Development of Emission Rates for 

Light-Duty Vehicles in the Motor Vehicle 

Emissions Simulator (MOVES2010) 

Final Report
NOTICE

This technical report does not necessarily represent final EPA decisions or 

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

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

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

technical developments.

EPA-420-R-11-011

August 2011

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Development of Emission Rates for 

Light-Duty Vehicles in the Motor Vehicle 

Emissions Simulator (MOVES2010) 

Final Report
i

Table of Contents

1. Gaseous Exhaust Emissions from Light-Duty Gasoline Vehicles (THC, CO, NOx) ..........................1

1.1 Introduction .................................................................................................................................1

1.1.1 MOVES Background. .............................................................................................................1

1.1.2 Light-Duty Vehicles................................................................................................................1

1.1.3 Differences between MOVES and MOBILE..........................................................................2

1.1.4 Overview.................................................................................................................................7

1.2 Emissions Sources (sourceBinID) and Processes (polProcessID)...............................................9

1.2.1 The emissionRateByAge Table.............................................................................................11

1.2.1.1 Age Groups (ageGroupID) ..........................................................................................11

1.3 Exhaust Emissions for Running Operation................................................................................13

1.3.1 Operating Modes (opModeID).............................................................................................13

1.3.2 Scope.....................................................................................................................................15

1.3.3 Emission-Rate development: Subgroup 1 (Model years through 2000) ..............................15

1.3.3.1 Data Sources................................................................................................................15

1.3.3.1.1 Vehicle Descriptors ...............................................................................................15

1.3.3.1.1.1 Track Road-Load Coefficients: Light-Duty Vehicles....................................16

1.3.3.1.2 Test Descriptors.....................................................................................................17

1.3.3.1.3 Candidate Data Sources.........................................................................................17

1.3.3.2 Data Processing and Quality-assurance.......................................................................19

1.3.3.2.1 Sample-design reconstruction (Phoenix only).......................................................20

1.3.3.3 Source selection...........................................................................................................21

1.3.3.4 Methods.......................................................................................................................22

1.3.3.4.1 Data-Driven Rates.................................................................................................22

1.3.3.4.1.1 Rates: Calculation of weighted means...........................................................23

1.3.3.4.1.2 Estimation of Uncertainties for Cell Means: .................................................23

1.3.3.4.2 Model-generated Rates (hole-filling) ....................................................................25

1.3.3.4.2.2 Rates ..............................................................................................................25

1.3.3.4.2.2.1 Coast/Cruise/Acceleration.......................................................................27

1.3.3.4.2.2.2 Braking/Deceleration ..............................................................................30

1.3.3.4.2.3 Estimation of Model Uncertainties................................................................32

1.3.3.4.2.4 Reverse transformation..................................................................................32

1.3.3.4.3 Table Construction ................................................................................................33

1.3.3.5 Verification and Adjustment for High-Power Operating modes.................................34

1.3.3.6 Estimating Rates for non-I/M Areas............................................................................43

1.3.3.7 Stabilization of Emissions with Age ...........................................................................53

1.3.3.7.2 non-I/M Reference Rates.......................................................................................58

1.3.4 Emission-Rate Development: Subgroup 2 (MY 2001 and later) ..........................................60

1.3.4.1 Data Sources................................................................................................................60

1.3.4.1.1 Vehicle Descriptors ...............................................................................................60

1.3.4.2 Estimating I/M Reference Rates..................................................................................60

1.3.4.2.1 Averaging IUVP Results.......................................................................................61

1.3.4.2.2 Develop Phase-In Assumptions.............................................................................65

1.3.4.2.3 Merge FTP results and phase-in Assumptions ......................................................69

1.3.3.2.4 Estimating Emissions by Operating Mode.............................................................75

1.3.4.2.4.1 Running Emissions ........................................................................................75

1.3.4.2.5 Apply Deterioration...............................................................................................81

1.3.4.2.5.1 Recalculate the logarithmic mean..................................................................81

1.3.4.2.5.2 Apply a logarithmic Age slope ......................................................................81
ii

1.3.4.2.5.3 Apply the reverse transformation ..................................................................84

1.3.4.2.6 Estimate non-I/M References................................................................................85

1.4 Exhaust Emissions for Start Operation......................................................................................86

1.4.1 Subgroup 1: Vehicles manufactured in model year 1995 and earlier ..................................86

1.4.1.1 Methods.......................................................................................................................86

1.4.1.1.1 Data Sources...........................................................................................................86

1.4.1.1.2 Defining Start Emissions........................................................................................87

1.4.1.1.3 Relationship between Soak Time and Start Emissions ..........................................87

1.4.2 Subgroup 2: Vehicles manufactured in MY1996 and later...................................................89

1.4.3 Applying Deterioration to Starts ...........................................................................................90

1.4.3.1 Assessing Start Deterioration in relation to Running Deterioration............................90

1.4.3.2 Translation from Mileage to Age Basis.......................................................................99

1.4.3.3 Application of Relative Multiplicative Deterioration................................................101

1.7 Replication and Data-Source Identification.............................................................................108

2. Particulate-Matter Emissions from Light-Duty Vehicles.................................................................112

2.1 Introduction and Background ..................................................................................................112

2.1.1 Particulate Measurement in the Kansas City Study ............................................................113

2.1.2 Causes of Gasoline PM Emissions......................................................................................116

2.2 New Vehicle or Zero Mile Level (ZML) Emission Rates.......................................................118

2.2.1 Longitudinal Studies ...........................................................................................................120

2.2.2 New Vehicle, or ZML Emission Rates and Cycle Effects..................................................121

2.2.3 Aging or Deterioration in Emission Rates ..........................................................................126

2.2.2.4 Age Effects or Deterioration Rates............................................................................126

2.3 Estimating Elemental and Organic Carbon Fractions .............................................................129

2.4 Modal PM Emission Rates ......................................................................................................135

2.4.1 Typical behavior in particulate emissions as measured by the Dustrak and Photoacoustic

Analyzer ...........................................................................................................................................136

2.5 Conclusions .............................................................................................................................144

3. Gaseous and Particulate Emissions from Light-Duty Diesel Vehicles (THC, CO, NOx, PM)........146

3.1 Gaseous Emissions: MY2009 and earlier, Particulate Emissions: MY2003 and earlier.........146

3.1.1.2 Estimating Bag Emissions: .........................................................................................147

3.1.1.3 Assigning Operating Modes for Starts (Adjustment for Soak Time) .........................150

3.1.2 Running Emissions by Operating Mode .............................................................................152

3.2 Gaseous Emissions: MY2010 and Later, Particulate Emissions: MY2004 and Later. ...........156

3.2.1 Gaseous Emissions..............................................................................................................156

3.2.2 Particulate Emissions ..........................................................................................................156

3.3 Particulate Emissions: Estimating Elemental and Organic Carbon Components (EC, OC) ...156

3.3.1 Group 1: MY 2003 and earlier ............................................................................................156

3.3.2 Group 2: MY 2004 and later ...............................................................................................157

4.0 Crankcase Emissions ...................................................................................................................158

Appendix A: Peer-Review Comments and Response: Reviewer 1..........................................................161

Appendix B: Peer-Review Comments and Response: Reviewer 2..........................................................182

References.................................................................................................................................................190
1

1. Gaseous Exhaust Emissions from Light-Duty Gasoline

Vehicles (THC, CO, NOx)

1.1 Introduction

1.1.1 MOVES Background

The material presented in this document is a component of a much larger effort, including the

estimation of emission rates for heavy-duty vehicles, estimation of evaporation emissions,

estimation of usage and activity patterns for vehicles, the compilation and storage of all types of

input data in the MOVES database, and the algorithms that combine and process input

information during model runs, translating inputs and modeling assumptions into inventory

estimates.

Readers not familiar with MOVES may find it useful to access additional documentation

providing a broader view of MOVES, the rationale for its development as a replacement for

MOBILE6, and broad overviews of its design.

• The “Initial Proposal” for MOVES describes the impetus behind the effort to design a

new inventory model from the ground up, with the goal of developing a tool both more

comprehensive and flexible than its predecessor1.

• A subsequent “Draft Design and Implementation Plan” describes the MOVES design and

introduces the reader to concepts and terminology developed for the new model2.

• Readers wishing to further understand the development of the modal design for running

emissions can consult the “Methodology for Developing Modal Emission Rates,”3as well

as the “Shoot Out”

4

conducted among several candidate approaches.

• This document focuses on development of inputs to the MOVES Database. Readers

interested in further understanding the processes used by the model to process inputs into

inventory estimates can consult the MOVES Software Design Reference Manual

(SDRM)5.

A large volume of additional documentary and supporting materials can be obtained at

http://www.epa.gov/otaq/models/moves/movesback.htm. In general, the most recent and

relevant materials are at the top of the page, with older material located further down. However,

as the previous references show, references posted throughout the page are still relevant to the

MOVES model and database in its most recent versions.

1.1.2 Light-Duty Vehicles

This chapter describes the technical development of emission rates for gaseous exhaust

pollutants for light-duty vehicles. These pollutants include total hydrocarbons (THC), carbon
2

monoxide (CO) and oxides of nitrogen (NOx). The resulting model inputs are included in the

MySQL database supporting the MOVES2010 model.

Light-duty vehicles are defined as cars and trucks with gross vehicle weight ratings (GVWR) of

less than 8,500 lbs. For purposes of emissions standards “cars” are designated as “LDV” or

“passenger cars” (PC), and are distinguished from “trucks” which are further subclassified as

“light light-duty trucks” (LLDT) and “heavy light-duty trucks” (HLDT), on the basis of GVWR

≤ 6000 lbs and GVWR > 6000 lbs, respectively. The two broad classes, LLDT and HLDT, are 

further subdivided into LDT1/LDT2, and LDT3/LDT4. As these subdivisions are highly specific

and technical, we do not describe them here. Interested readers can find more information at

http://www.epa.gov/otaq/standards/weights.htm. As MOVES pools all truck classes for

purposes of inventory estimation, we will refer to “cars” and “trucks” throughout.

Exhaust emissions from light-duty vehicles have contributed substantially to urban air pollution,

and have received a great deal of scientific, political and regulatory attention over the past forty

years. The Clean Air Act (CAA), passed in 1970 (and amended in 1977 and 1990), set “National

Ambient Air-Quality Standards” (NAAQS) for HC, CO and NOx. Carbon monoxide is targeted

for its respiratory toxicity, and HC and NOx largely for their roles in production of ground-level

ozone, another pollutant targeted under the CAA. Regulations designed to reduce automobile

emissions to facilitate achievement of compliance with the NAAQS include Tier-1 standards

introduced in the mid 1990’s, followed by National Low-Emission Vehicle (NLEV) standards

starting in 2001, and Tier-2 standards starting in 2004. Concurrently, the state of California and

additional states electing to adopt “California” in lieu of “Federal” standards have implemented

the “LEV-I” and “LEV-II” standards. In addition to introducing more stringent tailpipe

standards, requiring introduction of oxygenated gasolines, and modifying test procedures, the

1990 CAA Amendments expanded requirements for Inspection-and-Maintenance programs

(I/M). The role played by I/M programs in many urban areas over the past twenty years means

that accounting for the existence of such programs is a primary consideration in modeling

tailpipe emissions from light-duty vehicles.

Through a combination of regulation and improved technology, gaseous tailpipe emissions from

light-duty vehicles have declined substantially over the past several decades. Important

milestones in engine and emissions control technology have included the introduction of fuel

injection (replacing carburetion), positive crankcase ventilation (PCV), exhaust gas recirculation

(EGR), catalytic converters, electronic engine controls, and on-board diagnostic systems (OBD).

Development of emission rates thus largely involves constructing a “numerical” account of this

history. However, a detailed account of these developments is beyond the scope of this

document which will focus on the development of emission rates as inputs to the MOVES

database. However, this history has been well described elsewhere, and we refer interested

readers to the EPA website6,7, as well as to the peer-reviewed literature8,9,10,11,12,13.

1.1.3 Differences between MOVES and MOBILE

At the outset, it is useful to highlight four important different between MOVES and MOBILE.

(1) While intending to estimate average emissions across the entire vehicle fleet, MOVES does
3

not distinguish between “normal” and “high emitters,” (2) MOVES inverts MOBILE’s approach

to inspection and maintenance, (3) MOVES is a “modal” model, whereas MOBILE is “nonmodal,” and (4) emission rates developed for MOVES are expressed in time-specific, rather than

distance specific terms, i.e., mass/time (g/hr), rather than mass/distance (g/mi, g/km).

1. A fundamental difference between MOVES and MOBILE is that MOVES does not classify

vehicles into “emitter classes.” The MOBILE model(s) provided different sets of emission rates

for “normal” and “high” emitters. While arbitrary, this distinction made qualitative and practical

sense because the emission rates were themselves averages of FTP test results.

We didn’t attempt a similar approach in MOVES for several reasons, some conceptual, some

practical. The main conceptual reason is that in review of data, we did not see clear evidence of

distinct “high emitter” subpopulations. Rather, review of emissions data seems to show highly

skewed but continuous distributions with long tails, which we treat as log-normal for modeling

purposes. Clearly, the vehicles in the upper percentiles of the distributions make

disproportionate contributions to the inventory, assuming similar driving patterns to cleaner

vehicles in the lower percentiles. Based on these observations, our approach has been to capture

the mean of the entire distribution, including the upper tail. We illustrate these concepts using

two examples, based on aggregate cycle means from the Phoenix I/M program, measured on the

IM147 cycle.

Figure 1 shows cumulative distributions of NOx emissions for “young” cars, aged 0-3 years,

representing two sets of emissions standards. The blue distribution represents “Tier 0” vehicles,

manufactured prior to 1994; the green represents “Tier 1” vehicles, manufactured in 1996-97,

and the red represents a mix of the two, during the Tier-1 phase-in period (1994-95). Note that

the combination of reduced standards and improved technology pushes the entire distribution

“leftward” or towards lower emission levels.
4

Figure 1. Cumulative distributions of running NOx for cars, Age 0-3, measured on the IM147 cycle (Source:

Phoenix I/M program).

A similar example, Figure 2, shows NOx distributions for Tier-1 vehicles (MY1996-97) at two

different age levels, 0-3 and 8-9 years old, shown in blue and red, respectively. Qualitatively, the

picture looks very similar to Figure 1, except that in this case we can see the effect of age in

pushing the entire distribution “rightwards,” towards higher emission levels. Note that the entire

distribution shifts, including the lower percentiles, not only the “high emitters” in the upper

percentiles.
5

Figure 2. Cumulative distributions of running NOx for Tier-1 cars, at two age levels, measured on the IM147

cycle (Source: Phoenix I/M program).

A pattern not necessarily apparent in Figure 1 emerges if we view the same distributions on a

logarithmic scale, as shown in Figure 3. In the logarithmic view, we can see that the distribution

at 8-9 years is the same is that at 0-3 years, but shifted to the right; that is, the shapes (variances)

of the two distributions are very similar, but the means are shifted. These figures illustrate the

“logarithmic” or “multiplicative” scaling typical in emissions data. The utility of logarithms in

modeling follows from the fact that multiplicative patterns representing actual changes can be

represented and projected very conveniently as additive changes in logarithmic space. These

patterns obtain whether the data are analyzed with respect to technology, age or power. The

development of emission rates, as described in this chapter (and for PM in Chapter 2), relies

heavily on these concepts. Figure 4 shows a similar picture to Figure 2, except for THC; what is

notable is that the THC distributions are even more skewed than the NOx distributions.
6

Figure 3. Cumulative distributions of running NOx for Tier-1 cars, at two age levels, measured on the IM147

cycle (LOGARITHMIC SCALE) (Source: Phoenix I/M program).

Figure 4. Cumulative distributions of running THC for Tier-1 cars, at two age levels, measured on the

IM147 cycle (Source: Phoenix I/M program).
7

In addition to the conceptual reason just illustrated, there were practical reasons for not creating

one or more “high-emitter” classes: (1) a vehicle or test showing high emissions for one

pollutant need not show high emissions for other pollutants, (2) high emissions may be a

transitory phenomenon in many cases, i.e., vehicles with high results for one set of

measurements may not show similar results if re-measured; in such cases it is very difficult to

determine whether the apparent change is due to an actual change in the vehicle or the

notoriously high variability of emissions measurements, (3) given that rate development for

MOVES operating modes is not coupled to the FTP (or any particular cycle), convenient and

non-arbitrary definitions of “high emitter” are not readily available, and (4) distinction of emitter

classes would require that the intensive process of rate development be repeated for each class,

including the projection of emissions by age and power, and development of distinct adjustments

for temperature and fuel (performed separately). The detailed data required for these analyses

and their projection into the future is not available.

2. A second important difference between MOVES and MOBILE is that MOVES inverts

MOBILE’s approach to inspection and maintenance. That is, the emission rates provided with

MOBILE represented “non-I/M” conditions, and MOBILE represented I/M conditions by

making adjustments during model runs. By contrast, the MOVES input table contains two sets

of rates, representing “I/M reference” and “non-I/M” conditions, respectively. In development

of these rates, “I/M conditions” were assigned as the default case, and rates representing “nonI/M” conditions were developed in relation to rates representing “I/M conditions.” During model

runs, MOVES represents particular I/M programs as a function of both sets of rates, modified by

adjustments calculated to represent the parameters of specific programs. These topics are

discussed in greater detail in 1.3.3.6.

3. A third major difference between MOVES and MOBILE is that MOVES is modal, whereas

MOBILE was not. This feature gives MOVES tremendous flexibility, allowing users to

represent any driving pattern, across a range of temporal and spatial scales. The modal emission

rates are applied consistently at the different analysis scales under which MOVES operates –

national, county and project.

4. Finally, emission rates in MOVES are expressed as “time-specific” rates (mass/time, g/hr), as

opposed to “distance-specific” rates (g/mi), as were rates in MOBILE. With respect to model

design, the purpose for this change was to introduce a measurement basis that would be

applicable to all emissions sources, processes, and operating modes, including those for which a

distance-specific basis is not applicable. Examples include all emissions for nonroad equipment,

which are expressed on a mass/work basis in the NONROAD model (g/hp-hr, g/kW-hr), and idle

or “hotelling” emissions for all sources, which occur while the source is stationary.

1.1.4 Overview

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

gaseous-pollutant emissions from gasoline-fueled light-duty vehicles. The values in this table

describe the “base rates” (meanBaseRate). These values represent mean emissions on the

MOVES reference fuel on a temperature range of 68-86 oF, and unadjusted for the effects of

temperature, humidity, air-conditioning and inspection-and-maintenance programs (I/M). The

adjustments for these factors, applied during MOVES runs, are described in a separate report:
8

“MOVES2010 Highway Vehicle Temperature, Humidity, Air Conditioning, and Inspection and

Maintenance Adjustments.”

34

The emissionRateByAge table includes rates representing start and running operation, defined as

distinct “processes” in MOVES. Rates representing “running operation” are described in Section

1.3, and those for “start operation” are described in section 1.4.

For running emissions, section 1.3.3 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 (subsection 1.3.3) or by development and application of statistical “hole-filling” models (sub-section

1.3.4). 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 program14.

Because the analyses described in sub-sections 1.3.3 and 1.3.4 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 I/M cycles. The development and application of adjustments

to rates in operating modes at high power is discussed in sub-section 1.3.3.5.

As mentioned, the rates described in 1.3.3 and 1.3.4 represent emission rates for vehicles under

the requirements of an inspection-and-maintenance program, specifically the program in

Phoenix, AZ, during calendar years 1995-2005. For this reason, we refer to these rates as “I/M

reference rates.” With respect to the I/M reference rates, we describe the approach taken to

estimating rates in non-I/M areas, designated as the “non-I/M reference rates”, in 1.3.3.6. For

runs representing areas without an I/M program, MOVES uses the non-I/M reference rates. For

runs representing areas with I/M programs, MOVES adjusts the I/M reference rates to account

for the particular aspects of the program(s) represented. It is important to note the I/M reference

rates assume full compliance with program requirements within the area. MOVES discounts

estimated emissions for non-compliance during a model run, which is then represented in the

results34.

We have observed, as have other researchers, that emissions deterioration tends to follow

exponential, or log-linear trends over the first 8-9 years. However, after this point, the trends

enter a declining phase, during which increases in mean emissions continue at a reduced rate. For

the I/M reference rates, we assume that rates stabilize between 12 and 15 years of age. For the

non-I/M reference rates, we assume that they continue to increase at reduced rates through 20+

years of age. The analyses guiding these assumptions are described in 1.3.3.7.

For start emissions, we also applied different methods to different datasets to derive two sets of

rates. For vehicles manufactured in 1995 and earlier, the process of rate development is

described in 1.4.1. For vehicles manufactured in 1996 and later, the process of rate development

is described in 1.4.2. We assume that emissions deterioration affects start as well as running

emissions. Sub-section 1.4.3 describes how we estimate deterioration in start emissions in

relation to deterioration in running emissions.
9

1.2 Emissions Sources (sourceBinID) and Processes (polProcessID)

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

pollutants are the gaseous criteria pollutants: total hydrocarbons (THC), carbon monoxide (CO)

and oxides of nitrogen (NOx). The relevant processes are exhaust emissions emitted during

engine start and running processes, i.e., “exhaust start” and “exhaust running.” Combinations of

pollutant and process relevant to this chapter are shown in Table 1. For start emissions, the

meanBaseRate is expressed in units of g/start, and for running emissions, the meanBaseRate is

expressed in units of g/hr, which MOVES terminology designates more specifically as “g/SHO,”

where SHO denotes “source-hours operating.”

Note that this document describes only emission rates for exhaust hydrocarbons. Modeling of

emission rates for evaporative hydrocarbons is described in a separate report: Development of

Evaporative Emissions Calculations for the Motor Vehicle Emissions Simulator15

.

For these pollutants and processes emissions sources include light-duty vehicles (cars and

trucks). Note that the engine-size and weight-class attributes are not used to classify vehicles.

For light-duty vehicles, these parameters are assumed not to influence emissions, as these

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

In the emissionRateByAge table, the emissions source is described by a label known as the

“sourceBinID”. This identifier is constructed as a pattern variable incorporating the attributes

shown in Table 2. Assignment of the attributes just described allows assignment of the sourcebin identifier. The identifier is a 19-digit numeric label, of the form “1fftteeyysssswwww00,”

where each component is defined as follows:

1 is the literal value “1,” which serves as a leading value to set the magnitude of the entire

label,

ff represents the fueltypeID,

tt represents the engTechID,

ee represents the regClassID,

yy represents the shortModYrGrpID,

ssss represents the engSizeID,

wwww represents the weightClassID, and

00 is the literal value “00,” which serves to provide two trailing zeroes at the end of the label.

The individual attributes are assembled in the proper sequence by constructing the sourceBinID

as a pattern variable, where
10

weightCla 10ssID

engSizeID 10

shortModYrGroupID 10

regClassID 10

engTechID 10

fuelTypeID 10

101DsourcebinI

2

6

10

12

14

16

18

+ ×

+ ×

+ ×

+ ×

+ ×

+ ×

×=

Equation 1

As an example, Table 3 shows the construction of sourceBin labels for light-duty gasoline

vehicles, manufactured in model years 1998 and 2010.

Table 1. Combinations of pollants and processes for gaseous pollutant emissions.

pollutantName1

pollutantID1processName2processID2polProcessID3Section

HC 1 Running exhaust 1 101

Start exhaust 2 102

CO 2 Running exhaust 1 201

Start exhaust 2 202

NOx 3 Running exhaust 1 301

Start exhaust 2 302

1

as shown in the database table “pollutant.”

2

as shown in the database table “emissionProcess.”

3

as shown in the database table “emissionRateByAge.”

Table 2. Construction of sourceBins for Exhaust Emissions for light-duty vehicles.

Parameter MOVES Database Attribute1 Values

Fuel type fuelTypeID Gasoline = 01

Diesel = 02

Ethanol = 05

Engine Technology engtechid 01= “Conventional internal

Combustion”

Regulatory Class regClassID 20 = “Car” (LDV)

30 = “Truck” (LDT)

Model-Year group shortModYrGroupID Varies2

Engine Size Class engSizeID <not used>

Vehicle Test Weight weightClassID <not used>

1

as used in the database table “emissionRateByAge.”

2

as defined in the database table “modelYearGroup.”
11

Table 3 Examples of sourceBinID construction for cars and trucks in model years 1998 and 2010.

fuelTypeID engTechID regClassID shortModYrGroupID sourceBinID

1 (Gasoline) 1 (conventional) 20 (Car) 98 (MY 1998) 1 01 01 20 98 0000 0000 00

1 1 30 (Truck) 30 (MY 2010) 1 01 01 30 98 0000 0000 00

1 1 20 (Car) 98 (MY 1998) 1 01 01 20 30 0000 0000 00

1 1 30 (Truck) 30 (MY 2010) 1 01 01 30 30 0000 0000 00

1.2.1 The emissionRateByAge Table.

The rates described in this document are stored in the MOVES emissionRateByAge table. This

table includes five fields, as shown in Table 4. Consistent with the MOVES modal approach, the

table contains mean base emission rates (meanBaseRate) and associated estimates of uncertainty

in these means for motor vehicles classified as “emissions sources” (sourceBinID), and by

“operating mode” (opModeID). The table includes rates for vehicles inside and outside of

Inspection-and-Maintenance Areas. The uncertainty estimates are expressed as coefficients of

variation for the mean (meanBaseRateCV); this term is synonymous with the “relative standard

error (RSE). In this section, we will describe the processes of data classification by source bin

and operating mode, calculation of mean emission rates, and statistical evaluation of the results.

1.2.1.1 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 9 years.
12

Table 4. Description of the EmissionRateByAge Table.

Field Symbol Description

SourceBinID --- Source Bin identifier. See Table 2

and Table 3 and Equation 1.

PolProcessID --- Combines pollutant and process. See

Table 1.

opModeID Operating mode: defined separately

for running and start emissions. See

Table 5.

ageGroupID Indicates age range for specific

emission rates. See 1.2.1.1.

meanBaseRate EcellMean emission rates in areas not

influenced by inspection and

maintenance programs.

meanBaseRateCV CVE

Coefficient of variation of the cell

mean (relative standard error, RSE),

for the meanBaseRate.

meanBaseRateIM Mean emission rate in areas subject

to an I/M program with features

similar to the Phoenix program . See

1.3.7.

meanBaseRateIM

CV

Coefficient of variation of the cell

mean (relative standard error, RSE),

for the meanBaseRateIM.

dataSourceID Numeric label indicating the data

source(s) and method(s) used to

develop specific rates.
13

1.3 Exhaust Emissions for Running Operation

Running operation is defined as operation of internal-combustion engines after the engine and

emission control systems have stabilized at operating temperature, i.e., “hot-stabilized”

operation.

1.3.1 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 passengers16. It is estimated in terms of a vehicle’s speed and mass

(commonly referred to as weight), as shown in Equation 2

m

amvCvBvAv

P

ttttt

t

+++

=

32

,V

Equation 2

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

• speed at time t (vt, m/sec),

• acceleration at (m/sec2) ,

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

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

3

, representing rolling resistance, rotational

resistance and aerodynamic drag, in units of kW-sec/m, kW-sec2/m2and kW-sec3/m3,

respectively.

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. Note that during

model operation, MOVES does account for grade when characterizing vehicle activity. For a

description of this process, see the “Vehicle Population and Activity” report17.

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

running-exhaust process (Table 5). Aside from deceleration/braking, which is defined in 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/metric ton. For reference, each mode is identified by a numeric label, the

“opModeID.”
14

Table 5. Definition of MOVES Operating Modes for Running-Exhaust operation.

Operating

Mode

Operating Mode

Description

Vehicle-Specific

Power

(VSPt, kW/metric

ton)

Vehicle Speed

(vt,mi/hr)

Vehicle Acceleration

(at, 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 1 ≤ vt < 25

12 Cruise/Acceleration 0 ≤ VSPt < 3 1 ≤ vt < 25

13 Cruise/Acceleration 3 ≤ VSPt < 6 1 ≤ vt < 25

14 Cruise/Acceleration 6 ≤ VSPt < 9 1 ≤ vt < 25

15 Cruise/Acceleration 9 ≤ VSPt < 12 1 ≤ vt < 25

16 Cruise/Acceleration 12 ≤ VSPt 1 ≤ 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
15

1.3.2 Scope

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

various sources without regard for the residence locations for vehicles measured. In contrast,

when turning attention to the regulated gaseous pollutants, it is essential to know with some

degree of confidence whether vehicles had been subject to inspection-and-maintenance (I/M)

requirements at or previous to the time of measurement. After reviewing data sources, it became

clear that the volumes of data collected within I/M areas vastly exceeded those collected in nonI/M areas. We also concluded that I/M programs themselves could provide large and valuable

sources 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 “reference”

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 MOBILE6, in which “non-I/M” is the “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.

