MOVES2010 

Highway Vehicle Temperature, 

Humidity, Air Conditioning, and 

Inspection and Maintenance Adjustments 
 

MOVES2010
 

Highway Vehicle Temperature, 


Humidity, Air Conditioning, and 


Inspection and Maintenance Adjustments
 

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments. 

EPA-420-R-10-027 

December 2010 
Table of Contents 

Table of Contents............................................................................................................................. i
 

List of Tables .................................................................................................................................. ii
 

List of Figures ................................................................................................................................iii
 

Glossary of Acronyms.................................................................................................................... iv
 

1. Introduction..............................................................................................................................1

2. Temperature Adjustments........................................................................................................1
 

2.1 Data Sources for Temperature Effects ............................................................................. 1
 

2.2 Temperature Adjustment Methodology ........................................................................... 2
 

2.3 Effects of Temperature on Gasoline Fueled Vehicles...................................................... 3
 

2.3.1 Temperature Effects on Gasoline Start Emissions.................................................... 3
 

2.3.1.1 HC and CO Start Emissions for Gasoline-Fueled Vehicles: ............................ 3
 

2.3.1.2 Temperature Effects on Gasoline NOx Start Emissions................................... 5
 

2.3.1.3 Temperature Effects on Gasoline PM Start Emissions..................................... 7
 

2.3.2 Temperature Effects on Gasoline Running Emissions ............................................ 7
 

2.4 Effects of Temperature on Diesel Fueled Vehicles.......................................................... 9
 

2.5 Cold Weather Effects ..................................................................................................... 12
 

2.5.1 Cold Weather CO Requirement............................................................................. 12
 

2.5.2 Cold Weather HC Requirement............................................................................. 12
 

2.5.3 Cold Weather PM Effects ...................................................................................... 14
 

3. Humidity Adjustments .......................................................................................................... 17
 

4. Air Conditioning Adjustments.............................................................................................. 17
 

4.1 Air Conditioning Effects Data ............................................................................................ 18
 

4.2 Mapping Data to VSP Bins............................................................................................... 20
 

4.3 Air Conditioning Effects on Emissions ............................................................................ 22
 

4.3.1 A/C Adjustments for HC, CO and NOx Emissions..................................................... 22
 

4.3.2 Full A/C Adjustments for Energy Consumption.......................................................... 22
 

4.3.3 Uncertainty Analysis.................................................................................................... 23
 

4.4 Adjustments to Air Conditioning Effects ........................................................................... 25
 

5. Inspection and Maintenance Programs ................................................................................. 28
 

5.1 Inspection & Maintenance in MOBILE6 ....................................................................... 28
 

5.2 Inspection & Maintenance in MOVES .......................................................................... 29
 

5.3 Development of MOVES I/M Factors ........................................................................... 30
 

5.4 Development of MOVES I/M Compliance Inputs......................................................... 32
 

6. References............................................................................................................................. 34
 

Appendix A – Mean Start Emission by Temperature ................................................................... 36
 

Appendix B – Calculation of Specific Humidity.......................................................................... 38
 

Appendix C – Air Conditioning Analysis Vehicle Sample .......................................................... 39
 

Appendix D – Toros Topaloglu, Comments................................................................................ 41
 

Appendix E – ENVIRON International Corporation, Comments ................................................ 48
 

Appendix F – Julio Vassallo, Comments...................................................................................... 52
 

Appendix G – Coordinating Research Council Project E-68a Comments ................................... 54
 

i 
List of Tables
 

Table 2-1 Average NOx Emission Results by Temperature........................................................... 7
 

Table 2-2 Diesel Vehicle Emissions by Temperature..................................................................... 9
 

Table 2-3 Phase-In of Vehicles Meeting Cold Weather HC Standard.......................................... 13
 

Table 2-4 Multiplicative Increases of PM at 20° Fahrenheit ........................................................ 16
 

Table 2-5 Exponential Equation Constant Terms......................................................................... 16
 

Table 3-1 Humidity Correction Coefficients Used by MOVES ................................................... 17
 

Table 4-1 Distribution of Test Vehicles by Model Year............................................................... 19
 

Table 4-2 Distribution of Tests by Schedule Type ....................................................................... 19
 

Table 4-3 VSP Bin Definitions..................................................................................................... 21
 

Table 4-4 Full Air Conditioning Adjustments for HC, CO and NOx........................................... 22
 

Table 4-5 Full Air Conditioning Adjustments for Energy............................................................ 23
 

Table 4-6 Fraction of Vehicles Equipped with Air Conditioning................................................. 26
 

Table 4-7 Fraction of Air Conditioning Units Still Functioning By Age ..................................... 27
 

Table 4-8 Effect of Heat Index on Air Conditioning Activity...................................................... 27
 

Table 5-1 MOBILE6.2 Runs Used to Populate the MOVES IMFactor........................................ 31
 

Table 5-2 I/M Coverage Table Data Sources................................................................................ 33
 

Table A-1 Change in Mean Start Emissions at Various Temperatures ........................................ 36
 

Table A-2 Change in Mean Start Emissions at Various Temperatures ........................................ 37
 

Table C-1 Vehicle Sample for the Air Conditioning Analysis ..................................................... 39
 

ii 
1

2

3

4

5

6

List of Figures
 

Figure 2- Effects of Ambient Temperature on Changes in Cold-Start NOx................................. 6
 

Figure 2- Logarithm of Bag-2 HC Versus Temperature ............................................................... 8
 

Figure 2- Cold-Start HC Emissions (in grams) with Confidence Interval.................................. 10
 

Figure 2- Bag-1 minus Bag-3 CO Emissions (in grams) with Confidence Interval ................... 11
 

Figure 2- Bag-1 minus Bag-3 NOx Emissions (grams) with Confidence Intervals.................... 11
 

Figure 2- FTP Bag 1 PM and FTP Bag 1 NMHC for Tier 2 Vehicles........................................ 15
 

iii 
Glossary of Acronyms
 

A/C Air Conditioning 

ACCF Air Conditioning Correction Factor 

ASM Acceleration Simulation Mode 

CO Carbon Monoxide 

EPA Environmental Protection Agency 

F Fahrenheit 

FID Flame Ionization Detection 

FTP Federal Test Procedure 

g/mi Grams per Mile 

GVWR Gross Vehicle Weight Rating 

HC HydroCarbons 

HLDT Heavy Light Duty Truck 

I/M Inspection and Maintenance 

LDT Light Duty Truck 

LDV Light Duty Vehicle 

LLDT Light Light Duty Truck 

MDPV Medium Duty Passenger Vehicle 

MOVES MOtor Vehicle Emission Simulator 

MSAT Mobile Source Air Toxics 

NMHC Non-Methane HydroCarbons 

NOx Oxides of Nitrogen 

OBD On-Board Diagnostic 

PM Particulate Matter 

RSD Remote Sensing Data 

SFTP Supplemental Federal Test Procedure 

THC Total HydroCarbons (FID detection) 

VIN Vehicle Identification Number 

VOC Volatile Organic Compounds 

iv 
1. Introduction 

The emission rates in the MOVES model database represent 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 a methodology for adjusting 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 being addressed in a separate report. 

This report describes adjustments that affect running exhaust, start exhaust and extended 

idling emissions. The crankcase emission processes are chained to running exhaust, engine start 

and extended idling emissions, and thus are similarly affected by the temperature adjustments 

describe in this report. The impact of fuels, temperatures and I/M programs on vapor venting, 

permeation and liquid leaks is addressed in a separate report on evaporative emissions. 

2. Temperature Adjustments 

In EPA's previous emissions model (MOBILE6), passenger car and light-duty truck tailpipe 

emissions were adjusted relative to its base emission rates at 75 degrees Fahrenheit based on: 

1.	 ambient temperature [1], and 

2.	 for start emissions, an adjustment based on the length of the soak time. [2] 

MOVES will take a similar approach, but we will substantially alter the nature of the temperature 

adjustments. 

2.1 Data Sources for Temperature Effects 

For this analysis, we used almost entirely “Bagged” tests. Those data sets consisted of 

Federal Test Procedure (FTP) and LA-92 tests for start emissions. For the temperature effects on 

running emissions we used the Bag-2 emissions of those FTPs as well as US06 tests (without 

engine starts). Some second-by-second test data were also used but only to validate the effects of 

temperature on running emissions (HC, CO, and NOx). The data used in these analyses come 

from the following four sources: 

1.	 EPA’s Mobile Source Observation Database (MSOD) as of April 27, 2005. Over the past 

decades, EPA has performed emission tests (usually the FTP) on tens of thousands of 

vehicles under various conditions. EPA has stored those test results in its Mobile Source 

Observational Database (MSOD). (EPA has supplemented those tests with the results 

from many non-EPA testing programs.) 

For the MSOD data, we limited our analysis to only tests from the vehicles that were 

tested at two or more temperatures. In this analyses, those paired (MSOD) tests covered 

1
 
the temperature range from 15 to 110 degrees Fahrenheit. Many of those bagged tests 

(FTPs) were also used in our earlier MOBILE6 analyses. 

Information on EPA's MSOD is available on EPA's website: 

http://www.epa.gov/otaq/models.htm 

2.	 A testing program in Kansas City also yielded pairs of test (using LA92s tests rather than 

FTPs) from the vehicles that were tested at two or more temperatures. Information on 

this study is available at: 

http://www.epa.gov/otaq/emission-factors-research/420r08009.pdf 

3.	 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 at 

temperatures of: 75, 40, 20, 0 and –20 oF. These five vehicles supplemented the vehicles 

from the MSOD and Kansas City . [3] 

4.	 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 at temperatures of: 75, 

20, and 0 oF. These four vehicles also supplemented the vehicles from the MSOD and 

Kansas City. 

2.2 Temperature Adjustment Methodology 

For our analyses of gasoline vehicles, we stratified the paired-test data by the same 

parameters that MOVES uses to define the Source Bins, namely: fuel type, regulatory class, and 

model year groups. 

For this analysis, we started with the model year groups used in MOVES for start emission 

rates. By combining several finer model year groups, we consolidated those (MOVES) model 

year groups into these six model year groups: 

-- 1960 to 1980
 

-- 1981 to 1982
 

-- 1983 to 1985
 

-- 1986 to 1989
 

-- 1990 to 2004
 

-- 2005 and later
 

A preliminary analysis of the test data indicated that vehicles meeting the Tier 0, Tier 1, and 

LEV standards all exhibited similar increases in emissions as the ambient temperature drops 

from 75o F to 20o F. A single additive adjustment (for each of HC, CO, NOx) can represent this. 

Both the Federal FTP and California’s Unified Cycle are 3-mode (or 3-bag) tests in which the 

first and third modes are identical driving cycles, but the first mode begins with a cold-start (cold 

2
 
engine and emission control equipment) and the third mode begins with a hot-start (relatively 

warm engine and control equipment).. We used the adjusted difference of Bag-1 minus Bag-3 

emissions to estimate the cold-start emissions (in grams) for each test. 

Similarly, we used the emissions from the FTP Bag-2, IM240, and US06 tests to estimate the 

ratios (i.e., multiplicative changes) in the hot-running emission rates. 

We combined the test data from the passenger cars and the light-duty trucks. Therefore, the 

only stratifying parameter in this analysis was the model year grouping. 

Then, within each model year group, we used regression analysis of cold-start and hotrunning emissions versus temperature to find a polynomial fit to describe the change in emissions 

as a function of temperature. 

For simplicity, we only considered functions that were a multiple of “temperature minus 75o 

F” raised the first, second, or third degree. This produced (additive) adjustments that exhibit zero 

change at 75 degrees Fahrenheit. 

2.3 Effects of Temperature on Gasoline Fueled Vehicles 

2.3.1 Temperature Effects on Gasoline Start Emissions 

The effects of ambient temperature on HC, CO, and NOx start emissions were modeled using 

the following algorithm: 

1.	 Using additive (rather than multiplicative) adjustments. 

2.	 Calculating these adjustments as simple functions of one of the following measures: 

-- the temperature minus 75° F, or 

-- the square of the difference of the temperature minus 75° F, or 

-- the cube of the difference of the temperature minus 75° F. 

This approach guarantees a value of zero change for the additive adjustment at 75° F (i.e., 

the nominal temperature of EPA’s FTP test). Those coefficients are stored in the 

MOVES database table named StartTempAdjustment. 

3.	 Setting the value of the adjustments equal to zero for temperatures higher than 75°
 

Fahrenheit.
 

