Emission Adjustments for Temperature,
Humidity, Air Conditioning and
Inspection and Maintenance for
Onroad Vehicles in MOVES3
£% United States
Environmental Protect
Agency
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Emission Adjustments for Temperature,
Humidity, Air Conditioning and
Inspection and Maintenance for
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.
Onroad Vehicles in MOVES3
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
4>EPA
United States
Environmental Protection
Agency
EPA-420-R-22-030
November 2022
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1. Introduction 5
2. Temperature Adjustments 6
2.1 Data Sources for Gasoline Temperature Effects for THC, CO, and NOx emissions 6
2.2 Temperature Effects on Gasoline Start Emissions 7
THC and CO Start Emissions for Gasoline-Fueled Vehicles 8
Temperature Effects on Gasoline NOx Start Emissions 17
Temperature Effects on Gasoline PM2.5 Start Emissions 19
2.3 Temperature Effects on Running Exhaust Emissions from Gasoline Vehicles 25
THC, CO and NOx Running Exhaust Temperature Effects 25
PM2.5 Running Exhaust Temperature Effects 25
2.4 Temperature Effects on Diesel Vehicles 28
THC, CO and NOx Temperature Effects for Diesel Vehicles 28
PM Temperature Effects for Diesel Vehicles 31
2.5 Temperature Effects on Compressed Natural Gas Vehicles 32
2.6 Temperature Effects on Start Energy Consumption 32
2.7 Conclusions and Future Research 34
3. Humidity Adjustments 35
3.1 Humidity Adj ustment Equation 35
3.2 Future Research 36
4. Air Conditioning Adjustments 37
4.1 Air Conditioning Effects Data 37
4.2 Air Conditioning Effects on Emissions and Energy 38
Full A/C Adjustments for THC, CO and NOx Emissions 39
Full A/C Adjustments for Energy Consumption 39
4.3 Adjustments to Air Conditioning Effects 40
4.4 Conclusions and Future Research 41
5. Inspection and Maintenance Programs 42
5.1 Inspection & Maintenance in MOBILE6 42
5.2 Inspection & Maintenance in MOVES 42
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5.3 Development of MOVES I/M Factors 43
5.4 I/M Compliance Factors 46
5.5 Calculation of I/M Emission Rates 46
5.6 Default I/M Program Descriptions (IMCoverage) 47
6. References 52
Appendix A Derivation of Default Temperature and Humidity Values
and Other Meteorology Calculations 57
Appendix B OTAQ Light-duty gasoline 2012 Cold Temperature
Program 63
Appendix C Air Conditioning Analysis Vehicle Sample 64
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List of Acronyms
A/C air conditioning
ACCF air conditioning correction factor
ASM Acceleration Simulation Mode
CO Carbon Monoxide
CDB county database
CF critical flow factor coefficient
CFR Code of Federal Regulations
CNG Compressed Natural Gas
CV coefficients of variation
DPF diesel particulate filter
EPA U.S. Environmental Protection Agency
E85 gasoline containing 70-85 percent ethanol by volume
F Fahrenheit
FTP Federal Test Procedure
GDI Gasoline Direct Injection
GHG Greenhouse Gases
GVWR Gross Vehicle Weight Rating
HC hydrocarbons
HP horsepower
I/M Inspection and Maintenance program
IM240 Inspection and Maintenance roadside vehicle driving schedule
KCVES Kansas City Light-Duty Vehicle Emissions Study
kW Kilowatt
LA-92 California dynamometer driving schedule for light-duty vehicles
LDT Light-Duty Truck
LDV Light-Duty Vehicle
LHDT Light Heavy-Duty Truck
LLDT Light Light-Duty Truck
MDPV Medium-Duty Passenger Vehicle
MOBILE6 EPA Highway Vehicle Emission Factor Model, Version 6
MOVES Motor Vehicle Emission Simulator Model
MSAT Mobile Source Air Toxics rules
MSOD Mobile Source Observation Database
NEI National Emission Inventory
NMHC Non-Methane Hydrocarbons
NMIM National Mobile Inventory Model
NOx Oxides of Nitrogen
OBD On-Board Diagnostics
ORD Office of Research and Development
OTAQ Office of Transportation and Air Quality
PFI Port Fuel Injection
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PM
Particulate Matter
RIA
Regulatory Impact Analysis
SFTP
Supplemental Federal Test Procedure
SIP
state implementation plan
SRC
selective reduction catalysts
STP
scaled tractive power
SwRI
Southwest Research Institute
THC
Total Hydrocarbons
US06
A drive cycle that is part of the SFTP
VIN
Vehicle Identification Number
voc
Volatile Organic Compound
VSP
vehicle specific power
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1. Introduction
The United States Environmental Protection Agency's Motor Vehicle Emission Simulator—commonly
referred to as MOVES—is a set of modeling tools for estimating air pollution emissions produced by
onroad (highway) and nonroad mobile sources. MOVES estimates the emissions of greenhouse gases
(GHGs), criteria pollutants, and selected air toxics. The MOVES model is currently the official model for
use for state implementation plan (SIP) submissions to EPA and for transportation conformity analyses
outside of California. The model is also the primary modeling tool to estimate the impact of mobile
source regulations on emission inventories.
MOVES calculates emission inventories by multiplying emission rates by the appropriate emission-
related activity, applying correction and adjustment factors as needed to simulate specific situations, and
then adding up the emissions from all sources and regions. The highway vehicle emission rates in the
MOVES model 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 MOVES3 adjustments that affect running exhaust, start exhaust, and extended idling
exhaust emissions for Total Hydrocarbons (THC), carbon monoxide (CO), nitrogen oxides (NOx), fine
particulate matter (PM2 5) and energy consumption. The temperature effects that impact these pollutants,
also affect the pollutants that are calculated from these pollutants in MOVES, such as volatile organic
compounds (VOC)2 and individual toxics such as benzene3 (chained to THC), NO2 (chained to NOx)10:,n,
elemental carbon (chained to PM2 s)2, and CO2 emissions (chained to energy)4. The definitions of these
pollutants and the relationship to the primary pollutants are discussed in the cited MOVES reports. The
crankcase emission processes1011 are chained to running exhaust, engine start, and extended idling
exhaust 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.5
This report replaces a version released in 2020. It now describes changes to I/M coverage for
MOVES3.0.4 as well changes from MOVES20146 to MOVES3. Updates for MOVES3 were minor.
They include removing the running exhaust temperature effect for particulate matter and correcting an
error in the I/M adjustments. The update to the running temperature effect was included in peer-review of
MOVES updates conducted in 2017, and comments and responses are located on the EPA Science
Inventory webpage.7 This report also includes an appendix describing temperature and humidity in
MOVES defaults.
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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 two considerations:
1. Ambient temperature8
2. The latent engine heat from a previous trip, applied as an adjustment based on the length of time
the vehicle has parked since operating (soak time)9'10
This report describes the adjustment based on ambient temperature. Soak time and start emissions are
addressed in the light-duty10 and heavy-duty11 emission rate reports.
This report addresses temperature sensitivity of emissions from gasoline vehicles in Sections 2.1 through
2.3. Although the gasoline temperature effects are developed based on emissions data from light-duty
gasoline vehicles, they 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 diesel
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 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.
These effects are applied only to vehicle starts.
2.1 Data Sources for Gasoline Temperature Effects for THC, CO, and NOx emissions
To determine the impact of ambient temperature on running emissions, our analysis included the Bag 2
emissions of Federal Test Procedure (FTP) tests as well as US06 tests (without engine starts).
For start emissions, measurements from both the Federal FTP and California Unified Cycle (3-phase / 3-
bag tests) were used. 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.
The data used in these analyses are from the following sources:
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Table 2-1 Summary of Data Sources
Data Source
Test
Temperatures Tested (deg. F)
# of Vehicles
MY Range
MSOD
FTP +
15-110
Hundreds
Pre-2005
ORD
FTP, IM240
-20, 0, 20, 40, 75
5
1987-2001
MSAT
FTP
0, 20, 75
4
2005
OTAQ
FTP, US06
0, 20, 75
9
2006,2010
• MSOD - EPA's Mobile Source Observation Database (MSOD) as of April 27, 2005.
EPA has 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).12
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.
• 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 IM240
cycles under controlled conditions at temperatures of: 75, 40, 20, 0 and -20°F.13
• 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°F. This program was used in
the Regulatory Impact Analysis of Final Rule: Control of Hazardous Air Pollutants from
Mobile Sources14, which is referred to as MSAT-2 in this report to distinguish it from an
earlier mobile source air toxics (MSAT) rulemaking.15 The MSAT-2 rule required Tier 2
vehicles to meet a non-methane hydrocarbon (NMHC) standard on the FTP cycle of 0.3
g/mile for light-duty vehicles (<6,000 lbs) beginning phase-in for model year 2010
vehicles.16
OTAQ Cold Temperature Program - EPA's Office of Transportation and Air Quality
(OTAQ) contracted the testing of nine Tier 2 vehicles (2006 and 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 tested vehicles is summarized in Appendix B . Note
that for the estimation of the THC and CO cold start effects the two GDI vehicles were
excluded from the analysis.
2.2 Temperature Effects 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.
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The effects of ambient temperature on THC, 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, depending on model year group and pollutant:
Additive Grams = A*(Temp-75) + B*(Temp-75)2 Equation 2-1
Additive Grams = Be A*(TemP-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, A and B, for the adjustment equations are stored in the
StartTempAdjustment table. This table contains temperature effect coefficients for each model year
group, operating mode, and pollutant.
In MOVES, the temperature effects for older model year groups use polynomial function (Equation 2-1)
and more recent model year vehicles use log-linear function (Equation 2-2). The data processing and the
model fitting process differed for the polynomial and log-linear fits and each is described separately
below. Temperature effects in MOVES3 were retained from earlier versions of MOVES.
THC and CO Start Emissions for Gasoline-Fueled Vehicles
In developing temperature adjustments for THC and CO start emissions, both polynomial and log-linear
regression models were used to fit the data. Data anomalies were resolved by combining 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) and 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
vehicles.16
Polynomial Pits
The coefficients for THC emissions for pre-2006 gasoline vehicles and CO emissions for pre-2001
gasoline vehicles 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 relevant vehicle test in the MSOD, ORD and MSAT data. 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
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• 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.
Table 2-2 Polynomial Model Coefficients for CO Temperature Effects for 2000 Model Year and Earlier
Gasoline Vehicles and THC Temperature Effects for 2005 and Earlier Gasoline Vehicles
CO
THC
Model Year Group
A
B
A
B
Pre-1981
-4.677
-0.631
1981-1982
-4.631
-0.414
1983-1985
-4.244
-0.361
1986-1989
0.002
1986-2000
0.023
1990-2005
0.003
The THC 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" THC 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 THC 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°. However, these coefficients are applied to all ambient temperatures below 75 °F in
MOVES.
For CO, the temperature effect developed based on the 1994-2000 model year vehicles was applied to all
model years from 1986-2000, because including 1986-1993 model year vehicles in the analysis resulted
in cases where older model years were modeled with substantially lower CO emissions than newer model
years. Note that the base CO emission rates still vary across this model year range.