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

1.3.3.1 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.

• Vehicles were subject to I/M program requirements at the time of measurement.

1.3.3.1.1 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 6.
16

Table 6. 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 cars (LDV)

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

1.3.3.1.1.1 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 318.

( )

( ) 















⋅

⋅

⋅=

















⋅

⋅

⋅=

















⋅

⋅

⋅=

3

250

1

2

250

1

250

1

TRLHP

TRLHP

TRLHP

cv

c

PFC

cv

c

PFB

cv

c

PFA

C

B

A

Equation 3

where:

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

PFB = default power fraction for coefficient 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.19
17

1.3.3.1.2 Test Descriptors

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

specific vehicles. Essential items for use in rate development are listed in Table 7.

Table 7. 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 correctly to pass or fail categories

Test weight lb Calculate vehicle-specific power

1.3.3.1.3 Candidate Data Sources

In addition to the parameters listed in Table 6 and Table 7, datasets with historic depth and large

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

emissions as well as trends with vehicle age.

At the outset, a large volume of emissions data was available, representing over 500,000 vehicles

when taken together (Table 8). In some cases they could be combined as broadly comparable

pairs representing I/M and non-I/M conditions. While not all available data could receive

detailed attention, due to limitations in time and resources, a selection of likely candidates was

subjected to a high degree of scrutiny and quality-assurance, after which some were excluded

from further consideration for specific reasons.

Table 8. Datasets available for use in estimating Running emissions from cars and trucks.

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

NY (New York) Maryland/N Virginia VA (Richmond)

GA (Atlanta) GA (Augusta/Macon)

NE (Omaha)

OK (Tulsa)
18

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

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

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 hydrocarbon species relative to other techniques, such as 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.20 (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 data21, 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, deceleration/braking or idle modes. For these

reasons we reserved the RSD for additional roles, such as verification of results obtained from

dynamometer data.

Table 9. 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 1995-1999

2002-2005

1999-2002 2002-2005

Size (no. tests) 8,900 62,500 8,100 2,200,000

Dynamometer datasets that received serious consideration are described below and summarized

in Table 9.

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 prior to conduction of IM240 tests. 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
19

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.

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-screening bias” proved

intractable, 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 gaseous pollutants.

1.3.3.2 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
20

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

seconds of tests (for IM240s only).

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 (e.g., 300

mpg). 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.

1.3.3.2.1 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 5. 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 4.

CYMY,p,

CYMY,p,

CYMY,p,

CYMY,f,

CYMY,f,

CYMY,f,

N

n

f

N

n

f = = Equation 4

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 5.
w MY,f, CY

Figure 5. Stratified sampling as applied in selection of the random evaluation sample in the Phoenix I/M

Program (CY 2002-05).

1.3.3.3 Source selection

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

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

other datasets, including selected remo

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 assessme

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.

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

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

In addition, pooling the three datasets would have

may not be apparent at first glance

sizes. Thus, if the datasets were pooled without

have exerted much stronger influence than the

disparities in influence by assigning the different datasets similar or proportional influence would

21

CYMY,p,

CYMY,p,

CYMY,f,

CY

1 1

f

w

f

= =

. Stratified sampling as applied in selection of the random evaluation sample in the Phoenix I/M

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

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

d only by Phoenix in any case.

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

may not be apparent at first glance. Table 9 shows that the datasets were of greatly

sizes. Thus, if the datasets were pooled without some 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

Equation 5

. Stratified sampling as applied in selection of the random evaluation sample in the Phoenix I/M

and NY datasets,

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

sensing data, for purposes of comparison. This course

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

nt 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,

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

several difficult technical issues that

greatly differing

type of relative weighting, Phoenix would

others in most shared calendar years. To rectify

disparities in influence by assigning the different datasets similar or proportional influence would
22

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 described)

above), 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.

Finally, the selection of the Phoenix data provided a relatively consistent basis for specification

of a “reference fuel,” and development of associated fuel adjustments34.

1.3.3.4 Methods

1.3.3.4.1 Data-Driven Rates

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

statistics for each combination of sourceBinID, ageGroup and operating mode (i.e., table cell).

We classified the data by regulatory class (LDV=”cars”, LDT=”trucks”), model-year group, age

group and operating mode (Table 5). The model-year groups used are shown in Table 7, along

with corresponding samples of passing and failing tests.

Table 10. Test sample sizes for the Phoenix random evaluation sample (n = no. tests)

Model-year

group1

Cars Trucks

fail2pass 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

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

model-year groups used in the MOVES database.

2 Note that ‘failure’ can indicate failure for CO, HC or NOx, as applicable.
23

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’.

1.3.3.4.1.1 Rates: Calculation of weighted means

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

∑

∑

=

= =

test

test

1

1

,

n

i

i

n

i

tii

h

w

Rw

E Equation 6

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.

1.3.3.4.1.2 Estimation of Uncertainties for Cell Means:

A new feature of MOVES is its ability to estimate uncertainty in emissions projections. In the

emissionRateByAge table, uncertainties for individual rates are stored in the

“meanBaseRateCV” fields (Table 4).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-test variability22. To enable estimation of variances under this approach, we

calculated a set of summary statistics, as listed below:

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

given cell.

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

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

test on an individual vehicle.
24

Cell sample size (nh,i): the total number of individual measurements on all vehicles in a cell,

where each count represents a measurement collected at an approximate frequency of 1.0 Hz,

(i.e., “second-by-second”).

Test variance (

2

i

s ): the variance of measurements for each 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.

Weighted Between-Test variance component (

2

b

s ): 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

( )

∑

∑

=

=

−

−

=

i

i

n

i

i

hi

n

i

i

w

EEw

s

1

2

2 1

b

1

Equation 7

Weighted Within-Test Variance Component (

2

w

s ): the variance component due to variability

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

Equation 8

Variance of the cell mean (

2

E

s ): 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 += Equation 9

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 = Equation 10
25

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

1.3.3.4.2 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

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

required “hind-casting” emissions for younger vehicles for older model years, as well 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/metric ton.

Figure 6. Model-year by Age Structure of the Phoenix I/M Random Evaluation Sample.

1.3.3.4.2.2 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. Note that the extrapolated values were

modified on a case-by-case basis as described in section 1.5.5.

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/metric ton ( 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

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
26

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 5.

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

brake/deceleration and idle, we fit one model incorporating 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 other terms (Table 5). Overall, we fit

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

10, 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 11). This sampling was performed to fit models on smaller volumes of data that

a standard desktop computer could handle. The sample was stratified by test result (pass, fail)

and age, with allocation proportional to that in the sample pool. Within each result age stratum,

tests were drawn using simple random sampling, and sampling frequencies and weights, fstrat and

wstrat, calculated as

strat

strat

strat

strat

strat

strat

strat

1

,

n

N

f

w

N

n

f = == Equation 11

where nstrat and Nstrat are the number of tests selected from a stratum and total number of tests in

the stratum, respectively. Then, for each test selected, a final weight was calculated as the

product of the stratum weight and the initial sampling weight (wresult,MY,CY), as shown in Equation

5.

final = www stratCYMY,result, Equation 12
27

Table 11. Sample sizes for statistical modeling, by regulatory class and test result.

Model-year group LDV LDT

fail pass fail pass

1981-82 645 554 476 723

1983-85 569 631 508 691

1980-89 375 828 343 856

1990-93 260 944 209 991

1994-95 406 1,995 378 2,021

1996-98 663 1,738

1996 346 854

1997-8 671 1,730

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

described below.

1.3.3.4.2.2.1 Coast/Cruise/Acceleration

Means model

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

Eh 7V654tsPsaPPP i ++++++++= εγβββββββ

3

V3

2

V2V10

ln Equation 13

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

(VSP, kW/metric ton),

• 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 PV, 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

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
28

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 11). 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

Equation 14

where PV 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 12.

Table 12. 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 groups As in Table 11

× 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
29

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(5)250(543210 Equation 15

Then, for each table cell, we constructed a vector of predictors (Xh). Equation 16 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 13.

[ ] V

3

V

2

VV Xh = 1 001 PaPPP Equation 16

Table 13. 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 + 34.0

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.

ln E = X

'

hh ‰ Equation 17
30

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

nine terms

[ ] α = α α α α3210 Equation 18

[ aPaP ] Xh = 1 V V Equation 19

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

s = hhl ‡X

2

, Equation 20

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 cars, and the 1997-98 model-year group for trucks.

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 slopes from the available data would have given 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, denoted as β0*, were recalculated by rearranging Equation 13 to evaluate the model in

operating mode 24, using the age slope from the previous model-year group (β4*) and an

estimate of ln-emissions 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/metric ton and the speed

class is 25-50 mph.

)5025(5 6

*

43

3

2

2

* 1

*

β0 Ea a*5.75.75.7ln βββββ − −−−−−−= 5.7 β Equation 21

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.

1.3.3.4.2.2.2 Braking/Deceleration

Means model

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

modes, however, 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:
31

= β + β +γ +ε h i E ta 710ln Equation 22

Variances model

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

of age.

sl 10 a ++= εαα

2

Equation 23

Model application

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

calculated as in Table 12, except that the number of operating modes is 2, rather than 21. We set

up coefficient and predictor vectors, as before.

For the means and variances sub-models the vectors are

[ ] β = β β10 Equation 24

and

[ a ] Xh = 1 Equation 25

respectively.

For the variances model the coefficients vector is

[ ] = α α10‡ Equation 26

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

As with coast/cruise/accel 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 27 is a rearrangement of Equation 22.

ln 4**

*

β0 a −= β aE Equation 27

After these steps, the imputed values of lnEh were calculated, as in Equation 19.
32

1.3.3.4.2.3 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 Equation 28

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 Equation 29

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 cell23. 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 co-variances represent the uncertainties in each coefficient in relation

to itself and the others.

1.3.3.4.2.4 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 = Equation 30

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

The left-hand “means” term represents the geometric mean, or the center of the implied lognormal distribution, whereas the right-hand “variance” term reflects the influence of the “highemitting” vehicles representing the tail of the distribution.
33

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

30 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

e

x

s ln2

2

Equation 31

In practice one of these options was selected based on 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 30 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 (

2

Eh

s ), as in Equation 9. Finally,

we calculated the CV-of-the-mean (CVEh) for each modeled emission rate, as in Equation 10.

1.3.3.4.3 Table Construction

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

(Equation 6 to Equation 10), shaded area in Figure 6) and the complete set generated through

modeling (Equation 13 to Equation 31) 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% ( Eh

CV <

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

illustration, results of both methods are presented separately.

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 14.
34

Table 14. Mapping “Analytic” Model-year Groups onto MOVES database Model-year groups

“Analytic” “MOVES database” modelYearGroupID shortModYrGroupID

Cars Trucks

1981-82 1981-82 1980 and previous 19601980 1

1981-82 1981-82 1981-82 19811982 61

1983-85 1983-85 1983-84 19831984 62

1983-85 1983-85 1985 1985 85

1986-89 1986-89 1986-87 19861987 63

1986-89 1986-89 1988-89 19881989 64

1990-93 1990-93 1990 1990 90

1990-93 1990-93 1991-1993 19911993 65

1994-95 1994-95 1994 1994 94

1994-95 1994-95 1995 1995 95

1996-98 1996 1996 1996 96

1996-98 1997-98 1997 1997 97

1996-98 1997-98 1998 1998 98

1996-98 1997-98 1999 1999 99

1996-98 1997-98 2000 2000 20

1.3.3.5 Verification and Adjustment for High-Power Operating modes

The rates described were derived from data measured on IM240 or IM147 cycles, 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/metric ton 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/metric ton.

Based on initial review and comment on this aspect of the analysis, 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)24

and the remainder on selected EPA programs, all stored in OTAQ’s Mobile-Source 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 specific

driving patterns, as does the FTP, but rather to exercise vehicles through the ranges of speed,

acceleration and power comprising the performance of most light-duty vehicles. Several variants

of the MEC were developed to provide a database to inform the development of the
35

Comprehensive Modal Emissions Model (CMEM)24. Driving traces for the US06 and MEC

cycles are shown in Figure 7 and Figure 8. Both cycles range in speed up to over 70 mph and in

VSP up to and exceeding 30 kW/metric ton.

Figure 7. Example Speed Traces for the US06 and MEC Drive Cycles.

Figure 8. Example vehicle-specific power (VSP) traces for the US06 and MEC cycles.
36

Table 15 summarizes the numbers of available tests by regulatory class, model-year group and

drive cycle, with numbers of tests differing in each model-year group. Samples were somewhat

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

Table 15. Sample sizes for US06 and MEC Samples (No. tests)

Model-year group Car Truck 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 9, Figure 10 and Figure 11 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 9. CO emissions (g/sec) on aggressive cycles , vs. VSP, by regulatory class and model-year group.
37

Figure 10. THC emissions (g/sec) on aggressive cycles , vs. VSP, by regulatory class and model-year group.

Figure 11. NOx emissions (g/sec) on aggressive cycles, vs. VSP, by regulatory class and model-year 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 15. After averaging, we

calculated ratios from 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/metric ton. With mode 27 as a reference, we calculated ratios

to modes 28, 29 and 30.
38

3029,28,for,

27,

,

27: = i =

E

E

R

h

ih

i Equation 32

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

4039,38,for,

37,

,

37: = i =

E

E

R

h

ih

i Equation 33

After calculating the ratios, we calculated ratio-based emissions estimates (E

R

) 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:, Equation 34

respectively, where Eh

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

previously described.

The next step, the process by which ratio-based rates were selected as rates for particular

operating modes on a case-by-case basis has changed substantially for the final rates. In the draft,

we calculated upper and lower confidence limits for E

R

and replaced the initial rate with E

R

if it

fell outside the confidence band, i.e., if the initial rate was greater than the upper bound or lower

than the lower bound. Evaluation of the results of this approach showed, however, that it gave

spurious results in many cases. We found it impossible to assign a confidence level for the band

that would work in all cases, i.e., sufficiently sensitive to identify and correct problem cases, but

not so sensitive so as to make unnecessary modifications.

For the final rates, we developed a different logic for applying the ratio-based rates. One change

from the draft is that ratio-based rates were considered only for modes 29,30, 39 and 40, i.e.,

modes spanning the range of VSP beyond the IM147. Modes 28 and 38 are partially covered by

the I/M cycles, and the differences among the data, model and ratios were generally much

smaller than for the four highest modes. The steps in the revised process are:

1) Identify acceptable candidate values (data, model or ratio). The data values were considered

acceptable if (1) a value was present, (2) it met the acceptability criteria (described above on

page 33) and (3) it was greater than the value in the next lowest mode. Similarly, predicted

values were acceptable if they exceeded the value for the preceding operating mode.

Following these evaluations, the final value was selected as the minimum of the acceptable

candidates. These criteria were applied sequentially to prevent declining VSP trends with

increasing power. As a first step, values were selected for operating modes 29 and 39, relative to

modes 28 and 38. In a successive step, values were selected for 30 and 40, relative to those

selected for 29 and 39, respectively. We present some examples below, showing differences

between the draft and final rates.
39

In the THC example (Figure 12), the final values are substantially reduced, particularly for

modes 29 and 30. In the draft (a), 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. In the final rates, the results vary. For

modes 29 and 30, the data values meet the criterion of the minimum value giving an increasing

trend from mode 28 – 30. However, for modes 39 and 40, the ratio and the model give the

values meeting the criterion, as shown in (c).

The example for CO shows different behavior in the draft, but a similar outcome in the final

(Figure 13). In the draft (a), the initial values for modes 28-30 all fall within the confidence

intervals for the ratio-based value 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. For operating modes 29 and 30,

the data is selected as the minimum option available, as with HC. For modes 39 and 40, the

model is similarly selected. In the final rates, the ratio based values are not adopted for this

example, as they had been in the draft, and the net result is a decrease in CO rates in the affected

operating modes.

Finally, in the NOx example (Figure 14), the initial rates are replaced in five out of six cases in

the draft (a). The initial values for 28-30 and 40 all fall below the lower confidence limit,

whereas that for 30 falls above the upper confidence limit. In the final, the ratio is used more

sparingly, as in the HC and CO examples. Model values are used in two cases (modes 30 and

40) and the ratio in one case (mode 39).

These examples highlight the uncertainty of projecting emissions at high power and of projecting

beyond the range of the IM147. Uncertainties are much smaller for opModes 28 and 38 than for

29,30 , 39 and 40. This pattern may be due to the fact that, for modes 28 and 38, the power range

for the IM147 overlaps somewhat the range of the aggressive cycles. For this reason, the degree

of extrapolation is lower and the power trends are similar.
40

Figure 12. THC emission rates (g/hr), vs. VSP for MY 1998 cars at ages 4-5 years: (a) options

for draft rates, (b) Options for final model (data, model and ratio) and (c) options selected for

final rates.

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

(c) Final: options selected

(b) Final: options available

(a) Draft
41

Figure 13. CO emission rates (g/hr), vs. operating mode for MY-1998 trucks at ages 6-7: (a) options for draft

rates , (b) Options for final model (data, model and ratio and (c) options selected for final rates.

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

(c) Final: options selected

(b) Final: options available

(a) Draft
42

Figure 14. NOx emission rates (g/hr) vs. operating mode for MY-1995 Cars at ages 8-9: (a) options for draft

rates , (b) Options for final model (data, model and ratio and (c) options selected for final rates.

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

(a) Draft

(b) Final: options available

(c) Final: options selected
43

1.3.3.6 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 1.3.3.2 and 1.3.3.3. 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.

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 15(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 15(b).
Figure 15. General 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.

For convenience, we refer to the first approach as the “between

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 t

“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

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

vehicles are presented in Table 16

Table 16. Criteria used to identify vehicles migrating into the Phoenix Program.

logic

OR

AND NOT

AND

AND

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

44

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

to the first approach as the “between-area” approach, and the second

area” approach. Neither approach attempts to measure the incremental difference

gram from one cycle to the next.

ted 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

t yet been influenced by the program. The specific criteria to identify particular migrating

16.

iteria used to identify vehicles migrating into the Phoenix Program.

Criterion

The vehicle comes from from out-of-state

From a non-I/M county in AZ

From other I/M areas

Receiving very first test in Phoenix program

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

e vehicles. The last registered location of vehicles was identified

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

area” approach, and the second

area” approach. Neither approach attempts to measure the incremental difference

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

t yet been influenced by the program. The specific criteria to identify particular migrating

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

e vehicles. The last registered location of vehicles was identified
45

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 program25.

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 16. Geographic Distribution of Vehicles migrating into the Phoenix I/M area, 1995-2005.

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 35. 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.

I/M

I/M-non Ratio

E

E

= Equation 35

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 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 the previous results for our purposes, we

translated averages for migrating and program fleets into ratios as in Equation 35.

6%

14% 23% 24% 0%

15% 5%

12%

1%
46

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

Continuous Atlanta Fleet Evaluation (CAFE) Program26,27. Unlike our own analysis, this

program involves a comparison between two geographic areas. The “I/M area” is the thirteencounty 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 nonI/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 17. 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

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. The 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.
47

Figure 17. 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).

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 to have shorter exemptions; for

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

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

(a) CO

(b) THC

(c) NOx
48

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 year age Group to a value of 1.0 at 0 years of age, as shown in Figure 18. 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 18. 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.

Figure 19 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 years (for NOx) and 10 years (for HC and CO).

MOVES AgeGroup

Analysis AgeGroup
49

Figure 19. 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 10-1 4 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
50

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

rates.

I/Mnon, I/M,

*Ratio Eh − = Eh Equation 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

= +

















∂

∂

+ 















∂

∂

=

−

−

− −

Equation 37

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 and appropriate given the additional

assumptions involved in developing the non-I/M reference rate.

Figure 20 shows an example of the reference rates vs. operating mode, for all three pollutants.

Note that not all the modes are shown, to allow examination of differences between non-I/M and

I/M rates at lower VSP. Figure 21 shows corresponding trends by age for two operating modes.

The first is opmode 11, (speed = 1-25 mph, VSP <0 kW/metric ton) and 27 (speed = 25-50 mph,

VSP = 12-18 kW/metric ton). An clear observation from both plots 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, in absolute (but not relative), terms.
51

Figure 20. Non-I/M and I/M Reference Rates by Operating Mode (Example: Cars, MY 1994, at 8-9 years of

age)

0

10

20

30

40

50

60

70

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

opModeID

Emission Rate (g/hr)

I/M Reference

non-I/M Reference

0

500

1000

1500

2000

2500

3000

3500

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

opModeID

Emission Rate (g/hr)

I/M Reference

non-I/M Reference

0

50

100

150

200

250

300

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

opModeID

Emission Rate (g/hr)

I/M Reference

non-I/M Reference

(a) THC

(b) CO

(c) NOx
52

Figure 21. Non-I/M and I/M Reference Rates vs. Age for Two Operating Modes (Example: Cars,

MYG 1994).

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25

Age (years)

Emissions (g/hr)

I/M Ref: opMode 11

non-I/M Ref: Mode 11

I/M Ref: Mode 27

non-I/M Ref: Mode 27

0

100

200

300

400

500

600

700

800

0 5 10 15 20 25

Age (years)

Emissions (g/hr)

I/M Ref: opMode 11

non-I/M Ref: Mode 11

I/M Ref: Mode 27

non-I/M Ref: Mode 27

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20 25

Age (years)

Emissions (g/hr)

I/M Ref: opMode 11

non-I/M Ref: Mode 11

I/M Ref: Mode 27

non-I/M Ref: Mode 27

(a) THC

(b) CO

(c) NOx
53

1.3.3.7 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 between 12 and 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 studies12. Figure 22 and

Figure 23 show age trends by model year for cars and trucks, respectively. The values shown are

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

stabilization of emissions with age is another departure with the approach used on MOBILE,

which allowed emissions to increase indefinitely.

From these figures, as well as Figure 1, it is clear that no data were 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 8-10 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 observed patterns of fleet emissions. To compensate for this limitation, we employed a

simple approach to represent the decline and stabilization of the rates.

We calculated ratios of means between the 10-14 and 15-19 year ageGroups, each relative to the

8-9 year age group, using the 1986-89 and 1990-93 model-year groups, which contain data for

vehicles as old as 19 years. For this purpose we used Phoenix data averaged by MOVES modelyear and age groups, as shown in Figure 24. Data points in the figure represent aggregate tests

(g/mi). After averaging by model-year group and ageGroup, we calculated ratios of means for

the 10-14 and 15-19 ageGroups.

98

1915

age

98

1410

age ,

−

−

−

− = =

E

E

R

E

E

R Equation 38

We calculated modified rates for the 10-14 and 15-19 year ageGroups as the product of the rate

for the 8-9 year ageGroup and the corresponding ratio (Rage). Assuming that emissions would be

fully stable by 20 years, we set the rate for the 20+ year ageGroup equal to that for the 15-19

year ageGroup. We calculated variances for the ratios as in Equation 37, but did not propagate

the uncertainty through to the final result.
54

Table 17. Ratios used to stabilize emission rates for the 10-14 and 15-19 year ageGroups, calculated relative

to the 8-9 year ageGroup.

Regulatory Class ageGroup Ratios (Rage) Variances (VR)

THC CO NOx THC CO NOx

Cars 10-14 1.338 1.226 1.156 0.000000032 0.000160 0.00000009

Cars 15-19 1.571 1.403 1.312 0.00000411 0.00268 0.00000261

Trucks 10-14 1.301 1.220 1.156 0.00000173 0.000758 0.00000138

Trucks 15-19 1.572 1.479 1.312 0.0000518 0.0666 0.0000499
55

Figure 22. Aggregate IM147 Emissions (g/sec) for Cars, by Model year and Age, for the

Phoenix Random Sample.

(a) CO

(b) THC

(c) NOx
56

Figure 23. Aggregate IM147 Emissions (g/sec) for trucks, by Model year and Age, for the Phoenix

Random Sample.

(a) CO

(b) THC

(c) NOx
57

Figure 24. Aggregate IM147 Emissions (g/mi) by Model-Year Group and Age Group.
58

1.3.3.7.2 non-I/M Reference Rates

The ratios developed in 1.3.3.7.1 are assumed to 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

reflective of 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 requirements in some

way. However, in the absence of a program, high-emitting vehicles are not identified and owners

have less 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

were collected within I/M areas. Thus, given the absence of information, we adopted an

assumption that, absent the existence of a program, emissions would increase after 19 years. We

applied this assumption by assuming that the ratio observed between the 10-14 and 15-19 year

ageGroups would persist in linear fashion from the 15-19 to the 20+ year ageGroups.

Table 18 shows the deterioration stabilization ratios for both the I/M and non-I/M references

rates. As mentioned above, all ratios are applied by multiplication by values for the 8-9 year

ageGroup in all operating modes. The ratios for I/M areas (Rage,I/M) are identical to those in Table

17. The center column shows the ratio of values of Rage,I/M for the 15-19 to the 10-14 year

ageGroups. Ratios for the non-I/M references (Rage,non-I/M ) are identical to those for I/M in the

10-14 and 15-19 year ageGroups. In the 20+ year ageGroup, the non-I/M ratio is equal to the

product of the 15-19 value and the ratio of the 15-19 and the 10-14 values.
59

Table 18. Deterioration-stabilization ratios as applied to I/M and non-I/M reference rates.

Pollutant Regulatory

Class

ageGroup Rage,I/M

1 Ratio (15-19:10-14) Rage,non-I/M

THC

Cars

10-14 1.338 1.338

15-19 1.571 1.174 1.571

20+ 1.571 1.845

Trucks

10-14 1.301 1.301

15-19 1.572 1.206 1.572

20+ 1.572 1.898

CO

Cars

10-14 1.226 1.226

15-19 1.403 1.144 1.403

20+ 1.403 1.606

Trucks

10-14 1.220 1.220

15-19 1.479 1.213 1.479

20+ 1.479 1.795

NOx

Cars

10-14 1.159 1.159

15-19 1.312 1.132 1.132

20+ 1.312 1.486

Trucks

10-14 1.159 1.159

15-19 1.312 1.132 1.132

20+ 1.312 1.486

1 Values in this column are identical to those in Table 17.

2

. Calculated as the ratio of the values in the current and previous rows.

3

for 10-14 and 15-19 year ageGroups, values in this column identical to the I/M column; for the 20+ year ageGroup, values in

this column equal the product of the value in the previous row (15-19) and the value in the center column.
60

1.3.4 Emission-Rate Development: Subgroup 2 (MY 2001 and later)

1.3.4.1 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.

1.3.4.1.1 Vehicle Descriptors

In addition to the parameters listed above in Table 7, the IUVP data provides engine-family

information. Using engine family, the IUVP files can be merged with certification test records

by model year. The certification test records provide information on standard level and specific

emissions standards applicable to each vehicle. The standard 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 combinations of

standard level and vehicle class (LDV, LDT1-4).

Table 19. Vehicle Descriptors available in IUVP files and certification test records.

Parameter Units Source Purpose

IUVP Cert. Records

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 associated with the correct

standard level and emissions standard, which allows inference of the correct vehicle class.

1.3.4.2 Estimating I/M Reference Rates

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

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

process involves six steps, each of which is discussed in more detail in Section 1.3.4.2.1, below.
61

1. Average IUVP results by standard level and vehicle class.

2. Develop phase-in assumptions for MY 2001 – 2021, by standard level, 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.

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 three additional age Groups (4-5, 6-7 and 8-9).

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. For the remaining three groups, emissions are assumed to stabilize as described above

on page 53.

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 19).

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

1.3.4.2.1 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 25

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 effects

of HC or NOx control on CO emissions.

Figure 25. Composite FTP Results for Tier1, NLEV and Tier 2 Vehicles, as measured by IUVP, in

relation to corresponding certification and useful-life standards.
62

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

FTP Emissions (g/mi)

Certification Standards (g/mi)

Useful Life Standards (g/mi)

FTP Composite estimates (g/mi)

0.000

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

10.000

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

FTP Emissions (g/mi)

Certification Standards (g/mi)

Useful Life Standards (g/mi)

FTP Composite estimates (g/mi)

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

FTP Emissions (g/mi)

Certification Standards (g/mi)

Useful Life Standards (g/mi)

FTP Composite estimates (g/mi)

(c) NOx

(b) CO

(a) HC
63

Figure 26. 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).