2.3.1.1 HC and CO Start Emissions for Gasoline-Fueled Vehicles: 

As described above we used the difference between the Bag-1 emissions and the 

corresponding Bag-3 emissions to estimate the cold-start emissions (in grams per start) for each 

3
 
test. For the gasoline-fueled vehicles, those cold-start emissions were then stratified by model 

year group. The mean emissions at 75 °F were subtracted from each of the means to determine 

the change in emissions as functions of ambient temperature. (See Appendix A for the resulting 

average changes.) 

As noted at the beginning of this section, we decided to model the changes in cold-start 

emissions as a polynomial (linear, or a quadratic, or a cubic) function of the temperature minus 

75° F. Thus, the shape of each adjustment curve at temperature below 75° F would determine 

the shape of that curve at temperatures above 75° F. However, the predetermined shape of the 

curve at temperatures above 75° F was not always in agreement (directionally) with the test data 

above 75° F. Therefore, we set the value of those additive adjustments 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 (68o to 86o F); however, those values are included (if available) 

in Appendix A. 

We performed a linear, quadratic, and cubic regressions on the data in Appendix A and then 

selected the best fit from among those three. The following equations were, thus, chosen as the 

"best fit" predictors of the change in cold-start emissions (in grams) as functions of the ambient 

temperature: 

For the Pre-1981s:
 

HC temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -0.630705748 R-sqr = 0.99271
 

CO temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -4.677330289 R-sqr = 0.98973
 

Each of those linear coefficients is stored in table StartTempAdjustment. (for the cold-start, 

i.e., opModeID of 108) 

For the 1981-1982s:
 

HC temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -0.413584322 R-sqr = 0.98368
 

CO temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -4.630546442 R-sqr = 0.97761
 

For the 1983-1985s:
 

HC temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -0.360706640 R-sqr = 0.88660
 

CO temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -4.244442967 R-sqr = 0.96367
 

For the 1986-1989s:
 

HC* temperatureAdjustment = tempAdjustTermB * Sqr_of (Temp. – 75)
 

where: tempAdjustTermB = 0.002413998 R-sqr = 0.98895
 

4
 
CO temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -1.089740827 R-sqr = 0.99401
 

Note: HC test data for this model year range were available down to an ambient 

temperature of -20° F. However, the "best fit" HC regression curves (linear, 

quadratic, and cubic) all exhibited less than ideal fits to those data at temperatures 

from zero through 20° F. Deleting the test data at -20° F and rerunning the 

regressions produced an improved estimate of the cold-start HC emissions in that 

critical temperature range. Therefore, this proposed quadratic regression is based on 

the changes in cold-start emissions at only temperatures from zero through 75° F. 

For the 1990-2005s:
 

HC* temperatureAdjustment = tempAdjustTermB * Sqr_of (Temp. – 75)
 

where: tempAdjustTermB = 0.002924240 R-sqr = 0.99409
 

CO* temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -1.141434345 R-sqr = 0.99017
 

Note: As with the regressions performed on the test data from the 1986 through 1989 

model years, both the HC and CO regressions produced superior estimators of both 

HC and CO cold-start emissions (at temperatures above zero degrees F) when the test 

data at -20° F was omitted. Therefore, both of these regressions were based on the 

changes in cold-start emissions only at temperatures from zero through 75° F. 

2.3.1.2 Temperature Effects on Gasoline NOx Start Emissions 

For the effects on cold-start NOx emissions associated changes in ambient temperature, we 

attempted the same model year stratification that we used for the HC and CO emissions. 

However, as is illustrated in the following graph (Figure 2-1), the "by model year" temperature 

effects on cold-start NOx emissions did not lend themselves to linear, quadratic, or cubic 

regressions (possibly due to insufficient sample size). Also, not unexpectedly, most of the 

coefficients produced by those regression analyses were not statistically significant. 

5
 
Figure 2-1 Effects of Ambient Temperature on Changes in Cold-Start NOx 

-3.0 

0.0 

3.0 

6.0 

9.0 

P re -8 1 

8 1 -8 2 

8 3 -8 5 

8 6 -8 9 

9 0 -9 3 

9 4 -9 9 

2 0 0 1 

2 0 0 5 

-20 0 20 40 60 80 100 120 

T e mpe ra ture (de gre e s Fa hre nhe it) 

A visual inspection of Figure 2-1 suggests that only three model year groups (1990-1993, 

2001, and 2005) exhibited patterns that would result in meaningful regression analyses. We 

attempted to group the data into various other model year groups. The only grouping that 

produced useful regression analysis results were the ones in which we average together all of the 

NOx results (from Appendix A) to obtain the following table: 

6
 
Table 2-1 Average NOx Emission Results by Temperature 

Temp 

-20.0 

0.0 

Delta NOx 

(grams) 

1.201 

1.227 

19.4 

20.7 

0.202 

0.089 

22.4 -0.155 

Temp 

31.0 

40.0 

Delta NOx 

(grams) 

-0.007 

0.876 

48.8 

49.8 

0.127 

0.333 

51.0 0.325 

Temp 

54.2 

76.3 

Delta NOx 

(grams) 

0.438 

0.000 

95.3 

97.1 

0.225 

0.370 

105.8 0.543 

Performing regression analyses on these data (again, using only the changes in the NOx coldstart emissions for temperatures below 86o F as explained in Section 3.2), we found the "best fit" 

equation to be: 

NOx temperatureAdjustment = tempAdjustTermA * (Temp. – 75) 

where: tempAdjustTermA = -0.009431682 R-sqr = 0.611349 

Although the value of R-squared is not as high as for the HC and CO regression equations, 

the coefficient is statistically significant. If we were to evaluate that equation for temperatures 

higher than 75° F, it would predict a negative change (i.e., a decrease) in the cold-start NOx 

emissions (i.e., a decrease in cold-start NOx emissions), but the actual data indicate that the coldstart NOx emissions increase as the ambient temperature rises above 90° F. Therefore (as with 

the previous adjustments), this additive adjustment is set to zero for temperatures higher than 75° 

F. 

2.3.1.3 Temperature Effects on Gasoline PM Start Emissions 

The temperature effects on particulate matter (PM) start emissions modeled in MOVES 

using a multiplicative (not additive) exponential (not polynomial) adjustment. Thus analysis is 

included as Chapters 7 and 8 of a separate report ("Analysis of Particulate Matter Emissions from 

Light-Duty Gasoline Vehicles in Kansas City"). [4] 

2.3.2 Temperature Effects on Gasoline Running Emissions 

The test data analyzed to determine the effects of different ambient temperatures on running 

emissions consisted of: 

1. Bag-2 of the FTP for vehicles tested at multiple temperatures, 

2. US06 for vehicles tested at multiple temperatures, and 

3. Remote sensing data (RSD) on a random sample of vehicles
 

tested at Kansas City over a wide range of temperatures.
 

4. FTP and IM240 tests on a random sample of vehicles tested
 

at Kansas City
 

Those test data suggest that there is very little variation in those running emissions of HC, 

CO, or NOx. Regression analyses found that the coefficients (slopes) were not statistically 

significant (that is, the slopes were not distinguishable from zero). This is consistent with what 

7
 
we found in our analysis of the Kansas City data. This lack of correlation between running 

emissions and ambient temperature is illustrated (as an example) by the following graph of the 

HC data: 

Figure 2-2 Logarithm of Bag-2 HC Versus Temperature 

lnHC(bag2) 

4 

3 

2 

1 

0 

10.0 100.0
 

-1
 

-2 

-3 

-4 

-5 

-6 

20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 

Temp 

In this plot, each point represents a single FTP Bag-2 test result from the Kansas City 

program. A visual inspection of this plot of the natural log of the FTP Bag-2 HC emissions 

suggests no strong relationship between the hot-running HC emissions and the ambient 

temperature. 

The CO and NOx plots are similar in that they also do not indicate a significant trend. 

We looked at the second-by-second test data from IM240 tests run in Chicago (as part of 

Chicago's I/M program) to validate this conclusion. To avoid variable preconditioning, we used 

only second IM240s when back-to-back IM240s were performed, and for the other IM240s we 

examined the last 120 seconds of full duration IM240s. We found no evidence of a temperature 

effect between 5 and 95 degrees F. 

The effect of temperature on hot running HC, CO, and NOx emissions is coded in MOVES 

using polynomial functions as multiplicative adjustments. In this version of MOVES, we 

propose to set all of those adjustments equal to 1.0, that is, no change in those running emissions 

with temperature. 

This was not the case for PM emissions. Our data did show a temperature effect for running 

emissions of particulate matter. As for start emissions, the temperature effects on particulate 

matter (PM) running emissions modeled in MOVES using a multiplicative (not additive) 

8
 
exponential (not polynomial) adjustment. This analysis is detailed in as Chapters 7 and 8 of the 

"Analysis of Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas City"). 

[4]. 

2.4 Effects of Temperature on Diesel Fueled Vehicles 

We were able to identify only 12 diesel-fueled vehicles with FTPs at multiple temperatures 

(nine passenger cars and 3 light-duty trucks). However, only two of those 12 vehicles were 

tested at temperatures within the normal FTP range (68o to 86o F). None of these diesel trucks 

were equipped with after-treatment devices. The Bag-1 minus Bag-3 emissions for those tests 

are shown in Table 2-2. We stratified the test results into four temperature bands which yielded 

the following emission values (grams per start) and average temperature value: 

Table 2-2 Diesel Vehicle Emissions by Temperature 

(grams per start) 

Temperature Count HC 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 

When we plotted the mean HC start emissions (above) versus temperature, we obtained the 

following graph (where the vertical lines represent 90 percent confidence intervals and the 

"dashed" line represents a linear regression through the data). 

9
 
Figure 2-3 Cold-Start HC Emissions (in grams) with Confidence Interval 

4 

3 

2 

1 

0 

30 40 50 60 70
 

T emperature (degrees F) 

The dashed (blue) line in the figure is a linear regression line having as its equation: 

HC = (-0.0420985982 * Temperature ) + 4.22477812 R-sqr = 0.9040467 

Transforming this equation into an equation that predicts the (additive) change/adjustment in 

the cold-start HC emissions from light-duty diesel-fueled vehicles (in the MOVES format), we 

obtain: 

HC temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = -0.0420985982
 

The coefficient associated with this temperature adjustment term is statistically significant 

although its coefficient of variation is relatively large (23.04 percent). 

Again, this HC adjustment factor represent the difference of Bag-1 minus Bag-3 and must be 

adjusted to estimate the cold-start HC emissions. 

It proved more difficult to estimate a diesel light-duty vehicle temperature effect for CO and 

NOx. because 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: 

10
 
Figure 2-4 Bag-1 minus Bag-3 CO Emissions (in grams) with Confidence Interval 

0 

1 

2 

3 

4 

5 

30 40 50 60 70
 

T emperature (degrees F)
 

Figure 2-5 Bag-1 minus Bag-3 NOx Emissions (grams) with Confidence Intervals 

0 

2 

4 

6 

30 40 50 60 70
 

T emperature (degrees F)
 

11
 
 

Statistical analyses of both the diesel cold-start CO and NOx emissions failed to produce 

coefficients that were significantly different from zero. Therefore, for both cold-start CO and 

NOx adjustments from light-duty diesel-fueled vehicles, we propose to set the temperature 

adjustment for start emissions to zero: 

CO temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = 0.0
 

NOx temperatureAdjustment = tempAdjustTermA * (Temp. – 75)
 

where: tempAdjustTermA = 0.0
 

Since gasoline adjustments were set to zero, the temperature effects for diesel running 

exhaust were also set to zero. 

Because temperature effects data was not available for heavy duty trucks, the light duty 

results were extrapolated to these vehicles including thethe extended idling emission process for 

heavy duty long haul diesel trucks. No attempt has been made to adjust the effects of 

temperature on emissions to account for the introduction of after-treatment devices (such as 

diesel particulate filters or oxidation catalysts) that will become more common on future diesel 

fueled vehicles. 

2.5 Cold Weather Effects 

There are two sets of regulations that can affect our estimates of emissions at low temperature 

(i.e., at 20 degrees Fahrenheit), namely the cold weather CO requirement and the cold weather 

HC requirement. 

2.5.1 Cold Weather CO Requirement 

The cold weather CO requirement for the 1994 and newer model year LDVs and LDTs limits 

the composite FTP CO emissions to 10.0 grams per mile at a temperature of 20 degrees 

Fahrenheit. However, the FTP test results used for our analysis (for those model years) were 

from vehicles that were certified as meeting that cold weather composite CO requirement. Thus, 

the temperature adjustments (based on regressions of those FTP results) already incorporated that 

cold weather CO requirement into MOVES. 

2.5.2 Cold Weather HC Requirement 

TheMobile Source Air Toxic (MSAT-2) rule included a limit on low temperature (i.e., at 20 

degrees Fahrenheit) non-methane hydrocarbon (NMHC) emissions for light-duty and some 

medium-duty gasoline-fueled vehicles. 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 FTP NMHC emissions should not exceed 0.3 

grams per mile. 

