The Effects of Ultra-Low Sulfur 

Gasoline on Emissions from Tier 2 

Vehicles in the In-Use Fleet 

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.

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

The Effects of Ultra-Low Sulfur 

Gasoline on Emissions from Tier 2 

Vehicles in the In-Use Fleet 

Final Report

EPA-420-R-14-002

March 2014
1

Table of Contents

Acronyms........................................................................................................................................ 3

1. Executive Summary................................................................................................................. 4

2. Introduction ............................................................................................................................. 8

2.1. Background ...................................................................................................................... 8

2.2 Motivation for this Study ............................................................................................... 11

3. Study Design.......................................................................................................................... 11

3.1. Measurement of Reversible In-Use Loading (Clean-Out Effect) .................................. 12

3.2 Effect of Sulfur Level..................................................................................................... 13

4. Test Vehicle Selection, Recruitment, and Delivery .............................................................. 13

4.1 Choice of Makes and Models......................................................................................... 13

4.2 Vehicle Recruitment Criteria ......................................................................................... 15

4.3 Initial Checks and Test Vehicle Delivery ...................................................................... 15

5. Test Fuel Specs and Procurement.......................................................................................... 16

6. Test Procedures...................................................................................................................... 17

6.1 Initial fuel exchange and vehicle prep............................................................................ 17

6.2 Test procedure description ............................................................................................. 18

7. Data Analysis and Results ..................................................................................................... 22

7.1. Data Preparation............................................................................................................. 24

7.1.1. Imputation of measurements with low concentration............................................. 24

7.1.2. Detection of outliers................................................................................................ 27

7.2. Modeling Methodology.................................................................................................. 28

7.3. Statistical Analysis and Results ..................................................................................... 30

7.3.1. Effect of clean-out at 28 ppm.................................................................................. 30

7.3.2. Effect of clean-out at 5 ppm.................................................................................... 35

7.3.3. Effect of Sulfur level............................................................................................... 39

7.3.3.1. Tier 2 Vehicles........................................................................................................ 39

7.3.3.2. Tier 3-like Vehicles................................................................................................. 53

7.3.4. Sensitivity Analysis ................................................................................................ 56

8. Summary and Conclusions .................................................................................................... 59

9. References ............................................................................................................................. 62

Appendix A. Details of On-Road Mileage Accumulation Route

Appendix B. Emission Concentration Plots
2

Appendix C. Additional Information on Emission Measurements

Appendix D. Discussion of Univariate and Multivariate Analysis of Variance

Appendix E. Plots of All Pollutants and Bags

Appendix F. Sensitivity Analysis of Influential Vehicles
3

Acronyms

A/F Ratio Air-to-Fuel Ratio

BIC Schwarz Bayesian Criterion

CH4 Methane

CO Carbon Monoxide

CRC Coordinating Research Council

CS Compound Symmetry Covariance

EPA Environmental Protection Agency

F Fahrenheit

FID Flame Ionization Detector

FTP Federal Test Procedure

HC Hydrocarbons

LOQ Limit of Quantification

MIL Malfunction Indicator Lamp

ML Maximum Likelihood

MSAT Mobile Source Air Toxics

NMHC Non-Methane Hydrocarbons

NMOG Non-Methane Organic Gases

NOx Oxides of Nitrogen

NVFEL National Vehicle and Fuel Emission Laboratory

PGM Platinum Group Metals

PM Particulate Matter

PPB Parts per Billion

PPM Parts per Million

PSI Pounds per Square Inch

PZEV Partial Zero-Emissions Vehicle

REML Restricted Maximum Likelihood

RLD Restricted Likelihood Distance

RVP Reid Vapor Pressure

SAS Statistical Analysis Systems

THC Total Hydrocarbons

UN Unstructured Covariance
4

1. Executive Summary

Fuel sulfur content has long been understood to affect the performance of emission

aftertreatment catalysts in light duty vehicles, where the sulfur and its oxides occupy active

precious metal sites and oxygen storage materials, reducing the catalyst’s efficiency in

destroying pollutants. Numerous studies have shown the direct impact of fuel sulfur levels

above 30 ppm on emissions, data that formed the basis of the sulfur controls in EPA’s Tier 2

rulemaking for light duty vehicle emissions, published in 2000.1

Following the successful implementation of the Tier 2 sulfur standards, new research has

focused on the emission reduction potential of lowering sulfur levels below 30 ppm, particularly

on Tier 2 and newer technology vehicles, under the hypothesis that increased reliance on the

catalytic convertor will result in a higher sensitivity to fuel sulfur content. A 2005 study

conducted jointly by EPA and several automakers found large decreases in NOx and HC

emissions from vehicles meeting Tier 2 Bin 5 emission levels when operating on 6 ppm versus

32 ppm sulfur test fuel.2In order to gain further understanding of how these emission reductions

would translate into the in-use fleet, EPA conducted the study described here to assess the state

of sulfur loading (poisoning) in typical in-use vehicles, as well as the effect of fuel sulfur level

on these vehicles during subsequent mileage accumulation. It was designed to take into

consideration what was known from prior studies on sulfur build-up in catalysts over time and

the effect of periodic regeneration events that can occur during higher speed and load operation

in day-to-day driving.