To adapt the additive ambient temperature adjustments to account for intermediate soak times, the A and
B coefficients for start operating modes other than cold starts were reduced by multiplying by a factor
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equal to the ratio between emissions at the desired soak time and the cold start emissions for catalyst
equipped vehicles as used in MOBILE6.9 These factors are summarized in Table 2-3.
Table 2-3 Soak Time Multipliers for Additive Start Temperature Effects
Operating Mode ID
Nominal Soak Time (min)
THC
CO
NOx
108
720
1
1
1
107
540
0.908778
0.91377
1.053118
106
240
0.733962
0.79137
1.117624
105
105
0.64496
0.72996
1.128799
104
75
0.599625
0.6285
1.129778
103
45
0.444825
0.44136
1.02786
102
18
0.208548
0.199678
0.58398
101
3
0.037593
0.035422
0.20508
Log-linear Fits
In estimating the THC temperature effect for model years 2006 and later and the CO temperature effect
for model years 2001 and later3, data from ORD, MSAT and OTAQ cold temperature programs'3 were
used to fit regression models. We used linear mixed models, with both continuous and categorical
variables, to fit to the logarithm of the start emissions. Second-order polynomial models exhibited non-
intuitive behaviors (e.g., negative values, non-monotonically increasing 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 nlme.17 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
aThe CO temperature effects for 2001-2005 model years were estimated using the log-linear fit 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.
b We excluded the two GDI vehicles from the OTAQ cold temperature program from the model fit because they
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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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:
log(y) = oc + /?-, ¦ Temp Equation 2-4
We then converted the mean logarithmic model to real-space, yielding:
y = gK+Pi Temp Equation 2-5
We then normalized to degrees below 75°F, by setting T' = 75 — Temp , and substituting Temp = 75 —
T' into the above equation and rearranging. This yields the equations:
y= e°c+Pi(75-T') Equation 2-6
y = /irT) Equation2-7
y — gx+75'ft eft(7'em!'-75) Equation 2-8
Then setting A = /?l5 and B= ea+7S'^. B is essentially the 'Base Cold Start' at 75°F, with units of
(g/start). The eA(Temv~7^) term is a multiplier which increases the cold start at temperatures below 75°F.
To convert the model to an additive adjustment, we calculated the additive difference from the cold start:
y - y(75) = BeA(.TemP~75) — g, This model form can be used in the current MOVES temperature
calculator for THC and CO, by setting C = -B, yielding Equation 2-2:
Additive Grams = Be A *(TemP-75) + C Equation 2-2
The initial estimated fixed effects (including p-values) for the linear model fit for CO are displayed in
Table 2-4. The model estimates that the Portable Fuel Injection (PFI) MSAT-2 compliant vehicles (Model
year 2010) tested in the OTAQ 2012 test program have consistently lower CO start emissions than the
pre-MSAT-2 vehicles (pre-2010), as shown by the positive pre-MSAT coefficient (a.2). However, no
statistically significant difference in the log-linear impact of temperature (coefficient (3) was found
between the 2001-2009 and the 2010 model year groups for CO emissions, as shown in Table 2-4 (p-
value of the Temperature x pre-MSAT effect is >0.90).
Table 2-4 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)
Value
Std.Error
DF
t-value
p-value
Intercept (ai)
3.5502
0.1433
80
24.8
2.8E-39
Temperature ((3i)
-0.0380
0.0022
80
-17.5
4.3E-29
pre-MSAT (012)
0.7378
0.2066
11
3.6
0.0044
Temperature ((3i) x pre-MSAT (012)
-0.0003
0.0032
80
-0.1
0.9225
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Because there was not a significant temperature effect between the pre- and post-MSAT-2 vehicles, we
estimated the temperature effect (|3i) from a model fit where the pre-MSAT-2 and post-MSAT-2 vehicles
are pooled together as shown in Table 2-5.
Table 2-5 Fixed Effects for the Final CO Model Fit to Data from 2001+ Model Year Vehicles from the ORD,
MSAT and Cold Temperature Programs (13 vehicles, 95 observations)
Value
Std.Error
DF
t-value
p-value
Intercept (ai)
0.6914
0.1400
81
4.94
4.1E-06
Temperature (Pi)
-0.038
0.0016
81
-24.08
1.1E-38
pre-MSAT (a2)
0.7284
0.1815
11
4.01
0.0020
The data along with the final model fits are displayed m Figure 2-1. The MSAT-2 compliant group
(2010+) has significantly lower base cold start (coefficient a), which causes the emissions to be lower
across all temperatures for the newer model year vehicles. The CO model coefficients in the form of
Equation 2-2 for use in MOVES are provided in Table 2-8. The 2009 and 2013 model year B values are
derived from the linear mixed model for the pre-MSAT-2 and the MSAT-2 compliant groups,
respectively. The 2010 through 2012 model year B values are derived by linearly interpolating the 2009
and 2013 values.
0 25 50 75
Degrees (F)
Figure 2-1 FTP CO Start Emissions with Log-linear Model Fit
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For THC emissions, a statistically 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 2-6 (p-value of the
Temperature x pre-MSAT term is much smaller than 0.05).
Table 2-6. Fixed Effects for the Final THC Model Fit to Data from 2006+ Model Year Vehicles from the
MSAT Program and the Cold Temperature Program (11 vehicles, 69 observations)
Value
Std.Error
DF
t-value
p-value
Intercept (ai)
1.8613
0.1321
56
14.1
4.6E-20
Temperature ((3i)
-0.0394
0.0011
56
-34.6
1.7E-39
pre-MSAT (a2)
0.7503
0.2254
9
3.3
0.0088
Temperature ((3i) x pre-MSAT (012)
-0.0111
0.0021
56
-5.2
2.7E-06
The THC model fit to the cold start emissions data is graphed in Figure 2-2. As shown, the pre-MSAT-2
cold start emissions for THC are much more sensitive to cold temperature than the MSAT-2 compliant
vehicles.
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t
V
\
\
\
\
\
\
\
V
Program
o MSAT Vehicle Data 2010+
• pre-MSAT Vehicle Data 2006-2009
\
\
\ ~
\
%
V
\
V
Model
— Model Fit 2010+
t -Model Fit 2006-2009
•
\ 9 \
\ O s
^ 1
8 ^—i
1
0 25 50 75
Degrees (F)
Figure 2-2 FTP THC Start Emissions with Log-linear Model Fit
The differences in the THC 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 vehicles.16
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 light heavy-duty trucks (LHDTs) (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, as shown in Table 2-7.
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Table 2-7 Phase-in of Vehicles Meeting Cold Weather THC Standard
Model Year
LDVs / LLDTs
LHDTs / MDPVs
2010
25%
0%
2011
50%
0%
2012
75%
25%
2013
100%
50%
2014
100%
75%
2015
100%
100%
For the phase-in years, the coefficients for the THC temperature effect equation in the
startTempAdjustment table were adjusted linearly according to the light-duty vehicle phase-in. Equation
2-9 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) + ^2O13(0.25) Equation2-9
With this approach, the log-linear temperature effect (coefficient A) for THC emissions is reduced from
2009 to 2013 while the base 75° F THC 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., LHDTs/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 LHDTs/MDPVs
or heavy-duty temperature effects were available to assess this approach.
Table 2-8 summarizes the coefficients used with Equation 2-2 (log-linear) to estimate additive start
temperature adjustments for more recent model year gasoline vehicles.
Table 2-8. Coefficients Used for Log-linear Temperature Effect Equation for All Gasoline Source Types
CO
THC
Model Year Group
A
B
C
A
B
C
2001-2009
-0(V,x
4 1 V.
-4 1 '<¦
2006-2009
-U.051
0.308
-0.308
2010
-0.038
3.601
-3.601
-0.048
0.315
-0.315
2011
-0.038
3.066
-3.066
-0.045
0.322
-0.322
2012
-0.038
2.531
-2.531
-0.042
0.329
-0.329
2013 & Later
-0.038
1.996
-1.996
-0.039
0.336
-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 MOVES for CO and THC, respectively. These include both the polynomial fits
and the log-linear curve fits to the data.
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30CH
to
£
03
O)
(D 200
>
TD
TJ
CO
O100
u
20 40 60
Temperature (deg F)
Pre 1981
MY81_82
MY83_85
MY86_05
MY06_09
MY10
MY11
MY12
MY13 50
Note; In MOVES3, "MY13_50" applies to all model years 2013-2060.
Figure 2-3 CO Additive Cold Start Temperature Effects for Gasoline Vehicles by Model Year Groups
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501
6 20 40 60
Temperature (deg F)
Note: In MOVES3, "MY13_50" applies to all model years 2013-2060.
Figure 2-4 THC Additive Cold Start Temperature Effects for Gasoline Vehicles by Model Year Groups
To adapt the additive adjustments for intermediate soak times, the B and C coefficients for start operating
modes other than cold starts were reduced by multiplying by a factor equal to the ratio between emissions
at the desired soak time and the cold start emissions for catalyst equipped vehicles as used in MOBILE6
as summarized in Table 2-3.9
Temperature Effects on Gasoline NOx Start Emissions
Cold-start NOx emissions are not as sensitive to ambient temperature changes as THC and CO emissions,
because the fuel-rich conditions at engine start favor incomplete combustion of fuel, forming CO and
THC; 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.
Due to the small temperature effects and the variability of the data, the NOx temperature effect was
calculated in MOVES 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. Table 2-9 lists the average
incremental cold start NOx emissions, compared to 76.3°F, from the MSOD, ORD, and MSAT programs.
17
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Table 2-9. Average Incremental Cold Start NOx Emissions by Temperature for Gasoline Vehicles Calculated
from the MSOD, OR I) and MSAT Programs
Temp F
Delta
NOx (grams)
-20
1.201
0
1.227
19.4
0.202
20.7
0.089
22.4
-0.155
31
-0.007
40
0.876
48.8
0.127
49.8
0.333
51
0.325
54.2
0.438
76.3
0
95.3
0.225
97.1
0.37
105.8
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) Equation 2-10
where: A = -0.009
R2 = 0.61
Although the value of R2 is not as high as for the THC and CO regression equations, the fit is statistically
significant.
Note that Equation 2-10 predicts a decrease in cold-start NOx emissions for temperatures greater than
75°F, while the data in Table 2-9 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. Therefore, as with the other
temperature adjustments, we have set the NOx additive adjustment to zero in MOVES for temperatures
higher than 75 °F.
18
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In addition, we investigated whether different NOx temperature correction is needed for vehicles subject
to the MSAT-2 rule. Figure 2-5 shows a comparison between NOx start emissions data from OTAQ Cold
Temperature Program, including both the port-fuel injection (PFI) and gasoline-direct injection (GDI)
2006-2010 model year vehicles, and the emissions predicted using temperature effects calculated from the
MY2005-and-earlier vehicles. Because start emissions compose such a small percentage of total NOx
emissions, the differences between the MOVES temperature effects and the NOx data from the OTAQ
Cold Temperature Program were considered negligible. Thus, we applied the NOx temperature
adjustment estimated in Equation 2-10 for all model years.
1.5-
U)
c/>
§1.0
if)
LLJ
0.5-
o.o-
20
75
Temperature (degF)
Figure 2-5 FTP Start NOx Emissions, Bag 1 - Bag 3, Model Years 2006-2010
To adapt the additive adjustments for intermediate soak times, the A coefficients for start operating modes
other than cold starts were adjusted by multiplying by a factor equal to the ratio between emissions at the
desired soak time and the cold start emissions for catalyst equipped vehicles as used in MOBILE6 and
summarized in Table 2-3.