12
 
 

 

 

● For heavy light-duty trucks (HLDTs) (those with GVWR from 6,001 up to 8,500 pounds) 

and for medium-duty passenger vehicles (MDPVs), the composite FTP NMHC emissions 

should not exceed 0.5 grams per mile. 

These cold weather standards are to be phased-in beginning with the 2010 model year, 

specifically: 

Table 2-3 Phase-In of Vehicles Meeting Cold Weather HC Standard 

Model Year 

2010 

2011 

LDVs / LLDTs 

25% 

50% 

HLDTs / MDPVs 

0% 

0% 

2012 

2013 

75% 

100% 

25% 

50% 

2014 

2015 

100% 

100% 

75% 

100% 

To incorporate this set of HC requirements into MOVES, we must first determine its impact 

on the start emissions (both cold-start and hot-start) as well as on the running emissions for each 

class of vehicles. 

We already observed that changes in the ambient temperature do not have a significant effect 

on the running THC emissions. Therefore, we will assume that the full impact of this 

requirement will be on the start emissions. 

Our earlier analysis of temperature effects on the emissions of Tier 2 vehicles was based on a 

single gasoline-fueled passenger car and three light-duty trucks that were each FTP tested at zero, 

20, and 75 degrees Fahrenheit. The average nonmethane HC (NMHC) composite FTP emissions 

at 75o F were: 

● 0.02 (0.0180) g/mile for the passenger car and 

● 0.04 (0.0353) g/mile for the heavy light-duty trucks (over 6,000 GVWR). 

Considering the MSAT-2 standards (0.30 and 0.50, respectively), this would mean the 

NMHC composite FTP emissions could increase by no more than 0.28 grams per mile (i.e., 0.30 

minus 0.02) for LDVs/LLDTs and by no more than 0.46 grams per mile for HLDTs/MDPVs as 

the ambient temperature drops from 75o F to 20o F. 

Applying those increases in NMHC emission rates to the composite FTP (which simulates a 

trip of 7.45 miles in length), those rates convert to total NMHC increases of 2.086 grams (for 

LDVs/LLDTs) and 3.427 grams (for HLDTs/MDPVs). Since a composite FTP is composed of a 

7.45 mile driving cycle plus a generic engine start (57 percent hot-start and 43 percent cold-start), 

those increases represent the increases in the generic start emissions. Using the ratio of hot-start 

13
 
 

 

 

 

to cold-start from our earlier analysis, the increase in NMHC cold-start emissions (as the ambient 

temperature drops from 75o F down to 20o F) are: 

● 0.5611592 grams for the LDVs/LLDTs and 

● 0.9219045 grams for the HLDTs/MDPVs. 

Since the analysis for the MSAT-2 rule assumed that increase in NMHC is linear with 

temperature (decreasing 55 degrees from 75 down to 20), those rates convert to decreases in total 

NMHC per cold-start of: 

● -0.0102029 grams per degree F for the LDVs/LLDTs and 

● -0.0167619 grams per degree F for the HLDTs/MDPVs. 

These are the rates (slopes) that we propose to use in MOVES for cold-starts (i.e., starts that 

follow a 12 hour engine soak). For the seven shorter soak periods (that MOVES uses as 

opModes), we will continue to use the ARB soak adjustments for HC emissions for catalyst 

equipped vehicles to estimate those HC emissions (following the seven shorter soak periods). 

2.5.3 Cold Weather PM Effects 

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) document [5] that 

accompanied that rule noted there is a strong linear correlation between NMHC and PM2.5 

emissions. That correlation is illustrated in the following graph (reproduced from that RIA) of 

the logarithm of the Bag-1 PM2.5 versus the logarithm of the Bag-1 NMHC (for various Tier-2 

vehicles). 

14
 
Figure 2-6 FTP Bag 1 PM and FTP Bag 1 NMHC for Tier 2 Vehicles 

Therefore, the limitation on cold weather HC (or NMHC) emissions is expected to result in a 

proportional reduction in cold weather PM2.5 emissions. In the MSAT-2 RIA (Table 2.1.-9), 

EPA estimated that this requirement would result in a 30 percent reduction of VOC emissions at 

20o 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 20o 

F (for Tier 2 cars and trucks). 

EPA's earlier analysis (for MOVES) [4] indicated that ambient temperature affects both start 

and running PM emissions, and that effect (for Tier 2 vehicles) is best modeled by (exponential) 

multiplicative adjustments of the form: 

A*(72-t) Multiplicative factor = e , where "t" is the ambient temperature 

and where A = 0.0463 for cold-starts and 

0.0318 for hot running 

(See Table 12 in Reference [4], page 46.) 

15
 
 

 

 

 

Therefore, for Tier 2 vehicles not affected by the MSAT-2 requirements, as the temperature 

decreases from 72o to 20o F, EPA expects PM emissions to increase by factors of: 

● 11.10727 for cold-starts and 

● 5.22576 for hot running. 

Applying the 30 percent reduction for vehicles affected by the MSAT-2 requirements implies 

a PM increase as the temperature decreases from 72o to 20o F of: 

● 7.77509 for cold-starts and 

● 3.65803 for hot running. 

Combining this information with the MSAT-2 phase-in schedule from Table 2-3 leads to the 

following (multiplicative) increases as the temperature decreases from 72o to 20o F: 

Table 2-4 Multiplicative Increase in PM from 72o to 20° Fahrenheit 

LDVs / LLDTs HLDTs / MDPVs 

Model Year Start Running Start Running 

2008 11.10727 5.22576 11.10727 5.22576 

2009 11.10727 5.22576 11.10727 5.22576 

2010 10.27423 4.83383 11.10727 5.22576 

2011 9.44118 4.44189 11.10727 5.22576 

2012 8.60814 4.04996 10.27423 4.83383 

2013 7.77509 3.65803 9.44118 4.44189 

2014 7.77509 3.65803 8.60814 4.04996 

2015 7.77509 3.65803 7.77509 3.65803 

Solving for the corresponding constant terms so that the preceding exponential equation will 

yield these increases, gives us these "A" values: 

Table 2-5 Exponential Equation Constant Terms 

LDVs / LLDTs HLDTs / MDPVs 

Model Year Cold-Start Running Cold-Start Running 

2008 0.046300 0.031800 0.046300 0.031800 

2009 0.046300 0.031800 0.046300 0.031800 

2010 0.044801 0.030301 0.046300 0.031800 

2011 0.043175 0.028675 0.046300 0.031800 

2012 0.041398 0.026898 0.044801 0.030301 

2013 0.039441 0.024941 0.043175 0.028675 

2014 0.039441 0.024941 0.041398 0.026898 

2015 0.039441 0.024941 0.039441 0.024941 

16
 
We assume that the increases in the PM2.5 emissions apply proportionally to the Elemental 

Carbon (EC) and Organic Carbon (OC) portions of the PM2.5 emissions. Sulfate PM emissions 

are not affected by temperature. 

Although the ARB factors that adjust the start emissions based on soak time were not 

developed for PM emissions from gasoline-fuel vehicles, the finding that Tier 2 PM emissions 

are proportional to HC emissions supports our decision to apply the HC soak adjustments to the 

start PM emissions. 

3. Humidity Adjustments 

In EPA's previous emissions model, MOBILE6, only gasoline vehicle exhaust NOx 

emissions were adjusted for humidity. MOVES adjusts both gasoline and diesel vehicle exhaust 

NOx emissions. The base exhaust emission rates for NOx in all modes and all processes are 

multiplied by a humidity adjustment. This factor is calculated using the following formula: 

K = 1.0 – ( (Bounded Specific Humidity – 75.0) * Humidity Correction Coefficient) 

The bounded specific humidity is in units of grains of water per pound of dry air. The 

specific humidity is not allowed to be lower than 21 grains and is not allowed to be larger than 

124 grains. If the specific humidity input exceeds these limits, the value of the limit is used to 

calculate the humidity adjustment. Appendix B shows how the hourly relative humidity values 

are converted to specific humidity used in this equation using temperature and barometric 

pressure. 

Table 3-1 Humidity Correction Coefficients Used by MOVES 

Fuel Type Humidity Correction Coefficient 

Gasoline 0.0038 

Diesel Fuel 0.0026 

The diesel humidity correction coefficient is taken directly from the Code of Federal 

Regulations[6]. The gasoline humidity correction coefficient is carried over from the coefficient 

used in the MOBILE6 model. 

4. Air Conditioning Adjustments 

The air conditioning adjustments in MOVES are based on the same data as was used in the 

previous MOBILE6 model, but the adjustments themselves were recalculated to be consistent 

with the MOVES methodology. 

The proposed factors are based on a test procedure meant to simulate air conditioning 

emission response under extreme “real world” ambient conditions. These factors predict 

emissions which would occur during full loading of the air conditioning system, and will then be 

17
 
scaled down in MOVES according to ambient conditions in a modeling run. The second-bysecond emission data were analyzed using the MOVES methodology of binning the data 

according to vehicle characteristics (source bins in MOVES) and vehicle specific power bins 

(operating modes in MOVES). The results of the analysis showed statistically significant and 

consistent results for three bin combinations (deceleration, idle and cruise/acceleration) and the 

three primary exhaust pollutants (hydrocarbon, carbon monoxide and nitrous oxides). This 

report shows the results of the analysis for the air conditioning adjustments used in MOVES for 

HC, CO, NOx and energy consumption. 

MOVES will make adjustments to total energy consumption and exhaust running HC, CO 

and NOx emissions separately for each operating mode. The criteria pollutants (HC, CO and 

NOx) are only affected for passenger car, passenger truck and commercial light truck source 

types. Energy consumption is affected for all source types. The same adjustment values are used 

for all source use types affected within a pollutant type. 

4.1 Air Conditioning Effects Data 

The data for the MOVES A/C Correction Factor (ACCF) was collected in calendar year 1997 

and 1998 in specially designed test programs. In the programs the same set of vehicles were 

tested at standard FTP test conditions (baseline) and at a nominal temperature of 95 F. Use of 

the same set of vehicles and test cycles should eliminate most of the vehicle and test procedure 

variability and highlight the difference between a vehicle operating at extreme ambient 

conditions and at a baseline condition. 

The data used to develop the MOVES ACCF consisted of 54 individual cars and light 

trucks tested over a variety of test schedules. Overall the database consisted of a total of 625 test 

cycles, and 1,440,571 seconds of emission test and speed / acceleration data. Because of the 

need to compute vehicle specific power on a modal basis, only test results which consisted of 

second by second data were used in the analysis. All second by second data were time aligned 

and quality controlled checked. 

The distribution of test vehicles by model year is shown in Table 4-1. Model years 1990 

through 1999 were included. The data set consists of 30 cars and 24 light trucks. No test data 

were available on other vehicle types (i.e., motorcycles, heavy trucks, etc). The individual test 

cycles which the vehicles were run on are shown with the test counts in Table 4-2. The data 

shows a nice balance between different test cycles, and cars and trucks. Unfortunately, the study 

does not contain any pre-1990 model years. A complete list of the individual vehicles and a 

basic description is shown in Appendix C. 

Only vehicles which were coded as having an emission test with the A/C system on were 

selected. The A/C On tests and the A/C Off (default for most EPA emission tests in general) 

were matched by VIN, test schedule and EPA work assignment. The matching ensured that the 

same vehicles and test schedules were contained in both the A/C On sample and the A/C Off 

sample. 

18
 
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 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 

19
 
4.2 Mapping Data to VSP Bins
 

The overall dataset consisted of a sample of vehicle tests with the A/C system on and a 

sample of vehicle tests with the A/C system off. Both samples consisted on the same vehicles 

and all tests were modal with a data sampling of 1 hertz (second-by-second data collection). 

Prior to analysis the data for each vehicle / test cycle combination was time aligned to insure that 

the instantaneous vehicle operating mode was in-sync with the emission collection system. 

Following time alignment, the vehicle specific power (VSP) was calculated for each vehicle test / 

second combination. This was done using Equation 1. 

VSP =	 985.5357 * Speed * Acoeff / Weight + 

440.5729 * Speed^2 * Bcoeff / Weight + 

196.9533 * Speed^3 * Ccoeff / Weight + 

0.19984476 * Speed * Accel + GradeTerm Eq 1 

Where 

VSP is the vehicle specific power for a given second of operation in units of KW / tonne.
 

Speed is the instantaneous vehicle speed for a given second in units miles / hour.
 

Accel is the instantaneous vehicle acceleration for a given second in unit of miles/hr-sec
 

Weight is the test vehicle weight in pounds.
 