The main study sample described in this analysis consisted of 93 cars and light trucks

recruited from owners in southeast Michigan, covering model years 2007-9 with approximately

20,000-40,000 odometer miles. The makes and models targeted for recruitment were chosen to

be representative of high sales vehicles covering a range of types and sizes. The main study

vehicle selection was also consistent with the EPAct/V2/E-89 study to allow potential linking of

the two programs. While the main study vehicle selection did not specifically target cleaner

emission vehicle technologies, the supplemental study acquired additional vehicles with “Tier 3-

like” emission levels and technologies as discussed later. Test fuels were two non-ethanol
5

gasolines with properties typical of certification fuel, one at a sulfur level of 5 ppm and the other

at 28 ppm, the higher level chosen to be representative of retail fuel available to the public in the

vehicle recruiting area. All emissions data was collected using the FTP cycle at a nominal

ambient temperature of 75°F.

Using the 28 ppm test fuel, emissions data were collected from vehicles in their asreceived state, and then following a high-speed/load “clean-out” procedure consisting of two

back-to-back US06 cycles intended to reduce sulfur loading in the catalyst. A statistical analysis

of this data showed highly significant reductions in several pollutants including NOx and

hydrocarbons (Table ES-1), suggesting that reversible sulfur loading exists in the in-use fleet and

has a measurable effect on aftertreatment performance. For example, Bag 2 NOx emissions

dropped 31 percent between the pre- and post-cleanout tests on 28 ppm fuel.

Table ES-1 Percent Reduction in In-Use Emissions After the Clean-Out Using 28 ppm Test

Fuel

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

PM

(p-value)

Bag 1 – –

6.0%

(0.0151) – –

15.4%

(< 0.0001)

Bag 2 31.4%

(0.0003)

14.9%

(0.0118) –

18.7%

(0.0131)

14.4%

(0.0019) –

Bag 3 35.4%

(<0.0001)

20.4%

(<0.0001)

21.5%

(0.0001)

27.7%

(<0.0001)

10.3%

(<0.0001)

24.5%

(<0.0001)

FTP

Composite

11.4%

(0.0002)

3.8%

(0.0249)

6.8%

(0.0107)

3.5%

(0.0498)

6.0%

(0.0011)

13.7%

(<0.0001)

Bag 1 – Bag 3 – –

7.2%

(0.0656) – – –

The clean-out effect is not significant at α = 0.10 when no reduction estimate is provided.

To assess the impact of lower sulfur fuel on in-use emissions, further testing was

conducted on a representative subset of Tier 2 vehicles on 28 ppm and 5 ppm fuels with

accumulated mileage. A first step in this portion of the study was to assess differences in the

effectiveness of the clean-out procedure under different fuel sulfur levels. Table ES-2 presents a

comparison of emissions immediately following (<50 miles) the clean-out procedures at the low

vs. high sulfur level. These results show significant emission reductions for the 5 ppm fuel
6

relative to the 28 ppm fuel immediately after this clean-out; for example, Bag 2 NOx emissions

were 34 percent lower after a clean-out on the 5 ppm fuel compared to that following the cleanout on the 28 ppm fuel. This indicates that the catalyst is not fully desulfurized, even after a

clean out procedure, as long as there is sulfur in the fuel. This further indicates that current

sulfur levels in gasoline continue to have a long-term, adverse effect on exhaust emissions

control that is not fully removed by intermittent clean-out procedures that can occur in day-today operation of a vehicle and demonstrates that lowering sulfur levels to 10 ppm on average

will significantly reduce the effects of sulfur impairment on emissions control technology.