Temperature Effects on Gasoline PM2.5 Start Emissions
The temperature effects for particulate matter emissions from gasoline engines were obtained
from the Kansas City Light-Duty Vehicle Emissions Study (KCVES)18, conducted between 2004
19
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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 too small temperature differences between the phases) as described in the EPA report18 and
Nam et al.19
The analysis of the KCVES data indicated that ambient temperature affects for start PM emissions is best
modeled by (log-linear) multiplicative adjustments of the form:
Equation 2-11
Multiplicative Factor = eA*(72~TemP)
Where
Temp = Temperature
A = log-linear temperature effect. A = 0.0463 for cold starts from the KCVES analysis1819
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) document14 that accompanied
the 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).
20
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CN _
^ _
E
CT)
CL
X
CD _
V O A
V o
V
4- V
£
# ¥
At
+
%
+
™i
CD
m
oo
V v
A o
-3
-2
-1
0
1
Bag 1 NHMC - ln(g/mi)
Plot Icons are Vehicle-Specific
Figure 2-6 FTP Bag 1 PM and FTP Bag 1 NMHC for Tier 2 Vehicles
Therefore, the limitation on cold weather THC (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+) MSAT-2 vehicles implies a PM increase as the
temperature decreases from 72° to 20°F of:
Multiplicative Factor at 20 Ffor MSAT-2 Vehicles = 0.7*ea0463*(72~20> Equation 2-12
= 7.8
Using Equation 2-12 with the MSAT-2 phase-in schedule from Table 2-7 leads to the following
(multiplicative) increases as the temperature decreases from 72° to 20° F:
21
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Table 2-10 Multiplicative Increase in Cold Start PM2.sfrom 72° to 20° Fahrenheit for Gasoline Vehicles
Model Year
LDVs / LLDTs
LHDTs / MDPVs
2008
11.1
11.1
2009
11.1
11.1
2010
10.3
11.1
2011
9.4
11.1
2012
8.6
10.3
2013
7.8
9.4
2014
7.8
8.6
2015+
7.8
7.8
Solving for the corresponding log-linear terms gives us these "A" values:
Table 2-11 Log-linear Temperature Effect for Start PM2.5 Emissions (Coefficient A) for Gasoline Vehicles
Model Year
LDVs / LLDTs
LHDTs / MDPVs
2008
0.0463
0.0463
2009
0.0463
0.0463
2010
0.0448
0.0463
2011
0.0432
0.0463
2012
0.0414
0.0448
2013
0.0394
0.0432
2014
0.0394
0.0414
2015+
0.0394
0.0394
We confirmed this theoretically derived temperature effect for MSAT-2 compliant vehicles by comparing it to data
from the OTAQ Cold Temp Study, which including only the MY 2010 PFI vehicles (Appendix B). The temperature
effect developed for MOVES fits this data well, as shown in Note: In MOVES3, "MY13_50" applies to all model
years 2013-2060.
Figure 2-7. Note, as discussed in the light-duty report, we significantly updated the start PM2 5 emission
rates to account for GDI vehicles in MOVES310, but we did not revisit the temperature effects for start
emissions.
22
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0 20 75
Temperature (degF)
Note: In MOVES3, "MY13_50" applies to all model years 2013-2060.
Figure 2-7. FTP PM2.5 Start Emissions, MSAT-2 Compliant Vehicles (7 PFI Vehicles, 40 Tests with Nonzero
PM Measurements on E10 Fuel) from OTAQ Cold Temperature Program
Figure 2-8 presents the light-duty multiplicative temperature effects using the coefficient from Table
2-11, and the model form of Equation 2-11.
23
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2 20-
c
o
if)
"E|_
LLJ .9r
05 C
£
10-
0
— Pre2010
— MY201 0
— MY2011
— MY201 2
MY2013
2050
20 40 60
Temperature (deg F)
Note: In MOVES3, "MY13_50" applies to all model years 2013-2060.
Figure 2-8. PM Start Exhaust Emissions Effect for Gasoline Light-Duty Vehicles in MOVES
Because the PM2 5 speciation profile for gasoline vehicles did not change significantly between the winter
and summer rounds of the KCVES,2" we apply the same temperature adjustment to each component of
the PM emissions, including elemental carbon, organic carbon, sulfate and other species. The PM start
temperature adjustment does not vary with soak time since it is multiplicative.
24
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2.3 Temperature Effects on Running Exhaust Emissions from Gasoline Vehicles
While MOVES is designed to model the impact of ambient temperature on running exhaust emissions,
current data suggests that there is little effect of temperature on THC, CO, NOx or PM. The sections
below discuss the relevant data and analysis for gaseous pollutants and for particulate matter.
THC, CO and NOx Running Exhaust Temperature Effects
We examined the same data described above for starts to evaluate potential running temperature effects.
These test data suggest that there is very little effect of temperature on running emissions of THC, CO, or
NOx. Regression analyses found that the coefficients (slopes) were not statistically significant (that is, the
slopes were not distinguishable from zero). This contrasts with the significant temperature effect in THC,
CO, and NOx Bag 2 of the Kansas City Light-Duty Vehicle Emissions Study (KCVES) with higher
emissions at colder temperatures.18 As discussed for PM emissions in the next subsection, we attribute the
temperature effect on THC, CO, and NOx emissions observed in the KCVES to the short duration and
mild acceleration of Bag 1 of the LA-92 driving cycle, such that the vehicles had not fully reached hot-
stabilized condition by the beginning of Bag 2.
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 only used the second IM240s when back-to-back IM240s 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 for THC, CO, and NOx between 5
and 95°F.
Because most of the data sets evaluated did not find a significant temperature effect, and the temperature
effect observed in the KCVES is attributed to the test conditions not achieving hot-stabilized running
conditions, we do not model temperature effects for THC, CO, and NOx in MOVES for running exhaust
for all gasoline vehicles. In MOVES, these effects are coded using polynomial functions as multiplicative
adjustments. Therefore, in MOVES, we set all of those adjustments equal to 1.0.
PM2.5 Running Exhaust Temperature Effects
The initial analysis of the Kansas City Light-Duty Vehicle Emissions Study (KCVES) data18'19 indicated
that significant ambient temperature effects existed for both start (Bagl-Bag3) and running (Bag 2) PM
emissions on the LA-92 cycle0. Thus, MOVES2010 and MOVES2014 applied a temperature effect for
running emissions for all model year vehicles based on the Bag 2 measurements from paired vehicles tests
conducted in the winter and summer of the KCVES.
For MOVES2014, we updated the PM temperature effect for running emissions for Tier 2 and later model
year vehicles (2004+) based on data from the 2012 Cold Temperature Program (documented in Section
2.1.). 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 of Tier 2 and MSAT-2-
compliant vehicles and PFI and GDI (See Appendix B). The results from these programs are plotted
against temperature in Figure 2-9. We also fit log-linear models to the data and found the effect of
0 The temperature effects in MOVES2010 and MOVES2014 for pre-2004 vehicles were substantial. Emissions
increased by a factor of 10 between ambient temperatures of 72°F and 0°F.
25
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temperature was not statistically significant on either cycle. Based on these results, we removed the
temperature effect for Tier 2 vehicles (model year 2004 and later) in MOVES2014.
0.04
^0.03
CD
to
c
o
'(/>
— 0 02
c
LU
0.01
Figure 2-9. Hot-running PM Emissions Measured on Two Cycles (FTP Bag 2, US06) on MSAT-2 Compliant
MY 2010 Gasoline Vehicles, Reported as Grams/cycle
These results contrast with the significant PM running temperature effect detected for Bag 2 emissions in
the KCVES. Upon further analysis of the PM emissions from the KCVES study, we determined that
muchd of the temperature effect observed in the KCVES Bag 2 emissions was due to the short duration
and relatively mild accelerations of the cold-start phase of the LA92 cycle, which is only 310 sec (1.18
mi) in length. We note that the PM temperature effect was much larger at the beginning of Bag 2 than at
the end. In contrast, the cold-start phase of the FTP, used in the Cold Temperature Program is 505
seconds (3.59 miles) in length.
FTP
-1-
0
1
20
US06
—i—
20
75 , or
Temperature (degF)
75
d We believe that the small, but statistically significant temperature effect that persists at the end of Bag 2, even after
1,025 seconds (17 minutes) of operation on the LA-92 in KCVES may be an artifact of this particular study, because
this persistent temperature effect on hot-stabilized running emissions was not observed in other studies.
26
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For MOVES3, we conducted a literature review from other studies that conducted measurements of
particulate matter emissions from gasoline vehicles including model years before 2004 at different
ambient temperatures. The results are summarized in Table 2-12.
Table 2-12. Literature Review of Temperature Effects on Running PM2.5 emissions from Gasoline Vehicles
Study
Vehicles and Test conditions
Findings on PM2.5 emissions
Measurements of Exhaust
Particulate Matter Emissions from
In-Use Light-Duty Motor Vehicles
in the Denver, Colorado Area21'22
71 light-duty gasoline vehicles from
model year 1970 to 1996 tested in
the summer of 1996 and winter of
1997 on a chassis dynamometer
using bag 2 of the FTP driving
schedule.
Linear mixed model was fit and no
significant temperature effect was
observe.
Comprehensive particle
characterization of modern gasoline
and diesel passenger cars at low
ambient temperature23
Two Euro-3 (apply to 2000 -2004
model year vehicles) port-injection
gasoline vehicles (Renault Megane
and Alfa 406 TS)
Tested +23, -7 and -20 °C on a
chassis dynamometer on the
common Artemis driving cycle
(CADC), after warmed up on 50-
minute IUFC15 driving cycle.
No temperature effect observed on
running emissions.
Characterization of Metals Emitted
from Motor Vehicles24
Emission rates derived from PM2 5
concentrations measured at the
entrance and exit concentration of
the Howell tunnel in Milwaukee,
WI in the summer of 2000 and the
winter of 2000-2001. Light-duty
vehicles constituted between 90.6
percent to 93.9 percent of the
vehicle fleet, with 6.1 percent to 9.4
percent heavy-duty trucks.
Chemical mass balance methods
were used to estimate the
contribution of tunnel emission
rates to gasoline tailpipe, diesel
tailpipe, brake wear, resuspended
road dust, and tire wear emissions.
Carbonaceous PM2 5 (EC+OC)
emission rates (mg/km) were
significantly lower (49-51 percent)
in the winter than the summer.
Gasoline tailpipe emissions are
estimated to be the largest
contributors to EC and OC
emissions3; more than diesel
tailpipe, brake wear, and
resuspended road dust.
The winter tests had comparable or
larger PM measurements of
inorganic ions and metals
(including Na and CI) presumably
due to road salt in the winter.
a Gasoline tailpipe and tire wear are combined together because they have similar source profiles. However, gasoline tailpipe
emissions in MOVES contribute a much larger share of PM2.5 emission rates (and thus EC and OC) than brake wear emissions.25
The result of the literature review (Table 2-12) suggested no temperature effects on PM exhaust
emissions, even for model year vehicles similar to the years measured in the KCVES. Thus, we now
believe the significant running PM temperature effect in KCVES was an artifact of the measurement
conditions of the study, including the short Bag 1 of the LA-92 cycle. Therefore, in MOVE3, we have
27
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removed the running temperature effect for exhaust particulate matter emissions for all model year light-
duty gasoline vehicles.