Acoeff = 0.7457*(0.35/(50*0.447)) * ROAD_HP
 

Bcoeff = 0.7457*(0.10/(50*50*0.447*0.447)) * ROAD_HP
 

Ccoeff = 0.7457*(0.55/(50*50*50*0.447*0.447*0.447)) * ROAD_HP
 

Where 

ROAD_HP = 4.360117215 + 0.002775927 * WEIGHT (for cars) 

ROAD_HP = 5.978016174 + 0.003165941 * WEIGHT (for light 

trucks) 

GradeTerm (KW/tonne) = 4.3809811 * Speed * Sin(Radians(GradeDeg)) 

Where 

GradeDeg is the road grade in units of degrees. This term is zero for 

dynamometer tests. 

4.3809811 (m^2 * hr / (s^3 * miles) = 

9.80665(m/s^2) * 1609.34(m/mile) / 3600(secs/hr) 

KW / tonne = m^2 / s^3 

9.80665(m/s^2) is the gravitation constant. 

20
 
After computing the VSP for each vehicle test / second combination, we assigned the 

individual secondsto the MOVES VSP bins. These VSP bins are defined in Table 4-3. VSP bins 

26 and 36 were not defined because bins 27-30 and bins 37-40 overlap them. 

Table 4-3 VSP Bin Definitions 

VSP Label Definition 

0 Braking 

1 Idling 

11 Low Speed Coasting; VSP< 0; 1<=Speed<25 

12 Cruise/Acceleration; 0<=VSP< 3; 1<= Speed<25 

13 Cruise/Acceleration; 3<=VSP< 6; 1<=Speed<25 

14 Cruise/Acceleration; 6<=VSP< 9; 1<=Speed<25 

15 Cruise/Acceleration; 9<=VSP<12; 1<=Speed<25 

16 Cruise/Acceleration; 12<=VSP; 1<=Speed<25 

21 Moderate Speed Coasting; VSP< 0; 25<=Speed<50 

22 Cruise/Acceleration; 0<=VSP< 3; 25<=Speed<50 

23 Cruise/Acceleration; 3<=VSP< 6; 25<=Speed<50 

24 Cruise/Acceleration; 6<=VSP< 9; 25<=Speed<50 

25 Cruise/Acceleration; 9<=VSP<12; 25<=Speed<50 

26 Cruise/Acceleration; 12<=VSP; 25<=Speed<50 

27 Cruise/Acceleration; 12<=VSP<18; 25<=Speed<50 

28 Cruise/Acceleration; 18<=VSP<24; 25<=Speed<50 

29 Cruise/Acceleration; 24<=VSP<30; 25<=Speed<50 

30 Cruise/Acceleration; 30<=VSP; 25<=Speed<50 

33 Cruise/Acceleration; VSP< 6; 50<=Speed 

35 Cruise/Acceleration; 6<=VSP<12; 50<=Speed 

36 Cruise/Acceleration; 12 <= VSP; 50<=Speed 

37 Cruise/Acceleration; 12<=VSP<18; 50<=Speed 

38 Cruise/Acceleration; 18<=VSP<24; 50<=Speed 

39 Cruise/Acceleration; 24<=VSP<30; 50<=Speed 

40 Cruise/Acceleration; 30<=VSP; 50<=Speed 

An average emission result for each pollutant (HC, CO and NOx) with and without A/C 

operation was computed for each VSP Bin. This resulted in 69 (23 VSP bins x 3 pollutants) 

pairs of emission averages. However, preliminary analysis of the data grouped into the 23 bins 

(defined in Table 4-3) showed unsatisfactory statistical results. In the general, no trends were 

evident across VSP bins or within similar subsets of VSP bins. The trends were highly erratic 

and the results were generally not statistically significant. In addition, most of the bins labeled 

30 or higher had very few data members. An analysis of cars versus trucks was also performed, 

and showed no statistical difference between the two. 

To produce more consistent results, the individual VSP bins were collapsed down to three 

principal bins. These are the Braking / Deceleration bin, the Idle bin and the Cruise / 

Acceleration bin. These large bins are quite different in terms of engine operation and emissions 

performance. The Braking bin consisted of VSP Bin 0 in Table 4-3, the Idle bin was VSP Bin 1 

and the Cruise / Acceleration bin contained the remaining 21 bins. 

21
 
4.3 Air Conditioning Effects on Emissions 

4.3.1 A/C Adjustments for HC, CO and NOx Emissions 

Full A/C adjustments were generated for each of the nine VSP Bin and pollutant 

combinations. This was done by dividing the mean “With A/C” emission factor by the mean 

“Without A/C” emission factor for each of the VSP Bin / pollutant combinations. The Full A/C 

adjustments are shown in Table 4-4. Measures of statistical uncertainty (coefficient of variation 

of the mean) were also computed using the standard error of the mean. They are shown in Table 

4-4 as “Mean CV of CF.” 

Table 4-4 Full Air Conditioning Adjustments for HC, CO and NOx 

Pollutant Operating Mode Full A/C CF Mean CV of CF 

HC Braking / Decel 1.0000 0.48582 

HC Idle 1.0796 0.74105 

HC Cruise / Accel 1.2316 0.33376 

CO Braking / Decel 1.0000 0.31198 

CO Idle 1.1337 0.77090 

CO Cruise / Accel 2.1123 0.18849 

NOx Braking / Decel 1.0000 0.19366 

NOx Idle 6.2601 0.09108 

NOx Cruise / Accel 1.3808 0.10065 

4.3.2 Full A/C Adjustments for Energy Consumption 

The use of a vehicle’s A/C system will often have a sizeable impact on the vehicle’s energy 

consumption. This was found statistically by analyzing the available second by second data on 

CO2 and other gaseous emissions, and converting them to an energy basis using standard EPA 

vehicle fuel economy certification equations. The vehicle emission data were binned by VSPBin 

(see above). A mean value was computed for each combination of VSPBin. Separate analysis 

was done as a function of sourcebinid (combination of vehicle type, fuel type and model year), 

and the results were not statistically different versus sourcebinid given the relatively small 

sample sizes. As a result, the A/C adjustments for energy are a function of only VSPBin. The 

resulting A/C adjustments are shown in Table 4-5. 

22
 
Table 4-5 Full Air Conditioning Adjustments for Energy 

VSPBin A/C Factor VSPBin A/C Factor VSPBin 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 

Only very small amounts of data were available for VSPBins 26 through 29 and VSPBins 

37 through 40. As a result, the data from these bins was averaged together and binned into two 

groups. The resulting group averages were used to fill the individual VSPBins. This averaging 

process has the effect of leveling off the effect of A/C at higher power levels for an engine. This 

is an environmentally conservative assumption since it is likely that the engine power devoted to 

an A/C compressor probably continues to decline as the overall power demand of the engine is 

increased. In fact, in some newer vehicle designs the A/C unit will be shut off by an engine 

controller if the driver demands a very high level of power from the vehicle. If and when new or 

additional data become available on this issue, EPA will re-evaluate the assumption of a constant 

A/C factor for the high VSPBins. 

4.3.3 Uncertainty Analysis 

Measures of statistical uncertainty as indicated by the coefficient of variation of the mean 

(mean CV) were calculated using the following formula. The same set of equations were used 

for each of the three pollutants (although the equations are shown only once). The values of X 

and Y represent second by second emissions from either HC, CO or NOx. The variable “X” 

represents emissions with the A/C On and “Y” represents emission with the A/C Off. 

Given: 

Z = X / Y 

Mean CV = SEz / Z 

Where Z is the ratio of A/C On emissions (X) to A/C Off emissions (Y) 

SEz is the standard error of Z 

Mean CV is the coefficient of variation of the mean 

23
 
Vz2 =	 (dZ/dX)2 * Vx

2 + (dZ/dY)2 * Vy2 

Where	 Vz is the variance of Z, Vx is the variance of X and Vy is the variance of Y 

dZ/dX is the partial derivative of Z with respect to X 

dZ/dY is the partial derivative of Z with respect to Y 

(Vz / Z)2	 = ((1/Y2

)*Vx

2

) / (X2/Y2) + ((X2/Y4) * Vy

2

) / (X2/Y2) 

This equation reduces to: 

(Vz / Z)2	 = (Vx / X)2 + (Vy / Y)2 

And ultimately to: 

SEz / Z	 = SQRT [ (SEz / X)2 + (SEz / Y)2 ] 

The variance term is defined as: 

Vz =	 (1/Y)2 * Sy2x + (-X/Y2) * (-X/Y2) * Sy2y; 

Where 

X = A/C On emissions
 

Y = A/C Off emissions
 

The term Vz represents a contribution from both the X and Y emissions terms (A/C On and A/C 

Off). The terms Sy2x and Sy2y also include variance contributions of the “across sample 

variance” and the “within a given vehicle test” variance. The “across sample variance” is the 

standard variance of the sample and is computed within a given sourcetype (vehicle type such as 

car, light truck, heavy truck, etc) and operating mode bin (one of the 23 VSP bin types – See 

Table 4-3). The “within a given vehicle test” variance is the additional variance due to the fact 

that each vehicle test contributes hundreds or even thousands of test data elements. Because two 

data elements may come from the same vehicle, they are not strictly independent of each other. 

Sy2x = SA2x / nVeh + SB2x / nCell
 

Sy2y = SA2y / nVeh + SB2y / nCell
 

SA2x = ( 1 / (nVeh-1) ) * Sum1x
 

SB2x = ( 1 / (nCell – nVeh) ) * Sum2x
 

SA2y = ( 1 / (nVeh-1) ) * Sum1y
 

SB2y = ( 1 / (nCell – nVeh) ) * Sum2y
 

24
 
And 

Sum1x 

Sum2x 

= 

= 

S ( YbarVehx – YbarCellx )

2 

S ( varVehx – ( nMeas – 1) )2 

Sum1y 

Sum2y 

= 

= 

S ( YbarVehy – YbarCelly )

2 

S ( varVehy – ( nMeas – 1) )2 

Where 

The sums ( S	 ) are across sourcetype and operating mode. 

nMeas	 Count of data elements within a given sourcetype, operating mode and vehicle 

test. 

nVeh	 Count of data elements within a given vehicle test 

nCell	 Count of data elements within a given sourcetype and operating mode 

varVeh	 Variance for each vehicle test. Separate values for both X and Y are calculated. 

YbarVeh	 Mean emission rate for each vehicle test. Separate values for both X and Y are 

calculated. 

YbarCell	 Mean emission rate for each sourcetype and operating mode. Separate values for 

both X and Y are calculated. 

For HC, CO and NOx, detailed VSP was not found to be an important variable in regards to 

A/C adjustment and A/C usage. However, Full A/C adjustments greater than one were found for 

all pollutants for both Idle and Cruise / Acceleration modes. For NOx Idle mode, a fairly large 

multiplicative adjustment of 6.2601 was obtained. This large factor reflects the relatively low 

levels of NOx emissions during idle operation. A moderately high multiplicative A/C adjustment 

of (2.1123) for CO cruise / Accel was also obtained. These adjustments will double CO 

emissions under extreme conditions of A/C usage. A/C adjustments of less than or equal to one 

were found for the Braking / Deceleration mode for all three pollutants. These were set to one 

for use in the MOVES model. 

4.4 Adjustments to Air Conditioning Effects 

The adjustments for each operating mode are weighted together by the operating mode 

distribution calculated from the driving schedules used to represent the driving behavior of 

vehicles. Average speed, road type and vehicle type will affect the operating mode distribution. 

weightedFullACAdjustment = SUM( fullACAdjustment*opModeFraction ) 

25
 
Since not all vehicles are equipped with air conditioning and air conditioning is normally not 

on all of the time, the full air conditioning effect on emissions is adjusted before it is applied to 

the emission rate. The SourceTypeModelYear table of the MOVES database contains the 

fraction of vehicles in each model year that are equipped with air conditioning [7]. 

Table 4-6 Fraction of Vehicles Equipped with Air Conditioning 

(ACPenetration) 

Model Year Passenger Cars All Trucks and Buses 

1971* 0.592 0.287 

1972 0.592 0.287 

1973 0.726 0.287 

1974 0.616 0.287 

1975 0.631 0.287 

1976 0.671 0.311 

1977 0.720 0.351 

1978 0.719 0.385 

1979 0.694 0.366 

1980 0.624 0.348 

1981 0.667 0.390 

1982 0.699 0.449 

1983 0.737 0.464 

1984 0.776 0.521 

1985 0.796 0.532 

1986 0.800 0.544 

1987 0.755 0.588 

1988 0.793 0.640 

1989 0.762 0.719 

1990 0.862 0.764 

1991 0.869 0.771 

1992 0.882 0.811 

1993 0.897 0.837 

1994 0.922 0.848 

1995 0.934 0.882 

1996 0.9484 0.9056 

1997 0.9628 0.9292 

1998 0.9772 0.950 

1999 0.980 0.950 

2000** 0.980 0.950 

* 1971 model year fractions are applied to all previous model years. 