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

FTP Emissions (g/mi)

Cold start (g/mi) Hot Running (g/mi)

0.000

2.000

4.000

6.000

8.000

10.000

12.000

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

FTP Emissions (g/mi)

Cold start (g/mi) Hot Running (g/mi)

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

FTP Emissions (g/mi)

Cold start (g/mi) Hot Running (g/mi)

(c) NOx

(b) CO

(a) HC
64

Figure 27 Composite, 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, normalized to respective Tier-1 levels.

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

Ratio to Tier-1 Level

Composite Cold Start Hot Running

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

Ratio to Tier-1 Level

Composite Cold Start Hot Running

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

T1 TLEV LEV ULEV bin8 bin7 bin6 bin5 bin4 bin3 bin2

T1 NLEV NLEV NLEV T2 T2 T2 T2 T2 T2 T2

Ratio to Tier-1 Level

Cold Start Hot Running Composite

(c) NOx

(b) CO

(a) HC
65

Figure 26 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, divided by the nominal bag distance (3.59

miles) which expresses the values as a “start rate” in g/mi. The “hot-running” emissions are

represented by Bag 2 emissions, also divided by the appropriate distance to obtain an aggregate

rate, in g/mi. Additionally, Figure 23 shows composite, start and running values normalized to

their respective Tier-1 levels, which clearly displays the greater relative levels of control for

running as opposed to start emissions. Not surprisingly then, 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 to a lesser degree. In any case, the results show

that sole reliance on composite results in projecting future emissions declines would give

misleading results in projecting either start or running emissions. Hence, the method described

below emphasizes treating them separately.

1.3.4.2.2 Develop Phase-In Assumptions

To estimate emissions levels for specific model years, we developed assumptions describing the

phase-in of new emissions standards after model year 2000. For rates stored in the MOVES

default database, we developed assumptions intended to apply to vehicles sold in states where

Federal, rather than California standards applied. Thus, the phase-is designed to represent the

phase-in of National-Low-Emission-Vehicle (NLEV) and Tier-2 standards.

To achieve these steps, we obtained certification records and test results for a selection of model

years28. These records contain information on certified vehicles, including model year, engine

family, standard level (Tier-1, LEV, Bin 5, etc.), and sales area, as well as numerical standards

used for certification on the Federal Test Procedure (e.g., 0.05 g NMOG/mile, etc.). For each

engine family, we inferred the vehicle class (LDV, LDT1-LDT4) based on combination of

standard and numerical values. Examples illustrating this process are shown in Table 20.

After compling lists of engine families by standard, model year and vehicle class, we obtained

estimates of final sales from the EPA VERIFY database for MY 2001-200729. We merged the

certification records with the sales estimates, by model year and engine family.

Then to estimate the default “Federal” phase-in, we summed the sales by model year, standard

level and vehicle class, for a subset of sales areas in which Federal or California standards

applied, excluding those sales areas in which only California standards applied. Estimates of

numbers of engine families certified for various sales areas are listed in Table 21. Sales-weighted

phase-in scenarios for each vehicle class are shown in Figure 28 through Figure 31. As noted, the

results in the Figures reflect the certifications in the “Fed” or “Both” groups shown in Table 21.

Proportions of each standard represent actual phase-in history for MY 2001-2007. We projected

phase-in assumptions through MY2010, after which we held assumptions constant, under

assumption that the Tier-2 phase-in would be complete.
66

The National LEV (NLEV) standards apply only to LDV, LDT1 and LDT2 vehicle classes, for

which Tier 1 certification ended in MY 2000. Certification to NLEV standards began in 2001

and ended in 2006, however, NLEV vehicles dominate the (Federal) fleet between 2001 and

2003. Tier 2 vehicles enter the fleet in 2003 and completely comprise new sales by 2010.

The phase-in for LDV, LDT1 and LDT2 are broadly similar in that LEV and Bin 5 vehicles

dominate certifications and sales. There are relatively small differences in that LDV-T1 contains

higher fractions of ULEV and Bin 8.

The phase-in for heavy light-duty trucks is simpler in that Tier-1 certifications continue through

2004, after which Tier-2 standards are introduced. After 2003, certifications are dominated by

Bin 8, Bin 5 and Bin 4.

Table 20. Examples of Information obtained from Certification Test Records, with Vehicle Class inferred

from combinations of Standard, and FTP Certification values.

Standard Engine Family Sales Area FTP Standard Vehicle-Class

50,000-mi 100,000-mi 120,000-mi

LEV 2HNXV02.0VBP NLEV all states 0.075 0.09 LDV, LDT1

LEV 2MTXT02.4GPG NLEV all-states 0.100 0.13 LDT2

Tier 1 2CRXT05.95B2 Federal all-altitude 0.32 0.46 LDT3

Tier 1 2CRXT05.96B0 Federal all-altitude 0.39 0.56 LDT4

Table 21. Approximate Numbers of Engine Families Certified, by Model Year and Age Group, Model Years

2001-2007.

Sales Area Code Group1 Model Year Total

2001 2002 2003 2004 2005 2006 2007

California CA CA 114 116 118 240 251 275 255 1,369

Clean Fuel Vehicle CF Fed 38 46 81 76 69 61 55 426

California + NLEV (all

states)

CL Both 149 140 129 418

Federal All Altitude FA Fed 79 75 86 209 219 271 274 1,213

Federal + CA Tier 2 FC Both 16 81 41 33 16 187

Clean Fuel Veh +

NLEV(ASTR)2

+ CA

NF Both 57 56 45 158

NLEV (All States) NL Fed 31 47 74 152

TOTAL 468 480 549 606 580 640 600 3,923

1

“Fed” denotes areas for which vehicles were certified to Federal Tier 1, NLEV or Tier 2 standards, “CA” denotes vehicles

certified to California LEV-I or LEV-II standards, including the “section 177” states, “Both” denotes vehicles certified for

Federal or California Sales Areas.

2

. “ASTR” = “All-state trading Region.”
67

Figure 28. Phase-in Assumptions for Tier 1, NLEV, and Tier 2 standards, for LDVand LDT1

Figure 29. Phase-in Assumptions for Tier 1, NLEV and Tier 2 standards, for LDT2.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000200120022003200420052006200720082009201020112012

Model Year

Percent

Tier-2(Bin 2)

Tier-2(Bin 3)

Tier-2(Bin 4)

Tier-2(Bin 5)

Tier-2(Bin 6)

Tier-2(Bin 7)

Tier-2(Bin 8)

LEV-II (ULEV)

LEV-II (LEV)

NLEV(ULEV)

NLEV(LEV)

NLEV(TLEV)

Tier 1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000200120022003200420052006200720082009201020112012

Model Year

Percent

Tier-2(Bin 3)

Tier-2(Bin 4)

Tier-2(Bin 5)

Tier-2(Bin 7)

Tier-2(Bin 8)

NLEV(ULEV)

NLEV(LEV)

NLEV(TLEV)

Tier 1
68

Figure 30. Phase-in Assumptions for Tier 1 and Tier 2 standards, for LDT3

Figure 31. Phase-in Assumptions for Tier 1 and Tier 2 standards, for LDT4.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000200120022003200420052006200720082009201020112012

Model Year

Percent

Tier-2(Bin 4)

Tier-2(Bin 5)

Tier-2(Bin 8)

Tier 1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000200120022003200420052006200720082009201020112012

Model Year

Percent

Tier-2(Bin 5)

Tier-2(Bin 8)

Tier 1
69

1.3.4.2.3 Merge FTP results and phase-in Assumptions

The goal of this step is to calculate weighted averages of the FTP cold-start and running results

for all standards 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 fractions also derived from the weighted sales estimates. Through MY

2007, where we had actual history, these fractions vary by model year, but are held stable after

2008. See Figure 32.

Figure 33 shows an example of the Phase-in calculation for NOx from cars between model years

2000 and 2010. The figure 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 cold-start emissions for young vehicles in each model year (ages 0-3). For

running emissions, however, the averages are not used directly; rather, each is expressed as a

ratio to the corresponding Tier-1 value.

Table 22 shows weighted average values for model-years 2001-2010 for simulated FTP

composites, cold-start and hot-running emissions. The start values, expressed as the cold-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 34 to Figure 36 below.
70

Figure 32. Relative Fractions of Truck Classes, by Model Year.

Figure 33. Example of Phase-In Calculation, for NOx from Cars, for MY 2000-2010.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000200120022003200420052006200720082009201020112012

Model Year

Percent

LDT1

LDT2

LDT3

LDT4

Standard Cold start Hot Running

(g) (g/mi) 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Tier 1 Tier 1 0.888 0.127 1 0.011 0.004 0.002 0 0 0 0 0 0 0

TLEV 0.888 0.127 0 0.052 0.018 0.011 0 0 0 0 0 0 0

NLEV LEV 0.566 0.040 0 0.801 0.752 0.613 0.175 0.110 0.132 0.103 0.070 0.035 0

ULEV 0.566 0.040 0 0.136 0.226 0.192 0.042 0 0 0 0 0 0

bin8 0.418 0.035 0 0 0 0.115 0.251 0.163 0.095 0.002 0 0 0

bin7 0.364 0.052 0 0 0 0.017 0.004 0.005 0.004 0 0 0 0

Tier 2 bin5 0.165 0.008 0 0 0 0.049 0.491 0.682 0.698 0.799 0.830 0.855 0.890

bin4 0.090 0.005 0 0 0 0 0.016 0.021 0.033 0.042 0.050 0.060 0.060

bin3 0.071 0.004 0 0 0 0 0.008 0.009 0.003 0.013 0.010 0.010 0.010

bin2 0.067 0.000 0 0 0 0 0 0.010 0.011 0.014 0.015 0.015 0.015

LEV-II LEV 0.165 0.008 0 0 0 0 0.0052645 0.000 0.000 0.000 0.000 0.000 0.000

ULEV 0.071 0.004 0 0 0 0 0.0074988 0.000 0.024 0.026 0.025 0.025 0.025

Start (g) 0.888 0.586 0.573 0.530 0.314 0.248 0.237 0.199 0.185 0.170 0.156

Running (g/mile) 0.127 0.046 0.042 0.039 0.022 0.016 0.015 0.011 0.010 0.009 0.008

RATIO to Tier 1 1.00 0.36 0.33 0.31 0.17 0.13 0.12 0.087 0.079 0.070 0.061

Phase-in by Model Year
71

Table 22. Weighted Average FTP Values Projected for Trucks and Cars 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)

Trucks 2000 2.28 17.90 1.01 0.175 1.87 0.104 0.304 1.12 0.174

2001 1.43 12.56 0.566 0.0965 1.23 0.0400 0.171 0.843 0.0876

2002 1.41 12.40 0.552 0.0941 1.21 0.376 0.169 0.836 0.0865

2003 1.47 12.73 0.586 0.100 1.25 0.0424 0.181 0.863 0.0934

2004 0.92 7.92 0.393 0.0535 0.786 0.0123 0.0849 0.473 0.0434

2005 0.78 7.05 0.315 0.0440 0.703 0.00574 0.0596 0.367 0.0291

2006 0.70 6.12 0.296 0.0378 0.612 0.00511 0.0381 0.264 0.0183

2007 0.66 5.85 0.281 0.0361 0.587 0.00490 0.0315 0.226 0.0148

2008 0.65 5.75 0.270 0.0356 0.580 0.00479 0.0285 0.208 0.0130

2009 0.63 5.67 0.260 0.0350 0.571 0.00470 0.0258 0.192 0.0115

2010 0.62 5.58 0.251 0.0345 0.564 0.00462 0.0233 0.177 0.0101

Cars 2000 1.62 11.40 0.805 0.126 1.53 0.0571 0.209 0.888 0.127

2001 0.856 7.68 0.287 0.0361 0.954 0.00509 0.0948 0.586 0.0457

2002 0.821 7.27 0.284 0.0333 0.893 0.00451 0.0898 0.573 0.0421

2003 0.808 7.05 0.299 0.0340 0.839 0.00462 0.0824 0.530 0.0394

2004 0.714 6.16 0.298 0.0356 0.664 0.00488 0.0461 0.315 0.0220

2005 0.672 5.91 0.274 0.0358 0.634 0.00477 0.0351 0.248 0.0161

2006 0.657 5.85 0.257 0.0350 0.633 0.00462 0.0335 0.239 0.0150

2007 0.621 5.63 0.234 0.0341 0.608 0.00443 0.0271 0.201 0.0112

2008 0.611 5.55 0.232 0.0341 0.592 0.00443 0.0248 0.187 0.0101

2009 0.601 5.47 0.231 0.0339 0.574 0.00442 0.0224 0.172 0.00896

2010 0.591 5.38 0.229 0.0339 0.557 0.00442 0.0201 0.158 0.00784
72

Figure 34. Weighted Ratios for Composite, Start and Running CO Emissions, for (a) Trucks and (b) Cars.

1.000

0.562 0.547 0.581

0.390

0.313 0.294 0.279 0.267 0.257 0.248

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

Composite

Cold start

Hot Running

1.000

0.357 0.353 0.372 0.371 0.340 0.320 0.290 0.288 0.286 0.284

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

(a) Trucks

(b) Cars
73

Figure 35. Weighted Ratios for Composite, Start and Running THC Emissions, for (a) Trucks and (b) Cars.

1.000

0.384 0.361

0.407

0.118

0.055 0.049 0.047 0.046 0.045 0.044 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

Composite

Cold start

Hot Running

1.000

0.0 0.089 0.079 0.081 0.085 0.084 0.081 0.078 0.078 0.077 0.077

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

(a) Trucks

(b) Cars
74

Figure 36. Weighted Ratios for Composite, Start and Running NOx Emissions, for (a) Trucks and (b) Cars.

1.000

0.502 0.496

0.536

0.249

0.167

0.105 0.0 0.085 0.074 0.066 0.058

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

Composite

Cold start

Hot Running

1.000

0.359 0.331 0.310

0.173

0.127 0.118 0.0 0.088 0.080 0.070 0.062

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

(a) Trucks

(b) Cars
75

1.3.3.2.4 Estimating Emissions by Operating Mode

With the introduction of the NLEV standards, new emissions requirements were imposed, in

addition to standards defined in terms of the Federal Test Procedure. The new requirements,

under the “Supplemental Federal Test Procedure” (SFTP), imposed more stringent emissions

control under conditions of high speed and power (through the US06 cycle), and with airconditioning running (through the SC03 cycle). 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/metric ton),

and the ranges covered by the US06 cycle. For convenience, we refer to these two regions as

“the hot-running FTP region” and “US06 region,” respectively (See Figure 37). Data measured

on the SC03 cycle did not play a role in emission rate development.

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 from 2001 to 2010, to

represent emissions for that year. For the FTP operating modes, we applied the ratios shown in

Figure 34 to Figure 36 above.

1.3.4.2.4.1 Running Emissions

For the “US06” operating modes, we followed a different approach from that described above in

1.3.4.2.3. 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,

giving pronounced declines in emissions on the FTP. For our purposes, we are referring

specifically to declines in running emissions, as shown by changes in Bag-2 emissions. However,

it was not obvious that the degree of control would increase as dramatically for the SFTP

standards, as shown by the US06. Thus, in preparation of the draft rates, we adopted a

conservative assumption that emissions in the “US06” region would not drop as sharply as those

in the “hot-running FTP” region.

It was therefore necessary to estimate different sets of ratios. Two alternative approaches were

developed.

The first involved returning to the Phoenix I/M data. To create pre- and post-SFTP estimates, 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 US06 operating modes (as a group), and

calculated ratios between the two groups.

0098SFTP,poll,

0301,SFTPpoll,

SFTP

−

−

=

E

E

R Equation 39

The resulting ratios were used for CO and HC, as shown in Figure 38.

The second approach involved compilation of results on the US06 cycle and calculation of ratios

in a manner similar to that used for FTP data as described in 1.3.4.2.3 above. It was possible to
76

obtain data representing US06 tests representing vehicles in MY 1996-97 from the MobileSource Observation Database (MSOD), developed and maintained by EPA/OTAQ.30 For NLEV

and Tier-2 vehicles manufactured after MY2000, US06 results were available from the IUVP

program. Emissions results on the US06 by standard were weighted by the phase-in assumptions

for MY 2001-2007 as with the FTP results. Resulting ratios were used for NOx, as shown in

Figure 38.

Figure 39 and Figure 40 show application of the ratios to the hot-running FTP and US06

operating modes in model years 2000 (the reference year), 2005, and 2010, both calculated with

respect to 2000. The sets of ratios shown in Figure 38 are used for both sets of modes. Note that

the values for the SFTP modes are equal in 2005 and 2010 for HC and CO, because the SFTP

ratios are constant by model year. In these figures, 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.
77

Figure 37. Operating modes for running Exhaust Emissions, divided broadly into “hot-running FTP” and

“US06” regions.

V

e

hicle-S

p

e

cific

P

o

wer (k

W/to

n

n

e)

The “hot-running FTP Region”

Low to Moderate

Power

(also includes braking (0)

and idle (1)

The “US06 Region”

High Power
78

Figure 38. Weighted Ratios for Cars, for hot-running emissions, representing the “hot-running FTP Region”

(FTP) and the “US06 Region” (US06), for (a) CO, (b) THC and (c) NOx.

1.000

0.357 0.353 0.372 0.371 0.340 0.320 0.290 0.288 0.286 0.284

1.000

0.555 0.552 0.551 0.550 0.550 0.550 0.550 0.550 0.550 0.550

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

FTP

US06

1.000

0.089 0.079 0.081 0.085 0.084 0.081 0.078 0.078 0.077 0.077

1.000

0.357 0.353 0.352 0.350 0.347 0.347 0.345 0.345 0.345 0.345

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

FTP

US06

1.000

0.258 0.253 0.240

0.183 0.167 0.165 0.156 0.153 0.149 0.145

1.000

0.359

0.331

0.310

0.173

0.127 0.118

0.088 0.080 0.070 0.062

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Model Year

Weighted Ratio

US06

FTP

(a) CO

(b) THC

(c) NOx
79

Figure 39. Projected Emission Rates for Cars, by Operating mode for ageGroup 0-3 years, for three model

years (LINEAR SCALE).

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

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

Emission Rate (g/hr)

2000

2005

2010

0

2

4

6

8

10

12

14

16

18

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

Emission Rate (g/hr)

2000

2005

2010

0

20

40

60

80

100

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

Emission Rate (g/hr)

2000

2005

2010

(a) CO

(b) THC

(c) NOx
80

Figure 40. Projected Emission Rates for Cars, by Operating mode for ageGroup 0-3 years, for three model

years (LOGARITHMIC SCALE).

0

1

10

100

1,000

10,000

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

Emission Rate (g/hr)

2000

2005

2010

0

0

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

Emission Rate (g/hr)

2000

2005

2010

0

0

1

10

100

1,000

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

Emission Rate (g/hr)

2000

2005

2010

(a) CO

(b) THC

(c) NOx
81

1.3.4.2.5 Apply Deterioration

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.

1.3.4.2.5.1 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 40. Note that this equation is simply a rearrangement of

Equation 30.

2

ln

2

l

l a

xx

σ

−= Equation 40

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 of σl

2

used for this step were 1.30,

0.95 and 1.60 for CO, THC and NOx, respectively.

1.3.4.2.5.2 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 Equation 41

The values of the logarithmic slope are adapted from values developed for the 1996-98 model –

year group. The values applied are shown in Table 23 . 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,

as shown in Equation 41.

Figure 41 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
82

deterioration. In this view, deterioration appears as the magnitude of the gaps between a family

of similar trends against power.

Table 23. Values of the logarithmic deterioration slope applied to running-exhaust emission rates for MY

following 2000.

pollutant opMode Group Logarithmic slope (ml)

CO “hot-running FTP”10.13

“US06”20.06

THC “hot-running FTP” 0.09

“US06” 0.09

NOx “hot-running FTP” 0.15

“US06” 0.15

1

Includes opModeID = 0,1, 11-16, 21-25, 27, 33,35,37.

2 Includes opModeID = 28,29,30, 38,39,40.
83

Figure 41. Example of Logarithmic Deterioration Model for THC (Cars, MYG 96-98): (a)

lnTHC vs age, by VSP level (kW/metric ton), and (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
84

1.3.4.2.5.3 Apply the reverse transformation

After the previous step, the values of xl,age were reverse-transformed, as in Equation 30. 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 1.3.4.2.5.1 and may vary with age.

Values of logarithmic variances for all three pollutants are shown in Table 24.

Table 24. Values of Logarithmic Variance Used to Calculate Emissions Deterioration by Reverse

Transformation of Logarithmic Means.

Age Group Pollutant

CO THC NOx

0-3 years 1.30 0.95 1.60

4-5 2.05 1.50 1.60

6-7 2.00 1.70 1.40

8-9 1.80 1.90 1.40

No values are presented in the table for the 10-14, 15-19 and 20+ year age Groups. This

omission is intentional, in that we did not want to extrapolate the deterioration trend beyond the

8-9 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. For the 10-14, 15-19 and 20+ age Groups, the

“stabilization of emissions with age” was estimated as described in section 1.3.3.7.

Figure 42 shows the same results as Figure 41, 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, in absolute terms, with increasing age.
85

Figure 42. Example of Reverse Transformation for THC (LDV, MYG 96-98): (a) THC vs. Age, by

VSP level (kW/metric ton), (b) THC vs. VSP, by Age (yr).

1.3.4.2.6 Estimate non-I/M References

Completion of steps 1.3.4.2.1 – 1.3.4.2.6 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.

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

A ge (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
86

1.4 Exhaust Emissions for Start Operation

1.4.1 Subgroup 1: Vehicles manufactured in model year 1995 and earlier

In EPA's previous emissions model (MOBILE6), start emissions for passenger cars and lightduty trucks, were dependent upon three factors:

1. the (base) emissions of that vehicle at 75 degrees Fahrenheit31,

2. an adjustment factor based on the length of soak time32, and

3. an adjustment factor based on the ambient temperature33

Within the MOVES modal structure, operating modes for start emissions are defined in terms of

soak time (preceding an engine start). The following sections will discuss the development of

base rates for “cold starts” (operating mode 108), as well as those for “warm” or “hot” starts

following seven soak times of varying length (operating modes 101-107).

Note that the development and application of temperature adjustments is discussed in a separate

report. 34

1.4.1.1 Methods

1.4.1.1.1 Data Sources

Data used in these analyses come from the following four sources:

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

the past decades, EPA has performed emission tests (usually the Federal Test

Procedure) on tens of thousands of vehicles under various conditions. EPA has

stored those test results in its Mobile Source Observation Database (MSOD).

We identified (in the MSOD) 549 gasoline-fueled vehicles (494 cars and 55

trucks) that had FTPs performed at temperatures both within the normal FTP range

(68o to 86o Fahrenheit) as well as outside that range (i.e., either below 68o or above

86o). Aside from the differences in ambient temperature, the test parameters for

the paired FTPs on each vehicle were identical. The FTPs were performed at

temperatures from 16 through 111o F.

2. EPA’s Office of Research and Development (ORD) contracted (through the Clean

Air Vehicle Technology Center, Inc.) the testing of five cars (model years 1987

through 2001). Those vehicles were tested using both the UDDS and the IM240

cycle at temperatures of: 75, 40, 20, 0 and –20 oF35.

3. Under a contract with EPA, Southwest Research Institute (SwRI) tested four Tier2 vehicles (2005 model year car and light-duty trucks) over the UDDS at

temperatures of: 75, 20, and 0 oF [citation?]
87

4. During 2004-05, USEPA OTAQ and ORD, in conjunction with the Departments of

Energy and Transportation, conducted a program in the Kansas-City Metropolitan

Area. During this study, designed to measure particulate emissions, gaseous

emissions were also measured on the LA92 cycle36.

1.4.1.1.2 Defining Start Emissions

Using the FTP data described above, we estimated cold-start emissions as the difference in mass

between Bag-1 and Bag-3 (g). However, because Bag 1 follows a 12-hour (720 minute) soak

and Bag 3 follows a 10-minute soak, it is possible to use soak/time relationships to modify the

Bag1-Bag3 difference so as to account for the respective soak periods. The start/soak

relationships we applied were adapted from a study performed by the California Air Resources

Board37 Based on these data, we derived a correction factor “A” as shown in Equation 42 and

Table 25.

− A

=

1

3)Bag-1(Bag

start-Cold Emissions Equation 42

Table 25. Correction Factor A for application in Equation 39.

Vehicle Type HC CO NOx

No Catalyst 0.37101 0.34524 1.57562

Catalyst Equipped 0.12090 0.11474 0.39366

Heated Catalyst 0.05559 0.06937 1.05017

1.4.1.1.3 Relationship between Soak Time and Start Emissions

In the MOVES input database, “operating modes” for start emissions are defined in terms of

soak time preceding an engine start. The “cold-start,” as defined and calculated above, is

represented as opModeID=108. An additional seven modes are defined in terms of soak times

ranging from 3 min up to 540 min (opModeID = 101-107). To estimate start rates for the

additional seven modes, we applied the soak-time/start relationships mentioned above. The

specific values used are adapted from the MOBILE6 soak-effect curves for catalyst and noncatalyst equipped vehicles

15

. To adapt these relationships to the MOVES operating modes, the

soak time was divided into eight intervals, each of which was assigned a "nominal" soak time, as

shown in Table 26.
88

Table 26. Operating-Mode Definitions for Start Emissions, Defined in terms of Soak Time.

Nominal Soak Period

(min)

OpModeID OpModeName

3 101 Soak Time < 6 minutes

18 102 6 minutes <= Soak Time < 30 minutes

45 103 30 minutes <= Soak Time < 60 minutes

75 104 60 minutes <= Soak Time < 90 minutes

105 105 90 minutes <= Soak Time < 120 minutes

240 106 120 minutes <= Soak Time < 360 minutes

540 107 360 minutes <= Soak Time < 720 minutes

720 108 720 minutes <= Soak Time

We have adapted and applied soak time adjustments used in MOBILE6.2 for gasolinefueled vehicles, as shown in Table 27. Additionally, all pre-1981 model year passenger cars and

trucks use the catalyst equipped soak curve adjustments, although some of these vehicles are not

catalyst equipped.

Table 27. Calculated soak-time adjustments, derived from MOBILE6 soak-time coefficients for catalystequipped vehicles.

opModeID Soak

period

(min)

Adjustment

HC CO NOx

101 3 0.051 0.034 0.093

102 18 0.269 0.194 0.347

103 45 0.525 0.433 0.872

104 75 0.634 0.622 1.130

105 105 0.645 0.728 1.129

106 240 0.734 0.791 1.118

107 540 0.909 0.914 1.053

108 720 1.000 1.000 1.000

Model-year groups used to calculate start rates for vehicles in model year 1995 and

earlier are shown in Table 28. In some cases, model-year groups were adjusted to compensate for

sparsity of data in narrower groups. For example, the average NOx emissions for MY 1983-

1985 trucks are slightly negative. This result is possible, but is likely due to erratically behaving

means from small samples. Thus, these model years were grouped with the 1981-1982 model

years, which for trucks had similar emission standards. In addition, the MY1994-1999 gasoline

truck sample includes a very high-emitting vehicle, which strongly influences the results for CO.

