Emission Adjustments for Temperature,
Humidity, Air Conditioning, and
Inspection and Maintenance for On-road
Vehicles in MOVES2014
&EPA
United States
Environmental Protection
Agency
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Emission Adjustments for Temperature,
Humidity, Air Conditioning, and
Inspection and Maintenance for On-road
Vehicles in MOVES2014
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
&EPA
United States
Environmental Protection
Agency
EPA-420-R-15-020
November 2015
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Table of Contents
Table of Contents 1
Glossary of Acronyms 3
1 Introduction 4
2 Temperature Adjustments 4
2.1 Data Sources for Gasoline Temperature Effects for HC, CO, and NOx emissions 5
2.2 Effects of Temperature on Gasoline Start Emissions 6
2.2.1 HC and CO Start Emissions for Gasoline-Fueled Vehicles 6
2.2.2 Temperature Effects on Gasoline NOx Start Emissions 15
2.2.3 Temperature Effects on Gasoline PM Start Emissions 17
2.3 Temperature Effects on Running-Exhaust Emissions from Gasoline Vehicles 22
2.3.1 HC, CO and NOx Running-Exhaust Temperature Effects 22
2.3.2 PM Running-Exhaust Temperature Effects 23
2.4 Effects of Temperature on Diesel Fueled Vehicles 28
2.4.1 HC, CO and NOx Temperature Effects for Diesel Vehicles 28
2.4.2 PM Temperature Effects for Diesel Vehicles 31
2.5 Compressed Natural Gas Temperature Effects 31
2.6 Temperature Effects on Start Energy Consumption 32
2.7 Conclusions and Future Research 34
3 Humidity Adjustments 35
3.1 Humidity Adjustment Equation 35
3.2 Future Research 35
4 Air Conditioning Adjustments 36
4.1 Air Conditioning Effects Data 36
4.2 Mapping Data to VSP Bins 39
4.3 Air Conditioning Effects on Emissions 41
4.3.1 Full A/C Adjustments forHC, CO and NOx Emissions 41
4.3.2 Full A/C Adjustments for Energy Consumption 41
4.4 Adjustments to Air Conditioning Effects 42
1
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4.5 Conclusions and Future Research 43
5 Inspection and Maintenance Programs 44
5.1 Inspection & Maintenance inMOBILE6 44
5.2 Inspection & Maintenance inMOVES 44
5.3 Development of MO VESI/M Factors 45
5.4 I/M Compliance Factors 47
5.5 Calculation of I/M Emission Rates 48
5.6 Development of Default MO VESI/M Program Inputs 48
6 References 52
Appendix A OTAQ Light-duty gasoline 2012 Cold Temperature
Program 55
Appendix B Calculation of Specific Humidity 56
Appendix C Air Conditioning Analysis Vehicle Sample 57
Appendix D Response to Peer Review Comments on Chapter 2:
Temperature Adjustments 59
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Glossary of Acronyms
A/C Air Conditioning
ACCF Air Conditioning Correction Factor
ASM Acceleration Simulation Mode
CO Carbon Monoxide
EPA Environmental Protection Agency
F Fahrenheit
FID Flame lonization Detection
FTP Federal Test Procedure
g/mi Grams per Mile
GVWR Gross Vehicle Weight Rating
HC Hydrocarbons
HLDT Heavy Light Duty Truck
I/M Inspection and Maintenance
LOT Light Duty Truck
LDV Light Duty Vehicle
LLDT Light Light Duty Truck
MDPV Medium Duty Passenger Vehicle
MOVES MOtor Vehicle Emission Simulator
MSAT Mobile Source Air Toxics
NMHC Non-Methane Hydrocarbons
NOx Oxides of Nitrogen
OBD On-Board Diagnostic
PM Paniculate Matter
RSD Remote Sensing Data
SFTP Supplemental Federal Test Procedure
THC Total Hydrocarbons (FID detection)
VIN Vehicle Identification Number
VOC Volatile Organic Compounds
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1 Introduction
The highway vehicle emission rates in the MOVES model database represent emissions under a
single (base) scenario of conditions for temperature, humidity, air conditioning load and fuel
properties. MOVES is designed to adjust these base emission rates to reflect the conditions for
the location and time specified by the user. MOVES also includes the flexibility to adjust the
base emission rates to reflect the effects of local Inspection and Maintenance (I/M) programs.
This report describes how these adjustments for temperature, humidity, I/M and air conditioning
were derived. Adjustments for fuel properties are addressed in a separate report.1
This report describes adjustments that affect running exhaust, start exhaust and extended idling
emissions. The crankcase emission processes are chained to running exhaust, engine start and
extended idling emissions, and thus are similarly affected by the temperature adjustments
described in this report. The impact of fuels, temperatures and I/M programs on vapor venting,
permeation and liquid leaks is addressed in a separate report on evaporative emissions.2
This report is an update to the previously posted MOVES2014 report (EPA-420-R-14-012,
December 20143). These changes include Section 2.6, which documents the temperature
adjustments for energy consumption. We have also revised the description of the development of
the inspection and maintenance benefits for MOVES in Section 5.3, along with Equation 18 in
that section, and have documented MOVES2014a updates to the default MOVES I/M Program
inputs in Section 5.6.
2 Temperature Adjustments
Emission rates in MOVES are adjusted by the ambient temperature to account for temperature
effects that impact emissions such as inefficient oxidation of emissions at cool catalyst
temperatures and additional fuel needed to start an engine at cold temperatures. In MOVES,
exhaust emissions are adjusted relative to their base rates at 75 degrees Fahrenheit based on:
1. Ambient temperature4
2. The latent engine heat from a previous trip, applied as an adjustment based on the length
of the soak time5'6
This report contains the adjustment based on ambient temperature. The second point regarding
soak time and start emissions is addressed in the light-duty6 and heavy-duty7 emission rates
reports.
This report addresses temperature sensitivity of emissions from gasoline vehicles in Sections 2.1
through 2.3. All the gasoline emissions data used to estimate temperature effects are obtained
from light-duty gasoline vehicles. However, the gasoline temperature effects are applied to all
gasoline vehicles in MOVES, including motorcycles, heavy-duty gasoline vehicles, and light-
duty vehicles fueled on ethanol-gasoline blends.
Section 2.4 discusses the temperature effects derived for diesel vehicles. The data used to derive
temperature effects is based on light-duty diesel vehicles, but are applied to all diesel vehicles in
MOVES due to a lack of temperature effect data on heavy-duty diesel vehicles. The diesel
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temperature effects are also applied to CNG buses as discussed in Section 2.5. Section 2.6
discusses the temperature effects for energy consumption for all vehicle types in MOVES.
2.1 Data Sources for Gasoline Temperature Effects for HC, CO,
andNOx emissions
For the analysis of start emissions, the data consists of Federal Test Procedure (FTP) and LA-92
tests. For running emissions, analysis includes the bag 2 emissions of FTP tests as well as US06
tests (without engine starts). Measurements from both the Federal FTP and California Unified
Cycle (3-phase / 3-bag tests) are used to determine the effect of temperature on vehicle
emissions. Within each test cycle, the first and third phases are identical driving cycles, but the
first phase begins with a cold-start (cold engine and emission control equipment) while the third
phase begins with a hot-start (relatively warm engine and control equipment). The difference
between Bag 1 and Bag 3 (in grams) are the emissions attributed to the cold start of the vehicle.
Some second-by-second test data were also used but only to validate the effects of temperature
on running emissions (HC, CO, and NOx). The data used in these analyses are from the
following sources:
Table 2-1 Summary of Data Sources
Data Source
MSOD
ORD
MSAT
OTAQ
Test
FTP +
FTP, IM240
FTP
FTP, US06
Temperatures Tested (degF)
15-110
-20, 0, 20, 40, 75
0, 20, 75
0, 20, 75
# of Vehicles
Hundreds
5
4
9
MY Range
Pre-2005
1987-2001
2005
2010
MSOD - EPA's Mobile Source Observation Database (MSOD) as of April 27,
2005. Over the past decades, EPA has performed or acquired data representing
emissions measurements over various cycles (often the FTP) on tens of thousands
of vehicles under various conditions. EPA has stored those test results in its
Mobile Source Observational Database (MSOD).
For the data stored in MSOD, we limited our analysis to those tests for
which vehicles were tested at two or more temperatures. The subset of
tests meeting this criterion covered a temperature range from 15 to 110°F.
Note that the results acquired from MSOD were collected in aggregate or
"bag" modes.
Information on EPA's MSOD is available on EPA's website:
http://www.epa.gov/otaq/models.htm
ORD Program- EPA's Office of Research and Development (ORD) contracted
(through the Clean Air Vehicle Technology Center, Inc.) the testing of five cars
(model years 1987 through 2001). Those vehicles were tested using both the FTP
and the EVI240 cycles under controlled conditions at temperatures of: 75, 40, 20, 0
and -20 °F8.
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MSAT Program - Under a contract with EPA, the Southwest Research Institute
(SwRI) tested four Tier 2 vehicles (2005 model year car and light-duty trucks)
over the FTP under controlled conditions at temperatures of: 75, 20, and 0 °F9.
OTAQ Cold Temperature Program - EPA's Office of Transportation and Air
Quality (OTAQ) contracted the testing of nine Tier 2 vehicles (2010 model year
car and light-duty trucks). Eight of the nine vehicles were Mobile Source Air
Toxics (MSAT-2) rule compliant. Vehicles were tested on the FTP and US06
under controlled conditions 75, 20, and 0°F. Information on the vehicle test design
is located in Appendix A.
2.2 Effects of Temperature on Gasoline Start Emissions
When a vehicle engine is started, emissions can be higher than during normal operation due to
the relatively cold temperature of the emissions control system. As these systems warm up to
their ideal operating temperature, emissions from the vehicle can be dramatically reduced. The
cold start effect can vary by pollutant, temperature, and vehicle technology.
The effects of ambient temperature on HC, CO, and NOx start emissions were developed using
the following approach:
No adjustment for temperatures higher than 75°F. 75°F is the midpoint of the allowable
temperature range (68°F-86°F) per the FTP.
Additive adjustments for temperatures below 75°F. These adjustments are added to the
emissions that would occur at 75°F.
Calculate the adjustments as either polynomial (Equation 2-1) or log-linear (Equation
2-2) functions:
Additive Grams = A*(T-75) + B*(T-75)2 Equation 2-1
Additive Grams = Be A*f-75) + C Equation 2-2
This approach provides a value of zero change for the additive adjustment at 75° F (i.e., the
temperature of the federal FTP test). The coefficients for the adjustment equations are stored in
the MOVES database table Start!empAdjustment. This table contains temperature effect
coefficients for each model year group and pollutant. In MOVES2010, the temperature effects
for all model years used polynomial functions (Equation 2-1) and these are retained in
MOVES2014 for older model year groups. Reanalyzing data from our previous test programs
was outside the scope of the update for MOVES2014. For MOVES2014, we used the log-linear
form for more recent model year vehicles for which we had new data, as detailed in Section
2.2.1.2. The data processing and the model fitting process differed for the polynomial and log-
linear fits, and each is described separately below.
2.2.1 HC and CO Start Emissions for Gasoline-Fueled Vehicles
In developing temperature adjustments for HC and CO start emissions, both polynomial and log-
linear regression models were used to fit the data. Data anomalies were resolved by combining
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two or more model year groups to obtain a larger dataset, or by removing anomalous data points.
We also distinguish temperature effects between pre-MSAT-2 (Mobile Source Air Toxics)aand
MSAT-2 compliant vehicles, which began phase-in starting in 2010. The MSAT-2 rule included
the first regulation on low temperature (20 ° F) non-methane hydrocarbon (NMHC) emissions
for light-duty and some medium-duty gasoline-fueled vehicles.
2.2.1.1 Polynomial Fits
MOVES2014 retained the MOVES2010 coefficients for HC emissions for all pre-2006 gasoline
vehicles, and for CO emissions for pre-2001 gasoline vehicles.
These coefficients were calculated with polynomial fits to data processed in the following steps.
First, the cold start emissions (grams/start) were calculated as the difference between bag 1 and
bag 3 emissions for each vehicle test. Next, the cold start emissions were stratified by model year
groups. The data was initially grouped according to the following model year groups:
1960 to 1980
1981 to 1982
1983 to 1985
1986 to 1989
1990 to 1993
1994 to 1999
2000 to 2005
Then, the mean emissions at 75°F were subtracted from the mean emissions at the other
temperatures to determine the change in emissions as functions of ambient temperature. Then,
we modeled the changes in cold-start emissions as a polynomial function of temperature minus
75° F. The additive adjustments are set equal to zero for temperatures higher than 75° F. Thus,
we did not use the changes in emissions from temperature above the FTP temperature range (68°
to 86° F). The model year groups were aggregated to larger intervals when the less aggregated
groups yielded non-intuitive results (e.g. older model year group had lower cold start emissions).
Table 2-2 summarizes the coefficients used with Equation 2-1 (polynomial) to estimate additive
start temperature adjustments for older model year gasoline vehicles.
a http: //www. epa. go v/otaq/fuels/gasolinefuels/MSAT/index.htm
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Table 2-2 Polynomial model coefficients for CO temperature effects for 2000 model year and earlier gasoline
vehicles and HC temperature effects for 2005 and earlier gasoline vehicles.
Model Year
Group
Pre-1981
1981-1982
1983-1985
1986-1989
1986-2000
1990-2005
CO
A
-4.677
-4.631
-4.244
B
0.023
HC
A
-0.631
-0.414
-0.361
B
0.002
0.003
The HC test data for the 1986-1989, and 1990-2005 model year groups included the ORD
program vehicles that were tested at an ambient temperature of -20° F. However, when this ultra-
low temperature data was included, the "best fit" HC regression curves (linear, quadratic, and
cubic) all exhibited poor fits for temperatures from zero through 20° F. We removed the five
ORD vehicle tests conducted at -20° F, which improved the estimate of the cold-start HC
emissions in the more common 0° F to 20° F range. Therefore, the coefficients in MOVES are
based on the changes in cold-start emissions for temperatures from zero through 75°, but in
MOVES these coefficients are applied to all ambient temperatures < 75° F.
In MOVES2014, the CO temperature effect that MOVES2010 used for the 1994-2000 model
years was applied to all model years from 1986-2000. The MOVES2010 temperature effect for
1986-1993 vehicles was dropped because it led to cases where older model years were modeled
with substantially lower CO emissions than newer model years. (The base CO emission rates,
however, are unchanged from MOVES2010, and still vary across this model year range.)
2.2.1.2 Log-linear Fits
In updating the start temperature effects for MOVES2014, we focused on the most recent model
year groups and implemented an improved methodology. For the updated cold temperature
effects in MOVES2014, we fit regression models to data from the ORD, MSAT and OTAQ cold
temperature programs51. These datasets were analyzed to determine an HC temperature effect for
model years 2006+ and a CO temperature effect for model years 2001+. The CO temperature
effects were applied to the 2001-2005 model years because the temperature correction for these
model years in previous versions of MOVES caused the model to estimate cold start CO
emissions that were unrealistically high relative to older model year vehicles.
We used linear mixed models, with both continuous and categorical variables, to fit to the
logarithm of the start emissions. Second-order polynomial models fit to the data exhibited
nonintuitive behavior when fitted to the data (negative values, non-monotonically increasing
a We excluded the two GDI vehicles from the OTAQ cold temperature program from the model fit because were not deemed
representative of the predominate technology in the 2010 vehicle fleet. In addition, they were believed to be transitional GDI
technologies that were not necessarily representative of future GDI technology.
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emissions). Thus we chose to fit the data with log-linear models because they provide
monotonically increasing emissions at colder temperatures and can model the strong curvature
evident in the cold start data (See Figure 2-1 and Figure 2-2).
The model parameters were fit using linear mixed models using the function Ime within the R
statistical package nlme10. Using random effects for vehicle, and the test temperature as a fixed
effect, we accounted for the paired test design of the data set, yielding robust temperature effect
estimates for the entire data set (e.g. not all vehicles were tested at the same set of temperatures
which is evident at -20 ° F in Figure 2-1).