Table ES-2 Percent Reduction in Emissions from 28 ppm to 5 ppm

for the First Three Repeat FTP Tests Immediately Following Clean-Out

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value) PM

Bag 1 5.3%

(0.0513)

6.8%

(0.0053)

6.2%

(0.0083)

5.7%

(0.0276)

14.0%

(<0.0001) –

Bag 2 34.4%

(0.0036)

33.9%

(<0.0001) –

26.4%

(0.0420)

49.4%

(<0.0001) –

Bag 3 42.5%

(<0.0001)

36.9%

(<0.0001)

14.7%

(0.0041)

51.7%

(<0.0001)

28.5%

(<0.0001) –

FTP Composite 15.0%

(0.0002)

13.3%

(<0.0001)

8.5%

(0.0050)

10.9%

(0.0012)

23.6%

(<0.0001) –

Bag 1 – Bag 3 – – – – – –

The effectiveness of clean-out cycle is not significant at α = 0.10 when no reduction estimate is provided.

To assess the overall in-use reduction between high and low sulfur fuel, a mixed model

analysis of all data as a function of fuel sulfur level and miles driven after cleanout was

performed. This analysis found highly significant reductions for several pollutants, as shown in

Table ES-3; the reductions for Bag 2 NOx were particularly high, estimated at 52 percent

between 28 ppm and 5 ppm. For all pollutants, the model fitting did not find a significant milesby-sulfur interaction, suggesting that the effect of sulfur level does not depend on miles driven

after the fuel change, and therefore, the emission benefits of lower fuel sulfur occurred

immediately and continued as miles were accumulated.
7

Table ES-3 Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur on In-Use

Tier 2 Vehicles

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

NOx+NMOG

(p-value) PMa

Bag 1 7.1%

(0.0216)

9.2%

(0.0002)

6.7%

(0.0131)

8.1%

(0.0017)

16.6%

(< 0.0001) N/A –

Bag 2 51.9%

(< 0.0001)

43.3%

(< 0.0001) –

a 42.7%

(0.0003)

51.8%

(< 0.0001) N/A –

Bag 3 47.8%

(< 0.0001)

40.2%

(< 0.0001)

15.9%

(0.0003)

54.7%

(< 0.0001)

29.2%

(< 0.0001) N/A –

FTP

Composite

14.1%

(0.0008)

15.3%

(< 0.0001)

9.5%

(<0.0001)

12.4%

(< 0.0001)

29.3%

(< 0.0001)

14.4%

(<0.0001) –

Bag 1 - Bag 3 –

a 5.9%

(0.0074) –

a

–

b

–

b N/A –

a

Sulfur level not significant at α = 0.10.

b

Inconclusive because the mixed model did not converge.

Major findings from this study include:

 Reversible sulfur poisoning is occurring in the in-use fleet of Tier 2 vehicles and has a

measureable effect on emissions of NOx, hydrocarbons, and other pollutants of interest.

 The effectiveness of high speed/load procedures in restoring catalyst efficiency is limited

when operating on higher sulfur fuel.

 Reducing the fuel sulfur levels from 28 to 5 ppm is expected to achieve significant

reductions in emissions of NOx, hydrocarbons, and other pollutants of interest from a

broad range of common in-use Tier 2 vehicles.

 Relatively large effect of sulfur on Bag 2 NOx were found to be robust based on the

sensitivity analyses examining the impact of influential vehicles and measurement

uncertainty at low emission levels.

 Lower-emitting Tier 3 vehicles are expected to show similar, if not greater, sensitivities

to the fuel sulfur levels compared to the conventional Tier 2 vehicles currently in-use,

based on the analyses of “Tier 3-like” vehicles.

A draft version of this report underwent an independent peer review covering the design,

analysis methods, and results. This process was conducted according to guidelines described in

EPA’s Peer Review Handbook, and did not produce any significant adverse findings. A detailed

description of the process and results is available on the EPA Science Inventory website.3
8

2. Introduction

2.1. Background

Sulfur in gasoline has long been found to reduce the conversion efficiency of automotive

three-way catalysts, with some studies suggesting an increase in catalyst sensitivity (in terms of

percent conversion efficiency) to sulfur as tailpipe emission levels decrease.4The Tier 2 light

duty emission standards recognized the importance of sulfur and required lower gasoline sulfur

levels in accordance with the relative level of the new emission standards. Since that time, little

work has been published on the effect of further reductions of fuel sulfur level, especially on inuse vehicles that may have some degree of catalyst deterioration due to real world operation.

This study investigates the effects of lower sulfur gasoline on in-use Tier 2 vehicles that have

been operated on commercial gasoline (with relatively higher sulfur levels) under real world

driving conditions.

Aftertreatment systems utilize catalysts containing precious metals and metal oxides to

selectively oxidize hydrocarbons and reduce nitrogen oxides in the exhaust gases. Sulfur oxides

from fuel combustion preferentially bind to active sites in the catalyst, inhibiting their ability to

participate in the intended conversion reactions (this is often referred to as sulfur poisoning).