To compensate, these vehicles were grouped with the 1990-1993 model years. The values in the

table represent the difference of Bag-1 minus Bag-3, adjusted, as described above, to estimate

cold-start emissions.
89

Table 28. Cold-start emissions (Bag 1 – Bag 3,) for gasoline-powered cars and trucks

Model-year

Group n Mean (g) Standard deviation (g) CV-of-the-Mean (RSE)

Years THC CO NOx THC CO NOx THC CO NOx

Cars

1960-1980 1,488 5.172 75.832 0.608 6.948 83.812 2.088 0.035 0.029 0.089

1981-1982 2,735 3.584 52.217 1.118 7.830 60.707 1.682 0.042 0.022 0.029

1983-1985 2,958 2.912 34.286 0.922 5.216 44.785 1.321 0.033 0.024 0.026

1986-1989 6,837 2.306 21.451 1.082 2.740 32.382 1.034 0.014 0.018 0.012

1990-1993 3,778 1.910 17.550 1.149 1.728 13.953 1.034 0.015 0.013 0.015

1994-1995 333 1.788 16.233 1.027 1.203 31.648 0.742 0.037 0.107 0.040

Trucks

1960-1980 111 9.008 115.849 0.155 9.179 113.269 2.682 0.097 0.093 1.641

1981-1985 910 4.864 94.608 0.0412 4.992 67.871 1.797 0.034 0.024 1.445

1986-1989 1192 3.804 45.918 2.107 2.298 36.356 2.152 0.017 0.023 0.030

1990-1995 1755 3.288 40.927 2.192 4.211 42.478 2.158 0.031 0.025 0.024

1.4.2 Subgroup 2: Vehicles manufactured in MY1996 and later

Start rates for vehicles manufactured in model year 1996 and later were estimated using data

from the EPA In-use Verification Program (IUVP), as with running rates for MY 2001 and later

(see 1.3.4 above).

For model years 1996-2000, rates for vehicles at 0-3 years of age (ageGroup=0003), are shown

above in Table 22, in the row for MY2000.

For MY 2001 and later, cold-start rates (opModeID=108) were estimated as described in 1.3.4

above, using the data and approaches described in steps 1-4 and step 6 (as described on page 60).

We applied the FTP averages as shown in Figure 26 and Figure 27, and the phase-in assumptions

shown in Figure 28, Figure 29, Figure 30, Figure 31 and Figure 32. As with running emissions,

Figure 33 illustrates the calculation of weighted average FTP results by model year.

To estimate start emissions for the remaining seven operating modes, we applied “soak

fractions” to the “cold-start” emissions, as described above. The soak fractions were adapted

from the approach applied in the MOBILE model20. Specifically, the part-wise regression

equations used in MOBILE6 for “conventional catalyst” engines 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 43 shows the soak fractions for HC,

CO and NOx, with each value plotted at the midpoint of the respective soak period.
90

Figure 43. Soak Fractions Applied to Cold-Start Emissions (opModeID = 108) to Estimate

Emissions for shorter Soak Periods (operating modes 101-107).

1.4.3 Applying Deterioration to Starts

1.4.3.1 Assessing Start Deterioration in relation to Running Deterioration

The large datasets used to develop rates for running emissions provided much information about

deterioration for hot-running emissions, but no information on deterioration for start emissions.

After some consideration, it occurred to us that the data from the IUVP program, used to develop

running rates for NLEV and Tier-2 vehicles, could also be useful to evaluate the relationship

between deterioration trends for start and running emissions. A valuable aspect of these data is

that they provide FTP results with the measurement phases separated. As before, we focused on

cold-start emissions, calculated as Bag1 - Bag3 (g), and hot-running emissions, represented by

Bag2 (g/mi). For this purpose, these data are also valuable because they provide emissions

measured over a wide range of mileage, up to 100,000 mi, although the corresponding range of

vehicle age is relatively narrow (0-5 years). Thus, we elected to evaluate trends in emissions vs.

mileage.

At the outset, we plotted the data for NMOG and NOx vs. odometer reading, on linear and

natural log scales. Scatterplots of start and running NMOG emissions are shown in Figure 44 and

Figure 45; corresponding plots for lnNMOG are shown in Figure 46 and Figure 47. Similarly,

scatterplots of start and running NOx emissions are shown in Figure 48 and Figure 49;

corresponding plots for lnNOx are shown in Figure 49 and Figure 50.

In viewing the data, some observations are apparent. The data are grouped, with one group

representing vehicles measured at less than 50,000 miles, centered around 10,000-20,000 miles,

and a second group representing vehicles measured at 50,000 to 100,000 miles. Given the
91

purpose of the IUVP program, the two groups are designed to assess compliance with

certification (< 50,000 mi) and useful-life (>50,000 mi) standards, respectively. As expected,

distributions of emissions are skewed, but with running emissions more skewed than start

emissions. In the log plots, the degree of skew is shown by the variability of the transformed

data, with the ln(start) spanning 3-3.5 factors of e, and the ln(running) spanning 6-7 factors of e.

Finally, and of most relevance to this analysis, deterioration trends are visible in the ln plots,

with the masses of points at >50,000 miles centered higher than those for < 50,000 miles.

To assess the presence of trends in emissions and mileage more rigorously, we ran linear

statistical models on the ln-transformed data. To illustrate, we will focus on models run on

vehicles certified to LEV standards, as shown in Table 31 and Table 32. The model structure

includes a uniform intercept for all vehicle classes (LDV, LDT1-4), with separate intercepts for

each vehicle class. All parameters are highly significant, both for lnNMOG and lnNOx. A

more complex model structure was attempted, which included individual mileage slopes for

different vehicle classes. However, this model was not retained, as it did not improve the fit, nor

were the interaction terms themselves significant. The covariance structure applied was simple,

in that a single residual error variance was fit for all vehicle classes.

Models were fit to vehicles certified to other standards, such as ULEV and Tier-2/Bin-5, the

results for which are not shown here. The models for ULEV show very similar patterns to those

for LEV, whereas the models fit to Bin5 data were not considered useful as the range of mileage

covered for these more recent vehicles was not wide enough to demonstrate deterioration trends

(i.e., < 25,000 mi).

The models confirm the visual impression given by the plots of lnNMOG and lnNOx. Positive

trends in emissions do appear evident in these data, but the increase in emissions with mileage is

very gradual. The trends in lnNOx are steeper than those for lnNMOG, and the trends for

running emissions are steeper than those for start emissions. For lnNOx, the running slope is 1.65

times that for starts, and for lnNMOG, the running slope is 1.25 times that for starts.
92

Figure 44. Cold-start FTP emissions for NMOG (g) vs. Odometer (mi), for LEV vehicles, from the IUVP

program

Figure 45. Hot-running (Bag 2) FTP emissions for NMOG (g/mi) vs. Odometer (mi), for LEV vehicles, from

the IUVP program
93

Figure 46. Cold-start FTP emissions for ln(NMOG) vs. Odometer (mi), for LEV vehicles, from the IUVP

program

Figure 47. Hot-running (Bag 2) FTP emissions for ln(NMOG) vs. Odometer (mi), for LEV vehicles, from the

IUVP program
94

Figure 48. Cold-start FTP emissions for NOx (g) vs. Odometer (mi), for LEV and ULEV vehicles, from the

IUVP program

Figure 49. Hot-running (Bag 2) FTP emissions for NOx (g/mi) vs. Odometer (mi), for LEV and ULEV

vehicles, from the IUVP program
95

Figure 50. Cold-start FTP emissions for ln(NOx) vs. Odometer (mi), for LEV vehicles (Source: IUVP

program).

Figure 51. Hot-running (Bag 2) FTP emissions for ln(NOx) vs. Odometer (mi), for LEV vehicles (source:

IUVP program).
96

Table 29. Model fit parameters for lnNMOG, for LEV vehicles

Parameter Predictor Estimate Standard error Denom. D.F. t-value Pr > t

Cold-Start (Bag 1 – Bag 3) (residual error = 0.1942)

Slope Odometer (mi) 0.000004982 0.0 2,404 ∞ <0.0001 

intercept LDV-T1 -1.9603 0.02224 2,404 -88.14 <0.0001

intercept LDT2 -1.7353 0.02429 2,404 -71.43 <0.0001

intercept LDT3 (MDV2) -1.5735 0.03520 2,404 -44.70 <0.0001

intercept LDT4 (MDV3) -1.2937 0.03233 2,404 -40.01 <0.0001

Hot-Running (Bag 2) (residual error = 1.3018)

Slope Odometer (mi) 0.000008237 0.0 2,225 ∞ <0.0001 

intercept LDV-T1 -6.1604 0.05961 2,225 -103.34 <0.0001

intercept LDT2 -6.2554 0.06577 2,225 -95.11 <0.0001

intercept LDT3 (MDV2) -5.9018 0.09239 2,225 -63.88 <0.0001

intercept LDT4 (MDV3) -5.5949 0.08766 2,225 -63.83 <0.0001

Table 30. Model fit parameters for lnNOx, LEV+ULEV vehicles ,

Parameter Predictor Estimate Standard error Denom. D.F. t-value Pr > t

Cold-Start (Bag 1 – Bag 3) (residual error = 0.68)

Slope Odometer (mi) 0.000009541 0.0 1,657 ∞ <0.0001 

intercept LDV-T1 -2.6039 0.05231 1,657 -50.74 <0.0001

intercept LDT2 -2.4538 0.06056 1,657 -40.52 <0.0001

intercept LDT3 (MDV2) -2.0769 0.08173 1,657 -25.41 <0.0001

intercept LDT4 (MDV3) -1.645 0.08882 1,657 -18.52 <0.0001

Hot-Running (Bag 2) (residual error = 2.9643)

Slope Odometer (mi) 0.000012 0.00000165 1,622 7.13 <0.0001

intercept LDV-T1 -4.7396 0.1092 1,622 -43.40 <0.0001

intercept LDT2 -4.9527 0.1304 1,622 -37.98 <0.0001

intercept LDT3 (MDV2) -4.3144 0.1740 1,622 -24.80 <0.0001

intercept LDT4 (MDV3) -4.1214 0.1835 1,622 -22.47 <0.0001
97

Having drawn these conclusions, we developed an approach to apply them to emission rate

development. To begin, we applied the statistical models by calculating predicted values of

lnNMOG and lnNOx at mileages from 0 (the intercept) to 155,000 miles. We reversetransformed the models using Equation 30 (page 32) to obtain predicted geometric and

arithmetic means with increasing mileage, as shown in Table 31for NMOG and Table 32 for

NOx.

We normalized the predicted means at each mileage to the value at 0 miles to obtain a

“deterioration ratio” Rdet, by dividing each predicted value at a given mileage by the predicted

value at 0 miles (i.e., the intercept); Rdet for the intercept = 1.0 (Equation 43 ).

0,

miles,

det

a

a

x

x

R = Equation 43

We took this step to express start and running trends on a comparable relative multiplicative

basis, as trends in absolute running and start emissions are clearly not comparable.

Finally, to relate start and running trends, we calculated the ratio in Rdet for start to that for

running, designated as Rrel

runningdet,

startdet,

rel R

R

R = Equation 44

Values or Rdet and Rrel for NMOG and NOx are shown in Table 31 and Table 32, respectively,

with corresponding results shown graphically in Figure 52 and Figure 53, respectively.
98

Table 31. Application of Models for NMOG, representing emissions trends for LDV-T1 vehicles certified to

LEV standards.

Parameter Odometer (mi, ×10,000)

0 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5

Cold Start

lnNMOG -1.960 -1.886 -1.836 -1.786 -1.736 -1.686 -1.636 -1.587 -1.537

Geometric mean 0.141 0.152 0.159 0.168 0.176 0.185 0.195 0.205 0.215

Arithmetic mean 0.156 0.168 0.176 0.185 0.195 0.205 0.215 0.226 0.238

Deterioration

ratio (Rdet) 1.000 1.078 1.133 1.190 1.251 1.315 1.382 1.453 1.527

Hot Running

lnNMOG -6.160 -6.037 -5.954 -5.872 -5.790 -5.707 -5.625 -5.543 -5.460

Geometric mean 0.00211 0.00239 0.00259 0.00282 0.00306 0.00332 0.00361 0.00392 0.00425

Arithmetic mean 0.00404 0.00458 0.00497 0.00540 0.00586 0.00636 0.00691 0.00750 0.00815

Deterioration

ratio (Rdet) 1.000 1.132 1.229 1.334 1.449 1.573 1.708 1.855 2.014

Relative Ratio (Rrel) 1.000 0.9952 0.922 0.892 0.864 0.836 0.809 0.783 0.758

Table 32. Application of Models for NOx, representing emissions trends for LDV-T1 vehicles certified to

LEV standards

Parameter Odometer (mi, ×10,000)

0 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5

Cold Start

lnNOx -2.604 -2.461 -2.365 -2.270 -2.175 -2.079 -1.984 -1.888 -1.793

Geometric mean 0.0740 0.0854 0.0939 0.1033 0.1137 0.1250 0.1376 0.1513 0.1665

Arithmetic mean 0.1039 0.1199 0.1319 0.1452 0.1597 0.1757 0.1933 0.2126 0.2339

Deterioration

ratio (Rdet) 1.000 1.154 1.269 1.396 1.536 1.690 1.859 2.045 2.250

Hot Running

lnNOx -4.740 -4.560 -4.440 -4.320 -4.200 -4.080 -3.960 -3.840 -3.720

Geometric mean 0.0087 0.0105 0.0118 0.0133 0.0150 0.0169 0.0191 0.0215 0.0242

Arithmetic mean 0.0385 0.0461 0.0520 0.0586 0.0660 0.0745 0.0840 0.0947 0.1067

Deterioration

ratio (Rdet) 1.000 1.097 1.350 1.522 1.716 1.935 2.181 2.460 2.773

Relative Ratio (Rrel) 1.000 0.964 0.940 0.918 0.895 0.874 0.852 0.832 0.811
99

Figure 52. Deterioration Ratios for Cold-Start and Hot-Running NMOG Emissions.

Figure 53. Deterioration Ratios for Cold-Start and Hot-Running NOx Emissions.

For NOx (Figure 53) we decided to assign start NOx the same multiplicative relative

deterioration as running NOx. However, for HC, the difference between running and start

deterioration was greater, large enough that we reduce to reduce start deterioration relative to

running deterioration.

1.4.3.2 Translation from Mileage to Age Basis

The question remained, as to how the results derived from the IUVP data and presented above

could be applied during the generation of emission rates. At the outset, a question arises from the

fact that the results shown above were generated on the basis of mileage, whereas MOVES
100

assigns deterioration on the basis of age. It was therefore necessary to translate the Rrel from a

mileage basis to an age basis. We achieved the translation through a series of steps.

First, we assumed a rate of mileage accumulation of about 10,000 miles per year, from which it

follows that the Rrel at 125,000 miles would occur at about 12.5 years of age, or would be

represented by the 10-14 year ageGroup. Accordingly, we assigned midpoints to the 0-3 and 10-

14 year ageGroups of 2 and 12.5 years, respectively, and assume that Rrel declines linearly with

age. These assumptions allow calculation of a declining trend in the ratio with respect to age.

The slope of the trend is the change in ratio (ΔRrel) over the corresponding change in time

(Δtime). 

30952.0

10.5

325.0

12.5 2

0.1675.0

time

rel

rel −=

−

=

−

−

=

∆

∆

=

R

mR Equation 45

The calculation of the slope lets us estimate a value of Rrel for each ageGroup.

000.1 age R agerel, = − mRrel Equation 46

The results, as applied for hydrocarbons and CO, are shown in Table 33 and Figure 54. The net

result is a 15-40% reduction in multiplicative start deterioration, relative to running deterioration.

Table 33. Relative deterioration ratios (Rrel), for NMOG (and CO), assigned to each ageGroup.

AgeGroup Age (years) Relative Ratio (Rrel)

0-3 2 1.000

4-5 5 0.845

6-7 7 0.783

8-9 9 0.721

10-14 12.5 0.613

15-19 17.5 0.613

20 + 23 0.613
101

Figure 54. Relative Deterioration Ratios (Rrel), for NMOG (and CO), assigned to each ageGroup.

1.4.3.3 Application of Relative Multiplicative Deterioration

An advantage of the modal approach is that any driving cycle can be represented as a weighted

average of the MOVES emission rates and the “operating-mode distribution” for the cycle. In

this case, we applied an operating-mode distribution for the “hot-running” phase of the FTP. This

phase is 860 seconds long that represents urban driving over a 3.86 mile route after the engine

has stabilized at its normal operating temperature. We estimated an operating-mode distribution

using the “Physical Emission Rate Simulator” (PERE)16. This distribution, shown in Table 34,

represents a “typical” car, with engine displacement and test weight of 2.73 L and 3,350 lb. A

corresponding “typical” truck was represented with displacement and test weight of 4.14 L and

4,364 lb, respectively.

Combining emission rates for hot-running emissions with the operating-mode distributions, we

calculated aggregate cycle emissions for the second hot-running phase of the FTP (g/mi), for all

model-year and age groups. Figure 55 and Figure 56 show resulting cycle aggregates for THC

and NOx. Note that the underlying rates for model years 1995 (representing Tier 0) and 2000

(representing Tier 1) were derived using data and methods described above in Section 1.3.3

(starting on page 15), and those for model years 2005 and 2010 were derived using data and

methods described above in Section 1.3.4 (starting on page 60).

It is important to note that this step is performed both for vehicles in inspection-and-maintenance

areas (I/M, using the meanBaseRateIM) and for vehicles outside I/M areas (using

meanBaseRate). Because deterioration is represented differently for the non-I/M and I/M

reference rates (see 1.3.3.7.2, page 58), and this difference is carried into deterioration for the

start rates, the result is that the MOVES rates represent that I/M programs have effects on start as

well as running emissions.
102

FTP2, 3MYG,0

FTP2, AgeMYG,

AgeMYG,det,

−

=

E

E

R Equation 47

Table 34. Operating-mode distributions for running emissions, representing a “typical” car and truck on the

hot-stabilized phase of the FTP (Bag 2).

opModeID Cars (LDV) Trucks (LDT)

Time in mode

(sec)

Time in mode

(%)

Time in mode

(sec)

Time in mode

(%)

0 97 11.27 97 11.27

1 155 18.00 155 18.00

11 77 8.94 74 8.59

12 121 14.05 112 13.01

13 83 9.64 88 10.22

14 59 6.85 66 7.67

15 22 2.56 19 2.21

16 4 0.46 7 0.81

21 42 4.88 41 4.76

22 111 12.89 102 11.85

23 62 7.20 69 8.01

24 18 2.09 21 2.44

25 7 0.81 7 0.81

27 2 0.23 2 0.23

28 0 0.00 0 0.00

29 0 0.00 0 0.00

30 1 0.12 1 0.12

33 0 0.00 0 0.00

35 0 0.00 0 0.00

37 0 0.00 0 0.00

38 0 0.00 0 0.00

39 0 0.00 0 0.00

40 0 0.00 0 0.00
103

Figure 55. Cycle-aggregate THC emission rates by age, projected from MOVES running-exhaust emission

rates, for the hot-stabilized phase of the FTP, representing vehicles in I/M areas.

Figure 56. Cycle-aggregate NOx emission rates by age, projected from MOVES running-exhaust emission

rates, for the hot-stabilized phase of the FTP, representing vehicles in I/M areas.

For each model-year group, we divided cycle aggregate for each ageGroup (EFTP2,MYG,Age) by the

estimate for the 0-3 year ageGroup (EFTP2,MYG,0-3), to obtain a deterioration ratio (Rdet,MYG,Age) as

shown in Equation 47. As examples, ratios for cars are shown for THC in Figure 57(I/M) and

Figure 59 (non-I/M). Corresponding ratios for NOx are shown in Figure 59 (I/M) and Figure 60

(non-I/M). The ratios show that, in relative multiplicative terms, the MOVES rates represent

greater deterioration for running exhaust THC than for NOx.
104

Figure 57. Deterioration Ratios for THC, representing the hot-stabilized phase of the FTP (Bag 2),

Representing vehicles in I/M areas.

Figure 58. Deterioration Ratios for THC, representing the hot-stabilized phase of the FTP (Bag 2),

Representing vehicles in non-I/M areas.
105

Figure 59. Deterioration Ratios for NOx, representing the hot-stabilized phase of the FTP (Bag 2),

representing vehicles in I/M areas.

Figure 60. Deterioration Ratios for NOx, representing the hot-stabilized phase of the FTP (Bag 2),

representing vehicles in non-I/M areas.
106

At this point, projecting deterioration for start emissions is a simple matter of multiplying the

start rate for the 0-3 year ageGroup in each relevant operating mode (opModeID =101-108) by

the deterioration ratio (Rdet) and the relative deterioration ratio (Rrel) for each ageGroup. The

projected start rate in each agegroup (Estart,age) is

agestart, = − RREE agerel,agedet,30,start Equation 48

Note that for NOx the values of Rrel are 1.0 for all agegroups, i.e., relative multiplicative

deterioration for start emissions is the same as for running emissions. For THC and CO,

however, Rrel takes the values shown in Table 33, which reduces reduced relative start emissions

in comparison to relative running emissions. To illustrate the results, Figure 61 and Figure 62

show deterioration for cold-start emissions (opModeID=108) for THC and NOx, respectively.

Figure 61. Projected Deterioration for Cold-start THC Emissions (opModeID=108), in four Model years,

representing vehicles in I/M areas.
107

Figure 62. Figure 63. Projected Deterioration for Cold-start NOx Emissions (opModeID=108), in four Model

years, representing vehicles in I/M areas.
108

1.7 Replication and Data-Source Identification

The rates developed as described in Section 1 represent gasoline-fueled conventional-technology

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. However, for

ethanol blends, the effect of ethanol (and other effects related to blending) is represented through

fuel adjustments, rather than through the base rates, as described in this document. The

development and application of fuel adjustments is described in a separate document34.

Table 35. Fuel types and Engine technologies represented for gaseous-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)

30 Electric

Throughout the process, we assigned dataSourceIDs to subgroups of rates, which identify the

data and methods used to develop particular subsets of rates. The dataSourceIDs developed for

these analyses are listed and described in Table 36. Note that the table also lists the numbers of

records in each dataSourceID and relevant report section describing rate development for each

dataSourceID.

Finally, Table 37 shows the accounting for all rates developed for light-duty gaseous-pollutant

emissions and included in the emissionRateByAge table. The leftmost four columns delineate

subsets of rates by the pollutant processes included (Running, Start), and the respective fuel

types (fuelTypeID), engine technologies (engTechID) 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, as follows. Fueltype and engtech always represent single categories, as only

one of each is represented in a single row. Two regClasses in each row always refer to two

categories, cars (LDV) and trucks (LDT). The numbers of model-year groups (MYG), age

groups and operating modes (opModes) covered varies with combinations of process and

dataSource. Each row always represents the three gasous pollutants (CO, THC and NOx).

The rates for datasourceID = 4400 – 4601 were summed as a single category, as these groups

represent the outcome of a set of interrelated processes, as described in “Section 1.3.3

Emission-Rate development: Subgroup 1 (Model years through 2000).” The count of 15

modelyeargroups includes MY 2000 and earlier. The dataSourceIDs 4800 and 4801 represent

running emissions for MY 2001+, as described in “Section 1.3.4 Emission-Rate Development:
109

Subgroup 2 (MY 2001 and later).” For these rows, a count of one agegroup refers to the 0-3 year

ageGroup, whereas a count of three refers to the 4-5, 6-7, and 8-9 year ageGroups.

For these rates, the total of 21modelyeargroups represent groups 2001 – 2021-2050.

DataSourceIDs 4805 – 4807 represent start emissions for MY2001 – 2031-2050. For these

groups, counts of 26 or 36 modelyeargroups denote MY 1996-2031 and 1980 and earlier -

20212050, respectively. Counts of one or six ageGroups refer to the 0-3 ageGroup and the

remaining six ageGroups, respectively. Counts of one or seven opModes refer to the cold-start

emissions (opmode 108) or the remaining seven start modes, respectively.

The dataSourceID 4900 refer to the replication of the gasoline/conventional rates to provide

base rates for ethanol blends. 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; 8 modes for start emissions and 23 modes for running emissions.

The count for dataSourceID 4910 is similar, except that the 12 modelyeargroups include only

2010 – 2021-2050, as mentioned previously.
110

Table 36. Description of data sources and methods used in development of gaseous-pollutant emission rates

for light-duty vehicles.

DataSourceID Description No.

Records

Report

Section

4400 Data driven rates: averaged from continuous (second-by-second) IM240/IM147

data from Phoenix random evaluation sample, CY1995-99 and CY2002-05, on

temperature range of 68-86 °F.

6,968

4027 For opModeID = 29,39 only; use meanBaseRateIM calculated by ratio relative

to value in opMode 27; neither data mean or model prediction eligible

118

4037 For opModeID = 30,40 only; use meanBaseRateIM calculated by ratio relative

to value in opMode 27; neither data mean or model prediction eligible.

252

4427 For opModeID = 29,30,39,40 only; use meanbaseRatIM calculated by ratio

realative to value in opMode 27; either data mean or model prediction, or both

is eligible

138

4500 imputed using statistical hole-filling models. 3,572

4527 For opModeID = 29,30,39,40 only; use meanbaseRateIM calculated by ratio

relative to value in opMode 27; model prediction eligible, data mean not

eligible.

268

4601 calculated by ratio relative to ageGroup 8-9, modelyeargroups 2000 and

earlier, ageGroupID 10-14, 15-19 and 20+ only)

3,174

SUBTOTAL 14,490

4601 calculated by ratio relative to ageGroup 8-9, modelyeargroups 2001 and later,

ageGroupID 10-14, 15-19 and 20+ only)

8,694

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).

2,898

4801 calculated by applying deterioration to 4800 values, (modelyeargroups 2001 and

later only, ageGroups 4-5 through 8-9)

8,694

4805 calculated from IUVP FTP results, as Bag 1 - Bag 3 mass (cold start, opMode

108 only, ageGroup 0-3 only).

156

4806 calculated by applying deterioration ratios to 4805 values (cold start, opMode

108 only, ageGroup 4-5 and older).

1,296

4807 calculated by applying soak fractions and deterioration ratios to 4805 values

(opModes 101-107 only, all ageGroups).

10,584

4900 replicated from gasoline rates (fueltypeid = 1) to represent ethanol blends

(fueltypeid = 5).

46,872

4910 replicated from gasoline rates for all engine technologies to represent rates for

tier-2 light-duty diesel engines (MY 2010 and later only).

15,624
111

Table 37. Accounting for the segment of the emissionRateByAge table contributed by rates for gaseous-pollutant emissions for light-duty vehicles.

Process(es) fuelTypeID engTechID dataSourceID Accounting (No. classes or groups) No. records

fueltypes engTechs regClasses MYG ageGroups opModes pollutants

Start 01 01 101 1 1 2 10 1 1 3 60

Running 01 01 4400 1 1 2 15 7 23 3 14,490

Running 01 01 4027

Running 01 01 4037

Running 01 01 4500

Running 01 01 4527

Running 01 01 4601

4601 1 1 2 21 3 23 3 8,694

Running 01 01 4800 1 1 2 21 1 23 3 2,898

Running 01 01 4801 1 1 2 21 3 23 3 8,694

Start 01 01 4805 1 1 2 26 1 1 3 156

Start 01 01 4806 1 1 2 36 6 1 3 1,296

Start 01 01 4807 1 1 2 36 7 7 3 10,584

SUBTOTAL 46,872

Running & start 05 01 4900 1 1 2 36 7 31 3 46,872

Running & start 02 01 4910 1 1 2 12 7 31 3 15,624

TOTAL 109,368
112

2. Particulate-Matter Emissions from Light-Duty Vehicles

2.1 Introduction and Background

A large body of research is available 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 PM research programs on light-duty gasoline vehicles was

employed to generate emission rates for MOVES. The emission rates developed by this

approach embody a strictly “bottom-up” method whereby emission rates are developed from

actual vehicle measurements following intensive data analysis, and are then structured as inputs

to the 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 2004-200538. 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 and winter

phases, 261 and 278 vehicles were measured, respectively, with some overlap between the

phases. The measurements were conducted on a portable dynamometer using the LA92 driving

cycle under ambient temperature conditions.

Analyses of some of the data from this program are presented in the report: “Analysis of

Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas City”

39 This

“analysis report” (which is the partner to this chapter) presented preliminary emission rates for

PM, elemental carbon fraction (EC), organic carbon fraction (OC), as well as temperature

adjustment factors for start and hot-running emissions processes. These preliminary results form

the basis for the emission rates developed in this chapter. The rates in the analysis report are

based on aggregate or “bag” emissions measured on the filters, and are thus presented as

grams/start for start emissions and grams/mile for hot running operation.

The dataset included vehicles manufactured over several decades, measured at various ages

during CY2004-05. Thus, the program taken alone did not enable us to forecast emissions for

current vehicles as they age, or to hindcast emissions of older vehicles when they were young.

This chapter describes the development of a deterioration model based on a comparison of

former PM studies with the 2005 Kansas City study. The rates from this deterioration model

allow both forecasting and hindcasting as required by MOVES.