The linear mixed model had the following form:
log(y) = oc + /?! Temp + Veh Equation 2-3
Where: y = start emissions (grams), Temp= temperature in Fahrenheit, Veh = random effect for
each individual vehicle. The mean model simply removes the random vehicle effects:
l°g(y) = K + /?l ' Temp Equation 2-4
We then converted the mean logarithmic model to real-space, yielding:
y _ goc+ftremp Equation 2-5
We then changed the intercept to 75F, by setting T' = 75 Temp , and substituting Temp =
75 T' into the above equation and rearranging. This yields equation:
y _ goc+remp Equation 2-6
Where A = 01} and B= ea+75'/?1. B is essentially the 'Base Cold Start' at 75F, with units of
(g/start). The eA(-Temp~7^ term is a multiplier which increases the cold start at lower
temperatures.
To convert the model to an additive adjustment, we calculated the additive difference from the
cold start: y - y(75) = BeA(-Temp~7^ - B. This model form can be used in the current MOVES
temperature calculator for HC and CO, by setting C = -B, yielding Equation 2-2:
Additive Grams = Be Aif(T-J5) + C Equation 2-2
The initial estimated fixed effects (including p-values) for the linear model fit are displayed in
Table 2-3. The model estimates that the PFI MS AT -2 compliant vehicles (2010) tested in the
OTAQ 2012 test program have consistently lower start emissions than the pre-MSAT-2 vehicles
(pre-2010), as shown by the positive pre-MSAT coefficient (012). No statistical difference in the
log-linear impact of temperature (coefficient P) was found between the 2001-2009 and the 2010
model year groups for CO emissions, as shown in Table 2-3 (p-value of the Temperature * pre-
MSAT effect is >0.90).
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Table 2-3 Fixed Effects for the initial CO model fit to data from 2001+ model year vehicles from the ORD,
MSAT, and Cold Temperature Programs (13 vehicles, 95 observations).
Intercept (ai)
Temperature (Pi)
pre-MSAT (
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Figure 2-1 FTP CO start emissions with log-linear model fit
150-
g
o
M
OT
LJJ
O
50
o-
Model
Fit_2010_and_newer
- Fit_2001_2009
Program
* MSAT Vehicle Data 2010+
« pre-MSAT Vehicle Data 2001-2009
o
25
Degrees (F)
50
75
For HC emissions, a significant difference was detected in the log-linear temperature effect (Pi)
between the pre-MSAT-2 and MSAT-2 compliant vehicles as shown in Table 2-5 (p-value of the
Temperature x pre-MSAT term is much smaller than 0.05).
Table 2-5. Fixed effects for the final HC model fit to data from 2006+ model year vehicles from the MSAT
Program and the Cold Temperature Program (11 vehicles, 69 observations).
Intercept (ai)
Temperature (Pi)
pre-MSAT (
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Figure 2-2 FTP HC start emissions with log-linear model fit
30-
20
10
o-
Program
° MSAT Vehicle Data 2010+
pre-MSAT Vehicle Data 2006-2009
Model
Model Fit 2010+
-Model Fit 2006-2009
25
Degrees (F)
50
75
The differences in the HC cold start temperature effect represent the impact of the Mobile Source
Air Toxic (MSAT-2) rule. The MSAT-2 rule included a limit on low temperature (20 ° F) non-
methane hydrocarbon (NMHC) emissions for light-duty and some medium-duty gasoline-fueled
vehicles9. Specifically:
For passenger cars (LDVs) and for the light light-duty trucks (LLDTs) (i.e., those with
GVWR up to 6,000 pounds), the composite (combined cold start and hot running) FTP
NMHC emissions should not exceed 0.3 grams per mile.
For heavy light-duty trucks (HLDTs) (those with GVWR from 6,001 up to 8,500 pounds)
and for medium-duty passenger vehicles (MDPVs), the composite FTP NMHC emissions
should not exceed 0.5 grams per mile.
These cold weather standards are phased-in beginning with the 2010 model year, specifically:
12
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Table 2-6 Phase-in of vehicles meeting cold weather HC standard
Model Year
2010
2011
2012
2013
2014
2015
LDVs / LLDTs
25%
50%
75%
100%
100%
100%
HLDTs/MDPVs
0%
0%
25%
50%
75%
100%
For the phase-in years, the coefficients for the HC temperature effect equation in the MOVES
database start!empAdjustment table were adjusted linearly according to the light-duty vehicle
phase-in. Equation 2-7 shows how the temperature effect is calculated for a model year 2010
LDV, where A2oio is the 2010 emissions rate
^2010 = ^2009(1 - 0.25) + 42013(0.25)
Equation 2-7
With this approach, the log-linear temperature effect (coefficient A) for HC emissions is reduced
from 2009 to 2013 while the base 75° F HC cold start (coefficient B) is relatively constant.
Within the current MOVES design, temperature effects are applied by fuel types and model year
vehicles, but not by regulatory class (e.g. HLDTS/MDPVs). As such, the light-duty rates,
including the light-duty MSAT-2 phase in are applied to all the gasoline-fueled vehicles in
MOVES. No data on HLDTs/MDPVs or heavy duty temperature effects were available to assess
this approach.
Table 2-7 summarizes the coefficients used with Equation 2-2 (log-linear) to estimate additive
start temperature adjustments for newer model year gasoline vehicles.
Table 2-7. Coefficients used for log-linear temperature effect equation for all gasoline source types
Model Year Group
2001-2009
2006-2009
2010
2011
2012
2013 & Later
CO
A
-0.038
-0.038
-0.038
-0.038
-0.038
B
4.136
3.601
3.066
2.531
1.996
C
-4.136
-3.601
-3.066
-2.531
-1.996
HC
A
-0.051
-0.048
-0.045
-0.042
-0.039
B
0.308
0.315
0.322
0.329
0.336
C
-0.308
-0.315
-0.322
-0.329
-0.336
Figure 2-3 and Figure 2-4 graphically compare all the cold start temperature effects for gasoline
vehicles by model year groups in MOVES2014. These include both the polynomial fits and the
log-linear curve fits to the data.
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Figure 2-3 CO additive cold start temperature effects for gasoline vehicles by model year groups
300-
CO
O)
I
O100
O
o-
Pre1981
MY81_82
MY83_85
MY86_05
MY06_09
MY10
MY11
-MY12
MY13_50
20
40
60
Temperature (deg F)
14
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Figure 2-4 HC additive cold start temperature effects for gasoline vehicles by model year groups
Pre1981
MY81_82
MY83_85
MY86_89
MY90J35
MY06J39
MY 10
-MY11
MY12
MY13 50
60
Temperature (deg F)
2.2.2 Temperature Effects on Gasoline NOx Start Emissions
Cold-start NOx emissions are not as sensitive to ambient temperature changes as HC and CO
emissions, because the fuel-rich conditions at engine start favor incomplete combustion of fuel,
forming CO and HC; NOx is favored under the lean burn, high temperature engine operation
more typical of running emissions. However, NOx emissions are impacted by the inefficiencies
of the three-way catalyst at low temperatures, and a small cold start temperature sensitivity is
expected.
MOVES2014 applies the same NOx temperature effect as was used in MOVES2010. Due to the
small temperature effects and the variability of the data, for MOVES2010, this effect was
calculated by averaging all the available NOx results (i.e. the 2005-and -earlier model year data)
together across model year groups and then performing regression. The following table lists the
average incremental cold start NOx emissions from the MSOD, ORD, and MSAT programs.
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Table 2-8. Average incremental cold start NOx emissions by temperature for gasoline vehicles calculated
from the MSOD, ORD, and MSAT programs
Temp F
-20
0
19.4
20.7
22.4
31
40
48.8
49.8
51
54.2
76.3
95.3
97.1
105.8
Delta
NOx (grams)
1.201
1.227
0.202
0.089
-0.155
-0.007
0.876
0.127
0.333
0.325
0.438
0
0.225
0.37
0.543
Using the data above, we fit a linear regression to the emission averages for temperatures of 76.3
°F and lower, and obtained the following fit:
NOx temperature additive adjustment = A * (Temp. - 75)
where: A =-0.009
R2 = 0.61
Equation 2-8
Although the value of R2 is not as high as for the HC and CO regression equations, the fit is
statistically significant.
Note that Equation 2-8 predicts a decrease in cold-start NOx emissions for temperatures greater
than 75° F, while the data in Table 2-4 indicates an increase in cold-start NOx emissions as the
ambient temperature rises above 90° F. The increase is small and may be an artifact of how these
data were analyzed, since only a subset of vehicles were measured above 75° F. As with the
other temperature adjustments, for MOVES2014, we have set the NOx additive adjustment to
zero for temperatures higher than 75° F.
For MOVES2014, we investigated whether the NOx temperature correction needed to be
updated for vehicles subject to the MSAT-2 rule. Figure 2-5 shows a comparison between NOx
start emissions data from OTAQ Cold Temperature Program (all vehicles, PFI and GDI, 2006-
2010 model year vehicles) and the emissions predicted using MOVES2010 temperature effects.
Because start emissions compose such a small percentage of total NOx emissions, the differences
between the MOVES2010 effects and the NOx data from the OTAQ Cold Temperature Program
were considered negligible. Thus we have maintained the MOVES2010 NOx temperature
adjustment estimated in Equation 2-8 for all model years in MOVES2014.
16
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Figure 2-5 FTP start NOx emissions, Bag 1 - Bag 3, model years 2006+
1.5-
£1.0-
0,5-
o.o-
Test Data
MOVES
temperature Effect
for NOx starts
MOVES201 Ob Temperature Effect
Applied to median data
20
75
Temperature (degF)
2.2.3 Temperature Effects on Gasoline PM Start Emissions
The temperature effects for particulate matter emissions from gasoline engines were
obtained from the Kansas City Light-Duty Vehicle Emissions Study (KCVES)11,
conducted between 2004 and 2005. The KCVES measured emissions from 496 vehicles
collected in the full sample, with 42 vehicles sampled in both the winter and summer
phases of the program. The EPA conducted an analysis of the temperature effects of
gasoline vehicles from the KCVES by estimating the temperature effect on PM emissions
from 34 paired vehicle tests that were sampled in both winter and summer ambient
conditions (10 paired vehicle tests were removed due to missing values and/or small
temperature differences between the phases) as derived in the EPA report (200811) and
Nametal. (201012).
The analysis of the Kansas City data indicated that ambient temperature affects for start PM
emissions is best modeled by (log-linear) multiplicative adjustments of the form:
17
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Multiplicative Factor =
Equation 2-9
Where T= Temperature
A = log-linear temperature effect. A = 0.0463 for cold starts from the KCVES analysis11'12
The log-linear temperature effect of 0.0463 is used in MOVES for gasoline vehicles of
model year 2009-and-earlier, i.e vehicles not affected by the MSAT-2 requirements.
The MSAT-2 rule (signed February 9, 2007) does not explicitly limit cold weather emissions of
particulate matter (PM). However, the Regulatory Impact Analysis (RIA) document9 that
accompanied that rule noted there is a strong linear correlation between NMHC and PM2.5
emissions based on the MSAT program discussed in Section 2.1. That correlation is illustrated in
Figure 2-6 (reproduced from that RIA) as the logarithm of the Bag-1 PM2.5 versus the logarithm
of the Bag-1 NMHC (for various Tier-2 vehicles).
Figure 2-6 FTP Bag 1 PM and FTP Bag 1 NMHC for Tier 2 vehicles
CN _
E
"01
O) OO _
m
0
§
y
V
V O A
V ^> o
V V
A
-3
-2
-1
0
1
Bag 1 NHMC-ln(g/mi)
Plot Icons are Vehicle-Specific
18
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Therefore, the limitation on cold weather HC (or NMHC) emissions is expected to result in a
proportional reduction in cold weather PM2.5 emissions. In the MSAT-2 RIA (Table 2.1 .-9), EPA
estimated that this requirement would result in a 30 percent reduction of VOC emissions at 20° F.
Applying the same analytical approach that was used in the RIA means that a 30 percent
reduction in VOC emissions would correspond to a 30 percent reduction in PM emissions at 20°
F (for Tier 2 cars and trucks).
Applying the 30 percent reduction for vehicles affected by the MSAT-2 requirements to the
temperature effects calculated for the fully phased-in (2015+) pre-MSAT-2 vehicles implies a
PM increase as the temperature decreases from 72° to 20° F of:
Multiplicative Factor at 20° F for MSAT-2 Vehicles = 0.7*e<>.0463*(72-20) Equation 2-10
= 7.8
Using Equation 2-10 with the information with the MSAT-2 phase-in schedule from Table 2-6
leads to the following (multiplicative) increases as the temperature decreases from 72° to 20° F:
Table 2-9 Multiplicative increase in cold start PIVh.s from 72° to 20° Fahrenheit for gasoline vehicles
Model Year
2008
2009
2010
2011
2012
2013
2014
2015
LDVs / LLDTs
11.1
11.1
10.3
9.4
8.6
7.8
7.8
7.8
HLDTs/MDPVs
11.1
11.1
11.1
11.1
10.3
9.4
8.6
7.8
Solving for the corresponding log-linear terms gives us these "A" values:
Table 2-10 Log-linear temperature effect for Start PIVh.s emissions (Coefficient A) for gasoline vehicles
Model Year
2008
2009
2010
2011
2012
2013
2014
2015
LDVs / LLDTs
0.0463
0.0463
0.0448
0.0432
0.0414
0.0394
0.0394
0.0394
HLDTs / MDPVs
0.0463
0.0463
0.0463
0.0463
0.0448
0.0432
0.0414
0.0394
For MOVES2014, we confirmed this theoretically derived temperature effect for MSAT-2
compliant vehicles by comparing it to data from the OTAQ study, which was collected on actual
19
-------
MY2010 MSAT-2 compliant vehicles. The temperature effect previously developed for
MOVES2010 fits this data well, as shown in Figure 2-7Figure 2-7. FTP PM2.5 start emissions,
MSAT-2 compliant vehicles. Thus we have retained the PM start temperature effects estimated
for the MSAT-2 rule in MOVES2014.
Figure 2-7. FTP PIVh.s start emissions, MSAT-2 compliant vehicles (7 PFI vehicles, 40 tests with nonzero PM
measurements on E10 fuel)
0.25 -
0.20 -
l.15
111
0,05 -
0.00 -
MOVES201 Ob Temperature Effect
jptied to median data
20
Temperature (clegF)
75
Figure 2-8 graphs the light-duty multiplicative temperature effects using the coefficient in Table
2-10, and the model form of Equation 2-9.
20
-------
Figure 2-8. PM start exhaust emissions effect for gasoline vehicles in MOVES2014
.9. 20-
8 IT
gjg
LU 9-
+-j
t:
o-
Pre2010
MY2010
- MY2011
MY2012
-MY2013
2050
0
20 40
Temperature (deg F)
60
Because the PIVb.s speciation profile for gasoline vehicles did not change significantly between
the winter and summer rounds of the Kansas City Light-duty vehicle emissions study,13 we apply
the same temperature adjustment to each component of the PM emissions, including elemental
carbon, organic carbon, sulfate and other species.
21
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2.3 Temperature Effects on Running-Exhaust Emissions from
Gasoline Vehicles
2.3.1 HC, CO and NOx Running-Exhaust Temperature Effects
MOVES is designed to model temperature effects for running-exhaust for HC, CO, and NOx.
However, the available data does not support a running temperature effect for any model year
groups. In MOVES2010, we examined the same data as the start temperature effects, to evaluate
potential running temperature effects. These test data suggest that there is very little effect of
temperature on running emissions of HC, CO, or NOx. Regression analyses found that the
coefficients (slopes) were not statistically significant (that is, the slopes were not distinguishable
from zero). This finding is consistent with what we found in our analysis of the Kansas City
Light-Duty Vehicle Emissions Study (KCVES)11. The lack of correlation between running
emissions and ambient temperature is illustrated (as an example) in EPA (2008)ufor the data
from the full-sample (496 vehicles) in KCVES:
Figure 2-9 Logarithm of Bag-2 HC emission rate versus temperature (deg F) from the Kansas City Light-
Duty Vehicle Emissions Study
CM
(0
-if
K
O
^ -2
-4
-5
-6
.0 2C
.0 3{
.0
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Temp
In this plot, each point represents a single LA-92 Bag-2 test result from the Kansas City program.