The amount of sulfur retained by the catalyst is a function of the type and arrangement of active

materials and coatings used within the catalyst, its operating temperature, as well as the air-tofuel ratio and concentration of sulfur in the exhaust gas.5,6 Modern vehicle engines operate with

rich-lean oscillations that maintain the proper oxidation-reduction condition of the catalyst, but

under typical driving conditions, there is a nonzero equilibrium level of sulfur retained.

Regularly operating the catalyst at a high temperature under net reducing conditions can release

much the sulfur oxides from the catalyst (Figure 2-1), and could minimize the effects of fuel

sulfur on catalyst efficiency. However, producing these conditions at sustained and/or regular

intervals would accelerate thermal degradation of the catalyst and may also raise other

challenges for emissions control and fuel economy.
9

Figure 2-1 Catalyst-Sulfur Interaction by Temperature and A/F Ratio (Adapted from

Information from Ford Motor Company)

Additionally, not being able to maintain these catalyst temperatures (e.g., cold weather,

idles), and the rich air-to-fuel ratios can result in increased PM, NMOG and CO emissions.

Therefore, reducing fuel sulfur levels has been the primary regulatory mechanism to minimize

sulfur contamination of the catalyst and ensure optimum emissions performance over the useful

life of a vehicle.

In 2005, EPA and several automakers jointly conducted a program that examined the

effects of sulfur, benzene, and volatility on gaseous emissions from a fleet of nine Tier 2

compliant vehicles (referred to as the Mobile Source Air Toxics or MSAT study7). The fuel

matrix consisted of four non-ethanol fuels blended in a stepwise manner, starting with a

purchased base fuel containing the lowest levels of the three properties of interest. The base fuel

Hot-Rich Hot-Lean

Cold-LeanCold-Rich

900°C

500°C

Rich Air/Fuel

Sulfur oxidation & sulfate

decomposition

SO2release & conversion

to SO3, H2SO4

Lean Air/Fuel

Sulfate reduction &

release as SO2, H2S

SO2 adsorption &

reduction to elemental S

(reversible poisoning)

SO2 adsorption &

oxidation to SO3, SO4
10

contained 6 ppm sulfur, and after additions of butane (to arrive at 9 psi RVP) and benzene (to

arrive at 1.0 vol. %), sulfur was increased to 32 ppm using a small amount of doping agent.

Thus, the sulfur effect could be deduced by comparing emission results between the final fuel

and the one just before it in the blending sequence. The test vehicles were production samples

owned by the manufacturers, which had emission catalysts and oxygen sensors bench-aged to the

equivalent of 120,000 miles. The dataset consisted of tests performed at four different labs, with

three manufacturers testing two of their own vehicles, and EPA testing the remaining three at its

Ann Arbor, Michigan, laboratory. Tests conducted on high-sulfur fuel were preceded by an

extended cruise period to simulate sulfur loading, and tests on low-sulfur fuels were preceded by

a high-speed/load cycle used in previous programs performed by the Coordinating Research

Council.8

Mixed model analyses were performed on the data using vehicle and lab as random

effects and fuel properties as fixed effects. Reductions in FTP-weighted emissions for 6

compared to 32 ppm sulfur were 33 percent for NOx, 11 percent for THC, 17 percent for CO,

and 32 percent for methane (all statistically significant at α = 0.10). While the test procedures

may have produced sulfur loading in some vehicles beyond what would normally be expected inuse, these data suggested that there were likely to be significant emission reductions possible

with further reductions in gasoline sulfur level.

A study published in 2011 by Umicore Autocat USA describes tests performed on a

model year 2009 Chevrolet Malibu calibrated to PZEV levels (20 mg/mi NOx), intended to

examine the hypothesis that very-low emitting vehicles are more sensitive to fuel sulfur content.

To examine the impact of sulfur on the underfloor catalyst efficiency, the study used two test

fuels, at 3 and 33 ppm sulfur, over FTP, US06, and a high speed/load clean-out cycle similar to

one used in the MSAT study described above,. The authors observed consistent degradation of

catalyst performance when repeat FTPs were performed using the 33 ppm fuel, versus none with

the 3 ppm test fuel. Emission measurements showed a NOx reduction of 41 percent between

averages of three replicate FTPs on low versus high sulfur level.9
11

2.2 Motivation for this Study

The MSAT study described in the previous section motivated the design of a new study

to further investigate the effects of lower sulfur gasoline on a larger number of vehicles.

