Development of Emission Rates for 

Light-Duty Vehicles in the Motor 

Vehicle Emissions Simulator 

(MOVES2009) 

Draft Report 
Development of Emission Rates for 

Light-Duty Vehicles in the Motor 

Vehicle Emissions Simulator 

(MOVES2009) 

Draft Report
 

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-P-09-002 

August 2009 
Table of Contents 

1. Criteria Pollutant Emissions from Light-Duty Gasoline Vehicles (THC, CO, NOx) ............ 1 

1.1 Emissions Sources (sourceBinID) .................................................................................. 1 

1.2 Age Groups (ageGroupID) ............................................................................................. 2 

1.3 Operating Modes (opModeID) ....................................................................................... 2 

1.4 Scope............................................................................................................................... 5 

1.5. Emission-Rate development: Subgroup 1 (Model years through 2000)........................ 5 

1.5.1 Data Sources ........................................................................................................... 5 

1.5.1.1 Vehicle Descriptors............................................................................................ 5 

1.5.1.1.1 Track Road-Load Coefficients: Light-Duty Vehicles.................................. 6 

1.5.1.2 Test Descriptors................................................................................................. 6 

1.5.1.3 Candidate Data Sources..................................................................................... 7 

1.5.2 Data Processing and Quality-assurance.................................................................. 9 

1.5.2.1 Sample-design reconstruction (Phoenix only)................................................. 10 

1.5.3 Source selection .................................................................................................... 11 

1.5.4 Methods................................................................................................................. 12 

1.5.4.1 Data Driven Rates............................................................................................ 12 

1.5.4.1.1 Rates: Calculation of weighted means...................................................... 12 

1.5.4.1.2 Estimation of Uncertainties for Cell Means: ............................................. 13 

1.5.4.2 Model-generated Rates (hole-filling)............................................................... 14 

1.5.4.2.2 Rates........................................................................................................... 15 

1.5.4.2.2.1 Coast/Cruise/Acceleration ................................................................... 16 

Means model......................................................................................................... 16 

Model application ................................................................................................. 17 

1.5.4.2.2.2 Braking/Deceleration ........................................................................... 19 

Means model......................................................................................................... 19 

Variances model.................................................................................................... 20 

Model application ................................................................................................. 20 

1.5.4.2.3 Estimation of Model Uncertainties............................................................ 21 

1.5.4.2.4 Reverse transformation .............................................................................. 21 

1.5.4.3 Table Construction........................................................................................... 22 

1.5.5 Verification and Adjustment for High-Power Operating modes.......................... 23 

1.5.6 Estimating Rates for non-I/M Areas..................................................................... 29 

1.5.7 Stabilization of Emissions with Age..................................................................... 40 

1.5.7.1 I/M Reference Rates ........................................................................................ 40 

1.5.7.2 non-I/M Reference Rates................................................................................. 45 

1.5.8 Deterioration for Start Emissions.......................................................................... 46 

1.6. Emission-Rate Development: Subgroup 2 (MY 2001 and later).................................. 49 

1.6.1 Data Sources ......................................................................................................... 49 

1.6.1.1 Vehicle Descriptors.......................................................................................... 49 

1.6.2 Estimating I/M Reference Rates........................................................................... 50 

1.6.2.1 Averaging IUVP Results.................................................................................. 50 

1.6.2.2 Develop Phase-In Assumptions....................................................................... 53 

1.6.2.3 Merge FTP results and phase-in Assumptions ................................................ 55 

1.6.2.4 Estimating Emissions by Operating Mode ...................................................... 60 

i
1.6.2.4.1 Running Emissions .................................................................................... 60 

1.6.2.4.2 Start emissions........................................................................................... 65 

1.6.2.5 Apply Deterioration ......................................................................................... 66 

1.6.2.5.1 Recalculate the logarithmic mean.............................................................. 67 

1.6.2.5.2 Apply a logarithmic Age slope .................................................................. 67 

1.6.2.5.3 Apply the reverse transformation............................................................... 68 

1.6.2.6 Estimate non-I/M References .......................................................................... 70 

1.7 Replication and Data-Source Identification.................................................................. 71 

2. Particulate-Matter Emissions from Light-Duty Vehicles..................................................... 76 

2.1 Introduction and Background ....................................................................................... 76 

2.1.1 Particulate Measurement in the Kansas City Study.............................................. 77 

2.1.2 Causes of Gasoline PM Emissions ....................................................................... 80 

2.2 New Vehicle or Zero Mile Level (ZML) Emission Rates............................................ 82 

2.2.1 Longitudinal Studies............................................................................................. 83 

2.2.2 New Vehicle, or ZML Emission Rates and Cycle Effects.................................... 85 

2.2.3 Aging or Deterioration in Emission Rates............................................................ 90 

2.2.2.4 Age Effects or Deterioration Rates.................................................................. 90 

2.3 Modal PM Emission Rates............................................................................................ 96 

2.2.1 Typical behavior in particulate emissions as measured by the Dustrak and 

Photoacoustic Analyzer ........................................................................................................ 96 

2.4 Conclusions................................................................................................................. 105 

3. Criteria Pollutant Emissions from Light-Duty Diesel Vehicles (THC, CO, NOx) ............ 107 

3.1. Estimating Zero-Mile FTP Emissions: ....................................................................... 107 

3.1.2 Estimating Bag Emissions: ................................................................................. 108 

3.1.2 Assigning Operating Modes for Starts (Adjustment for Soak Time)................. 111 

3.2 Running Emissions by Operating Mode ..................................................................... 113 

4. Crankcase Emissions .......................................................................................................... 116 

. References........................................................................................................................... 119 

ii
1. Criteria Pollutant Emissions from Light-Duty Gasoline 

Vehicles (THC, CO, NOx) 

This chapter describes the technical development of emission rates for criteria pollutants (HC, 

CO, NOx) from light-duty vehicles for use in the draft MOVES model . 

Section 1 describes the structure of the MOVES emissionRateByAge table, as it applies to 

criteria-pollutant emissions from gasoline-fueled light-duty vehicles. Section 1.5 describes the 

development of emission rates for vehicles manufactured prior to model year 2000. Sub-sections 

1.5.1 and 1.5.2 describe the process of data selection and quality assurance. Rates were generated 

either directly from available data (sub-section 1.5.3) or by development and application of 

statistical “hole-filling” models (subsection 1.5.4). These rates were derived using data from the 

Phoenix I/M program and represent rates characteristic of a program with features similar to 

those in the Phoenix program. 

Because steps 1.5.3 and 1.5.4 relied on data collected on IM240 and IM147 cycles, we thought it 

appropriate to evaluate the extrapolation with power to high levels beyond those covered by the 

IM cycles. The development and application of adjustments to rates in operating modes at high 

power is discussed in sub-section 1.5.5. 

In MOVES terminology, pollutants are emitted by “sources” via one or more “processes.” 

Within processes, emissions may vary by operating mode, as well as by age Group. 

The relevant processes are exhaust emissions of total hydrocarbons (THC), carbon monoxide 

(CO) and oxides of nitrogen (NOx), during running operation (running exhaust) (pollutantID = 

1,2,3, respectively). The pollutant process is running exhaust emissions (process 01). Thus, the 

pertinent values of polProcessID are 101, 201 and 301, respectively. 

For these pollutant processes, the meanBaseRate is expressed in units of g/SHO, where SHO 

denotes “source-hours operating.” 

1.1 Emissions Sources (sourceBinID) 

For these pollutant processes emissions sources include light-duty vehicles (cars and trucks). 

The corresponding sourceBins are defined as shown in Table 1 - 1. Note that the engine-size and 

weight-class attributes are not used, as they were for energy consumption. Unlike fuel or energy 

consumption, these parameters are assumed not to influence emissions, since light-duty vehicles 

are required to meet applicable standards irrespective of size and weight. 

1
Table 1 - 1. Construction of sourceBins for Running-Exhaust Emissions for runningexhaust emissions from light-duty vehicles 

Parameter MOVES Database Attribute Values 

Fuel type fuelTypeID Gasoline = 01 

Ethanol = 05 

Engine Technology engtechid Conventional = 01 

Regulatory Class regClassID LDV = 20 

LDT = 30 

Model-Year group shortModYrGroupID <as shown where?> 

Engine Size Class engSizeID <not used> 

Vehicle Test Weight weightClassID <not used> 

1.2 Age Groups (ageGroupID) 

To account for emissions deterioration, MOVES estimates emission rates for vehicles in a series 

of age ranges, identified as age groups (ageGroupID). Seven groups are used, as follows: 0-3, 

4-5, 6-7, 8-9, 10-14, 15-19, and 20+ years. The values of the attribute ageGroupID for these 

classes are 3, 405, 607, 809, 1014, 1519, and 2099, respectively. These groups assume that the 

most rapid change in emissions as vehicles age occurs between 4 and 10 years. 

1.3 Operating Modes (opModeID) 

For running emissions, the key concept underlying the definition of operating modes is “vehiclespecific power” (VSP, PV ). This parameter represents the tractive power exerted by a vehicle to 

move itself and its cargo or passengers1. It is estimated in terms of a vehicle’s speed and weight, 

as shown in Equation 1 - 1. 

m

amvCvBvAv P ttttt

t

+++ =

32

,V

1 - 1 

In this form, VSP (PV,t, kW/tonne) is estimated in terms of vehicles’: 

• speed at time t (v , m/sec), t 

• acceleration at (m/sec2) , 

• - mass m (tonne) (usually referred to as “weight, ”), 

• - track-road load coefficients A, B and C3

,representing rolling resistance, rotational 

resistance and aerodynamic drag, in units of kW-sec/m, kW-sec2

/m2 and kW-sec3/m3, 

respectively. 

Note that this version of the equation does not include the term accounting for effects of road 

grade, because the data used in this analysis was measured on chassis dynamometers. 

On the basis of VSP, speed and acceleration, a total of 23 operating modes are defined for 

running-exhaust processes (Table 1 - 2). Aside from deceleration/braking, which is defined in 

2
terms of acceleration, and idle, which is defined in terms of speed alone, the remaining 21 modes 

are defined in terms of VSP within broad speed classes. Two of the modes represent “coasting,” 

where VSP < 0. and the remainder represent “cruise/acceleration,” with VSP ranging from 0 to 

over 30 kW/tonne. For reference, each mode is identified by a numeric label, the “opModeID.” 

3
Table 1 - 2. Definition of the MOVES Operating Mode Attribute for Motor Vehicles 

(opModeID). 

Operating 

Mode 

Operating Mode 

Description 

Vehicle-Specific 

Power 

(VSPt, kW/tonne) 

Vehicle Speed 

(vt,mi/hr) 

Vehicle Acceleration 

(a, mi/hr-sec) 

0 Deceleration/Braking at ≤ -2.0 OR 

(at < -1.0 AND 

at-1 <-1.0 AND 

at-2 <-1.0) 

1 Idle -1.0 ≤ vt < 1.0 

11 Coast VSPt< 0 0 ≤ vt < 25 

12 Cruise/Acceleration 0 ≤ VSPt < 3 0 ≤ vt < 25 

13 Cruise/Acceleration 3 ≤ VSPt < 6 0 ≤ vt < 25 

14 Cruise/Acceleration 6 ≤ VSPt < 9 0 ≤ vt < 25 

15 Cruise/Acceleration 9 ≤ VSPt < 12 0 ≤ vt < 25 

16 Cruise/Acceleration 12 ≤ VSPt 0 ≤ vt < 25 

21 Coast VSPt< 0 25 ≤ vt < 50 

22 Cruise/Acceleration 0 ≤ VSPt < 3 25 ≤ vt < 50 

23 Cruise/Acceleration 3 ≤ VSPt < 6 25 ≤ vt < 50 

24 Cruise/Acceleration 6 ≤ VSPt < 9 25 ≤ vt < 50 

25 Cruise/Acceleration 9 ≤ VSPt < 12 25 ≤ vt < 50 

27 Cruise/Acceleration 12 ≤ VSP < 18 25 ≤ vt < 50 

28 Cruise/Acceleration 18 ≤ VSP < 24 25 ≤ vt < 50 

29 Cruise/Acceleration 24 ≤ VSP < 30 25 ≤ vt < 50 

30 Cruise/Acceleration 30 ≤ VSP 25 ≤ vt < 50 

33 Cruise/Acceleration VSPt< 6 50 ≤ vt

35 Cruise/Acceleration 6 ≤ VSPt < 12 50 ≤ vt

37 Cruise/Acceleration 12 ≤ VSP <18 50 ≤ vt

38 Cruise/Acceleration 18 ≤ VSP < 24 50 ≤ vt

39 Cruise/Acceleration 24 ≤ VSP < 30 50 ≤ vt

40 Cruise/Acceleration 30 ≤ VSP 50 ≤ vt

4
1.4 Scope 

In estimation of energy consumption for MOVES2004, it was possible to combine data from 

various sources without regard for the places of residence for various vehicles. In contrast, when 

turning attention to the criteria pollutants, it was clear that it would be essential to know with a 

high degree of confidence whether vehicles had been subject to inspection-and-maintenance 

(I/M) requirements at the time of measurement. After reviewing data sources, it became clear 

that the amounts of data collected within I/M areas vastly exceeded those collected in non-I/M 

areas. We also concluded that I/M programs themselves could provide a large and valuable 

source of data. In consideration of the demanding analytic tasks posed by the ambitious MOVES 

design, we elected to estimate rates for vehicles in I/M areas first, as the “base-line” or “default” 

condition. Following construction of a set of rates representing I/M conditions, the plan was to 

estimate rates for non-I/M areas relative to those in I/M areas. This approach is an inversion of 

that used in MOBILE, in which “non-I/M” is that “default condition” relative to which “I/M” 

emissions are calculated during a model run. 

In addition, the rates described below represent emissions on the FTP temperature range (68 – 86 

°F), to provide a baseline against which temperature adjustments would be applied during model 

runs. 

1.5. Emission-Rate development: Subgroup 1 (Model years through 2000) 

1.5.1 Data Sources 

For emissions data to be eligible for use in MOVES development, several requirements were 

imposed: 

• To derive rates for operating modes, it was essential to acquire data measured on 

transient tests. 

• Data had to be measured at a frequency of approximately 1 Hz., e.g., continuous or 

“second-by-second” measurements. 

• To make allowance for application of temperature adjustments (developed 

separately), it was necessary to know the temperature at the time of test. 

1.5.1.1 Vehicle Descriptors 

In addition to the requirements listed above, complete descriptive information for vehicles was 

required. Vehicle parameters required for incorporation into MOVES are shown in Table 1 - 3. 

5
Table 1 - 3. Required Vehicle Parameters. 

Parameter Units Purpose 

VIN Verify MY or other parameters 

Fuel type 

Make 

Model 

Model year Assign sourceBinID, calculate age-at-test 

Vehicle class Assign sourceBinID 

GVWR lb Distinguish trucks from LDV 

Track road-load power hp Calculate track road-load coefficients A, B and C

1.5.1.1.1 Track Road-Load Coefficients: Light-Duty Vehicles 

For light-duty vehicles, we calculated the track load coefficients from the “track road load power 

at 50 mph” (TRLP, hp), based on Equation 1 - 2. 

( )

( ) ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⋅

⋅ ⋅=

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⋅

⋅ ⋅=

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⋅

⋅ ⋅=

3

250

1

2

250

1

250

1

TRLHP

 PF 

TRLHP

 PF 

TRLHP

 PF 

cv

c

C

cv

c

B

cv

c

A

C

B

A

1 - 2 

where: 

PFA = default power fraction for coefficient A at 50 mi/hr (0.35), 

PFB = default power fraction for coefficient B B at 50 mi/hr (0.10), 

PFC = default power fraction for coefficient C at 50 mi/hr (0.55), 

c1 = a constant, converting TRLP from hp to kW (0.74570 kW/hp), 

v50 = a constant vehicle velocity (50 mi/hr), 

c2 = a constant, converting mi/hr to m/sec (0.447 m⋅hr/mi⋅sec)). 

In the process of performing these calculations, we converted from english to metric units, in 

order to obtain values of the track road-load coefficients in SI units, as listed above. Values of 

TRLP were obtained from the Sierra I/M Look-up Table.2

1.5.1.2 Test Descriptors 

6
In addition, a set of descriptive information was required for sets of emissions measurements on 

specific vehicles. Essential items for use in MOVES are listed in Table 1 - 4. 

Table 1 - 4. Required Test Parameters 

Parameter Units Purpose 

Date Determine vehicle age at test 

Time of day Establish sequence of replicates 

Ambient temperature °F Identify tests on target temperature range 

Test Number Identify 1st and subsequent replicates 

Test duration sec Verify full-duration of tests 

Test result pass/fail Assign tests to correct result stratum 

Test weight lb Calculate vehicle-specific power 

1.5.1.3 Candidate Data Sources 

In addition to the parameters listed in Table 1 - 3 and Table 1 - 4, datasets with historic depth and 

large sample sizes were highly desirable, to characterize the high variability typical of exhaust 

emissions as well as trends against age. 

Various datasets were available, representing several million vehicles when taken together 

(Table 1 - 5). In some cases they could be combined as broadly comparable pairs representing 

I/M and non-I/M conditions. Likely candidates were subjected to a high degree of scrutiny and 

quality-assurance, after which some were excluded from further consideration for specific 

reasons. 

Table 1 - 5. Datasets considered for use in Estimating Light-duty Runnning Emissions. 

Dynamometer Remote-Sensing (RSD) 

I/M non-I/M I/M non-I/M 

AZ (Phoenix) AZ Phoenix 

IL (Chicago) IL (Chicago) 

MO (St. Louis) MO St. Louis 

British Columbia 

CO (Denver) 

Indiana 

Ohio 

Wisconsin 

NY (New York) 

Other MSOD

Maryland/N Virginia VA (Richmond) 

CA (Los Angeles) 

TX (Houston) 

GA (Atlanta) GA (Augusta/Macon) 

NE (Omaha) 

OK (Tulsa) 

Several remote-sensing datasets received consideration. However, we elected not to use remotesensing data directly to estimate rates, for several reasons: (1) For the most part, the RSD 

7
datasets on hand had very restricted model-year by age coverage (historic depth), which limited 

their usefulness in assigning deterioration. (2) The measurement of hydrocarbons by RSD is 

highly uncertain. The instruments are known to underestimate the concentrations of many VOCs 

relative to flame-ionization detectors. In inventory estimation, a multiplicative adjustment of 2.0-

2.2 is often applied to allow comparison to HC measurements by other methods.3 (3) In 

MOVES, emissions are expressed in terms of mass rates (mass/time). While fuel-specific rates 

(mass emissions/mass fuel) can be estimated readily from remote-sensing data4

, mass rates 

cannot be calculated without an independently estimated CO2 mass rate. It followed that RSD 

would not provide rates for any MY×Age combinations where dynamometer data were not 

available. In these cases, RSD would be dependent on and to some extent redundant with 

dynamometer data. (4) Because remote-sensing measurements are typically sited to catch 

vehicles operating under light to moderate acceleration, results can describe emissions only 

selected cruise/acceleration operating modes. However, RSD cannot provide measurements for 

coasting, decel/braking or idle modes. For these reasons we reserved the RSD for secondary 

roles, such as verification of results obtained from dynamometer data. 

Table 1 - 6. Characteristics of Candidate Datasets 

Chicago Phoenix NYIPA St. Louis 

Type Enhanced Enhanced Basic/Enhanced Enhanced 

Network Centralized Centralized De-centralized Centralized 

Exempt MY 4 most recent 4 most recent 2 most recent 2 most recent 

Collects random 

sample? 

YES YES n/a NO 

Program Tests Idle, IM240, OBD-II Idle/SS, IM240, 

IM147, OBD-II 

IM240 IM240 

Fast-pass/Fast-fail? YES YES n/a YES 

Test type (for 

random sample) 

IM240 IM240, IM147 IM240 n/a 

Available CY 2000-2004 1995-99 

2002-2004 

1999-2002 2002-2005 

Size (no. tests) 8,900 62,500 8,100 

Dynamometer datasets that received serious consideration are described below and summarized 

in Table 1 - 6. 

Metropolitan Chicago. We acquired data collected over four calendar years (2000-04) in 

Chicago’s centralized enhanced program. In addition to routine program tests, the program

performed IM240 tests on two random vehicle samples. One is the “back-to-back” random 

sample. This sample is relatively small (n ~ 9,000 tests), but valuable because each selected 

vehicle received two full-duration IM240 tests in rapid succession, obviating concerns about 

conditioning. A second is the “full-duration” random sample, in which selected vehicles 

received a single full-duration IM240. This sample is much larger (n > 800,000) but less valuable 

due to the lack of replication. Despite its size, the full-duration sample has no more historic 

depth than the back-to-back sample, and thus sheds little additional light on age trends in 

emissions. Both samples were simple random samples, indicating that in the use of the data, 

users must assume that the samples are self-weighting with respect to characteristics such as high 

emissions, passing/failing test results, etc. 

8
St. Louis. Another large program dataset is available from the program in St. Louis. While a 

large sample of program tests is available, this program differed from the others in that no 

random evaluation sample was available. Because vehicles were allowed to “fast-pass” their 

routine tests, results contained many partial duration tests (31 – 240 seconds). At the same time, 

the lack of replication raised concerns about conditioning. Partial duration was a concern in itself 

in that the representation of passing vehicles declined with increasing test duration, and also 

because it compounded the issue of conditioning. In addition, while OBD-equipped vehicles 

failing a scan received IM240s, those passing their scans did not. Because addressing the 

interwoven issues of inadequate conditioning, “fast-pass bias” and “OBD-screen bias” proved 

impractical, we excluded this dataset from further consideration. 

Phoenix. At the outset, the random samples from the Phoenix program appeared attractive in 

that they had over twice the historic depth of any other dataset, with model-year × age coverage 

spanning 11 calendar years. Usage of these samples is somewhat complicated by the fact that no 

random samples were collected for two years (2000-01) and by the fact that the sample design 

employed changed in the middle of the ten-year period. During the first four years, a simple “2% 

random sample” was employed. During the last four years, a stratified design was introduced 

which sampled passing and failing vehicles independently and at different rates. In the stratified 

sample, failures were over-sampled relative to passing vehicles. Thus, using these data to 

estimate representative rates and to combine them with the 2% sample, assumed to be selfweighting, required reconstruction of the actual stratified sampling rates, as described below. 

 

New York Instrumentation/Protocol Assessment (NYIPA). This dataset differs from the others in 

that while it was collected within an I/M area in New York City, it is not an I/M program dataset 

as such. It is, rather, a large-scale research program designed to establish correlation between the 

IM240 and an alternative transient test. It is not entirely clear whether it can be considered a 

random sample, in part because estimation of representative averages was not a primary goal of 

the study. All data that we accessed and used was measured on full-duration IM240s during a 

four-year period. There was a high degree of replication in the conduction of tests, allowing 

fully-conditioned operation to be isolated by exclusion of the initial test in a series of replicates. 