2.4 Temperature Effects on Diesel Vehicles
The data used to evaluate and estimate temperature effects on diesel vehicles were limited to laboratory
tests on pre-2007 model year light-duty diesel vehicles. From this analysis, MOVES models a
temperature effect only for THC start emissions. The THC start temperature effect estimated from the
light-duty diesel was applied to all model year diesel vehicles in MOVES, including heavy-duty diesel
vehicles. None of the other pollutants in MOVES have temperature effects for diesel start emissions, and
MOVES has no temperature adjustments for running emissions.
As described below, in developing MOVES3, we reviewed more recent studies conducted on modern
diesel and heavy-duty diesel vehicles, but additional analysis of temperature effects data for US light-duty
and heavy-duty diesel are needed to fully evaluate the values now in MOVES.
THC, CO and NOx Temperature Effects for Diesel Vehicles
For the development of the diesel temperature effects in MOVES, we were able to identify only 12 diesel
vehicles tested on FTP at multiple temperatures (9 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 aftertreatment devices.
Diesel Start Effects
The average start (Bag-1 minus Bag-3) emissions for those tests are shown in Table 2-13. We stratified
the test results into four temperature bands which yielded the following emission values (grams per start)
and average temperature value:
Table 2-13 Average Light-duty Diesel Vehicle Incremental Start Emissions (Bag 1- Bag3) by Temperature
(grams per start)
Temperature, F
Count
THC
CO
NOx
34.6
6
2.55
2.44
2.6
43.4
7
2.68
2.03
0.32
61.5
10
1.69
3
0.67
69.2
2
1.2
1.91
0.36
Figure 2-10 shows the plot of mean THC start emissions versus temperature (where the vertical lines
represent 90 percent confidence intervals and the "dashed" line represents a linear regression through the
data).
28
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4
3
2
1
0
30 40 50 60 70
Temperature (degrees F)
Figure 2-10 Mean Light-duty Diesel Cold-start THC Emissions (in grams, shown on the y-axis) with 90
percent Confidence Intervals vs Temperature
The dashed (blue) line in Figure 2-10 represents a linear regression line:
THC = (-0.04 * Temperature) + 4.22 R2 = 0.90 Equation 2-13
Transforming this equation into an equation that predicts the (additive) change/adjustment in the cold-
start THC emissions from light-duty diesel vehicles (in the MOVES format), we obtain:
THC additive temperature adjustment = A * (Temp. - 75) Equation 2-14
where: A = -0.04 and Temp, is <75F.
The coefficient associated with this temperature adjustment term is statistically significant although its
coefficient of variation is relatively large (23 percent). We apply this adjustment to heavy-duty as well as
light-duty vehicles due to limited data on heavy-duty diesel starts.
The modified temperature adjustments for diesel THC emissions for starts with shorter soak times
(operating modes 101-107) are described in the MOVES heavy duty exhaust report.11
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:
29
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1
<
>
1 4
1
1
1—~
30 40 50 60 70
Temperature (degrees F)
Figure 2-11 Mean Light-duty Diesel Cold-start CO Emissions (in grams) with 90 percent Confidence
Intervals vs Temperature
<
>
1—~—
1 ~—
1 ~
30 40 50 60 70
Temperature (degrees F)
Figure 2-12 Mean Light-duty Diesel Cold-start NOx Emissions (grams) with 90 percent Confidence Intervals
vs Temperature
Statistical analyses of both the diesel cold-start CO and NOx emissions showed that the coefficients were
not significantly different from zero. Therefore, for both cold-start CO and NOx adjustments for diesel
vehicles, we set the temperature adjustment for start emissions to zero.
30
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Diesel Running Effects
Since the diesel start temperature effects were either very small or zero, we did not evaluate the
diesel running temperature effect for THC, CO and NOx for MOVES - we set temperature
effects for diesel running exhaust to zero, similar to the gasoline running exhaust adjustments.
More recently some researchers have suggested that diesel NOx may be underestimated in
current US emission inventories during the wintertime.26 In the US, one road-side study27 and
one tunnel study28 have suggested that there is an increase in heavy-duty diesel NOx emissions
at cold temperatures. A recent European study using road-side measurements estimated a strong
temperature dependence of light-duty diesel NOx emissions, with higher NOx emissions
observed at low temperatures.29 However, the penetration of light-duty diesel vehicles and
diesel emission control technologies employed in Europe and the US differ significantly. These
studies suggest that US diesel NOx emissions may increase in wintertime, but additional analysis
of data on US light-duty and heavy-duty diesel are needed to confirm and quantify the increase
for incorporation into MOVES.
PM Temperature Effects for Diesel Vehicles
MOVES 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. (2005)23 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. Although the researchers observed an increasing trend in particle mass
emissions (g/start) from the conventional diesel vehicle at colder temperatures, 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. (201430) also reported significant increase in PM start emissions from a light-duty diesel
engine tested in a laboratory at +20 and -20 °C. 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 engines31'32, 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
31
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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 in MOVES can be revisited in the future as additional data become
available.
, riperature Effects on Compressed Natural Gas Vehicles
MOVES3 models emissions from heavy-duty vehicles running on compressed natural gas. However, at
the time the temperature corrections were developed, no data were available on temperature impacts of
compressed natural gas emissions. As discussed in the heavy-duty report11, the start emissions for CNG
emissions for THC, CO, NOx and PM are set equal to diesel start emissions. We also applied the same
temperature adjustments to CNG as diesel, that is, only start temperature effects on THC emissions.
, riperature Effects on Start Energy Consumption
The temperature effects on energy consumption in MOVES have not been updated since MOVES2004.
No temperature correction is applied to energy consumption from running activity. As presented in
heavy-duty report11, 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 on energy consumption in MOVES.
The analysis used to derive the temperature effects on start energy consumption in MOVES is
documented in the MOVES2004 energy report.33 No significant temperature effects for energy
consumption were found for warmed-up vehicles in the analysis, thus MOVES does not apply a
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 2-15,
which is applied to all ambient temperatures. Unlike the temperature adjustments for criteria pollutants,
MOVES does not limit the energy consumption adjustments to only cold temperatures, but also adjusts
the energy consumption for hot temperatures. This ambient temperature adjustment is separate from the
air conditioning adjustment described in Section 4, below.
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 in the greenhouse gas and energy report4
for light-duty vehicles and heavy-duty exhaust report.11
Multiplicative temperature adjustment
= 1.0 + tempAdjustTermA X (temperature — 75) Equation 2-15
+ tempAdjustTermB x (temperature — 75)2
Table 2-14 displays the coefficients used to adjust start energy consumption for gasoline, E85, diesel and
CNG-fueled vehicles. MOVES has no correction for electric 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
32
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use the same energy rates as comparable gasoline-fueled vehicles.4 CNG vehicles use the same
adjustments as diesel vehicles, because they use the same energy start rates as comparable diesel
vehicles.11
Table 2-14. Multiplicative Temperature Coefficients Used in MOVES
tempAdjustTermA
tempAdjustTermB
Fuel types
Model Years
-0.01971
0.000219
Gasoline, E85
1960-2050
-0.0086724
0.00009636
Diesel, CNG
1960-2050
Figure 2-13 displays the multiplicative temperature adjustments for starts as a function of temperature. At
75°F, the multiplicative adjustment is one. Gasoline 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.
D
a
:
p
03
O
P,
Temperature (F)
Figure 2-13. Multiplicative Temperature Adjustments for Starts from Energy Consumption as a Function of
Ambient Temperature
33
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2.7 Conclusions and Future Research
With improved calibration and temperature management, ambient temperatures have less impact on
emissions of newer vehicles than older ones, but MOVES3 continues to estimate temperature effects for
start THC, CO, NOx and PM emissions from gasoline vehicles and THC start temperature effect for
diesel and CNG vehicles.
We recognize that additional data and analysis could improve the MOVES temperature effects.
Additional studies and analyses could include:
• Evaluating the benefits of applying log-linear or other mathematical models for pre-MSAT2
gasoline vehicle THC & CO temperature effects and considering whether all temperature
effects could be multiplicative rather than using additive effects for THC/CO/NOx start
emissions.
• Investigating ambient temperature effects on cold start emissions at temperatures warmer than
75 °F.
• Evaluating the interaction of ambient temperature effects and fuel effects.
• Evaluating the interaction of ambient temperature effects and deterioration.
• Analyzing ambient temperature effects for modern (2007 and later) diesel vehicles from recent
studies, 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
Tier 3 gasoline direct injection and dual port-fuel and direct injection, stop-start technologies,
battery electric vehicles and hybrid technologies.
• Analyzing the effect of temperature on other pollutants estimated in MOVES including
ammonia (NH3)
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3. Humidity Adjustments
Water in the air cools the peak combustion temperature and lowers engine out NOx emissions. The NOx
exhaust emissions data used to develop emission rates for MOVES are adjusted from actual measurement
conditions to a standard humidity, including the emissions data from the Evaluation Sample for the
Denver Metropolitan I/M Program used to develop NOx emission rates for MY 1990 and later gasoline
vehicles10 and the emissions data from the Heavy-Duty Diesel In-Use Testing Dataset used to develop
NOx emission rates for MY 2010 and later heavy-duty diesel vehicles.11 At run time, these base NOx
exhaust emission rates are adjusted from the standard humidity level to the humidity conditions specified
by the run spec as described below.
3.1 Humidity Adjustment Equation
In MOVES, the base exhaust emission rates for NOx in all modes and all processes are multiplied by a
(unitless) humidity factor, K. This factor is calculated using the following formula:
K= 1.0- ((Bounded Specific Humidity - 75.0) * Humidity Correction Coefficient) Equation
3-1
The form of Equation 3-1 is based on the humidity adjustment equation given in the Part 86 of the Code
of Federal Regulations (CFR) for light-duty vehicle emissions testing.34 Equation 3-1 is the inverse of the
Part 86 CFR equation, because the Part 86 CFR equation is used adjust the NOx emissions data from test
conditions to the standard humidity level (75 grains water vapor per pound of dry air); whereas MOVES
adjusts the base emission rates from the standard humidity level to the conditions requested in the run
spec. 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 higher than 124 grains. If the
specific humidity input exceeds these limits, the value of the limit is used to calculate the humidity
adjustment. Appendix A.6 shows how the hourly relative humidity values are converted to specific
humidity used in this equation using temperature and barometric pressure. Table 3-1 lists the humidity
correction coefficients used in MOVES.
Table 3-1. Humidity correction coefficients used by MOVES
Fuel Type
Humidity Correction Coefficient
Gasoline
0.0038
Diesel Fuel
0.0026
CNG
0
E-85
0.038
Electricity
0
The gasoline humidity correction coefficient is carried over from the coefficient used in the MOBILE6
model. The E-85 coefficient is set equal to the coefficient for gasoline. Coefficients for CNG and
electricity are set to zero. The humidity correction coefficients are recorded in the MOVES database
fuelType table. In our 2017 peer-review of heavy-duty emission rates,35 we proposed updating the
humidity corrections to be based on the same equations and coefficients as are used for emission testing
35
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in the Code of Federal Regulations for both light-duty34 and heavy-duty vehicles.36 This change is not
incorporated in MOVES3, but we anticipate incorporating these updates in a future version of MOVES.®
3.2Future Research
Future work could investigate whether the real-world emissions impact of humidity is similar to the
corrections developed from laboratory testing used in the Code of Federal Regulations. Additional work
could evaluate the emission impact of humidity on more recent gasoline, diesel and alternative-fueled
engines and consider whether modern engine calibration and emission control technologies impact the
humidity effect.
e The update equations required a code change that was not incorporated into MOVES3.