A visual inspection of this plot of the natural logarithm of the LA-92 Bag-2 HC emissions
suggests no strong relationship between the hot-running HC emissions and the ambient
temperature. Though not shown, the paired data showed similar relationships.
The CO and NOx plots are similar in that they also do not indicate a significant trend.
22
-------
As an additional test, we examined a set of continuous data collected on the IM240 cycle in the
Chicago I/M program. To avoid potential confounding due to variable levels of conditioning
vehicles experienced in the queues at the I/M stations, we used only second EVI240s when back-
to-back EVI240s were performed, and for single IM240s we examined only the final 120 seconds
of full duration IM240s. Based on this analysis, we found no evidence of a temperature effect
between 5 and 95°F.
The effect of temperature on hot running HC, CO, and NOx emissions is coded in MOVES using
polynomial functions as multiplicative adjustments. In MOVES2014, we continue to set all of
those adjustments equal to 1.0, that is, we estimate no change in running emissions with
temperature for all model year gasoline vehicles.
2.3.2 PM Running-Exhaust Temperature Effects
The analysis of the Kansas City data11'12 indicated that significant ambient temperature effects
exist for both start and running PM emissions. The temperature effect for hot-running conditions
was estimated using the same equation as starts, but a different cold start effect, as shown in
Equation 2-11:
,,,,.,. ,. r , A*i72 Tt Equation 2-11
Multiplicative jactor = eA ' ' M
Where T= Temperature
A = temperature effect, A = 0.0318 for bag-2 from the KCVES
In MOVES2010, we applied the 0.0318 temperature effect for PM running-exhaust emissions for
all model year gasoline vehicles.
For MOVES2014, we re-evaluated the PM temperature effect for running emissions for Tier 2
and MSAT-2-compliant vehicles, because our data tested on these vehicles suggested there was
little impact of temperature on running PM emissions. Experimental data collected in the 2012
OTAQ program involved measurement of PM emissions on both the FTP (by phase) and the
US06 cycles at temperatures of 0, 20, and 75°F. The results from these programs are plotted
against temperature in Figure 2-10. We also fit log-linear models to the data, and found the effect
of temperature was not statistically significant on either cycle. This evidence suggested that for
Tier 2 vehicles, PM emissions are not influenced by ambient temperature when the engines are
fully warmed up.
23
-------
Figure 2-10. Hot-running PM Emissions measured on two Cycles (FTP Bag 2, US06) on MSAT-2 compliant
MY 2010 gasoline vehicles, reported as grams/cycle.
0.04 -
,0,03
o>
to
^0.02
0.01 -
FTP
'
US06
75 0 20
Temperature(degF)
75
These results contrast with the significant PM running temperature effect detected for bag 2
emissions in the Kansas City Study. We hypothesized that the temperature effect observed in the
KCVES bag 2 emissions may have been due in part to the short duration of the cold-start phase
of the LA92 cycle, which is only 310 sec (1.18 mi) in length. In contrast, the cold-start phase of
the FTP, used in the more recent studies, is 505 seconds (3.59 miles) in length. Bag 1 of the
LA92 is also a considerably "milder" drive schedule in terms of accelerations, than bag 1 of the
FTP, thus giving less opportunity for the engine and catalyst to obtain more optimum
temperature regimes to avoid PM formation. One interpretation of the trend observed in the
Kansas City results is that vehicles were not fully conditioned at the end of the first phase of the
LA92. The implication is that emissions observed in the early portion of the hot-running phase
could have reflected "start" rather than "running" emissions, which could have explained the
apparent presence of a temperature effect for hot-running emissions. Similarly, Mathis et al.
(2004) did not observe a temperature effect on PM emissions from running emissions for two
modern three-way catalyst equipped port-fuel injected (PFI) vehicles tested in a laboratory at
+23,-7, and-20°C14.
To evaluate this hypothesis, we re-analyzed the continuous (second-by-second) data from the
Kansas City program. Three sets of time series were considered, including second-by-second
measurements of PM (DustTrak measurements normalized to the Teflon filter measurements),
24
-------
black carbon (photoacoustic analyzer) and hydrocarbon emissions (flame ionization detector).
The second-by-second measurements were analyzed to evaluate whether an effect of ambient
temperature could be observed only during the first portion of hot-running phase in the LA92.
An aggregate time series for PM emissions, averaged for the set of paired measurements (20
vehicles measured in both the summer and winter) are graphed in Figure 2-11. Except for model-
year group 1981-1990, the winter time measurements are noticeably higher than the summer
measurements even beyond 1,000 seconds.
Figure 2-11 Second-by-second average PIVh.s emissions for paired vehicle tests in the KCVES.
25-
20
1C
It)
1 n
10
5
25
on
20
15
ID-
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CL
6-
[
,
(
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WkJL
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i
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ll.L(4JLjL IJjjJLllrrt
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5C
f
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ittl*
10
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00 15
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00
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to
rn
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i
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0
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CO
CO
CO
Ol
1
RND
summer
- venter
sec
We fit log-linear models (of the form of Equation 2-3) to test the statistical significance of
temperature on the log of PM emissions for bag 1, bag 1 + bag 2, and varying segments of each
25
-------
of the bags. The estimated temperature effect (Pi) are shown in Table 2-11 for both a pooled
sample (419 vehicles), and the paired sample (20 vehicles). The pooled data includes all the
vehicles measured in Kansas City that had valid second-by-second measurements. All the
temperature effects were statistically significant (p-value <0.05), except for the tests noted with
asterisks. The statistical models confirm the observations made in Figure 2-11. The temperature
effect is largest for the segment of emissions closest to the cold start (bag 1), and decreases as the
engine warms up with time. However, the PM emissions in bag 2 were influenced by
temperature even after removing the first 570 seconds (bag 2 >570 s) and first 1,025 seconds
(bag2>l,025 s)..
Table 2-11 Log-linear temperature effect (Pi) measured in the KCVES
Model
bag 1
bag 1 + bag 2 < 570 s
bag 1+bag 2
bag 2
bag 2 > 570 s
bag 2 > 1,025 s
PM
pooled
-0.047
-0.039
-0.029
-0.020
-0.017
-0.008
paired
-0.051
-0.048
-0.041
-0.035
-0.032
-0.020
BC
pooled
-0.047
-0.045
-0.036
-0.015
-0.012
-0.004**
paired
-0.050
-0.049
-0.044
-0.033
-0.030
-0.022
HC
pooled
-0.018
-0.017
-0.014
-0.003 **
-0.001**
-0.003**
paired
-0.020
-0.019
-0.017
-0.006
-0.004**
-0.005*
*p-value > 0.05 ,** p-value >0.10
The re-analysis of Kansas City study suggested that, as suspected, much of the running
temperature effect apparent in bag 2 is due to the short warm-up in bag 1 of the LA-92.
However, it also showed that a temperature effect on bag 2 emissions persists even after 1,025
seconds (17 minutes) of operation on the LA-92 cycle. One of the difficulties in reconciling the
results from the cold temperature PM test programs is that both the driving cycles and the vehicle
technologies differ between test programs (i.e. driving cycle and vehicle technologies are
confounding variables). This makes it difficult to determine if the differing temperature effects
observed for running conditions are due to technology differences, driving cycle, or both.
Based on the available data, in MOVES2014, we have retained the PM running temperature
effect estimated from Kansas City for all 2004-and-earlier model year vehicles. This step was
taken for several reasons:
1. Kansas City was conducted in 2004/2005 and includes measurements from 1960's era
vehicles to 2005 model year vehicles. The temperature effect estimated in MOVES is
applicable to the vehicle technologies tested in Kansas City. Kansas City only tested a
few 2005 vehicles, none of which were compliant with the Tier 2 standards.
2. A large portion of the PM running temperature estimated in Kansas City appears to be
due to the short length of bag 1 in the LA-92 cycle. However, the temperature effect was
found to still be significant at the end of bag 2. The trip length for light-duty gasoline
vehicles used in MOVES ranges from 2 to 9 miles. This length is less than the combined
length of bag 1 and bag 2 of the LA-92 (9.81 miles). Therefore, we believe that retaining
the running temperature effect in MOVES will not lead to an overestimation of PM
emissions for typical emission inventories.
26
-------
For 2005-and-later model year vehicles, we removed the running temperature effect. This step
was taken for the following reasons:
1. The available data on Tier 2 light-duty gasoline vehicles did not show a temperature
effect on bag 2 of the FTP cycle or the US06. Because the light-duty gasoline phase-in of
Tier 2 standards began with model year 2005, we have removed the running temperature
effect for 2005 and later model year vehicles.
2. MOVES PM start effects used to model the Tier 2 MSAT-2 vehicles provides a relatively
good fit to the start emission data as shown in Figure 2-7. We appear to be capturing the
magnitude of PM emissions from the cold start and associated warm-up period from
these vehicles with the cold start temperature effects alone.
Figure 2-12 displays the temperature adjustments for running exhaust particulate matter
emissions from gasoline vehicles in MOVES.
Figure 2-12. PM running exhaust emissions effect for gasoline vehicles in MOVES2014
10.0-
c/)
g
7.5-
Pre2004
MY2004 2050
5.0-
o_
2.5-
20 40
Temperature (deg F)
60
27
-------
2.4 Effects of Temperature on Diesel Fueled Vehicles
2.4.1 HC, CO andNOx Temperature Effects for Diesel Vehicles
We were able to identify only 12 diesel-fueled vehicles with FTP tests at multiple temperatures
(nine passenger cars and 3 light-duty trucks). However, only two of those 12 vehicles were tested
at temperatures within the normal FTP range (68° to 86° F). None of these diesel trucks were
equipped with after-treatment devices. The average bag-1 minus bag-3 emissions for those tests
are shown in Table 2-12. We stratified the test results into four temperature bands which yielded
the following emission values (grams per start) and average temperature value:
Table 2-12 Average light-duty diesel vehicle incremental start emissions (Bag 1- Bag3) by temperature (grams
per start)
Temperature, F
34.6
43.4
61.5
69.2
Count
6
7
10
2
HC
2.55
2.68
1.69
1.2
CO
2.44
2.03
3
1.91
NOx
2.6
0.32
0.67
0.36
When we plotted the mean HC start emissions (above) versus temperature, we obtained the
following graph (where the vertical lines represent 90 percent confidence intervals and the
"dashed" line represents a linear regression through the data).
Figure 2-13 Mean light-duty diesel cold-start HC emissions (in grams) with 90% confidence intervals vs
temperature.
30
40 50 60
Temperature (degrees F)
70
The dashed (blue) line in Figure 2-13 is a linear regression line having as its equation:
28
-------
HC = (-0.0421 * Temperature ) + 4.22
R2 = 0.90
Equation 2-12
Transforming this equation into an equation that predicts the (additive) change/adjustment in the
cold-start HC emissions from light-duty diesel-fueled vehicles (in the MOVES format), we
obtain:
HC additive temperature adjustment = A * (Temp. - 75)
where: A = -0.0421
Equation 2-13
The coefficient associated with this temperature adjustment term is statistically significant
although its coefficient of variation is relatively large (23.04 percent). We apply this adjustment
to heavy-duty as well as light-duty vehicles.
On the other hand, the cold-start CO and NOx emissions did not exhibit a clear trend relative to
the ambient temperature. Plotting the mean CO and NOx cold-start emissions versus ambient
temperature (with 90 percent confidence intervals) produced the following two graphs:
Figure 2-14 Mean light-duty diesel cold-start CO emissions (in grams) with 90% confidence intervals vs
temperature
5
4
30
40 50 60
Temperature (degrees F)
70
29
-------
Figure 2-15 Mean light-duty diesel cold-start NOx emissions (grams) with 90% confidence intervals vs
temperature
1
30
40 50 60
Temperature (degrees F)
70
Statistical analyses of both the diesel cold-start CO and NOx emissions failed to produce
coefficients that were significantly different from zero. Therefore, for both cold-start CO and
NOx adjustments from diesel-fueled vehicles, we propose to set the temperature adjustment for
start emissions to zero.
Given the small diesel start temperature effects, we did not evaluate the diesel running
temperature effect for HC, CO, and NOx. We set temperature effects for diesel running exhaust
to zero, similar to the gasoline running exhaust adjustments. The light duty diesel HC start
emissions were also applied to heavy-duty diesel vehicles in MOVES. Similar to light-duty
vehicles, all other temperature effects in MOVES are set to zero, including extended idle
exhaust. Because of a lack of data no attempt has been made to calculate temperature effects for
diesel vehicles with after-treatment devices (such as diesel particulate filters or oxidation
catalysts) that are now required to meet current emission standards.
30
-------
2.4.2 PM Temperature Effects for Diesel Vehicles
MOVES2014 does not include any temperature effects for particulate matter emissions from
diesel vehicles. As presented in the previous section, hydrocarbon emissions from conventional
diesel engines have much lower temperature sensitivity than catalyst-controlled light-duty
gasoline emissions. Limited data exists on the ambient temperature effects of particulate matter
emissions from diesel engines.
The EPA does not have data on PM start emissions on US-certified diesel vehicles tested across
different ambient temperatures. From a literature search, we were able to find two European test
programs that measured PM diesel start emissions from European light-duty diesel engines and
vehicles at cold and warm ambient temperatures.
Mathis et al. (200414) evaluated particle mass and number emissions from a conventional light-
duty diesel vehicle, and a light-duty diesel equipped with a diesel particulate filter (DPF) at
laboratory conditions measured at +32, -7, and -20° C. The researchers measured an increasing
trend in particle mass emissions (g/start) from the conventional diesel vehicle at colder
temperatures, but over the entire drive cycle the particle number emission rates were not
significantly impacted by the cold start contribution. The particle mass emissions from the DPF-
equipped vehicle were two orders of magnitude smaller than the conventional diesel engines, but
the start contributed the majority of the particle number emissions over the entire test cycle.
Sakunthalai et al. (201415) also reported significant increase in PM start emissions from a light-
duty diesel engine tested in a laboratory at +20 and -20C. However, they only reported the PM
mass concentrations of the exhaust, and not emission rates. Additionally, the engine was not
equipped with an emission control system. Other researchers have reported that PM emissions
are larger at cold start than hot start from diesel engines16'17, but have not investigated the
relationship of cold starts with ambient temperatures.
The reviewed studies suggest that temperature does influence cold start PM emissions from
diesel vehicles. However, at this time MOVES does not include temperature adjustments to
diesel start emissions due to limited data on diesel engines and because diesel starts are a minor
contributor to particulate mass emissions to the mobile-source emission inventory. The diesel
particulate matter emission temperature effects can be revisited as additional data become
available.
2.5 Compressed Natural Gas Temperature Effects
MOVES2014 currently models emissions from compressed natural gas used to fuel transit buses.
However, no data were available on temperature impacts of compressed natural gas emissions.
As discussed in the heavy-duty report, the start emissions for CNG emissions for HC, CO, NOx,
and PM are set equal to diesel start emissions. We also applied the same temperature adjustments
to CNG as diesel, which only includes the start temperature effects on HC emissions.
31
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2.6 Temperature Effects on Start Energy Consumption
The temperature effects on energy consumption in MOVES have not been updated since
MOVES2004. As presented in heavy-duty report7, the energy consumption from starts is a small
fraction compared to the total energy use of both gasoline and diesel vehicles. As such, we have
not prioritized updating the start energy rates or temperature adjustments in subsequent versions
of MOVES.
In this section, we provide a summary of the start temperature effects used in MOVES. The
analysis used to derive the temperature effects on start energy consumption in MOVES is
documented in the MOVES2004 energy report.18 No significant temperature effects for energy
consumption were found for warmed-up vehicles in the analysis, thus MOVES does not contain
temperature effect on running energy consumption.
MOVES applies temperature adjustments to the start energy consumption through a
multiplicative adjustment. The form of the multiplicative adjustments used in MOVES is shown
in Equation 2-14, which is applied to all ambient temperatures. Unlike the criteria emission rates
temperature adjustments, MOVES does not limit the energy consumption adjustments to only
cold temperatures, but also adjusts the energy consumption for hot temperatures.