While these data played a prominent role in development of energy consumption rates for 

MOVES2004, the four-year duration of the program limits its usefulness in analysis of age 

trends for criteria pollutants. 

1.5.2 Data Processing and Quality-assurance 

We performed several quality-assurance steps to avoid known biases and issues in using I/M data 

to estimate mean emissions. One source of error, “inadequate conditioning” can occur when 

vehicles idle for long periods while waiting in line. To ensure that measurements used reflected 

fully-conditioned vehicles we excluded either portions of tests or entire tests, depending on test 

type and the availability of replicates. If back-to-back replication was performed, we discarded 

the first test in a series of replicates. If replication was not performed, we excluded the first 120 

seconds of tests (for IM240s only). 

9
Another problem occurs when calculation of fuel economy for tests yields values implausible 

enough to indicate that measurements of one or more exhaust constituents are invalid. To 

identify and exclude such tests, we identified tests with outlying measurements for fuel 

economy, after grouping vehicles by vehicle make, model-year and displacement. 

An issue in some continuous or second-by-second datasets is that cases occur in which the 

emissions time-series appears to be “frozen” or saturated at some level, not responding to 

changes in power. We found that the occurrence of such problems was more or less evenly 

distributed among the fleet regardless of age or model year, and that severe instances were rare. 

We excluded tests in which 25% or more of the measurements were “frozen.” 

For a modal analysis assuming that emissions respond to power on short time scales, 

It is critical that the emissions time-series be aligned to the power time-series. Consequently, we 

examined alignment for all tests. As necessary, we re-aligned emissions time series to those for 

VSP by maximizing correlation coefficients, using parametric Pearson coefficients for CO2 and 

NOx, and non-parametric Spearman coefficients for CO and THC. 

1.5.2.1 Sample-design reconstruction (Phoenix only) 

For data collected in Phoenix during CY 2002-05, we constructed sampling weights to allow use 

of the tests to develop representative means. The program implemented a stratified sampling 

strategy, in which failing vehicles were sampled at higher rates than passing vehicles. 

It is thus necessary to reconstruct the sample design to appropriately weight failing and passing 

vehicles in subsequent analyses. After selection into the random sample, vehicles were assigned 

to the “failing” or “passing” strata based on the result of their routine program test, with the 

specific test depending on model year, as shown in Figure 1 - 1. Within both strata, sample 

vehicles then received three replicate IM147 tests. 

Based on test records, reconstructing sampling rates simply involved dividing the numbers of 

sampled vehicles by the total numbers of vehicles tested, by model year and calendar year, for 

failing (f) and passing (p) strata, as shown in Equation 1 - 3. 

CYMY,p,

CYMY,p,

CYMY,p,

CYMY,f,

CYMY,f,

CYMY,f, N

n

f N

n

f = = 1 - 3 

Corresponding sampling weights indicate the numbers of vehicles in the general fleet represented 

by each sample vehicle. They were derived as the reciprocals of the sampling fractions, as shown 

in Equation 1 - 4. 

CYMY,p,

CYMY,p,

CYMY,f,

CYMY,f,

1 1

f

w

f

w = = 1 - 4 

10
Figure 1 - 1. Stratified Sampling as applied in selection of the Random Evaluation Sample 

in the Phoenix I/M Program (CY 2002-05) 

Official Test

MY 1980 and previous: Loaded-mode + Idle

MY 1981 – 1995: IM147

MY 1996 and later: OBD II 

Result

Failing Stratum

Oversampled 

“higher” sampling rate

Passing Stratum

“lower” sampling rate 

 Triplicate IM147

1.5.3 Source selection 

After excluding the St. Louis dataset, and comparing the Phoenix, Chicago and NY datasets, 

analysis, we elected to rely on the Phoenix dataset for purposes of rate estimation and to use the 

other datasets, including selected remote-sensing data, for purposes of comparison. This course 

was chosen for several reasons. 

For our purposes, the greater historic depth of the Phoenix data was a tremendous advantage. It 

was the only set deep enough to allow direct and independent assessment of deterioration. The 

limited depth of the other datasets would have meant that the subset of calendar years that could 

be covered by pooled data would have been relatively limited. Only a single calendar year, 

2002, is covered by all three datasets. Several years would be covered by two out of three. 

Calendar 1999 is covered by Phoenix and NY; 2000 and 2001 would have been covered by NY 

and Chicago, and 2003 and 2004 by Chicago and Phoenix. The remaining years, 1996-98 and 

2005 could have been covered only by Phoenix in any case. 

In addition, pooling the three datasets would have involved several difficult technical issues. 

Table 1 - 6 shows that the datasets were of strongly differing sizes. Thus, if the datasets were 

pooled without any type of relative weighting, Phoenix would have exerted much stronger 

influence than the others in most shared calendar years. To rectify disparities in influence by 

assigning the different datasets similar or proportional influence would have required 

development of some sort of a weighting scheme, but a rational basis for such relative weighting 

is not immediately apparent. 

The question of pooling is further complicated by the fact that use of the Phoenix data collected 

in CY 2002 to 2005 requires use of sampling weights for passing and failing tests (as discussed), 

11
whereas the Chicago and NYIPA datasets are assumed to be self-weighting. Again, no rational 

basis for incorporating weighted and self-weighted tests from various programs in the same CY 

was immediately apparent. 

1.5.4 Methods 

 

1.5.4.1 Data Driven Rates 

Where data was present, the approach was simple. We calculated means and other summary 

statistics for each combination of source and operating mode (i.e., table cell). We classified the 

data by regulatory class (LDV, LDT), model-year group, age group and operating mode (Table 

1). The model-year groups used are shown in Table 7, along with corresponding samples of 

passing and failing tests. 

 

Table 1 - 7. Test sample sizes for the Phoenix random-evaluation sample. 

Model-year 

groupb

LDV LDT 

faila pass fail pass 

1981-82 562 539 340 495 

1983-85 1,776 2,078 1,124 1,606 

1980-89 3,542 6,420 1,745 3,698 

1990-93 2,897 8,457 1,152 4,629 

1994-95 997 4,422 703 3,668 

1996-98 1,330 3,773

1996 526 1,196 

1997-98 858 2,320 

1999-2000 176 753 136 624 

Total 11,285 26,478 6,589 18,254 a Note that ‘failure’ can indicate failure for CO, HC or NOx, as applicable. 

b

 Note that these are the model-year groups used for analysis; NOT the 

model-year groups used in the MOVES database.

We calculated means and other summary statistics for each combination of sourceBinID, 

ageGroupID and opModeID. For simplicity, we will refer to a specific combination of 

sourceBinID, and opModeID as a “cell,” to be denoted by label ‘h’. 

1.5.4.1.1 Rates: Calculation of weighted means 

The emission rate (meanBaseRate) in each cell is a (Eh) simple weighted mean 

12
∑

∑

=

= = test

test

1

1

,

n

i

i

n

i

tii

h

w

Rw

E 1 - 5 

where wi is a sampling weight for each vehicle in the cell, as described above, and Ri,t is the 

“second-by-second” emission rate in the cell for a given vehicle at a given second t. 

1.5.4.1.2 Estimation of Uncertainties for Cell Means: 

 

To estimate sampling error for each cell, we calculated standard-errors by weighted variance 

components. In estimating variances for cell means, we treated the data within cells as effective 

cluster samples, rather than simple random samples. This approach reflects the structure of the 

data, which is composed of sets of multiple measurements collected on individual vehicles. Thus, 

measurements on a specific vehicle are less independent of other measurements on the same 

vehicle than of measurements on other vehicles. Accordingly, means and variances for individual 

vehicle tests were calculated to allow derivation of between-test and within-test variance 

components. These components were used in turn to calculate the variance of the mean for each 

cell, using the appropriate degrees of freedom to reflect between-vehicle variability5. To enable 

estimation of variances under this approach, we calculated a set of summary statistics, as listed 

below: 

Test mean ( ): the arithmetic mean of all measurements in a given test on a specific vehicle in a 

given cell. 

Ei

Test sample size (nh), the number of individual vehicle tests represented in a cell. 

Measurement sample size (ni): the number of measurements in a cell representing an individual 

test on an individual vehicle. 

Cell sample size (nh,i): the number of individual measurements in a cell, where each count 

represents a measurement collected at an approximate frequency of 1.0 Hz, (i.e., “second-bysecond). 

Test variance ( ): the variance of measurements for each vehicle test represented in a cell, 

calculated as the average squared deviation of measurements for a test about the mean for that 

test. Thus, we calculated a separate test variance for each test in each cell. 

2

i s

Weighted Between-Test variance component ( ): the component of total variance due to 

variability among tests in a cell, or stated differently, the weighted variance of the test means 

about the cell mean, calculated as 

2

b s

13
( )

∑

∑

=

=

−

−

= i

i

n

i

i

hi

n

i

i

w

EEw

s

1

2

2 1

b

1

1 - 6 

Weighted Within-Test Variance Component ( ): the variance component due to variability 

within tests, or the variance of measurements within individual tests (R

2

ws

i,t) about their respective 

test means, calculated in terms of the test variances, weighted and summed over all tests in the 

cell: 

( )

( ) hh,i

n

i

i

ii

n

i

i

nnw

snw

s h

h

− ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

−

=

∑

∑

=

=

1

2

2 1

w

1

1 - 7 

Variance of the cell mean ( 2

Es ): this parameter represents the uncertainty in the cell mean, and is 

calculated as the sum of the between-vehicle and within-test variance components, with each 

divided by the appropriate degrees of freedom. 

ihh

E

n

s

n

s

s h

,

2

w

2

2 b += 1 - 8 

Coefficient-of-Variation of the Mean (CVEh): this parameter gives a relative measure of the 

uncertainty in the cell mean, allowing comparisons among cells. It is calculated as the ratio of the 

cell standard error to the associated cell mean 

h

E

E E

s h

h

2

CV = 1 - 9 

Note that the term CVE is synonymous with the term “relative standard error” (RSE). 

1.5.4.2 Model-generated Rates (hole-filling) 

Following averaging of the data, it was necessary to impute rates for cells for which no data was 

available, i.e., “holes.” Empty cells occur for age Groups not covered by available data (Figure 1 

- 2). In the figure, “age holes” are represented by unshaded areas. Filling in these un-shaded 

areas required “hind-casting” emissions for younger vehicles for older model years, as 

“forecasting” deterioration of aging vehicles for more recent model years. Empty cells occur as 

well in high-power operating modes not covered by the IM147 or IM240, meaning operating 

modes with power greater than about 24 kW/tonne. 

14
Figure 1 - 2. Model-year by Age Structure of the Phoenix I/M Random Evaluation 

Sample. 

MY Vehicle Age at Test (years)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990 95 96 97 98 99 00 01 02 03 04 05

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

1.5.4.2.2 Rates 

To estimate rates in empty cells (holes), we constructed statistical models of emissions data to 

extrapolate trends in VSP and age. For this purpose, we generated a series of models based on 

the MOVES operating-mode/ageGroup structure. 

As a preliminary step, data were averaged for each test within a set of classes for VSP and speed. 

We averaged emissions by model-year-group, regClass, age, VSP class, speed class and test. 

Classes for VSP followed intervals of 3.0 kW/tonne ( e.g., 0-3, 3-6, ... 27-30, 30+). Speed 

classes followed those used for the MOVES operating modes (e.g., 1-25 mph, 25-50 mph, 50+ 

mph). The resulting dataset had a single mean for each test in each 6-way cell. The purpose for 

this averaging was to give the resulting statistical model an appropriate number of degrees of 

freedom for each of the class variables, i.e., the d.f. would be determined by the number of tests 

rather than the number of individual “second-by-second” measurements. Note that the matrix 

used for this purpose was finer than that represented in Table 1 - 1. 

We fit separate models in three groups of operating modes. For all operating modes except 

brake/deceleration and idle, we fit one model that incorporates VSP. We call this group 

“coast/cruise/acceleration.” For braking/deceleration and idle, we fit two additional models not 

incorporating VSP, as these modes are not defined in terms of VSP (Table 1). Overall, we fit 

three models for each combination of LDV and LDT, for the model-year groups shown in Table 

7, giving a total of 60 models. 

Before fitting a model, we drew a sample of vehicle tests in each model-year group (n = 1,200 to 

3,500, see Table 8). This sampling was performed to fit model on smaller amount of data that a 

standard desktop computer could handle. The sample was stratified by test result and age with 

allocation proportional to that of the total sample. 

15
Table 1 - 8. Sample sizes for statistical modeling, by regulatory class and test result 

Model-year 

group 

LDV LDT 

fail pass fail pass 

1980 & earlier 

1981-82 

1983-85 

1980-89 

1990-93 

1994-95 

1996-98 663 1,738 

1996 346 854 

1997-8 671 1,730 

1999-2000 247 954 

Each model included two sub-models, one to estimate means and one to estimate variances, as 

described below. 

1.5.4.2.2.1 Coast/Cruise/Acceleration 

 

Means model 

For the means sub-model, the dependent variable was the natural logarithm of emissions 

Eh aPPP 7V654 tP i ++++++++= εγβββββββ 3

V3

2 ln V2V10 vv 1 - 10 

where : 

• lnEh = natural-logarithm transform of emissions (in cell h), 

• PV, PV

2

, PV

3

 = first-, second- and third-order terms for vehicle-specific power 

(kW/tonne), 

• a = vehicle age at time of test (years), 

• s = speed class (1 -25 mph, 25-50 mph and 50+ mph), 

• t = test identifier (random factor) 

• ε = random or residual error 

 

• β = regression coefficients for the intercept and fixed factors p, a and s. 

• γ = regression coefficients for the random factor test. 

The model includes first-, second- and third-order terms in PV to describe curvature in the power 

trend, e.g., enrichment for CO and the corresponding decline in NOx at high power. The age 

term gives an ln-linear trend in age. The speed-class term allows for a modified intercept in each 

16
speed class, whereas the power/speed-class interaction allows slightly different power slopes in 

each speed class. The random factor term for test fits a random intercept for each test, which 

does not strongly affect the mean estimates but does affect the estimation of uncertainties in the 

coefficients. 

After fitting models, we performed basic diagnostics. We plotted residuals against the two 

continuous predictors, VSP and age. We checked the normality of residuals across the range of 

VSP and age, and we plotted predicted vs. actual values. 

Variances model

The purpose of this sub-model was to model the variance of lnEh, i.e., the logarithmic variance 

sl

2

, in terms VSP and age. To obtain a dataset of replicate variance estimates, we drew sets of 

replicate test samples. Each replicate was stratified in the same manner as the larger samples 

(Table 1 - 8). To get replicate variances, we calculated ln-variance for each replicate within the 

VSP/age matrix described above. 

Models were fit on set of replicate variances thus obtained. The dependent variable was 

logarithmic variance 

sl V3V210 aPPa ++++= εαααα 2 1 - 11 

where p and a are VSP and age, as above, and α are regression coefficients. After fitting we 

examined similar diagnostics as for the means model. 

Model application 

Application of the model was simple. The first step was to construct a cell matrix including all 

emission rates to be calculated, as shown in Table 1 - 9. 

Table 1 - 9. Construction of emission-rate matrix for light-duty gasoline vehicles 

Count Category MOVES Database attribute 

1 Fuel (gasoline) fuelTypeID = 01 

× 2 Regulatory Classes (LDV, LDT) regClassID = 20, 30 

× 10 Model-year groups1

× 21 Operating modes opModeID = 11-16, 21-30, 33-40 

× 7 Age Groups ageGroupID = 3, 405, 607, 809, 

1014, 1519, 2099 

× 3 Pollutant processes (running HC, 

CO, NOx) polProcessID = 101, 201, 301 

= 9,660 TOTAL cells 

17
Next, we constructed a vector of coefficients for the means sub-model (β) and merged it into the 

cell matrix. 

[ ] β = β β β β β β − β − β + β6)50(5)5025(45)250(543210 1 - 12 

Then, for each table cell, we constructed a vector of predictors (Xh). Equation 13 shows an 

example for an operating mode in the 1 – 25 mph speed class, e.g., the value for the 1-25 mph 

class is 1 and the values for the 25-50 and 50+ speed classes are 0. To supply values for VSP 

(PV) and age group (a), cell midpoints were calculated and applied as shown in Table 10. 

[ ] V

3

V

2 Xh = 1 VV 001 PaPPP 1 - 13 

Table 1 - 10. Values of VSP used to apply statistical models 

opModeID Range Midpoint 

11, 21 < 0 -2.0 

12, 22 0 - 3 -2.5 

13, 23 3 - 6 4.5 

14, 24 6 - 9 7.5 

15, 25 9 - 12 10.5 

16 12 + 14.5 

27,37 12 - 18 15.0 

28,38 18 - 24 21.0 

29,39 24 - 30 27.0 

30 30 + 34.0 

40 30 + 32.0 

33 < 6 0.5 

35 6 - 12 9.0 

The final step was to multiply coefficient and predictor vectors, which gives an estimated 

logarithmic mean (lnEh) for each cell h. 

X β

' ln E = hh 1 - 14 

18
The application of the variances model is similar, expect that the vectors have four rather than 

nine terms 

[ ] α = α α α α3210 1 - 15 

[ aPaP ] Xh = 1 V V 1 - 16 

Thus, the modeled logarithmic variance in each cell is given by 

s = Xhhl α 2

, 1 - 17 

In some model-year groups, it was not always possible to develop plausible estimates for the age 

slope β4, because the data did not cover a wide enough range of calendar years. For example, in 

the 99-00 model-year group, the available data represented young vehicles without sufficient 

coverage of older vehicles. We considered it reasonable to adapt the age slope for the 96-98 

model-year group for LDV, and the 1997-98 model-year group for LDT. 

In the groups 83-85 and 81-82, the data covered vehicles at ages of 10 years and older but not at 

younger ages. Simply deriving a slope from the available data would give values that were much 

too low, resulting in very high emissions for young vehicles. In these cases we considered it 

more reasonable to adopt an age slope from a subsequent model year group. When making this 

assumption, it is necessary to recalculate the intercept, based on the assumed slope and the 

earliest available data point. 

Intercepts were recalculated by rearranging Equation 1 - 10 to evaluate the model in operating 

mode 24, using the age slope from the previous model-year group (β4*) an estimate of lnemissions from the available dataset at the earliest available age (lnEa*) at age a*. In operating 

mode 24, the midpoint of the VSP range (6-9) is 7.5 kW/tonne and the speed class is 25-50 mph. 

43 )5025(5 6

3

2

2

* 1

* β0 Ea a*5.75.75.7ln βββββ − −−−−−−= 5.7 β 1 - 18

On a case by case basis, age slopes were adopted from earlier or later model-year groups. In a 

similar way, ln-variance models or estimates could be adopted from earlier or later model years. 

1.5.4.2.2.2 Braking/Deceleration 

Means model 

We derived models similar to those one used for coast/cruise/acceleration. For these operating 

modes, the models were much simpler, in that they did not include VSP or the speed classes used 

to define the coast/cruise/accel operating modes. Thus, emissions were predicted solely in terms 

of age, although random intercepts were fit for each test as before: 

19
= β + β + γ +ε h i ln E 710 ta 1 - 19 

Variances model 

In addition, we fit variances models for these operating modes, which were also simple functions 

of age. 

sl 10 a ++= εαα 2 1 - 20 

Model application 

In these operating modes, rates were to be modeled for a total of 840 cells. This total is 

calculated as in Table 1 - 9, except that the number of operating modes is 2, rather than 21. We 

set up coefficient and predictor vectors, as before. 

For the means model the vectors are 

[ ] β = β β10 1 - 21 

and 

[ a ] Xh = 1 1 - 22 

respectively. 

For the variances model the coefficients vector is 

[ ] α = α α10 1 - 23 

and the predictor vector is identical to that for the means model. 

As with CCA modes, we considered it reasonable in some model-year groups to adopt a slope or 

ln-variance from a previous or later model-year group. In model-year groups where the purpose 

was to hindcast rates for younger vehicles, rather than forecast rates for aging vehicles, it was 

again necessary to recalculate the intercept based on a borrowed age slope and an estimate of 

lnEh calculated from the sample data for the youngest available age class. In this case, equation 

24 is a rearrangement of Equation 1 - 19. 

ln *

4*

*

0 β a −= β aE 1 - 24 

20
After these steps, the imputed values of lnEh were calculated, as in Equations 1 - 14 and 1 - 17. 

1.5.4.2.3 Estimation of Model Uncertainties 

We estimated the uncertainty for each estimated lnEh in each cell. During each model run, we 

saved the covariance matrix of the model coefficients (sβ

2

). This matrix contains covariances of 

each of the nine coefficients in relation to the others, with the diagonal containing variances for 

each coefficient. 

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎢

⎣

⎡

=

+

−

−

.

... ...

....... .

...... ..

..... ...

... ...

........

........

........

... ...

2

6

2

4,6

2

0,6

2

)50(5

2

)5025(5

2

)250(5

2

4,0

2

4

2

0,4

2

3

2

2

2

1

2

6,0

2

4,0

2

0

σ σ σ

σ

σ

σ

σ σ σ

σ

σ

σ

σ σ σ

2

β s 1 - 25 

Using the parameter vectors Xh and the covariance matrix sβ

2

, the standard of error of estimation 

for each cell was calculated as 

h hhE s XsX

2

β

' = 2

ln 1 - 26 

The standard error of estimation in each cell represents the uncertainty of the mean estimate in 

the cell, based on the particular values of the predictors defining the cell6

. The pre- and postmultiplication of the covariance matrix by the parameter vectors represents the propagation of 

uncertainties, in which the parameters represent partial derivatives of each coefficient with 

respect to all others and the covariances represent the uncertainties in each coefficient in relation 

to itself and the others. 

1.5.4.2.4 Reverse transformation 

To obtain an estimated emission rate Eh in each cell, the modeled means and variances are 

exponentiated as follows 

2

, ln 5.0

ee h hl E s Eh = 1 - 27 

21
The two exponential terms use the results of the means and variances sub-models, respectively 

(Equations 1 - 6 and 1 - 7). The left-hand “means” term represents the geometric mean, or the 

center of the implied log-normal distribution, whereas the right-hand “variance” term reflects the 

influence of the “high-emitting” vehicles representing the tail of the distribution. 

The estimate of ln-variance could be obtained in several different ways. The first and preferred 

option was to use the modeled variance as described above. A second option was to use an 

estimate of variance calculated from the available sample of ln-transformed data. A third option, 

also based on available data, was an estimate calculated from averaged emissions data and the 

mean and variance of ln-transformed emissions data. This process involves reversing Equation 1 

- 19 to solve for sl

2

. If the mean of emissions data is xa and mean of ln-transformed data is xl, 

then the logarithmic variance can be estimated as 

⎟

⎠

⎞ ⎜

⎝

⎛ = l x

a

l

x

s

e

2 ln 2 1 - 28 

In practice one of these options was selected based which most successfully provided model 

estimates that matched corresponding means calculated from the data sample. 