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4. Air Conditioning Adjustments
MOVES3 applies air conditioning adjustments to THC, CO, NOx and energy consumption from
passenger cars, passenger trucks and commercial light trucks. The air conditioning (A/C) effects
described below and incorporated in MOVES3 were originally derived for MOVES2010.
The air conditioning adjustment factors used in MOVES are based on data collected from light-duty
vehicles in 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 the ambient conditions
specified in a modeling run. The second-by-second emission data were analyzed using the MOVES
methodology of binning the data according to vehicle characteristics (MOVES source bins) and vehicle
specific power bins (MOVES operating modes). The results of the analysis showed statistically
significant and consistent air conditioning effects for three types of operation (deceleration, idle and
cruise/acceleration) and the three primary exhaust pollutants (hydrocarbon, carbon monoxide and nitrous
oxides) and energy consumption. This section shows the results of the analysis for the air conditioning
adjustments used in MOVES for THC, CO, NOx and energy consumption. The impact of A/C on
particulate matter has not been evaluated for MOVES and therefore, MOVES currently has no air
conditioning effect for PM emissions.
The MOVES A/C adjustment varies by operating mode for total energy consumption and exhaust running
THC, CO and NOx emissions and applies only to passenger cars, passenger trucks and commercial light
trucks. The HD emission rates do not require explicit A/C adjustments because they are based on real-
world driving that includes A/C usage depending on ambient conditions when the test was conducted.
For example, the model year 2010 and later HD diesel energy rates are based on manufacturer-run Heavy-
Duty In-Use Testing (HDIUT) data.11
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
was intended to eliminate most of the vehicle and test procedure variabilities 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 emission results from 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, speed and acceleration data. Because of the need to compute
vehicle specific power on a modal basis, only test results which consisted of second-by-second data were
used in the MOVES analysis. All second-by-second data were time-aligned and checked for errors.
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 or heavy-duty 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 balance between different test
cycles and cars and trucks. The individual vehicles are listed in Appendix C.
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Only vehicles which were coded as having an emission test with the A/C system on were selected for this
analysis. 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
Count
1990
5
1991
5
1992
6
1993
5
1994
7
1995
5
1996
13
1997
4
1998
3
1999
1
TOTAL
54
Table 4-2 summarizes the distribution of test-cycles analyzed. The test-cycles are defined in a MOBILE6
report.37
Table 4-2 Distribution of tests by test cycle
Schedule Name
Count
ART-AB
36
ART-CD
36
ART-EF
36
F505
21
FTP
21
FWY-AC
57
FWY-D
36
FWY-E
36
FWY-F
36
FWY-G
36
FWY-HI
36
LA4
23
LA92
35
LOCAL
36
NONFRW
36
NYCC
36
RAMP
36
ST01
36
TOTAL
625
4.2 Air Conditioning Effects on Emissions and Energy
The data described above was then used to estimate factors to account for increases in emissions and
energy consumption with full loading of the air conditioning system. These factors are recorded for
running and extended idle emissions by sourcetype, pollutant and operating mode in the
38
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fullACadjustment table of the MOVES database. Thus, the same effects are applied for all fueltypes and
model years.
Full A/C Adjustments for TUC, CO and NOx Emissions
Average emissions for each pollutant (HC, CO and NOx) with and without A/C operation were
computed for each of the MOVES light-duty running operating modes as defined using vehicle specific
power (VSP).10 This resulted in 69 (23 VSP bins x 3 pollutants) pairs of emission averages. However, the
trends were erratic and the results were generally not statistically significant. In addition, most of the
high-speed bins had little data. An analysis of cars versus light-duty trucks showed no statistical
difference between the two. To produce more consistent results, the individual VSP bins were
consolidated to three principal bins: Braking / Deceleration, Idle, and Cruise / Acceleration as defined in
Table 4-3. These consolidated operating mode bins are quite different in terms of engine operation and
emissions performance.
Full A/C adjustments were then generated by dividing the mean "With A/C" emission factor by the mean
"Without A/C" emission factor for each combination of consolidated operating mode and pollutant. Full
A/C adjustments are shown in Table 4-3. 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 4-3 as
"Mean CV of CF"
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.
Table 4-3 Full air conditioning adjustments for THC, CO and NOx
Pollutant
Consolidated
Operating Mode
opModelDs
Full A/C CF
Mean CV ofCF
THC
Braking / Decel
0
1.0000
0.48582
THC
Idle
1
1.0796
0.74105
THC
Cruise / Accel
11-40
1.2316
0.33376
CO
Braking / Decel
0
1.0000
0.31198
CO
Idle
1
1.1337
0.77090
CO
Cruise / Accel
11-40
2.1123
0.18849
NOx
Braking / Decel
0
1.0000
0.19366
NOx
Idle
1
6.2601
0.09108
NOx
Cruise / Accel
11-40
1.3808
0.10065
These adjustments are applied to passenger cars, passenger trucks and light commercial trucks only.
Note the higher air conditioning effect for NOx at idle. These results are consistent with those obtained
from Nam et al. (2000)38 who showed that at low load conditions, A/C greatly increased NOx emissions
due to reduced residual gas fractions in-cyUnder.
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 running operating mode and mean values were
39
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computed. A separate analysis was done as a function of sourceBinID (combination of vehicle type, fuel
type and model year), however, 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 only of running
operating mode.. The resulting A/C adjustments are shown in Table 4-4.
Table 4-4 Full air conditioning adjustments for energy
opModelD
A/C Factor
opModelD
A/C Factor
opModelD
A/C Factor
0
1.342
21
1.294
30
1.294
1
1.365
22
1.223
33
1.205
11
1.314
23
1.187
35
1.156
12
1.254
24
1.167
37
1.137
13
1.187
25
1.157
38
1.137
14
1.166
26
1.127
39
1.137
15
1.154
27
1.127
40
1.137
16
1.128
28
1.127
29
1.127
These adjustments are applied to passenger cars, passenger trucks and light commercial trucks only
Only very small amounts of data were available for operating modes 26 through 29 and 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 VSP bins. 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, sometimes to zero, as the overall power demand of the engine is increased.
Fuel economy and GHG regulations are expected to reduce energy consumption with air conditioning.
However, because, the MOVES A/C factors are multiplicative adjustments to the running energy rates, a
reduction in running energy rates also reduces energy consumption from air conditioning. In MOVES, we
project the light-duty A/C improvements of regulatory rules using the running energy rates as documented
in the MOVES3 Greenhouse Gas and Energy Consumption Rates Report.4
4.3 Adjustments to Air Conditioning Effects
In MOVES, 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 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 index39) of the air outside their vehicles. These MOVES defaults are documented in the Population
and Activity report.40 The fraction of vehicles equipped with air conditioning, the fraction of operational
40
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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 also 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.
4.4 Conclusions and Future Research
MOVES applies air conditioning effects to emissions from passenger cars, passenger trucks and
commercial light trucks. 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.
• Evaluation of air conditioning effects in the highest VSP/STP bins.
• Updates to information on the fraction of vehicles equipped with air conditioning and their
malfunction rates.
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5. Inspection and Maintenance Programs
Inspection and Maintenance (I/M) programs are generically any state or locally mandated inspection of
highway motor vehicles intended to identify those vehicles most in need of emissions-related repair and
require repairs of those vehicles. There is great variation 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.
5.1 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.41
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.
5.2Inspection & Maintenance in I S
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). As detailed in
the MOVES light-duty emission rate report, the mean base rates for light-duty gasoline vehicles (the
principal target of I/M programs1), initially developed for MOVES2010 and MOVES2014, were based on
data from the enhanced I/M program in Phoenix, Arizona (as operated from calendar year 1995 through
2005) and represent the design features of that program. The ratio differences between the non-I/M and
f Starting in MOVES3.1, we also estimate different effects of I/M and non-I/M for gasoline LHD2b3
trucks as detailed in the MOVES3 Heavy-Duty report (EPA-420-R-22-031). Error! Bookmark not
defined.
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I/M reference rates, which differ by age, were also primarily derived from analyses of data from within
the Phoenix program, and are assumed to represent the I/M benefit of the Phoenix program design
assuming perfect compliance. However, we did compare these differences to analogous differences in two
other geographic areas, Northern Virginia and Atlanta, and found them in broad agreement with the
Phoenix results. Equation 5-1 shows this relationship in a mathematical form.
Standard IM Difference = EnonIM — Em Equation 5-1
where Enon-iM and Eim are the non-I/M and I/M reference rates in a given age group, respectively.
The Phoenix program design was selected as the reference program because at the time, most of the
underlying data for MOVES light-duty emission rates 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)EnonIM Equati,,n 5"2
where Ep is the adjusted emission rate for a "target" I/M program, Ejm is the reference rate, /?n0niM 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 I^„,m\w- fall between /^llolll\i and A'm. 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
^ EnonIM Equation 5-3
Eim ~ EnonIM
5.3 Development of. ;tors
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
43
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old model years at young ages (i.e., a 1985 model year at age five).
As a result, EPA developed 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 used to compute estimated values for IMFactor. The
IMFactor table includes the following fields:
• Pollutant / Process
• Test Frequency
• Test Standard (see Table 5-1 below)
• Source Type
• Fuel Type (Only gasoline/ethanol fuels have IMFactors)
• Model Year Group
• Age Group
• IMFactor
Table 5-1 MOVES I/M Test Standards
testStandardsID
testStandardsDesc
shortName
11
Unloaded Idle Test
Unloaded Idle
12
Two-mode, 2500 RPM/Idle Test
2500 RPM/Idle
13
Loaded / Idle Test
Loaded/Idle
21
ASM 2525 Phase-in Cutpoints
A2525 Phase
22
ASM 5015 Phase-in Cutpoints
A5015 Phase
23
ASM 2525/5015 Phase-in Cutpoints
A2525/5015 Phase
24
ASM 2525 Final Cutpoints
A2525 Final
25
ASM 5015 Final Cutpoints
A5015 Final
26
ASM 2525/5015 Final Cutpoints
A2525/5015 Final
31
IM240 Phase-in Cutpoints
IM240 Phase
33
IM240 Final Cutpoints
IM240 Final
41
Evaporative Gas Cap Check
Evp Cap
42
Evaporative System Pressure Check
Evp Pressure
43
Evaporative System OBD Check
Evp OBD
44
Evaporative Gas Cap and Pressure Check
Evp Cap, Prs
45
Evaporative Gas Cap and OBD Check
Evp Cap, OBD
46
Evaporative Pressure and OBD Check
Evp Prs, OBD
47
Evaporative Gas Cap, Pressure and OBD Check
Evp Cap,OBD,Prs
51
Exhaust OBD Check
Exhaust OBD
61
HDDV Engine Reflash Program
HDDV Reflash
The IMFactor value was computed for all reasonable combinations of the parameters listed in the
IMFactor table. A separate MOBILE6.2 run was done for each parameter combination (Target design, Ev)
and a second set of runs were done for the reference program (Reference design, Em). The IMFactor (R)
was then calculated from the mean emission results from these two runs and the non-I/M case using
Equation 5-3. The reference program has inputs matching the Phoenix, Arizona I/M program during the
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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 (IM240, IM147) 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 5-2 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 THC, CO and NOx.