The multiplicative temperature adjustments are applied to all start operating modes of varying
soak lengths. MOVES does have different baseline (75°F) start energy consumption rates for
different soak times, which are documented with the baseline energy start rates.7'21
Multiplicative temperature adjustment
= 1.0 + tempAdjustTermA x (temperature - 75) Equation
+ tempAdjustTermB x (temperature 75)"
,2 2-14
Table 2-13 displays the coefficients used to adjust start energy consumption for gasoline, E85,
diesel, and CNG fueled-vehicles. The temperature coefficients are stored in the MOVES
temperatureadjustment table by pollutant, emission process, fuel type, and model year range. E85
fueled vehicles use the same energy adjustments as gasoline vehicles, because they also use the
same energy rates as comparable gasoline-fueled vehicles.19 CNG vehicles (CNG transit buses)
use the same adjustments as diesel vehicles, because they use the same energy start rates as
comparable diesel transit buses.7
Table 2-13. Multiplicative temperature coefficients used in MOVES
tempAdjustTermA
-0.01971
-0.0086724
tempAdjustTermB
0.000219
0.00009636
Fuel types
Gasoline, E85
Diesel, CNG
Model Years
1960-2050
1960-2050
32
-------
Figure 2-16 displays the multiplicative temperature adjustments for starts as a function of
temperature used in MOVES2014. At 75°F, the multiplicative adjustment is 1. Gasoline fueled-
vehicles have a larger temperature effect than diesel vehicles, increasing to 4.8 at -20°F, while
decreasing to 0.64 at 100°F. Whereas, the adjustment for diesel vehicles only increases to 2.7 at
20°F, and decreases to 0.85 at 100°F.
Figure 2-16. Multiplicative temperature adjustments for starts from energy consumption as a function of
ambient temperature.
6
5
1 4
Multiplicative ad;
O i1 to u>
\
\
^^
^x^
^s
\
'*--
Ga
"""--^
>oline
Diese
1
1
~
00 20 40 60 80 100 120
Temperature (F)
33
-------
2.7 Con elusions an d Future Research
The temperature adjustments within MOVES have a significant impact on the emissions
estimated for gasoline vehicles. The OTAQ Cold Temperature program was an important study
to evaluate, validate, and update the temperature sensitivities in MOVES for modern vehicles.
Based on our evaluation of the study, we updated the temperature emission effects for HC and
CO starts, and removed the PM running-exhaust temperature effect for Tier 2 compliant
vehicles.
We recognize that the current temperature effects in MOVES have limitations. Additional
studies/analyses could include:
Evaluating the benefits of applying log-linear or other mathematical models for pre-
MSAT2 gasoline vehicle HC & CO temperature effects.
Investigating ambient temperature effects on cold start emissions above certification
levels, i.e. temperatures warmer than 75°F)
Evaluating the interaction of ambient temperature effects and fuel effects
Evaluating the interaction of ambient temperature effects and deterioration
Conducting studies of ambient temperature effects in heavy-duty diesel vehicles,
especially those equipped with emission control devices, including diesel particulate
filters (DPF) and selective reduction catalysts (SCR).
Conducting studies of temperature effects in vehicles using alternative fuels such as
compressed natural gas and ethanol blends
Incorporating data on the impact of temperature effects on new technology vehicles,
including gasoline direct injection, stop-start technologies and hybrid technologies
34
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3 Humidity Adjustments
Water in the air cools the peak combustion temperature and lowers NOx emissions. MOVES
adjusts both gasoline and diesel vehicle exhaust NOx emissions to account for humidity.
3.1 Humidity Adjustment Equation
In MOVES, the base exhaust emission rates for NOx in all modes and all processes are
multiplied by a humidity adjustment. This factor is calculated using the following formula:
K = 1.0 - ( (Bounded Specific Humidity - 75.0) * Humidity Correction Coefficient) Equation
3-1
The bounded specific humidity is in units of grains of water per pound of dry air. The specific
humidity is not allowed to be lower than 21 grains and is not allowed to be larger than 124
grains. If the specific humidity input exceeds these limits, the value of the limit is used to
calculate the humidity adjustment. Appendix B shows how the hourly relative humidity values
are converted to specific humidity used in this equation using temperature and barometric
pressure.
Table 3-1. Humidity correction coefficients used by MOVES
Fuel Type
Gasoline
Diesel Fuel
Humidity Correction Coefficient
0.0038
0.0026
The diesel humidity correction coefficient is derived from the Code of Federal Regulations20.
The gasoline humidity correction coefficient is carried over from the coefficient used in the
MOBILE6 model.
3.2 Future Research
Future research could investigate the emission impact of humidity on more recent gasoline,
diesel and alternatively-fueled engines and consider whether emission control technologies
impact the humidity effect.
35
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4 Air Conditioning Adjustments
The air conditioning (A/C) effects described below, and incorporated in MOVES2014 were
originally derived for MOVES2010. No changes to air conditioning calculations and parameters
were made for MOVES2014, although there have been significant improvements to A/C energy
efficiencies. As part of the analysis supporting the 2012-2016 Light Duty Greenhouse Gas
standards, and the 2017-2025 Light Duty Greenhouse Gas Standards, we estimated significant
improvements in air conditioning system efficiencies, starting in model year 2012 with full
phase-in by 2019. In MOVES, we project the light-duty A/C improvements of these rules using
the running energy rates as documented in MOVES2014 Greenhouse Gas and Energy
Consumption Rates Report21, rather than changing the A/C factors within MOVES. The MOVES
A/C factors are multiplicative adjustments from the running energy rates, so a reduction in
running energy rates also reduces energy consumption from light-duty vehicle air conditioning.
The air conditioning adjustment factors used in MOVES are based on a test procedure meant to
simulate air conditioning emission response under extreme "real world" ambient conditions.
These factors predict emissions which would occur during full loading of the air conditioning
system, and are then scaled down in MOVES according to ambient conditions in a modeling run.
The second-by-second emission data were analyzed using the MOVES methodology of binning
the data according to vehicle characteristics (source bins in MOVES) and vehicle specific power
bins (operating modes in MOVES). The results of the analysis showed statistically significant
and consistent results for three types of operation (deceleration, idle and cruise/acceleration) and
the three primary exhaust pollutants (hydrocarbon, carbon monoxide and nitrous oxides). This
report shows the results of the analysis for the air conditioning adjustments used in MOVES for
HC, CO, NOx and energy consumption. The impact of A/C on particulate matter has not been
evaluated for MOVES. MOVES currently has no air conditioning effect for PM emissions.
MOVES adjusts total energy consumption and exhaust running HC, CO and NOx emissions
separately for each operating mode. MOVES models A/C effects for criteria pollutants (HC, CO
and NOx) only for passenger car, passenger truck and commercial light truck source types.
Energy consumption is affected for all source types. The same adjustment values are used for all
source use types affected within a pollutant type.
4.1 Air Conditioning Effects Data
The data for the MOVES A/C Correction Factor (ACCF) was collected in 1997 and 1998 in
specially designed test programs. In the programs, the same set of vehicles were tested at
standard FTP test conditions (baseline) and at a nominal temperature of 95 F. Use of the same set
of vehicles and test cycles should eliminate most of the vehicle and test procedure variability and
highlight the difference between a vehicle operating at extreme ambient conditions and at a
baseline condition.
The data used to develop the MOVES ACCF consisted of 54 individual cars and light trucks
tested over a variety of test schedules. Overall the database consisted of a total of 625 test cycles,
and 1,440,571 seconds of emission test and speed / acceleration data. Because of the need to
compute vehicle specific power on a modal basis, only test results which consisted of second-by-
36
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second data were used in the MOVES analysis. All second-by-second data were time aligned and
quality controlled checked.
The distribution of test vehicles by model year is shown in Table 4-1. Model years 1990 through
1999 were included. The data set consists of 30 cars and 24 light trucks. No test data were
available on other vehicle types (e.g. motorcycles, heavy trucks). The individual test cycles on
which the vehicles were run are shown with the test counts in Table 4-2. The data shows a nice
balance between different test cycles, and cars and trucks. Unfortunately, the study does not
contain any pre-1990 or post-1999 model years. A complete list of the individual vehicles and a
basic description is shown in Appendix C.
Only vehicles which were coded as having an emission test with the A/C system on were
selected. The A/C On tests and the A/C Off (default for most EPA emission tests in general)
were matched by VIN, test schedule and EPA work assignment. The matching ensured that the
same vehicles and test schedules were contained in both the A/C On sample and the A/C Off
sample.
Table 4-1 Distribution of test vehicles by Model Year
Model Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
TOTAL
Count
5
5
6
5
7
5
13
4
3
1
54
Table 4-2 contains the distribution of test-cycles analyzed. A definition of the test-cycles is
included in a MOBILE6 report.22
37
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Table 4-2 Distribution of tests by test cycle
Schedule Name
ART-AB
ART-CD
ART-EF
F505
FTP
FWY-AC
FWY-D
FWY-E
FWY-F
FWY-G
FWY-HI
LA4
LA92
LOCAL
NONFRW
NYCC
RAMP
ST01
TOTAL
Count
36
36
36
21
21
57
36
36
36
36
36
23
35
36
36
36
36
36
625
38
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4.2 Mapping Data to VSP Bins
The overall dataset consisted of a sample of vehicle tests with the A/C system on and a sample of
vehicle tests with the A/C system off. Both samples consisted on the same vehicles and all tests
were modal with a data sampling of 1 hertz (second-by-second data collection). Prior to analysis
the data for each vehicle / test cycle combination was time aligned to ensure that the
instantaneous vehicle operating mode was in-sync with the emission collection system.
Following time alignment, the vehicle specific power (VSP) was calculated for each vehicle test
/ second combination. This was done using Equation 4-1.
Equation 4-1
VSP = 985.5357 * Speed * Acoeff / Weight +
440.5729 * SpeedA2 * Bcoeff / Weight +
196.9533 * SpeedA3 * Ccoeff/Weight +
0.19984476 * Speed * Accel + GradeTerm
Where
VSP is the vehicle specific power for a given second of operation in units of KW / tonne.
Speed is the instantaneous vehicle speed for a given second in units miles / hour.
Accel is the instantaneous vehicle acceleration for a given second in unit of miles/hr-sec
Weight is the test vehicle weight in pounds.
Acoeff = 0.7457*(0.35/(50*0.447)) * ROAD_HP
Bcoeff = 0.7457*(0.107(50*50*0.447*0.447)) * ROAD_HP
Ccoeff = 0.7457*(0.55/(50*50*50*0.447*0.447*0.447)) * ROAD_HP
Where
ROAD_HP = 4.360117215 + 0.002775927 * WEIGHT (for cars)
ROAD_HP = 5.978016174 + 0.003165941 * WEIGHT (for light trucks)
GradeTerm (KW/tonne) = 4.3809811 * Speed * Sin(Radians(GradeDeg))
Where
GradeDeg is the road grade in units of degrees. This term is zero for dynamometer tests.
4.3809811 (mA2 * hr/(sA3 * miles) =
9.80665(m/sA2) * 1609.34(m/mile)/3600(secs/hr)
KW / tonne = mA2 / sA3
9.80665(m/sA2) is the gravitation constant.
After computing the VSP for each vehicle test / second combination, we assigned the individual
seconds to the MOVES VSP bins. These VSP bins are defined in Table 4-3. VSP bins 26 and 36
were not defined because bins 27-30 and bins 37-40 overlap them.
39
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Table 4-3 VSP bin definitions
VSP Label
0
1
11
12
13
14
15
16
21
22
23
24
25
26
27
28
29
30
33
35
36
37
38
39
40
Definition
Braking
Idling
Low Speed Coasting;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
VSP< 0; K=Speed<25
0<=VSP< 3; 1<= Speed<25
3<=VSP< 6; K=Speed<25
6<=VSP< 9; K=Speed<25
9<=VSP<12; K=Speed<25
12<=VSP; K=Speed<25
Moderate Speed Coasting; VSP< 0; 25<=Speed<50
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
Cruise/ Acceleration;
0<=VSP< 3; 25<=Speed<50
3<=VSP< 6; 25<=Speed<50
6<=VSP< 9; 25<=Speed<50
9<=VSP<12; 25<=Speed<50
12<=VSP; 25<=Speed<50
12<=VSP<18; 25<=Speed<50
18<=VSP<24; 25<=Speed<50
24<=VSP<30; 25<=Speed<50
30<=VSP; 25<=Speed<50
VSP< 6; 50<=Speed
6<=VSP<12; 50<=Speed
12 <= VSP; 50<=Speed
12<=VSP<18; 50<=Speed
18<=VSP<24; 50<=Speed
24<=VSP<30; 50<=Speed
30<=VSP; 50<=Speed
An average emission result for each pollutant (HC, CO and NOx) with and without A/C
operation was computed for each VSP Bin. This resulted in 69 (23 VSP bins x 3 pollutants) pairs
of emission averages. However, preliminary analysis of the data grouped into the 23 bins
(defined in Table 4-3) showed unsatisfactory statistical results. In the general, no trends were
evident across VSP bins or within similar subsets of VSP bins. The trends were highly erratic
and the results were generally not statistically significant. In addition, most of the bins labeled 30
or higher had very few data members. An analysis of cars versus trucks was also performed, and
showed no statistical difference between the two.
To produce more consistent results, the individual VSP bins were collapsed down to three
principal bins. These are the Braking / Deceleration bin, the Idle bin and the Cruise /
Acceleration bin. These large bins are quite different in terms of engine operation and emissions
performance. The Braking bin consisted of VSP Bin 0 in Table 4-3, the Idle bin was VSP Bin 1
and the Cruise / Acceleration bin contained the remaining 21 bins.
40
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4.3 Air Conditioning Effects on Emissions
4.3.1 Full A/C Adjustments for HC, CO and NOx Emissions
Full A/C adjustments were generated for each of the nine VSP Bin and pollutant combinations.
This was done by dividing the mean "With A/C" emission factor by the mean "Without A/C"
emission factor for each of the VSP Bin / pollutant combinations. The Full A/C adjustments are
shown in Table 4-4. Measures of statistical uncertainty (coefficient of variation of the mean)
were also computed using the standard error of the mean. They are shown in Table 4-4 as "Mean
CVofCF."
Table 4-4 Full air conditioning adjustments for HC, CO and NOx
Pollutant
HC
HC
HC
CO
CO
CO
NOx
NOx
NOx
Operating Mode
Braking / Decel
Idle
Cruise / Accel
Braking / Decel
Idle
Cruise / Accel
Braking / Decel
Idle
Cruise / Accel
opModelD
0
1
11-40
0
1
11-40
0
1
11-40
Full A/C CF
1.0000
1.0796
1.2316
1.0000
1.1337
2.1123
1.0000
6.2601
1.3808
Mean CVofCF
0.48582
0.74105
0.33376
0.31198
0.77090
0.18849
0.19366
0.09108
0.10065
Note the higher air conditioning effect for NOx at idle. These results are consistent with those
obtained from Nam et al. (2000)23 who showed that at low load conditions, A/C greatly
increased NOx emissions due to reduced residual gas fractions in-cylinder.
4.3.2 Full A/C Adjustments for Energy Consumption
The use of a vehicle's A/C system will often have a sizeable impact on the vehicle's energy
consumption. This was found statistically by analyzing the available second-by-second data on
CO2 and other gaseous emissions, and converting them to an energy basis using standard EPA
vehicle fuel economy certification equations. The vehicle emission data were binned by VSPBin
(see above). A mean value was computed for each combination of VSPBin. Separate analysis
was done as a function of sourcebinid (combination of vehicle type, fuel type and model year),
and the results were not statistically different across sourcebinid given the relatively small
sample sizes. As a result, the A/C adjustments for energy are a function of only VSPBin. The
resulting A/C adjustments are shown in Table 4-5.