The uncertainties mentioned above represent uncertainties in lnEh. Corresponding standard errors 

for the reverse-transformed emission rate Eh were estimated numerically by means of a MonteCarlo process. At the outset, we generated a pseudo-random set of 100 variates of lnEh, based on 

a normal distribution with a mean of 0.0 and variance equal to slnE

2

. We applied Equation 1 - 28

to reverse-transform each variate, and then calculated the variance of the reverse-transformed 

variates. This result represented the variance-of-the-mean for Eh ( ), as in Equation 2

Eh s 1 - 8. 

Finally, we calculated the CV-of-the-mean (CVEh) for each modeled emission rate, as in 

Equation 1 - 9. 

1.5.4.3 Table Construction 

After compilation of the modeling results, the subset of results obtained directly from the data 

(Equations 1 - 4 to 1 - 9), shaded area in Figure 1 - 1) and the complete set generated through 

modeling (Equations 1 - 10 to 1 - 28) were merged. A final value was selected for use in the 

model data table. The value generated from data was retained if two criteria were met: (1) a 

subsample of three or more individual vehicles must be represented in a given cell (nh ≥ 3), and 

(2) the CVEh (relative standard error, RSE) of the data-driven Eh must be less than 50% ( < 

0.50). Failing these criteria, the model-generated value was substituted. For purposes of 

illustration, results of both methods are presented separately. 

Eh CV

At this point, we mapped the analytic model-year groups onto the set of model-year groups used 

in the MOVES database. The groups used in the database are designed to mesh with heavy-duty 

standards and technologies, as well as those for light-duty vehicles. To achieve the mapping, we 

replicated records as necessary, in cases where the analytic group was broader than the database 

group. Both sets of groups are shown in Table 11. 

22
Table 1 - 11 Mapping 'Analytic" Model-Year Groups onto MOVES database Model-Year 

Groups. 

“Analytic” “MOVES database” 

LDV LDT 

1981-82 1981-82 1980 and previous 

1981-82 1981-82 1981-82 

1983-85 1983-85 1983-84 

1983-85 1983-85 1985 

1986-89 1986-89 1986-87 

1986-89 1986-89 1988-89 

1990-93 1990-93 1990 

1990-93 1990-93 1991-1993 

1994-95 1994-95 1994 

1994-95 1994-95 1995 

1996-98 1996 1996 

1996-98 1997-98 1997 

1996-98 1997-98 1998 

1999-2000 1999-2000 1999 

1999-2000 1999-2000 2000 

1.5.5 Verification and Adjustment for High-Power Operating modes 

The rates described were derived from data measured on the IM240 or IM147, which are limited 

in terms of the ranges of speed and vehicle-specific power that they cover. Specifically, these 

cycles range up to about 50 mph and 24 kW/tonne for speed and VSP, respectively. Some 

coverage does exist outside these limits but can be sporadic and highly variable. The operating 

modes outside the I/M window include modes 28,29,30, 38, 39 and 40, which we’ll refer to as 

the ‘high-power’ operating modes. For these modes, the statistical models described above were 

used to extrapolate up to about 34 kW/tonne. 

Based on comments from members of the FACA MOVES Review Workgroup, we thought it 

advisable to give additional scrutiny to the high power extrapolation. To obtain a framework for 

reference, we examined a set independently measured data, collected on drive cycles more 

aggressive than the IM cycles, namely, the US06 and the “Modal Emissions Cycle” or “MEC.” 

Much of the data was collected in the course of the National Cooperative Highway Research 

Program (NCHRP) and the remainder on selected EPA programs, all stored in OTAQ’s MobileSource Observation Database (MSOD). Unlike the US06, which was designed specifically to 

capture speed and acceleration not captured by the FTP, the MEC is an engineered cycle, 

designed not to be representative of any specific driving pattern but rather to exercise vehicles 

through a wide range of speed and acceleration. Driving traces for both cycles are shown in 

Figures 3 and 4. Both cycles range in speed up to over 70 mph and in VSP up to and over 30 

kW/tonne. 

23
Figure 1 - 3. Example Speed Traces for the US06 and MEC Drive Cycles. 

Figure 1 - 4. Example VSP Traces for the US06 and MEC Cycles 

Table 12 summarizes the numbers of available tests by regulatory class, model-year group and 

drive cycle. had a certain number of tests, as shown in Table 12. Samples were somewhat larger 

for LDV for both cycles, which represented a broad range of model-years. 

24
Table 1 - 12. Sample Sizes for US06 and MEC Samples (No. tests) 

Model-year group LDV LDT Total 

US06 MEC US06 MEC

1980 & earlier 4 14 6 24 

1981-85 15 23 8 19 65 

1986-89 21 24 13 31 89 

1990-93 54 57 22 36 169 

1994-95 49 45 22 30 146 

1996-99 58 28 56 17 159 

Total 201 191 121 139 652 

Figure 1 - 5 to Figure 1 - 7 show trends in emissions vs. VSP for CO, HC and NOx for LDV and 

LDT by model year group. Both cycles were averaged and plotted as aggregates. 

Figure 1 - 5. CO emissions (g/ssec) on Aggressive Cycles vs. VSP, by Regulatory Class and Modelyear Group. 

Figure 1 - 6 THC Emissions (g/sec) on aggressive Cycles vs. VSP, by Regulatory Class and Modelyear Group. 

25
Figure 1 - 7. NOx Emissions (g/sec) on Aggressive Cycles vs. VSP by Regulatory Class and Modelyear Group. 

To construct a basis for reference, we averaged the data by regulatory class, model-year group 

and operating mode, using the model-year groups shown in Table 1 - 12. After averaging, we 

calculated ratios frrm high-power operating modes to a selected reference mode. Specifically, we 

selected two modes covered by the IM cycles (27 and 37) to serve as reference points. The 

midpoint VSP for each is ~15 kW/tonne. With mode 27 as a reference, we calculated ratios to 

modes 28, 29 and 30 

26
30 29, 28, for ,

27,

,

27: = i = E

E

R

h

ih

i 1 - 29 

and with mode 37 as a reference, we calculated ratios to modes 38, 39 and 40. 

40 39, 38, for ,

37,

,

37: = i = E

E

R

h

ih

i 1 - 30 

Calculating uncertainties in the ratios was an important step. If the ratio R is calculated as a 

numerator divided by a denominator (N/D), the variance in the ratio is propagated by summing 

the products of the squared partial derivatives of R to N and D and the variances of their 

respective means. 

2

2

2

2

2

R N Ds

D

R

s

N

R

s ⎟

⎠

⎞ ⎜

⎝

⎛

∂

∂ ⎟ + ⎠

⎞ ⎜

⎝

⎛

∂

∂ = 1 - 31 

Note that this expression contains only variances and neglects potential covariances between N

and D. In the case of N/D the partial derivatives, which express the sensitivity of the ratio to 

each, are simply calculated as 

2 and 1

D

N

D

R

N D

R − = ∂∂ = ∂

∂

1 - 32 

To apply the ratios to operating modes 28-30 and 38-40, we calculated ratio-based emissions 

estimates (ER

) as the products of their respective ratios and the initial rate for modes 27 or 37 

initial

hi

R

ih

initial

hi

R

ih = 27,27:, or, = EREERE 37,37:, 1 - 33 

respectively, where Eh

initial is the initial data-driven or model-generated rate calculated as 

previously described. 

We used the variances of the ratios to calculate upper and lower confidence limits on the ratiobased rates. 

iR

R

i

R

i

iR

R

i

R

i

stE

stE

,80.0

,80.0

UCL

LCL

+=

−= 1 - 34 

In applying the confidence band as an evaluation criterion, each of the high-power operating 

modes i, the initial value Einitial was a candidate for replacement by ER

 if it fell outside the 80% 

confidence band of ER

, or 

R

i

initial

i

R

i

initial Ei < ORLCL E > UCL 1 - 35 

27
Due to volatility in the ratios, the confidence limits were quite wide in some cases. After initially 

calculating and evaluating a 95% confidence band, we settled on using a somewhat narrower 

80% band, for the reason that it was more sensitive in identifying implausible values of Einitial, 

whether high or low. 

We present some examples below. In the THC example (Figure 1 - 8), the initial rates fall outside 

the confidence intervals for the ratio-based rates for three out of six possible cases, i.e., in modes 

30, 39 and 40. The resulting rate is higher for modes 30 and 40, but lower for 39. The example 

for CO is different (Figure 1 - 9). The initial values for modes 28-30 all fall within the 

confidence intervals and are thus retained. The values for 39 and 40, fall outside the band on the 

low side and are replaced by the ratio-based rates. Finally, in the NOx example (Figure 1 - 10), 

the initial rates are replaced in five out of six cases. The initial values for 28-30 and 40 all fall 

below the LCL, whereas that for 30 falls above the UCL. 

Figure 1 - 8. THC emission rates (g/hr) vs. Operating Mode for MY-1998 LDV at ages 6-7: initial 

(data or statistical model) and calculated by ratio based on aggressive cycles. 

0

10

20

30

40

50

60

70

80

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

Initial

ratio

Figure 1 - 9. CO emission rates (g/hr) vs. operating mode for MY-1998 LDT at ages 6-7: initial 

(data or statistical model) and calculated by ratio based on aggressive cycles. 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

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

Initial

ratio

28
Figure 1 - 10. NOx Emission Rates (g/hr) vs. Operating Mode, for MY1995 LDV at ages 8-9: initial 

(data + statistical model) and calculated by ratio based on aggressive cycles. 

0

50

100

150

200

250

300

350

400

450

500

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

Initial

ratio

1.5.6 Estimating Rates for non-I/M Areas 

In modeling emission inventory for light-duty vehicles, it is necessary at the outset to consider 

the question of the influence of inspection and maintenance (I/M) programs. In this regard a 

fundamental difference between MOVES and MOBILE is that MOVES inverts MOBILE’s 

approach to representing I/M. In MOBILE, the emission rates stored in the input data tables 

represent non-I/M conditions. During a model run, as required, emissions for I/M conditions are 

modeled relative to the original non-I/M rates. 

In MOVES, however, two sets of rates are stored in the input table (emissionRateByAge). One 

set represents emissions under “I/M conditions” (meanBaseRateIM) and the other represents 

rates under “non-I/M conditions” (meanBaseRate). The first set, representing vehicles subject to 

I/M requirements, we call the “I/M reference rates”. The second, representing vehicles not 

subject to I/M requirements, we call the “non-I/M reference rates.” 

For the I/M reference rates, the term “reference” is used because the rates represent a particular 

program, with a specific design characteristics, against which other programs with differing 

characteristics can be modeled. Thus, the I/M references are, strictly speaking, regional rates, and 

not intended to be (necessarily) nationally representative. Development of the I/M reference rates 

is discussed above in sections 1.5.2 and 1.5.3. As the I/M references represent Phoenix, the 

program characteristics implicitly reflected in them include: 

• A four-year exemption period, 

• transient tailpipe tests for MY 81-95, 

• OBD-II for MY 96+, 

• Biennial test frequency. 

In addition, the Phoenix program provides a relatively stable basis against which to represent 

other program designs and for application of fuel adjustments. 

29
Our approach is to derive the non-I/M rates relative to the I/M references, by adjustment. One 

reason for adopting this approach is that , as mentioned, the volumes of data available in I/M 

areas vastly exceed those collected in non-I/M areas. An additional practical reason is that major 

work-intensive steps such as “hole-filling” and projection of deterioration need only be 

performed once. 

In contrast to the I/M references, the non-I/M reference rates are designed to be nationally 

representative. Broadly speaking, they are intended to represent all areas in the country without 

I/M programs. In general, estimating the influence of I/M areas on mean emissions is not trivial, 

and efforts to do so commonly follow one of two broad approaches. One approach is to compare 

emissions for two geographic areas, one with and one without I/M (Figure 1 - 11(a)). A second 

and less common approach is to compare emissions between two groups of vehicles within the 

same I/M area, but with one group representing the main fleet ostensibly influenced by the 

program, and the second, far smaller, representing vehicles measured within the program but 

presumably not yet influenced by the program (Figure 1 - 11(b)). 

Figure 1 - 11. Basic approaches to estimating differences attributable to I/M programs: (a) 

comparison of subsets of vehicles between two geographic areas, with and without I/M, and (b) 

comparison within a program area. 

“non I/M ”

I/M I/M

non-I/M

(b) Comparison within a program

 Area

(a) Comparison between a program Area 

 and a non-program area

For convenience, we refer to the first approach as the “between-area” approach, and the second 

as the “within-area” approach. Neither approach attempts to measure the incremental difference 

attributable to a program from one cycle to the next. 

The approach we adopted emphasizes the “within-area’ approach, based on a sample of vehicles 

“migrating” into Phoenix. To lay the basis for comparison, the primary goal was to identify a set 

of vehicles that had been measured by the program after moving into the Phoenix area, but that 

30
had not yet been influenced by the program. The specific criteria to identify particular migrating 

vehicles are presented in Table 13. 

Table 1 - 13. Criteria Used to Identify Vehicles Migrating into the Phoenix Program. 

logic Criterion 

The vehicle comes from from out-of-state 

OR From a non-I/M county in AZ 

AND NOT From other I/M areas 

AND Receiving very first test in Phoenix program 

AND Selected for random evaluation sample 

After applying these criteria, we identified a sample of approximately 1,400 vehicles. The origin 

of vehicles entering the Phoenix Area was traced by following registration histories of a set of 

approximately 10,000 candidate vehicles. The last registered location of vehicles was identified 

prior to registration in Phoenix or the vehicle’s first test in the Phoenix program. Vehicles were 

excluded if their most recent registration location was in a state or city with an I/M program7

. 

Figure 14 shows the distribution of incoming vehicles, by Census Region. Most vehicles 

migrating to Phoenix came from the Midwest (47%), followed by the South (32%), the West 

(20%) and the Northeast (1%). The low incidence from the NE may be attributable to the large 

number of I/M programs in that region. 

Figure 1 - 12 Geographic Distribution of Vehicles Migrating into the Phoenix I/M Area, 1995 - 

2005. 

6%

14% 23% 24% 0%

15% 5%

12%

1%

31
To assess the differences between migrating (non-I/M) and “local” (I/M) vehicles, we adopted a 

simple approach. We calculated ratios between means for the migrating and local groups, as 

shown in equation 36. We used aggregate tests, after preliminary analyses suggested that the 

ratios did not vary significantly by VSP. Because the sample was not large in relation to the 

degree of variability involved, we also aggregated tests for cars and trucks in all model years. 

However, we did calculate ratios separately for three broad age groups (0-4, 5-9, and 10+) years. 

We propagated uncertainty for these ratios as in Equations 1 - 31 and 1 - 32. 

I/M

I/M-non Ratio

E

E = 1 - 36 

For purposes of verification, we compared our results to previous work. An initial and obvious 

comparison was to previous work based on an out-of-state fleet migrating into Phoenix that 

provided a model for our own analysis7

. This previous effort, by T. Wenzel, identified a 

migrating fleet, and analyzed differences between it and the program fleet for vehicles in model 

years 1984 – 1994 measured during calendar years 1995-2001. To adapt his results for our 

purposes, we converted averages for migrating and program fleets into ratios as in Equation 1 - 

36. 

A another valuable source for comparison was remote-sensing data collected in the course of the 

Continuous Atlanta Fleet Evaluation (CAFE) Program8,9

. Unlike our own analysis, this program 

involves a comparison between two geographic areas. The “I/M area” is the thirteen-county 

Atlanta area, represented by measurements for approximately 129,000 vehicles. The other (the 

non-I/M area) is the twelve-county non-I/M area, surrounding Atlanta, represented by 

measurements for approximately 28,000 vehicles. Both areas have been under a low-sulfur fuel 

requirement since 1999. Results used for this analysis were collected during CY 2004. 

The non-I/M : I/M ratios calculated from the RSD are based on concentrations, rather than mass 

rates. 

A third source was an additional remote-sensing dataset collected in N. Virginia/D.C. area 

The I/M area was the “northern-Virginia” counties, and the non-I/M area was Richmond. The 

I/M and non-I/M areas were represented by about 94,000 and 61,000 vehicles, respectively, 

collected in CY 2004. In this case, the molar ratios were converted to mass rates, with use of 

fuel-consumption estimates derived from energy-consumption rates in MOVES2004. After this 

step, non-I/M : I/M ratios were calculated using the mass rates. 

Results are shown in Figure 1 - 13. The charts show mean ratios for the three age groups for our 

migrating vehicle analysis, as well as the remote-sensing studies. The diamonds represent 

approximate values from Wenzel’s earlier work with the Phoenix data. For our analyses (solid 

bars) the ratios are generally lower for the 0-4 year age Group, and larger for the 5-9 and 10+ age 

groups, but differences between the two older groups are small. The Atlanta results show a 

similar pattern for HC and NOx, but not for CO, for which the ratios are very similar for all three 

age groups. The Virginia results are the other hand, show increasing trends for CO and HC, but 

not for NOx. The ratios in Atlanta are slightly higher than those for Phoenix in the 0-4 year age 

group. This difference may be attributable to the shorter exemption period in Atlanta (2 years) 

vs. the four-year period in Phoenix, but it is not clear that these differences are statistically 

32
significant. In all three programs, ratios for the two older age classes generally appear to be 

statistically significant. 

In interpreting the ratios derived from the Phoenix data, it is important to note that they assume 

full program compliance. In the migrating vehicle analysis this is the case because all emissions 

measurements were collected in I/M lanes. Thus, vehicle owners who evaded the program in one 

way or another would not be represented. On the whole, results from multiple datasets, using 

different methods, showed broad agreement. 

If we calculate non-IM reference rates from the I/M references by ratio, with the ratios constant 

by model-year group and VSP, it follows that the absolute differences must increase with power. 

Similarly, absolute differences increase with age, for two reasons. A first reason is the same as 

that for VSP, that for a constant ratio, the absolute difference increases as emissions themselves 

increase, and on top of this, the second reason is that the ratios themselves increase with age 

(Figure 15). A third implication is the absolute differences would be smaller for successive 

model-year groups as tailpipe emissions decline with more stringent standards. 

33
Figure 1 - 13. Non-I/M : I/M ratios for CO, HC and NOx for the Phoenix Area (this analysis) compared to 

remote-sensing results for Atlanta and N. Virginia, and previous work in Phoenix (diamonds). 0.97 1.26 1.00 1.19 1.30 1.17 1.25 1.29 1.43

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

0-4 5-9 10+

Age Class

Ratio

AZ I/M GA RSD (CY04) VA RSD (CY04) 0.85 1.04 1.15 1.29 1.16 1.29 1.32 1.21 1.50

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

0-4 5-9 10+

Age Class

Ratio

AZ I/M GA RSD (CY04) VA RSD (CY04) 0.88 1.05 1.20 1.24 1.15 1.17 1.17 1.21 1.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0-4 5-9 10+

Age Class

Ratio

(a) CO

(b) THC

(c) NOx

AZ I/M GA RSD (CY04) VA RSD (CY04)

A final practical step is to translate these results into terms corresponding to the MOVES age 

groups. As mentioned, the program in Phoenix has a four-year exemption period for new 

vehicles. However, it is not uncommon for other programs have shorter exemptions; for 

example, both the Atlanta and N. VA programs have two-year exemptions. 

34
An additional factor is that the coarser age groups used for the migrating-vehicle analysis don’t 

mesh cleanly with the MOVES age groups. It was therefore necessary to impute values to the 

first two MOVES age groups (0-3 and 4-5 years). We achieved this step by linearly interpolating 

the value for the 5-9 age Group to a value of 1.0 and 0 years of age, as shown in Figure 1 - 14. 

To anchor the interpolation, we associated the value of the ratio for the 5-9 year age group with 

the midpoint of the group (7.5 years). Then, based on a straight line interpolation, we imputed 

values for the 0-3 and 4-5 MOVES age groups, by taking the value on the line associated with 

the midpoint of each class, 1.5 and 5 years, respectively. 

Figure 1 - 14. Imputation of Non-I/M Ratios for the 0-3 and 4-5 year MOVES AgeGroups by 

Linear Interpolation from the Midpoint of the 5-9 year Analysis Age Group. 

MOVES AgeGroup

Analysis AgeGroup

 

Figure 1 - 15 shows final values of the non-I/M ratios for CO, THC and NOx, with error-bars 

representing 95% confidence intervals. The values for each pollutant start at 5.0% and increase 

with age, stabilizing at maximum values at 6-10 years. 

35
Figure 1 - 15. Final non-I/M ratios for CO, HC and NOx, by MOVES AgeGroups, with 95% 

confidence intervals. 1.05 1.13

1.191.191.251.251.25

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0-3 4-5 6-7 8-9 10-14 15-19 20+

Age Class

Ratio

(a) CO 1.05

1.191.291.291.321.321.32

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0-3 4-5 6-7 8-9 10-14 15- 19 20+

Age Class

Ratio

(b) THC 1.05 1.16

1.211.211.211.211.21

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

0-3 4-5 6-7 8-9 10-14 15-19 20+

Age Class

Ratio

(c) NOx

The ratios shown in Figure 17 are applied to the I/M reference rates to derive non-I/M reference 

rates. 

Eh −I/Mnon, = *Ratio Eh I/M, 1 - 37 

36
The uncertainty in Eh,non-I/M was calculated by propagating the uncertainty in the Ratio with that 

of the corresponding I/M rate EhI/M. 

22 22

I/M,

2

2

2

I/M,

2 I/Mnon,

2

2 I/Mnon,

I/Mnon, I/M,

I/Mnon, I/M,

h h

h h

E Rh E

E

h

h

R

h

E

s sE sR

s

E

E

s

R

E

s

= +

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂ =

−

−

− −

1 - 38 

Thus, for any given cell h, the uncertainty in the non-I/M reference rate is larger than that for the 

corresponding I/M reference rate, which is reasonable given the additional assumptions involved 

in developing the non-I/M reference rate. 

Figure 1 - 16 shows an example of the reference rates by operating mode, for all three pollutants, 

with error bars representing 95% confidence intervals. Note that not all the modes are shown, to 

allow examination of differences between non-I/M and I/M rates at lower VSP. It is 

immediately apparent that uncertainties are considerably larger for the non-I/M rates, which 

reflects the uncertainties in the non-I/M:I/M ratios, in relation to the relatively small uncertainties 

in the I/M references derived directly from data. The very large uncertainties in high-power 

operating modes (28-30, 39) reflect the combined uncertainties in the high operating mode ratios 

(see 1.5.5) and the non-I/M ratios. 

Figure 1 - 17 shows corresponding trends by age for two operating modes. The first is opmode 

11, (speed = 1-25 mph, VSP <0 kW/tonne) and 27 (speed = 25-50 mph, VSP = 12-18 

kW/tonne). As before, the uncertainties are visibly larger for the non-I/M rates. Trends level off 

at in the 10-14 year age Group. An obvious observation is that the I/M difference is much larger 

in the more aggressive mode (27) than in the less aggressive one (11), with the inference that I/M 

differences will be more strongly expressed for more aggressive than less aggressive driving. 