Specifically, we wanted to assess what the benefits of sulfur control might be under in-use

driving conditions for typical modern vehicles, as previous studies have generally not provided

data that can be utilized for this type of analysis in a straightforward manner. Many studies have

designs that look for a change in emissions at a single point in time shortly after a fuel sulfur

change and/or a clean-out cycle, while in fact, catalysts of in-use vehicles are normally operating

at a point somewhere between being clean of sulfur (e.g., following high speed, high load and

therefore high catalyst temperature operation) and fully loaded with sulfur (e.g., following low

speed, low load, cool catalyst temperature operation). Others have measured the effects of aging

catalysts on different fuel sulfur levels, which do not give any information on what happens

when fuel sulfur is changed partway through a vehicle’s life. Thus, the design of this study

sought to assess the impact of reversible sulfur loading on in-use Tier 2 vehicles recruited from

their owners, as well as the emission performance after a change to lower sulfur fuel and

subsequent mileage accumulation. Additionally, the supplemental testing of “Tier 3-like”

vehicles sought to assess the impact of fuel sulfur levels on vehicles and technologies at the

lowest emission levels not yet prevalent in the Tier 2 fleet but similar to vehicles expected in

coming years due to LEV III and Tier 3 requirements.

3. Study Design

This section outlines the hypotheses and theoretical bases for the specific procedures that

follow in the testing section.

In order to characterize the effect of sulfur on the in-use fleet, vehicles were recruited

from their owners and tested in EPA’s National Vehicle and Fuel Emission Laboratory (NVFEL)

in Ann Arbor, Michigan. Given this arrangement, it was not feasible to directly measure sulfur

loading in the catalysts themselves by means that involved damaging or destroying them.
12

Instead, the behavior of emissions during various test procedures was used as a proxy for sulfur

loading.

The program design used two fuels differing only in sulfur level, with the higher level of

30 ppm chosen to match the current average Tier 2 sulfur limit. All other fuel properties were

similar to Tier 2 certification gasoline used today to demonstrate compliance with Tier 2

emission standards (see Table 5-1).i

3.1. Measurement of Reversible In-Use Loading (Clean-Out Effect)

The level of reversible in-use sulfur storage (or loading) and release within an exhaust

catalyst system can be assessed by measuring emissions from the vehicle as received, performing

a high-speed, high-load clean-out cycle, then measuring emissions again. This change in

emissions represents the amount of reversible deactivation relative to a “clean” baseline state.

Prior to the start of this test program, we consulted previous studies as well as vehicle calibration

and emission controls experts to select an appropriate clean-out cycle for this test program. An

example of such a cycle is presented in Appendix C of the CRC E-60 emissions study report.10

The US06 certification cycle, while not as extreme as the E-60 cycle, does provide similar

conditions that are favorable for desulfurization (rich-lean switching at high temperatures) and is

commonly used in the auto industry to desulfurize catalysts prior to emissions testing. Though

the E-60 cycle would have been preferred, there were concerns that it would cause undesirable

wear and tear on recruited vehicles. As a compromise, two back-to-back US06 cycles were

selected for use in this test program.

The level of sulfur loading in a vehicle catalyst is primarily a function of the fuel sulfur

level and catalyst temperature, the latter of which is a function of vehicle and catalyst design and

driver behavior. A vehicle with a relatively high exhaust temperature at the catalyst location,

and/or significant excess loading of certain platinum group metals (PGM) and other active

i

Since sulfur’s effect on (non-sulfur) emissions is understood to occur solely in the catalyst, a non-ethanol fuel was

chosen as a base for practical reasons. Moreover, since sulfur was varied independently of other fuel properties, we

do not anticipate that the relative difference in emission results would have been adversely affected by this design

choice.
13

materials in the catalyst may be relatively insensitive to sulfur loading regardless of driver

behavior. These effects of vehicle design were accounted for in the study by recruiting different

makes and models covering a range of engine and vehicle sizes. We attempted to account for the

effects of the owner’s behavior by testing several samples of the same make and model.

3.2 Effect of Sulfur Level

Following vehicle operation that raises catalyst temperature and removes sulfur,

accumulation immediately resumes as temperature declines under more typical (milder) driving

conditions. This loading continues over a period of time with vehicle operation, and can be

observed as an increase in emissions (sometimes referred to as “NOx creep”). To capture this

effect, repeated emission tests were performed at two different fuel sulfur levels. Given that the

exhaust stream contains different concentrations of sulfur according to the fuel sulfur level, the

rate of reloading is expected to be lower for the lower sulfur fuel. Additionally, if an equilibrium

sulfur loading (represented by a stable level of emissions) were to be reached after many miles of

driving, this procedure could investigate whether this equilibrium loading is also lower for the

lower sulfur fuel.