The first four runs represent the Non-I/M reference and the three Arizona I/M references.
Table 5-2 MOBILE6.2 runs used to populate the MOVES I/M adjustment factor
RUN#
Description
Type
1
Non I/M Base
Non I/M Reference
2
IM240 Base (Biennial IM240/147)
I/M Reference
3
OBD Base (Biennial OBD Test)
I/M Reference
4
Basic Base (Loaded - Idle Test)
I/M Reference
5
Biennial - IM240 - Phase-in Cutpoints
Target I/M Design
6
Annual - IM240 - Phase-in Cutpoints
Target I/M Design
7
Biennial - IM240 - Final Cutpoints
Target I/M Design
8
Annual - IM240 - Final Cutpoints
Target I/M Design
9
Biennial - ASM 2525/5015 - Phase-in Cutpoints
Target I/M Design
10
Annual - ASM 2525/5015 - Phase-in Cutpoints
Target I/M Design
11
Biennial - ASM 2525/5015 - Final Cutpoints
Target I/M Design
12
Annual - ASM 2525/5015 - Final Cutpoints
Target I/M Design
13
Biennial - ASM 2525 - Phase-in Cutpoints
Target I/M Design
14
Annual - ASM 2525 - Phase-in Cutpoints
Target I/M Design
15
Biennial - ASM 2525 - Final Cutpoints
Target I/M Design
16
Annual - ASM 2525 - Final Cutpoints
Target I/M Design
17
Biennial - ASM 5015 - Phase-in Cutpoints
Target I/M Design
18
Annual - ASM 5015 - Phase-in Cutpoints
Target I/M Design
19
Biennial - ASM 5015 - Final Cutpoints
Target I/M Design
20
Annual - ASM 5015 - Final Cutpoints
Target I/M Design
21
Annual - OBD -
Target I/M Design
22
Annual - LOADED/IDLE
Target I/M Design
23
Biennial - IDLE
Target I/M Design
24
Annual - IDLE
Target I/M Design
25
Biennial - 2500/IDLE
Target I/M Design
26
Annual - 2500/IDLE
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
IMFactor is a function of running and start processes).
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Note that MOVES2014 had an error in the calculation of the IMFactor values which affected the 1981
through 1995 model years for vehicles 10 years and older. This problem was noted by the Coordinating
Research Council in their E-101 report42 on the MOVES2014 version of the model. This problem was
rectified in MOVES3 by recalculating the IMFactor values for all source types in this model year and age
range. The new IMFactor values increase THC, CO and NOx emissions compared to MOVES2014 in I/M
areas with programs that cover these model years by less than 1 percent in calendar year 1999 increasing
to nearly 3 percent in calendar year 2010. The impact of this problem diminishes after calendar year 2010
as these model years are retired from the fleet. The impact of this change for specific areas will vary
depending on the age distribution and other factors.
5.4I/M Compliance Factors
In addition to the IMFactor, MOVES adjusts rates for particular programs by applying an additional
multiplicative "Compliance Factor." While the IMFactor (R, see Equation 5-3) represents the theoretical
effectiveness of a specific I/M program design relative to the reference design, as described above, the
values of the I/M compliance factor (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. The compliance factor may vary
from 0 to 100 where zero would represent a totally failed program and 100 a perfectly successful
program. Factors which tend to reduce the compliance factor include the systematic waiver of failed
vehicles from program requirements, large numbers of motorists who completely evade the program,
technical losses from improperly functioning equipment or inadequately trained technicians.
The MOBILE6 compliance rate, waiver rate and effectiveness rate were used to determine the MOVES
I/M Compliance Factor. The MOVES Compliance Factor is a broader concept that incorporates three
separate MOBILE6.2 inputs. Equation 5-4 shows the relationship.
C = M6ComplianceRate * M6Ef f ectivenessRate Equation 5-4
* (1 — M6WaiverRate)
MOVES does not have separate inputs for the effect of waivers on I/M benefits. The Inspection
Maintenance section of the technical guidance document for MOVES3 43 describes how to calculate the
MOVES compliance rate to include the effect of waivers.
In MOVES, it is assumed that any repairs attempted on vehicles receiving waivers are not effective and
do not result in any reduced emissions.
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 IM 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 * ComplianceFactor * 0.01) Equation 5-5
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The next step is to 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 Equation 5-6
+NonIMRate * (1.0 — IMAdjustFract)
5.6.Default I/M Program Descriptions (IMCoverage)
Information about which pollutant-processes are covered by I/M programs in various counties and
calendar years is listed in the MOVES database table IMCoverage. This coverage information may vary
by pollutant, process, county, year, sourcetype and fuel type. The table also lists the I/M compliance
factors described above.
The IMCoverage 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 sourcetypes, model year groups or pollutant-process combinations.
For example, a county in calendar year 2007 may have an IMProgramID=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 IMCoverage table also shows other important I/M parameters for each IMProgramlD. These include
the relevant model year range (beginning and ending model year), the frequency of inspection (annual,
biennial, continuous/monthly), test type (Idle, IM240, ASM, OBD-II) and the test standard.
The structure of the IMCoverage table in the MOVES database is:
• Pollutant / Process
• State / County
• Calendar Year
• Source Use Type
• Fuel Type (only gasoline and ethanol fuels)
• IMProgramlD
• Beginning Model Year of Coverage
• Ending Model Year of Coverage
• InspectFreq
• I/M Test Standards (see Table 5-1)
• UselMyn
• Compliance Factor
The UselMyn toggle is a user feature that allows the user to completely disable the modeling of I/M for
one or more of the parameter combinations.
When modeling for regulatory purposes, it is expected that a state will enter their own set of program
descriptive parameters and compliance factors which reflect current and expected future program
operation. However, MOVES contains a set of I/M program descriptions for all calendar years intended to
reflect our best assessment of the programs in each state.
47
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The 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 NMIM model to compute the National Emission Inventory of 2011.
The MOBILE6 data fields listed in Table 5-3 were extracted and processed into the various fields in the
MOVES IMCoverage table for each state and county.
As seen in Table 5-3, MOBILE6.2 and MOVES do not have exactly compatible parameter definitions.
The MOBILE6.2 I/M Cutpoints data were used only to determine level of stringency of a state's IM240
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
sourcetype.
Table 5-3 I/M Coverage table data sources
MOBILE6 Data
MOVES I/M Coverage Parameter
Compliance Rate
Used in the MOVES Compliance Rate Calculation
I/M Cutpoints
Used to determine MOVES I/M Test Standards
Effectiveness Rate
Used in the MOVES Compliance Rate Calculation
Grace Period
Used in MOVES to Determine Beginning Model Year of Coverage
Model Year Range
Used in MOVES to Determine Ending Model Year of Coverage
Test Type
Used to determine MOVES I/M Test Type
Vehicle Type
Used to determine MOVES Sourcetype
Waiver Rate
Used in the MOVES Compliance Rate Calculation
For calendar year 2011 through 2013, the IMCoverage table default parameters were derived using the
IMCoverage tables from the county databases (CDBs) provided to EPA for the 2011 National Emission
Inventory (NEI) project44 (Versionl). 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 2013 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 calendar year 2014 and later values were initially derived from the 2014 NEI (Version l)45 CDBs
following review by the states with the 2015 and later calendar year assuming no changes in the basic
2014 I/M program design, but updating the model year coverage values to properly account for the
existing grace periods in the future calendar years. All of the I/M program descriptions were 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 without coverage were left between model years. This check also looked for
cases where the coverage beginning model year occurred later than the ending model year coverage.
Each problem identified was compared to the I/M program descriptions found in the 2013 EPA I/M
Program Data, Cost and Design Information report46 to resolve conflicts. The county coverage values in
some states were also updated for some calendar years. In addition to the updates in the I/M program
descriptions, the table was updated to make sure each I/M program covered E85-fueled vehicles in the
48
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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. MOVES does not offer any benefits from inspection programs to detect liquid fuel leaks.
For MOVES3, the table was further updated based on state supplied data through the OBD Clearinghouse
website47 and 2017 National Emissions Inventory (NEI).48 The updates include adding I/M program for
Ascension Parish, Iberville Parish, and Livingston Parish in Louisiana; adding I/M program for Hamilton
County, Tennessee, and for Cache County, Utah. We also updated the program stop years for terminated
I/M programs. These terminated programs include ones in Anchorage Borough, Alaska; Grundy County,
Illinois; Clark County and Floyd County, Indiana; Shelby County, Tennessee; and seven counties in
Minnesota, 26 counties in North Carolina, and six counties in Ohio.
We also deleted the I/M program for Harrison County, Indiana (for all the CY years), since it was
confirmed that Harrison County, IN has never been in nonattainment for any National Ambient Air
Quality Standards (NAAQS) and does not have a I/M program. We also updated the beginning model
year for North Carolina I/M counties to reflect changes to their program for 2020 and later.49 In
addition, to reflect the termination of I/M program in Washington state, I/M programs have been removed
from IMCoverage table for all counties in Washington state after CY2019.
For version 3.0.4 of MOVES3, we updated compliance factors using data from the 2020 National
Emissions Inventory for existing IM programs that match the description in the default database, for year
2020 and after. We also used the 2020 NEI information to add Cache County, UT for calendar year 2020
and beyond. Finally, we removed I/M program information for Montgomery County, OH for 2020 and
beyond, and removed all counties in Tennessee starting with calendar year 2023.
California currently has three different I/M programs: an enhanced program, basic program, and
ownership change program. These may vary by ZIP code region. We mapped California counties with
I/M program types by checking all the ZIP codes in each county. We use the basic program to represent a
county if it has mixed programs. This methodology is consistent with previous work. We updated I/M
program details for ten counties in California based on our research.
Table 5-4 shows the states with I/M program descriptions in the updated I/M coverage table and shows
the number of counties covered by the programs by calendar year. For example, Idaho has two counties
that have I/M programs; one county's program covers from CY 1990 to CY 2060, while the other
county's program covers from CY 2011 to CY 2060.