37
Figure 1 - 16. Non-I/M and I/M Reference Rates by Operating Mode (Example: LDV, MY 1994, at 8-9 years 

of age) 

0

5

10

15

20

25

30

35

40

45

50

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

Operating Mode

Emissions (g/hr)

IM reference

non-I/M Reference

0

200

400

600

800

1000

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

Operating Mode

Emissions (g/hr)

IM reference

non-I/M Reference

0

100

200

300

400

500

600

700

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

Operating Mode

Emissions (g/hr)

IM reference

non-I/M Reference

(a) THC

(b) CO

(c) NOx

38
Figure 1 - 17. Non-I/M and I/M Reference Rates vs. Age for Two Operating Modes (Example: 

LDV, MYG 1994). 

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25

Operating Mode

Emissions (g/hr)

IM Ref: Mode 11

non-I/M Ref: Mode 11

IM Ref: Mode 27

non-I/M Ref: Mode 27

0

100

200

300

400

500

600

700

800

0 5 10 15 20 25

Operating Mode

Emissions (g/hr)

IM Ref: Mode 11

non-I/M Ref: Mode 11

IM Ref: Mode 27

non-I/M Ref: Mode 27

0

50

100

150

200

0 5 10 15 20 25

Operating Mode

Emissions (g/hr)

IM Ref: Mode 11

non-I/M Ref: Mode 11

IM Ref: Mode 27

non-I/M Ref: Mode 27

(a) THC

(b) CO

(c) NOx

39
1.5.7 Stabilization of Emissions with Age 

One characteristic of the data is that fleet-average emissions do not appear to increase 

indefinitely with age, but rather tend to stabilize at some point around 15 years of age. 

This behavior is visible in datasets with enough historical depth for age trends to be observable, 

including the Phoenix random sample and long-term remote-sensing studies10. Figure 1 - 18 and 

Figure 1 - 19 show age trends by model year for LDV and LDT, respectively. The values shown

are aggregate mass rates over the IM147 expressed as g/sec for CO, THC and NOx. 

1.5.7.1 I/M Reference Rates 

From Figures 11 and 12, as well as Figure 1, it is clear that no data was available at ages older 

than 10 years of age for model years later than 1995, and that no data was available at ages older 

than 15 years for model years older than 1990. Thus for model years more recent than about 

1995 it was necessary to project emissions for ages greater than 8-10 years. 

However, it is not appropriate to simply extrapolate the statistical models past about 10-12 years. 

As described above, emissions were modeled as ln-linear with respect to age, which implies 

exponential trends for reverse-transformed values. However, exponential trends will increase 

indefinitely if extrapolated much beyond the range of available data, which obviously does not 

describe the down-turn as the trend levels off and stabilizes. To compensate for this limitation, 

we employed a simple approach to represent the decline and stabilization of the rates. 

40
Figure 1 - 18. Aggregate IM147 Emissions (g/sec) for LDV, by Model year and Age, for the 

Phoenix Random Sample. 

(a) CO

(b) THC

(c) NOx

41
Figure 1 - 19. Aggregate IM147 Emissions (g/sec) for LDT, by Model year and Age, for the 

Phoenix Random Sample. 

(a) CO

(b) THC

(c) NOx

42
We calculated ratios of means between the 15-19 and 10-14 year age Groups for the model-year 

group 1986-89, which contains data for vehicles as old as 19 years. For this purpose we used 

Phoenix data aggregated by MOVES model-year and age groups, as shown in Table 1 - 15. 

For this purpose, we used aggregated tests (g/mi). After averaging by model-year group and 

ageGroup, we calculated ratios of means between the 15-19 and 10-14 ageGroups. 

1410,8986

1915,8986

age

−−

−− = E

E

R 1 - 39 

We calculated modified rates for the 15-19 and 20+ age Groups as the product of the rate for the 

10-14 ageGroup and the resulting ratio (Rage, Error! Reference source not found. ). The 

resulting rate was the same for 15-19 and 20+. We calculated variances for the ratios, but did not 

propagate the uncertainty through to the final result. 

Table 1 - 14. Age-Group Ratios (Rage) between the 15-19 and 10-14 ageGroups (MYG 

1986-89) 

Regulatory Class CO THC NOx 

LDT 1.22 1.19 1.08 

LDV 1.15 1.14 1.00 

43
Table 1 - 15. Aggregate IM147 Emissions (g/mi) by Model-Year Group and Age Group. 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

0 5 10 15 20 25

Age (years)

Emissions (g/mile)

pre-80

81-82

83-85

86-89

90-93

94-95

96-98

99-00

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

0 5 10 15 20 25

Age (years)

Emissions (g/mile)

pre-80

81-82

83-85

86-89

90-93

94-95

96-98

99-00

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20 25

Age (years)

Emissions (g/mile)

pre-80

81-82

83-85

86-89

90-93

94-95

96-98

99-00

(c) NOx

(b) THC

(a) CO

44
1.5.7.2 non-I/M Reference Rates 

The ratios developed in 1.5.7.1 apply in I/M areas, as the underlying data was collected in the 

Phoenix I/M area. It is therefore plausible that the patterns observed may be specific to I/M areas 

The program places some pressure on high-emitting vehicles to improve their emissions, leave 

the fleet, leave the area, or, it could be added, evade the program in some way. However, in the 

absence of a program, high-emitting vehicles are not identified and owners have little incentive 

to repair or replace them. Thus, the question arises as to whether deterioration patterns would 

necessarily be identical in non-I/M as in I/M areas. Two plausible scenarios can be proposed. In 

the first, the pattern of deterioration followed by stabilization is similar in non-I/M as in I/M 

areas, but emissions stabilize at a higher level, and perhaps at a later age. In the second, 

emissions continue to increase in non-I/M areas, but at a slower rate after 10-15 years. 

Data that sheds light on these questions are very limited, as the datasets with sufficient history 

are collected within I/M areas. But one possibility exists. As mentioned, we analyzed and 

considered data fromt the Chicago program for this project. A characteristic of this program is 

that vehicles were evaluated only on results for HC and CO; NOx was measured in some lanes 

but did not inform test results. Thus, with respect to NOx, we considered the Chicago data as a 

rough surrogate for a non-I/M area. It was therefore helpful to compare NOx results in Chicago 

to those in Phoenix, on an aggregate basis, as shown in Figure 1 - 20. In the figure, NOx 

emissions for the 1990-93 model-year grouip appear to be increasing at a higher rate in Chicago 

than in Phoenix between the ages 8 to 12.5 years. Based on this increment (15%), we assumed 

that emissions for all pollutants would increase by 15% between the 15-19 and 20+ year 

ageGroups. Note that the effects of this adjustment can be seen in the non-I/M series in Error! 

Reference source not found. above. 

Figure 1 - 20. Aggregate IM147 NOx Emissions for LDV in Phoenix (unlabeled) and Chicago (IL) 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 5 10 15 20

Age (years)

 IM147 Emissions (g/mi)

90-93

94-95

96-98

99-00

90-93 (IL)

96-98 (IL)

94-95 (IL)

99-00 (IL)

45
1.5.8 Deterioration for Start Emissions 

Because MOBILE assumed that start emissions would deteriorate, based on analyses performed 

at the time of its development, we thought it reasonable to include start deterioration in draft 

MOVES. Due to the complete lack of data in this area, we elected to model start deterioration as 

a function of running deterioration. 

As described, start rates are defined in terms of the FTP, but the rates for running are represented 

by operating mode. However, the modal approach enables simulation of multiple driving 

patterns, as represented by test cycles, such as the Federal Test Procedure (FTP), or the US06 

cycle, which represents high-speed driving in the Supplemental Federal Test Procedure (SFTP). 

Any driving cycle can be represented as a weighted average of the MOVES emission rates and 

the “operating mode distribution” for that cycle. In this case we developed an operating-mode 

distribution for the “hot-running” phase of the FTP (Bag 2). This phase is an 860 second long 

trace that represents urban driving over a 3.9 mile route after the engine has stabilized at its 

normal operating temperature. We estimated an operating-mode distribution using the “Physical 

Emission-Rate Estimator” (PERE)2

. This distribution, shown in Error! Reference source not 

found., represents a typical LDV, with an engine displacement of 2.73 L and test weight of 

3,350 lb. Estimating the Bag 2 emissions simply involves calculation of averages of the emission 

rates weighted by the mode distribution. 

Table 1 - 16. Operating-mode distribution for a typical LDV on the hot-stabilized phase of 

the FTP (Bag 2)

Operating Mode Time in Mode 

(seconds) 

Time in Mode (%) 

0 97 11.3 

1 155 18.0 

11 77 8.9 

12 121 14.1 

13 83 9.6 

14 59 6.9 

15 22 2.6 

16 4 0.50 

21 42 4.9 

22 111 12.9 

23 62 7.2 

24 18 2.1 

25 7 0.80 

27 2 0.2 

28 

29 

30 1 0.1 

33 

35 

37 

38 

39 

40 

Total 861 100 

46
After calculation, the simulated FTPs were converted to ratios by dividing the value for each age 

Group by the value for the 0-3 year age Group. 

A major uncertainty in our knowledge of start emissions is whether it is reasonable to assume 

that start emissions would deteriorate at the same (relative) rate as running emissions. Given the 

lack of data in this area, we adapted assumptions applied in the MOBILE model. In MOBILE we 

assumed that start emissions for NOx would deteriorate at same relative rate as those for 

running,11 but that HC and CO emissions would deteriorate at lower relative rates.12 Thus, we 

adapted the equations for “normal” and “high” emitters applied in MOBILE and developed a set 

of ratios for each age Group that reduce HC and CO start deterioration relative to the running 

deterioration, as shown in Error! Reference source not found.. Final ratios after application of 

the relative reductions are shown in Figure 1 - 21. Deteriorated start rates are developed (in all 

operating modes) by multiplying the rate in the 0-3 age Group by the ratio for each successive 

age Group. 

NOTE: if possible, these assumptions will be reviewed, and considered for revision before 

release of the final model. 

 

Table 1 - 17. Ratios Expressing Relative Reduction in Start Deterioration, relative to 

Running Deterioration, by Age Group. 

ageGroup HC CO 

0-3 1.00 1.00 

4-5 0.58 0.57 

6-7 0.47 0.46 

8-9 0.41 0.39 

10-14 0.36 0.33 

15-19 0.36 0.33 

20+ 0.36 0.33 

47
Figure 1 - 21. Example: Start Deterioration Ratios Applied to MY 2001-2021. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 5 10 15 20 25

AgeGroup Midpoint (years)

Ratio

LDT-IM

LDT-nonIM

LDV-IM

LDV-nonIM

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 5 10 15 20 25

AgeGroup Midpoint (years)

Ratio

LDT-IM

LDT-nonIM

LDV-IM

LDV-nonIM

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0 5 10 15 20 25

AgeGroup Midpoint (years)

Ratio

LDT-IM

LDT-nonIM

LDV-IM

LDV-nonIM

(a) CO

(b) THC

(c) NOx

48
1.6. Emission-Rate Development: Subgroup 2 (MY 2001 and later) 

1.6.1 Data Sources 

Data for vehicles in model years 2001 and later was acquired from results of tests conducted 

under the In-Use Verification Program. This program, initiated in 2003, is run by manufacturers 

and administered by EPA/OTAQ through the Compliance and Innovative Strategies Division 

(CISD). 

To verify that in-use vehicles comply with applicable emissions standards, customer-owned 

vehicles at differing mileage levels are tested on an as-received basis with minimal screening. 

Emissions are measured on the Federal Test Procedure, US06 and other cycles. The FTP is most 

relevant to our purposes, but the US06 is also important. 

1.6.1.1 Vehicle Descriptors 

In addition to the parameters listed above in Table 1 - 3. Required Vehicle Parameters., the 

IUVP data provides engine-family information. Using engine family, the IUVP files can be 

merged with certification logs by model year. The certification logs provide information on Tier 

level and specific emissions standards applicable to each vehicle. The Tier level refers to the 

body of standards to which vehicles were certified (Tier 1, NLEV, LEV-I, LEV-II), and the 

standards refer to specific numeric standards for HC, CO or NOx, where HC are represented by 

non-methane hydrocarbons (NMHC) or non-methane organic gases (NMOG), depending on Tier 

Level. 

Table 14. Vehicle Descriptors Available in IUVP files and Certification Logs 

Parameter Units Source Purpose 

IUVP Cert. Log

VIN Y Verify MY or other parameters 

Fuel type Y 

Make Y Y 

Model Y Y 

Model year Y Y Assign sourceBinID, calculate age-at-test

Engine Family Y Y 

Tier Y 

Emissions Standard Y Assign Vehicle Class 

Combining data from both sources allows individual test results to be properly associated with 

the correct Tier Level and emissions standard, which allows inference of the correct vehicle class 

(LDV, LDT1, LDT2, LDT3, LDT4). 

49
1.6.2 Estimating I/M Reference Rates 

The goal of this process is to represent I/M reference rates for young vehicles, i.e., the first age 

Group (0-3 years). The rates are estimated by Tier, model year and regulatory class. The process 

involves six steps. 

1. Average IUVP results by Tier and vehicle class 

2. Develop phase-in assumptions for MY 2001 – 2021, by Tier, vehicle class and model year 

3. Merge FTP results and Phase-in assumptions. For running emissions, calculate weighted 

ratios of emissions in each model year to those for Tier 1 (MY2000). Then calculate emissions 

by operating mode in each model year by multiplying the MY2000 emission rates by the 

weighted ratio for each model year. For start emissions, use weighted average FTP starts 

directly. 

4. Estimate Emissions by Operating Mode. We assumed that the emissions control at high 

power (outside ranges of speed and acceleration covered by the FTP) would not be as effective 

as at lower power (within the range of speed and acceleration covered by the FTP). 

5. Apply Deterioration to estimate emissions for remaining six age Groups. We assume that 

NLEV and Tier-2 vehicles will deteriorate similarly to Tier-1 vehicles, when viewed in 

logarithmic terms. We therefore apply ln-linear deterioration to the rates developed in steps 1-4. 

6. Estimate non-I/M reference rates. The rates in steps 1-6 represent I/M references. 

Corresponding non-I/M references are calculated by applying the ratios applied to the Tier-1 and 

pre-Tier-1 rates (Figure 16). 

Each of these steps is described in greater detail in the sub-sections below. 

1.6.2.1 Averaging IUVP Results 

In using the IUVP results, “cold-start” emissions are represented as “Bag 1 – Bag 3” i.e., the 

mass from the cold-start phase less that from the corresponding hot-start phase. Similarly, “hotrunning” emissions are represented by the “Bag 2,” or the “hot-stabilized” phase, after the initial 

cold-start phase has conditioned the engine. 

The first step is to average the IUVP results by Tier and vehicle Class. Results of this process 

are shown below. In the figures, note that the HC values represent non-methane hydrocarbons 

(NMHC) for Tier 1 and non-methane organic gases (NMOG) for NLEV and Tier 2. Figure 1 - 22

shows FTP composite results in relation to applicable certification and useful-life standards. For 

THC and NOx, the data show expected compliance margins in the range of 40-60% in most 

cases. For CO, compliance margins are even larger, ostensibly reflecting the concomitant effect 

of HC control on CO emissions, whereas CO standards were not stringent enough in themselves 

to substantially reduce emissions. 

50
Figure 1 - 22. Composite FTP Results for Tier1, NLEV and Tier 2 Vehicles, as measured by IUVP, 

in relation to certification and useful-life standards. 

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

T1

TLEV

LEV

ULEV

bin8bin7bin6bin5bin4bin3bin2

HC Emissions (g/mi)

Certification Standards

Useful Life Standards

Composite Results

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

T1 TLEV LEV ULEV bin5 bin4 bin3 bin2

CO Emissions (g/mi) 0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Tier 1

TLEV

LEV

ULEV

Bin 8Bin 7Bin 6Bin 5Bin 4Bin 3Bin 2

NOx Emissions (g/mi)

Certification

Useful Life

Composite

(a) THC

(b) CO

(c) NOx

51
Figure 1 - 23. Cold-start (Bag 1 – Bag 3) and Hot-running (Bag 2) FTP emissions for Tier 1, NLEV 

and Tier 2 vehicles, as measured by IUVP (g/mi). 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Tier 1

TLEV

LEV

ULEV

Bin 8Bin 7Bin 6Bin 5Bin 4Bin 3Bin 2

NOx Emissions (g/mi)

Cold Start

Running 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

T1 TLEV LEV ULEV bin5 bin4 bin3 bin2

CO Emissions (g/mi)

Cold Start

Running

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

T1

TLEV

LEV

ULEV

bin8bin7bin6bin5bin4bin3bin2

HC Emissions (g/mi)

Cold Start

Running

(a) THC

(b) CO

(c) NOx

52
Figure 1 - 23 shows results for separate phases of the FTP, to examine differential effects of 

standards on start and running emissions. As mentioned, the “cold-start” emissions are 

represented by the difference between Bags 1 and 3, expressed as a “start rate” in g/mi. The “hotrunning” emissions are represented by Bag 2 emissions, also divided by the appropriate distance 

to obtain an aggregate rate, in g/mi. Distinguishing start and running emissions shows that 

composite FTP values for HC and CO are strongly influenced by start emissions. Starts are also 

important for NOx, but not as markedly so. In any case, the results show that use of the 

composite results could give misleading results in projecting either start or running emissions. 

1.6.2.2 Develop Phase-In Assumptions 

For rates stored as MOVES defaults, we developed assumptions intended to apply to vehicles 

sold in states where Federal, rather than California standards applied. Thus, the phase-in 

represents the phase-in of NLEV and Tier-2 standards. To construct the default scenarios, we 

divided the vehicle classes into two groups. The first group includes LDV, LDT1 and LDT2, to 

which NLEV standards applied; the second includes LDT3 and LDT4, which transitioned 

directly from Tier 1 to Tier 2. 

Assumptions for LDV, LDT1 and LDT2 are shown in Figure 1 - 24. For these classes, The 

transition between Tier 1 and NLEV is abrupt, occurring between 2000 and 2001. For the first 

two model years, the fleet is a mixture of “transitional LEV” (TLEV) and LEV, and entirely 

LEV for the following four model years. However, a three-year phase-in of Tier 2 began in 2003, 

and was complete by 2007, after which the Federal fleet is entirely Tier 2. The breakdown of 

Tier 2 Bins during the transition is shown in Figure 1 - 25. This scenario reflects a tendency for 

Bin 5 to dominate the fleet by 2007, followed by Bin 3, and with Bins 4 and 2 playing minor 

roles. 

For the second group, LDT34, the transition from Tier 1 to Tier 2 lasts three years, as shown in 

Figure 1 - 26 and Figure 1 - 27. Like the LDV group, the transition to Tier 2 is complete in MY 

2007. Tier 2 vehicles are primarily represented by Bin 8 for the first several years, after which 

the heavier LDTs also move into Bin 5. For both groups, we assume that all phase-in fractions 

are stable between MY2010 and 2021. 

53
Figure 1 - 24. Phase-in Assumptions for Tier 1, NLEV, and Tier 2, for LDV, LDT1 and LDT2 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

2000200120022003200420052006200720082009201020112012201320142015201620172018201920202021

Model Year

Fraction

Tier 1

TLEV

LEV

Tier 2

Figure 1 - 25. Phase-in Assumptions for Tier 2, by Bin, for LDV, LDT1 and LDT2. 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Model Year

Fraction

bin2f rac

bin3f rac

bin4f rac

bin5f rac

54
Figure 1 - 26. Phase-in Assumptions for Tier 1 and Tier 2 Vehicles, for LDT3 and LDT4. 

0.00

0.20

0.40

0.60

0.80

1.00

1.20

2000200120022003200420052006200720082009201020112012201320142015201620172018201920202021

Model Year

Fraction

T1Frac

T2Frac

Figure 1 - 27. Phase-in Assumptions for Tier 2 Vehicles, By Bin, for LDT3 and LDT4. 

0.000

0.200

0.400

0.600

0.800

1.000

1.200

20002002200420062008201020122014201620182020

Model Year

Fraction

bin4frac

bin5frac

bin8frac

1.6.2.3 Merge FTP results and phase-in Assumptions 

For running emissions, the goal of this step is to calculate a weighted average of the FTP results 

for different tiers in each model year, with the emissions results weighted by applicable phase-in 

fractions. We do this step for each vehicle class separately, then we weight the four truck classes 

together using a set of constant fractions adapted from the MOBILE model (0.15, 0.60, 0.15, 

0.10 for LDT1, 2, 3, and 4, respectively). We did not vary these fractions by model year. 

55
Figure 1 - 28 shows an example of the Phase-in calculation for LDV NOx in four model years. 

The tables shows cold start and running FTP values for Tier 1, NLEV and Tier 2 standards, as 

well as the phase-in fractions for each standard in each model year. Start and running emissions 

in each model year are simply calculated as weighted averages of the emissions estimates and the 

phase-in fractions. The resulting weighted start estimates are used directly to represent coldstart emissions for young vehicles in each model year. For running emissions, however, the 

averages are not used directly; rather, each is expressed as a ratio to the corresponding Tier-1 

value. 

Figure 1 - 28. Example of Phase-in Calculation, LDV NOx, for four Model years. 

Tier Standard Cold Start Running 

(g) (g/mi) 2000 2002 2005 2010

Tier 1 Tier 1 0.983 0.149 1 0 0 0

TLEV 0.827 0.123 0 0.2 0 0

LEV 0.569 0.052 0 0.8 0.5 0

ULEV 0.453 0.048 0 0 0 0

Bin 8 0.481 0.032 0 0 0 0

Bin 7 0.378 0.025 0 0 0 0

Bin 6 0.275 0.018 0 0 0 0

Bin 5 0.172 0.011 0 0 0.45 0.595

Bin 4 0.098 0.007 0 0 0.03 0.035

Bin 3 0.036 0.005 0 0 0.01 0.368

Bin 2 0.049 0.00005 0 0 0.01 0.002

Start (g) 0.98 0.62 0.37 0.12

Running (g/mile) 0.14895 0.06577 0.0312 0.00887

RATIO to Tier 1 1.00 0.44 0.21 0.06

Tier 2

NLEV

Phase-In by MY

Table 1 - 18 shows weighted average values for model-years 2001-2010 for simulated FTP 

composites, start and running emissions. The Start values, expressed as the start mass increment 

(g) are used directly in the MOVES emission rate table to represent cold-start emissions 

(operating mode 108). The composites and running emissions, expressed as rates (g/mi) are 

presented for comparison. For running emissions, however, the averages shown in the table are 

not used directly; rather, each is expressed as a ratio to the corresponding Tier-1 value, as shown 

in Figure 1 - 28, and in Figure 1 - 29,Figure 1 - 30 and Figure 1 - 31 below. 

NOTE: We are aware of the anomalous increasing trends in running CO and HC shown in 

Figures 1-29 and 1-30, which are due to discontinuities in the underlying FTP results. We plan to 

revisit and correct this ratios before formulation of the final set of rates. 