In addition, the previous analyses did not attempt to translate results measured on the LA92 cycle

(used in Kansas City) into terms of other cycles (such as the FTP) or to “real-world” driving. As

with the gaseous pollutants, MOVES has the capability to represent hot running “modal”

emission rates so that emissions vary depending on the driving pattern represented. The
113

operating modes defined for PM are the same as for the gaseous emissions (see Table 5, page

10). This chapter describes how the continuous PM measurements collected in the study were

used to populate the modal rates for MOVES. Because of the reliance on continuous PM

measurement, it is worth describing the measurement procedures used in 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 64). The LA92 cycle consists of three phases or “bags”.

Phase 1 (“bag 1”) is a “cold start” that lasts the first 310 seconds (1.18 miles). “Cold start” is

technically defined as an engine start after the vehicle has been “soaking” in a temperature

controlled facility (typically ~72°F) with the engine off. In the Kansas City study, the vehicles

were soaked overnight under ambient conditions. Phase 1 is followed by a stabilized Phase 2 or

“hot running” (311 – 1427 seconds or 8.63 miles). At the end of Phase 2, the engine is turned off

and the vehicle is allowed to “soak” in the test facility for ten minutes. At the end of the soak

period, the vehicle is started again, and is driven on the same driving schedule as Phase 1. This

Phase 3 is called a “hot start” because the vehicle is started when the engine and after-treatment

systems are still hot. Criteria pollutants were measured both in continuous and aggregate modes.

Particulate was collected during each of the three phases on 47 mm Teflon filters at 47°C ± 2°C.

Figure 64. Phases 1 and 2 of the LA92 Cycle, representing “cold-start” and “hot-running” operation,

respectively.

In addition to the gaseous pollutants measured via the constant-volume sampler (CVS),

continuous measurements of total PM mass were taken using two instruments. The first was an

Booker Systems Model RPM-101 Quartz-crystal microbalance (QCM) manufactured by Sensors,

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
114

Inc.; the second was a Thermo-MIE Inc. DataRam 4000 Nephelometer. In addition to total

mass, estimated black carbon was measured continuously with a DRI photoacoustic instrument.

In addition, integrated samples were collected and analyzed by DRI for PM gravimetric mass,

elements, elemental and organic carbon, ions, particulate and semi-volatile organic compounds,

and volatile organic air toxics. All sampling lines were heated and maintained at 47°C ± 2°C.

The samples were extracted from the dilution tunnel through a low particulate loss 2.5 μm

cutpoint pre-classifier. Further details and a schematic of the sampling instrumentation are

shown in Figure 65 and Figure 66.

Figure 65. Schematic of the constant-volume sampling system used in the Kansas-City Study.

(4 6 C )

D ilu te d e x h a u s t

a t 4 6 C
Figure 66. Continuous PM analyzers and their locations in the sample line.

It is worth briefly describing the apparatus used to measure

thorough description may be found in the contractor’s report

measuring continuous particulate was a daunting technical challenge.

specific advantages and disadvantages. For this study, the cumulative mass as measured

Teflon filters was treated as a benchmark. Thus

estimate modal emissions, the sums of the time series for the continuous

normalized to their corresponding

The Quartz Crystal Microbalance mea

face by measuring the change in

such as water vapor and desorption of lighter organic constituents. Due to the high degree of

noise in the continuous time series, the measurements were averaged over 10 seconds, thus

damping the temporal effects of transients. The QCM can accurately capture cumulative PM

over time, however, measurement uncertainties increase

values depend on a calculated difference between two sequential, and similar, measurements.

Due to the resulting high variability, including large and rapid fluctuations from positive to

negative emissions at any given instant, and vice versa,

viewed as a practical option for use

a check on the other instruments.

The Dustrak and Dataram both work on light

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 shou

concentrations though it may miss a substantial portion of the smaller (nano) particles. To

provide a qualitative check on this

instruments were aligned and checked to ensure that significant mass was not missed. Based on

this analysis, the Dustrak instrument was observed to be the more reliable of the 3 instruments,

and mass correction at low loads was not judged to be worth the effort given the uncerta

115

Continuous PM analyzers and their locations in the sample line.

is worth briefly describing the apparatus used to measure PM on a continuous basis.

thorough description may be found in the contractor’s report40. As of the date of this

measuring continuous particulate was a daunting technical challenge. Each technique

specific advantages and disadvantages. For this study, the cumulative mass as measured

benchmark. Thus, prior to using the continuous measurements to

estimate modal emissions, the sums of the time series for the continuous measurements

ir corresponding filter masses to compensate for systematic instrument errors.

The Quartz Crystal Microbalance measures the cumulative mass of the PM deposited on a crystal

face by measuring the change in its oscillating frequency. It is highly sensitive to many artifacts

such as water vapor and desorption of lighter organic constituents. Due to the high degree of

oise in the continuous time series, the measurements were averaged over 10 seconds, thus

the temporal effects of transients. The QCM can accurately capture cumulative PM

measurement uncertainties increase for successive points in time

difference between two sequential, and similar, measurements.

Due to the resulting high variability, including large and rapid fluctuations from positive to

given instant, and vice versa, use of the QCM measurements w

for use in emission rate development MOVES at this time, except as

the other instruments.

The Dustrak and Dataram both work on light-scattering principles. As such, they have very

rapid response times and can measure larger PM volumes with reasonable accuracy. However,

their accuracy degrades when measuring low PM volumes. Since most PM mass lies within the

larger particles, the instruments should be able to capture most of the continuous

concentrations though it may miss a substantial portion of the smaller (nano) particles. To

this supposition, the time-series for the QCM and optical

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 uncerta

on a continuous basis. A more

. As of the date of this program,

technique has

specific advantages and disadvantages. For this study, the cumulative mass as measured on the

g the continuous measurements to

measurements were

systematic instrument errors.

sures the cumulative mass of the PM deposited on a crystal

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

oise in the continuous time series, the measurements were averaged over 10 seconds, thus

the temporal effects of transients. The QCM can accurately capture cumulative PM

for successive points in time because the

difference between two sequential, and similar, measurements.

Due to the resulting high variability, including large and rapid fluctuations from positive to

QCM measurements was not

MOVES at this time, except as

rinciples. 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

continuous mass

concentrations though it may miss a substantial portion of the smaller (nano) particles. To

series for the QCM and optical

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
116

involved. This time-consuming analysis was done by eye for each test and the results are not

presented in this chapter.

The photoacoustic analyzer (PA) is unique among the continuous instruments in its ability to

capture only the soot or elemental carbon components of PM. The fast analyzer detects the

resonances coming off the carbon-carbon bonds in soot. Unfortunately, there were insufficient

Thermal Optical Reflectance (TOR) elemental carbon (EC) measurements from quartz filters to

normalize the PA data, but some comparisons are shown in the contractor’s report40. 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.

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

means of determining modal emission rates, and the use of such data does not qualify them as

EPA recommended or approved devices or processes.

2.1.2 Causes of Gasoline PM Emissions

In gasoline-powered spark-ignition engines, particulate matters is formed during 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

make comparisons 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 agglomerate. This form of PM is generally composed 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 but does form in larger quantities

under relatively rich conditions, e.g., low 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 fraction is about 20% of

PM mass compared to about 70% for a diesel engine.

Other compounds in the fuel or engine oil, such as trace levels of sulfur and phosphorus, form

form sulfates and phosphates during combustion, both of which form particulate. The sulfur

level in gasoline is now very low, almost eliminating sulfate formation from gasoline sulfur
117

content but motor oil contains higher fractions of 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.

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 replaced; thus, one might expect older model-year fuel-injected

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 control, however it should be noted that the correlation between these

emissions is far from perfect. Many examples of this relation are shown in the 2008 analysis

report39.

Amounts of PM emitted are very sensitive to the amount of fuel in combustion as well as the

stoichiometry of the reaction. 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). Additionally, under cold
118

conditions (e.g., below 20oF), additional enrichment of the fuel-air mixture is needed to start the

engine and lasts longer while the engine warms up.

When high acceleration rates or loads are encountered (such as 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 get into the combustion chamber via several pathways.

Some engines 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, the amount of HC from oil is typically small.

However, organic PM from oil consumption can be quite significant because oil is a high

molecular weight hydrocarbon, and more likely to persist as unburned 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, the first two are presumably less of a factor in the Kansas City program than aromatics.

In the calendar years that MOVES models, lead is not a significant portion of the inventory, thus

is largely ignored.

Some of these PM forming mechanisms clearly affect HC emissions. 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 an approach to extend the PM results from the Kansas City Study to

estimate average emissions across the fleet. The section also compares the new vehicle results
119

from many different studies in order to estimate zero mile level (ZML) emission rates for all

model years. Before modeling deterioration, it is first necessary to capture ZML emission rates.

In constructing a model of emissions from the Kansas City data (Figure 67), the most significant

challenge is distinguishing model year and age effects. As with most datasets, this issue arises

because the program was conducted over a two-year period, thus ensuring a direct

correspondence between model year and age. As a result, it is very difficult to distinguish the

reduction in emissions with model year from the increase in emission with age. Emissions tend

to decrease as technologies are introduced on vehicles (with later model years) in order to

comply with more stringent emissions standards. However, these technologies and vehicles tend

to deteriorate over time, thus for the same model year vehicle, older vehicles (greater age) will

have higher emissions (on average) than newer vehicles.

Figure 67. Average Particulate Emission Rates from the Kansas City Study, by Model year, shown as cycle

aggregates on the LA92.

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

miles (odometer) on 3 different cycles

unfortunately, PM results were not reported as a function of mileage. The

“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

(Figure 68).

Figure 68. Hydrocarbon emissions a function of mileage (Gibbs et al., 1979)

Hammerle et al. (1992) measured PM from two

results for PM deterioration are somew

deterioration seems to occur mainly in the beginning of life, with very little occurring after

20,000 miles. Also, the study is limited to

120

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

ed emissions from 56 vehicles with mileage ranging from 0 to 55,000

miles (odometer) on 3 different cycles41. Hydrocarbon emissions were analyzed, but

unfortunately, PM results were not reported as a function of mileage. The authors’

tes 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

. Hydrocarbon emissions a function of mileage (Gibbs et al., 1979)

1992) measured PM from two vehicles over 100,000 miles.42 However, their

results for PM deterioration are somewhat inconclusive, as the following figure shows, since the

deterioration seems to occur mainly in the beginning of life, with very little occurring after

20,000 miles. Also, the study is limited to two specific vehicle models.

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

from 0 to 55,000

. Hydrocarbon emissions were analyzed, but

authors’ state that

tes 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

However, their

hat inconclusive, as the following figure shows, since the

deterioration seems to occur mainly in the beginning of life, with very little occurring after
Figure 69. Particulate emissions as a function of odometer for two Ford vehicles (Hammerle et al., 1992)

Both of these studies assume that odometer is a surrogate for age. While there are some

deterioration mechanisms that worsen wi

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)

account for all causes of deterioration.

Whitney (2000) re-recruited 5 vehicles that had been

(CRC-E24)43. There are two significant li

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

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

121

Particulate emissions as a function of odometer for two Ford vehicles (Hammerle et al., 1992)

Both of these studies assume that odometer is a surrogate for age. While there are some

deterioration mechanisms that worsen with mileage accumulation, there are others that

deteriorate with effects that occur over time, such as number of starts, corrosion due to the

elements, deposits and impurities collecting in the gas tank and fuel system, etc. Therefore, we

study that describes deterioration as a function of odometer (alone)

account for all causes of deterioration.

recruited 5 vehicles that had been measured in previous study 2 years prior

. 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

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

missions (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

New Vehicle, or ZML Emission Rates and Cycle Effects

Particulate emissions as a function of odometer for two Ford vehicles (Hammerle et al., 1992)

Both of these studies assume that odometer is a surrogate for age. While there are some

th 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

study that describes deterioration as a function of odometer (alone) may not

previous study 2 years prior

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

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

missions (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

are minimal for PM.

The three longitudinal studies described above are inconclusive, though they do hint that
122

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 studies. New vehicle emission rates tend to have lower variability than

older vehicles (in absolute terms) since they have lower emissions that comply with more

stringent HC standards. These standards, which decrease over time, tend to affect PM emissions

as well since many of the mechanisms for HC formation also form PM.

Several independent studies have measured PM emissions from nearly new vehicles. For our

purposes, we will define “new” as a vehicle less than 3 years old, i.e., vehicles within the 0-3

year age Group. Table 38 lists the 15 studies employed for this analysis.

Table 38. Historical gasoline PM studies including new vehicles at time of study.

Program

Year of

Study

No.

vehicles

Drive

cycle

Gibbs et al.

41 1979 27 FTP

Cadle et al.

44 1979 3 FTP

Urban & Garbe45,

46 1979, 1980 8 FTP

Lang et al.47 1981 8 FTP

Volkswagen48 1991 7 FTP

CARB49 1986 5 FTP

Hammerle et al., 199242 1992 2 FTP

CRC E24-1 (Denver)50 1996 11 FTP

CRC E24-2 (Riverside)51 1997 20 FTP

CRC E24-3 (San

Antonio)52 1998 12 FTP

Chase et al.53 2000 19 FTP

Whitney (SwRI)43 1999 LA92

KC (summer)39,40 2004 13 LA92

EPA (MSAT)54 2006 4 FTP

Before we examine 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,

Riverside55. 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
123

this program. However Figure 70 shows the HC emissions compared for the two cycles. The

trends indicate that there is little cycle effect for HC.

Figure 70. Hydrocarbon emissions on the LA92 versus corresponding results on the FTP

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 71 shows the new-vehicle emission rates from the 11 studies listed in Table 38. The data

points represent each individual test, and the points with error bars represent the average for each

source. The plot presents evidence of an exponential trend (fit included) of decreasing emissions

with increasing model year. The fit is also nearly identical if we omit the two programs that

employed the LA92 cycle. We will use this exponential ZML relationship as the baseline on

which to build a deterioration model. However, the measurements from the older programs

primarily measured total particulate matter. These have been converted to 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)56. 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 documentation is not sufficient to even

attempt it. Therefore no attempts at adjustment or normalization were made except for size

fraction, lead and sulfur, as described above.
124

Figure 71. Particulate emission rates for new vehicles compiled from 11 independent studies.

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 cycle phase. 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 cars (LDV) (EHR,y) is:

y

y EE

-0.814

,HR 2005,HR = e Equation 49

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 a factor of 1.43. This

value is based on an average of all the studies with new vehicles from 1992 onward (before this

model year, there were no trucks measured). It is also multiplied by 0.898 to give hot running

bag 2 rates and 1.972 to give the cold start emission rate (here defined as bag 1-bag 3 in units of

g/mi). These values were estimated by running a general linear model of bag 2 and bag1-3 with

respect to composite PM, respectively, using the SPSS statistical software tool. The averages of

these ratios by model year are shown in Figure 72, in which no clear trend is discernable. The

parameters of the model are summarized in Table 39.
Figure 72. Ratios of hot-running/composite and cold

averaged by model year.

Table 39. Best-fit parameters for cold

Parameter

LDV hot

Exponential slope

Truck/car ratio

Bag

Cold

Figure 73 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 rep

125

running/composite and cold-start/composite, Bag2 and Bag1-Bag3, respectively,

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

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 rep

Bag3, respectively,

ort.
Figure 73. Particulate ZML emission rates (g/mi) for cold

LDT.

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 emis

hindcast the past as well as forecast the future, as

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

two points. This section describes the procedure and the assumptions made to determine the rate

at which vehicle PM emissions age.

We first break the data into age Groups. We use the MOVES age

following age intervals: 0-3 (new), 4

category for 20 years and older implies that emission rates have stabilized by 20 years of age.

The bag measurements from all of the vehicles measured in Kansas City were

temperature using the equation derived in the analysis

126

Particulate ZML emission rates (g/mi) for cold-start and hot-running emissions, for LDV and

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 required in inventory models.

Age Effects or Deterioration Rates

The ZMLs determined in the previous section represent baseline emissions for new vehicles in

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. 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 groups which correspond to the

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.

The bag measurements from all of the vehicles measured in Kansas City were first adjusted for

temperature using the equation derived in the analysis report39. The equation used is:

running emissions, for LDV and

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

sions from the fleet and to

The ZMLs determined in the previous section represent baseline emissions for new vehicles in

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

many ways to connect

two points. This section describes the procedure and the assumptions made to determine the rate

which correspond to the

19, 20+. Having a single age

category for 20 years and older implies that emission rates have stabilized by 20 years of age.

first adjusted for

. The equation used is:
127

)72(03344.0

72,PM PM, e

T EE T− −

= Equation 50

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, i.e., the rates in each model-year

group represent emissions for that group at the age of measurement in 2004-05, at 72oF rather

than at the actual ambient temperature.

The method adopted is to ratio the aged rates with the new rates so that the changes with

deterioration rates are all proportional. This approach will be referred to as the “multiplicative

deterioration model”, and is analogous to the approach used with the gaseous emissions (Chapter

1).

It is likely that some of the same mechanisms that cause HC and CO to increase over time would

also result in PM increases. These factors include deterioration in the catalyst, fuel control,

air:fuel-ratio control, failed oxygen sensors, worn engine parts, oil leaks, etc. Figure 74 shows

trends in the natural logarithm of THC rates over approximately 10 years, based on randomevaluation samples in the Phoenix I/M program. On a ln-linear scale, the deterioration rates

appear approximately linear over this time period, suggesting that the deterioration rates are

exponential over this time interval. This observation, combined with the approximate

parallelism of the trends for successive model years, implies that emissions follow a

multiplicative pattern across model-year or technology groups, calling for a multiplicative

deterioration model. In such a model, the aged rates and the new rates are converted to a

logarithmic scale, after which the slopes are estimated by fitting a general linear model. The

average slope is estimated, with the ZMLs determined earlier defining the y-axis offsets. The

result is a series of ladder-like linear trends in log scale as shown in Figure 75. The lines fan out

exponentially on a linear scale as shown in Figure 76. 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.
128

Figure 74. The natural logarithm of THC emissions vs. Age for LDV in the Phoenix (AZ) Inspection and

Maintenance program over a ten-year period (1995-2005).

Figure 75. 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.
129

Figure 76. The multiplicative deterioration model shown on a linear scale. The y-axis offsets capture the

new-vehicle ZML rates. The dotted lines and points with error bars represents the Kansas-City results (with

95% confidence intervals).

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 estimate remaining rates for starts other than the cold

start (opmodeID=101-107). Because no data is available describing how particulate start

emissions vary by soak time, we have used the HC soak curves shown previously (see p. 87 ).

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 Estimating Elemental and Organic Carbon Fractions

After performing the analyses described above to estimate total particulate ( PM2.5), we

partitioned the total into components representing elemental and organic carbon, EC and OC,
130

respectively. Following this step, the values for EC and OC were loaded into the

emissionRateByAge table, using the pollutant and process codes shown in Table 1.

Table 40. Combinations of pollutants and processes for particulate emissions.

pollutantName1

pollutantID1processName2processID2polProcessID3

Primary PM2.5 - Organic Carbon 111 Running exhaust 1 11101

Start exhaust 2 11102

Primary PM2.5 - Elemental

Carbon

112 Running exhaust 1 11201

Start exhaust 2 11202

1

as shown in the database table “pollutant.” Note that MOVES will reaggregate the particulate components to

construct “Primary Exhaust PM10” (pollutantID 100) and “Primary Exhaust PM2.5” (pollutantID 110).

2

as shown in the database table “emissionProcess.”

3

as shown in the database table “emissionRateByAge.”

This discussion in this section is reproduced and adapted from the Kansas-City analysis report39.

Vehicle exhaust particulate matter consists of many different chemical species, including

elemental carbon (EC), organic carbon (OC), sulfates, nitrates, trace metals and elements. The

vast majority of the PM emissions is in the form of EC or OC. Elemental carbon, also known as

soot or black carbon, is produced during combustion when fuel or fuel droplets are pyrolyzed (or

carbonized) under low oxygen levels. In this process hydrogen is stripped from the carbon atoms

in the hydrocarbon, and carbon soot residue remains. Elemental carbon is formed in gasoline

engines primarily when the fuel air mixture is rich (even in localized portions of the air/fuel

mixture of the engine). The hot oxygen-starved and fuel rich environment favors pyrolysis

reactions.

We might expect to see higher EC fractions in gasoline engines following engine starts, or when

during enrichment mode such as under heavy engine load. These fine soot particles are generally

non reactive in the atmosphere, though they may act as agglomerization centers for particle

growth both in the exhaust stream and in the atmosphere. In other words, other compounds

including organic carbon adsorb onto the surface of the elemental carbon. In turn, these

adsorbed organic carbon compounds can react in the atmosphere, generally in oxidation

reactions.

Organic carbon forms clusters of organic molecules that agglomerate and grow throughout

combustion, as the exhaust cools, and finally as it disperses into the atmosphere. In gasoline

engines OC can be formed normally during combustion from the fuel or the lubricating oil.

Sulfate emissions have largely been controlled through fuel sulfur controls, and, previously, by

the closer control of air:fuel ratio necessary for the three-way catalyst to effectively function.

We expect the sulfate emissions to be much lower than past studies. Likewise, we also assume

that nitrates and trace metals and elements are small on a mass basis by comparison. Therefore,

we spend the remainder of this section discussing EC and OC only.
131

It is important to separate EC and OC in inventory estimation since photochemical models treat

these fractions of particulate separately. Also, the ratios are helpful for comparing emissions

(and air quality) models to source apportionment studies. Finally, EC is easier to measure and

more stable in the atmosphere than OC, therefore it is useful to track for a variety of purposes.

In the Kansas City study, EC was measured using two different methods. The first was the

technique of thermal optical reflectance (TOR). This procedure also measured OC and total PM,

but unfortunately, not all the vehicles in the study were measured using this technique.

Elemental carbon was also measured uing the photoacoustic analyzer, which measures EC on a

continuous basis. More information can be found on these techniques and their calibration and

comparison results in the contractor’s report40 and Fujita et al. (2006)57. The former reference

indicates that the photacoustic analyzer has good correlation with TOR EC measurement

especially at higher PM levels, however, at lower levels (in bag 3 for example), the correlation is

poorer. This is not surprising since all instruments have limited ability to measure small signals.

To accentuate the full range of operation, Figure 77 shows a plot of a comparison of the two

instruments on a natural-log scale. The plot reinforces the excellent agreement between the two

instruments in bag 1 of the test, when emissions levels are at their highest. The correlation (and

slope) is also good for the high values in bag 2, however, as the measurements get smaller, the

photoacoustic analyzer seems to be shifted by about 2.4 mg/mi (near the origin of the plot). An

adjustment equation may be appropriate if the TOR is the accepted standard, but since this offset

mainly affects small measurements only, it will probably have little impact on emissions

inventory models.

Figure 77. Comparison of Photoacoustic to TOR EC measurements on a logarithmic scale.

We present trends of the ratio of EC to total PM (EC/PM) only. Since in most cases the sum of

EC+OC = PM, generalizations can be extended to OC/PM as well, accounting, of course, for the

y = 0.9825x - 0.2107

R

2

= 0.9417

y = 1.0666x - 1.1628

R

2

= 0.7295

y = 0.6468x - 0.6774

R

2

= 0.485

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

ln(TOR EC)

ln(Photoacoustic EC)

bag 1

bag 2

bag 3

Linear (bag 1)

Linear (bag 2)

Linear (bag 3)
132

inverse relationship between EC and OC. There may be a small amount of non-carbon emission

in the PM, but we assume that it is negligible.

We explore the EC/PM ratio for the four measurement techniques employed in this study:

photoacoustic analyzer (PM, continuous EC), Dustrak analyzer (DT, continuous optical PM),

gravimetric filter (PM), and thermal optical reflectance (TOR, which measured both EC and total

carbon, TC). Table 41 shows the comparison of the 3 different ratio methods using these

instruments. The values were determined from ratios of average values in the numerator and

denominator. The TOR ratios have two major limitations: the ratios are unexpectedly high and,

after eliminating bad data points, there are only 75 valid measurements. Due to the latter

condition (primarily), the TOR ratios will not be used in subsequent analysis. The photoacoustic

to dustrak ratios present a reasonable approach, however, since the Dustrak and PM are not

perfectly correlated40, we elected to use the photacoustic to gravimetric filter ratios for EC/OC

rate estimation.

Table 41. Elemental to total PM ratio for 4 different measurement techniques.

all start running

PA/DT 0.128 0.188 0.105

PA/PM 0.197 0.340 0.164

EC/TC TOR 0.382 0.540 0.339

In the next 3 plots, we look for other factors that may affect the EC/PM variability. Temperature,

model year and vehicle weight are all examined. Figure 78 shows the relationship between

EC/PM to test temperature. These values were averaged for all test values within a 10oF bin and

then ratios were calculated between corresponding means. We conclude from this plot that there

is very little temperature dependence to this ratio (though there may be a very small effect for hot

running bag 2). Any temperature dependence is miniscule compared to the temperature effects

presented earlier for total PM. One might have expected cold start EC ratios to be higher in

colder temperatures due to the potential for extended rich starts, however the data does not seem

to support this hypothesis.
133

Figure 78. Elemental Carbon to Total PM ratio as a function of test temperature.

Figure 79 shows the EC/PM ratio within model year groups. We conclude from this plot that

there seems to be very little model year or age dependence on the EC/PM ratio.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 20 40 60 80 100

temperature

ec/pm

ec/pm start

ec/pm running
134

Figure 79. Elemental Carbon to Total PM ratio as a function of vehicle model year.

Figure 80 shows the EC/PM ratio as function of vehicle weight. This plot shows a clear trend of

decreasing EC/PM ratio as weight increases. This could be a function of engine displacement

(and peak power) as much as vehicle weight (the two tend to be correlated with each other). The

trend may also be a function of the drive schedule since lighter (and possibly underpowered

vehicles) may be more likely to go into enrichment than more powerful vehicles if driven on

identical drive cycles. In subsequent modeling (in MOVES), cars and light trucks are modeled

as separate vehicle types, which will capture some of this weight effect.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Model year

ec/pm

ec/pm start

ec/pm running
135

Figure 80. EC/PM ratio as a function of vehicle inertial weight.

An analysis shows the following statistics, with the breakdown of car vs truck in Table 42:

♦ avg Start EC/PM = 0.337

♦ avg Running EC/PM = 0.132

♦ Composite EC/PM ratio = 0.173

♦ The respective OC ratios can be calculated from the above by subtracting the fraction

from 1.0.

The markedly higher level of EC during starts is not surprising given the rich fuel conditions that

exist during this mode of operation.

These results are roughly consistent with past studies, which found the OC:PM fraction in

Denver to range from 61-89%50, in the South Coast of California to range from 37-80%51, and in

San Antonio to range from 53-93%52. For emission rate development, we use the values derived

from the Kansas-City study, summarized in Table 42. Non-carbon PM are included with OC and

is assumed to be small.

Table 42. Elemental and Organic Carbon PM fractions in from vehicles in the KC study.

Process

EC/PM

car

EC/PM

Truck

OC/PM

car

OC/PM

truck

Start 0.345 0.325 0.655 0.675

Running 0.179 0.068 0.821 0.932

2.4 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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 1000 2000 3000 4000 5000 6000

inertial weight (lb)

ec/pm

car

truck
136

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-emitting and high-emitting vehicles. We conclude by

describing the procedure by which results from the Dustrak were used to develop emission rates

by operating mode.

2.4.1 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 increase 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 81, which includes

two 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.

Figure 81. A typical time-series plot of continous particulate emissions as measured by several instruments.
137

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 82 shows an older MY1976

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.
138

Figure 82. Continous particulate emissions from a 1976 Nova measured at 54°F.

Figure 83 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

these vehicles need to go into enrichment during this relatively mild acceleration.

Figure 83. Measured Particulate time series for a recent model year vehicle.
139

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 84 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 power-based approach. 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 84. Particulate time-series for a 1988 Dynasty.

Figure 85 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

dustrak, nepholometer, and the QCM all register the hump, while the PA shows very little EC,
140

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 nonrepeatability of some of these humps arising from oil consumption explains how some vehicles

can flip from “high” to “normal” emissions or vice-versa in replicate measurements. These

observations have ramifications for future PM research, in that sample sizes should be large and

fleets properly representative.

Figure 85. Continuous particulate time series for a 1995 Lincoln Continental.

The next figure (Figure 86) shows a more typical high PM emitter, where the bag 2 emission rate

is 266 g/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.
141

Figure 86. Continuous particulate (and HC) time series for a 1978 MG.