** 2000 model year fractions are applied to all later model years. 

Motorcycles are not adjusted for air conditioning. 

The fraction of vehicles whose air conditioning is operational varies by age of the vehicle and 

is stored in the SourceTypeAge table of the MOVES database. 

26
 
Table 4-7 Fraction of Air Conditioning Units Still Functioning By Age 

Age Functioning Age Functioning Age Functioning 

1 1.00 11 0.98 21 0.95 

2 1.00 12 0.98 22 0.95 

3 1.00 13 0.96 23 0.95 

4 0.99 14 0.96 24 0.95 

5 0.99 15 0.96 25 0.95 

6 0.99 16 0.96 26 0.95 

7 0.99 17 0.96 27 0.95 

8 0.98 18 0.95 28 0.95 

9 0.98 19 0.95 29 0.95 

10 0.98 20 0.95 30 0.95 

An equation is used to predict 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 index [7]) of the air outside their vehicles. The heat index values are stored in the 

ZoneMonthHour table of the MOVES database. 

ACOnFraction = ACActivityTermA 

+ heatIndex*(ACActivityTermB + ACActivityTermC*heatIndex) 

Table 4-8 Effect of Heat Index on Air Conditioning Activity 

-3.63154 ACActivityTermA 

0.072465 ACActivityTermB 

-0.000276 ACActivityTermC 

Heat Index AC On Fraction 

67.44 0.000 

70 0.089 

75 0.251 

80 0.399 

85 0.534 

90 0.655 

95 0.762 

100 0.855 

105 0.934 

110 1.000 

The fraction of vehicles equipped with air conditioning, the fraction of operational air 

conditioning and the fraction of air conditioning use are used to adjust the amount of "full" air 

conditioning that occurs in each hour of the day. 

ACAdjustment = 1+ ( (weightedFullACAdjustment-1) 

* ACPenetration*functioningACFraction*ACOnFraction ) 

27
 
The air conditioning adjustment is a multiplicative adjustment applied to the emission rate 

after it has been adjusted for fuel effects. 

Air conditioners are employed for defogging at all temperatures, particularly, at lower 

temperatures. This secondary use of the A/C along with associated emission effects is not 

addressed in MOVES2010. 

5. Inspection and Maintenance Programs 

Inspection and Maintenance (I/M) programs are generically any state-run or locally mandated 

inspection of highway motor vehicles intended to identify those vehicles most in need of repair 

and requires repairs on those vehicles. Since these programs are locally run, there is great 

variability in how these programs are designed and the benefits that they generate in terms of 

emission reductions from highway motor vehicles. 

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 MOBILE documentation [9]. 

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 nonI/M deterioration rates were assumed to be the same. 

With the passage of time, the non-I/M and I/M emission cases diverged from each other 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. 

28
 
5.2 Inspection & Maintenance in MOVES
 

The MOVES emission rates contain estimates of emission levels as a function of age, model 

year group and vehicle type for areas where no I/M program exists (the mean base rate, or the 

non-I/M reference rates) and for an area representing the “reference I/M program” (the I/M 

reference rates). The I/M reference rates were derived using data from the enhanced I/M 

program in Phoenix, Arizona, and represent the design features of that program. The difference 

between the non-I/M and I/M reference rates are assumed to represent theI/M benefit of the 

Phoenix program design assuming perfect compliance. Equation 1 shows this relationship in a 

mathematical form. 

Standard I/M difference = EnonIM – EIM Eq 1 

where Enon-IM and EIM are the non-I/M and I/M reference rates, respectively. 

The Phoenix program design was selected as the reference program because virtually all of 

the underlying data for MOVES came from this source. The selection does not imply any 

judgment on the strengths or weaknesses of this specific program. In MOVES, it is this general 

I/M design which is the model, not the actual Arizona I/M program as it is operated. 

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. This goal was achieved by comparing 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 2, 

Ep = REIM + (1- R)EnonIM Eq 2 

where Ep is the adjusted emission rate for a “target” I/M program, EIM is the reference rate, EnonIM 

is the non-I/M reference rate, and R is an aggregate adjustment representing the difference in 

average emission rates between the target program and the reference program. Depending on the 

value of R, Ep may be greater than EnonIM, fall between EnonIM and EIM, or be less than EIM. Thus 

this framework can represent target programs as more effective or less effective than the 

reference program. In MOVES, R is referred to as the “IMFactor.” 

Re-arranging Equation 2 and solving for R gives leads to Equation 3. This equation shows 

the I/M adjustment as the ratio of the emission difference between a proposed I/M program 

design and the Standard I/M Difference 

Ep - EnonIM R = Eq 3 EIM - EnonIM 

29
 
5.3 Development of MOVES I/M Factors 

Early in the MOVES development process it was decided that developing the I/M adjustment 

factors based on a completely new analysis would prove 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 old model years at young ages (i.e., a 1985 

model year at age five). As a result, EPA decided to develop I/M adjustment factors based on the 

information incorporated in MOBILE6.2. Mechanically, this step was achieved by running the 

MOBILE6.2 model about 10,000 times over a complete range of pollutant–process 

combinations, inspection frequencies, calendar years, vehicle types, test types, test standards, and 

model year group / age combinations. The mean emission results for each combination were 

extracted from the output and utilized. The IMFactor table includes the following fields: 

• Pollutant / Process 

• Test Frequency 

• Test Type 

• Test Standard 

• Regulatory Class 

• Fuel Type (Only gasoline/ethanol fuels have IMFactors) 

• Model Year Group 

• Age Group 

• IMFactor 

The IMFactor value was computed for each combination of the parameters listed in the 

IMFactor table. A separate MOBILE6.2 run was done for each parameter combination (Target 

design, Ep), and a second set of runs were done describing the reference program (Reference 

design, ER). The IMFactor is the ratio of the mean emission results from these two runs. 

Equation 4 illustrates the simple formula. 

Ep Rp = Eq 4 ER 

The Reference program has inputs matching the Phoenix I/M program during the time in 

which the data used in the MOVES emission rate development were collected (CY 1995-2005). 

The Reference design represents a biennial frequency with an exemption period for the four most 

recent model years.. It uses three different I/M test types (basic idle test for MY 1960-1980, 

transient tailpipe tests for MY 1981-1995 (IM240, IM147), and OBD-II scans for MY 1996 and 

late). Each of these test types became the Reference for the respective model year groups. 

The specific combinations of MOBILE6.2 runs performed are shown in Table 5-1 below. 

Each of these runs represents a particular test type and test standard design which was expressed 

as a ratio to the standard reference tests. The first four runs represent the Non I/M reference and 

30
 
the three Phoenix I/M references. A set of these runs were done for each calendar year 1990 

through 2030, for cars, light trucks and heavy-duty gasoline vehicles and for pollutants HC, CO 

and NOx. 

Table 5-1 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). 

In addition to the IMFactor, MOVES adjusts rates for particular programs by applying an 

additional multiplicative "Compliance Factor" (IMCompliance). The IMFactor (R) represents the 

theoretical effectiveness of a specific I/M program design, relative to the reference design, as 

described above. 

Values of the IMComplianceFactor (C ) are specific to individual programs and represent its 

overall operational effectiveness and efficiency, aside from the effectiveness inherent in its 

design. Variables which impact the IMCompliance factor include waiver rates, compliance rates 

and overall operational efficiency. Default IMComplianceFactors are provided in the MOVES 

database, but alternate values may be entered by the user for specific analyses. The default 

factors were taken from the 2005 EPA National Emission Inventory (NEI) [10], and are based on 

31
 
data submitted by individual states in their State Implementation Plan (SIP) processes. The vast 

majority of the default IMCompliance factors are greater than 90 percent. 

5.4 Development of MOVES I/M Compliance Inputs 

The default I/M Compliance inputs are contained in the IMCoverage table in the MOVES 

database. The structure of the table is: 

• Pollutant / Process 

• State / County 

• Year 

• Source Use Type 

• Fuel Type (only gasoline fuels) 

• Beginning Model Year of Coverage 

• Ending Model Year of Coverage 

• InspectFreq 

• IMProgramID 

• I/M Test Type 

• I/M Test Standards 

• Ignore I/M toggle (user control variable) 

• Compliance Factor 

The IMCoverage table structure shows that the IM Compliance Factor is a function of 

numerous variables that include geography, time, vehicle type / fuel / coverage factors, program 

test frequency and specific I/M test / I/M test standards types. 

For state SIPs, it is expected that the state will enter their own set of Compliance Factors 

which reflect current and expected future program operation. The data in the default MOVES 

table is likely out of date (i.e., 2005 NEI), and has not been cross referenced or updated with 

recent state I/M program designs / changes. 

The underlying data used to construct the default Compliance Factors were taken from 

MOBILE6.2 input files used in the NMIM model to compute the National Emission Inventory of 

2005. The data files listed in Table 5-2 were extracted and processed into the various fields in 

IMCoverage table. 

32
 
Table 5-2 I/M Coverage Table Data Sources 

NMIM Data Source MOVES I/M Coverage Parameter 

MOBILE6 Compliance Rate Used in the MOVES Compliance Rate 

Calculation 

I/M Cutpoints Used to determine MOVES I/M Test Standards 

MOBILE6 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 Regulatory Class 

input 

MOBILE6 Waiver Rate Used in the MOVES Compliance Rate 

Calculation 

As seen in Table 5-2, MOBILE6.2 and MOVES do not have exactly compatible parameter 

definitions. Extraction and processing of the MOBILE6.2 inputs for all of the individual states 

was required. The MOBILE6 compliance rate, waiver rate and Effectiveness rate were used to 

determine the MOVES Compliance Rate. The new MOVES Compliance Rate is a broader 

concept that incorporates three separate MOBILE6.2 inputs. Equation 5 shows the relationship. 

C = M6 Compliance Rate · M6 Effectiveness Rate · (1- M6 Waiver Rate) Eq 5 

MOVES does not have separate inputs for the effect of waivers on I/M benefits. Section 

3.10.6.2 of the document, “Technical Guidance on the Use of MOVES2010 for Emission 

Inventory Preparation in State Implementation Plans and Transportation Conformity” 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. 

Other fields in the IMCoverage table complete the description of the I/M program in effect in 

each county. The MOBILE6.2 I/M Cutpoints data were used only to determine level of 

stringency of a state’s 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 regulatory class. The Ignore I/M 

toggle is a user feature that allows the user to completely disable the effects of I/M for one or 

more of the parameter combinations. 

33
 
6. References 

1.	 E. Glover and D. Brzezinski, "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) 

2.	 E. Glover and P. Carey, "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) 

3.	 Stump, F. D., D. L. Dropkin, S. B. Tejada, C. Loomis, and C. Pack, "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/R01/053 (NTIS PB2004-106735), July 2002. 

(Available at: http://www.epa.gov/nerl/nerlmtbe.htm#mtbe7c) 

4.	 "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) 

5.	 "Regulatory Impact Analysis for Final Rule: Control of Hazardous Air Pollutants from 

Mobile Sources" EPA Report Number EPA420-R-07-002, February 2007, Chapter 2, pages 

2-15 to 2-17. 

(Available at: http://www.epa.gov/otaq/regs/toxics/fr-ria-sections.htm) 

6.	 Code of Federal Regulations 86.1342-90 (Page 309). 

(Available at: http://www.access.gpo.gov/nara/cfr/cfr-table-search.html) 

7.	 "Air Conditioning Activity Effects in MOBILE6", EPA Report Number EPA420-R-01-054 

(M6.ACE.001), November 2001. 

(Available at: http://www.epa.gov/otaq/models/mobile6/m6tech.htm) 

8.	 "Air Conditioning Correction Factors in MOBILE6", EPA Report Number EPA420-R-01-

055 (M6.ACE.002), November 2001. 

(Available at: http://www.epa.gov/otaq/models/mobile6/m6tech.htm) 

9.	 "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) 

34
 
10.	 "Documentation for the 2005 Mobile National Emissions Inventory (NEI) 2005, Version 

2", December 2008, and National Mobile Inventory Model (NMIM) County Database 

(NCD) for 2005 V2. 