56
Table 1 - 18. Weighted Average FTP Values Projected for LDT and LDV for MY 2001-

2010 

regClass MY CO HC NOx 

Composite 

(g/mi) 

Start 

(g) 

Running

(g/mi) 

Composite

(g/mi) 

Start

(g) 

Running

(g/mi) 

Composite 

(g/mi) 

Start

(g) 

Running

(g/mi) 

LDT 2000 2.17 14.72 1.23 0.171 1.77 0.0587 0.298 1.34 0.184 

2001 1.81 14.15 0.848 0.140 1.45 0.0486 0.248 1.12 0.144 

2002 1.76 13.81 0.846 0.135 1.37 0.0497 0.222 1.04 0.126 

2003 1.72 13.48 0.843 0.129 1.28 0.0508 0.197 0.956 0.109 

2004 1.34 10.73 0.647 0.097 1.03 0.0337 0.144 0.726 0.0780 

2005 1.06 8.74 0.497 0.074 0.85 0.0220 0.104 0.553 0.0554 

2006 0.77 6.63 0.342 0.051 0.67 0.0102 0.0632 0.376 0.0327 

2007 0.47 4.27 0.193 0.032 0.49 0.0022 0.0286 0.206 0.0140 

2008 0.46 4.20 0.190 0.031 0.48 0.00209 0.0244 0.175 0.0120 

2009 0.46 4.18 0.189 0.031 0.47 0.00208 0.0243 0.174 0.0120 

2010 0.45 4.07 0.185 0.029 0.45 0.00197 0.0186 0.132 0.00922 

LDV 2000 1.604 10.24 0.983 0.112 1.294 0.027 0.241 0.983 0.149 

2001 1.173 8.86 0.579 0.0764 0.932 0.016 0.138 0.672 0.080 

2002 1.170 8.38 0.613 0.0702 0.835 0.017 0.118 0.621 0.0662 

2003 1.167 7.91 0.647 0.064 0.738 0.018 0.0980 0.569 0.052 

2004 1.004 7.11 0.539 0.0557 0.671 0.014 0.0786 0.465 0.0415 

2005 0.857 6.46 0.435 0.0482 0.616 0.0101 0.0599 0.366 0.0312 

2006 0.693 5.68 0.325 0.0404 0.556 0.00615 0.0407 0.262 0.0208 

2007 0.430 3.92 0.180 0.0286 0.437 0.00193 0.0180 0.127 0.00899 

2008 0.420 3.83 0.176 0.0283 0.432 0.00191 0.0177 0.125 0.00886 

2009 0.418 3.80 0.175 0.0282 0.432 0.00190 0.0176 0.124 0.00884 

2010 0.402 3.65 0.169 0.0277 0.423 0.00187 0.0171 0.119 0.00863 

57
Figure 1 - 29. Weighted Ratios for Composite, Start and Running CO Emissions, for LDT and 

LDV. 

1.000

0.687 0.685 0.683

0.524

0.403

0.277

0.156 0.154 0.153 0.150

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999200020012002200320042005200620072008200920102011

Model Year

Weighted Ratio

composite

start

Running

1.000

0.589

0.624

0.658

0.548

0.443

0.330

0.183 0.179 0.178 0.172

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999200020012002200320042005200620072008200920102011

Model Year

Weighted Ratio

composite

start

Running

(a) LDT

(b) LDV

58
Figure 1 - 30. Weighted Ratios for Composite, Start and Running THC Emissions, for LDT and LDV. 

1.000

0.827 0.846 0.865

0.574

0.374

0.173

0.037 0.036 0.036 0.034

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999200020012002200320042005200620072008200920102011

Model Year

Weighted Ratio

composite

start

Running

1.000

0.593

0.630

0.667

0.519

0.374

0.228

0.071 0.071 0.070 0.069

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999200020012002200320042005200620072008200920102011

Model Year

Weighted Ratio

composite

start

Running

(a) LDT

(b) LDV

59
Figure 1 - 31. Weighted Ratios for Composite, Start and Running NOx Emissions, for LDT and 

LDV 

1.000

0.785

0.688

0.591

0.425

0.302

0.178

0.076 0.065 0.065 0.0 0.050

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999200020012002200320042005200620072008200920102011

Model Year

Weighted Ratio

composite

start

Running

1.000

0.540

0.444

0.349

0.278

0.209

0.140

0.060 0.059 0.059 0.058 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1999200020012002200320042005200620072008200920102011

Model Year

Weighted Ratio

composite

start

Running

(b) LDV

(a) LDT

1.6.2.4 Estimating Emissions by Operating Mode 

1.6.2.4.1 Running Emissions 

To project emissions for NLEV and Tier-2 vehicles, we divided the operating modes for running 

exhaust into two groups. These groups represent the ranges of speed and power covered by the 

FTP standards (< ~18 kW/tonne), and the ranges covered by the SFTP standards (primarily the 

60
US06 cycle). For convenience, we refer to these to regions as “the FTP region” and “SFTP 

region,” respectively (See Figure 1 - 32). 

Figure 1 - 32. Operating Modes for Running Exhaust Pollutant Processes, divided into "FTP " and 

"SFTP" Regions. Vehicle-Specific Power (kW/tonne)

The “FTP Region”

Low to Moderate 

Power

(also includes braking (0)

and idle (1)

The “SFTP Region”

High Power 

To estimate emissions by operating mode, the approach was to multiply the emission rates for 

MY 2000, representing Tier 1, by a specific ratio for each model year, to represent emissions for 

that year. For the FTP operating modes, we applied the ratios shown in Figure 1 - 29 to Figure 1 

- 31 above. 

For the SFTP operating modes, we followed a different approach. At the outset, we noted that 

the degree of control in the FTP standards increases dramatically between MY 2000 through MY 

2010, following phase-in of the Tier-2 standards. However, it was not obvious that the degree of 

control would increase as dramatically for the SFTP standards. Thus, in preparation of the draft 

rates, we adopted a conservative assumption that emissions in the SFTP region would not drop as 

dramatically as those in the FTP region. 

It was therefore necessary to estimate a different set of ratios. However, it was not feasible to 

calculate ratios for the SFTP modes as described above for the FTP modes because SFTP 

standards do not apply to Tier 1 engines, leaving no point of reference for the ratio calculation. 

So, as an initial effort for the draft, we adopted an alternative approach. Returning to the Phoenix 

I/M data, we pooled tests for two model-year groups, 1998-2000, representing Tier 1 vehicles 

not subject to SFTP requirements, and 2001-2003, representing NLEV vehicles subject to the 

SFTP. For each group, we calculated means for each pollutant for the SFTP operating modes (as 

a group), and calculated ratios between the two groups. 

61
0098SFTP,poll,

0301,SFTPpoll,

SFTP

−

− = E

E

R 1 - 40 

The resulting ratios are 0.55, 0.35 and 0.30 for CO, THC and NOx, respectively. These values 

exceed those for the FTP modes for MY ~2004 and later. However, the calculation and 

application of these values is considered preliminary and will be reevaluated for development of 

the final MOVES rates. 

Figure 1 - 33 and Figure 1 - 34 show application of the ratios to the FTP and SFTP operating 

modes in model years 2000 (the reference year), 2005, and 2010, both calculated with respect to 

2000. The ratios shown in Figure 1 - 29 to Figure 1 - 31 are used for the FTP modes, and the 

uniform values RSFTP are used for the SFTP modes. Note that the values for the SFTP modes are 

equal in 2005 and 2010, because the SFTP ratios are constant by model year. The results are 

presented on both linear and logarithmic scales. The linear plots display the differences in the 

high-power modes, but obscure those in the low-power modes. The logarithmic plots 

supplement the linear plots by making visible the relatively small differences between MY 2005 

and 2010 in the lower power modes. 

NOTE: the assumptions described in this section as slated for review and potential revision 

before releases of the final model. 

62
Figure 1 - 33. Projected Emission Rates by Operating Mode for Age group 0-3 years, in three 

Model Years (LINEAR SCALE). 

0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

4,000.00

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

Operating Mode

Emisson Rate (g/hr)

2000

2005

2010

0

5

10

15

20

25

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

Operating Mode

Emisson Rate (g/hr)

2000

2005

2010

0

10

20

30

40

50

60

70

80

90

100

110

120

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

Operating Mode

Emisson Rate (g/hr)

2000

2005

2010

(a) CO

(b) THC

(c) NOx

63
Figure 1 - 34. Projected Emission Rates by Operating Mode for Age group 0-3 years, in three 

Model Years (LOGARITHMIC SCALE). 

64
0.01

0.10

1.00

10.00

100.00

1,000.00

10,000.00

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

Operating Mode

Emisson Rate (g/hr)

2000

2005

2010

0.0001

0.001

0.01

0.1

1

10

100

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

Operating Mode

Emisson Rate (g/hr)

2000

2005

2010

0.01

0.10

1.00

10.00

100.00

1,000.00

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

Operating Mode

Emisson Rate (g/hr)

2000

2005

2010

(a) CO

(b) THC

(c) NOx

1.6.2.4.2 Start emissions 

65
As mentioned, the values for “start” shown above in Table 1 - 18 are assumed to represent coldstart emissions, denoted by opModeID 108, and defined as start emission following a soak period 

of 12 hours or longer (720 min). Additional operating modes for starts are defined in terms of 

shorter soak periods, as shown in Table 1 - 19. 

Table 1 - 19. Operating-Mode Definitions for Start Exhaust Emissions

opModeID Description Soak Period (min)1

101 “hot start” ≤ 6 

102 6 - 30 

103 30 – 60 

104 60 – 90 

105 90 – 120 

106 120 – 360 

107 360 - 720 

108 “cold start” ≥ 720 1

 Defined in terms of lower-bound ≤ soak period < upper-bound. 

To estimate start emissions for the other operating modes, we applied “soak fractions” to the 

“cold-start” emissions. The soak fractions were adapted from the approach applied in the 

MOBILE model13. Specifically, the part-wise regression equations used in MOBILE were 

evaluated at the midpoint of the soak period for each operating mode. For each mode, the start 

rate is the product of the cold-start rate and the corresponding soak fraction. Figure 33 shows the 

soak fractions for HC, CO and NOx, with each value plotted at the midpoint of the respective 

soak period. 

Figure 1 - 35. Soak Fractions Applied to Cold-Start Emissions (opModeID = 108) to Estimate Emissions 

for shorter Soak Periods (operating modes 101-107). 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

0 120 240 360 480 600 720

 Soak Time (minutes) 

Ratio of Start to Cold-Start 

NOx

CO

HC

1.6.2.5 Apply Deterioration 

66
Based on review and analysis of the Phoenix I/M data, we assume that deterioration for different 

technologies is best represented by a multiplicative model, in which different technologies, 

represented by successive model-year groups, show similar deterioration in relative terms but 

markedly different deterioration in absolute terms. We implemented this approach by translating 

emissions for the 0-3 age Group, as calculated above, into natural logarithms and applying 

uniform logarithmic age trends to all model-year groups. We derived logarithmic deterioration 

slopes for Tier-1 vehicles (MY 1996-98) and applied them to NLEV and Tier-2 vehicles. In this 

process we applied the same logarithmic slope to each operating mode, which is an extension of 

the multiplicative deterioration assumption. 

Note that we applied deterioration only to the running emissions, not to the start emissions. We 

carry this process out in four steps. 

1.6.2.5.1 Recalculate the logarithmic mean 

Starting with the values of the arithmetic mean (xa) calculated above, we calculate a logarithmic 

mean (xl), as shown in Equation 1 - 41. Note that this equation is simply a rearrangement of 

Equation 1 - 27. 

2

ln

2

l

l a xx

σ

−= 1 - 41 

The values of the logarithmic variance are intended to represent values for young vehicles, as the 

estimates for xa represent the 0-3 year age Group. The values applied were 1.30, 0.95 and 1.6 for 

CO, THC and NOx, respectively. 

1.6.2.5.2 Apply a logarithmic Age slope 

After estimating logarithmic means for the 0-3 age class (xl,0-3), we estimate additional 

logarithmic means for successive age classes (xl,age), by applying a linear slope in ln-space (ml). 

1.5)-age( l = l −30,age, + mxx l

1 - 42 

The values of the logarithmic slope are adapted from values developed for the 1996-98 model –

year group. The values applied were 0.18, 0.15 and 0.17 for CO, THC and NOx, respectively. 

When calculating the age inputs for this equation, we subtracted 1.5 years to shift the intercept to 

the midpoint of the 0-3 year age Group. 

Figure 1 - 36 shows an example of the approach, as applied to THC from LDV in the 1996-98 

model-year group. The upper plot (a) shows lnTHC vs Age, by VSP, where the VSP acts as a 

surrogate for operating mode. The defining characteristics of the plot are a series of parallel 

lines, with the gaps between the lines reflecting the magnitude of the VSP differences between 

them. Similarly, the lower plot shows lnTHC vs. VSP, by Age, where age acts as a surrogate for 

67
deterioration. In this view, deterioration appears as the magnitude of the gaps between a family 

of similar trends against power. 

Figure 1 - 36. Example of Logarithmic Deterioration Model for THC (LDV, MYG 96-98): (a) lnTHC vs. 

Age, by VSP level (kW/tonne), (b) lnTHC vs. VSP, by Age (yr). 

-10.0

-9.0

-8.0

-7.0

-6.0

-5.0

-4.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Age (Years)

ln(THC)

3

6

9

12

21

30

-10.0

-9.0

-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

VSP (kW/tonne)

ln(THC)

0

3

6

9

12.5

(a) Age

(b) VSP

1.6.2.5.3 Apply the reverse transformation 

68
After the previous step, the values of xl,age were reverse-transformed, as in Equation 1 - 27. The 

values of the logarithmic variance used for this step were adapted from the Phoenix I/M results 

and are intended to represent emissions distributions for “real-world” vehicle populations, 

meaning that the values are higher than the value used in step 1.6.2.5.1 and may vary with age. 

Values of logarithmic variances for all three pollutants are shown in Table 17. 

Table 1 - 20. Values of Logarithmic Variance Used to Calculate Emissions Deterioration by 

Reverse Transformation of Logarithmic Means.

Age Group Pollutant 

CO THC NOx 

0-3 years 2.5 1.50 1.95 

4-5 2.7 1.80 2.10 

6-7 2.7 2.00 2.00 

8-9 2.7 2.10 2.00 

10-14 2.7 2.10 2.00 

No values are presented in Table 1 - 20 for the 15-19 and 20+ year age Groups. This omission is 

intentional, in that we did not want to extrapolate the deterioration trend beyond the 10-14 year 

age Group. Extrapolation beyond this point is incorrect, as we know that emissions tend to 

stabilize beyond this age, while the ln-linear emissions model would project an increasingly 

steep and unrealistic exponential emissions trend. 

Figure 1 - 37 shows the same results as Figure 1 - 36, following reverse transformation. The 

families of parallel logarithmic trends are replaced by corresponding “fans” of diverging 

exponential trends. An implication of this model is that as deterioration occurs, it is expressed 

more (in absolute terms) at high power. Similarly, the relationship between emissions and VSP 

becomes more pronounced with increasing age. 

69
Figure 1 - 37. Example of Reverse Transformation for THC (LDV, MYG 96-98): (a) THC vs. Age, 

by VSP level (kW/tonne), (b) THC vs. VSP, by Age (yr). 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

110.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Age (Years)

THC Emissions (g/hr)

3 kW/tonne

6 kW/tonne

9 kW/tonne

12 kW/tonne

21 kW/tonne

30 kW/tonne

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0

VSP (kW/tonne)

THC Emissions (g/hr)

0

3

6

9

12.5

(a) Age

(b) VSP

1.6.2.6 Estimate non-I/M References 

Completion of steps 1.6.2.1 – 1.6.2.6 provided a set of rates representing I/M reference rates for 

MY 2001-2021. As a final step, we estimated non-I/M reference rates by applying the same 

ratios applied to the I/M references for MY 2000 and previous, as described above. 

70
1.7 Replication and Data-Source Identification 

The rates developed as described in Sections 2 and 3 represent gasoline-fueled conventionaltechnology engines. For purposes of the draft version of the emissionRateByAge table, we 

replicated these rates to represent other fuels and technologies. 

At the outset, we replicated the entire set of gasoline rates for ethanol (blends?) In addition, we 

replicated all the gasoline rates for the advanced engine technologies. The fuel types and engine 

technologies represented in the table segment are listed in Error! Reference source not found.. 

Table 1 - 21 Fuel Types and Engine Technologies Represented for Criteria-pollutant 

Emissions from Light-Duty Vehicles. 

Attribute sourceBin attribute Value Description 

Fuel type fuelTypeID 01 Gasoline 

02 Diesel 

05 Ethanol 

Engine Technology engTechID 01 Conventional internal combustion (CIC) 

02 Advanced internal combustion (AIC) 

11 Moderate hybrid – CIC 

12 Full hybrid – CIC 

20 Hybrid – AIC 

21 Moderate hybrid – AIC 

22 Full hybrid - AIC 

Throughout the process, we assigned dataSourceIDs to subgroups of rates, which identify the 

data and methods used for particular rates. The dataSourceIDs developed for these analyses are 

listed and described in Table 1 - 22. 

Table 1 - 22. Descriptions of Data Sources and Methods used in Development of CriteriaPollutant Emission Rates for Light-Duty Vehicles. 

DataSourceID Description 

4400 Data driven rates: averaged from second-by-second IM240/IM147 data from Phoenix random 

evaluation sample, CY1995-99 and CY2002-05, on temperature range of 68-86 °F. 

4427 replaces a 4400 value for opModes 28-30, calculated by ratio relative to opMode 27 

4437 replaces a 4400 value for opModes 38-40, calculated by ratio relative to opMode 37 

4500 imputed using statistical hole-filling models. 

4527 replaces a 4500 value for opModes 28-30, calculated by ratio relative to opMode 27 

4537 replaces a 4500 value for opModes 38-40, calculated by ratio relative to opMode 37 

4601 calculated by ratio relative to ageGroup 10-14 (modelyeargroups 2000 and previous only, 

ageGroupID 15-19 and 20+ only) 

71
4602 calculated by ratio relative to ageGroup 15-19, modelyeargroups 2000 and previous only, 

ageGroupID 20+ only, meanBaseRate only (corresponding meanBaseRateIM is 4601). 

4800 calculated by ratio from MY2000 rates, with ratios calculated from IUVP FTP Bag-2 data, 

(modelyeargroups 2001 and later only ageGroup 0-3 only). 

4801 calculated by applying deterioration to 4800 values, (modelyeargroups 2001 and later only, 

ageGroups 4-5 through 10-14) 

4802 calculated by ratio relative to ageGroup 10-14 (modelyeargroups 2001 and later / ageGroupID 

15-19 and 20+ only). 

4803 calculated by ratio relative to ageGroup 15-19, modelyeargroups 2001 and later / ageGroupID 

20+ only, meanBaseRate only (corresponding meanBaseRateIM is 4802). 

4805 calculated from IUVP FTP results, as Bag 1 - Bag 3 mass (cold start, opMode 108 only, 

ageGroup 0-3 only). 

4806 calculated by applying deterioration ratios to 4805 values (cold start, opMode 108 only, 

ageGroup 4-5 and older). 

4807 calculated by applying soak fractions and deterioration ratios to 4805 values (opModes 101-107 

only, all ageGroups). 

4900 replicated from gasoline rates (fueltypeid = 1) to represent ethanol blends (fueltypeid = 5). 

4901 replicated from gasoline or ethanol rates with conventional internal combustion to represent rates 

for advanced engine technologies. 

4910 replicated from gasoline rates for all engine technologies to represent rates for tier-2 light-duty 

diesel engines (MY 2010 and later only). 

Finally, Table 1 - 23 shows the accounting for all rates developed for light-duty criteria-pollutant 

emissions and included in the draft emissionRateByAge table. The leftmost four columns 

delineate subsets of rates by the pollutant processes included (Running, Start), and the respective 

fueltypes, engtechs and dataSourceIDs. The next seven “accounting” columns show the 

construction of subtotals corresponding to combinations of fueltype, engtech, and dataSource. 

The values in these columns represent numbers of groups or categories covered, i.e., two 

regClasses always refers to LDV and LDT. 

The rates for datasourceID = 4400 – 4602 were summed as a single category, as these groups 

represent the outcome of a set of interrelated processes, as described in section 1.5. The count of 

15 modelyeargroups includes groups through MY 2000 (see Table 1 - 11). The dataSourceIDs 

4800 – 4803 represent running emissions for MY 2001+, as described in Section 1.6. The total 

of 21 modelyeargroups represent groups 2001 – 2021-2050. For these rows, a count of one 

agegroup refers to the 0-3 year ageGroup, whereas a count of four refers to the 4-5, 6-7, 8-9 and 

10-14 year age Groups. 

DataSourceIDs 4805 – 4807 represent start emissions for MY2001-2021. For this group, counts 

of 26 or 36 modelyeargroups denote MYG 1996-2021 and 1980 and earlier - 20212050, 

respectively. Counts of one or six ageGroups refer to the 0-3 ageGroup and the remaining six 

72
73

ageGroups, respectively. Counts of one or seven opModes refer to the cold-start emissions 

(opmode 108) or the remaining seven start modes, respectively. 

The dataSourceIDs 4900 and 4901 refer to the replication of the gasoline/conventional rates for 

ethanol and the advanced engine technologies, respectively. The Count of 36 modelyeargroups 

includes all groups from 1980 & earlier through 2021-2050. The count of 31 opModes includes 

all modes for both the start and running processes. The counts for dataSourceID 4910 is similar, 

except that the 12 modelyeargroups include only 2010-20212050, as mentioned.
Table 1 - 23. Accounting for the Segment of the Draft emissionRateByAge Table contributed by Rates fo

Emissions, Light-Duty Vehicles. 