4. Test Vehicle Selection, Recruitment, and Delivery

4.1 Choice of Makes and Models

The test fleet was chosen to be representative of latest-technology light duty vehicles

being sold at the time the program was launched. The study did not attempt to analyze or model

details of aftertreatment design specific to each vehicle model such as catalyst position, precious

metal types and quantities used, or related engine control strategies such as timing advance at

cold start or fuel cut during deceleration. While these things undoubtedly influence the behavior

of emissions and may interact with the fuel sulfur effects being investigated, including them in

an analysis requires correctly assessing and parameterizing them for all vehicles in the study.

Instead, this program’s aim was to characterize overall effects of sulfur on emission inventories

by observing the aggregate behavior of a representative fleet of vehicles.
14

The main and largest group of vehicles was intended to conform on average to the Tier 2

Bin 5 exhaust certification level and employ a variety of emission control technologies. These

goals could be achieved by including a range of vehicle sizes, engine displacements, and

manufacturers. A list of 19 high-sales-volume makes and models based on 2006-8 sales data and

projections had been used for test fleet selection in the EPAct/V2/E-89 study that was launched

shortly before this study.11 Given that we would be targeting recruitment of vehicles 1-3 years

old, this list seemed relevant; another benefit would be that the emission behavior of these same

models would also be characterized in the other study’s results. Grouping sales data by engine

family allowed additional transparency and flexibility in choosing test vehicles that represent a

wider group with identical powertrains without targeting one specific make and model. The

resulting target list of 19 vehicle models for recruitment is shown in Table 4-1.

Table 4-1 Test Vehicles Targeted for Recruitment

Make Brand Model Engine

Size Engine Family Tier 2 Cert Bin

GM Chevrolet Cobalt 2.2L I4 8GMXV02.4025 5

GM Chevrolet Impala FFV 3.5L V6 8GMXV03.9052 5

GM Saturn Outlook 3.6L V6 8GMXT03.6151 5

GM Chevrolet Silverado FFV 5.3L V8 8GMXT05.3373 5

Toyota Toyota Corolla 1.8L I4 8TYXV01.8BEA 5

Toyota Toyota Camry 2.4L I4 8TYXV02.4BEA 5

Toyota Toyota Sienna 3.5L V6 8TYXT03.5BEM 5

Toyota Toyota Tundra 4.0L V6 8TYXT04.0AES 5

Ford Ford Focus 2.0L I4 8FMXV02.0VD4 4

Ford Ford Taurus 3.5L V6 8FMXV03.5VEP 5

Ford Ford Explorer 4.0L V6 8FMXT04.03DB 4

Ford Ford F150 FFV 5.4L V8 8FMXT05.44HF 8

Chrysler Dodge Caliber 2.4L I4 8CRXB02.4MEO 5

Chrysler Dodge Caravan FFV 3.3L V6 8CRXT03.3NEP 8

Chrysler Jeep Liberty 3.7L V6 8CRXT03.7NE0 5

Honda Honda Civic 1.8L I4 8HNXV01.8LKR 5

Honda Honda Accord 2.4L I4 8HNXV02.4TKR 5

Honda Honda Odyssey 3.5L V6 8HNXT03.54KR 5

Nissan Nissan Altima 2.5L I4 8NSXV02.5G5A 5

Following the main group of vehicles representing the Tier 2 in-use fleet, a smaller group

of vehicles meeting lower emission standards was tested using the same fuels and procedures as

applicable. The purpose of this follow-on work was to evaluate the sulfur sensitivity of the
15

newest, lowest-emission technologies that could represent a portion of the future in-use fleet. A

detailed list of these vehicles is shown in Table 7-12.

4.2 Vehicle Recruitment Criteria

The vehicles recruited from private owners were selected to have odometer mileage

between 12,000 and 40,000 miles and model year less than three years old. The recruitment

process used the State of Michigan vehicle owner registration database to identify candidate

vehicle owners within 50 miles of NVFEL. Solicitation letters were sent to these owners to find

participants willing to allow their vehicles to be held and tested for up to six weeks, in exchange

for cash and/or a loaner vehicle. Once an owner agreed to participate, the vehicle was scheduled

for testing.

4.3 Initial Checks and Test Vehicle Delivery

Recruited vehicles were driven to the test facility either by their owners or NVFEL

personnel sent to retrieve them. NVFEL drivers were instructed to avoid hard accelerations and

high speeds in an effort to preserve the existing state of the emission controls. Once in custody,

vehicles were inspected for damage or malfunction. The condition of the MIL (malfunction

indicator, or “check engine”, lamp) was noted, and if illuminated, the vehicle was rejected from

the study. Minor exhaust leaks were repaired as needed; however, damage to the catalyst,

muffler, or sections of exhaust pipe disqualified the vehicle from the study. Gas caps and other

fuel systems components were visually inspected and repaired as needed. All repairs were

documented.