41
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Table 4-5 Full air conditioning adjustments for energy
VSPBin
0
1
11
12
13
14
15
16
A/C Factor
1.342
1.365
1.314
1.254
1.187
1.166
1.154
1.128
VSPBin
21
22
23
24
25
26
27
28
29
A/C Factor
1.294
1.223
1.187
1.167
1.157
1.127
1.127
1.127
1.127
VSPBin
30
33
35
37
38
39
40
A/C Factor
1.294
1.205
1.156
1.137
1.137
1.137
1.137
Only very small amounts of data were available for VSPBins 26 through 29 and VSPBins 37
through 40. As a result, the data from these bins was averaged together and binned into two
groups. The resulting group averages were used to fill the individual VSPBins. This averaging
process has the effect of leveling off the effect of A/C at higher power levels for an engine. This
is an environmentally conservative assumption since it is likely that the engine power devoted to
an A/C compressor probably continues to decline as the overall power demand of the engine is
increased. In fact, in some vehicle designs the A/C unit will be shut off by an engine controller if
the driver demands a very high level of power from the vehicle. In the future, EPA hopes to re-
evaluate the assumption of a constant A/C factor for the high VSPBins.
For HC, CO and NOx, detailed VSP was not found to be an important variable in regards to A/C
adjustment and A/C usage. However, Full A/C adjustments greater than one were found for all
pollutants for both Idle and Cruise / Acceleration modes. For NOx Idle mode, a fairly large
multiplicative adjustment of 6.2601 was obtained. This large factor reflects the relatively low
levels of NOx emissions during idle operation. A moderately high multiplicative A/C adjustment
of (2.1123) for CO cruise / Accel was also obtained. These adjustments will double CO
emissions under extreme conditions of A/C usage. A/C adjustments of less than or equal to one
were found for the Braking / Deceleration mode for all three pollutants. These were set to one for
use in the MOVES model.
4.4 Adjustments to Air Conditioning Effects
The adjustments for each operating mode are weighted together by the operating mode
distribution calculated from the driving schedules used to represent the driving behavior of
vehicles. Average speed, road type and vehicle type will affect the operating mode distribution.
meanBaseRateACAdj = SUM(meanBaseRate*(fullACAdjustment-l .0)*opModeFraction)
Since not all vehicles are equipped with air conditioning, and air conditioning is normally not on
all of the time, the full air conditioning effect on emissions is adjusted before it is applied to the
emission rate. The adjustment account for (a) the fraction of vehicles in each model year that are
equipped with air conditioning, (b) the fraction of vehicles equipped with air conditioning of
each age that have an operational air conditioning system and (c) the fraction of those vehicle
owners who have air conditioning available to them that will turn on the air conditioning based
on the ambient temperature and humidity (heat index24) of the air outside their vehicles. These
42
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MOVES defaults are documented in the Population and Activity report. 25The fraction of vehicles
equipped with air conditioning, the fraction of operational air conditioning and the fraction of air
conditioning use are used to adjust the amount of "full" air conditioning that occurs in each hour
of the day.
EmissionRate = (meanBaseRateACAdj *
ACPenetration*functioningACFraction*ACOnFraction) + meanBaseRate
The air conditioning adjustment is a multiplicative adjustment applied to the emission rate
after it has been adjusted for fuel effects.
Air conditioners are employed for defogging at all temperatures, particularly, at lower
temperatures. This secondary use of the A/C along with associated emission effects is not
addressed in MOVES.
4.5 Con elusions an d Future Research
MOVES applies air conditioning effects to emissions from all vehicles except motorcycles. The
impact depends on pollutant, operating mode, ambient temperature and humidity, and the
anticipated availability of air conditioning in the vehicle type, model year and age being
modeled.
There are a number of areas where our understanding of air conditioning impacts could be
improved. These include:
Evaluation of the impact of air conditioning use on particulate matter emissions.
Studies of air conditioning effects in a broader range of model years, particularly those
with the most recent emission control technologies.
Studies of air conditioning effects in a broader range of vehicles, particularly in heavy-
duty diesel vehicles.
Evaluation of air conditioning effects in the highest VSP/STP bins.
Evaluation of the emissions impact of air conditioners in their role as defoggers.
43
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5 Inspection and Maintenance Programs
Inspection and Maintenance (I/M) programs are genetically any state or locally mandated
inspection of highway motor vehicles intended to identify those vehicles most in need of
emissions-related repair and requiring repairs of those vehicles. Since these programs are
location specific, there is great variability in how vehicles are selected for inclusion in the
programs, how and when vehicles are tested, and what happens when vehicles fail. MOVES is
designed to take these variations into the account when estimating the emission benefits of these
programs.
5.7 Inspection & Maintenance in MOBILE6
Because MOVES draws heavily on the approaches developed for MOBILE6.2 to represent the
design features of specific I/M programs, it is useful to briefly review these methods. Readers
interested in a more thorough treatment of the topic are encouraged to review the relevant
MOBILE6 documentation.26
The MOBILE6.2 model used a methodology that categorized vehicles according to emitter status
(High emitters and Normal emitters), and applied a linear growth model to project the fraction of
the fleet that progresses from the Normal emitter to the High emitter status as a function of age.
Average emission rates of High and Normal emitters were weighted using the High emitter
fraction to produce an overall average emission rate as a function of age, model year group and
vehicle type. The emissions generated represented the emissions of the fleet in the absence of
I/M (the No I/M emission rate).
A similar approach was used to generate I/M emission rates. In this case the initial starting point
for the function (where age=0) was the same as the No I/M case. However, the effects of I/M
programs and associated repairs were represented by reductions in the fraction of high emitters,
which consequently affected the average emission level of the fleet. Balancing these emissions
reductions due to I/M repairs were the re-introduction of high emitters in the fleet due to
deterioration of vehicle emission control systems after repairs. The underlying I/M and non-I/M
deterioration rates were assumed to be the same.
MOBILE6 modeled the non-I/M and I/M emission cases diverging from each other over time,
with the I/M rates being lower. The percentage difference between these two rates is often
referred to as the overall I/M reduction or I/M benefit.
5.2 Inspection & Maintenance in MOVES
The MOVES emission rates contain estimates of emission levels as a function of age, model year
group and vehicle type for areas where no I/M program exists (the mean base rate, or the non-
I/M reference rates) and for an area representing the "reference I/M program" (the I/M reference
rates). The I/M reference rates for light duty gasoline vehicles (the principal target of I/M
programs) were derived using data from the enhanced I/M program in Phoenix, Arizona (as
operated from calendar year 1995 through 2002) and represent the design features of that
program. The difference between the non-I/M and I/M reference rates are assumed to represent
the I/M benefit of the Phoenix program design assuming perfect compliance. Equation 5-1 shows
this relationship in a mathematical form.
44
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Standard IM Difference = EnonIM EIM Equation 5-1
where En0n-iM and EIM are the non-I/M and I/M reference rates, respectively.
The Phoenix program design was selected as the reference program because virtually all of the
underlying data for MOVES came from this source. The selection does not imply any judgment
on the strengths or weaknesses of this specific program.
The object of this process is to generate a general model which can be used to represent all I/M
programs in the United States. The MOVES approach is to compare individual program designs
against the reference program for purposes of developing adjustment to the "standard I/M
difference" representing design features differing from those in the reference program. This
concept is shown mathematically in Equation 5-2,
Ep = REIM + (1 R)EnonlM Equation 5-2
where Ep is the adjusted emission rate for a "target" I/M program, Em is the reference rate,
-EnoniM is the non-I/M reference rate, and R is an aggregate adjustment representing the difference
in average emission rates between the target program and the reference program.
Depending on the value of R, Ep may be greater than En0niM, fall between En0niM and EIM, or be
less than EIM. Thus this framework can represent target programs as more effective or less
effective than the reference program. In MOVES, R is referred to as the "IMFactor."
Re-arranging Equation 5-2 and solving for R gives leads to Equation 5-3. This equation
shows the I/M adjustment as the ratio of the emission difference between a proposed I/M
program design and the Standard I/M Difference
Ep EnonIM
R = Equation 5-3
c1 n
5.3 Development of MOVES I/M Factors
Early in the MOVES development process it was decided that developing the I/M adjustment
factors based on a completely new analysis was infeasible. A major obstacle was a lack of
suitable emissions and I/M program data representing the full range of program designs. Data
sets for certain I/M programs (i.e., transient test based programs) were generally quite complete
and robust. However, mass emission results and random vehicles samples were quite scarce for
other test types such as the Acceleration Simulation Mode (ASM), steady-state, idle tests and
OBD-II scans. This situation was particularly true for data on old model years at young ages (i.e.,
a 1985 model year at age five).
45
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As a result, EPA decided to develop I/M adjustment factors based on the information
incorporated in MOBILE6.2. Mechanically, this step was achieved by running the MOBILE6.2
model about 10,000 times over a complete range of pollutant-process combinations, inspection
frequencies, calendar years, vehicle types, test types, test standards, and model year group / age
combinations. The mean emission results for each combination were extracted from the output
and utilized. The IMF actor table includes the following fields:
Pollutant / Process
Test Frequency
Test Type
Test Standard
Regulatory Class
Fuel Type (Only gasoline/ethanol fuels have EVIFactors)
Model Year Group
Age Group
IMF actor
The IMF actor value was computed for all reasonable combinations of the parameters listed in
the EVIFactor table. A separate MOBILE6.2 run was done for each parameter combination
(Target design, Ep~), and a second set of runs were done for the reference program (Reference
design, EM). The EVIFactor is then calculated from the mean emission results from these two
runs and the non-I/M case. Equation 5-4 illustrates the formula, which was derived in the
previous section as Equation 5-3.
(Ep EnonIM)
R = Equation 5-4
The reference program has inputs matching the Phoenix, Arizona I/M program during the time in
which the data used in the MOVES emission rate development were collected (CY 1995-2005).
The reference design represents a biennial frequency with an exemption period for the four most
recent model years. It uses three different I/M test types (basic idle test for MY 1960-1980,
transient tailpipe tests for MY 1981-1995 (EVI240, EVI147), and OBD-II scans for MY 1996-and-
later). Each of these test types became the reference for the respective model year groups.
The specific combinations of MOBILE6.2 runs performed are shown in Table 5-1 below. Each
of these runs represents a particular test type and test standard design which was expressed as a
ratio to the standard reference tests. A set of these runs were done for each calendar year 1990
through 2030, for cars, light trucks and heavy-duty gasoline vehicles and for pollutants HC, CO
and NOx.
The first four runs represent the Non I/M reference and the three Arizona I/M references.
46
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Table 5-1 MOBILE6.2 runs used to populate the MOVES I/M adjustment factor
RUN*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Description
Non I/M Base
IM240 Base (Biennial IM240/147)
OBD Base (Biennial OBD Test)
Basic Base (Loaded - Idle Test)
Biennial - IM240 - Phase-in Cutpoints
Annual - IM240 - Phase-in Cutpoints
Biennial - IM240 - Final Cutpoints
Annual - IM240 - Final Cutpoints
Biennial - ASM 2525/5015 - Phase-in Cutpoints
Annual - ASM 2525/5015 - Phase-in Cutpoints
Biennial - ASM 2525/5015 - Final Cutpoints
Annual - ASM 2525/5015 - Final Cutpoints
Biennial - ASM 2525 - Phase-in Cutpoints
Annual - ASM 2525 - Phase-in Cutpoints
Biennial - ASM 2525 - Final Cutpoints
Annual - ASM 2525 - Final Cutpoints
Biennial - ASM 5015 - Phase-in Cutpoints
Annual - ASM 5015 - Phase-in Cutpoints
Biennial - ASM 5015 - Final Cutpoints
Annual - ASM 5015 - Final Cutpoints
Annual - OBD -
Annual - LOADED/IDLE
Biennial - IDLE
Annual - IDLE
Biennial - 2500/IDLE
Annual - 2500/IDLE
Type
Non I/M Reference
I/M Reference
I/M Reference
I/M Reference
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
Target I/M Design
The MOBILE6.2 database output option was chosen for all runs. This step produced large sets of
results which were further stratified by facility-cycle / start process and age. This output format
necessitated additional processing of the facility rates into composite running and start factors (in
MOVES the IMF actor is a function of running and start processes).
5.4 I/M Complian ce Factors
In addition to the IMFactor, MOVES adjusts rates for particular programs by applying an
additional multiplicative "Compliance Factor" (IMCompliance). While the IMFactor (R)
represents the theoretical effectiveness of a specific I/M program design, relative to the reference
design, as described above, the values of the EVIComplianceFactor (C) are specific to individual
programs and represent their overall operational effectiveness and efficiency. Program
characteristics which impact the I/M compliance factor include waiver rates, compliance rates
and overall operational efficiency. It may vary from 0 to 1.0 where zero would represent a totally
failed program and 1.0 a perfectly successful program. Factors which tend to reduce the
complianceFactor are the systematic waiver of failed vehicles from program requirements, the
47
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existence of large numbers of motorists who completely evade the program requirements,
technical losses from improperly functioning equipment or inadequately trained technicians.
Most default IMCompliance factors are greater than 90 percent.
5.5 Calculation of I/M Emission Rates
Calculation of the emission rate for vehicles subject to an I/M program begins with the
calculation of the IMAdjustFract. The IMAdjustFract combines the EVI Factor for the program
design and the Compliance Factor for the program characteristics to create a single factor. The
Compliance Factor is in units of percent and is converted to a fraction.
IMAdjustFract = (IMFactor * ComplainceF'actor * 0.01) Equation 5-5
The next step is estimate a program-specific "with I/M" emission rate by weighing together the
emission rate for the I/M reference program and the non-I/M emission rate, using the
IMAdjustFract.
TargetRate = IMRate * IMAdjustFract 5 K
+NonIMRate * (1.0 - IMAdjustFract) quation
5.6 Development of Default MOVES I/M Program Inputs
Information about which pollutant-processes are covered by I/M programs in various
counties and calendar years is contained in the MOVES database table EVICoverage. This
coverage information is allowed to vary by pollutant (process, county, year, regulatory class, and
fuel type). The table also lists each the I/M compliance factor described above
The EVICoverage table includes the use of I/M program identifiers called IMProgramlDs. A
particular county will likely have several IMProgramlDs that reflect different test types, test
standards or inspection frequencies being applied to different regulatory classes, model year
groups or pollutant-process combinations. For example, a county in calendar year 2007 may
have an EVIProgramID=l that annually inspects pre-1981 model year cars using an Idle test, and
an IMProgramID=2 that biennially inspects 1996-and-later model year light-trucks using an
OBD-II test.
The EVICoverage table also shows other important I/M parameters for each IMProgramlD.
These include the model year information as a model year range (beginning and ending model
year), the frequency of inspection (annual, biennial and continuous/monthly), test type (Idle,
IM240, ASM, OBD-II) and test standard.
48
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The structure of the IMCoverage table in the MOVES database is:
Pollutant / Process
State / County
Year
Source Use Type
Fuel Type (only gasoline and ethanol fuels)
Beginning Model Year of Coverage
Ending Model Year of Coverage
InspectFreq
EVIProgramlD
I/M Test Type
I/M Test Standards
Ignore I/M toggle (user control variable)
Compliance Factor
A full update to the IMCoverage table was beyond the scope of MOVES0214. Much of the data
in the default IMCoverage table is out of date. For official state submissions, it is expected that
the state will enter their own set of program descriptive parameters and compliance factors which
reflect current and expected future program operation.
The underlying data used to construct the default inputs for I/M programs before calendar year
2011 were taken from MOBILE6.2 input files used in the NMEVI model to compute the National
Emission Inventory of 2011. The MOBILE6 data fields listed in Table 5-2 were extracted and
processed into the various fields in the MOVES IMCoverage table.