(Available at: http://www.epa.gov/ttn/chief/net/2005inventory.html) 

35
 
Appendix A – Mean Start Emission by Temperature 

Table A-1 Change in Mean Start Emissions at Various Temperatures
 

By Model Year Group
 

Relative to 75° F
 

Model Yr HC CO NOx 

Group Temp (grams) (grams) (grams) 

Pre-81 19.75 36.090 226.941 -0.274 

Pre-81 20.67 33.018 254.386 -0.925 

Pre-81 22.63 30.560 276.341 -1.445 

Pre-81 47.55 18.569 129.472 -0.380 

Pre-81 49.78 15.252 120.931 -0.034 

Pre-81 52.52 18.099 115.776 0.101 

Pre-81 60.14 11.120 53.617 1.790 

Pre-81 77.31 0 0 0 

Pre-81 95.36 -2.122 -58.656 1.640 

Pre-81 98.06 -1.755 -67.555 1.975 

Pre-81 105.06 -4.935 -86.689 3.769 

Model Yr HC CO NOx 

Group Temp (grams) (grams) (grams) 

81-82 19.36 21.120 231.180 -0.374 

81-82 20.69 23.363 242.806 -0.252 

81-82 22.33 25.496 253.865 -0.135 

81-82 49.20 7.782 109.851 -0.066 

81-82 50.31 8.202 120.239 0.065 

81-82 51.43 9.209 132.360 0.194 

81-82 59.15 6.432 135.063 -1.416 

81-82 75.73 0 0 0 

81-82 95.22 -4.659 -144.116 1.915 

81-82 97.75 -5.450 -174.532 1.814 

81-82 105.00 -9.958 -343.847 4.568 

36
 
APPENDIX A Continued 

Table A-2 Change in Mean Start Emissions at Various Temperatures
 

By Model Year Group
 

Relative to 75° F
 

Model Yr HC CO NOx 

Group Temp (grams) (grams) (grams) 

83-85 19.32 23.299 218.857 0.665 

83-85 21.00 17.755 218.151 -0.017 

83-85 22.48 14.599 216.439 -0.414 

83-85 28.80 20.594 186.549 -0.126 

83-85 48.99 5.213 94.414 0.513 

83-85 50.33 5.946 93.032 0.250 

83-85 51.30 6.490 95.495 0.183 

83-85 76.20 0 0 0 

83-85 95.81 -1.044 -29.275 0.903 

83-85 97.19 -1.209 -35.995 0.868 

83-85 105.79 -1.124 -25.407 -1.010 

Model Yr HC CO NOx 

Group Temp (grams) (grams) (grams) 

86-89 -20 27.252 178.536 -2.558 

86-89 0 25.087 147.714 -1.360 

86-89 20 14.011 104.604 -0.749 

86-89 40 8.316 78.525 0.312 

86-89 75 0 0 0 

86-89 95.03 -0.127 -4.257 -0.137 

86-89 96.43 -0.139 -5.354 -0.091 

86-89 106.29 -0.729 -1.017 -0.084 

Model Yr 

Group 

1990-2005 

1990-2005 

Temp 

-20 

0 

HC 

(grams) 

38.164 

16.540 

CO 

(grams) 

143.260 

92.926 

NOx 

(grams) 

1.201 

1.227 

1990-2005 

1990-2005 

20 

40 

8.154 

4.872 

56.641 

33.913 

1.082 

0.876 

1990-2005 75 0 0 0 

37
 
Appendix B – Calculation of Specific Humidity 

Equations to convert relative humidity in percent to specific humidity (or humidity ratio) in 

units of grains of water per pound of dry air (ref. CFR section 86.344-79, humidity calculations). 

Inputs: 

TF is the temperature in degrees F. 

Pb is the barometric pressure. 

Hrel is the relative humidity 

T = 5( )[T -32 ] + 273 K 9 F 

T0 =647.27 -TK 

Hratio or specifichumidity = 4347.8* PV /( Pb -PV ) 

P =� Hrel �

V � �Pdb 100 Ł ł 

3 Ø(3.2437 +0.00588 T +0.0000000117 T )ø 0 0 (-T /TK )Œ œ 1+0.00219 T o 0 ß Pdb = 29.92*218.167*10 

0 Œ œ 

3 (3.2437 +0.00588 T +0.0000000117 T ) 0 0 (-T /TK )Œ

Ø œø 

0 Œ 1+0.00219 T0 œ

=6527.557*10 o ß 

38
 
Appendix C – Air Conditioning Analysis Vehicle Sample 

Table C-1 Vehicle Sample for the Air Conditioning Analysis 

Model Year Make Model Vehicle Class Weight 

1990 DODGE DYNA CAR 3625 

1990 NISSAN MAXI 0 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 SUMM 0 CAR 2500 

1993 HONDA ACCO 0 CAR 3250 

1993 TOYOTA CAMR 0 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 ASTR 0 LDT1 4250 

1991 PLYMOUTH VOYA LDT1 3750 

1992 CHEVROLET LUMI LDT1 3875 

1993 CHEVROLET S10 LDT1 2875 

1994 CHEVROLET ASTR LDT1 4750 

1994 PONTIAC TRAN LDT1 4250 

1996 FORD EXPL LDT1 4500 

1996 FORD RANG LDT1 3750 

1990 CHEVROLET SURB LDT2 5250 

39
 
Model Year Make Model Vehicle Class Weight 

1991 FORD E150 0 LDT2 4000 

1994 FORD F150 LDT2 4500 

1996 FORD F150 LDT2 4500 

1996 DODGE DAKO PICK TRUCK 4339 

1996 DODGE D250 RAM TRUCK 4715 

1996 DODGE GRAN CARA TRUCK 4199 

1996 DODGE CARA TRUCK 4102 

1996 FORD F150 PICK TRUCK 4473 

1997 DODGE GRAN CARA TRUCK 4318 

1997 DODGE DAKOT TRUCK 4382 

1997 PONTIAC TRANSSPOR TRUCK 4175 

1998 DODGE CARA GRAN TRUCK 4303 

1999 FORD WIND TRUCK 4500 

40
 
Appendix D – Toros Topaloglu, Comments 

Peer Review of US EPA’s
 

“Draft MOVES2009 Highway Vehicle Temperature, Humidity, Air Conditioning, and
 

Inspection & Maintenance Adjustments”
 

September 29, 2009
 

As part of the MOVES2010 Peer Review process, EPA solicited comments from Toros 

Topaloglu, Ph.D., P.Eng. on the August 2009 draft of report Draft MOVES2009 Highway 

Vehicle Temperature, Humidity, Air Conditioning, and Inspection & Maintenance Adjustments. 

Dr. Topaloglu is an Environmental Systems Specialist at the Ministry of Transportation Ontario, 

Canada. 

Dr. Topaloglu’s comments are copied below, with EPA response in italics. 

1. Introduction 

The development of MOVES and its predecessor, MOBILE, represent enormous achievements: 

estimating past, present and future emissions of an infinitely diverse and variable vehicle/driver 

population under highly variable and ever changing conditions. The US EPA deserves our 

sincere gratitude for this unparalleled effort, which continues to deliver ever more powerful and 

user-friendly emission simulators. 

It has not been easy to think of a few meaningful comments on the above captioned report that 

describes various adjustments employed in MOVES. I have limited myself to “constructive 

criticism”, assuming that this is what you expect from me and that this will be viewed in a 

positive vein coming from someone who has a direct and genuine interest in making MOVES as 

useful as possible. Where I am silent, I fully concur with the adopted approach and its 

presentation. This happens to be the case for over 99% of the report. 

In this review, I relied primarily on my personal experience and knowledge but consulted also the 

relevant MOBILE6 documentation and a few specific publications listed under Section 5 

(references). 

2. General Comments 

2.1. I agree with the empirical/statistical approach adopted in the derivation of the 

adjustments - given the imprecise nature of cars and the near infinite variability in their 

population. Some scientist and engineers may feel more comfortable with relations that 

have a theoretical basis; however, even with the “best” data `the multitude of 

41
 
mechanisms involved in each adjustment make a mechanistic approach very difficult to 

implement. 

2.2. Adjustments for greenhouse gas emission factors may not have been uniformly 

addressed. The vehicle emissions certification process does not automatically yield 

adjustments for CO2, CH4 and N2O emissions. Given the urgency to address Climate 

Change, MOVES will be called upon frequently to derive more accurate GHG emission 

factors. 

2.3. Adjustments for individual air toxic emission factors may not have been fully addressed. 

It is conceivable that adjustments for NMHC may not apply equally to each and every air 

toxic, since they are not formed by identical physical and chemical mechanisms. 

2.4. It is not clear that the US EPA adjustments deal fully with up-and-coming technologies 

such as hybrid, plug-in hybrid and battery-powered electric vehicles. Emissions of these 

vehicles, where they exist, are less sensitive to variations in ambient conditions, airconditioning (A/C), and inspection and maintenance (I/M). In fact, they are generally 

exempt from I/M. The number of electric hybrids in the US fleet exceeds one million 

already and is expanding rapidly. Hence, it will become progressively more important to 

account for their characteristics. 

2.5. In future updates of MOVES, it may be worthwhile to try and correlate adjustments with 

major vehicle technologies and fuel types – beyond what is in place. This may improve 

the ability of the model to simulate future emissions. Vehicle manufacturers often have 

this information and might share it with the US EPA. 

2.6. The accuracy of the adjustments depends, in part, on the representativeness of the test 

vehicle sample. It is obvious that the US EPA has spent enormous effort to achieve a 

high degree of representativeness. However, limitations with the test data and, to a 

lesser extent, unexpected but deliberate efforts to alter the vehicle population such as the 

recent “cash for clunkers” program of the US government may have somewhat thwarted 

this effort. Given these factors, it is rather difficult for a regular MOVES user to judge 

the adequacy of the proposed adjustments. 

3. Specific Comments 

3.1 Ambient Temperature 

3.1.1.	 I concur with the observation that the principal influence of ambient temperature 

(Tamb) on emissions is during the warm-up phase of cold-starts. Its influence on 

warmed-up vehicle running emissions is relatively small – albeit not nil, 

particularly, under extremely cold conditions when steady-state temperatures of 

vehicle components (lubricants, tires, etc.) may stay below their “normal” values. 

3.1.2.	 It is not certain that the difference in emissions between Bags 1 and 3 of the FTP 

cycle can fully account for cold-start emissions under extremely cold conditions 

42
 
(below 32°F) when it takes extended periods of time to reach stead-state. Hence, 

adjustments based on these data will probably result in underestimates. 

EPA has seen increased emphasis by manufacturers on decreasing the amount of time it 

takes to light off the catalytic convertor in order to address tighter emission standards, 

and since the 1990’s vehicles have had to meet emission standards even at low 

temperatures. Once the catalytic convertor is fully operational, any small effects from 

the ambient temperature on emission formation in the engine are easily lost in the 

catalyst. EPA believes that any temperature effects not captured in the first bag (505 

seconds) of the FTP are negligible and existing data bears that out. 

3.1.3.	 The decision to neglect adjustment for ambient temperatures above 75°F is a 

reasonable but not a perfect one. Reference (1) provides some evidence for less fuel 

consumption and CO2 emissions at higher ambient temperatures – perhaps due to 

less throttling (higher volumetric efficiency). Other emissions are probably also 

affected, but the test data do not seem to allow for these smaller effects - as noted on 

page 7 of the report, in the discussion of the Tamb adjustment for NOx emissions. 

3.1.4.	 Part of the difficulty with adjusting for Tamb in the general fleet may be due to the 

many vehicle parking options: outdoors, unheated indoors, heated indoors or with a 

plugged-in block heater. If a vehicle is parked outdoors, the wind chill factor might 

also influence cold-start emissions. The test data do not seem to account for all of 

these factors. 

The temperature adjustments in MOVES are intended to represent the effects on vehicle 

emissions when the ambient temperature to which the vehicle is subjected is known. 

There may be factors that cause difficulty in determining the appropriate temperature to 

apply to the fleet, such as the variation of ambient temperature over the area you wish to 

model. However, these are issues for guidance on how best to use the model for specific 

scenarios. 

3.2. Humidity 

3.2.1.	 One would expect a weak dependence of carbon dioxide emissions on ambient 

humidity, as reported for NOx. The EPA certification humidity adjustments 

should, however, account for this effect. 

3.2.2.	 I expect that the EPA certification humidity adjustments are sufficient for
 

inventory work.
 

3.3. Air Conditioning 

3.3.1.	 The US EPA report indicates that all emission tests with the A/C on were carried 

out at 95°F only. This implies that the A/C adjustments are not based on emission 

data obtained at the same temperature, with A/C on and A/C off. If so, according to 

Reference 1, a significant “error” may have been incurred by not accounting for the 

co-existing effect of Tamb on some emissions such as those of CO2. 

43
 
On the contrary, because the MOVES model does not apply temperature adjustments to 

running emissions (except for particulate matter), the comparison of A/C on emissions at 

high temperature and A/C off emissions at low temperature allows the A/C correction to 

incorporate any necessary temperature effect. However, as discussed in Section 2.3.2. 

our data suggests such effects are not significant. 