Once the inclusion criteria were met, the existing fuel was drained and a sample was

analyzed for its sulfur content to provide information about whether any vehicles that had been

operating on unusually high or low sulfur levels (no vehicles were removed from the study based

on this criterion). Federal fuel sulfur standards applicable in the recruiting area specified a 30
16

ppm annual refinery average with an 80 ppm per-gallon cap. Figure 4-1 shows the distribution

of sulfur levels found in the fuel tanks of test vehicles as received. The mean of the fuel samples

for which data were available (86 of 93 vehicles) was 25.4 ppm with a standard deviation of 11.2

ppm. At the time of the study, fuel in the recruiting area typically contained 10% ethanol.

Figure 4-1 Fuel Sulfur Levels Recruited Vehicles’ Fuel Tanks

5. Test Fuel Specs and Procurement

A bulk quantity of non-ethanol, low-sulfur, certification-type gasoline was purchased

from a commercial fuel blender (Haltermann Solutions, Houston, TX) and divided between two

underground storage tanks prior to start of the program. One tank was spiked with a small

amount (on the order of a liter of volume) of dibutyl disulfide to increase the sulfur content from

0

5

10

15

20

25

30

35

<10 10-20 20-30 30-40 40-50 50-60 >60

Number of Vehicles

Fuel Sulfur Level (ppm)
17

the as-received 5 ppm to 28 ppm. Due to the small volume of sulfur agent added, changes in

other fuel properties were negligible. Other properties of the test fuels are listed in Table 5-1,

and fall in the range of what is encountered in typical commercial gasoline in the vehicle

recruiting area. Relative to national average 2010 summer conventional gasoline (much of

which contained ethanol) as reported in the Auto Alliance U.S fuel survey database, these test

fuels are somewhat higher in aromatics (by about 6 vol. %), somewhat lower in olefins (by about

6 vol. %), somewhat higher in T50 (by about 20°F), and slightly lower in T90 (by about 3°F).

Table 5-1 Test Fuel Properties

Fuel Property ASTM Method Low S Test Fuel High S Test Fuel†

Sulfur D2622 5 ppm 28 ppm

Benzene D5769 0.34 Vol. % 0.34 Vol. %

Total Aromatics D5769 31.2 Vol. % 31.2 Vol. %

Olefins D1319 0.5 Vol. % 0.5 Vol. %

Saturates D1319 68.3 Vol. % 68.3 Vol. %

Oxygenates D5599 0.0 Vol. % 0.0 Vol. %

T50 D86 221°F 221°F

T90 D86 317°F 317°F

RVP D5191 9.0 psi 9.0 psi

†

Sulfur content was confirmed for the higher-sulfur test fuel, while other properties were assumed to be the same as

the base fuel given the small amount of dopant added.

6. Test Procedures

6.1 Initial Fuel Exchange and Vehicle Prep

Fuel drains were generally done via the fuel rail by energizing the vehicle’s pump. Next,

the key was turned to the “on” position, without starting the vehicle, for 30 seconds before

keying off. This allowed the vehicle to register the new fuel level to trigger any fast fuel trim
18

learning procedures the vehicle may have.ii Next, the vehicle was filled to 50 percent capacity

with test fuel. This fuel drain and fill process was then repeated a second time to ensure that the

fuel system had been flushed fully prior to testing.

Following the fuel change the vehicle was moved to the dynamometer to conduct an LA4

preconditioning drive cycle.iii At the completion of this prep cycle, the vehicle was idled in park

for two minutes before engine shutdown, a step intended to ensure fuel trim data was updated for

idle operation. The vehicle was then moved to a storage (“soak”) area using an electric towing

device for a 12-36 hour holding period at approximately 70°F prior to testing.

6.2 Test Procedure Description

Vehicles recruited for testing in this program fell into one of two test groups: “Long Test

Procedure” vehicles or “Short Test Procedure” vehicles. Among the main group of Tier 2

vehicles, for a given vehicle model class (or family ID), one of the five vehicles was tested under

the Long procedure while the other four were tested under the Short procedure. The choice of

vehicle to undergo the Long procedure was made at random from willing participants in a given

vehicle class. The original Long and Short procedures used at the beginning of the program are

shown in Figure 6-1 and discussed in greater detail below. They are identical in structure for the

first six emission tests. The follow-on group of “Tier 3-like” vehicles followed the modified

Long procedure as described below with the exception of the manufacturer-owned vehicle. It

was not expected to have a use history relevant to the in-use loading question, and therefore

underwent a somewhat abbreviated procedure focused only on the effect of sulfur level.

ii Fuel trim refers to the ability of the engine control system to adapt to a change in fuel properties, which may affect

vehicle or emissions performance. Some vehicles may accelerate this “learning” process if a fuel change is detected.

iii The LA4 drive cycle covers the first 7.5 miles of the FTP test cycle, consisting of typical urban driving behavior.