Table 5-2 I/M Coverage table data sources
NMIM Data Source
MOBILE6 Compliance Rate
I/M Cutpoints
MOBILE6 Effectiveness Rate
Grace Period
Model Year Range
Test Type
Vehicle Type
MOBILE6 Waiver Rate
MOVES I/M Coverage Parameter
Used in the MOVES Compliance Rate Calculation
Used to determine MOVES I/M Test Standards
Used in the MOVES Compliance Rate Calculation
Used in MOVES to Determine Beginning Model Year of
Coverage
Used in MOVES to Determine Ending Model Year of
Coverage
Used to determine MOVES I/M Test Type
Used to determine MOVES Regulatory Class input
Used in the MOVES Compliance Rate Calculation
49
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As seen in Table 5-2, MOBILE6.2 and MOVES do not have exactly compatible parameter
definitions. Extraction and processing of the MOBILE6.2 inputs for all of the individual states
was required. The MOBILE6 compliance rate, waiver rate and effectiveness rate were used to
determine the MOVES Compliance Rate. The new MOVES Compliance Rate is a broader
concept that incorporates three separate MOBILE6.2 inputs. Equation 5-7 shows the relationship.
C = M6ComplianceRate * M6EffectivenessRate .
* (1 - M6WaiverRate quation
MOVES does not have separate inputs for the effect of waivers on I/M benefits. Section 3.10.6.2
of the technical document for MOVES201027 describes how to calculate the MOVES
compliance rate to include the effect of waivers. An updated version of this report will be
published for MOVES2014.
In MOVES, it is assumed that any repairs attempted on vehicles receiving waivers are not
effective and do not result in any reduced emissions.
Other fields in the IMCoverage table complete the description of each I/M program in effect in
each county. The MOBILE6.2 I/M Cutpoints data were used only to determine level of
stringency of a state's EVI240 program (if any). The MOBILE6.2 Test Type inputs provided a
description of the specific I/M tests performed by the state and test standards for the ASM and
Basic I/M tests. The MOBILE6.2 inputs of Grace Period and Model Year Range were used to
determine the MOVES Beginning and Ending model year data values for each I/M program. The
MOBILE6.2 Vehicle type input was mapped to the MOVES regulatory class. The Ignore I/M
toggle is a user feature that allows the user to completely disable the effects of I/M for one or
more of the parameter combinations.
The IMCoverage table default parameters for calendar year 2011 and later were updated for
MOVES2014 using the IMCoverage tables from the county databases (CDBs) provided to EPA
for the 2011 National Emission Inventory (NET) project28 (Versionl). A CDB was created for
every county in the nation containing an IMCoverage table. These tables were available for
review by states and updated as needed. The I/M program descriptions from these CDBs were
extracted from the CDBs and compiled in the default IMCoverage table for calendar year 2011.
The I/M descriptions for 2012 and later calendar years were derived from the 2011 I/M
descriptions, assuming no changes in the basic I/M program design, but updating the model year
coverage values to properly account for the existing grace periods in the future calendar years.
The State of Georgia provided a complete set of I/M program descriptions for their 13 counties
with I/M programs for all calendar years 1999 through 2050 after the NEI. These changes were
also included in the update.
For MOVES2014a, all of the I/M program descriptions were further checked using a script to
look for cases where a model year coverage either conflicted with other rows in the I/M
description or where gaps were left between model years without coverage. This check also
looked for cases where the coverage beginning model year occurs later than the ending model
year coverage. Each problem identified was compared to the I/M program descriptions found in
50
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the 2013 EPA I/M Program Data, Cost and Design Information report29 to resolve conflicts. The
county coverages in some states was also changed for some calendar years.
The counties with coverage changes are:
Six Florida counties (12011, 12031, 12057, 12086, 12099 and 12103) were removed for
all calendar years.
Three Louisiana counties (22005, 22047, and 22063) were removed for all calendar
years.
Three Texas counties (48071, 48291 and 48473) were removed for all calendar years.
One Minnesota county (27171) was removed for all calendar years.
One Pennsylvania county (42073) was removed for all calendar years.
Two Colorado counties (8041 and 8097) were removed from all calendar years.
Four Kentucky counties (21015, 21037, 21111 and 21117) were removed for 2006 and
later calendar years.
One Alaska county (2090) was removed for 2010 and later calendar years.
Seven Colorado counties (8001, 8005, 8013, 8014, 8031, 8035, 8059) were populated
with new I/M data for 2011 and later calendar years.
Two Colorado counties (8069 and 8123) were replaced with a copy of the (new) 2015
and later calendar year coverage from county 8001. All previous calendar year I/M was
removed for these counties.
Thirteen Georgia counties (13057, 13063, 13067, 13077, 13089, 13097, 13113, 13117,
13121, 13135, 13151, 13223 and 13247) were populated with new I/M data for 1999 and
later calendar years from the GA_2002.imcoverage table provided by Georgia.
40 California counties were populated with new I/M data for 2011 and later calendar
years.
(6001, 6007, 6011, 6013, 6017, 6019, 6021, 6029, 6031, 6037,
6039, 6041, 6047, 6053, 6055, 6057, 6059, 6061, 6065, 6067,
6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087,
6089, 6095, 6097, 6099, 6101, 6103, 6107, 6111, 6113, 6115)In addition to the updates in the
I/M program descriptions, all of the counties were altered to make sure each I/M program
covered E85 fueled vehicles in the same way as for gasoline in all calendar years. Any program
elements claiming benefits for inspections to reduce liquid fuel leaks (pollutant process ID 113)
were dropped from the default I/M program descriptions. MOVES2014a does not offer any
benefits from inspection programs to detect liquid fuel leaks.
51
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6 References
1 USEPA (2015). Fuel Effects on Exhaust Emissions from On-road Vehicles inMOVES2014. EPA-420-
R-15-001. Assessment and Standards Division. Office of Transportation and Air Quality. US
Environmental Protection Agency. Ann Arbor, MI. November 2015.
http://www.epa.gov/otaq/models/moves/moves-reports.htm.
2 USEPA (2014). Evaporative Emissions from On-road Vehicles inMOVES2014. EPA-420-R-14-014.
Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental
Protection Agency. Ann Arbor, MI. September, 2014. http://www.epa.gov/otaq/models/moves/moves-
reports.htm.
3 USEPA (2014). Emission Adjustments for Temperature, Humidity, Air Conditioning, and Inspection and
Maintenance for On-road Vehicles inMOVES2014. EPA-420-R-14-012. Assessment and Standards
Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor,
MI. 2014. http://www.epa.gov/otaq/models/moves/moves-reports.htm.
4 E. Glover and D. Brzezinski, (2001) Exhaust Emission Temperature Correction Factors for MOBILE6:
Adjustments for Engine Start and Running LA4 Emissions for Gasoline Vehicles, EPA Report Number
EPA420 R 01 029 (M6.STE.004), April 2001. Available at:
www.epa.gov/otaq/models/mobile6/m6tech.htm
5 E. Glover and P. Carey (2001) Determination of Start Emissions as a Function of Mileage and Soak
Time for 1981-1993 Model Year Light-Duty Vehicles" EPA Report Number EPA420-R-01-058
(M6.STE.003), November 2001. Available at: www. epa.gov/otaq/models/mobile6/m6tech.htm
6 USEPA (2015). Exhaust Emission Rates for Light-Duty On-road Vehicles inMOVES2014. EPA-420-R-
15-005. Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental
Protection Agency. Ann Arbor, MI. October, 2015. http://www.epa.gov/otaq/models/moves/moves-
reports.htm.
7 USEPA (2015). Exhaust Emission Rates for Heavy-Duty On-road Vehicles in MOVES2014. EPA-420-
R-15-004a. Assessment and Standards Division. Office of Transportation and Air Quality. US
Environmental Protection Agency. Ann Arbor, MI. September, 2015.
http://www.epa.gov/otaq/models/moves/moves-reports.htm.
8 Stump, F. D., D. L. Dropkin, S. B. Tejada, C. Loomis, and C. Pack (2002). Characterization of
Emissions from Malfunctioning Vehicles Fueled with Oxygenated Gasoline-Ethanol (E-10) Fuel Part
III, US EPA's National Exposure Research Laboratory (NERL), U. S. Environmental Protection Agency,
Washington, D.C., EPA Report Number EPA/600/R-01/053 (NTIS PB2004-106735), July 2002.
Available at: http://www.epa.gov/nerl/nerlmtbe.htm#mtbe7c
9 USEPA (2007). "Regulatory Impact Analysis for Final Rule: Control of Hazardous Air Pollutants from
Mobile Sources" EPA Report Number EPA420-R-07-002, February 2007, Chapter 2, pages 2-15 to 2-17.
Available at: http://www.epa.gov/otaq/regs/toxics/fr-ria-sections.htm
10 Jose Pinheiro, Douglas Bates, Saikat DebRoy, Deepayan Sarkar and the R Development Core Team
(2013). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-109.
52
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11 USEPA (2008). Analysis of Paniculate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas
City, EPA Report Number EPA420-R-08-010, April 2008. Available at: http://epa.gov/otaq/emission-
factors-research/420r08010.pdf)
12 Nam, E.; Kishan, S.; Bauldauf, R.; Fulper, C. R.; Sabisch, M.; Warila, J. Temperature Effects on
Particulate Matter Emissions from Light-Duty, Gasoline-Powered Motor Vehicles. Environ. Sci. Technol.
2010,44,4672-4677
13 Sonntag, D. B., R. W. Baldauf, C. A. Yanca and C. R. Fulper (2013). Particulate matter speciation
profiles for light-duty gasoline vehicles in the United States. Journal of the Air & Waste Management
Association, 64 (5), 529-545.
14 Mathis, U.; Mohr, M.; Forss, A. Comprehensive particle characterization of modern gasoline and diesel
passenger cars at low ambient temperatures. Atmos. Environ. 2005, 39, 107-117.
15 Sakunthalai, R. A., H. Xu, D. Liu, J. Tian, M. Wyszynski and J. Piaszyk (2014). Impact of Cold
Ambient Conditions on Cold Start and Idle Emissions from Diesel Engines. SAE Technical Paper.
16 Calcagno, J. A. (2005). Evaluation of Heavy-Duty Diesel Vehicle Emissions during Cold-Start and
Steady-State Idling Conditions and Reduction of Emissions from a Truck-Stop Electrification Program.
PhD, University of Tennessee.
17 Bielaczyc, P., J. Merkisz and J. Pielecha (2000). Exhaust emission from diesel engine during cold start
in ambient temperature conditions. SAE Technical Paper.
18 USEPA (2005). Energy and Emissions Inputs. EPA-420-P-05-003. Office of Transportation and Air
Quality. US Environmental Protection Agency. Ann Arbor, MI. March, 2005.
http://www.epa.gov/otaq/models/moves/moves-reports.htm.
19 USEPA (2015). Greenhouse Gas and Energy Consumption Rates for On-road Vehicles: Updates for
MOVES2014. EPA-420-R-15-003. Assessment and Standards Division. Office of Transportation and Air
Quality. US Environmental Protection Agency. Ann Arbor, MI. October, 2015.
http://www.epa.gov/otaq/models/moves/moves-reports.htm.
20 Code of Federal Regulations 86.1342-90 (Page 309).Available at:
http://www.access.gpo.gov/nara/cfr/cfr-table-search.html
21 USEPA (2015). Greenhouse Gas and Energy Consumption Rates for On-road Vehicles: Updates for
MOVES2014. EPA-420-R-15-003. Assessment and Standards Division. Office of Transportation and Air
Quality. US Environmental Protection Agency. Ann Arbor, MI. October, 2015.
http://www.epa.gov/otaq/models/moves/moves-reports.htm.
22 USEPA (2001). Air Conditioning Correction Factors inMOBILE6. EPA420-R-01-055. Assessment
and Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency.
Ann Arbor, MI. November, 2011. http://www.epa.gov/otaq/models/mobile6/r01055.pdf
23 Nam, E. K., Understanding and Modeling NOx Emissions from Air Conditioned Automobiles, SAE
Technical Paper Series 2000-01-0858, 2000.
24 National Oceanic and Atmospheric Administration (2014). The Heat Index Equation. Weather
Prediction Center. National Weather
Service.http://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml
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25 USEPA (2015). Population and Activity ofOn-road Vehicles inMOVES2014. EPA-420-D-15-001.
Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental
Protection Agency. Ann Arbor, MI. 2015. http://www.epa.gov/otaq/models/moves/moves-reports.htm.
26 USEPA (2002) MOBILE6 Inspection /Maintenance Benefit Methodology for 1981 through 1995
Model Year Light Vehicles, USEPA Office of Transportation and Air Quality, Assessment and Standards
Division. EPA Report Number EPA420-R-02-014 (M6.IM.001) March 2002. Available at:
http://www.epa.gov/otaq/models/mobile6/r02014.pdf
27 USEPA (2012), Using MOVES to Prepare Emission Inventories in State Implementation Plans and
Transportation Conformity: Technical Guidance for MOVES2010, 20 Wa and 201 Ob (EPA-420-B-12-
028, April 2012) http://epa.gov/otaq/models/moves/documents/420bl2028.pdf
28 USEPA (2011). 2011 National Emission Inventory -
http://www.epa.gov/ttn/chief/net/201 linventory.html
29 USEPA (2013), I/MProgram Data, Cost and Design Information, Final Report, Prepared by ERG for
EPA, Project No.: 0303.00.009.001, August 2, 2013.
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Appendix A OTAQ Light-duty gasoline 2012 Cold Temperature
Program
Vehicle Name Model Year Injection Emissions Std MSAT? Odometer Displ (L) Cyl.
Buick Lucerne
Honda Accord
Hyundai Sante Fe
Jeep Patriot
Kia Forte EX
Mazda 6
Mitsubishi Gallant
Cadillac STS
VW Passat
2010
2010
2010
2010
2010
2010
2010
2010
2006
PFI
PFI
PFI
PFI
PFI
PFI
PFI
GDI
GDI
Tier 2/Bin 4
Tier 2/Bin 5
Tier 2/Bin 5
Tier 2/Bin 5
Tier 2/Bin 5
Tier 2/Bin 5
Tier 2/Bin 5
Tier 2/Bin 5
Tier 2/Bin 5
MSAT-2
MSAT-2
MSAT-2
MSAT-2
MSAT-2
MSAT-2
MSAT-2
MSAT-2
pre-MSAT
22000
24000
18000
22000
25000
24000
38000
21000
103000
3.9
2.4
2.4
2
2
2.5
2.4
3.6
2
V-6
1-4
1-4
1-4
1-4
1-4
1-4
V-6
1-4
Tested at O°F
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Appendix B Calculation of Specific Humidity
Equations to convert relative humidity in percent to specific humidity (or humidity ratio) in
units of grains of water per pound of dry air (ref CFR section 86.344-79, humidity calculations).
Inputs:
TF is the temperature in degrees F.