We are now ready to classify the emission rates into operating modes based on speed,

acceleration and vehicle-specific power (VSP) (Table 5). The following two figures show

Dustrak PM emissions binned by VSP and classified by model year Groups. Figure 87 shows

this relationship on a linear scale and Figure 88 shows the relationship on a logarithmic scale. It

is clear from the latter plot that VSP trends for PM tend to be exponential with VSP load, i.e.

they are approximately linear on a log scale, showing similar patterns to the gaseous emissions,

particularly CO. Thus we assume smooth log-linear relations when calibrating our VSP based

emission rates.
142

Figure 87. Particulate emissions, as measured by the Dustrak, averaged by VSP and model-year

Group (LINEAR SCALE).

Figure 88. Particulate emissions, as measured by the Dustrak, averaged by VSP and model-year

Group (LOGARITHMIC SCALE).
143

In order to determine the actual MOVES VSP based rates, followed seven steps:

1. The LA92 equivalent hot-running emission rate (g/mi) is determined for every model

year and age group from the model described in section 2.2.

2. The gram per second (g/s) emission rate is determined from the dustrak for cars and

trucks based on the KC data. These trends are then extrapolated to the higher VSP bin

levels where data is missing.

3. The VSP operating-mode distribution is calculated for bag 2 of the LA92 drive cycle for

cars and trucks separately – this step is equivalent to determining the number of seconds

in each mode.

4. The modal rates (Step 2) are then combined with the operating-mode distribution and

summed to give a total bag 2 emission factor that must match the aggregate LA92

emission rates in step 1 (as calculated from the filter measurements).

5. The emission rates are normalized to match the filter values through a normalization

factor that is applied to every combination of model year and age group.

6. The rates from step 5 are then multiplied by the corresponding EC and OC factors to

give rates for the hot-running process.

7. Steps above are repeated for all ages and model years.

The output from step 3 (operating-mode distribution) for cars and light trucks is shown in Figure

89. For operating-mode definitions, see Table 5.

Figure 89. Operating-Mode distribution for cars and light trucks representing the hot-running

phase (Bag 2) of the LA92 cycle.

The output of step 5 for each model year ZML (0-3 year age Group) is shown in Figure 90.

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
Figure 90. Particulate emissions for passenger cars (LDV) from Kansas City results

Group, normalized to filter mass measurements

After the rates were calculated, a quality check was performed to ensure that the aged rates in

any particular bin were not too high. A multiplicative model

excessively high emission rates under extreme conditions. For exa

was considered too high, this would be an extremely high

corrected in only two cases bins in operating mode 30, representing values for cars and trucks in

the 1975 model-year Group. In these

mode 30.

2.5 Conclusions

The previous discussion describes analyses of particulate

operating-mode based emission rates for use in the MOVES emissionRateByAge

incorporating the effects of temperature, model year and age. These rates include organic and

elemental carbon for cold-start and hot

and LDT). This analysis is crucial for understanding how

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

historical PM studies. The trends

with model year as the engine and fuel controls have improved and after

been installed. The new truck rates are found to be larger than the car rates. The deterioration

effect of age is determined by comparing the new vehicle rates to the Kansas City data

Based on patterns observed for the gaseous emissions, we have assumed that emissions

deteriorate exponentially with the age of the vehicle, but remain constant after abo

We also found that PM emission increase exponentially with VSP (or road or engine load).

144

. Particulate emissions for passenger cars (LDV) from Kansas City results

, normalized to filter mass measurements.

After the rates were calculated, a quality check was performed to ensure that the aged rates in

any particular bin were not too high. A multiplicative model with exponential factors risks

excessively high emission rates under extreme conditions. For example any rate over 100 g/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

year Group. In these cases, the value from operating mode 29 was copied into

The previous discussion describes analyses of particulate-matter emissions designed to develop

mode based emission rates for use in the MOVES emissionRateByAge

incorporating the effects of temperature, model year and age. These rates include organic and

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 from

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 have

been installed. The new truck rates are found to be larger than the car rates. The deterioration

fect of age is determined by comparing the new vehicle rates to the Kansas City data

Based on patterns observed for the gaseous emissions, we have assumed that emissions

deteriorate exponentially with the age of the vehicle, but remain constant after abo

also found that PM emission increase exponentially with VSP (or road or engine load).

. Particulate emissions for passenger cars (LDV) from Kansas City results, by model year

After the rates were calculated, a quality check was performed to ensure that the aged rates in

exponential factors risks

mple any rate over 100 g/sec

smoking vehicle. This behavior was

corrected in only two cases bins in operating mode 30, representing values for cars and trucks in

cases, the value from operating mode 29 was copied into

matter emissions designed to develop

mode based emission rates for use in the MOVES emissionRateByAge table,

incorporating the effects of temperature, model year and age. These rates include organic and

running emissions from cars and light trucks (e.g., LDV

PM emissions have changed over the

years and how new vehicle PM rates are projected to deteriorate over time. The new vehicle

vehicle emissions rates from

indicate that emissions have been decreasing exponentially

treatment devices have

been installed. The new truck rates are found to be larger than the car rates. The deterioration

fect of age is determined by comparing the new vehicle rates to the Kansas City data.

Based on patterns observed for the gaseous emissions, we have assumed that emissions

deteriorate exponentially with the age of the vehicle, but remain constant after about 20 years.

also found that PM emission increase exponentially with VSP (or road or engine load).
145

There is still much analysis that can be conducted with these data. In the future, it would be

important to examine trends in the speciated hydrocarbons and organic PM from the standpoint

of toxic emissions and also quantifying the PM emissions due to oil consumption. These

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 to explicitly capture the non carbon portion of particulate emissions.
146

3. Gaseous and Particulate Emissions from Light-Duty Diesel

Vehicles (THC, CO, NOx, PM)

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 emission rates

in relation to vehicle specific power. Therefore, we used aggregate results (in grams per mile)

from the Federal Test Procedure (FTP) to estimate aggregate rates, which we then translated into

corresponding modal rates (in grams per hour).

3.1 Gaseous Emissions: MY2009 and earlier, Particulate Emissions:

MY2003 and earlier.

The analyses in this section pertain to development of rates representing vehicles manufactured

prior to introduction of Tier-2 standards. For gaseous emissions, this grouping is represented by

MY 2009 and earlier. For particulate emissions, the grouping represents MY 2003 and

earlier.3.1.1 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 measured for purposes of engine

certification or generation of fuel economy estimates. These vehicles were new (age = zero

years), with each vehicle having accumulated about 4,000 miles. These data 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 U.S.) The sample sizes (by model year

group) and the mean composite FTP emissions are given in Table 43 for cars and Table 44 for

trucks:
147

Table 43. Mean Composite FTP Emissions (g/mile) for diesel-fueled Cars (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 ---

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.

Table 44. Mean Composite FTP Emissions (g/mile) for diesel-fueled light-duty trucks (LDT).

Model Year

Group

Sample

size

HC CO NOx PM1

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 ---

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).

3.1.1.2 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. The sources of these data are summarized in Table 45.
148

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):

Bag1HC, HC,composite E = 0.6433- + 4.702885 E Equation 51

Graphing this equation along with the 146 FTP test results, as shown in Figure 91 below,

illustrates the relationship between the individual bag HC emission and the composite HC

emission.

Table 45. Data Sources used to distinguish emissions by phase (bag) on the FTP for light-duty diesels.

Source No. Tests

Norbeck et al., (1998a)51 19

Norbeck et al., (1998b)58 15

USEPA In-Use Verification Program 12

Mobile-Source Observation Database (MSOD)30 105

Total 151

Figure 91. Example: Bag-1 HC (g) versus Composite FTP HC (g/mile)

FT P H C R ate (grams / mile)

0

4

8

12

16

0.0 0.5 1.0 1.5 2.0 2.5 3.0
149

We then applied those 14 equations (derived from the regression analyses) to the corresponding

composite FTP emissions shown in Table 43 and Table 44. 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

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 46 to Table

49), one table for each of the four pollutants.

Table 46. Estimated Aggregate HC Emission Rates.

Model Year

Group

Diesel-Fueled Passenger Cars Diesel-Fueled Light-Trucks

Running

(g/hr)

Cold-Start

(g)

Hot-Start

(g)

Running

(g/hr)

Cold-Start

(g)

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

Table 47. 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
150

Table 48. 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

Table 49. 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

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 3.3 below.

3.1.1.3 Assigning Operating Modes for Starts (Adjustment for Soak Time)

MOVES has start emission rates for eight operating modes (opModes), each based on the length

of the soak time prior to engine start. One mode corresponds to the 12 hour cold-soak

(opmodeID = 108). The remaining seven modes have soak times ranging from three minutes up

to nine hours (opModeID = 101-107).

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 EEβ α-

opModeID 108 = − e001.1 Equation 52
151

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

Equation 53

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 (Figure 92) 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.
152

Figure 92. Estimated THC Start Emissions (g) in terms of Soak Time (1995-2005 LDV).

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 MOBILE632.

3.1.2 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 5, page 14). However, we lacked the

requisite second-by-second data for the diesel-fueled 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 50.

T H C Sta rt Emissions (grams per sta rt)

1995-2005 LD DVs

Soa k T ime (min utes)

0

0.1

0.2

0.3

0 120 240 360 48 0 60 0 72 0
153

Table 50. 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 the light-duty diesels (Table 46 through

Table 49), by model-year group, yielded ratios of the light-duty emission rates to the light heavyduty 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 a scaling factor to multiply all of the modal LHDDV rates for that modelyear 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 plots (Figure 93 to Figure 96) of the estimated

modal emission rates for 1995-1998 model year diesel-fueled passenger cars and trucks.
154

Figure 93. Modal Emission Rates for THC, for MY1995-98 diesel-fueled Cars and Trucks.

Figure 94. Modal Emission Rates for CO, for MY1995-98 diesel-fueled Cars and Trucks.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

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

meanBaseRate (g/hr)

Operating Mode

Cars

Trucks

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

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

meanBaseRate (g/hr)

Operating Mode

Cars

Trucks
155

Figure 95. Modal Emission Rates for NOx, for MY1995-98 diesel-fueled Cars and Trucks.

Figure 96. Modal Emission Rates for PM, for MY1998 diesel-fueled Cars.

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

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

meanBaseRate (g/hr)

Operating Mode

Cars

Trucks

0

10

20

30

40

50

60

70

80

90

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

meanBaseRate (g/hr)

Operating Mode
156

3.2 Gaseous Emissions: MY2010 and Later, Particulate Emissions: MY2004

and Later.

3.2.1 Gaseous Emissions

For model years 2010 and later, we did not apply the analyses described above in 3.1. 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 36 and Table 37 (dataSourceID 4910).

3.2.2 Particulate Emissions

To achieve substantially lower PM emissions, manufacturers are now equipping their dieselfueled vehicles (cars and trucks) with particulate traps.

Similarly to the gaseous emissions, for MY 2004 and later, particulate emissions for light-duty

diesels were assumed to equal those for light-duty gasoline vehicles. Thus, for these model

years, corresponding gasoline rates, as described in Chapter 2.0 above, were replicated to

represent diesel vehicles.

3.3 Particulate Emissions: Estimating Elemental and Organic Carbon

Components (EC, OC)

3.3.1 Group 1: MY 2003 and earlier

For these model years, total PM was partitioned into EC and OC components using ratios

developed for application to heavy-duty diesels. Figure 97 below, which is reproduced from the

Heavy-Duty Emissions Report59, shows the ratios.
157

Figure 97. Elemental Carbon (EC) fractions running-exhaust particulate emissions, for Heavy-heavy-duty

and medium-heavy-duty diesel vehicles, by operating mode.

3.3.2 Group 2: MY 2004 and later

For these model years, total PM was partitioned into EC and OC components using ratios

developed for application to light-duty gasoline rates. See 2.3 And Table 42 above.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

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 bin

EC fraction

HHD

MHD
158

4.0 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.” These unburned gases are a potential

source of vehicle emissions.

To alleviate this source of emissions, the Positive Crankcase Ventilation (PCV) system was

designed as a calibrated air leak, whereby the engine contains its crankcase combustion gases.

Instead of the gases venting to the atmosphere, they are fed back into the intake manifold where

they reenter the combustion chamber as part of a fresh charge of air and fuel. A working PCV

valve should prevent virtually all crankcase emissions from escaping to the atmosphere.

PCV valve systems have been mandated in all gasoline vehicles, both light-duty and heavy-duty,

since model year 1969. Diesel vehicles with turbocharged engines , both light- and heavy-duty

have only been required to have PCV valves since model year 2008. Thus, MOVES emission

inputs 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 fraction of tailpipe exhaust emissions, which are equivalent to

engine-out emissions for pre-1969 vehicles. Crankcase emissions are calculated for selected

pollutants, including THC, CO, and NOx, and the particulate fractions organic carbon PM2.5,

elemental carbon PM2.5, sulfate PM2.5, and sulfate PM10. 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.

For vehicles with working PCV valves, we assume that emissions are zero. Based on EPA

tampering surveys, MOVES assumes a 4% PCV valve failure rate.60 Consequently, for

fuelType/model-year combinations equipped with PCV valves, we assume a crankcase ratio of

0.04; i.e., emission fractions for the crankcase process are estimated as 4% of the emission

fractions assumed for uncontrolled emissions. While this 4% estimate may be pessimistic for

new vehicles, and optimistic for old vehicles, available data does not support a more detailed

estimate. As older vehicles have higher overall emissions due to deterioration effects, use of the

aggregate rates may understate the impacts of crankcase emissions.

Very little information is available on crankcase emissions, especially those for gasoline

vehicles. A literature review was conducted in order to identify available data sources for

emission fractions (Table 51).
159

Table 51. Selected Sources of published data on crankcase emissions from gasoline and diesel vehicles (light- and heavy-duty).

Authors Year Type # Vehicles HC

PM(all

species) CO NOX Units

Hare and Baines61 1973 Diesel 1 0.2-4.1 0.9-2.9 0.005-0.43 0.005-0.43 % of exhaust

Heinen and Bennett62 1960 Gasoline 5 33 X x x % of exhaust

Bowditch63 1968 Gasoline x 70 X x x % of exhaust

Montalvo and Hare64 1985 Gasoline 9 1.21-1.92 X x x g/mi

Williamson65 1995 Diesel 1 50 35 x x % of exhaust

Kittelson66 1998 Diesel 1 x 0.038 x 0.005 g/hp-hr

Hill67 2005 Diesel 9 x 100 x x % of exhaust

Ireson68 2005 Diesel 12 x 25-28 x x % of exhaust

Zielinska69 2008 Diesel 2 x 20-70 x x % of exhaust

x = no data
160

Based on these sources, we estimated emission fractions for model years without mandated PCV

valves (Table 52). In absence of better information, gasoline emission fractions are a reflection

of diesel research, with the exception of the gasoline HC ratio. Given that the diesel vehicles

studied here are largely heavy duty, and most gasoline vehicles are light duty, there is a potential

mismatch between the data sources, which is necessitated by a paucity of data. As noted

previously, model years with PCV valves were assigned emission fractions calculated as 4% of

the fractions shown in the table.

Table 52. Emission Fractions for Vehicles without PCV systems (percent of exhaust emissions)

Emission Type Gasoline Diesel

HC 33%12%

NOx 0.03% 0.03%

CO 0.005% 0.005%

PM (all species) 20% 20%

1The gasoline HC fraction is substantially larger than the diesel ratio. This result may be driven by

differences between the Otto and diesel cycles, wherein the Otto cycle potentially allows a

significantly greater proportion of combustion gases to escape to the crankcase.

The crankcase emission fractions for HC, CO and NOx may underestimate emissions. These

percentages of exhaust emissions are generally based on [engine- out] 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

considerably higher than current tailpipe exhaust.

A 1995 study by Williamson65 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 fractions in MOVES rely on

the older set of data and maintain consistency with those emission factors used in the

NONROAD model. However, we note the wide range in the data sources.

Emission fractions 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.
161

Appendix A: Peer-Review Comments and Response: Reviewer 1

Reviewer 1: John M. German, International Council on Clean Transportation, Washington,

D.C.

Mr. German is a Senior Fellow and Program Director with the International Council on Clean

Transportation. He is a highly qualified expert in the areas of automotive engineering and

emissions control, whose career includes experience with both the industry and the USEPA. His

experience with the EPA includes managing the development of the US06 and SC03 test cycles

used to implement the Supplemental Federal Test Procedure, oversight of the development of the

Comprehensive Modal Emissions Model, a project conducted by engineers at the University of

California at Riverside, management of the cold-temperature CO Rule, and development of

facility cycles used in the MOBILE6 model. His industry experience includes power-train

engineering at both Chrysler and Honda over a period of 19 years.

This document contains comments received from Mr. German following conclusion of his

review of the draft report. Following each comment, I have included our specific response,

describing whether we have accepted the comment and made corresponding revisions in the final

report, or whether we have offered a rebuttal or otherwise declined to make revisions.

Note that page and paragraph numbers listed in the comments refer to the draft document: Development of

Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator (MOVES2009): Draft

Report. A copy of this document is included in the peer-review records, and is also available at

http://www.epa.gov/otaq/models/moves/techdocs/420p09002.pdf.
162

Comment Summary

In general, the MOVES draft did an excellent job of assessing emissions. This is a very difficult

task, especially considering the lack of data in many areas. Of course, given the lack of data and

the multitude of assumptions that have to be made, there were a number of places where

different approaches may yield better results, as discussed in my comments.

My detailed comments were written as annotations on the draft report itself. I did not have the

time or facilities to print out all 124 pages of the draft report with my annotated comments.

Thus, the detailed comments are submitted only in electronic form. It is perfectly OK if EPA

wants to print out these detailed comments.

PEER REVIEW CHARGE QUESTIONS:

1. For the most part, the report provides adequate description of selected data sources to allow

the reader to form a general view of the quantity and representativeness of data used in

development of emission rates. In some cases information about the sources of data used in

the report were lacking. My detailed comments note these places and asks for additional

description of the data.

2. The description of the analytic methods and procedures was generally excellent and allowed

the reader to understand the steps taken and assumptions made. There were a few cases

where additional explanation would be helpful, as noted in my detailed comments. The

examples chosen for tables and figures were also generally excellent.

3. Most of the methods and procedures employed were technically appropriate and reasonable.

However, there were some areas where alternative approaches might better achieve the goal

of developing accurate and representative model inputs. These are listed in my detailed

comments. The major areas of concern are as follows (these duplicate the summary at the

beginning of my detailed comments):

a. I did not see anything on ambient temperature adjustments. This is a very large

factor.

b. The use of IM data for pre 2000 vehicles needs to be validated. If I recall correctly,

there are offsets between IM data and the FTP and the correlation is not all that good.

While the IM data is needed to determine deterioration rates over time, it may not be

a good idea to use it directly for baseline emissions.

c. Diesels used yet another source of data - FTP data instead of IUVP or IM data.

Should establish a correlation between FTP and IUVP data and apply this as an offset

to the FTP data.

d. The use of bag 2 for running emissions is not appropriate. Running emissions plus

start emissions should equal the FTP. Using only bag 2 completely ignores the

running emissions from the 505 (bag 1/3). Running emissions should be determined

by subtracting start emissions from total FTP emissions and dividing by 7.5 miles.

4. In most cases, EPA’s assumptions when applicable data is meager or unavailable were

reasonable. My detailed comments note areas where different assumptions might be better.

The major area of concern is that the modeling of PM deterioration implicitly assumed that

PM correlated with HC (this is also in the summary at the beginning of my detailed
163

comments). However, CO is a much better predictor of air/fuel ratio than HC (reasons

explained in my detailed comments on page 88). I would investigate how well your

individual PM test results correlate with CO, or with a combination of HC and CO, instead of

just assuming they correlate with HC. If a reasonable correlation can be established, this

would be a much better way to establish PM cold start and running emissions and to assess

PM deterioration.

5. In general, the model inputs were appropriate and are reasonably consistent with physical and

chemical processes involved in exhaust emissions formation and control. Cases where better

inputs could be used are listed in my detailed comments.

My most important recommendation is outside the scope of reviewing the draft report. EPA

desperately needs to have better data upon which to base the MOVES model. Collection of

consistent data across the variety of vehicles and operation conditions would allow creation of a

much better model and help avoid all the assumptions needed in the current version.
164

Overall comments.

1. I did not see anything on ambient temperature adjustments. This is a very large factor.

RESPONSE:

The base rates described in this report represent the temperature range of 68-86 °F, i.e., the

“FTP temperature range”. They are not designed to represent the effect of temperature.

The revised report mentions this fact and refers readers to the appropriate report describing

adjustments for temperature (and other factors).

2. The use of IM data needs to be validated. If I recall correctly, there are offsets between

IM data and FTP and the correlation is not all that good. While the IM data is needed to

determine deterioration rates over time, it may not be a good idea to use it directly for

baseline emissions.

RESPONSE:

The I/M data used were not used as cycle aggregates. Rather, second-by-second data were

used, after being classified into operating modes on basis of vehicle-specific power and

speed, as described in 1.3. Breaking down the cycle in this way neutralizes the differences

that would be expected had we used cycle aggregate values, as in MOBILE6.

The potential offset is made worse by the use of FTP data for diesels. Need to establish a correlation

between FTP and IUVP data and apply this as an offset to the FTP data.

RESPONSE:

As with the data for gasoline vehicles, the FTP data for diesels was not used directly, as in

MOBILE, but rather to develop scaling factors that were applied to modal emission rates for

light-heavy-duty diesels so as to represent light-duty-diesels.

3. The use of bag 2 for running emissions is not appropriate. Running emissions plus start

emissions should equal the FTP. Using only bag 2 completely ignores the running

emissions from the 505 (bag 1/3). Running emissions should be set by subtracting start

emissions from total FTP emissions and dividing by 7.5 miles.

RESPONSE:
165

Again, we did not use FTP bag 2 emissions directly to assign emission rates for running

operation. Rather, we used them to derive relative changes in hot-running emissions for

vehicles in different standard levels relative to Tier 1. These relative changes, or ratios, were

used to scale down Tier 1 modal emissions appropriately to represent vehicles certified to

NLEV and Tier 2 standards. We used Bag-2 emissions for this purpose because we are

confident that the engine is conditioned before Bag 2 commences, and because we lack a

way to readily separate start and running emissions in Bags 1 and 3, i.e., none of the data

available to use included Bag 1 run under hot stabilized conditions (“hot-running505”). In

the revised report, this process is described in 1.3.4.2.

We explicitly avoided the use of FTP composites, which include both start as well as running

emissions. While the FTP standard represents the effects of control of both start and running

emissions, the relative levels of control for start and running differ both from each other and

from that for the composite. Generally, start emissions decline less than the standard would

suggest, and running emissions decline more. We elected to treat start and running

separately to account for these differences in levels of control. Thus, to represent start

emissions, we followed the common practice of estimating the cold start as the difference

between Bags1 and 3. We selected Bag 2 to represent hot-running emissions because unlike

Bags 1 and 3, it does not contain a “start increment”. We did not use Bags 1 or 3 to

represent running because it is not possible to isolate the “running component” from the

“start increment” in these bags, except for the cold start, as described.

4. Modeling of PM deterioration implicitly assumed that PM correlated with HC. However,

CO is a much better predictor of air/fuel ratio than HC (reasons explained in my comments

on page 88). I would investigate how well your individual PM test results correlate with

CO, or with a combination of HC and CO, instead of just assuming they correlate with HC.

If a reasonable correlation can be established, this would be a much better way to establish

PM cold start and running emissions and to assess PM deterioration.

RESPONSE:

We agree that PM, as well as CO, respond to enrichment. More generally, though, a major

component of PM is HC consisting of higher molecular weight compounds (about C10-12)

including semi-volatiles and non-volatile compounds, both of which are emitted in the

particulate phase, with particulate being formed from unburned and partially burned fuel

components. In the cylinder, processes such as wall quenching, particularly during cold

starts, tends to create both HC and particulate. In addition, since the introduction of NLEV

or LEV-I standards, targeted reductions in HC have yielded associated reductions in PM,

but have not driven compliance for CO, which the manufacturers have easily achieved.

Taking these factors in combination, we suggest that while a relationship between CO and

PM exists, the corresponding relationship between HC and PM is primary.

Chapter 1. Light-Duty Gasoline Criteria Exhaust Emissions (HC/CO/NOx)

1. Page 8, 1.5.1 :

Are you considering only IM data? If so, you should state so. If not, should state what the

data sources are and why FTP data was not included.
166

RESPONSE:

We did not restrict consideration to I/M data as such, although we did require that data was

measured on vehicles subject to I/M requirements. For example, data from the New York

Instrumentation Protocol Assessment (NYIPA) received serious attention. These data are

not I/M program data, but were measured on vehicles subject to the I/M program in New

York City between 1998 and 2002. Nor did we exclude the FTP as such. Had datasets

measured on the FTP been available and met all requirements, we would have considered

using them.

2. Page 9, 1.5.1.1.1, Re: Eq 1-2:

How were these equations derived? Should describe or include a reference. Also, are A, B,

and C derived directly from these equations? As A is proportional to v, B to v sq., and C to

v cubed, it seems like these equations are missing a step.

RESPONSE:

We have cited a reference for these equations in the final report. They are taken from the

“IM240 and Evap Technical Guidance”, April, 2000, EPA420-R-00-007. Note that the

squared and cubic exponents in the denominators of the equations for B and C lead to

correct proportionality in the resulting units.

3. Page 10, 1.5.1.3, 2nd para:

Table 1-6 only lists about 80,000 vehicles. If the large majority of these “several million”

are RSD or poor data, this statement probably should be revised.

RESPONSE:

The data sources listed in Table 1-5 in the draft report represent data determined to be

available and potentially suitable, before we began examination to verify suitability and

quality. Table 1-6 lists datasets that did received detailed scrutiny, and with the exception of

the St. Louis I/M data, were also confirmed to be suitable. The corresponding Tables in the

final report are Tables 8 and 9.

4. Page 11, 1.5.1.3, Table 1-6:

What about British Columbia, Denver, Indiana, Ohio, and Wisconsin listed in Table 1-5?

Why were they not discussed and/or excluded?

RESPONSE:

These data were not considered or discussed due to a combination of quality issues or lack

of time and people needed to process them. In the final report, we have removed references

to these datasets in Table 8 (formerly Table 1-5).

5. Page 18, 1.5.4.2.2, first para:

To help the reader, might want to mention here that extrapolation to high VSP bins was

modified based on actual high VSP data in section 1.5.5.
167

RESPONSE:

We have added a sentence in this paragraph to refer the reader to the description of the

adjustments in 1.3.3.5. (formerly 1.5.5).

6. Page 26, Table 1-11:

Emission rates for 81-82 are very different than for 1980 and previous. Probably not

appropriate to use 81-82 to represent older vehicles.

RESPONSE:

While acknowledging the differences between the two groups of vehicles, our difficulty with

the 1980 and older vehicles is that we lacked sufficient data to backcast their emissions to

young ages. Given this difficulty, and the negligible influence of the 1980 and older modelyear group on inventory, we considered the substitution reasonable.

7. Page 26, 1.5.5, 2nd para:

Should provide a reference for this program [NCHRP].

RESPONSE:

In the final report, we have cited a reference for this program: “Development of a

Comprehensive Modal Emissions Model: Final Report”, NCHRP Project 25-11, April,

2000.

8. Page 26, 1.5.5, 2nd para:

Should say something about why it [MEC cycle] was developed and by whom.

RESPONSE:

In the final report, we have added a brief description of the context and purposes for

development of the MEC cycles.

9. Page 27, 1st para, bottom of page:

Typo here [incomplete sentence].

RESPONSE:

We have corrected the incomplete sentence.

10. 1.5.6, Page 35, 1st para:

Were the vehicle compositions the same for the migrating and local I/M vehicles? Need to

demonstrate that the comparison did not include effects of different mix of vehicle types

(car, light trucks) and sizes within vehicle type. If there are effects, this should be corrected.

This is especially important, considering that the migrating vehicles had lower HC and NOx

than the local vehicles.
168

RESPONSE:

At the time lacked a means to verify standard levels or vehicle class, without information on

engine family for measured vehicles. Since release of the draft, we have made progress in

this area. It may be possible to revisit the analysis in terms of truck classes for revisions to

be considered during the next 24 months.