Process(es) fuelTypeID engTechID dataSourceID Accounting (No. classes or groups) 

fueltypes engTechs regClasses MYG ageGroups opModes polProce

Start 01 01 101 1 1 2 10 1 1 3 Running 01 01 4400 1 1 2 15 7 23 3 Running 01 01 4427 Running 01 01 4437 Running 01 01 4500 Running 01 01 4527 Running 01 01 4537 Running 01 01 4601 Running 01 01 4602 Running 01 01 4800 1 1 2 21 1 23 3 Running 01 01 4801 1 1 2 21 4 23 3 Running 01 01 4802 1 1 2 21 1 23 3 Running 01 01 4803 1 1 2 21 1 23 3 Start 01 01 4805 1 1 2 26 1 1 3 Start 01 01 4806 1 1 2 36 6 1 3 Start 01 01 4807 1 1 2 36 7 7 3 SUBTOTAL Running & start 05 01 4900 1 1 2 36 7 31 3 Running & start 01 02 4901 1 1 2 36 7 31 3 Running & start 01 11 4901 1 1 2 36 7 31 3 Running & start 01 12 4901 1 1 2 36 7 31 3 Running & start 01 20 4901 1 1 2 36 7 31 3 Running & start 01 21 4901 1 1 2 36 7 31 3 Running & start 01 22 4901 1 1 2 36 7 31 3 Running & start 05 02 4901 1 1 2 36 7 31 3 Running & start 05 11 4901 1 1 2 36 7 31 3 Running & start 05 12 4901 1 1 2 36 7 31 3 Running & start 05 20 4901 1 1 2 36 7 31 3 Running & start 05 21 4901 1 1 2 36 7 31 3 Running & start 05 22 4901 1 1 2 36 7 31 3 Running & start 02 01 4910 1 1 2 12 7 31 3 Running & start 02 02 4910 1 1 2 12 7 31 3 Running & start 02 11 4910 1 1 2 12 7 31 3 Running & start 02 12 4910 1 1 2 12 7 31 3 Running & start 02 20 4910 1 1 2 12 7 31 3 Running & start 02 21 4910 1 1 2 12 7 31 3 Running & start 02 22 4910 1 1 2 12 7 31 3 TOTAL 
75
2. Particulate-Matter Emissions from Light-Duty Vehicles 

2.1 Introduction and Background 

There is currently a large body of research on the formation and measurement of Particulate 

Matter (PM) emissions from combustion engines. This chapter describes the process by which 

emissions measured in a subset of the past PM research programs from light-duty gasoline 

vehicles was employed to generate emission rates for MOVES. The emission rates determined 

by this approach embody strictly “bottom-up” methodology whereby emission rates are 

developed from actual vehicle measurements following intensive data analysis, and are then 

input into an emissions inventory model. This is in contrast to a “top-down” approach which 

uses measurements of ambient PM concentrations from local regions and may apportion these 

emissions to vehicles (and other sources), which are then input into inventory models. 

The primary study that this chapter relies on is the “Kansas City Characterization Study” 

conducted in 2004-200514. The Environmental Protection Agency and several research partners 

conducted this study to quantify tailpipe particulate-matter emissions from gasoline-fueled light 

duty vehicles in the Kansas City Metropolitan Area. This study is the most comprehensive and 

representative study of its kind. In the context of a rigorous recruitment plan, strenuous efforts 

were made to procure a representative sampling of the fleet. During the summer phase, 261 

vehicles were tested, while 278 vehicles were tested in the winter. The testing was conducted on 

a portable dynamometer using the LA92 driving cycle in ambient temperature conditions. 

Much of the data from this program was analyzed in the report: “Analysis of Particulate Matter 

Emissions from Light-Duty Gasoline Vehicles in Kansas City”15 This “analysis report” (which 

is the partner to this “modeling” chapter) presented preliminary emission rates for PM, elemental 

carbon fraction (EC), organic carbon fraction (OC), as well as temperature adjustment factors for 

start as well as hot running emissions processes for MOVES. These emission rates form the 

basis for the emission rates developed in this chapter. The rates from the analysis report were 

based on the aggregate or “bag” emissions measured on the PM filters in the program, thus they 

are in units of grams per start for starts and grams/mile for hot running operations. The rates 

were inclusive of gasoline powered light-duty vehicles of all ages, but only modeled calendar 

year 2005, when the emission rates were actually measured. The measurement program could 

say very little of what the emission rates of vehicles were in, say 1990, or will be in 2020 

because the vehicle fleet looks very different in these other calendar year scenarios. This chapter 

describes the development of a deterioration model based on a comparison of past PM studies 

with the 2005 Kansas City study. The rates from this deterioration model include calendar years 

in the past, present and future as required by MOVES. 

The analysis in the 2008 paper also did not describe how the emissions would change if the 

driving differed from the LA92 (unified) drive cycle used in the program as it would in the realworld. MOVES has the capability to capture hot running “modal” emission rates so that 

emissions vary by the Vehicle Specific Power (VSP) of the vehicle. This chapter describes how 

the real-time PM measurements collected in the study were used to populate the VSP modal rates 

76
for MOVES. Because of the reliance on real-time PM measurement, it is worth describing the 

measurement procedures used in Kansas City. 

2.1.1 Particulate Measurement in the Kansas City Study 

For measurements conducted on the dynamometer, vehicles were operated over the LA92 

Unified Driving Cycle (see Figure 2 - 1). The LA92 cycle consists of three phases or “bags”. 

Phase 1 (“bag 1”) is a “cold start” that lasts the first 310 seconds (1.18 miles). “Cold start” is 

technically defined as an engine start after the vehicle has been “soaking” in a temperature 

controlled facility (typically ~72°F) with the engine off. In the Kansas City study, the vehicles 

were soaked over night in ambient conditions. Phase 1 is followed by a stabilized Phase 2 or 

“hot running” (311 – 1427 seconds or 8.63 miles). At the end of Phase 2, the engine is turned off 

and the vehicle is allowed to “soak” in the test facility for ten minutes. At the end of the soak 

period, the vehicle is started again, and is driven on the same driving schedule as Phase 1. This 

Phase 3 is called a “hot start” because the vehicle is started when the engine and after-treatment 

systems are still hot. Criteria pollutants were measured both in continuous and aggregate modes. 

Particulate was collected during each of the three phases on 47 mm Teflon filters at 47°C ± 2°C. 

Figure 2 - 1. Phases 1 and 2 of the LA92 Cycle, “cold-start” and “hot-running,” respectively. 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

0 200 400 600 800 1000 1200 1400

time

speed (mph)

Bag 1

Bag 2

In addition to the regulated gas pollutants measured via the constant-volume sampler (CVS), 

continuous measurements of PM mass were taken using an EPA-supplied Booker Systems 

Model RPM-101 QCM manufactured by Sensors, Inc. and a Thermo-MIE Inc. DataRam 4000 

Nephelometer. An estimate of black carbon was measured continuously with a DRI 

photoacoustic instrument and integrated samples were collected and analyzed by DRI for PM 

gravimetric mass, elements, elemental and organic carbon, ions, particulate and semi-volatile 

organic compounds, and volatile organic air toxics. All sampling lines were heated and 

77
maintained at 47°C ± 2°C. The samples were extracted from the dilution tunnel through a low 

particulate loss 2.5 μm cutpoint pre-classifier. Further details and a schematic of the sampling 

instrumentation are shown in Figure 2 - 2 and Figure 2 - 3. 

Figure 2 - 2. Schematic of the constant-volume sampling system used in the Kansas-City 

Study. 

(46 C) 

Diluted exhaust 

at 46 C 

Figure 2 - 3. Continuous PM analyzers and their locations in the sample line. 

78
It is worth briefly describing the real-time PM measurement apparatus used in the study. A more 

thorough description maybe found in the contractor’s report14. As of the date of this 

measurement program, there existed no perfect means of measuring real-time PM. Each of the 

devices has specific advantages and disadvantages. For this study, it has been assumed that the 

cumulative mass as measured (weighed) on the Teflon filters is the benchmark. Thus all realtime measurements have been normalized to the filters in order to minimize systematic 

instrument errors. 

The Quartz Crystal Microbalance measures the cumulative mass of the PM deposited on a crystal 

face by measuring the change in oscillating frequency. It is highly sensitive to many artifacts 

such as water vapor and desorption of lighter organic constituents. Due to the high degree of 

noise in the continuous time series, the measurements were averaged over 10 seconds, thus 

diluting the temporal effects of transients. The QCM can accurately capture cumulative PM that 

collects over time, however the measurement uncertainties increase at any given moment in time 

because they are dependent on a calculation difference between two sequential, and similar, 

measurements. Due to the resulting high variability, including large and rapid fluctuations from 

positive to negative emissions at any given instant, and vice versa, QCM measurements were not 

viewed as practical for use with MOVES at this time, except as a check for the other instruments. 

The Dustrak and Dataram both work on light-scattering principles. As such, they have very 

rapid response times and can measure larger PM volumes with reasonable accuracy. However, 

their accuracy degrades when measuring low PM volumes. Since most PM mass lies within the 

larger particles, the instruments should be able to capture most of the real-time mass 

concentrations though it may miss a substantial portion of the smaller (nano) particles. To 

provide a qualitative check on supposition, the time-series for the QCM and optical instruments 

aligned and checked to ensure that significant mass was not missed. Based on this analysis, the 

Dustrak instrument was observed to be the more reliable of the 3 instruments, and mass 

correction at low loads was not judged to be worth the effort given the uncertainties involved. 

This time-consuming analysis was done by eye for each test and the results are not presented in 

this chapter. 

The photoacoustic analyzer (PA) is unique among the real-time instruments in its ability to 

capture only the soot or elemental carbon components of PM. The fast analyzer detects the 

resonances coming off the carbon-carbon bonds in soot. It has been validated in a number of 

studies[references]. Unfortunately, there were insufficient Thermal Optical Reflectance (TOR) 

elemental carbon (EC) measurements from quartz filters to normalize the PA data, but some 

comparisons are shown in the contractor’s report . In this study, the PA data were compared 

qualitatively with the Dustrak and Dataram and found to be consistent with expected ratios of 

elemental to total carbon during transient events, leading to the conclusion that these instruments 

were largely consistent with one another. These results are also not presented in this chapter as 

every single trace was compared by eye. The data is used to determine the modal relationship of 

elemental to total PM. 

14

Due to the uncertainty of experimental measurement techniques for real-time PM at the time of 

the Kansas City study, these instruments are employed only as a semi-qualitative/quantitative 

79
means of determining modal emission rates, and the use of such data do not qualify them as EPA 

recommended or approved devices or processes. 

2.1.2 Causes of Gasoline PM Emissions 

Particulate matter is formed from gasoline-fueled engines originating from incomplete fuel and 

oil combustion (although the amount of oil consumed in combustion and its contribution to PM 

varies greatly from vehicle to vehicle). During operation, numerous distinct technologies used in 

vehicles are in various states of repair or disrepair which also affect PM emissions. Even brand 

new vehicles emit PM from combustion but at very low levels. A complete description of the 

causes of PM emissions and associated mechanisms is beyond the scope of this report, as many 

aspects of the science that are still not well understood. We will briefly summarize factors that 

contribute to gasoline PM in the vehicle fleet in this section. Where appropriate, we will also 

compare to the mechanisms of hydrocarbon (HC) formation, since parallels are often drawn in 

the literature. 

Simply put, particulate matter forms primarily during combustion when carbon-containing 

molecules condense or otherwise form particulate. This PM is generally ccomposed of higher 

molecular weight hydrocarbon compounds, some of which originate in the fuel/oil and some of 

which are formed during combustion. Unlike diesel engines, elemental (molecular) carbon or 

soot is not very prevalent with gasoline engines as compared to diesels but does form in larger 

quantities under relatively rich air:fuel ratios. The amount of elemental carbon in PM varies 

from vehicle to vehicle (and, even for a given vehicle, varies depending on operating conditions 

and state of repair). For gasoline-fueled vehicles, a typical number is about 20% of PM mass 

compared to about 70% for a diesel engine. There are also other compounds in the fuel or 

engine oil such as trace levels of sulfur and phosphorus which, in combustion, form sulfates and 

phosphates, both of which form particulate. The sulfur level in gasoline is now very low, almost 

eliminating sulfate formation from gasoline sulfur content but motor oil contains significant 

sulfur (and phosphorus) compounds. Also, trace metal constituents in gasoline and oil form PM 

in the combustion process as metallic oxides, sulfates, nitrates, or other compounds. Catalyst 

attrition products from the substrate and trace amounts of noble metals also form PM but not in 

the combustion process. The catalyst attrition products are mechanically generated and are 

usually in larger size ranges compared to exhaust PM. Exhaust PM as formed in the engine is 

generally very small in size (possibly much of it is nuclei mode PM in the range of 0.05 microns 

or smaller). In the exhaust system, including the muffler, some of the PM agglomerates and 

increases in size. 

The wide assortment of technologies used in vehicles can affect PM formation. These 

technologies were mainly developed to control HC, CO and NOx emissions, but most have the 

side benefit of reducing PM, since reducing exhaust HC generally also reduces exhaust PM 

although not to the same extent. Older engines from the 1980s and earlier that deliver fuel 

through a carburetor typically have poorer fuel droplet quality, as well as looser control of fuel 

air stoichiometry. These older vehicles are expected to produce more PM (on average) then their 

fuel injected counterparts that followed generally in the late 1980s and early 1990s. 

80
Among fuel injected engines, throttle body fuel injection (TBI) used in earlier engines with fuel 

injection typically has poorer fuel atomization quality and air:fuel ratio control than the port fuel 

injection (PFI) technology that supplanted it; thus, one might expect older model-year fuelinjected vehicles to have higher PM emissions (on average) than newer ones. Somewhat before 

the widespread adoption of fuel injection, closed-loop control systems were developed in tandem 

with oxygen sensors to improve the stoichiometric chemistry of combustion These closed loop 

controls improved combustion as well as the effectiveness of the after-treatment system. 

The after-treatment system on most vehicles consists of a 3-way catalyst. The 3-way catalyst was 

designed for simultaneous control of hydrocarbons, carbon monoxide, and nitrogen oxides. 

Vehicles with 3-way catalysts would meet more stringent hydrocarbon and carbon monoxide 

emission standards while also meeting the first stringent nitrogen oxide standard. In oxidizing 

hydrocarbons, these systems are resulted in additional PM control. These systems were utilized 

on almost all gasoline-fueled vehicles beginning in the 1981 model year. On some model-year 

vehicles in the 1980s and a few more recently, a secondary air injection system was added 

between the engine and oxidation portion of the catalyst in order to add supplementary air to the 

oxidation reactions on the catalyst. These systems also helped oxidize PM (though probably not 

to the extent that it oxidizes CO or HC). The deterioration of these technologies may affect PM 

and HC quite differently. Throughout this chapter, there are parallels drawn between HC and 

PM formation as well as controls, however it should be noted that the correlation between these 

emissions is far from perfect. Many examples of this are shown in the 2008 analysis report. 

Amounts of of PM emitted are very sensitive to the amount of fuel in combustion as well as the 

air:fuel ratio. As mentioned above, over-fueled mixtures result in higher PM formation and in 

some cases, also excess soot formation. Over-fueling can occur under several different 

conditions. During cold start, engines are often run rich in order to provide sufficient burnable 

fuel (i.e. light ends that vaporize at colder temperatures) to start combustion when the cylinder 

walls are still cold (which results in increased flame quench). When high acceleration rates or 

loads are encountered (such as in a wide-open throttle event), an extra amount of fuel is often 

injected for greater power or for catalyst and component temperature protection. Emission 

control systems in the late 1990s are better designed to control this enrichment. Finally, engines 

can run rich when a control sensor (e.g. oxygen, MAF, MAP, or coolant sensors) or the fuel 

system fails. 

 

In addition to fuel, lubricating oil can also get into the combustion chamber via several 

pathways. Some manufacturers may have poor tolerances for pistons and piston rings, thus the 

negative pressures (engine intake vacuum) can pull oil through these larger gaps during the 

intake stroke. Furthermore, engine components, such as valves, valve seals, piston rings, and 

turbochargers can wear and deteriorate resulting in increasing emissions over time. In all 

gasoline automotive engines, the crankcase (where the oil bathes the engine components) is 

vented back into the combustion chamber through the intake manifold. This is known as 

Positive Crankcase Ventilation (PCV), and is required in order to remove and burn the excess 

hydrocarbons in the hot crankcase. Unfortunately, it can also introduce PM precursors and oil 

into the engine combustion chamber. Because of the relatively small amount of oil consumption 

compared to the volume of gasoline burned in a vehicle, HC from oil is also small. However, 

organic PM from oil consumption can be quite significant because oil is a high molecular weight 

81
hydrocarbon, and more likely to remain as uncombusted droplets. Therefore as vehicles age, 

those that consume more oil will probably have very different emissions behavior for HC than 

for PM, compared to when they were new. However, oil consumption can "poison" the catalyst 

substrate, reducing the effectiveness of the catalyst at oxidizing HC. 

The fuel itself may have properties that exacerbate PM formation, which may be affected by 

concentrations of sulfur, lead, aromatics, and impurities. With the lower levels of lead and sulfur 

in fuels recently, the first two are less of a factor in the Kansas City program than aromatics 

would be. In the calendar years that MOVES models, lead is not a significant portion of the 

inventory, thus is largely ignored. Sulfur (as a fuel rather than a tailpipe phenomenon) is 

modeled separately and is described in another MOVES document. Impurities may be captured 

in a future version of MOVES. 

Some of these PM forming mechanisms clearly affect HC emissions. So a control technology or 

a deterioration path for HC may or may not similarly affect PM depending on the source. It is 

also likely that the processes that cause high PM may not be the same processes that cause 

organic PM. Some of the mechanisms also form visible smoke. Smoke takes on a variety of 

characteristics depending on the source, and can be due to oil consumption or overfueling. The 

smoke is visible because of the relative size of the particles compared to the light wavelengths 

that are scattered. However, visible smoke is not necessarily a reliable indicator of high PM 

emissions. 

2.2 New Vehicle or Zero Mile Level (ZML) Emission Rates 

In this section, we develop a modeling approach to extend the PM emission trends from Kansas 

City presented in the analysis report to average emissions across the fleet. The section also 

compares the new vehicle results from many different studies in order to determine the zero mile 

level (ZML) emission rates for all model years in MOVES. Before modeling deterioration, it is 

first necessary to capture ZML emission rates. 

In constructing a model of emissions from the Kansas City data (Error! Reference source not 

found.), the most significant challenge is distinguishing between model year and age effects. 

This problem arises because program was conducted over a two-year period, thus ensuring a 1 to 

1 correspondence between model year and age. As a result, one cannot know for sure whether 

emissions are decreasing with model year, or increasing with age, or both. Emissions tend to 

decrease as technologies are introduced on vehicles (with later model years) in order to comply 

with more stringent emissions standards. However, these technologies and vehicles tend to 

deteriorate over time, thus for the same model year vehicle, older vehicles (greater age) will have 

higher emissions (on average) than newer vehicles. 

82
Figure 2 - 4. Average PM Emissions from the Kansas City by Model Year. 

0

20

40

60

80

100

120

1975 1980 1985 1990 1995 2000

Model Year

PM (mg/mi)

KC measured

In concept, the most accurate means of quantifying emissions from vehicles over time is to 

conduct a longitudinal study, where emissions are measured for the same vehicles over several 

(or many) years. However, implementing such a study would be costly. Moreover, it is 

impossible to obtain recent model year vehicles that have been significantly aged. In the 

following sections, we will describe some limited longitudinal studies conducted in the past. 

Then we will present our modeling methodology to isolate model year (technology) in this 

chapter from age (deterioration) in the next. 

2.2.1 Longitudinal Studies 

There have been a few longitudinal studies conducted in the past that are relevant for PM 

emissions. Unfortunately, they are all limited in their ability to conclusively discern model from 

age effects. 

Gibbs et al. (1979) measured emissions from 56 vehicles from 0 to 55,000 miles (odometer) on 3 

different cycles for the EPA16. Hydrocarbon emissions were analyzed, but unfortunately, PM 

results were not reported as a function of mileage. The authors state that “emission rates of 

measured pollutants were not found to be a consistent function of vehicle mileage,” however, the 

following figure shows that some increasing trend seems to exist for HC. 

83
Figure 2 - 5. Hydrocarbon emission as a function of mileage (Gibbs et al., 1979) 

0

0.5

1

1.5

2

2.5

3

3.5

0 10 20 30 40 50 6

mileage (*1000)

HC (g/mi)

0

Hammerle et al. (1992) measured PM from two Ford model vehicles over 100,000 miles.17

However, their results for PM deterioration are somewhat inconclusive, as the following figure 

shows, since the deterioration seems to occur mainly in the beginning of life, with very little 

occurring after 20,000 miles. Also, the study is limited to only 2 vehicle models. 

Figure 2 - 6. Particulate emissions as a function of odometer for two Ford vehicles (Hammerle et 

al., 1992) 

y = 2E-05x + 0.8218

R2

 = 0.555

y = 8E-06x + 1.4717

R2

 = 0.1527

0

0.5

1

1.5

2

2.5

3

3.5

0 20000 40000 60000 80000 100000 120000

odometer

PM (mg/mi)

Explorer

Escort

Linear (Explorer)

Linear (Escort)

84
Both of these studies assume that odometer is a surrogate for age. While there are some 

deterioration mechanisms that worsen with mileage accumulation, there are others that 

deteriorate with effects that occur over time, such as number of starts, corrosion due to the 

elements, deposits and impurities collecting in the gas tank and fuel system, etc. Therefore, we 

believe that any study that describes deterioration as a function of odometer (alone) is not 

capable of accounting for all causes of deterioration. 

Whitney (2000) re-recruited 5 vehicles that had been tested from a previous large study 2 years 

prior (CRC-E24)18. There are two significant limitations of this follow-up study: (1) the interval 

between studies was only 2 years, though the odometers had increased 22,200 miles (on average) 

and (2) these vehicles were tested on a different drive cycle, the LA92 compared to the previous 

study, which used the FTP. We will explore the potential cycle differences on PM later, but 

assuming the cycles give similar PM results, the PM emissions were only 8% higher (on 

average). This increase is due to a single vehicle, which had significantly increased PM 

emissions (the rest were the same or slightly lower). Unfortunately, this is not a large enough 

sample and time period on which to resolve age effects, but it may be sufficient to conclude that 

the differences between PM from the FTP and LA92 drive cycles are minimal for PM. 

The three longitudinal studies described above are inconclusive, though they do hint that 

deterioration does occur. 

2.2.2 New Vehicle, or ZML Emission Rates and Cycle Effects 

In order to isolate the effect of model year (technology) from age (deterioration), it is useful to 

look at the model-year effect independently. This can be done by analyzing emissions from new 

vehicles from historical PM studies. This entails capturing the near ZML or Zero Mile Level 

emission rates. New vehicle emission rates tend to have a much smaller variability than older 

vehicles (in absolute terms) since they have lower emissions that comply with more stringent 

standards. These standards, which decrease over time, are for hydrocarbons, but tend to have an 

effect on PM emissions as well since many of the same mechanisms for HC formation also form 

PM. 

Several independent studies have been conducted , which have measured PM emissions from

nearly new vehicles. For our purposes, we will define “new” as a vehicle less than 3 years old, 

i.e., vehicles within the 0-3 year age Group. The following table lists the 15 studies employed for 

this analysis. 

Historical gasoline PM studies including new vehicles 

Table 2 - 1. Historical gasoline PM studies, including new vehicles. 

Program Yr of Study 

# new 

vehicles 

Drive 

cycle 

Gibbs et al.19 1979 27 FTP 

Cadle et al.20 1979 3 FTP 

Urban & Garbe21,

22 1979, 1980 8 FTP 

Lang et al.23 1981 8 FTP 

Volkswagen24 1991 7 FTP 

85
CARB25 1986 5 FTP 

Ford26 1992 2 FTP 

CRC E24-1 (Denver)27 1996 11 FTP 

CRC E24-3 (San 

Antonio)28 1996 12 FTP 

CRC E24-2 (Riverside)29 1997 20 FTP 

Ford,Chrysler,GM 

Chase et al.30 2000 19 FTP 

Whitney (SwRI)31 1999 2 LA92 

KC (summer)32 2004 13 LA92 

EPA (MSAT)33 2006 4 FTP 

 

Before, we plot these emissions, we should convince ourselves that the LA92 driving cycle will 

not give significantly different PM emissions than the FTP so that we can compare these test 

programs directly. As described above, the results from Whitney (2000) seem to indicate little 

difference between the two cycles. Even though the tests were conducted 2 years apart, one 

would expect that the aging effects in combination with the slightly more aggressive LA92 cycle 

(used later) would have given higher PM emissions. However, this was not the case, and only 

one of the 5 vehicles showed significantly increased emissions. 