Pb is the barometric pressure.
is the relative humidity
_ I 5
-
773
^/J
TQ= 647.27 -TK
ratio or specific humidity 4.34 /. O ry
'100
^=29.92*218.167*10
IT IT
I 10/1K
(3.2437+0.00588ro+0.0000000117ro3)
1+0.00219J0
= 6527.557*10
l3.2437+0.00588ro +0.0000000117T0
1+0.00219TC
o
56
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Appendix C Air Conditioning Analysis Vehicle Sample
Table C-l Vehicle Sample for the Air Conditioning Analysis
Model Year
1990
1990
1991
1991
1992
1992
1992
1992
1992
1993
1993
1993
1993
1994
1994
1994
1994
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
1997
1998
1998
1990
1990
1991
1991
1992
1993
1994
Make
DODGE
NISSAN
CHEVROLET
FORD
CHEVROLET
CHEVROLET
MAZDA
SATURN
TOYOTA
CHEVROLET
EAGLE
HONDA
TOYOTA
CHRYSLER
FORD
HYUNDAI
SATURN
BUICK
BUICK
FORD
SATURN
SATURN
CHEVROLET
HONDA
HONDA
PONTIAC
TOYOTA
FORD
MERCURY
TOYOTA
JEEP
PLYMOUTH
CHEVROLET
PLYMOUTH
CHEVROLET
CHEVROLET
CHEVROLET
Model
DYNA
MAXIO
CAVAO
ESCO GT
CAVA
LUMI
PROT
SL
CORO
CORS
SUMMO
ACCOO
CAMRO
LHS
ESCO
ELAN
SL
CENT
REGA LIMI
ESCO
SL
SL
LUMIO
ACCO
crvi
GRAN PRIX
CAMR
TAUR
GRAN MARQ
CAMRLE
CHER
VOYA
ASTRO
VOYA
LUMI
S10
ASTR
Vehicle Class
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
CAR
LDT1
LDT1
LDT1
LDT1
LDT1
LDT1
LDT1
Weight
3625
3375
2750
2625
3000
3375
2750
2625
2500
3000
2500
3250
3250
3750
2875
3000
2750
3995
3658
2849
2610
2581
3625
3500
2750
3625
3625
3650
4250
3628
3750
3375
4250
3750
3875
2875
4750
57
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Model Year
1994
1996
1996
1990
1991
1994
1996
1996
1996
1996
1996
1996
1997
1997
1997
1998
1999
Make
PONTIAC
FORD
FORD
CHEVROLET
FORD
FORD
FORD
DODGE
DODGE
DODGE
DODGE
FORD
DODGE
DODGE
PONTIAC
DODGE
FORD
Model
TRAN
EXPL
RANG
SURE
E1500
F150
F150
DAKO PICK
D250 RAM
GRAN CARA
CARA
F 150 PICK
GRAN CARA
DAKOT
TRANS SPOR
CARA GRAN
WIND
Vehicle Class
LDT1
LDT1
LDT1
LDT2
LDT2
LDT2
LDT2
TRUCK
TRUCK
TRUCK
TRUCK
TRUCK
TRUCK
TRUCK
TRUCK
TRUCK
TRUCK
Weight
4250
4500
3750
5250
4000
4500
4500
4339
4715
4199
4102
4473
4318
4382
4175
4303
4500
58
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Appendix D Response to Peer Review Comments on Chapter 2:
Temperature Adjustments
This section provides a verbatim list of peer reviewer comments submitted in response to the
charge questions for Chapter 2 (Temperature Adjustments), and includes EPA responses to the
peer-review. The other chapters of the report (Humidity adjustments, Air Conditioning
Adjustments, and Inspection and Maintenance Adjustments) document areas that did not have
major changes for MOVES2014 and thus were not subject to another round of peer-. To view the
peer-review for those sections, please see the MOVES2010 Report.30
D. 1 Adequacy of Selected Data Sources
Does the presentation give a description of selected data sources sufficient to allow the reader to
form a general view of the quantity, quality and representativeness of data used in the
development of emission rates? Are you able to recommend alternate data sources might better
allow the model to estimate national or regional default values?
D.I.I Dr. Chris Frey
The report appears to deal with the best available data sources as of the time that it was drafted.
The main difficulty with the current draft is the lack of sufficient specification/description of the
selected data and adequate or sufficient explanation in some cases of how it was used or
interpreted. See detailed comments for specifics.
RESPONSE: A tabular summary of data sources has been added (Table 2-1) to assist the
reader in understanding the details of particular test programs. We have also improved
the explanation of how the data was used.
D.I.2 Dr. Joe Zietsman
The description of the data sources is adequate and I am not aware of others that may be more
suitable. The limitations of the data, specifically with regard to the age of the datasets, have been
acknowledged in the report. There is clearly a need for more extensive and current data.
Specifically with regard to Section 2.1, it would help to better describe what data was used for
the analysis of start emissions versus the validation. Also it wasn't clear if the data used was
based on testing conducted in controlled test chambers, or just based on measured ambient/intake
air temperatures, or is a mix of both types of data.
RESPONSE: A tabular summary of data sources has been added (Table 2-1) to address
any shortcomings in the descriptions of test procedures. We have also clarified whether
the temperature was controlled or ambient.
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D. 2 Clarity of Analytical Methods and Procedures
Is the description of analytic methods and procedures clear and detailed enough to allow the
reader to develop an adequate understanding of the steps taken and assumptions made by EPA
to develop the model inputs? Are examples selected for tables and figures well chosen and
designed to assist the reader in understanding approaches and methods?
D.2.1 Dr. Chris Frey
In general, the answer to this question is a qualified yes. The general concepts are mentioned, but
could be better organized. There should be more emphasis on not just describing what was done
but also giving some rationale as to why (see specific comments). The examples are generally
well-chosen but not communicated with sufficient specificity. Lack of specificity will lead to
reader misinterpretation.
D.2.2 Dr. Joe Zietsman
Overall, the methods and procedures were clearly documented. However, there are numerous
examples where more clarity is desirable:
Under Section 2.2, it would help to clarify earlier what the applicable model year groups are for
the application of the polynomial versus the exponential functions. Also suggest labeling the
equations.
RESPONSE: We added text to clarify that we only implemented log-linear functions in
cases where we had additional data. We also labeled the equations. In order to keep the
text short, we did not specify the model years where the log-linear equations begin,
because it differs for CO, HC, andNOx, but this made clear in the following tables.
Table 2-1 [now Table 2-2] and related text - why is the terminology of polynomial function
(with c=0 for all groups) retained though the best fit model has been established as a quadratic?
RESPONSE: We removed Cfrom Equation 2-1 and Table 2-2.
The description of the polynomial model fit (page 10, paragraph 1) is unclear. Seems as though
last two sentences if interchanged could help with clarity.
RESPONSE: We removed the first sentence, to keep the focus onMOVES2014.
Figures 2-1, 2-2 - the legend indicates four fit lines, whereas only 2 are shown. Looks as though
in both cases two datasets were combined. Description and clarification is required.
RESPONSE: The legends were changed to distinguish the model fits from the data points.
Page 13, first paragraph, "the temperatures to be lower across all temperatures" - revise/clarify.
RESPONSE: The text has been changed to the intended meaning "emissions to be lower
across all temperatures."
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D. 3 Appropriateness of Technical Approach
Are the methods and procedures employed technically appropriate and reasonable, with respect
to the relevant disciplines, including physics, chemistry, engineering, mathematics and statistics?
Are you able to suggest or recommend alternate approaches that might better achieve the goal of
developing accurate and representative model inputs? In making recommendations please
distinguish between cases involving reasonable disagreement in adoption of methods as opposed
to cases where you conclude that current methods involve specific technical errors.
D.3.1 Dr. Chris Frey
In general, analysis should be reported with sufficient detail as to the input data and methods so
that an independent investigator can reproduce the analysis and get the same answer. Thus,
disclosure of the data used for modeling fitting (e.g., in an appendix) would be helpful.
The issue of developing accurate and representative model inputs is not a purely quantitative
one. Judgments have to be made regarding what data can reasonably represent fleet average
emission rates for a given vehicle type, fuel, and range of model years (and other factors). These
judgments are inherently qualitative. The report is making use of available data in a reasonable
manner. The report should include a section on key limitations and future needs to help prioritize
(if resources can be applied to do it) what data should be collected to better inform the
development of these adjustment factors. Stated another way, what lessons are learned from this
analysis that could inform future data collection that in turn would provide a better basis for
future correction factors?
RESPONSE: We added a Conclusions and Future Research Section where we address
limitations due to the scope oftheMOVES2014 update, and areas that could be
prioritized for future updates.
D.3.2 Dr. Joe Zietsman
As mentioned previously, I believe the methods are reasonable and most appropriate keeping in
mind the data limitations and context.
D.4 Appropriateness of Assumptions
In areas where EPA has concluded that applicable data is meager or unavailable, and
consequently has made assumptions to frame approaches and arrive at solutions, do you agree
that the assumptions made are appropriate and reasonable? If not, and you are so able, please
suggest alternative sets of assumptions that might lead to more reasonable or accurate model
inputs while allowing a reasonable margin of environmental protection.
D.4.1 Dr. Chris Frey
In general, the approach and assumptions are reasonable. See specific comments for some details
of where some additional explanation is needed.
D.4.2 Dr. Joe Zietsman
One area that is clearly lacking is with regards to temperature effects on diesel vehicles. As
noted, the set of 12 vehicles for which FTP data at multiple temperatures are available comprise
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passenger cars and light duty trucks - the extrapolation of these to heavy duty trucks (including
for extended idling) is a concern. I believe some data might exist that can be looked at to better
support or modify the current approach. This data is likely to be outside of FTP cycle data and
would include cold starts at different temperature ranges - I can think of ORNL and TTI work as
examples. While the data may not be directly usable for MOVES 2014 it can be used as a cross-
check.
RESPONSE: Updating the diesel temperature effects was not within the scope of the
MOVES2014 update, but could be revisited in future MOVES models. We mention this in
the Conclusions and Future Research section.
D. 5 Consistency with Existing Body of Data and Literature
Are the resulting model inputs appropriate, and to the best of your knowledge and experience,
reasonably consistent with physical and chemical processes involved in exhaust emissions
formation and control? Are the resulting model inputs empirically consistent with the body of
data and literature that has come to your attention?
D.5.1 Dr. Chris Frey
The report would benefit from a literature review of what is known about whether or how
temperature affects cold start emissions and hot stabilized emissions for gasoline and diesel
vehicles, with a focus on the most important factors and on issues that would help in explaining
and interpreting trends observed in the data used here. For example, statements are made several
times that cold start temperature adjustments are not made for temperatures over 75 F. Is there
some theoretical reason as to why such adjustments are not needed? In the absence of technical
context, this choice comes across as arbitrary and perhaps unjustified. Perhaps the explanation is
that cold start temperature adjustments exist at higher temperatures than 75 F, but that they
would tend to decrease the cold start by small amounts that are difficult to estimate. Therefore, a
choice was made not to estimate them, which is supported by some analysis based on empirical
data (explain). To the extent that this leads to bias in the emissions estimates from MOVES at
high temperatures, it will tend to slightly overestimate the emissions, which may be desirable
direction of bias for a regulatory model.
RESPONSE: We added text in Section 2.2 stating that 75F is considered normal
operation temperature per the FTP test. Because the FTP cycles served as the baseline
for the certification and the cold start emissions data, we did not investigate the impacts
of temperature beyond 75 F. This is a research area that could be worth investigating in
the future.
D.5.2 Dr. Joe Zietsman
Yes, considering the limited data I feel the resulting model inputs are appropriate. As more data
and analyses become available, they can be adjusted.
D. 6 Updates to Temperature Adjustment Data
For theMOVES2014 update of Chapter 2: Temperature Adjustments, certain temperature
adjustments were updated with new data (e.g. HC and CO cold starts from later model year
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gasoline vehicles, PM running effect for 2005+ my vehicles), while other adjustments were
deemed sufficient from MOVES2010 and were left unchanged (e.g. HC and CO cold starts for
pre-2000 MY vehicles, PM and NOx cold starts, PM running effect on pre-2005 vehicles). Did
the EPA give sufficient description for its rationale for making or not making these changes in
MOVES2014?
D.6.1 Dr. Chris Frey
The impression that the report gives is that the previous adjustments were generally left
unchanged, but not that they were evaluated and found to be adequate. Hence, the text could be
more clear as to the decision making process here and whether it was based on evaluation with
independent data not originally used to develop the existing MOVES 2010 adjustments.
RESPONSE: We added text in Section 2.2 stating that "We did not consider reanalyzing
data from our previous test programs with the log-linear fit (Equation 2-2), because it
was considered outside the scope of the update for MOVES2014. "
For adjustments for new model year groups, it is not really entirely clear as to why a log-linear
model is any better than a polynomial model in that the reader is not shown quantitative results
(with supporting data and graphs, and statistical summaries of goodness of fit and statistical
significance) to support such a choice. There is some qualitative discussion to justify the decision
on the bottom of page 10, but the description is vague. It would help if there was a quantitative
comparison of both types of models fit to the same data set to illustrate why the log linear mixed
model is better, and if some fundamental reason could be given for the preference. The
reader/user may wonder if a loglinear model would give a better fit to data for the earlier model
year groups and, thus, if the earlier model year groups should be reanalyzed with the newer
model form. EPA should report on whether they considered doing this or whether they made a
comparison upon which it was decided not to reanalyze the MOVES 2010 adjustments. If so,
then why would the loglinear model be better for newer data but not for older data? Is it because
newer data tend to be smaller in magnitude?
RESPONSE: We added text in Section 2.2.1.2, to further the rationale for using log-
linear models, focusing on the intuitive benefits of the log-linear approach (yields a
monotonically increasing, positive values). In our analysis, we did evaluate goodness of
fit statistics between different approaches (Mean Absolute Error, and Root Mean Square
Error), but they were of secondary importance because the other approaches yielded
spurious relationships, unless additional structure was imposed on the model (e.g.
forcing terms to be zero). As such, we decided that it was not necessary to compare
goodness of fits statistics to support our decision to use log-linear models.
D.6.2 Dr. Joe Zietsman
When I looked specifically for the information/rationale in the text, I find it is adequately
described. However, it would have been useful to provide a summary table containing a list of
updates vs what was left unchanged and listing the rationale(s).
RESPONSE: This may be appropriate for technical reviewers to focus on the updates for
MOVES2014. However, the intent of the technical documentation is to be comprehensive
regarding all the temperature effects in MOVES2014, whether they are newly
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incorporated or the same asMOVES2010. We decided not to address this comment,
because it would distract from the primary purpose of documenting all the temperature
effects currently used in MOVES2014.
D. 7 General/Catch-All Review
D.7.1 Dr. Chris Frey
This is a significant report that documents an important part of the MOVES emission factor
model, which is used nationally for a wide variety of regulatory and other analyses. As such, it is
critically important that the report be well written and very clear. While the current draft of the
report is good in many respects, it comes across as a draft and is not in final form in terms of the
critical thinking needed to make sure that it clearly communicates information to the reader.
For each of the major sections, it will help the reader to have clearly labeled sections that deal
with light duty vehicles and with all other vehicle source categories. It will also help to clearly
define and consistently use terms and concepts.
The communication of what was done, and why, should be more clear and complete. Ideally,
sufficient information should be communicated regarding the underlying data and inference
approaches such that an independent investigator can reproduce the results and obtain the same
answer. Many of the detailed comments given below under "specific comments" are aimed at
this objective.
Figure and table captions need to be more specific.
In general, be careful about significant figures. It is pretty rare in this type of work that data are
known with more than 3 significant figures. However, in various places, numbers are reported
with 5 or more significant figures. Even if the original data might be known with many
significant figures, its adoption for use in representing a national fleet introduces uncertainty,
since the original data may not represent the U.S. national fleet as it exists today.
Many specific comments are given below that elaborate on responses given above in response to
the charge questions.
RESPONSE: We addressed Dr. Chris Frey's comments regarding vehicle source
categories, figure and table captions, and significant figures in his specific comments
below.
Specific Comments: Numbers refer to page/paragraph/line in the paragraph
Section 2 - please give the reader some overview of this section. Is this about LDVs only? This
text starts out with LDVs but no objective or context is given. Define the scope. Give a Icear
statement of the objectives of this section.
RESPONSE: We added an overview to Section 2 to state that the temperature effects are
applicable to light-duty and heavy-duty vehicles. We revised Section 2.1, including the
heading, to be specific to gasoline vehicles. We specified in Table 2-4 that the
temperature effects apply to all gasoline vehicles in MOVES.
7/5: a study design would help the reader, before diving into details of individual studies. This
section 2.1 needs a summary table to help guide the reader through all of these studies. The table
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should include the following columns: Data Source (name of study, with references), Type of
Test (e.g., FTP, LA92, IM240, etc.), a column indicating if cold start is addressed (yes/no), a
column indicating if hot stabilized tailpipe emissions are addressed (yes/no), the temperature
range of the measurements, the number of vehicle measurements (reported separately for cold
start and hot stabilized), the range of model years, and the range of vehicle size (or other factor).
RESPONSE: A table summarizing the programs analyzed for modeling has been added to
aid the reader (Table 2-1).
7/5/2 "FTP tests" not "FTPs"
RESPONSE: Addressed.
8, section 2.2, statements are made here as to what was done without context or explanation.
State a purpose/objective followed by an overview, and save details for later. However, when
mentioned, explain WHY no adjustments are given for temperatures over 75 oF, and why there
are no "additive" adjustments for temperatures below 75 oF. to a reader who is reading this for
the first time, these out-of-context statements are very confusing, and this material is not
organized. Also, for all of the discussion of the first equation (number the equations), later it
turns out that in application this equation is reduced to just one term in each case. The text is
confusing also in that it states that HC, CO, and NOx cold start emissions "were modeled" but it
does not become clear until later as to the distinction between what is already in MOVES 2010
versus what is new for MOVES2014. Thus, in just 7 lines of material, the text offers far more
confusion than illumination.