11. Section 1.5.6, Page 35, 2nd para:

Can this data be sorted by the same age groups as the new data? This would help the

comparisons.

RESPONSE:

Unfortunately, we are unable to distinguish age groups in these data, which we acquired

from a published source.

12. Section 1.5.6, Page 35, 3rd para:

Were the locations controlled so that they produced similar vehicle speeds and accelerations

at the point of [remote] sensing? Some discussion of why these data are compatible should

be included. Also, were the fleet mixes similar?

RESPONSE:

For the data collected in the Atlanta area, the researchers attempted to select multiple sites

with similar driving characteristics, as described in the 2004 Biennial Evaluation Report. “

RSD sampling sites are selected to ensure physically consistent but demographically diverse

characteristics. Single straight lines of traffic with an average 35 mile-per-hour velocity are sought to

facilitate single vehicle measurements and speeds that maximize measurement opportunities. Driver

behavior and driving maneuvers are also observed at each site to ensure that remote sensing

measurements would not be biased high by acceleration or low by coasting.” (Reference 10 in the

Final Report). In the 2004 comparisons, the areas used were geographically continguous,

to account for the existence of a new low-sulfur fuel requirement in the 25-county Atlanta

area (13 I/M and 12 non-I/M counties), which suggests broad similarity in the composition

and age of the two fleets. In addition, before generating aggregate fleet-level results, we

compared the I/M and non-I/M remote-sensing data on a model-year basis, to control for

potential differences in fleet age.

13. Page 37, Figure 1-13:

It is troubling that the migratory vehicles had lower emissions than the local vehicles in

Phoenix for the 0-4 age group. Need to explore possible biases in the data, such as vehicle

composition, and correct if possible.

RESPONSE:

It is possible that fleet composition could contribute bias. As mentioned above, we lacked a

way of assigning vehicle class and standard level while performing these analyses. Recent

developments in this area may allow to reevaluation of this question in the future. In any

case, the differences shown in the figure are not statistically significant for HC or CO, and

perhaps marginally so for NOx.
169

14. Page 38, 1st para:

Does this mean that you ignored the data from the 0-4 year age group? If so, should explain

why and if not, should explain how the 0-4 year age data was used.

Also, how were the three data sets combined to determine the value at 7.5 years?

Also, why is the midpoint of the 4-5 MOVES age group 5 years instead of 4.5?

RESPONSE:

We did not combine the three datasets, but rather assigned the ratios on the basis of our

analysis of the Phoenix data. Accordingly, we used the other two datasets for verification.

We did tend to discount the 0-4 yr age group in the case of Phoenix. Because this program

had a four-yr exemption period, it did not make sense to assume that the program was

achieving benefits for vehicles that were exempt from testing. For Phoenix we assumed a

ratio of 1.0 during the exemption period. However, the development of the non-I/M reference

rates must allow for the fact that many programs have exemption periods shorter than those

in Phoenix. For this reason we did not think it reasonable to assign no I/M difference in the

MOVES 0-3 year ageGroup, and performed the interpolation to estimate a difference for this

group. In the interpolation, the value at 7.5 years was taken as the value for the 5-9 year

group from the Phoenix analysis.

When the midpoint of the 4-5 year ageGroup was set at 5 rather than 4.5 years is because

this age group spans two full years. Vehicles enter this group when they turn 4, and leave it

when they turn 6. When they turn 5 they have been in the group for one year, and will

remain in it for another year. When they turn 4.5, they have been in the group only 0.5 year,

but will remain in the group for another 1.5 years. On this basis, we concluded that 5,

rather than 4.5, is the actual midpoint of the ageGroup.

15. Page 38, 2nd para:

This implies that the 10+ data was also ignored.

RESPONSE:

No. The statement reflects the fact that the ratios in Figure 1-13 show that the ratios in the

10+ age class are very similar to those in the 5-9 year age class, suggesting that the ratio

has stabilized by 10 years of age.

You need to add explanations of how the three data sets were combined and how the data

was translated into the lines in Figure 1-14 and the bars in 1-15.

RESPONSE:

As mentioned above, the datasets were not combined; the ratios were assigned based on the

Phoenix data, as modified by the interpolation, with the remote-sensing data used for

verification.

It is potentially troubling that the non-I/M ratios stabilized at only 6 years of age. I would

expect that emissions from vehicles in I/M areas would stabilize after a certain age due to
170

being required to be fixed, but I would NOT expect emissions from vehicles in non-I/M

areas to stabilize with age. So, the ratios should continue to increase beyond 6 years.

RESPONSE:

Note that the fact that the ratio stabilizes does not mean that the absolute emissions in the

non-I/M area stabilize at six years, because the absolute emissions in the I/M area continue

to increase until after 10 years. Therefore, the absolute emissions in the non-I/M areas also

continue to increase.

16. Page 40, 3rd para:

I don’t understand this statement. The non-I/M % increases appear to be the same for both

modes. Mode 27 simply has much higher emissions whether the vehicle is from an I/M or

non-I/M area. The I/M factor has the same effect on both.

RESPONSE:

In percentage terms the effect is the same, but in absolute terms the effect is greater in

opMode 27 than in 11, simply because the emission rate is correspondingly higher.

17. Page 43, 1st para:

Again, this seems to be reasonable for vehicles in I/M areas, due to requirement to maintain

emissions. But is it also true in non-I/M areas? Do you have data supporting this?

RESPONSE:

We revised assumptions for non-I/M areas between the draft and final releases. The revised

assumptions are described in the final report and in the response to comment #20 below.

18. Page 46, 1st para:

For NOx, the average emissions for the 15-19 age group were lower than for the 10-14 age

group for your 1986-89 model year control group. Is it appropriate to lower NOx emissions

for higher age vehicles for model years after 1990? Probably better to assume that they

don’t change after 10-14 years.

RESPONSE:

The apparent decline with increasing age can be seen in various datasets. It is uncertain

whether it is a real effect, or due to erratic behavior in sub-samples of decreasing size. We

have thus assumed that rates do not decline after stabilizing.

19. Page 46, Table 1-14:

These ratios don’t match the data in graphs 1-15, which shows that the NOx for 15-19 year

old vehicles was lower than 10-14. However, the ratios in Table 1-14 are the reverse.

RESPONSE:

Table 1-14 has been obviated by revisions since release of the draft. Its counterpart in the

final report is Table 17. The approach used to stabilize emissions has been modified, as

described in 1.3.3.7 of the final report.
171

20. Page 48, 2nd para:

In section 1.5.6, you presented extensive analyses for non-I/M areas based upon Phoenix

I/M data. You need to explain what that data doesn’t work for this section.

RESPONSE:

The migrating vehicle sample used to develop the non-I/M reference rates was not sufficient

in itself to allow assessment of age trends in non-I/M areas past about 10 years. For this

reason, it was not useful to inform modeling of emissions stabilization in non-I/M areas.

I think this is a poor assumption. Catalyst problems identified by HC and CO monitors will

also lower NOx emissions. On the other hand, fixing air/fuel ratio problems may well

increase NOx emissions. Many malfunctions are being identified and repaired – and these

repairs have impacts on NOx emissions. This is not similar to a non-I/M area. For I/M areas,

the emission increase was not the same for all pollutants. Why would it be the same for non-I/M

areas?

RESPONSE:

After some consideration, we have come to agree with this comment. We have accordingly

revised the assumptions used to represent trends in emissions for vehicles over 15 years of

age in the non-I/M reference rates. In the final report, the revised assumptions are

described in 1.3.3.7.2. In the revisions, we assume that the relative trend observed between

the 10-14 and 15-19 year ageGroups will persist from the 15-19 year ageGroup to the 20+

ageGroups. Thus, in non-I/M areas, rates continue to rise after ten years, but at lower rates

than before ten years.

21. Page 49, 1.5.9, 1st para:

How are start emissions calculated? I don’t see any discussion or reference.

RESPONSE:

Discussion of start emissions, especially for vehicles manufactured prior to 1996, was

inadequate in the draft report. In the final document material has been added; the expanded

discussion has been inserted in the new section 1.4.

22. Page 49, 2nd para:

Where? [is the definition of start rates?].

RESPONSE:

In the final report, start emissions are defined in 1.3.4, and in 1.4.1.1.2.

23. page 50, para 1:

What simulated FTPs? All you have talked about is bag 2. Also, what relationship does bag

2 have to start emissions? This doesn’t make sense.
172

RESPONSE:

In this paragraph, the “simulated FTPs” would have been more accurately referred to as

“simulated FTP Bag 2.” The Bag 2s were simulated to describe the relative deterioration

rate for running emissions, as described in [draft] Table 1-16. The relative deterioration

trend for starts was then assessed in relation to the relative deterioration trend for running

emissions, as described in the following paragraphs.

24. page 50, para 2:

Are your “rates” multiplicative or additive?

RESPONSE:

The rates are multiplicative. For light-duty gaseous emissions, deterioration is applied

multiplicatively, and any adjustments or modifications are also multiplicative.

25. page 52, 1.6, 1st para:

How does the FTP data from the IUVP program correlate with the IM data used for pre2000 vehicles? If there is an offset between the IM and FTP data, this will lead to

discontinuities in your assessment.

Not to mention the simple correlation between IM data and real world data. You need to

validate that IM second-by-second data correlates with FTP data, or apply an offset factor to

the IM data.

RESPONSE:

We address this question in our responses to overall comments 2 and 3 above.

26. Page 53, 5th para (step 4):

This may be true for vehicles certified before SFTP phase-in. But for vehicles certified to

the SFTP, it may not be a valid assumption.

RESPONSE:

Based on supplementary analyses performed since release of the draft, we believe that the

assumption holds, although uncertainty remains in estimating differing degrees of control.

27. Page 53, 1.6.2.1, 1st para:

This means that you are throwing away running emissions on bags 1 and 3. Would be more

appropriate to subtract cold start emissions from total FTP emissions, then calculated the

running emissions over the entire drive cycle, not just bag 2.

RESPONSE:

While it would be a desirable step, we are unaware of away to distinguish running from start

emissions in Bags 1 and 3, to allow calculation of running emissions over the total FTP as

you suggest. See our response to overall comment 3 above.
173

28. Page 53, 1.6.2.1, 2nd para:

It’s not HC control, its NOx control. Manufacturers have found that catalysts are most

efficient when the air/fuel control is precisely at stoich, instead of cycling from slightly rich

to slightly lean. The elimination of the slightly rich events has reduced CO, as well as fast

catalyst lightoff.

RESPONSE:

While we don’t disagree that prevention of rich events and promotion of fast lightoff would

reduce CO, as well as NOx, we would suggest that as CO and exhaust hydrocarbons are

both products of incomplete hydrocarbon combustion, and that the overall control strategy

is to drive oxidation towards completion, CO control is more fundamentally linked to HC

control than to NOx control.

29. Page 56, 1st para:

Or there are running emissions in bags 1 and 3, which were not evaluated.

RESPONSE:

There are running emissions in Bags 1 and 3, of course. Incorporating them would not

fundamentally change the relationships shown in the figure 1-23, as most of the mass of Bag

1 is attributable to the start increment, with the imputed running component making a

relatively small contribution.

30. Page 56, 1.6.2.2, 3rd para:

This is not correct. LDT3/4 had interim standards that had the same phase-in as

LDV/LDT1/LDV2. However, the phase in to the final Tier 2 standards for LDT3 and 4 was

50% in 2008 and 100% in 2009.

RESPONSE:

Since release of the draft model, we have fundamentally revised the phase-in assumptions

based on certification records and sales figures, as described in 1.3.4.2.2 in the final report.

31. Page 58, 1.6.2.3, 1st para:

LDT1s are rapidly disappearing; although it probably doesn’t matter, as they are becoming

LDT2s which have the same standards.

RESPONSE:

Revised phase-in assumptions project low fractions of LDT1 , on basis of certification

records and sales.

32. Page 64, 1st para:

Your “FTP region” INCLUDES bag1/3 driving, but your running emissions don’t. This

mismatch needs to be fixed.

RESPONSE:
174

We agree that the text at this point is unclear, and not reflective of what the rates represent.

In the final report we redesignate the “FTP region” as the “hot-running-FTP” region, and

the “SFTP region” as the “US06 region.” Under this designation, the “hot-running-FTP”

region includes the speed and power ranges covered by the FTP Bag 2 (or the

IM240/IM147). It is not intended to include the somewhat more aggressive driving

represented in Bags 1/3. The revised discussion is in 1.3.3.2.4 in the final report.

33. Page 64, 3rd para:

You can justify this – you don’t have to just assume it. The SFTP standards were calibrated

to the Tier 1 and NLEV FTP standards. When the FTP standards were increased in

stringency with Tier 2, the SFTP standards were NOT increased correspondingly. Instead,

the SFTP standards are still calibrated to NLEV levels. Thus, the SFTP standards are not as

stringent and the expected reductions are less.

RESPONSE:

We agree on this point.

34. Page 64, 5th para:

You have SFTP data for 2001-2003? If so, you should discuss the data, similar to how you

discussed the data for vehicles prior to 2000 in section 1.5.5 and Table 1-12. Is the vehicle

composition for 2001-3 similar to 1998-2000?

RESPONSE:

In revisions to the rates for MOVES2010, SFTP results from the IUVP program were

applied for the NOx rates, but not for the HC or CO rates. For each pollutant, we adopted

the approach that appeared to most improve verification against external data.

35. Page 65, 2nd para:

If I follow this correctly, this means that SFTP emissions are the same for 2010 vehicles as

for 2005 vehicles. If this is accurate, should state this explicitly.

RESPONSE:

This is correct. In the final report, this point is made clear in Figure 34.

While I agree that the reduction in SFTP emissions should not track FTP reductions, as

SFTP standards were not reduced in conjunction with Tier 2 FTP reductions, it may not be

reasonable to assume that there is no reduction in SFTP emissions for Tier 2 vehicles.

In the rates released with MOVES2010, we assume some reduction in Tier 2 vehicles for

NOx, but not for HC or CO. Revisions made to the draft rates are described in 1.3.4.2.4.1.

36. Page 69, Table 1-19:
175

You have a minor problem here. You define cold start emissions as > 720 min soak period

minus a 10 minute soak period. But you then apply start emissions for a 10 minute soak.

This implies that your cold start emissions are understated and should be corrected by

adding the 10 minute soak start emissions to the emissions from bag 1 minus bag 3.

We have neglected the component associated with the 10-min soak in the cold start rates. We

have assumed this was reasonable in that for Tier-1 and later vehicles, we lacked data to

estimate the start for a “0-min” soak. We preferred not to apply the soak curve relationships

(Figure 39) for this purpose as to use the relationships to estimate the cold start and then to

reuse them to estimate warm starts would introduce circular reasoning into the process.

37. Page 69, 2nd para:

Some information on the vehicles used to derive these soak fractions would be very useful. The NOx

start fractions suggests that these are from older vehicles – I would not expect to see higher NOx for an

intermediate start on an NLEV or Tier 2 vehicle.

It is true that these relationships were derived for older vehicles, manufactured before 1995.

In the final report we cite the study, released by CARB, from which they were derived:

“Methodology for Calculating and redefining Cold and Hot Start Emissions”, California Air

Resources Board, El Monte, CA, March, 1996 (Reference 22).

38. Page 69, 3rd para:

How do you handle ambient temperatures other than 75 degrees? Colder temperatures will

dramatically change the soak fractions as a function of soak time. For example, at 20 F a

soak time of 240 to 360 minutes should result in a complete cold start and even a 30 minute

soak will have a completely cold catalyst.

Emissions for temperatures outside the “FTP range” of 68-86 F are estimated through the

application of temperature adjustments. Adjustments are described in a separate report:

“MOVES2010 Highway Vehicle Temperature, Humidity, Air Conditioning, and Inspection

and Maintenance Adjustments” EPA-420-R-10-027.

(http://www.epa.gov/otaq/models/moves/420r10027.pdf). At present, the model does not

have the sophistication to apply interactions of soak time and temperature. Soak

relationships and temperature relationships are assumed to be independent.

39. Page 74, 1.7, 2nd para:

Replicating gasoline data for ethanol and advanced gasoline technologies, including hybrids,

is reasonable. However, it is NOT reasonable to replicate the gasoline data for diesels.

Diesels are inherently lean-burn, which changes both the emissions from new vehicles and

how they deteriorate.

Lacking better data on light-duty diesel vehicles under Tier 2, we have retained this

assumption at the present.
176

Chapter 2 Light-Duty Gasoline Particulate Exhaust Emissions

1. Page 84, para 3:

Should note that at colder temperatures, additional enrichment is needed and the enrichment

lasts longer.

We have added a sentence to this effect.

2. Page 88, 2nd para:

Not a good assumption. LA92 is a higher load cycle which induces more enrichment. The

additional enrichment should be expected to cause additional PM emissions, so there might

not be any deterioration effects.

The effects of enrichment can be analyzed using CO emissions. There is a direct

relationship between enrichment and CO from the engine – virtually all of the excess fuel is

emitted as CO, not HC. In fact, air/fuel ratio can be calculated from engine-out CO

emissions. Of course, the catalyst interferes with this relationship, but during rich operation

CO conversion efficiency drops faster than HC conversion efficiency. Thus, tailpipe CO is

still a reasonable predictor of the amount of enrichment.

So, for example, you could see if the single vehicle with significantly increased PM also had

large increases in CO emissions. If there were large increases in CO, then it could have

been an enrichment effect caused by the additional load on the LA92 – especially if the

vehicle had a lower power to weight ratio. If the CO did not increase dramatically, then you

can be more confident that this is actually deterioration of PM.

Notwithstanding the aggressiveness of the LA92 with respect to the FTP, the data we

reviewed showed reasonably good and direct correlation between the two, when working

with cycle composites, that would probably not be expected if comparing individual bags.

Perhaps the inclusion of the longer start bag in the FTP compensates for the aggressiveness

of the hot-running bag of the LA92.

3. Page 89, para 2:

Not necessarily. Again, if the LA92 caused additional enrichment, the higher PM could be

from the enrichment. Comparing the CO emission rates, not the HC emission rates, will

give you a better handle on this.

See our response to overall comment #4 above.

4. Page 89, para 3:

Again, you shouldn’t compare just HC. Enrichment is reflected in CO emissions, not HC

emissions. The CO comparison is more important.
177

See our response to overall comment #4 above.

5. Page 90, para 1:

You can’t conclude this just looking at HC. It is more important to compare CO.

See our response to overall comment #4 above.

6. Page 91, para 2:

Instead of this general PM methodology, you should evaluate how well your PM test results

correlate with CO and/or HC emissions, after adjusting for fuel effects. If you can establish

a reasonable correlation, then you can adjust PM rates based on HC and CO emissions. This

would be especially useful in estimating PM deterioration.

At the outset, it is not clear to us how attempting to analyze PM through correlations with

HC and CO, and introducing associated uncertainty, would improve the resulting model

inputs when PM measurements are available. In addition, adjustment for fuel effects simply

not possible with the older data, as the needed fuel parameter information is not available.

7. Page 91, para 2:

Again, bag 2 is not representative of hot running emissions, which should be the average of

bag 2 and bag 3.

Bag 3 in the LA92 includes a hot-start component. As with the gaseous emissions, we lack a

way to separate the start and running components in Bag 3. For this reason, we have

focused on Bag 2, rather than Bag 3, to represent hot-running emissions.

8. Page 94, 2nd para:

You discuss this later, but it would help the reader if you would state here that the “aged”

data are affected both by vehicle age and model year.

We have added text to clarify this point: “the rates in each model-year group represent

emissions for that group at the age of measurement ...”

9. Page 96, 3rd para:

Again, CO correlates better with air/fuel ratio than HC and air/fuel ratio has a strong impact

on PM.

See our response to overall comment #4 above.
178

10. Page 98, Figure 2-15:

How does a multiplicative model based on CO instead of HC look? One based on both HC

and CO?

Multiplicative patterns for CO look very similar to those for HC. For reference, see Figures

1-18, 1-19 in the draft report (Figures 18 and 19 in the final report).

11. Page , 2.3.1, 1st para:

What about CO (the air/fuel surrogate)? How does this compare with PM emissions?

See our response to overall comment #4 above.

12. Page 100, 1st para:

Again, HC is not the proper metric to determine when the cold start ends. CO is directly

proportional to the air/fuel ratio and is a better predictor of closed-loop air/fuel control.

See our response to overall comment #4 above.

13. Page 101, 1st para:

Again, CO will tell you exactly when the vehicle runs rich and by how much.

See our response to overall comment #4 above.

14. Page 106, Figure 2-23:

Note that the PM emissions by VSP look a lot more like the CO emissions by VSP on page

28 than the HC emissions by VSP on page 29. An indication that PM may correlate more

strongly with air/fuel ratio (CO) than with HC.

See our response to overall comment #4 above.

15. Page 109, 1st para:

This wasn’t “determined” for PM. It was determined for other pollutants and assumed to be

the same for PM.

The comment is well taken. We have revised the text to reflect the assumption that

deterioration trends for PM would show patterns similar to those for the gaseous emissions.

Chapter 3 Light-Duty Diesel Criteria Exhaust Emissions (HC, CO, NOx)

1. Page 110, 1st para:
179

How do the FTP test results compare to the IUVP tests used for 2000+ gasoline vehicles and

IM data used for pre 2000 gasoline vehicles? If the use of the FTP for in-use emissions is

OK, then why wasn’t it used for gasoline vehicles?

It would not be relevant to compare the IUV P or I/M data, used to develop rates for

gasoline vehicles, to the FTP data used to develop rates for diesel vehicles. For gasoline

vehicles, we did make use of FTP results (by bag) when second-by-second data was not

available (for MY 2001+), as described above. Had second-by-second data for light-duty

diesels been available, we would have applied it, had it been measured on the FTP or

another cycle.

You need to establish a correlation between FTP and IUVP data and adjust the FTP data for the

offset.

Given the way we used the data, correlating FTP and other datasets is unnecessary, as

previously described.

2. Page 111, bottom of page:

Where were these other data from? Why aren’t they suitable for determining baseline data?

A short explanation (even if in a footnote) would be appreciated.

We have added a Table in the final report (Table 44) summarizing the sources of these data.

3. Page 112, 1st para:

Again, should subtract start emissions from total emissions to determine running emissions.

Then regress start emissions and running emissions against total emissions. More accurate

and much simpler than the method in this section.

It is unclear why the suggested approach would be either more accurate or simpler. Without

a matched HR505, we cannot cleanly separate the hot-start and running components, as you

suggest.

4. Page 112, bottom of page:

Bad assumption –and unnecessary.

We agree that this assumption is not appropriate, and leads to error in the resulting start

emissions. The errors are relatively small due to the great difference between start and

running components, particularly in Bag 1. Nonetheless, this assumption requires

reexamination when the rates are evaluated for revision.

5. Page 115, 1st para:

Why is this procedure different than the soak adjustments for gasoline vehicles? Also note that

my comment on gasoline vehicle soak time also applies here – determination of start emissions is bag 3 –

bag 1, which implicitly assumes zero soak emissions at 10 minutes.
180

The soak/start relationships for diesel engines was assessed independently from those for

gasoline engines, given that we lacked corresponding soak/start data for diesel engines .

6. Page 117, last para:

What about CO, NOx, and PM?

It would also be helpful to have graphs of HC, CO, NOx, and PM emissions versus VSP.

This will help the reader compare the impacts of high load on diesel emissions to those on

gasoline emissions on pages 28-29.

We have added similar plots for CO, NOx and PM (Figures 90 – 92 in the final report).

Chapter 4 Crankcase Emissions

1. Page 119, 5th para:

Should note that the emissions are actually a percentage of engine-out emissions, but that

vehicles before 1969 did not have catalysts, so the tailpipe correlation works.

We have added text to clarify this point.

2. Page 119, 6th para:

Is it appropriate to assume the rate is 4% for all vehicles? Should be virtually zero for newer

vehicles, especially those still under warranty, and be higher than 4% for old vehicles, with

some function in between.

Text has been added to clarify this point. While this 4% estimate may be pessimistic for new vehicles, and

optimistic for old vehicles, current data does not support a more detailed estimate. As older vehicles have higher

overall emissions due to deterioration effects, this may understate the impacts of crankcase emissions. Should

additional data become available, this may be a candidate for future updates.

3. Page 121, 1st para:

Probably not a good assumption. Diesels have much higher compression ratios and are likely to have

higher blow-by rates.

Text has been added to clarify this point. Diesel engines have both higher compression ratios and require

a tighter seal in order to operate. Otto cycle engines potentially allow a greater proportion of combustion

gases to escape to the crankcase. As a result, it is difficult to predict whether diesel engines have higher

or lower crankcase emissions.

That being said, we agree with the commenter that it would be preferable to have data on gasoline

engines.

4. Page 121, Table 4-2:

Why is HC crankcase emissions 16.5 times larger for gasoline than diesel? Deserves

explanation.
181

Text has been added to clarify this point. See previous response.
182

Appendix B: Peer-Review Comments and Response: Reviewer 2

Reviewer 2: Robert A. Harley, PhD., University of California at Berkeley.

Dr. Harley is a professor in the Department of Civil and Environmental Engineering. He has

conducted research and published extensively in the field of automotive emissions measurement

and control.

This appendix contains comments received from Dr. Harley following conclusion of his review

of the draft report. Following each comment, we have included our specific response, describing

whether we have accepted the comment and made corresponding revisions in the final report, or

whether we have offered a rebuttal or otherwise declined to make revisions.

Note that page and paragraph numbers listed in the comments refer to the draft document: Development of

Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator (MOVES2009): Draft

Report. A copy of this document is available at

http://www.epa.gov/otaq/models/moves/techdocs/420p09002.pdf
183

Review of Draft Report

Development of Emission Rates for Light-Duty Vehicles in the

Motor Vehicle Emissions Simulator (MOVES2009)

June 2009 version

Reviewed by

Robert Harley, Ph.D

Department of Civil and Environmental Engineering

University of California, Berkeley, CA 94720-1710

Prepared for

U.S. EPA Office of Transportation and Air Quality

September 2009
184

1. The draft report is missing introductory and concluding text.

Add Introduction. An introduction should be included, briefly describing motivation for

developing MOVES. It may not be obvious or known to all readers that MOVES is intended to

estimate emissions from both on-road and some off-road mobile sources, replacing the existing

MOBILE and OFFROAD modeling tools. A discussion of why a new modeling approach is

needed should be added (this might be simply a reference to other documents where more details

are available). Where should one look for other MOVES-related documents and information?

Clearly the present report is part of a larger effort, but that context is missing here.

RESPONSE:

We have added material to orient a reader to the broader context of MOVES and its

development, and to refer them to available sources of more detailed information.

The importance of light-duty (LD) vehicles as a source of air pollution should be summarized.

Readers may not be aware of an earlier related report on development of MOVES2004 for

estimating greenhouse gas emissions from LD vehicles. Some relevant background and

findings on mobile source emission trends from recent studies are that:

(a) LD vehicle emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides

(NOx), and particulate matter (PM) have declined substantially in recent years [Harley et al.,

2006; Bishop and Stedman, 2008; Ban-Weiss et al., 2008]

(b) in-use deterioration rates have declined for newer vehicles [Bishop & Stedman 2008]

(c) the effect of variations in engine load/driving conditions on emissions is not as large

as in the past. For example, Bishop and Stedman [2008] show that plots of exhaust emission

factors versus vehicle specific power (VSP) are flatter (i.e., there is less variation in emissions

with changes in engine load) for newer vehicles.

(d) the relative importance of other mobile sources of air pollution has increased as LD

vehicle emission control efforts have progressed. For example for NOx, diesel trucks now

dominate total on-road emissions, and there are also significant contributions to NOx from offroad diesel-powered equipment [Harley et al., 2005; Ban-Weiss et al., 2008].

RESPONSE:

We have added a brief summary of light-duty exhaust emissions and their control. Given the

length of the document, we did not attempt a lengthy or comprehensive discussion, but cite

several sources in the peer-review literature.

Provide Methodology Overview Key features of the modeling approach for LD vehicles

emissions that should be summarized at the outset include use of g/hr (rather than g/km or g/kg)

emission factors, binning of emission factors with vehicle specific power (VSP) serving as a

master variable, changes in how I/M vs. non-I/M areas are modeled, and reliance on emissions

data from Phoenix for pre-2000 vehicles, and manufacturer-conducted in-use vehicle emissions

testing for post-2000 vehicles.
185

RESPONSE:

We have added an overview describing the content of sections and subsections in Chapter 1,

and briefly discussing important differences between MOVES and MOBILE, including

changes with respect to time-based rather than distance-based emission rates, and changes

in the approach to modeling I/M.