Li et al., (2006) measured three vehicles on both cycles at the University of California, 

Riverside34. The PM emissions from the LA92 were 3.5 time larger (on average) than the FTP 

results. However, the HC emissions were only 1.2 times higher. These results seem rather 

contradictory and inconclusive. The 3.5 factor also seems excessive. 

Finally, the California Air Resources Board conducted an extensive measurement program over 

several years comparing many different drive cycles. Unfortunately, PM was not measured in 

this program. However the Figure 2 - 7 shows the HC emissions compared for the two cycles. 

The trends indicate that there is little cycle effect for HC. 

86
Figure 2 - 7. Hydrocarbon emissions on the LA92 versus corresponding results on the FTP. 

y = 0.9668x

R2

 = 0.3301

0

2

4

6

8

10

12

14

16

18

0 2 4 6 810

FTP

LA92

12

composite THC

Linear (composite THC)

Based on these studies, we conclude that there is little difference in PM emissions between the 

LA92 and FTP cycles on an aggregate basis (though their bag by bag emissions may differ). We 

shall demonstrate that, for the purposes of ZML analysis, the results will be nearly identical even 

if we omit the LA92 data, thus minimizing the significance of this issue. 

Figure 2 - 8 shows the new-vehicle emission rates from the 11 studies listed in Table 2 - 1 The 

data points represent each individual test, and the points with error bars represent the average for 

each test program. The plot presents evidence of an exponential trend (fit included) of 

decreasing emissions with increasing model year. The fit is also nearly identical if we omit the 2 

programs that employed the LA92 cycle. We will use this exponential ZML relationship as the 

baseline on which to build a deterioration model. However, the measurements from the older 

programs primarily measured total particulate matter. These have been converted to PM10 (for 

the plot), which is nearly identical (about 97% of total PM is PM10. We also assume that 90% of 

PM10 is PM2.5 (EPA,1981). For the older studies, we accounted for sulfur and lead directly if 

they were reported in the documentation. In those cases where sulfur was not reported, the levels 

were approximated using MOBILE6 sulfur emission factors and subtracted as an adjustment. 

Unfortunately, many of the older studies used a variety of methods for measuring particulate 

matter. There were many differences in filter media, sampling temperature, sample length, 

dilution, dynamometer load/settings etc. It is beyond the scope of this project to normalize all of 

the studies to a common PM metric. It is likely that there is insufficient documentation to even 

attempt it. Therefore no attempts at adjustment or normalization were made except for size 

fraction, lead and sulfur, as described above. 

 

87
Figure 2 - 8. Particulate emission rates for new vehicles compiled from 11 independent studies. 

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30

Model Year (+1975)

PM10 (mg/mi)

all

Gibbs et al., 1979

Cadle et al., 1979

Urban&Garbe, 1980

Lang et al., 1981

VW, 1991

CARB, 1986

Ford, 1992

E24-1 Denver

E24-2 UC Riverside

E24-3 San Antonio

Chrysler/Ford/GM

SwRi/NREL

KC-Summer LA92

MSAT-Tier2

mean for each program

Fit

PM = 15.6exp(-.0814 MY)

To determine the ZML emission rates from these data, the next step was to separate results for 

cars and trucks, and to separate cold-start from hot-running emissions. Unfortunately, the 

historical data does not present PM results by bag. Therefore, the 2005 hot-running ZMLs for 

cars vs trucks were determined from the KC dataset, and the model year exponential trend from 

the aggregate trendline (-0.08136) is used to extend the ZMLs back to model year 1975. The 

base hot running ZML emission rate for LDV (EHR,y) is: 

y

y EE -0.814,HR 2005,HR = e 2 - 1 

Where 

y = model year – 1975, and 

EHR,2005 = hot running zml rate for MY 2005. 

To estimate equivalent rates for trucks, we multiplied this expression by 1.43. This value is 

based on an average of all the studies with new vehicles from 1992 onward (before this model 

year, there were no trucks measured). It is also multiplied by 0.898 to give hot running bag 2 

rates and 1.972 to give the cold start emission rate (here defined as bag 1-bag 3 in units g/mi). 

These values were estimated by running a general linear model of bag 2 and bag1-3 with respect 

to composite PM respectively in the SPSS statistical software tool. The averages of these ratios 

by model year are shown in Figure 2 - 9, in which no clear trend is discernable. The parameters 

of the model are summarized in Table 2 - 2. 

88
Figure 2 - 9. Ratios of hot-running/composite and cold-start/composite, Bag2 and Bag1-Bag3, 

respectively, averaged by model year. 

0

1

2

3

4

5

6

7

8

1960 1970 1980 1990 2000 2010

model year

bg1-3/composite

coldstart/comp

bag2/comp

Table 2 - 2. Best-fit parameters for cold-start and hot-running ZML emission rates. 

Parameter Value 

LDV hot-running ZML (g/mi) 0.01558

Exponential slope 0.08136

Truck/car ratio 1.42600

Bag-2 coefficient 0.89761

Cold-start coefficient 1.97218

Figure 2 - 10 shows the ZML emission rates. The rates are assumed to level off for model years 

before 1975 and again after 2005. Elemental and organic carbon fractions are another 

modification to the ZML rates. These fractions are already reported in the analysis report. 

 

89
Figure 2 - 10. Particulate ZML emission rates (g/mi) for cold-start and hot-running emissions, for 

LDV and LDT. 

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

1975 1980 1985 1990 1995 2000 2005

model year

n ew PM emissions (g/mi)

truck new bag1_3

car new bag1_3

truck new bag2

car new bag2

2.2.3 Aging or Deterioration in Emission Rates 

In this section, a deterioration model is introduced that captures how new vehicles in all model 

years deteriorate over time so that gasoline PM from any given calendar year can be modeled in 

MOVES. The purpose of this model is to characterize the PM emissions from the fleet and to 

hindcast the past as well as forecast the future, as must be done in inventory models. 

 

2.2.2.4 Age Effects or Deterioration Rates 

The ZMLs determined in the previous section represent baseline emissions for new vehicles in 

each model–year group. By comparing the emissions from the “aged” Kansas City vehicles in 

calendar year 2005, to the new rates determined earlier, we can deduce the “age effect” for each 

corresponding age. However, simple an approach as this seems, there are many ways to connect 

two points (even if there are a family of 2 points). This section describes the procedure and the 

assumptions made to determine the rate at which vehicle PM emissions age. 

We first break the data into age Groups. We use the MOVES age bins which correspond to the 

following age intervals: 0-3 (new), 4-5, 6-7, 8-9, 10-14, 15-19, 20+. Having a single age 

category for 20 years and older implies that emission rates have stabilized by 20 years of age. 

90
The bag measurements from all of the vehicles measured in Kansas City were first adjusted for 

temperature using the equation derived in the analysis paper. The equation used is: 

)72(03344.0

72,PM PM, e T EE T

− − = 2 - 2 

where EPM,72, is the adjusted rate at 72oF for cold-start or hot-running emissions, EPM,T is the 

corresponding measured emissions for cold-start or hot-running, respectively, at temperature T, 

respectively. 

The temperature-adjusted measurements are the “aged” rates. 

There are two simple methods for determing the deterioration rates. The first method is to 

subtract the aged rates from the new rates, the difference by age would define the deterioration 

rate. This model will be referred to as the “additive deterioration model”. The second method is 

to ratio the aged rates with the new rates so that the deterioration rates are all proportional. This 

will be referred to as the “multiplicative deterioration model”. 

In the additive mode, the Kansas City data were grouped or averaged within the age Groupss, 

with the prerequisite that no newer age Group can have greater rates than an older age Group. 

This constraint is maintained by averaging 2 (or more) age bins together as necessary. Figure 2 - 

11 shows the additive age deterioration rates in units of g/mi for running and g/start for starts. 

Figure 2 - 12.shows the aggregate deterioration rates compared to the data. The cold start and hot 

running were combined with the proper weighting to calculate an LA92-equivalent rate. Finally, 

Error! Reference source not found. shows the LA92 equivalent emission rates, combining 

ZMLs and deterioration, compared to the data. 

91
Figure 2 - 11. The incremental effect of age on Particulate emissions based on the Kansas-City 

Results. 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0-3 4-5 6-7 8-9 10-14 15-19 20+

age bins

Deterioration Emission Rate (g/mi)

car hot running g/mi

truck hot running g/mi

car start g/mi

truck start g/mi

Figure 2 - 12. Cold-start and hot-running Particulate emissions by age, with estimated rates 

compared to data (as LA92 composite, mg/mi). 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35

Age

Adjusted PM (mg/mi)

cars

trucks

car age model

truck age model

92
Figure 2 - 13. Emission rates by age, as LA92 composites (mg/mi), compared to data. 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Age

Adjusted PM (mg/mi)

cars

trucks

car new model

truck new model

The model appears to fit the data well. One can also observe from Error! Reference source not 

found. the relative effect of ZML vs deterioration. The slight curvature after each large step 

represents the ZML exponential curves, whereas the larger steps represent the deterioration. 

This additive deterioration model suffers from some significant limitations. The first is that it 

assumes that all model years will deteriorate the same way independent of the emission control 

technologies employed. As an example, the model assumes that a 1975 carbureted engine will 

deteriorate in the same fashion as a 2005 fuel injected engine car; i.e. they will have different 

ZML rates to start, but both will add about 50 g/mi after 20 years. So, while new 1975 and 2005 

cars have emission rates of approximately 15 and 2 g/mi, respectively, (a factor of 7.5), after 20 

years the rates would be 65 and 52 g/mi, respectively, thus the cars will have nearly the same 

emission rates. While it is possible that a small number of fuel-injected vehicles would 

experience complete failures of their aftertreatment and fuel control systems, it is unlikely that 

they would (on average) have similar emission rates as a carbureted vehicle. The second 

problem with the model is that it is not consistent with the way hydrocarbon deterioration rates 

are represented in MOVES. 

It is likely that some of the same mechanisms that cause HC to increase over time would also 

result in PM increases. These factors include deterioration in the catalyst, fuel control, air:fuelratio control, failed oxygen sensors, worn engine parts, oil leaks, etc. Error! Reference source 

not found. shows trends in the natural logarithm of THC rates over approximately 10 years, 

based on random-evaluation samples in the Phoenix I/M program. On a ln-linear scale, the 

deterioration rates appear approximately linear over this time period. This pattern means that the 

deterioration rate is exponential over this time interval. This observation, combined with the 

93
approximate parallelism of the trends for successive model years, imply that emissions follow a 

multiplicative pattern across model-year or technology groups. thus calling for a multiplicative 

deterioration model. In such a model, the aged rates and the new rates are converted to a 

logarithmic scale, then the slopes are estimated by fitting a general linear model. The average 

slope is determined, and the ZMLs determined earlier define the y-axis offsets. This results in a 

series of ladder-like linear lines in on a log scale as show in Error! Reference source not 

found.. The lines fan out exponentially on a linear scale as shown in Error! Reference source 

not found.. The dotted lines and the points with uncertainty bars represent the Kansas City data 

overlaid onto the model and indicate that the model is consistent with the data. 

Figure 2 - 14. The natural log of THC emissions vs. Age for LDV in the Phoenix (AZ) Inspection 

and Maintenance program over a ten-year period (1995-2005). 

- 

94
Figure 2 - 15. The Multiplicative deterioration model applied to PM results from Kansas City. The y-axis 

offsets represent ZML rates. The dotted line represents the Kansas-City Data. 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 5 10 15 20 25

Age

ln (PM)

1975

1980

1985

1990

1995

2000

2005

KC model in 2004

Figure 2 - 16. The multiplicative deterioration model shown on a linear scale. The y-axis offsets 

capture the new-vehicle ZML rates. The dotted lines and points with error bars represente the 

Kansas-City results (with 95% confidence intervals). 

0

20

40

60

80

100

0 5 10 15 20 25 30

Age

PM (mg/mi)

1975

1980

1985

1990

1995

2000

2005

KC measured in 2004

KC model in 2004

2005 2000

1995

1990

1985

1980

1975

Because the model is multiplicative, the deterioration factors can be applied directly to trucks, 

cold start, hot-running, EC, and OC, since the order of operations does not matter. The start 

process requires only a soak time model to fill the remainder of the rates. Because no data is 

95
available describing how particulate start emissions vary by soak time, we have used the HC 

soak curves shown previously (see Figure 1 - 35). 

Substantial analysis is yet required to fill modal particulate emission rates for 

emissionRateByAge table in the MOVES input database. Because the simple multiplicative 

model can be applied across the range of VSP, deteriorated rates by operating mode can be 

directly generated, as described in the next section. 

2.3 Modal PM Emission Rates 

As mentioned earlier, the continuous emissions measurements from the Kansas City study were 

examined at great length, after which we determined that the Dustrak gave the most reliable 

second-by-second PM time-series data when compared to the quartz-crystal microbalance 

(QCM) and the Nephelometer. In the following sections, we describe some of the trends in 

continuous PM for “typical” normal and higher emitters. We conclude by describing the 

procedure by which results from the Dustrak were used to develop emission rates by operating 

mode.

2.2.1 Typical behavior in particulate emissions as measured by the Dustrak and 

Photoacoustic Analyzer 

After looking at over 500 second-by-second traces, it became apparent that most of the vehicles 

fell into certain general patterns. The most common behavior involved a highly non-linear PM 

emissions release as engine load increased. This pattern led to a monolithic “spike” in emissions 

during the most aggressive acceleration event in the LA92 drive cycle during the 2nd (hot 

running) bag at around 850 seconds. This peak is captured in Figure 2 - 17, which includes 2 

plots. The higher emissions prior to 300 seconds can be attributed to cold start, during which the 

engine is still cold and the fuel:air mixture tends to be on the rich side. The plot on the bottom 

confirms this supposition since it indicates that elemental carbon is relatively high during the 

start. The hydrocarbons are overlaid on the bottom plot merely for comparison, and provide a 

loose and qualitative comparison to organic PM emissions. Some vehicles had variations on this 

spike where it was much larger than even the cold start emissions, but this pattern is more typical 

of the newer vehicles tested on the warmer days. 

On the following series of plots the dustrak (most prominent), nephelometer and QCM are 

overlaid on the top chart, while the photoacoustic analyzer, hydrocarbon and speed are overlaid 

on the bottom chart. Ordinate values are all relative and not absolute. “Shifted” means timealigned, “Temp” means ambient temperature and the filter measurements as well as vehicle type 

and model year are written above the figures. 

96
Figure 2 - 17. A typical time-series plot of continous particulate emissions as measured by several 

instruments. 

The next series of two figures shows how in some cases, the cold-start emissions appear to be 

persist into the “hot-running” phase of the cycle (bag 2). Figure 2 - 18 shows an older 1976 

vehicle tested at 54°F, for which one might expect the cold start emissions to have a longer 

duration than a newer vehicle. In this case, the cold start emissions seem to end at around 550 

seconds (based on the HC trace). However, such cases where large portions of the cold start 

emissions leak occur during bag 2 were rare in the dataset, and thus they were not “corrected”. 

This step can be considered for future study. 

97
Figure 2 - 18. Continous particulate emissions from a 1976 Nova measured at 54°F. 

Figure 2 - 19 shows a similar but slightly more commonly seen effect for a newer vehicle. The 

difference is that the cold start seems to end at around 250 seconds in bag 1, but then is high 

again when bag 2 starts at around 350 seconds. Here the HC is low, but the EC (as indicated by 

the PA) is relatively high hinting at a slightly fuel rich mixture. It is uncertain at this time, why 

Figure 2 - 19. Particulate time-series for a 2002 Trailblazer at 50°F. 

these vehicles need to go into enrichment during this relatively mild acceleration. 

98
The traces shown so far have been “normal emitters” during hot running operation, i.e. they did 

not have unusually high emissions during bag 2. These vehicles represent the bulk of the data. 

However, some vehicles do exhibit higher or otherwise unusual hot-running PM emissions. 

Examples are shown in the following series of figures. 

Figure 2 - 20 shows a large “hump” of PM emissions starting at the beginning of bag 2 that lasts 

for nearly 600 seconds. The dustrak, nephelometer and the QCM all register this hump to 

varying degrees, so it’s unlikely that it is a mere instrument artifact. The bulk of the bag 2 PM 

emissions lies in this “hump,”, which does not coincide with a high load event. It is interesting 

that the PA is not detecting a broad EC portion, so this hump is most likely organic carbon (OC), 

which leads us to deduce that this hump probably represents OC particulate due to oil 

consumption. Because these humps are not load based events, they don’t suit themselves well to 

characterization by VSP as correlation to power should not be high during the event. Moreover, 

it is interesting to note that the broad hump does not repeat. Some vehicles have the hump at 

different locations in the cycle (or throughout the whole cycle in rare cases), thus making this 

effect impossible to model physically using only a VSP methodology. Therefore, the effect can 

only be captured on an aggregate level by simply averaging with the normal emitters described 

earlier. It follows logically that if the recruitment of these “high emitters” was representative in 

Kansas City, and these high emissions humps are not load dependent, then this effect on the 

inventory should be captured by normalizing the modal rates to the filter measurements; i.e. they 

are captured in the base emission rates. 

Figure 2 - 20. Particulate time-series for a 1988 Dynasty. 

99
Figure 2 - 21 shows another likely candidate for designation as an oil burner. The emissions 

humps are much broader, though the absolute emissions are similar to the Dynasty. Note again 

that the dustrack, nepholometer, and the QCM all register the hump, while the PA shows very 

little EC, one of the “fingerprints” of oil-based particulate. In one of the repeat test vehicles in 

the study, one test exhibited a hump in emissions and the repeat test did not. The inconsistency 

and non-repeatability of some of these humps arising from oil consumption explains how some 

vehicles can flip from high to normal emitter or vice-versa in back-to-back tests. These 

observations have ramifications for future PM test programs, in that sample sizes should be large 

and fleets properly representative. 

100
Figure 2 - 21. Continuous particulate time series for a 1995 Lincoln Continental. 

The next figure (Figure 2 - 22) shows a more typical high PM emitter, where the bag 2 emission 

rate is 266g/mi. Here the EC does mirror the high emissions seen in the other instruments. Even 

the HC measurements are saturated. This trace, representing a 1978 MG, is an indicator of poor 

fuel control, as might be expected with an older (1978) carbureted engine. 

101
Figure 2 - 22. Continuous particulate (and HC) time series for a 1978 MG. 

 

We are now ready to bin the emission rates into operating modes based on vehicle-specific 

power (VSP) The following two figures show Dustrak PM emissions binned by VSP and 

classified by model year Groups. Error! Reference source not found. shows this relationship 

on a linear scale and Error! Reference source not found. shows the relationship on a 

logarithmic scale. It is clear from the latter plot that VSP trends for PM tend to be exponential 

with VSP load, i.e. they are linear on a log scale. [This is consistent with some of the other 

criteria pollutant trends – is this true Jim?]. Thus we assume smooth log-linear relations when 

calibrating our VSP based emission rates. 

102
Figure 2 - 23. Particulate emissions, as measured by the Dustrak, averaged by VSP and model-year 

Group (LINEAR SCALE). 

Figure 2 - 24. Particulate emissions, as measured by the Dustrak, averaged by VSP and model-year 

Group (LOGARITHMIC SCALE).

In order to determine the actual MOVES VSP based rates, followed seven steps: 

103
1. The LA92 equivalent hot running emission rate in g/mi is determined for every model 

2. e dustrak for cars and 

n 

3. s calculated for bag 2 of the LA92 drive cycle for cars and 

4. n rates are then combined with the VSP activity rates by operating mode and 

5. normalization 

6. OC factors from 

7.

he output from step 3 (operating-mode distribution) for cars and light trucks is shown in Figure 

igure 2 - 25. Operating-Mode distribution for cars and light trucks representing the hot-running 

year and age group from the model described in section 2.2. 

The gram per second (g/s) emission rate is determined from th

trucks based on the KC data. These trends are then extrapolated to the higher VSP bi

levels where data is missing. 

The VSP activity distribution i

trucks separately – this step is equivalent to determining the number of seconds in each 

VSP bin. 

The VSP bi

then summed to give a total bag 2 emission factor that must match the aggregate LA92 

emission rates in step 2 (as calculated from the filter measurements). 

The emission rates are constrained to match the filter values through a 

factor that is applied to every model year age group (test by test??). 

The rates from step 5 are then multiplied by the corresponding EC and

the analysis report(cite) to give all of the hot running rates. 

Steps above are repeated for all ages and model years. 

T

2 - 25. For operating-mode definitions, see Table 1 - 2. 

F

phase (Bag 2) of the LA92 cycle. 

0

20

40

60

80

100

120

140

160

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

VSP Bin

frequnecy (seconds in bin)

car

light truck

104
The output of step 5 for each model year ZML (0-3 year age Group) is shown in Error! 

igure 2 - 26. Particulate emissions for passenger cars (LDV) from Kansas City results, by model 

Reference source not found.. 

F

year Group, normalized to filter mass measurements. 

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30 35 40 45

VSP bin

Emission Rate (g/s)

1960-1980

1981-1982

1983-1984

1985

1986-1987

1988-1989

1990

1991-1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

fter the rates were calculated, a quality check was performed to ensure that the aged rates in 

0 

2.4 Conclusions 

he previous discussion describes analyses of particulate-matter emissions designed to develop 

c and 

m 

e 

A

any particular bin were not too high. A multiplicative model that has exponential factors risks 

having excessively high emission rates under extreme conditions. For example any rate over 10

g/s was considered too high, this would be an extremely high-smoking vehicle. This behavior 

was corrected in only two cases bins in operating mode 30, representing values for cars and 

trucks in the 1975 model-year Group. In these cases, the value from operating mode 29 was 

copied into mode 30. 

T

operating-mode based emission rates for use in the MOVES emissionRateByAge table, 

incorporating the effects of temperature, model year and age. These rates include organi

elemental carbon for cold-start and hot-running emissions from cars and light trucks (e.g., LDV

and LDT).. This analysis is crucial for understanding how PM emissions have changed over the 

years and how new vehicle PM rates are projected to deteriorate over time. The new vehicle 

(zero mile level) PM emissions are estimated by analyzing the new-vehicle emissions rates fro

historical PM studies. The trends indicate that emissions have been decreasing exponentially 

with model year as the engine and fuel controls have improved and after-treatment devices hav

been installed. The new truck rates are found to be larger than the car rates. The deterioration 

105
effect of age is determined by comparing the new vehicle rates to the Kansas City data. It is 

determined that emissions deteriorate exponentially with the age of the vehicle, but remain 

constant after about 20 years. It is also found that PM emission increase exponentially with

(or road or engine load). 