RESPONSE: An overview has been added to this section as well as some explanation for
the guiding principles used in this analysis, including the decision to only model
temperature effects below 75 F temperature. We numbered the equations to be able to
reference them in the text. We also added text discussing our rationale for maintaining
temperature effects from MOVES2010, and incorporating new temperature effects in
MOVES2014 based on newer data.
It would help if there is a clear summary of what is in MOVES 2010 before discussing what is
new for MOVES 2014. The latter should be accompanied by an explanation of why.
RESPONSE: Clarification has been added as to what was in MOVES 2010 and what has
been changed for MOVES2014 in Section 2.2.
Section 2.2.1 is very difficult to follow. It would help to have an introduction paragraph that
gives an overview of this section. Otherwise, it feels like getting pulled along a path without
knowing to where it is leading.
RESPONSE: An overview has been added to describe the process of analyzing this
dataset before discussing the polynomial and log-linear model fitting procedures.
In Section 2.2.1, it is important to show, either here or in an appendix, the original data and the
fitted regression models, along with disclosure of the coefficient of determination of each model,
and the t-ratio and p-value of each coefficient. Just showing model predictions is not enough -
information should be given so that it is clear how the model parameters were estimated and
regarding the goodness-of-fit of these models. It is also important to be clear as to what results
are statistically significant.
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RESPONSE: We added tables with the model parameter estimates, and the t-ratio andp-
values of the estimated model parameters in Table 2-3, Table 2-4, Table 2-5. We also
added discussion about our model fitting procedures, and the rationale behind selecting
the final model.
The terms "significant difference " and "statistical difference " are used to indicate ap-
value of< 0.05. Also, the data can be viewed graphically in Figure 2-1 and Figure 2-2.
The text here tends to say "here's what we did" without explaining why. E.g., the next to last
paragraph declares what was done, but does not provide insight as to why a polynomial function
was used, or why additive adjustments are set to zero above 75 oF. After reading the entire
chapter, it is still not clear as to why no adjustments are made above 75 oF, except that maybe
they are small in value. Is there some theoretically reason as to why higher temperatures might
not shorten cold start duration or lower total cold start emissions?
RESPONSE: Clarification has been added to the beginning of the chapter on this issue.
75 is considered the ambient temperature at which standard certification FTP test cycle
is conducted. Theoretically, temperatures above 75 could have some impact on
shortening cold start duration but that analysis has not been performed.
Table 2-1 caption is unclear. Polynomial model coefficients for what model, for what vehicle, for
what variable? Captions should ALWAYS be specific and clearly communicate what the content
is about. Furthermore, information should be communicated regarding the R2 of each of these
models, and confirmation should be given that values not shown were statistically not
significantly different from zero and therefore were set to zero or, if there is some other reason,
then explain. To avoid confusion, for the CO results for the 2000-2005 (not 1990-2005) model
year range, indicate either n/a or use grey to 'grey out' so that it is clear that the missing values
here are intentional.
RESPONSE: The table [now Table 2-2] has been edited to help avoid confusing the
reader with overlapping model year groups. We added heading text stating the ranges of
model years that the effects apply to. We also added 'grey-out' boxes to indicate that
missing values are intentional.
While I don't have a significant concern in particular about the model forms used here, the text
could be more clear and organized, i.e. after reading this, my impression is that the linear form of
the first equation on page 8 is a legacy from the previous version of MOVES, and that the log-
linear form is now preferred... for the latter, a rationale should be given earlier for this
preference, supported by details later.
RESPONSE: A sentence has been added to this section overview (2.2) to address the
scope oftheMOVES2014 update, and why the polynomial form is retained in MOVES.
10/1: the explanation would be more clear if the data were shown graphically along with trend
lines representing the preferred model and the alternative model that was originally considered.
Some of the text is unclear... e.g., "unbalanced nature of the analysis" ... in particular, the word
"nature" is vague. If the issue here is that there was a smaller sample size for -20 oF than at other
temperature, then say so more specifically. Also, "This" in the last sentence has the wrong
antecedent and thus the sentence does not make sense.
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RESPONSE: The paragraph has been edited to explicitly state the smaller sample size of
vehicles tested at -2 OF. We removed the last sentence to the paragraph that did not value
to the paragraph, and was unclear to the reviewers.
10/2: [now page 11] a temperature effect is inferred, not developed. Here again, 'anomalies' are
best illustrated by visualizing the data either here or in an appendix.
RESPONSE: We changed the test to state that the temperature effects are inferred. We
also added Figures 2-3 and 2-4 to visualize all the temperature effects used in MOVES
(using both the polynomial and log-linear models).
10/2, [ page 11] next to last line... table indicates 1990 and later but text indicates 2000 and later.
RESPONSE: We added shading to Table 2-2, to make it clear the intended model year
groups for the CO andHC temperature effects between 1990 and 2005 model year
vehicles.
10/3: doubtful that "raw" data were used - the data probably underwent QA. Use a different
descriptor
RESPONSE: We removed "raw "
10, last paragraph, "linear mixed models" is not defined and should be explained. The term
"mixed" seems to refer to a mix of continuous and categorical variables. Avoid using the word
"nature" as in "paired nature" and "unbalanced nature" - these terms are vague.
RESPONSE: These changes have been made.
11: start emissions have units (e.g., grams). Always show units with numbers and in defining
numbers.
RESPONSE: Units have been added.
12: figures 2-1 and 2-2. Do not rely only on color to distinguish lines for two different series...
use different line styles also. The legend does not clearly define the data points. Is 2010 and new
the same as MSAT? Using two different descriptors for the same time period is confusing. Be
consistent.
RESPONSE: Labels have been updated and colors have been accompanied by
shading/line style differences
13/1/1: for clarify, was the value of A similar for both model year groups (i.e. did not have a
statistically significant difference)?
Response:. Yes, we have clarified derivation of A using Table 2-3 and Table 2-4. There
was no significant difference to the fiiXMSAT-2 interaction term that we added to the
model. We then refit the data without a fiiXMSAT-2 interaction term, to derive the A, B,
and C terms used in MOVES2014. Because A is derived from fii there is no difference in
the A term between the 2001-2009 and 2010 and later model year group vehicles.
13/1: Why is Table 2-3 cited before Table 2-2?
RESPONSE: Table citation removed.
13/2/4: "is used to representative" seems incorrect.
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RESPONSE: Reworded to "effect represents the impact of Mobile... "
13/2: the explanation of the MSAT-2 rule is very helpful, but should be given earlier since this
rule is mentioned previously.
RESPONSE: We added an explanation of the MSAT-2 rule to the chapter overview
13/3 - please explain what is "composite FTP NMHC emissions". Does this mean it includes
both cold start and hot stabilized emissions?
RESPONSE: Yes, we added to the report for clarification in section ...
13 - near bottom of page: "start!empAdjustment" table - please define or explain what this is.
Similarly, define or explain a2010, apre, and apost. Also explain/define HLDT and MDPV. This
seems to start a new section on other regulatory classes and thus should have a new header.
RESPONSE: These abbreviations are defined in the bullets above. An explanation has
been included for the other terms.
Table 2-3: caption should be clear as to the applicability of these data - i.e. for all source types?
OrjustLDGV?
RESPONSE: Caption has been changed to specify that the effects apply to all gasoline
source types.
14... why "not unexpectedly"? What was the expectation and its basis?
RESPONSE: This misleading term has been removed.
Figure 2-3: for what vehicle type is this applicable? Also, the "data" plotted here is confusing.
Does each "point" in the graph at a given temperature and model year range represent one
measurement, or is it the average of all (how many) measurements at that temperature for that
model year group? If the latter, then why not show the individual vehicle data, or show a range
of values associated with each average?
RESPONSE: The graphic is based on an older analysis that is not usedinMOVES2014.
We removed Figure 2-3, and kept the discussion of the analysis that is relevant to the
NOx effects used in MOVES2014.
Table 2-4 is for what type of vehicle? Fuel? Range of model years? Are these empirical data or
predictions from a model? If from a model, what model? i.e. be more specific. Figures and tables
should be self-documenting. Also, what is the "Emission Result" - is this an increment of cold
start emissions for cold starts at temperatures other than 75 oF? The caption is unclear, and thus
the data are highly likely to be misinterpreted.
RESPONSE: We added clarification that it is the average incremental cold start
emissions from gasoline vehicles calculated from theMSOD, ORD, andMSAT'programs
Page 16, equation: please justify that the "tempAdjustTerm" should have 7 significant figures.
Also, the R2 can be reported as 0.61 or maybe 0.611, but 6 decimal places is not necessary nor
justifiable.
RESPONSE: The number of significant figures has been reduced
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Page 16 "the actual data indicate that the cold start NOx emissions increase as the ambient
temperature rises above 90 oF. Therefore...." "set to zero." What precedes the "therefore" does
not actually explain why values that show an increase with higher temperatures should be set to
zero. Provide an explanation/justification for this decision. As an aside, what are "actual" data?
does this refer to "measurements"?
RESPONSE: The averaged cold start data in Table 2-8 indicate that the cold start NOx
emissions increase above 90. We add text to qualify that this may be an artifact of the
data since only some vehicles were measured above 75 F. We also state that we are not
adjusting temperatures above 75F to be consistent with the other temperature effects.
Page 16, last paragraph: "evaluated" how or in what way? "small" and "too minor" means what,
exactly... are these not statistically significantly different from zero incremental change, or are
these such a small percentage of baseline emissions as to be negligible. Justify, preferably
quantitatively.
RESPONSE: An explanation has been added to the report to explain why the NOx change
was evaluated as negligible, because the start NOx emissions are a small percentage of
the baseline NOx emissions.
Figure 2-4: Should also show mean values. Also indicate sample size.
RESPONSE: The sample size is indicated by the data points, and an overview of the
program is given in Appendix A.
Page 17: "report4"?
RESPONSE: We verified the citation to the EPA report is active to the Kansas City Study.
Page 18: for the equation given, report the R2 value for each case mentioned.
RESPONSE: The R2 value is not available in the cited reports. Additionally, the R2 value
for the log-transformed data, is not directly comparable to the model predictive power in
real-space, and does not add substantial value for the average reader.
Page 18 - please either explain way it is necessary or preferred to imply 6 to 7 significant figures
for these ratios, or use a reasonable number of significant figures (probably 2 or 3). Furthermore,
it is not really clear how one arrives at the ratio from a 30% NMHC reduction, which should be
explained or shown.
RESPONSE: The numbers have been reformatted to 3 significant digits. We also an
example calculation at how the multiplicative increases were reduced by 30%.
Table 2-5 and Table 2-6 [Now Table 2-9, and Table 2-10]: are these results specific to any
particular vehicle type or fuel? Significant figures?
RESPONSE: We added 'gasoline vehicles' to the heading of Table 2-9 and Table 2-10,
and have reduced the significant figures.
Page 19: last two lines of second paragraph- cannot figure out what this is about (unclear)
RESPONSE: These sentences are not needed in this report and are removed. The concept
is covered in the light-duty vehicle emission rate report.
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Figure 2-6 [ now Figure 2-7]: is this for total PM, PM10, PM2.5? It is useful to show mean
values also. Also indicate sample size.
RESPONSE: We addedPM2.5, and the sample size to the heading of Figure 2-7. For
purposes of the qualitative comparison, we believe that showing the distribution of the
data, without the mean, is sufficient to show that the data compare well.
Page 20: please indicate if this section is for all model years or is applicable to a particular range
of model years. Also please indicate sample size in Figure 2-7, and the range of model years of
the data shown.
RESPONSE: We added text at the beginning and end of Section 2.3.1 to specify that the
effects apply to all model year vehicles. We also added information on the sample size of
the KCVES in the beginning of Section 2.2.3, and in Section 2.3.1 with regard to the
sample size in Figure 2-7.
Page 21, 1st few lines: previous text indicates that there is no effect; thus, in such a context, this
text is confusing in that it seems to be contradictory. However, it ends with zero change. This
could be rewritten to make clear that MOVES was designed to allow for modeling of an effect,
but given that there is no observed effect, the coefficients are set to zero...
RESPONSE: We added text to the beginning of Section 2.3.1 stating that MOVES is
design to allow for modeling temperature effects for running-exhaust for HC, CO, and
NOx, but the data does not support it. This point is re-emphasized at the end of Section
2.3.1.
Page 21 - middle of page says "no significant temperature effect is observed in either cycle."
Figure 2-8 gives some hint that there may be an effect for the US06 cycle. However, if there is
an effect, it may be on the mean rather than the median "emissions". Is this total emissions or
some kind of emissions increment... not very clear. If this is a hot running emissions, why is it
not in units of mass per mile? In general, it is helpful to indicate sample sizes of data sets and to
also indicate the mean value in box and whiskers plots.
RESPONSE:. We added text explaining that we fit log-linear models to the data, and
found no statistically significant temperature effect. The units .
We specify the units in the heading as grams per cycle.
Page 23: After rereading this a few times I think I finally understood the logic here, but for one
thing the graphs are very hard to read, and thus it is hard to follow what is being stated in the
text. The text seems to deal with this later, but on page 23 my thought was that a real world trip
can be on the order of 500 to 1000 seconds, so even if somehow bag 2 emissions included a
delayed cold start effect, if there really was such an effect, then it needs to be considered
somehow in the emission inventory. Thus, it may not be "wrong" if it is averaged into the hot
stabilized emission rate for the purpose of improving the accuracy of the inventory for trips of
similar lengths. This seems to be the point made on page 24 in the first of the paragraphs
numbered as "2."
RESPONSE: We clarified the heading of Figure 2-11 to mention that it is the average of
sec/sec data. We also added text immediately follow ing Figure 2-11, to help transition
the discussion to the statistical tests using the same data. We also added text to better
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explain the results in Table 2-11, and added reference to the form of the equation used in
the statistical tests.
Page 25: first line of section 2.3, please clarify if these are light duty diesel vehicles. Even the
term "diesel trucks" later in this paragraph is not very clear... are these light duty trucks? - i.e.
be more specific as the types of vehicles represented here.
RESPONSE: We clarified that the diesel trucks refer to light-duty diesel trucks. We also
specified in the overview following Section 2 that we only tested light-duty diesel
vehicles, but apply the effects in MOVES to all diesel vehicles.
Table 2-8... please be clear as to what type of diesel vehicle is represented here (see above).
Also, explain the basis of the confidence intervals shown in Figure 2-10 - are these actually CI
on the mean, or are they a range of variability in the data?
RESPONSE: We added text to Table 2-9 heading stating that the data are from light-duty
diesel vehicles. We also added text on Figures 2-10, 2-11, and 2-12 stating that the graph
plots the means, to reflect the definition of the confidence interval on the mean as stated
in the text.
25/equations: significant figures. When a number is reported such as 4.22477812, it implies
precision of 4.22477812±0.000000005, which seems implausible.
RESPONSE: The numbers were reduced to 3 significant digits.
Figures 2-11,2-12: indicate the model year range and vehicle type.
RESPONSE: We specified in the Figure heading text that the data are for light-duty
diesel vehicles.
28: not clear on what basis it is reasonable to extrapolate results for gasoline vehicles to diesel
vehicles. This needs more explanation/justification.
RESPONSE: We revised the text to state that we did not evaluate the diesel running
temperature effect, but we set it to zero, similar to what was done for the gasoline vehicle
effects.
D.7.2 Dr. Joe Zietsman
In my review of Chapter 2 of the report documenting temperature adjustments for MOVES 2014,
I found the methods and assumptions to be overall reasonable and adequate. There are obviously
significant constraints, specifically with regard to available data, and I have touched upon these
limitations in my specific answers to the questions.
30 USEPA (2010). MOVES2010 Highway Vehicle Temperature, Humidity, Air Conditioning, and
Inspection and Maintenance Adjustments EPA-420-R-10-027. Assessment and Standards Division.
Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI.
December, 2010. http://www.epa.gov/otaq/models/moves/documents/420rl0027.pdf
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