49
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Table 5-4 States With I/M Programs as Listed in MOVES3.0.43
Calendar Years
State
StatelD
Minimum
Maximum
Counties
Alaska
2
1990
2009
1
1990
2012
1
Arizona
4
1990
2060
2
California
6
1990
2060
14
1999
2060
26
Colorado
8
1990
2060
7
8
2015
2060
2
Connecticut
9
1999
2060
8
Delaware
10
1990
2060
3
District of
Columbia
11
1990
2060
1
Georgia
13
1999
2060
13
Idaho
16
1990
2060
1
2011
2060
1
Illinois
17
1990
2060
10
1990
2005
1
Indiana
18
1990
2007
2
1990
2060
2
Kentucky
21
1990
2005
4
Louisiana
22
2000
2060
5
Maine
23
1990
2060
1
Maryland
24
1990
2060
14
Massachusetts
25
1990
2060
14
Minnesota
27
1990
1999
7
Missouri
29
1990
2060
5
Nevada
32
1990
2060
2
New Hampshire
33
2002
2060
3
2011
2060
7
New Jersey
34
1990
2060
21
New Mexico
35
1990
2060
1
New York
36
1990
2060
9
2001
2060
53
North Carolina
37
2003
2060
9
2006
2060
13
2003
2005
1
2003
2018
2
2006
2018
24
50
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Calendar Years
State
StatelD
Minimum
Maximum
Counties
Ohio
39
1990
2008
6
1990
2060
7
1990
2019
1
Oregon
41
1990
2060
4
2001
2060
2
Pennsylvania
42
1990
2060
11
2001
2060
14
Rhode Island
44
2000
2060
5
Tennessee
47
1990
2016
1
1990
2022
6
Texas
48
1990
2060
4
2000
2060
6
2011
2060
7
Utah
49
1990
2060
4
2020
2060
1
Vermont
50
1990
2060
14
Virginia
51
1990
2060
10
Washington
53
1990
2019
5
Wisconsin
55
1999
2060
7
aMOVES3.0.4 was updated based on data from the 2020 NEI. The I/M Coverage table in
earlier versions of MOVES3 is described in the text above.
51
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mces
1 USEPA (2016). Fuel Effects on Exhaust Emissions from On-road Vehicles in MOVES2014. EPA-420-R-
16-001. Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental
Protection Agency. Ann Arbor, Ml. 2016. https://www.epa.gov/moves/moves-technical-reports.
2 USEPA (2020). Speciation of Total Organic Gas and Particulate Matter Emissions from Onroad Vehicles
in MOVES3. EPA-420-R-20-021. Office of Transportation and Air Quality. US Environmental Protection
Agency. Ann Arbor, Ml. November 2020. https://www.epa.gov/moves/moves-technical-reports.
3 USEPA (2020). Air Toxic Emissions from Onroad Vehicles in MOVES3. EPA-420-R-20-022. Office of
Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml. November 2020.
https://www.epa.gov/moves/moves-technical-reports.
4 USEPA (2020). Greenhouse Gas and Energy Consumption Rates for Onroad Vehicles in MOVES3. EPA-
420-R-20-015. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor,
Ml. November 2020. https://www.epa.gov/moves/moves-technical-reports.
5 USEPA (2020). Evaporative Emissions from Onroad Vehicles in MOVES3. EPA-420-R-20-012. Office of
Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml. November 2020.
https://www.epa.gov/moves/moves-technical-reports.
6 USEPA (2014). Emission Adjustments for Temperature, Humidity, Air Conditioning, and Inspection and
Maintenance for On-road Vehicles in MOVES2014. EPA-420-R-14-012. Assessment and Standards
Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml.
2014. http://www.epa.gov/otaq/models/moves/moves-reports.htm.
7 USEPA (2017). Emission Adjustments for Temperature, Humidity, Air Conditioning, and Inspection and
Maintenance for Onroad Vehicles in MOVES201X - Draft Report. Draft report and peer-review
documents. Record ID 328830. EPA Science Inventory. September 2017.
https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryld=328830.
8 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
9 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
10 USEPA (2022). Exhaust Emission Rates for Light-Duty Onroad Vehicles in MOVES3. EPA-420-R-22-032.
Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml. November
2022. https://www.epa.gov/moves/moves-onroad-technical-reports
52
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11 USEPA (2022). Exhaust Emission Rates of Heavy-Duty Onroad Vehicles in MOVES3. EPA-420-R-22-
031. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml.
November 2022. https://www.epa.gov/moves/moves-onroad-technical-reports
12 USEPA (2008). Mobile Source Observation Database (MSOD): User Guide and Reference. EPA420-B-
08-017. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml.
December, 2008. https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockey=P10028RC.TXT
13 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
14 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: https://nepis.epa.gov/Exe/ZyPdf.cgi?Dockey=P1004LNN.PDF
15 USEPA (1994). Regulation of Fuels and Fuel Additives: Standards for Reformulated and Conventional
Gasoline 59 Federal Register: Issue 32. February 16, 1994, Docket Number: AMS-FRL-4817-8.
16 USEPA (2007). Control of Hazardous Air Pollutants From Mobile Sources 72 Federal Register
8428.February 26, 2007 . https://www.govinfo.gov/content/pkg/FR-2007-02-26/pdf/E7-2667.pdf
17 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.
18 USEPA (2008). Analysis of Particulate 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)
19 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
20 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. DOI: 10.1080/10962247.2013.870096.
21 Cadle, S. H., et al. (1998). Measurements of Exhaust Particulate Matter Emissions from In-Use Light-
Duty Motor Vehicles in the Denver, Colorado Area. CRC Project E-24-1 CRC Project E-24-1. Coordinating
Research Council. March 24, 1998. https://crcao.org/publications/emissions/index.html.
22 Cadle, S. H., et al. (1999). Composition of Light-Duty Motor Vehicle Exhaust Particulate Matter in the
Denver, Colorado Area. Environ Sci Technol, 33 (14), 2328-2339. DOI: 10.1021/es9810843.
53
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23 Mathis, U., M. Mohr and A.-M. Forss (2005). Comprehensive particle characterization of modern
gasoline and diesel passenger cars at low ambient temperatures. Atmospheric Environment, 39 (1), 107-
117. DOI: http://dx.doi.Org/10.1016/j.atmosenv.2004.09.029.
24 Schauer, J., et al. (2006). Characterization of Metals Emitted from Motor Vehicles. Health Effects
Institute Research Report Number 133 Health Effects Institute Research Report Number 133.
http://pubs.healtheffects.org/.
25 Sonntag, D., et al. (2018). Modeling Brake and Tire Wear Emissions in Regulatory Models in the United
States. 2018 ISES-ISEE Joint Annual Meeting. Ottawa, Canada.
26 McDonald, B., et al. (2019). Quantifying Urban Emissions Influencing Wintertime Ammonium Nitrate
Formation. AQUARIUS (Air Quality in the Western US). Salt Lake City, UT.
27 Hall, D. L., et al. (2020). Using near-road observations of CO, NOy, and C02 to investigate emissions
from vehicles: Evidence for an impact of ambient temperature and specific humidity. Atmospheric
Environment, 232, 117558. DOI: https://doi.Org/10.1016/j.atmosenv.2020.117558.
28 Wang, X., et al. (2019). Real-World Vehicle Emissions Characterization for the Shing Mun Tunnel in
Hong Kong and Fort McHenry Tunnel in the United States. Research Report 199. Health Effects
Institute. Boston, MA. March 2019. https://www.healtheffects.org/publication/real-world-vehicle-
emissions-characterization-shing-mun-tunnel-hong-kong-and-fort.
29 Grange, S. K., N. J. Farren, A. R. Vaughan, R. A. Rose and D. C. Carslaw (2019). Strong Temperature
Dependence for Light-Duty Diesel Vehicle NOx Emissions. Environ Sci Technol, 53 (11), 6587-6596. DOI:
10.1021/acs.est.9b01024.
30 Sakunthalai, R. A., et al. (2014). Impact of Cold Ambient Conditions on Cold Start and Idle Emissions
from Diesel Engines. SAE Technical Paper.
31 Calcagno, J. A. (2005). Evaluation of Heavv-Dutv 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.
32 Bielaczyc, P., J. Merkisz and J. Pielecha (2000). Exhaust emission from diesel engine during cold start in
ambient temperature conditions. SAE Technical Paper.
33 USEPA (2005). Energy and Emissions Inputs. EPA-420-P-05-003. Office of Transportation and Air
Quality. US Environmental Protection Agency. Ann Arbor, Ml. March, 2005.
http://www.epa.gov/otaq/models/moves/moves-reports.htm.
34 40 Code of Federal Regulations 86.144-94(c)(7)(iv-viii)(Page 706).Available at:
https://www.govinfo.gov/content/pkg/CFR-2019-title40-vol21/pdf/CFR-2019-title4Q-vol21-sec86-144-
94.pdf
54
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35 USEPA (2017). Exhaust Emission Rates for Heavy-Duty On-road Vehicles in MOVES201X - Draft Report.
Draft report and peer-review documents. Record ID 328830. EPA Science Inventory. September 2017.
https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryld=328830.
36 40 Code of Federal Regulations 1065.672 (a)(Page 230).Available at:
https://www.govinfo.gOv/content/pkg/CFR-2019-title40-vol37/pdf/CFR-2019-title40-vol37-secl065-
670.pdf
37 USEPA (2001). Air Conditioning Correction Factors in MOBILE6. EPA420-R-01-055. Assessment and
Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann
Arbor, Ml. November 2011. http://www.epa.gov/otaq/models/mobile6/r01055.pdf.
38 Nam, E. K., Understanding and Modeling NOx Emissions from Air Conditioned Automobiles, SAE
Technical Paper Series 2000-01-0858, 2000.
39 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
40 USEPA (2020). Population and Activity of Onroad Vehicles in MOVES3. EPA-420-R-20-023. Office of
Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, Ml. November 2020.
https://www.epa.gov/moves/moves-technical-reports.
41 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
42 Coordinating Research Council Report No. E-101, "Review of EPA's MOVES2014 Model", August 2016.
https://crcao.org/publications/emissions/index.html
43 USEPA (2020), MOVES3 Technical Guidance: Using MOVES to Prepare Emission Inventories for State
Implementation Plans and Transportation Conformity, EPA-420-B-20-052, November 2020
44 USEPA (2011). 2011 National Emission Inventory -
http://www.epa.gov/ttn/chief/net/2011inventory.html
45 USEPA (2016). 2014 National Emission Inventory, https://www.epa.gov/air-emissions-
inventories/2014-national-emissions-inventory-nei-data
46 USEPA (2013), l/M Program Data, Cost and Design Information, Final Report, Prepared by ERG for EPA,
Project No.: 0303.00.009.001, August 2, 2013.
47 OBD Clearinghouse (2019) "l/M Jurisdiction Report,"
https://www.obdclearinghouse.com/IMSolutions/
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48 USEPA (2020). 2017 National Emission Inventory, https://www.epa.gov/air-emissions-
inventories/2017-national-emissions-inventory-nei-data
49 USEPA (2019), Air Plan Approval; NC: Revision to l/M Program & Update to Charlotte Maintenance
Plan for the 2008 8-Hour Ozone NAAQS, Federal Register/Vol. 84, No. 176/Wednesday, September 11,
2019/Rules and Regulations.
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Appendix A Derivation of Default Temperature and Humidity
rology Calculations
The MOVES default database includes default ambient temperature and humidity values for every
county, month, and hour. If modelers do not enter local data, MOVES will use these values to calculate
the temperature and humidity adjustments described in the main body of this report. These values were
derived from 10-year average temperature and relative humidity values from calendar years 2001 through
2011 by month and by hour (standard time) for each county in the United States for all calendar years.
Due to the limited number of hourly observation stations (about 200 sites), interpolation of the available
data was required. This interpolation will not always produce accurate results, particularly in areas where
climate can vary significantly over distance, such as in mountainous terrain and near coastlines or deserts.
Moreover, it is important that the diurnal range of the average hourly temperatures match those of the
average monthly minimum and maximum values. This aspect arises due to the averaging process and to
the fact that daily maximum and minimum temperatures do not always occur at the same hourly
observation time.