Add Recommendations/Conclusions What are the data needs, and how will new data be

incorporated into the MOVES model to update it? What are key areas of uncertainty that would

benefit from additional study?

Most importantly, how will MOVES be verified against independent data that were not used in

model development? In addition to doing overall comparisons of MOVES against older

MOBILE/OFFROAD models, I recommend efforts to evaluate/verify each component of

MOVES as part of the model development process. Documenting the modeling approach and

input data can serve as a starting point for this, but the evaluation itself (or at least plans for such

an evaluation) is missing from the present LD vehicle emissions report. If this were a manuscript

being reviewed for publication in a scientific journal, verification of the model predictions using

independent observations would be an essential component. Given the potential future

importance of MOVES predictions to national air pollution control policy, I believe that

similarly high standards should apply with respect to including model evaluation/verification in

this report.

RESPONSE:

Since the release of the draft model and database, we have made substantial efforts to verify

several aspects of MOVES with respect to independent data. Of course, it is very difficult

and labor intensive to verify evaporative and start emissions, which is unfortunate, given

their importance. Nonetheless, we have verified running emissions for light-duty (and heavyduty) emissions and have identified several areas that merit attention and improvement. The

results of these analyses will be made available in a separate report, within which data and

research needs will be discussed.

2. There is unnecessary/imprecise use of acronyms and jargon where plain language would

be clearer and more accurate.

Light-duty vehicles (LDV) include cars as well as light trucks. The report often uses the LDV

acronym in text, figures, and tables when referring only to cars. In those situations, I

recommend using the term “cars”. Use of LDV when you mean cars is confusing: for me LDV

includes light trucks as well. The report title uses the phrase “Light-Duty Vehicles” which

emphasizes the point that both cars and LD trucks are included under the heading LDV.

RESPONSE:

We have added a paragraph describing the technical designations for “LDV” and “LDT.”

Throughout the rest of the document, we have simplified the discussion by substituting “car”
186

and “truck” for “LDV” and “LDT.”

I recommend changing the first chapter title to be “Gaseous” rather than “Criteria” pollutant

emissions. Criteria pollutant is confusing jargon. Isn’t PM also a criteria pollutant? Also I

suggest merging the discussion of gaseous emissions from LD diesel vehicles (currently

section 3) together with section 1. This would provide a more consistent parallel report

structure between gaseous and PM emissions, and between gasoline and diesel engines.

RESPONSE:

The comment is well taken, given that particulate matter is also a criteria pollutant.

Accordingly, we have revised the document to refer to HC, CO and NOx as the “gaseous

exhaust pollutants.”

3. Discussion of evaporative emissions and weather effects on all emissions is missing.

I did not find any discussion of evaporative emissions. If a separate report is planned to discuss

methods for representing evaporative emissions, that should be mentioned. Also climate

variables such as temperature and humidity affect vehicle emissions through increases in cold

start emissions as ambient temperature decreases, increased evaporative emissions with

increasing diurnal temperature range, and increased use of vehicle air conditioning on hot days. I

did not find any discussion of how changes in weather affect vehicle emissions, or how such

effects are modeled in MOVES. Ambient temperature affects gaseous as well as PM emissions,

including cold start effects and gas/particle partitioning of semi-volatile organics present in the

exhaust.

RESPONSE:

Both evaporative emissions and adjustments to exhaust emissions are discussed in separate

reports. The revised report refers readers to these additional documents.

A potential problem with using emissions data from Phoenix to represent all pre-2000 model

year vehicles nationwide is that mild winters in Phoenix may extend vehicle lifetimes and

reduce in-use emission deterioration rates relative to other parts of the country that

experience more severe weather.

RESPONSE:

This issue was raised and considered in the FACA MOVES Review workgroup. It is difficult

to address because datasets broad and deep enough to assess deterioration are difficult to

locate, and where they do exist (such as remote-sensing), observed differences potentially

attributable to “climate” are likely to be confounded by several other factors. Prominent

confounders can include measurement differences related to instrumentation and

calibration, differences in fuel composition, differences in I/M requirements, differences in

the degree of representativeness, and random error. Nonetheless, we attempted to evaluate
187

the issue by comparing the Phoenix data to evaluation data from the Chicago I/M program,

as well as data collected during the New York Instrumentation Protocol Assessment

(NYIPA). Both the Chicago and New York data represent fleets operating in colder climates

with harsher winters than in Phoenix. Aside from differences ostensibly attributable to I/M

requirements, e.g., the lack of a NOx requirement in Chicago, the three programs appeared

comparable enough to suggest that “climate” per se was not a major issue.

4. Reliance on IUVP Data Raises Concerns

Going forward, the pre-2000 vehicle model year data from Phoenix will play a minor role in

determining LD vehicle emissions, as those vehicles are 10+ years old already, and they will

constitute a declining fraction of the in-use vehicle fleet in assessments of future year emissions.

For emissions from 2000 model year and newer vehicles, the MOVES model relies on data

from the In-Use Verification Program (IUVP), a program that started in 2003 which is

administered by EPA and run by the vehicle manufacturers. Relying on IUVP data to model

vehicle emissions is a questionable approach. The IUVP appears to have been instituted as a

regulatory compliance program, and as such may not be well-suited to capturing the full range

of vehicles, operating conditions, and emissions that are relevant to the MOVES model and

developing emission inventory estimates. For example, will the vehicle sample in IUVP be

large and random enough to ensure that major emission contributions from malfunctioning/

high-emitting vehicles will be captured? How will high-emitting vehicles be represented in

MOVES? As fleet-average emissions decline, the remaining emissions are increasingly

dominated by contributions from high-emitting vehicles (Bishop and Stedman, 2008).

RESPONSE:

We agree that the IUVP is not necessarily designed to obtain representative data for the

entire “real world” fleet. But it is important to remember that similar issues apply equally

to most sources of emissions data, including high-quality laboratory studies reported in the

peer-reviewed literature, which very frequently use relatively small vehicle samples. This

situation is entirely understandable, given the difficulty and expense of measurement using

dynamometers or portable instruments combined with the difficulty of acquiring

representative samples. Despite these questions, we found the IUVP to be a very valuable

source, in that it provided information allowing assignment of standard level to individual

vehicles, which we found indispensable in projecting NLEV and Tier-2 emissions. However,

we did not take the representativeness of IUVP entirely for granted, and the approaches we

adopted compensated in three ways. (1) We used IUVP only to estimate rates for “young”

vehicles, aged 0-3 years. (2) we used the IUVP to develop scaling factors that we applied to

results from Phoenix I/M that represented Tier-1 rates. Thus the rates developed using the

IUVP data incorporate a direct link to “real-world” results, that probably more effectively

represent “high-emitting” or “mal-functioning” vehicles. (3) In projecting deterioration

from NLEV and Tier-2 vehicles, we used logarithmic variances “borrowed” from the

Phoenix I/M data, which are higher than those obtained directly from the IUVP. Because

this parameter represents the degree of skew in the distribution, increasing it effectively

represents an increase in the fraction of “high-emitting” vehicles, with associated increases

in mean emission rates.
188

5. PM Emissions

MOVES relies on data from the 2005 Kansas City PM study which focused on LD gasoline

vehicles. This was a comprehensive and well-conducted study and the resulting emissions data

are relatively current. Cold start as well as running emissions were measured. A limitation of

this study is the lack of information on how vehicle emissions for a given model year increase

with vehicle age/odometer reading.

On pp. 91-92, following equation 2-1, there is excessive precision and no associated uncertainty

reported for the multiplicative adjustment factors (0.898 and 1.972) used to give hot running and

cold start emission rates for LD trucks. Excessive precision and lack of uncertainty estimates

concern also applies to values presented in Table 2-2.

There is no discussion of PM emission rates from LD diesel vehicles; the Kansas City study

was for LD gasoline vehicles only. Chapter 3 on LD diesel vehicle emissions covers only the

gaseous pollutant emissions, not exhaust PM. Also non-exhaust PM emissions (e.g., tire wear,

brake wear) from LD vehicles are not discussed in this report.

RESPONSE:

Chapter 3 does cover PM emissions for light-duty diesel vehicles, as it does for

HC/CO/NOx. See tables 42-47 in the final report.

As a model evaluation case study, EPA staff may wish to consider long-term LD vehicle PM

emission trends reported by Ban-Weiss et al. (2008) at the Caldecott tunnel in California. LD

vehicle fleet-average PM2.5 mass emission rates were measured to have decreased by 36±17%

over a 9-year time period between 1997 and 2006, due to model year effects on zero mile levels

and/or deterioration rates. Both VSP and average vehicle age (i.e., calendar year–average model

year) were similar between the two field campaigns. Cold start emissions were not measured by

Ban-Weiss et al.

RESPONSE:

We thank the reviewer for pointing out the availability of this dataset as an opportunity to

verify PM predictions. It could make a good candidate for a future verification effort.

References

Ban-Weiss, G.A.; McLaughlin, J.P.; Harley, R.A.; Lunden, M.M.; Kirchstetter, T.W.; Kean,

A.J.; Strawa, A.W.; Stevenson, E.D.; Kendall, G.R. (2008). Long-Term Changes in Emissions of

Nitrogen Oxides and Particulate Matter from On-Road Gasoline and Diesel Vehicles.

Atmospheric Environment 42, 220-232.

Bishop, G.A.; Stedman, D.H. (2008). A Decade of On-Road Emissions Measurements.

Environmental Science & Technology 42, 1651-1656.
189

Harley, R.A.; Marr, L.C.; Lehner, J.K.; Giddings, S.N. (2005). Changes in Motor Vehicle

Emissions on Diurnal to Decadal Time Scales and Effects on Atmospheric Composition.

Environmental Science & Technology 39, 5356-5362.

Harley, R.A.; Hooper, D.S.; Kean, A.J.; Kirchstetter, T.W.; Hesson, J.M.; Balberan, N.T.;

Stevenson, E.D.; Kendall, G.R. (2006). Effects of Reformulated Gasoline and Motor Vehicle

Fleet Turnover on Emissions and Ambient Concentrations of Benzene. Environmental Science &

Technology 40, 5084-5088.
190

References

1 USEPA Mobile Source Emissions Modeling Workgroup. EPA’s New Generation Mobile Source Emissions

Model: Initial Proposal and Issues. EPA420-R-01-007. Office of Air and Radiation, Office of Research and

Development, Region 4. April, 2001. (http://www.epa.gov/otaq/models/ngm/r01007.pdf).

2 Koupal, J.; Cumberworth, M.; Michaels, H.; Beardsley, M.; Brzezinski, D. Draft Design and Implementation

Plan for EPA’s Multi-Scale Motor Vehicle and Equipment Emission System (MOVES). EPA420-P-02-006.

Assessment and Standards Division, USEPA Office of Transportation and Air Quality, Ann Arbor, MI. October,

2002. (http://www.epa.gov/otaq/models/ngm/p02006.pdf).

3 Computational Laboratory for Energy, Air, and Risk. Methodology for Developing Modal Emission Rates for

EPA’s Multi-Scale Motor Vehicle and Equipment Emission System. EPA420-R-02-027. Department of Civil

Engineering, North Carolina State University, Raleigh, NC. October, 2002.

(http://www.epa.gov/otaq/models/ngm/r02027.pdf).

4 Hart,C.; Koupal, J.; Giannelli, R. EPA’s Onboard Emissions Analysis Shootout: Overview and Results. EPA420-

R-02-026. Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. October,

2002. (http://www.epa.gov/otaq/models/ngm/r02026.pdf).

5 Cite latest version of SDRM.

6 Mobile-Source Emissions, Past, Present and Future: Pollutants.

(http://www.epa.gov/otaq/invntory/overview/pollutants/index.htm).

7 Mobile-Source Emissions, Past, Present and Future: Solutions.

(http://www.epa.gov/otaq/invntory/overview/solutions/index.htm).

8 Calvert, J.G.; Heywood, J.B.; Sawyer, R.F.; Seinfeld, J.H. 1993. Achieving acceptable air quality: some

reflections on controlling vehicle emissions. Science. 261:37-44.

9

Sawyer, R.F.; Harley, R.A.; Cadle, S.H.; Norbeck, J.M.; Slott, R.; Bravo, H.A. 2000. Mobile sources critical

review: 1998 NARSTO assessment. Atmospheric Environment. 34: 2161-2181.

10 Harley, R.A.; Marr, L.C.; Lehner, J.K.; Giddings, S.N. Changes in motor vehicle emissions on diurnal to decadal

time scales and effects on atmospheric composition. Environ. Sci. Technol. 39:5356-5362.

11 Harley, R.A.; Hooper, D.S.; Kean, A.J.; Kirchstetter, T.W.; Hesson, J.M.; Balberan, N.T.; Stevenson, E.D.;

Kendall, G.R. 2006. Effects of Reformulated Gasoline and Motor Vehicle Fleet Turnover on Emissions and

Ambient Concentrations of Benzene. Environ. Sci. Technol. 40: 5084-5088.

12 Bishop, G.A.; Stedman, D.H. 2008. A decade of on-road emissions measurements. Environ. Sci. Technol.

42:1651-1656.

13 Ban-Weiss, G.A.; McLaughlin, J.P.; Harley, R.A.; Lunden, M.M.; Kirchstetter, T.W.; Kean, A.J.; Strawa, A.W.;

Stevenson, E.D.; Kendall, G.R. 2008. Long-Term Changes in Emissions of Nitrogen Oxides and Particulate Matter

from On-Road Gasoline and Diesel Vehicles. Atmospheric Environment. 42: 220-232.

14 Wenzel, T. 2001. Reducing emissions from in-use vehicles: an evaluation of the Phoenix inspection and

maintenance program using test results and independent emissions measurements. Environmental Science & Policy.

4:359-376.

15 Gururaja, P.; Hawkins, D. Development of Evaporative Emissions Calculations for the Motor Vehicle Emissions

Simulator (Draft MOVES2010). USEPA Office of Transportation and Air Quality, Assessment and Standards

Division, Ann Arbor, MI. July, 2011.
191

16 Nam, E.K.; Giannelli, R. Fuel Consumption Modeling of Conventional and Advanced Technology Vehicles in the

Physical Emission Rate Estimator (PERE). USEPA Office of Transportation and Air Quality, Ann Arbor, MI.

EPA420-P-05-001, February, 2005. (http://www.epa.gov/otaq/models/ngm/420p05001.pdf).

17 USEPA Office of Air and Radiation, Office of Transportation and Air Quality. MOVES2010 Highway Vehicle

Population and Activity Data. EPA-420-R-10-026. Assessment and Standards Division, Ann Arbor, MI.

November, 2010. (http://www.epa.gov/otaq/models/moves/420r10026.pdf).

18 USEPA Office of Air and Radiation, Office of Transportation and Air Quality. IM240 and Evap Technical

Guidance. EPA420-R-00-007. Transportation and Regional Programs Division, Ann Arbor, MI. April, 2000.

(http://www.epa.gov/oms/regs/im/r00007.pdf). (page 106).

19 Heirigs, P.; Dulla, R.; Crawford, R.W. Processing of IM240 Data for Use in MOVES. SR007-05-02. Sierra

Research, Inc., Sacramento, CA. May, 2007.

20 Singer, B.C.; Harley, R.A.; Littlejohn, D.; Ho, J.; Vo, T. 1998. Scaling of infrared remote sensor hydrocarbon

measurements for motor vehicle emission inventory calculations. Environ. Sci. Technol. 32(21) 3241-3248.

21 Bishop, G.A.; Stedman, D.H. On-road Remote Sensing of Automobile Emissions in the Denver Area: Year 6,

January 2007. Department of Chemistry and Biochemistry, University of Denver, Denver, CO. June 2007.

22 Kish, L. 1965. Survey Sampling. John Wiley & Sons, New York.

23 Neter, J.; Kutner, M.H.; Nachtsheim, C.J.; Wasserman, W. Applied Linear Statistical Models. Irwin, Chicago.

Fourth Edition. 1996.

24 Barth, M.; An, F.; Younglove, T.; Scora, G.; Levine, C.; Ross, M.; Wenzel, T. Development of a Comprehensive

Modal Emissions Model: Final Report. National Research Council, Transportation Research Board, National

Cooperative Highway Research Program. NCHRP Project 25-11. April, 2000.

(http://www.cert.ucr.edu/cmem/docs/NCHRP_Final_Report.pdf).

25 McClement, D.; Dulla, R.G. Identification of Non-I/M Vehicles in I/M Program Vehicle Emission Datasets.

Draft Report, Sierra Research, Sacramento, CA., October, 2007.

26 Air Quality Group, Aerospace, Transportation and Advanced Systems Laboratory, Georgia Technical Research

Institute. Biennial Evaluation of the Emissions Reduction Effectiveness of the Atlanta Vehicle Inspection and

Maintenance Program for 2003-2004. Prepared for the Air Protection Branch, Environmental Protection Division,

Georgia Department of Natural Resources, Atlanta. September, 2007.

27 DeHart-Davis, L.; Corley, E.; Rodgers, M.O. 2002. Evaluating vehicle inspection/maintenance programs using

on-road emissions data. Evaluation Review. 26(2) 111-146.

28 Annual Certification Test Results & Data: Cars and Light Trucks. http://www.epa.gov/otaq/crttst.htm.

29 Engine and Vehicle Compliance System (VERIFY): http://www.epa.gov/otaq/verify/index.htm.

30 USEPA, Office of Transportation and Air Quality. Mobile Source Observation Database (MSOD): User Guide

and Reference. Assessment and Standards Division , Ann Arbor, MI. EPA420-B-08-017. December, 2008.

(http://www.epa.gov/otaq/models/msod/420b08017.pdf)

31 Enns, P.; Brzezinski , D. Comparison of Start Emissions in the LA92 and ST01 Test Cycles. EPA420-R-01-

025. (M6.STE.001), USEPA Office of Transportation and Air Quality, Ann Arbor, MI. April 2001.
192

32 Glover, E.; Carey, P. Determination of Start Emissions as a Function of Mileage and Soak Time for 1981-1993

Model-year Light-Duty Vehicles. EPA420-R-01-058 (M6.STE.003). USEPA Office of Transportation and Air

Quality, Ann Arbor, MI. November, 2001.

33 Glover, E.; Brzezinski, D. Exhaust Emission Temperature Correction Factors for MOBILE6: Adjustments for

Engine Start and Running LA4 Emissions for Gasoline Vehicles. EPA420-R-01-029 (M6.STE.004). USEPA Office

of Transportation and Air Quality, Ann Arbor, MI. April, 2001.

34 Assessment & Standards Division. MOVES2010 Highway Vehicle Temperature, Humidity, Air Conditioning,

and Inspection and Maintenance Adjustments. EPA-420-R-10-027. USEPA Office of Transportation and Air

Quality, Ann Arbor, MI, December, 2010 (http://www.epa.gov/otaq/models/moves/420r10027.pdf).

35 Stump, F.D.; Dropkin, D.L.; Tejada, S.B.; Loomis, C.; Pack, C. Characterization of Emissions from

Malfunctioning Vehicles Fueled with Oxygenated Gasoline-Ethanol (E-10) Fuel – Part III. EPA/600/R-01/053

(NTIS PB2004-106735). USEPA National Exposure research Laboratory (NERL). July 2002.

37 Sabate, S. Methodology for Calculating and Redefining Cold and Hot Start Emissions. California Air

Resources Board, El Monte, CA. March, 1996. As cited in: Glover, E.; Carey, P. Determination of Start Emissions

as a Function of Mileage and Soak Time for 1981-1993 Model-year Light-Duty Vehicles. EPA420-R-01-058

(M6.STE.003). November, 2001 (Reference 15).

39 Nam E.; Fulper, C.; Warila, J.E.; Somers, J.; Michaels, H.; Baldauf, R.; Rykowski, R.; Scarbro, C. Analysis of

Particulate Matter Emissions from Light-Duty Vehicles in Kansas City. EPA420-R-08-010. USEPA, Office of

Transportation and Air Quality, Ann Arbor, MI; Office of Research and Development, Research Triangle Park, NC.

April, 2008.

40 Kishan, S.; Burnette, A.; Fincher, S.; Sabisch, M.; Crews, W.; Snow, R.; Zmud, M.; Santos, R.; Bricka, S.;

Fujita, E.; Campbell, D.; Arnott, P. Kansas City PM Characterization Study: Final Report. EPA420-R-08-009.

USEPA Office of Transportation and Air Quality, Ann Arbor, MI. April, 2008.

41 Gibbs, R.E.; Wotzak, G.P.; Byer, S.M.; Kolak, N.P. Sulfates and Particulate Emissions from In-Use Catalyst

Vehicles. Regulated/Unregulated Emissions and Fuel Economy. EPA-600/9-79-047. US Environmental Protection

Agency, 1979.

42 Hammerle, R.H.; Korniski, T.J.; Weir, J.E.; Cladek, E.; Gierczak, C.A.; Chase, R.E.; Hurley, R.G. Effect of

Mileage Accumulation on Particulate Emissions from Vehicles Using Gasoline with Methylcyclopentadienyl

Manganese Tricarbonyl. SAE920731. Society of Automotive Engineers. 1992.

43 Whitney, K.A. Collection of In-Use Mobile Source Emission Samples for Toxicity Testing. SwRI 08.02602

Southwest Research Institute, San Antonio,TX, October, 2000.

44 Cadle, S.H.; Nebel, G.J.; Williams, R.L. Measurement of Unregulated Emissions from General Motors' LightDuty Vehicles. SAE790694. Society of Automotive Engineers. 1979.

45 Urban, C.M.; Garbe, R.J. Regulated and Unregulated Exhaust Emissions from Malfunctioning Automobiles.

SAE790696. Society of Automotive Engineers. 1979.

46 Urban, C.M.; Garbe, R.J. Exhaust Emissions from Malfunctioning Three-Way Catalyst Equipped Automobiles.

SAE800511. Society of Automotive Engineers. 1980.

47 Lang, J.M.; Snow, L.; Carlson, R.; Black, F.; Zweidinger, R.; Tejeda, S. Characterization of Particulate

Emissions from In-Use Gasoline-Fueled Motor Vehicles. Society of Automotive Engineers. SAE 811186. 1981.
193

48 Volkswagen AG. Unregulated Motor Vehicle Exhaust Emission Gas Components. Volkswagen Aktien

Gesellschaft, Forschung und Entwicklung [Research and Development], Wolfsburg, Germany. 1991

49 California Air Resources Board. Particulate Exhaust Emissions: Gasoline Powered Vehicles. Emissions

Studies Section, El Monte, CA. Project No. 2R8618. 1986.

50 Cadle, S.H.; Mulawa, P.; Hunsanger, E.C.; Nelson, K.; Ragazzi, R.A.; Barrett, R.; Gallagher, G.L.; Lawson,

D.R.; Knapp, K.T.; Snow, R. Measurement of Exhaust Particulate Matter Emissions from In-use Light-duty Motor

Vehicles in the Denver, Colorado Area. CRC E-24-1. Coordinating Research Council, Alpharetta, GA. NTIS

PB98-136401. March, 1998.

51 Norbeck, J.M.; Durbin, T.D.; Turex, T.J. Measurement of Primary Particulate Matter Emissions from LightDuty Motor Vehicles. 98-VE-RT2A-001-FR. University of California, College of Engineering, Center for

Environmental Research and Technology, Riverside, CA.; CRC E-24-2. Coordinating Research Council, Alpharetta,

GA. NTIS PB99-151755. 1998.

52 Whitney, K. Measurement of Primary Exhaust Particulate Matter Emissions from Light-duty Motor Vehicles.

CRC E-24-3, Coordinating Research Council, Alpharetta, GA. 1998. NTIS PB99-121279. November, 1998.

53 Chase, R.E.; Duszkiewicz, G.J.; Jensen, T.E.; Lewis, D.; Schlaps, E.J.; Weibel, A.T.; Cadle, S.; Mulawa, P.

Particle mass emission rates from current-technology light duty gasoline vehicles. J Air & Waste Manage. Assoc.

2000: (50) 930-935.

54 Southwest Research Institute. VOC/PM Cold Temperature Characterization and Interior Climate Control

emissions/Fuel Economy Impact. SwRI 03.11382.04. San Antonio, TX. Prepared for USEPA, Office of

Transportation and Air Quality, Ann Arbor, MI. October, 2005.

55 Li, W.; Collins, J.F.; Norbeck, J.M.; Cocker, D.R.; Sawant, A. Assessment of Particulate Matter Emissions

from a Sample of In-Use ULEV and SULEV Vehicles. Society of Automotive Engineers. SAE 2006-01-1076. 2006.

56 U.S. Environmental Protection Agency. Study of Particulate Matter Emissions from Motor Vehicles. 1981.

57 Fujita, E.M.; Campbell, D.E.; Zielinska, B. Chemical Analysis of Lubrication Oil Samples from a Study to

Characterize Exhaust Emissions from Light-Duty Gasoline Vehicles in the Kansas City Metropolitan Area. Desert

Research Institute, Division of Atmospheric Sciences, Reno, NV. CRCE-69a. Coordinating Research Council,

Alpharetta, GA. December, 2006.

58 Norbeck, J.M.; Durbin, T.D.; Truex, T.D.; Matthew R. Smith, M.R.; Characterizing Particulate Emissions

from Medium- and Light Heavy-Duty Diesel-Fueled Vehicles: Final Report. 98-VE-RT2E-004-FR. Center for

Environmental Research and Technology, College of Engineering, University of California, Riverside, CA.

September, 1998.

59 USEPA Office of Air and Radiation, Office of Transportation and Air Quality. Development of Emission Rates

for Heavy-Duty Vehicles in the Motor Vehicle Emissions Simulator (DRAFT REPORT). EPA-420-P-09-005.

Assessment and Standards Division, Ann Arbor, MI. August, 2009.

(http://www.epa.gov/otaq/models/moves/techdocs/420p09005.pdf).

60 US Environmental Protection Agency. EPA Motor Vehicle Tampering Survey. EPA 420-A-90-001. 1990.

61 Hare, C. T.; Baines, T. M.; Characterization of Diesel Crankcase Emissions. Society of Automotive Engineers,

Off-Highway Vehicle Meeting and Exhibition. MECA, Milwaukee. 1977.

62 Bennet, P.A. et al. Reduction of Air Pollution by Control of Emissions from Automotive Crankscase. SAE Paper

142A, January 1960.
194

63 Bowditch, F.W. The Automobile and Air Pollution. SAE Paper No. 680242. Presented at SAE Mid-Year

Meeting, Milwaukee, WI. May, 1968.

64 Montalvo and Hare, 1985. EPA 460/3-84-011 Crankcase Emissions with disabled PCV systems. Southwest

Research Inst., San Antonio, TX.;Environmental Protection Agency, Ann Arbor, MI. Emission Control Technology Div.

65 Williamson, J. 1995. Exhaust emissions and fuel economy evaluation of SV technologies power valve

crankcase ventilation and oil recovery system. SV Technologies report, Lake Elsinore, CA

66 Kittelson, D.B.; Spears, M.; Taubert, T.; Waytulonis, B. Measurement and Characterization of Crankcase Blowby Emissions: Phase I Report to Caterpillar, Inc. Center for Diesel Research, Department of Mechanical

Engineering, University of Minnesota, Minneapolis. February 25, 1998.

67 Hill, Zimmerman, Gooch. A Multi-City Investigation of the Effectiveness of Retrofit Emissions Controls in

Reducing Exposures to Particulate Matter in School Buses. Clean Air Task Force, Boston, MA . January, 2005.

68 Ireson, R. 2008. Intentional Tracer Measurements of Tailpipe and Crankcase Particulate Matter Concentrations

Inside School Buses: Results, Refinements, and Effects of Bus Operating Conditions . 18th CRC On-Road Vehicle

Emissions Workshop, San Diego, CA, March 31-April 2, 2008.

69 Zielinska, B.; Campbell, D.; Lawson, D. R.; Ireson, R. G.; Weaver, C. S.; Hesterberg, T. W.; Larson, T.; Davey,

M.; Liu, L.-J. S. 2008. Detailed characterization and profiles of crankcase and diesel particulate matter exhaust

emissions using speciated organics Environ. Sci. Technol. 42(15): 5661-5666.