3.3.2.	 A/Cs are employed for defogging at all temperatures – particularly, at lower 

temperatures. This secondary use of the A/C along with associated emission effects 

do not seem to have been accounted for (according to Ref. 1, defogging costs a 1.5 – 

7% in CO2 emissions at 55°F - depending on driving cycle). 

MOVES does not account for the A/C effects at low temperatures from the use of A/C for 

defogging. The text has been updated to describe this omission. 

3.3.3.	 Many modern vehicles are equipped with climate control systems, which are 

usually set by drivers to maintain automatically a preset optimum compartment 

temperature. The A/C systems of these vehicles switch on when this temperature 

set-point is exceeded – irrespective of humidity (as is the case with house 

thermostats). The conditioned air is cold and dry (often reheated with engine 

coolant). Hence, in these modern vehicles the compressor usage may be largely 

independent of humidity. The compressor load and hence energy use and some 

emissions are however very dependent on ambient air humidity. 

Modeling the behavior of modern A/C systems can be very tricky. As a first cut, MOVES 

simply addresses the need for A/C based on how comfortable humans will be based on 

the combination of temperature and humidity. This should adequately capture the need 

for A/C and the extra loads that result for inventory estimates. A better A/C load model 

may be developed as our understanding of these systems improves. 

3.4. Inspection and Maintenance 

3.4.1.	 The repeated application of MOBILE 6 to predict the relative emission 

consequences of various I/M program design features appears to involve certain 

assumptions; viz., all vehicles at a given age have the same odometer reading, are 

subject to the same deterioration rates, and, if repaired, experience the same 

emission improvements. It may be worthwhile to test the benefits of replacing these 

point assumptions with appropriate distributions or variables. 

Even with the use of distributions, the average impact of I/M programs on fleet emissions 

would be the same. We believe the added complexity of using distributions would only 

add to the complexity of our already complex modeling and provide very limited insight 

into the benefits of I/M repairs. 

3.4.2.	 Future failure rates will likely be smaller than current ones – largely due to 

incremental improvements in vehicle technology but also due to the observed shift 

to inherently low emission vehicles. 

44
 
All modeling of future model years is fraught with uncertainty. EPA has taken the 

position that improvements in emission performance will only occur if there is an 

incentive to improve, such as new emission standards. As such, it is reasonable that the 

existing failure rate, which is already very small, will continue into the future unless 

there is some regulatory reason why manufacturers would take the time and money to 

develop solutions that would significantly reduce their failure rates. Even without 

reductions in failure rates, the benefits of I/M programs will decrease as the emission 

impact of failure grows smaller on vehicles with new (lower) emission standards. 

3.4.3.	 MOBILE 6 apparently assumed that waived vehicle emission rates are invariably 

20% lower than those of failed vehicle emissions (see Ref, 2). Is this assumption 

carried through in MOVES? If so, it may be worthwhile to re-examine it. 

In MOVES, vehicles which receive waivers are assumed not to have been repaired at all. 

The text has been updated to include this information. Waived vehicles are typically a 

small fraction of the fleet and are difficult to study. Given the limited impact that these 

vehicles will have on total fleet emissions, determining a more precise impact from 

waiver vehicles will not be a high priority. 

3.4.4.	 The National Research Council (Ref. 2) raised a number of additional I/M related 

concerns with MOBILE 6: (a) assumption that vehicles with and without I/M 

deteriorate at the same rate; (b) no explicit allowance for those vehicles that are 

repaired before or after inspection but rapidly revert to high-emitter status; and (c) 

no I/M credit for high emitters that are scrapped or shipped outside of the region. It 

would be helpful to explain how these concerns were addressed in MOVES. 

The description of the I/M program effects in the report has been revised to more 

explicitly address the concerns of the National Research Council. 

3.4.5.	 Another concern in the I/M community, namely the effectiveness of OBD systems 

and OBD based I/M programs, deserves also a fuller discussion. 

4. Editorial Comments 

EPA has made many changes to the text of the report to address the following editorial 

comments. 

4.1. The term “adjustment”, as used in the title of the report, expresses the goal of the effort 

clearly and concisely. The terms “correction factor” and “adjustment factor”, as used in 

the body of the report, are less clear. First, a correction is not an adjustment. Second, 

the word “factor” implies a multiplication whereas most of the proposed adjustments are 

additive. I recommend that the report stick to the term “adjustment” throughout the 

report. 

4.2. This is not a free-standing report.	 Its contents cannot be fully understood without 

referring to a series of other reports (at least, the documentation of MOBILE 6). It would 

45
 
have been preferable to have a free-standing report – not for the sake of peer reviewers 

but for younger MOVES users who haven’t witnessed the evolution of MOBILE. 

4.3. A Table presenting the principal assumptions made and the resulting effectiveness 

estimates for major I/M program types would add to the value of the report. 

4.4. I am assuming that the final report will include lists of acronyms (with explanations), 

tables, figures as well as equation numbers, etc. - all the usual pieces that make a report a 

bit more accessible. 

4.5. Minor notes: 

•	 Last sentence on page 8 refers to Section 4.1.3, which does not exist. 

•	 Section 4.1 apologizes for lack of data with A/C on MC. Do you really mean 

motorcycles? 

•	 Section 4.1, third paragraph, refers to Appendix A for a list of vehicles and their 

description. It should instead refer to Table 4-1. Also, I don’t see any description of 

the vehicles. 

•	 The title of Section 4.3.2 reads “Energy Emissions”. It should probably read “Energy 

Consumption”. 

•	 On page 30, “OBD” is spelled “OBC”. 

5.	 Response to Specific Questions Posed in the US EPA Letter to Me 

5.1 The Clarity of the Presentation 

•	 The report is well written and very clear to individuals with a technical background in 

the subject area. It may however require some editing to make it more easily 

accessible to a wider audience – if this were necessary. 

5.2 The Integration of Information from Multiple Areas 

•	 The report is based on an enormous volume of previous work and the resulting 

information. Given the difficulty of condensing this vast volume of information into 

a relatively compact report, the author(s) have done very well. The information is 

well integrated. However, as noted in my general comments (Section 1 above), the 

report is not a stand-alone document. It cannot be fully understood without reading 

its references. 

5.3 The Appropriateness and Completeness of the Literature Discussed 

•	 The literature referenced in the report is highly appropriate and sufficiently complete. 

5.4 Appropriateness of the Resulting Adjustments 

46
 
•	 In spite of the inherent complexity of the subject and the limitations of the available 

data, the author(s) have succeeded in: 

o	 Identifying those effects that call for adjustments 

o	 Eliminating those effects (variables) that are too insignificant to adjust for 

o	 Deriving robust adjustments that reflect the totality of the empirical evidence 

available and also conform to theory 

•	 In my opinion, the adjustments are highly appropriate. The few comments provided 

in this review are intended to contribute to any future effort to update MOVES and 

make it as useful as possible to all potential users. 

6.	 References 

(1) Weilenmann, M.F.; Vasic A-M; Stettler P.; and Novak, P. Influence of Mobile AirConditioning on Vehicle Emissions and Fuel Consumption: A Model Approach for Modern 

Gasoline Cars Used in Europe. Environ. Sci. Technol. 2005, 39, 9601-9610. 

(2) National Research Council. Evaluating Vehicle Emissions Inspection and Maintenance 

Programs. The National Academies Press, Washington, DC. 2001. 

(3) Eisinger D.S. and Wathern, P. Policy Evolution and Clean Air: The Case of US Motor 

Vehicle Inspection and Maintenance. Transportation Research Part D. 2008, 13, 359-368. 

Toros Topaloglu, Ph.D., P.Eng. 

Thank you. 

47
 
Appendix E – ENVIRON International Corporation, Comments 

ENVIRON Review of EPA Draft Report:
 

“MOVES Temperature, Humidity, Air Conditioner, and Inspection and Maintenance
 

Adjustments”
 

As part of the MOVES2010 Peer Review process, EPA solicited comments from Christian E. 

Lindhjem of ENVIRON International Corporation on the August 2009 draft of report Draft 

MOVES2009 Highway Vehicle Temperature, Humidity, Air Conditioning, and Inspection & 

Maintenance Adjustments. 

Chris Lindhjem has a PhD. in Chemical Engineering from Rensselaer Polytechnic Institute and 

has more than 15 years of experience in automotive issues with particular focus on emissions 

from highway and non-road vehicles, engines, and engine fuels. 

Dr. Lindhjem’s comments are copied below, with EPA response in italics. 

Christian Lindhjem 

ENVIRON International Corporation 

773 San Marin Drive, Suite 2115 

Novato, California 94998 

415.899.0700 

30 September 2009 

Introduction 

This report appears to gather all of the adjustments to the MOVES basic emission rates into one 

document despite the seeming unrelated topics discussed. Temperature and humidity are ambient 

conditions that affect the engine and after-treatment control effectiveness. Air condition loads are 

influenced by ambient conditions, but in fact are only one of many potential loads. New engine 

standards and inspection and maintenance programs are primarily emission reduction program 

credit assessments. 

Yet despite the varied types of adjustments, it is reasonable to include all adjustments if indeed 

all adjustments to the model are included in this document. However, as new data or new 

approaches are required, updating the document could be more difficult or confusing to the 

reader because the document addresses so many issues. And if this document does not include all 

such adjustments, it might be confusing to understand all such adjustments split over many 

documents. 

48
 
Gasoline Vehicle Temperature Adjustments 

To the extent that temperature adjustments presented here differ from the official test procedures, 

EPA should make sure that the original data is free of temperature adjustments that the original 

researchers might have used on the reported data. Often researchers will follow the official test 

procedures to the letter and adjust the reported values to account for unique test conditions. 

Overall the method for temperature adjustments seem sound with a few comments noted here 

that might help the explanation for the reader. EPA correctly separated the start and running 

temperature adjustments to account for the likely different effects when the engine and aftertreatment are at operating temperature. 

For gasoline vehicle start emissions, hydrocarbon and carbon monoxide effects are presented 

sufficiently to understand the results. The NOx results were considerably more complex, and 

there may be reasons for the observed start NOx emission with regard to technology by model 

year grouping. However, given that the effect is more predominant with older model years, the 

NOx effect may be less important for most uses of MOVES. For all cases, it would be helpful to 

put the adjustment estimates in perspective of the base emission rates to demonstrate the relative 

importance of the temperature adjustment effect, such as inclusion of a description of the 

percentage effect. 

For gasoline vehicle running emissions, I agree with the assessment that ambient temperature has 

little effect on emissions. It might be helpful to note several same-vehicle tests at different 

ambient temperatures in Figure 2-2, such as by symbol and/or lines, to demonstrate that a 

temperature effect is not observed with the same equipment. Mixing vehicle tests and 

temperature conditions tests may mask an effect that could be observed in same vehicle tests. 

Diesel Vehicle Temperature Adjustments 

The diesel vehicle start emissions are presented, and I have no dispute with the results for start 

emissions determined. But this discussion could use some context in terms of vehicle types and 

applicability. For instance, based on the use of the FTP test cycles to determine the start 

emissions, I suspect that these 12 vehicles were pickup trucks, light heavy-duty vehicles, so EPA 

should discuss the relevance of using these vehicles to represent all diesel vehicles. 

The text of this document describing the temperature adjustments to diesel start emissions has 

been updated to better address the types of vehicles in the samples used. 

EPA makes no claim about particulate matter or running emissions temperature adjustments for 

diesel vehicles, so the report approach is inconsistent to that for gasoline vehicles. In addition, 

there was no discussion of whether these vehicles used after-treatment devices (either diesel 

particulate filters (DPF) or oxidation catalysts (OC) or future systems expected for 2010 model 

years and beyond) and how that might affect the results as was done to incorporate the cold 

weather CO and HC requirements for gasoline vehicle temperature adjustments. 

49
 
Less is known about the effects of temperatures on diesel particulate matter emissions. The text 

of this document describing the temperature adjustments to diesel particulate matter emissions 

has been updated to better address the technologies in the samples used. 

Cold Weather CO and HC Requirements 

The methodology to estimate the benefits credited to the light-duty cold weather regulations 

appears to be a reasonable approach as presented. However, it is questionable if the cold weather 

regulation adjustments should be applied to high emitters given that the control systems might 

not be functioning. To the extent that MOVES identifies high emitters, independent temperature 

adjustments should be applied to high emitters. 

Since MOVES does not identify high emitting vehicles during calculations, independent 

temperature adjustments for high emitters cannot be applied. 

Humidity 

Without performing additional testing, it is reasonable to use the Federal Register humidity 

corrections. Because these adjustments would be multiplicative, they would be applicable to the 

lower emission rates of later model years. 