To correct the diurnal range problem, EPA has developed a method to adjust the average hourly
temperatures so that the corresponding hourly-based maximum and minimum temperatures match those
of the true monthly maximum and minimum values. To correct the spatial problem, all of the daily and
monthly maximum and minimum temperature observations made by the National Weather Service
(NWS) and its Cooperative Observation branch (over 6000 sites) and the Federal Aviation
Administration (FAA) are used.
Note, temperature and humidity data are one of the many inputs that are averaged for simplified national
and state level onroad MOVES runs. The algorithms for this averaging ("aggregation") are described in
the MOVES code documentation at https://github.com/USEPA/EPA_MOVES_Model.
A.1 Data Sets and Quality Control
The National Climatic Data Center (NCDC) is the national and international depository for weather
observations. As part of its many duties, the NCDC publishes and maintains many climatic data sets.
"Quality Controlled Local Climatological Data" (QCLCD) files were obtained for all locations across the
United States, Puerto Rico and the Virgin Islands from the NCDC for this analysis.
There can be significant problems with this information. Primary among these problems is that many
stations with daily data do not have corresponding monthly averages, and vice-versa. Further, some
stations may have the same identification numbers while others may have missing or incorrect latitude
and longitude coordinates. During the processing of the 2009 data, nearly 10 percent of the 1654 stations
were found to have identification and/or location problems.
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Missing monthly temperatures can be calculated from the daily maximum and minimum observations for
these stations for the years of interest. To resolve the mislabeled station IDs and location data, it was
necessary to contact NCDC to obtain updated tables with corrected IDs before processing the data.
In addition to the hourly temperature and dew point data, the identification number and geographic
location (latitude and longitude) for all available weather stations across the United States, Puerto Rico,
and the Virgin Islands were obtained from the NCDC files. Using Geographical Information System
(GIS) software, the locations of the hourly weather observation stations were validated. To resolve
duplicate IDs and latitude/longitude issues, careful analysis of the station history files and conversations
with state climatologists and National Weather Service offices were made. Our contractor, Air
Improvement Resource Inc. (AIR), hand-edited the IDs and latitude/longitude data and supplied updates
to our data and to the NCDC.
For temperature disputes, such as maximum temperature less than minimum temperature (caused by
mistyped data), hourly and/or daily data from nearby sites were consulted and the data corrected
accordingly.
For each station, an inventory was made as to the number of hours with joint temperature and dew point
data. In order to be included in the analysis, each station had to have at least 50 percent data recovery for
each hour of each month.
The daily absolute maximum and minimum temperature data for all available stations were processed
into monthly averages. These stations covered all classifications, including First-Order (National Weather
Service), Second-Order (both Automated Surface Observing System (ASOS) and Automated Weather
Observing System (AWOS)) and cooperative (local). Following NCDC guidelines, a month's averages
were considered valid when no more than 5 days had missing data during that month. For each station,
the hourly temperature and dew point data was scanned for missing values. For missing data periods
lasting only 1 hour, the missing values was replaced with an interpolated value from the two adjacent
valid readings
After these filters were applied, the average monthly maximum and minimum temperature data were
adjusted to the common midnight-to-midnight observational period. This adjustment is necessary since
many of the cooperative stations take their observations either early in the morning or late in the
afternoon rather than at midnight. These observation times induce a bias into the monthly temperature
averages. Correction values were obtained from the NCDC and applied to the monthly averages.
1.2 County Temperature Assignment
An octal search with inverse distance weighting was used to assign the monthly maximum and minimum
temperatures to each U.S. county. Population centroids (latitude and longitude) for each county were
obtained from the 2010 United States Census. Population, rather than geographic, centroids were used to
provide a reasonable estimate of where the county's vehicle miles traveled and nonroad activity would be
concentrated. From each county's centroid, the distance and direction to each weather station was
calculated. The shortest distance was computed using the standard great circle navigation method and the
constant course direction was computed using the standard rhumb line method. A rhumb line is a line on
a sphere that cuts all meridians at the same angle; for example, the path taken by a ship or plane that
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maintains a constant compass direction. Based on the computed directions, the stations were assigned to
an octant, as follows:
• Octant 1: 0°
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After this adjustment is applied, the maximum and minimum of the adjusted hourly temperatures will
exactly match the average monthly maximum and minimum temperatures.
1.4 Relative Humidity Recalculation
Relative humidity depends on both temperature and dew point. Unfortunately, unlike daily maximum and
minimum temperatures, supplemental dew point data is not available. Consequently, an investigation and
literature search was made to determine a suitable estimation method. Surprisingly, few were found. The
scheme outlined below was suggested by the NCDC and was used in this analysis:
At any given time, the difference between the temperature and dew point is known as the dew point
depression (DPD). Since the dew point can never exceed the temperature, the minimum DPD is zero (100
percent relative humidity) while the maximum can be several tens of degrees, depending on how dry the
air is. From the original data, the DPD was computed at each hour.
After the hourly temperatures were adjusted to be consistent with the county minimum and maximum
temperatures as described above, the DPDs were subtracted from the hourly temperatures to estimate the
corresponding dew point. The corresponding relative humidity was then computed from these two values.
In keeping with standard meteorological practices, the relative humidity is always computed with respect
to water, even if the temperature is below freezing. Comparative tests showed that the new calculated
relative humidity results were very close to the original values, which is the desired outcome.
1.5 Calculation of 10 Year Averages
The monthly average hourly temperatures for each county from each calendar year from 2001 through
2011 were averaged to determine the default 10-year average temperatures stored in the MOVES
ZoneMonthHour table for each county. The relative humidity values were converted to specific humidity
(humidity ratio) for each hour before averaging and then converted back to relative humidity.
60
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1.6 Calculation of Specific Humidity
While the MOVES default humidity is stored as relative humidity, the humidity adjustment uses 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.
Hrei is the relative humidity
T0=647.27-Tk
H
ratio or specific humidity
mix*pvi(pb-pv)
7^=29.92*218.167*10
= 6527.557*10
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1.7 Calculation of Heat Index
MOVES air conditioning demand is calculated as a function of the heat index as described in the
MOVES Population and Activity report.40 In MOVES, the heat index is a function of temperature and
relative humidity.
Heat Index = min (( —42.379 + 2.049015237' + 10.14333127//
+ 0.2247554177/+ -0.00683783T2
+ 0.05481717W2 +0.00122874T2H
+ 0.0008528277/2 + 0.00000199T2H2), 120)
Where:
T= temperature
H = relative humidity
T >=78 0 F
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Appendix B OTAQ Light-duty gasoline 2012 Cold Temperature
Program
EPA's Office of Transportation and Air Quality (OTAQ) contracted the testing of nine Tier 2 vehicles
(2006 and 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. Note: we excluded the two GDI vehicles (Cadillac STS and the VW Passat) from the
estimation of the THC and CO cold starts'5 as mentioned in Section 2.2.
Information on the tested vehicles is summarized in Table B-l.
Table B-l Vehicles Tested in 2012 Cold Temperature Study
Vehicle Name
Model Year
Injection
Emissions Std
MSAT?
Odometer
Displ (L)
Cyl.
Buick Lucerne*
2010
PFI
Tier 2/Bin 4
MSAT-2
22000
3.9
V-6
Honda Accord*
2010
PFI
Tier 2/Bin 5
MSAT-2
24000
2.4
1-4
Hyundai Sante Fe
2010
PFI
Tier 2/Bin 5
MSAT-2
18000
2.4
1-4
Jeep Patriot*
2010
PFI
Tier 2/Bin 5
MSAT-2
22000
2
1-4
Kia Forte EX*
2010
PFI
Tier 2/Bin 5
MSAT-2
25000
2
1-4
Mazda 6*
2010
PFI
Tier 2/Bin 5
MSAT-2
24000
2.5
1-4
Mitsubishi Gallant*
2010
PFI
Tier 2/Bin 5
MSAT-2
38000
2.4
1-4
Cadillac STS
2010
GDI
Tier 2/Bin 5
MSAT-2
21000
3.6
V-6
VW Passat
2006
GDI
Tier 2/Bin 5
pre-MSAT
103000
2
1-4
Tested at 0°F
63
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64 |
Appendix C Air Conditioning Analysis Vehicle Sample
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.
Table C-l lists the vehicles in the test program.
Table C-l Vehicle Sample for the Air Conditioning Analysis
Model Year
Make
Model
Vehicle Class
Weight
1990
DODGE
DYNA
CAR
3625
1990
NISSAN
MAXIO
CAR
3375
1991
CHEVROLET
CAVA 0
CAR
2750
1991
FORD
ESCO GT
CAR
2625
1992
CHEVROLET
CAVA
CAR
3000
1992
CHEVROLET
LUMI
CAR
3375
1992
MAZDA
PROT
CAR
2750
1992
SATURN
SL
CAR
2625
1992
TOYOTA
CORO
CAR
2500
1993
CHEVROLET
CORS
CAR
3000
1993
EAGLE
SUMMO
CAR
2500
1993
HONDA
ACCOO
CAR
3250
1993
TOYOTA
CAMRO
CAR
3250
1994
CHRYSLER
LHS
CAR
3750
1994
FORD
ESCO
CAR
2875
1994
HYUNDAI
ELAN
CAR
3000
1994
SATURN
SL
CAR
2750
1995
BUICK
CENT
CAR
3995
1995
BUICK
REGA LIMI
CAR
3658
1995
FORD
ESCO
CAR
2849
1995
SATURN
SL
CAR
2610
1995
SATURN
SL
CAR
2581
1996
CHEVROLET
LUMI 0
CAR
3625
1996
HONDA
ACCO
CAR
3500
1996
HONDA
CIVI
CAR
2750
1996
PONTIAC
GRAN PRIX
CAR
3625
1996
TOYOTA
CAMR
CAR
3625
1997
FORD
TAUR
CAR
3650
1998
MERCURY
GRAN MARQ
CAR
4250
1998
TOYOTA
CAMR LE
CAR
3628
1990
JEEP
CHER
LDT1
3750
1990
PLYMOUTH
VOYA
LDT1
3375
1991
CHEVROLET
ASTRO
LDT1
4250
1991
PLYMOUTH
VOYA
LDT1
3750
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Modi
1992
1993
1994
1994
1996
1996
1990
1991
1994
1996
1996
1996
1996
1996
1996
1997
1997
1997
1998
1999
Make
Model
Vehicle Class
CHEVROLET
LUMI
LDT1
CHEVROLET
S10
LDT1
CHEVROLET
ASTR
LDT1
PONTIAC
TRAN
LDT1
FORD
EXPL
LDT1
FORD
RANG
LDT1
CHEVROLET
SURB
LDT2
FORD
E150 0
LDT2
FORD
F150
LDT2
FORD
F150
LDT2
DODGE
DAKO PICK
TRUCK
DODGE
D250 RAM
TRUCK
DODGE
GRAN CARA
TRUCK
DODGE
CARA
TRUCK
FORD
F150 PICK
TRUCK
DODGE
GRAN CARA
TRUCK
DODGE
DAKOT
TRUCK
PONTIAC
TRANS SPOR
TRUCK
DODGE
CARA GRAN
TRUCK
FORD
WIND
TRUCK
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