 VSP 

here is still much analysis that can be conducted with these data. In the future, it would be 

nt 

o 

T

important to examine trends in the speciated hydrocarbons and organic PM from the standpoi

of toxic emission and also quantifying the PM emissions due to oil consumption. This analyses 

are likely to expand the scientific understanding of PM formation and why certain gasoline 

fueled vehicles emit more PM than others under certain conditions. It would also be useful t

explicitly capture the non carbon portion of the PM. It is important to continue to collect PM 

emissions data in the field, since it validates the deterioration model. 

106
3. Criteria Pollutant Emissions from Light-Duty Diesel Vehicles 

(THC, CO, NOx) 

In MOVES, emission rates for running emissions are calculated for each operating mode. 

However, for the diesel-fueled passenger cars (LDV) and light-duty trucks (LDT), we lack the 

necessary continuous or “second-by-second” measurements to directly calculate the emission 

rates by VSP. Therefore, we used results (in grams per mile) from the Federal Test Procedure 

(FTP ) to estimate corresponding modal rates (in grams per hour). 

3.1. Estimating Zero-Mile FTP Emissions: 

We identified FTP results on the Annual Certification Test Results & Data website 

(http://www.epa.gov/otaq/crttst.htm) and on the Test Car List Report Files Website 

(http://www.epa.gov/otaq/tclrep.htm) for 513 diesel-powered LDV and 187 LDT from the 1978 

through 2008 model years. These vehicles had been tested either to certify or to generate fuel 

economy estimates (labels or CAFE). These test vehicles were all new (age = zero years), each 

vehicle having accumulated about 4,000 miles. These individual tests were used to calculate 

mean (composite) FTP emissions (grams per mile of HC, CO, NOx, and PM10) for each model 

year group. (We examined, but did not include data on European diesels since those vehicles 

might not be representative of those sold in the USA.) The sample sizes (by model year group) 

and the mean composite FTP emissions are given in Table 3 - 1 for LDV and Table 3 - 2 for 

LDT: 

Table 3 - 1. Mean Composite FTP Emissions (g/mile) for diesel-fueled LDV. 

Model Year 

Group 

Sample 

Size 

HC CO NOx PM1

Pre-1981 104 0.4883 1.3425 1.4126 ---* 

1981-82 114 0.2508 1.0861 1.1859 0.2999 

1983-84 116 0.2006 0.9809 1.0517 0.2881 

1985 73 0.2178 1.1386 0.8436 0.2751 

1986-90 79 0.2075 1.3581 0.5952 0.5668 

1991-93 13 0.2123 1.6854 0.5685 0.4990 

1994 3 0.2273 1.2233 0.8567 0.1747 

1995-2005 5 0.1364 0.4140 0.8180 0.0848 

2006-2008 6 0.0196 0.5367 0.3925 0.0312 

2011+2 --- 0.0196 0.5367 0.0500 0.0100 1 Measurements of PM emissions were not performed for the Pre-1981 model 

year cars (or trucks). For this analysis, we applied the (later) 1982 standard 

of 0.6 grams per mile to those earlier model years. 

2

 For 2011 and newer model years, we used the smaller of the Tier-2 Bin-5 

standard or actual test results from the 2006-2008 LDV. 

107
Table 3 - 2. Mean Composite FTP Emissions (g/mile) for diesel-fueled light-duty trucks. 

Model Year 

Group 

Sample 

size 

HC CO NOx PM

Pre-1981 13 0.6900 1.7923 1.6577 ---* 

1981-82 45 0.3478 1.3277 1.3748 0.3296 

1983-84 56 0.2578 1.0302 1.3052 0.2700 

1985 11 0.2297 1.1200 0.9473 0.2673 

1986-90 20 0.2364 0.9985 1.4435 0.2790 

1991-93 5 0.3020 1.7000 1.2600 0.1280 

1994 17 0.2213 1.6256 1.3814 0.1114 

1995-2005 14 0.1526 1.6179 1.4629 0.0960 

2006-2008 6 0.0181 0.2767 0.4583 ---* 

2011+** --- 0.0181 0.2767 0.0500 0.0100 

1 Because measurements of PM emissions were not performed for the Pre1981 model year cars (or trucks), we applied the (later) 1982 standard of 0.6 

grams per mile to those earlier model years. Due to questionable PM results 

for the 2006-2008 LDT, we used the LDV average PM value (0.0312 

grams/mile).

2

. For 2011 and newer model years, we used the smaller of the Tier-2 Bin-5 

standard or actual test results from the 2006-2008 LDT.

 

To achieve the substantially lower FTP PM emissions, manufacturers are now equipping 

their diesel-fueled vehicles (cars and trucks) with particulate traps. 

All of the Tier-2 diesel test vehicles in this sample were certified to the Bin-10 standards, 

implying that all 2006-2008 light-duty Tier-2 diesels sold in the USA were certified to the same 

standards. Since manufacturers are likely to target the Bin-5 standards in future model years, 

those average FTP emissions are probably not appropriate to represent model years beyond 2010. 

Therefore, we used the mean Bin 10 emissions of those Tier-2 vehicles to estimate the typical 

fleet emissions for only model years 2006 through 2010. For model years 2011 and later, we 

used the Bin-5 standards for NOx and PM. However, since the actual HC and CO (average) 

emissions (of those 2006 to 2008 test vehicles) were lower than the Bin-5 standards, EPA 

proposes using those lower test results for HC and CO (assuming that moving from Bin-10 to 

Bin-5 will not lead to an increase in HC or CO). 

3.1.2 Estimating Bag Emissions: 

The 700 certification (car and truck) test results were composite FTP results (HC, CO, NOx, 

and PM), not differentiated by test phase (bag). Therefore, the first task was to estimate the 

individual bag results based on the composite results. 

A smaller sample (151 tests) of FTPs from other data sets had emission results by bag. These 

FTPs of in-use vehicles (of various ages from various model years) were used only to develop 

correlations between the composite FTP emissions and the corresponding emissions of each of 

the three bags/modes. 

108
We regressed the Bag-2 emissions (in grams per hour) against the corresponding composite 

FTP emissions (in grams per mile) to obtain an estimate of running emissions. For these 

regressions, we used a piecewise linear approach rather than a polynomial regression to account 

for slight curvature in the relationships. Similar analyses were performed regressing Bag-1 

emissions and Bag-3 emissions (in total grams) each against the corresponding composite FTP 

emissions (in grams per mile). Each of the 14 regressions produces an equation, such as the 

following example, which correlates the Bag-1 “cold-start” HC emissions (EHC,Bag1, g) to the 

corresponding composite FTP HC emission rate (EHC,composite,g/mile): 

E Bag1HC, = + 4.702885 0.6433- EHC,composite 3 - 1 

Graphing this equation along with the 146 FTP test results, as shown in Figure 3 - 1 below, 

illustrates the relationship between the individual bag HC emission and the composite HC 

emission. 

Figure 3 - 1. Example: Bag-1 HC (g) versus Composite FTP HC (g/mile)

 FT P HC Rate (grams / mile) 

0

4

8

12

16

0.0 0.5 1.0 1.5 2.0 2.5 3.0

We then applied those 14 equations (derived from the regression analyses) to the 

corresponding composite FTP emissions from Tables 1 and 2. This step yielded (for each model 

year group in Tables 1 and 2) estimates of the emissions rate (in grams per hour) for Bag-2 as 

well as the total emissions (in grams) for each of Bag-1 and Bag-3. 

We then assumed that the running emission rates (in grams per hour) on Bag-2 were 

comparable to the rates on the running portion of the Bag-1 (and Bag-3). Subtracting the total 

emissions associated with those running rates from the estimated total emissions of Bag-1 (based 

on the regressions of Bag-1 versus composite FTP) yielded estimates of the cold-start emissions 

(by model year). Similarly, subtracting the estimated running emissions from the estimated total 

109
Bag-3 emissions produced estimates of hot-start emissions. Those estimated emission rates 

(running, cold-start, and hot-start) are summarized in the four following tables (Table 3 - 3 to 

Table 3 - 6), one table for each of the four pollutants. 

Table 3 - 3. Estimated Aggregate HC Emission Rates. 

Model Year 

Group 

Diesel-Fueled Passenger Cars Diesel-Fueled Light-Trucks 

Running 

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Running

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Pre-1981 8.0991 1.0961 0.1688 11.2131 1.6077 0.3280 

1981-82 4.0262 0.5505 0.1626 5.6533 0.7784 0.2161 

1983-84 3.1838 0.4325 0.1349 4.1427 0.5668 0.1664 

1985 3.4727 0.4729 0.1444 3.6724 0.5009 0.1510 

1986-90 3.2992 0.4486 0.1387 3.7835 0.5165 0.1546 

1991-93 3.3802 0.4600 0.1414 4.8847 0.6707 0.1908 

1994 3.6322 0.4953 0.1496 3.5308 0.4811 0.1463 

1995-2005 2.1069 0.2816 0.0995 2.3782 0.3196 0.1084 

2006-2008 0.1477 0.0071 0.0351 0.1226 0.0036 0.0342 

2011+ 0.1477 0.0071 0.0351 0.1226 0.0036 0.0342 

Table 3 - 4. Estimated Aggregate CO Emission Rates. 

Model Year 

Group 

Diesel-Fueled Passenger Cars Diesel-Fueled Light-Trucks 

Running 

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Running

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Pre-1981 21.3626 3.0900 1.0957 28.8186 4.0993 1.5010 

1981-82 17.1121 2.5146 0.8647 21.1168 3.0567 1.0824 

1983-84 15.3696 2.2787 0.7700 16.1856 2.3892 0.8144 

1985 17.9833 2.6326 0.9121 17.6745 2.5908 0.8953 

1986-90 21.6212 3.1250 1.1098 15.6605 2.3181 0.7858 

1991-93 27.0463 3.8594 1.4046 27.2886 3.8922 1.4178 

1994 19.3873 2.8226 0.9884 26.0552 3.7252 1.3508 

1995-2005 5.9718 1.0066 0.2592 25.9270 3.7079 1.3438 

2006-2008 8.0052 1.2818 0.3698 3.6954 0.6984 0.1355 

2011+ 8.0052 1.2818 0.3698 3.6954 0.6984 0.1355 

110
Table 3 - 5. Estimated Aggregate NOx Emission Rates. 

Model Year 

Group 

Diesel-Fueled Passenger Cars Diesel-Fueled Light-Trucks 

Running 

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Running

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Pre-1981 23.4257 1.6481 1.5561 27.6186 1.8543 1.7824 

1981-82 19.5462 1.4573 1.3466 22.7786 1.6162 1.5211 

1983-84 17.2503 1.3444 1.2227 21.5870 1.5576 1.4568 

1985 13.6886 1.1692 1.0304 15.4631 1.2565 1.1262 

1986-90 9.4389 0.9602 0.8009 23.9537 1.6740 1.5846 

1991-93 8.9815 0.9377 0.7762 20.8139 1.5196 1.4151 

1994 13.9128 1.1802 1.0425 22.8916 1.6218 1.5272 

1995-2005 13.2512 1.1477 1.0067 24.2849 1.6903 1.6025 

2006-2008 5.5883 0.8433 0.6673 6.4738 0.9325 0.7619 

2011+ 0.9813 0.3793 0.1751 0.9813 0.3793 0.1751 

Table 3 - 6. Estimated Aggregate PM Emission Rates. 

Model Year 

Group 

Diesel-Fueled Passenger Cars Diesel-Fueled Light-Trucks 

Running 

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Running

(g/hr) 

Cold-Start 

(g)rt 

Hot-Start 

(g) 

Pre-1981 7.0131 2.4362 1.2789 7.0131 2.4362 1.2789 

1981-82 3.3778 1.2427 0.6436 3.7378 1.3609 0.7065 

1983-84 3.2356 1.1960 0.6188 3.0160 1.1239 0.5804 

1985 3.0774 1.1441 0.5911 2.9830 1.1131 0.5746 

1986-90 6.6108 2.3041 1.2086 3.1250 1.1597 0.5995 

1991-93 5.7897 2.0346 1.0651 1.7460 0.4961 0.2167 

1994 2.4073 0.6682 0.3020 1.5101 0.4347 0.1863 

1995-2005 1.1338 0.3368 0.1378 1.2931 0.3782 0.1583 

2006-2008 0.3738 0.1390 0.0397 0.3738 0.1390 0.0397 

2011+ 0.0739 0.0609 0.0010 0.0739 0.0609 0.0010 

The PM rates in the preceding table represent the PM10 rates for all particulate matter on the 

collection filter (i.e., elemental carbon (EC), organic carbon (OC), sulfates, etc.). Disaggregating 

the PM estimates to obtain rates separately for EC and for OC, will be described in another 

report. 

NOTE: start and running rates for light-duty diesels in model years 2010 and later 

were assumed to equal those for light-duty gasoline vehicles, as vehicles running on both 

fuels would be certified to the same standards. See Table 1 - 23 (dataSourceID 4910) 

3.1.2 Assigning Operating Modes for Starts (Adjustment for Soak Time) 

MOVES has start emission rates for eight different operating modes (opModes), each based 

on the length of the soak time prior to engine start. One mode corresponds to the 12 hour coldsoak (opmodeID = 108). The remaining seven modes have soak times ranging from three 

minutes up to nine hours (opModeID = 101-107). 

111
Assuming that the start emissions change as functions of the temperature of the engine, and 

assuming that the engine temperature decreases (cools) exponentially with the soak period (i.e., 

length of time the engine is shut off), then we should be able to approximate the start emissions 

(following a soak EopModeID) by exponential functions of the form: 

( )t EE β α -

opModeID 108 = − e001.1 3 - 2 

 where E108 = cold-start emissions (g) and t = soak time (min), in minutes. 

(Note that the factor of 1.001 (rather than 1.0) in the preceding equation allows the exponential 

curve to pass through the cold-start value at 720 minutes rather than simply approaching it.) 

Using the estimated cold-start (CS) emissions i.e., emissions following a soak of at least 720 

minutes (E108) and the hot-start emissions i.e., the emissions following a soak of only 10 minutes 

(E101) from the preceding four tables, we solved algebraically for both the α and β coefficients, 

specifically: 

710

1ln 001.0ln

e

108

101

001.0ln720

−⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ −

=

= +

E

E

β

α β

3 - 3 

This approach yielded a unique start emission curve (as a function of soak time) for each 

pollutant and for each model year group. 

The effect of this exponential approach is illustrated in the following example plot which was 

created using the estimated cold-start THC emissions of 0.281593 grams for the 1995-2005 

model year diesel-fueled passenger cars and the estimated hot-start THC emissions of 0.099486 

grams from the preceding table. 

112
Figure 3 - 2. Estimated THC Start Emissions (g) in terms of Soak Time (1995-2005 LDV). 

T HC Start Emissions (grams per start) 

 1995-2005 LDDVs

 Soak T ime (minutes) 

0

0.1

0.2

0.3

0 120 240 360 480 600 720

This continuous concave curve is broadly comparable to the piecewise approach that the 

California Air Resources Board used in its analysis of the effect of soak time on the start 

emissions of gasoline-fueled vehicles and that EPA used in MOBILE635,36. 

3.2 Running Emissions by Operating Mode 

In MOVES, running emission rates are estimated for a set of operating modes defined in 

terms of vehicle-specific power, speed and acceleration ( see Table 1 - 2). However, we lacked 

the requisite second-by-second data for the diesel-fueled passenger cars and light-trucks to 

perform those calculations. Therefore, we developed modal rates for LDT from corresponding 

rates for light heavy-duty diesel-fueled trucks (LHD<=14K) (i.e., from trucks with gross vehicle 

weight ratings between 8,500 and 14,000 pounds). 

To adapt the LHDDT operating modes for application to LDDs, we developed operating 

mode frequencies in each mode for the 1,372-second LA-4 drive cycle (the first two phases of 

the FTP run sequentially). Due to differences in vehicle weight, we obtained separate (slightly 

different) distributions for passenger cars and and light-trucks, as shown in Table 3 - 7. 

113
Table 3 - 7. Operating-Mode Distribution for the LA-4 Drive Cycle. 

opModeID LDV LDT

0 164 164 

1 255 255 

11 93 96 

12 142 139 

13 99 103 

14 69 66 

15 34 33 

16 20 20 

21 68 70 

22 149 164 

23 123 110 

24 35 33 

25 21 19 

27 14 15 

28 8 7 

29 2 2 

30 0 0 

33 25 29 

35 35 33 

37 13 11 

38 3 3 

39 0 0 

40 0 0 

Applying the appropriate distribution to the modal emission rates for the LHDDVs, we 

obtained estimates of the emission rates (in grams per hour) over a simulated LA-4 driving cycle. 

Dividing those rates into the hour running rates for LDD (Table 3 - 3 through Table 3 - 6), by 

model-year group, yielded ratios of the light-duty emission rates to the light heavy-duty rates. 

The resulting ratios are then used as adjustment factors to scale the modal LHD rates to give 

estimated .modal LDD rates For example, applying the LA-4 operating-mode distribution to the 

NOx modal rates for the 1999-2002 model year LHDDVs produces an estimated NOx rate of 

143.66993 grams per hour compared to the actual passenger car average rate of 13.2512 grams 

per hour. Dividing yields a ratio of 0.092234. Therefore, we used that ratio (0.092234) as an 

adjustment factor to multiply all of the modal LHDDV rates for that model-year group to 

produce the corresponding VSP bins for the 1999-2002 model year diesel-fueled passenger cars. 

Thus, summing all of the LA-4 modal rates will exactly match the total estimate LA-4 (running) 

emissions. 

Not all of the operating modes are represented by the LA-4 driving cycle. Specifically, 

modes 30, 39, and 40 do not occur during the LA-4. For this analysis, we applied the same 

adjustment factor to all operating modes. 

This approach is illustrated by the following plot of the estimated zero-mile HC emission 

rates by VSP bins for 1995-1998 model year diesel-fueled passenger cars. 

114
Figure 3 - 3. Modal Emission Rates for HC (g/hour) for 1995-98 diesel-fueled LDV. 

HC Emissions (g/ hour) by VSP Bins

1995-1998 Diesel-Fueled Passenger Cars

 VSP Bin Operating Modes 

0

1

2

3

4

5

0 10 20 30 40

115
116

4. Crankcase Emissions 

In an internal combustion engine, the crankcase is the housing for the crankshaft. The 

enclosure forms the largest cavity in the engine and is located below the cylinder block. During 

normal operation, a small amount of unburned fuel and exhaust gases escape around the piston 

rings and enter the crankcase, and are referred to as “blow-by.” This blow-by is potentially a 

source of vehicle emissions. 

To alleviate this source of emissions, the Positive Crankcase Ventilation (PCV) system 

was designed as a calibrated air leak, whereby the engine contains its crankcase combustion 

gases. Instead of the gases venting to the atmosphere, they are fed back into the intake manifold 

where they reenter the combustion chamber as part of a fresh charge of air and fuel. A working 

PCV valve should prevent virtually all crankcase emissions from escaping to the atmosphere. 

PCV valve systems have been mandated in gasoline vehicles since model year 1969. 

Turbocharged diesel engines have only required PCV valves since model year 2008. MOVES 

emission rates assume that all 1968 and earlier gasoline vehicles, and 2007 and earlier diesel 

vehicles do not have PCV valves. 

The MOBILE series of models included crankcase emission factors solely for gasoline 

hydrocarbons. For purposes of MOVES, we have developed additional emission factors, as 

explained below. 

Crankcase emissions are calculated in MOVES by chaining the emission calculators 

which calculate start, running, or extended idling emissions to a crankcase emission ratio. 

Crankcase emissions are calculated as a percentage of tailpipe emissions. These emissions are 

calculated selected pollutant processes, including THC, CO, and NOx, and the particulate 

fractions Organic Carbon PM 2.5, Elemental Carbon PM 2.5, Sulfate PM 2.5, Sulfate PM 10,. 

For each of these pollutants, the crankcase emissions are calculated from the start, running 

exhaust, or extended idling emissions of the same pollutant and then multiplying by the 

appropriate ratio in the CrankcaseEmissionRatio table. 

With a working PCV valve, emissions are considered zero. Based on EPA tampering 

surveys, MOVES assumes a 4% PCV valve failure rate.37 Consequently, the emission rates in 

fuel type/model year combinations that have PCV valves are estimated as 4% of the emission 

rates of those years with uncontrolled emissions. 

Very little information is available on crankcase emissions, especially those for gasoline 

vehicles. A literature review was conducted in order to determine the best data sources for 

emission factors (Table 4 - 1).
117

Table 4 - 1. Selected Results for Crankcase Emissions Data 

Authors Year Type # Vehicles HC 

PM(all 

species) CO NOX 

Hare and Baines38 1973 Diesel 1 0.2-4.1 0.9-2.9 0.005-0.43 0.005-0.43 Heinen and Bennett39 1960 Gasoline 5 33 X x x Bowditch40 1968 Gasoline x 70 X x x Montalvo and Hare41 1985 Gasoline 9 1.21-1.92 X x x Williamson42 1995 Diesel 1 50 35 x x Kittelson43 1998 Diesel 1 x 0.038 x 0.005 Hill44 2005 Diesel 9 x 100 x x Ireson45 2005 Diesel 12 x 25-28 x x Zielinka46 2008 Diesel 2 x 20-70 x x 

x = no data 
Based on this literature review, emission factors were estimated for years without 

mandated PCV valves (Table 4 - 2). In absence of better information, gasoline emission factors 

are largely a reflection of diesel research. As noted previously, model years with PCV valves 

were assigned emission factors which were 4% of the emission factors belows 

Table 4 - 2. Emission Rates for Vehicles without PCV systems (percent of exhaust 

emissions) 

Emission Type Gasoline Diesel 

HC 33% 2% 

NOx 0.03% 0.03% 

CO 0.005% 0.005% 

PM (all speciations) 20% 20% 

The crankcase emission factors for HC, CO and NOx may underestimate emissions. 

These percentages of exhaust emissions are generally based on uncontrolled exhaust, which is 

not calculated by MOVES. MOVES produces exhaust estimates based on a number of control 

technologies (such as catalytic converters). Uncontrolled exhaust in the 1970s was significantly 

higher than current tailpipe exhaust. 

A 1995 study by Williamson estimated a significantly higher proportion of HC, CO, and 

NOx exhaust due to crankcase than earlier works. However, Williamson tested only a single 

engine. In absence of more consistent or compelling evidence, the emission factors in MOVES 

maintain consistency with those emission factors used in the NONROAD model. 

Emission factors for other fuels (LPG, methanol, etc) were set equivalent to diesel 

emission factors. Emission factors for electric vehicles were set to zero. 

Generally, the contributions of crankcase emissions to the overall emission inventory are 

expected to decrease as additional diesel vehicles acquire PCV systems.
. References 

 

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119
 

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