Air Conditioning 

The air conditioning adjustment approach appears to be counterintuitive to approach of MOVES 

defining power bins to reflect the engine loads. There may be some reasons for this approach 

given that idle and coasting\braking bins would not otherwise incorporate the auxiliary air 

conditioner loads. Another reason could be that air conditioner loads would oscillate between 

VSP bins when the compressor is engaged and disengaged unrelated to the driving demands. 

The approach presented is easy to follow in concept, but there should be more description of the 

overall air conditioning effect for sample vehicle types. To help the reader understand how 

important the air conditioning adjustment is, EPA should plot of the effect with respect to the 

humidity index, noting the heat index below which there is no air conditioning adjustment. The 

“ActivityTerm” coefficients for the ‘ACOnFraction’ estimates should be presented in the 

document. 

The text of this document describing the air conditioning adjustments has been updated to better 

display the effects of the activity adjustments versus the humidity index. 

Inspection and Maintenance (I/M) 

Using the I/M benefits from the MOBILE6 analysis is a reasonable approach without an 

extensive reanalysis of the benefits under the MOVES modeling framework. As with the 

assessment of new vehicle emission standards, the emission credits estimated for various 

programs may not be entirely based on a quantitative assessment of each program. Therefore, 

because the credits assigned have been well vetted under the MOBILE6 plan, it becomes a more 

accepted approach to use for MOVES as well. 

50
 
Because the MOBILE6.2 benefits only include HC, CO, and NOx and the Figure 2-6 was given 

as evidence of a relationship between PM and HC emission, PM benefits for I/M programs 

should also be considered. It would stand to reason, even without direct evidence, that emission 

reductions of the primary pollutions evaluation would also lead to PM emission reductions when 

malfunctioning vehicles are repaired. 

EPA does not yet have sufficient data to estimate PM emission reductions for I/M programs 

without further evidence that repairs that reduce HC, CO and NOx emissions will significantly 

affect PM emissions. 

Errata 

Numerous changes to the text have been made to address these minor edits. 

Page 12 above Figure 2-4; “adjustments” has an extra “s”
 

Page 25 below Table ?4-5? (label missing), just above section 4.3.3, “If and when ...”
 

Page 27 above Section 4.4: “A/C correction factors of less than unity or unity were found for...”
 

Some Tables have headings and some table headings are missing and references for those tables
 

in the text are not clear.
 

Section 5, Eq. 1, 2, 3 and variable description of nonIM emissions should read the same, such as 

“EnonIM” in all equations and descriptions. 

Equation 6 or should it be Equation 5? label on last page is incomplete 

51
 
Appendix F – Julio Vassallo, Comments 

Review of US EPA’s
 

“Draft MOVES2009 Highway Vehicle Temperature, Humidity, Air Conditioning, and
 

Inspection & Maintenance Adjustments”
 

September 25, 2009
 

Additional comments, not part of the formal MOVES2010 Peer Review process, were submitted 

by Julio E. Vassallo on the August 2009 draft of report Draft MOVES2009 Highway Vehicle 

Temperature, Humidity, Air Conditioning, and Inspection & Maintenance Adjustments. 

Julio Vassallo is a Chemical Engineer and the Technical Manager of Area new vehicles 

Approval and Certification in the the Laboratory of Vehicle Gaseous Emission Control (LCEGV) 

of the Ministry of Environment and Sustainable Development (SAyDS) in Buenos Aires, 

Argentina. 

Julio Vassallo’s comments are copied below, with EPA response in italics. 

Page 6: The behavior presented for vehicles without (or deactivated) catalyst (that might be 

included in the group pre 1981) is different with respect to the NOx emission, those with catalyst. 

The vehicle can be considered as two reactors in series, combustion reactor in homogeneous 

phase (cycle Otto engine) and oxidation-reduction catalytic reactor (catalytic converter). The 

generation of NOx in the engine is principally a function of temperature in the cylinder and the 

partial pressures of nitrogen and oxygen. Therefore when the engine is cold the issue (without 

catalyst) is the lowest and increases as the engine warms up (example in doc "Start Emission" 

vehicles without catalytic converters, emissions IM240 consecutive test series). In contrast to an 

engine with catalyst but also emissions start to increase with increasing temperature once you 

reached the temperature catalyst "light off" decreases again (example in doc "Cold Emission" 

vehicles with catalyst) Moreover, the emission of NOx is also heavily dependent on power (VSP) 

Then, depending on which portion of the emission is correlated is provable that the temperature 

hasn’t the same effect (function) for vehicles with catalyst and without catalyst. 

I think that the start NOx emission (FTP NOx emission Bag3 minus Bag1) of the vehicles 

without catalyst are highest than those with catalyst and has different start temperature 

dependence. 

With CO an HC start emission is different, because both reactors (engine and catalyst) the 

emission decreases with temperature (example in doc "Start Emission" vehicles with and without 

catalytic converters, emissions IM240 consecutive test series). 

The behavior of catalyst and non-catalyst vehicles is handled in MOVES by having separate 

temperature adjustments by model year group. 

52
 
Page 7: I think that is provably if you correlate taking account VSP and the vehicles with catalyst 

in other group that those without catalyst the R-square coefficient will be better. 

In MOVES, since the temperature adjustments are grouped by model year, some model year 

groupings will include catalyst and some non-catalyst vehicles in the correlations. Unless 

MOVES is redesigned to accommodate separate technologies in addition to model years, a 

separate correlation for each technology is not possible. 

Page 8: I agree that to reach working temperature (running) both the emission generated in the 

engine and removed by the catalyst that should not be so sensitive to the test temperature such as 

the start 

While the supply air temperature should influence the reactions of improving combustion 

efficiency at higher temperatures of income, is provable that the high working temperatures of 

engine determine less sensibility for that purpose, on the other hand those vehicles with catalyst 

in the regime temperature will have to be less sensibly since over 90% of the pollutants are 

converted and that masks any engine inefficiency specially to low exhaust flow (low rpm / VSP). 

Page 11: The analysis of diesel engine emissions are different from that of Otto cycle, in the case 

of CO are not as significant and therefore may be more affected by the measurement uncertainty 

when it comes to a small population, such as that of the reporter. For NOx, in this case normally 

pre 2007 alone technologies are oxidation catalysts (remove only CO and HC) therefore in this 

case has only effect the engine and NOx emissions should increase, ie emissions Bag 1 Bag 

minus 3 should be negative. For example the mean value obtained to 34.6 ° F will have to be 

negative -2.6? I haven’t studies with a diesel emission test series to different ambient temperature 

in the start, but you have studies for example that about humidity and temperature effects how I 

adjunct (in page 7 HUMIDITY AND TEMPERATURE CORRECTION FACTORS FOR NOx 

EMISSIONS FROM DIESEL ENGINES SwRI Project No. 03.30.10.06599). 

Page 13: as you get this value? This increasing of cold start emission (value 0,5611592) will be 

in grams per mile?. My doubt is because I think that if you have a total increase of 2.086 g 

NMHC = 0,43 (M Bag1+MBag2) + 0,57 (MBag2 + MBag3); then the value in grams of the cold 

start (M NMHC Bag 1) should be higher than 0,5611592. 

The effects of the MSAT rule on the cold temperature adjustment for HC emissions of engine 

starts will need to be revisited once vehicles compliant with these standards are available for 

testing. The current adjustment is based solely on the emission standard values. 

53
 
Appendix G – Coordinating Research Council Project E-68a 

Comments 

December 3, 2009 

Additional comments regarding the adjustments described in the report, “Draft MOVES2009 

Highway Vehicle Temperature, Humidity, Air Conditioning, and Inspection & Maintenance 

Adjustments , that were not part of the formal MOVES2010 Peer Review process, were submitted 

as part of the Coordinating Research Council (CRC) Project E-68a. 

Comments from the report that are relevant to the topics covered in the EPA report are copied 

below, with EPA response in italics. Readers are encouraged to obtain the entire CRC Project 

E-68a report in order to fully understand the comments in their full context. 

Correction Factors (Fuels and Temperature) 

Regarding temperature correction factors, EPA examined recent data and found that cold start 

HC, CO, and NOx emissions should be adjusted for temperature, but there is no ambient 

temperature effect on running exhaust emissions. EPA developed additive cold start increments 

for HC, CO, and NOx that increase with lower temperatures. 

One concern with the temperature increments is that there is no analysis of how these may 

change as vehicles age, and the available data seemed to omit the CRC E-74b testing program, 

which was completed in May of 2009. EPA could utilize the Kansas City temperature data to 

determine whether the temperature relationships change with vehicle age. Also, the CRC E-74b 

testing program data could be used to further check the MOVES cold start correction factors. 

EPA believes that studies, such as the Kansas City study and CRC E-74, which include vehicles 

of different ages, but do not follow the vehicle fleet over time, are inadequate to conclude that the 

effects of temperature vary by vehicle age. EPA in cooperation with others, is planning a study 

specifically designed to follow the vehicle fleet over time and should produce the type of 

information needed to determine the effects of vehicle age on temperature effects. 

A second concern is that the method used to develop HC temperature increments for the MSAT 

rule (which requires lower HC standards at cold temperatures) assumes a compliance margin 

with respect to the HC standards at 75° F, but no compliance margin with respect to the HC 

standards at 20° F. As a result, the HC increments for vehicles meeting the MSAT requirements 

are over-estimated. The method should be revised to include a compliance margin at 20° F to be 

consistent with the margin currently being utilized at 75° F. 

EPA believes that any compliance margin at 20 degrees Fahrenheit will likely differ significantly 

from the margin observed at 75 degrees. Further testing will be needed on vehicles compliant 

with the MSAT standards to determine the appropriate margin. 

54
 
A third concern is that vehicles subject to the lower MSAT HC standards will very likely have 

much lower CO emissions as well. Once vehicles are certified to the MSAT cold HC standards, 

an analysis should be conducted of certification or other data to determine how much the CO 

increments change for these vehicles as well. 

CO emission rates already assume the impact of explicit standards for CO emissions at low 

temperatures. EPA believes that strategies to reduce HC emissions at low temperatures will 

likely have minimal further impacts on CO emissions at low temperatures. 

Particulate Matter Emissions for Gasoline Vehicles 

Temperature correction factors were estimated from the matched vehicle pairs. Unlike HC, CO, 

and NOx emissions, where the temperature correction factors were only for cold start emissions, 

EPA found an increase in running PM emissions with decreasing ambient temperatures, albeit 

lower than for the cold start. 

The first concern is that the combined MSAT and Kansas City data on matched pairs does not 

appear to support a cold temperature adjustment for running emissions. Results from other 

studies such as NFRAQS should be included in the analysis, with special regard to high PM 

emitters. 

It is true that data from the matched pairs in the combined MSAT and Kansas City was 

inconclusive in determine the temperature effect on running emissions. However, using other 

analysis techniques, EPA was satisfied that a significant temperature effect could be determined. 

A third concern is that in the draft model, vehicles meeting lower HC standards in response to the 

MSAT rule currently are not assumed to have lower PM emissions. Since HC and PM emissions 

seem to correlate well, we believe there will be lower PM emissions with a lower HC standard at 

cold temperature. In the section on correction factors, we recommend evaluating certification 

data or other data to examine the effect of cold HC standards on HC and CO emissions. This 

should be extended to PM as well if possible. 

EPA has not been able to establish a clear correlation between HC emissions and PM 

measurements that would support assuming that PM emissions at low temperatures would be 

significantly affected by changes in the HC standard. EPA will be updating the emission 

estimates in future versions of MOVES as new data on vehicles certified to the new standards are 

tested. 

Summary of Recommendations 

EPA should utilize the Kansas City data to determine whether temperature correction factors 

change with vehicle age. Also, the CRC E-74b testing program data could be used to further 

check the MOVES cold start correction factors. 

As stated above, we believe the Kansas City data is inadequate for this purpose, but we hope to 

collect appropriate data to do this analysis in the future.. 

55
 
The Tier 2 cold temperature response should be lower than for Tier 1 vehicles. In addition, the 

MSAT rules should reduce CO emissions as well as HC emissions. 

The method used to develop HC temperature correction factors for the MSAT rule should be 

revised to include margin at 20° F to be consistent with the margin currently being utilized at 75° 

F. 

We don’t believe these changes are justified based on currently available data. Now that MSAT 

vehicles are entering the fleet, we hope to gather in-use data on vehicles meeting these 

standards. 

The combined MSAT and Kansas City data on matched pairs does not support a cold 

temperature adjustment for running emissions. Results from other studies such as NFRAQS 

should be included in the analysis, with special regard to high PM emitters. 

56