For more details on these test cycles see 40 CFR Part 86.
19

Figure 6-1 Original Short (S) and Long (L) Procedures

LO

N

G

P

R

O

C

E

D

UR

E

C

O

NTIN

UE

S INTO THE

S

E TE

STS

S

H

O

RT P

R

O

C

E

D

UR

E

Pre-clean-out (as-received) replicate

FTPs at 28 ppm S

Post-clean-out replicate FTPs at 28 ppm S

Add'l replicates for long-procedure vehicle at 28 ppm

S, alternating cold start FTPs for data collection

(numbered circles) with unsampled hot start FTPs

used for mileage accumulation (blank circles)

Sulfur clean-out cycle, 2 x US06 at 28 ppm S

1

2

3

4

5

6

1

2

3

4

5

6

1

2

3

Data used for "clean-out effect"

Data used for "sulfur level effect"

Fuel change to high-sulfur (28 ppm) test fuel

Data collection at 5 ppm S, alternating cold and hot

start FTPs as above

Fuel change to low-sulfur (5 ppm) test fuel

Sulfur clean-out cycle, 2 x US06 at 5 ppm S
20

All recruited vehicles were tested over three cold-start FTPs with high sulfur fuel to

assess “as received” emissions levels prior to performing a sulfur clean-out cycle (two back-toback US06 cycles) to remove sulfur accumulated in-use from the catalyst.iv Emissions were then

sampled again over three cold-start FTPs on the same high sulfur test fuel to assess the effect of

the sulfur clean-out cycle on emissions. This data was used to determine the “clean-out” effect

and is referred to as the clean-out dataset in the analysis section of this report. Testing of a

vehicle undergoing only the Short procedure was complete at this point and the vehicle was

returned to its owner.

Vehicles undergoing the Long procedure continued testing for an additional length of

time to determine the effects of both high and low sulfur fuel following mileage accumulation.

Beginning with the fourth post-cleanout FTP test on high sulfur fuel, the test sequence began to

alternate between sampled, cold-start FTPs and un-sampled hot-start FTPs (immediately

following the cold-start test) in order to accumulate additional miles between emission tests.

Following the sixth sampled FTP, the fuel was changed to low sulfur test fuel and a clean-out

cycle was run to remove accumulated sulfur from the catalyst and establish a new “cleaned-out”

emissions baseline on low sulfur test fuel. Six additional FTPs (with un-sampled FTPs inbetween) were conducted on low sulfur fuel. Together with the six high sulfur fuel tests, this

data is used to determine the “sulfur level effect,” which is the assessment of high and low sulfur

fuel use starting from a “clean” catalyst.

Under this original protocol, vehicles tested on the Long procedure were run for

approximately 100 miles of operation on each fuel following a cleanout. However, after an

interim data review mid-way through the program, we were concerned that 100 miles may not be

sufficient to cover loading levels associated with a wide variety of in-use driving. Therefore, we

modified the Long procedure to incorporate two 50-mile on-road mileage accumulation intervals

during the sequence of emission tests on each fuel. This modified Long procedure, the L/M

procedure, allowed us to accumulate an additional 50 miles on each fuel without occupying

iv A cold-start FTP refers to the FTP drive cycle being performed after the vehicle has been sitting with no engine

starts for between 12-36 hours at approximately 70°F. This is useful because emissions are generally highest in the

first few minutes of cold engine operation. A hot-start FTP refers to a test driven within a short time after a previous

test, such that the engine and aftertreatment are still well above room temperature. Hot-start tests were only used for

mileage accumulation and did not have emissions sampled.
21

additional test cell time. A flowchart of the modified Long procedure (L/M) is shown in Figure

6-2.

Figure 6-2 Modified Long (L/M) Procedure

On-road mileage accumulation for the L/M procedure was performed over a pre-defined

driving route selected for its resemblance to the FTP in terms of speed and load distribution.

Refer to Appendix A for more details on this drive route.

LO

N

G

P

R

O

CE

D

URE

C

O

NTIN

UE

S INTO THE

S

E TE

STS

S

H

O

RT P

R

O

C

E

D

UR

E

4

5

Pre-clean-out ("as-recieved") replicate FTPs

at 28 ppm S

Post-clean-out replicates at 28 ppm S

Add'l replicates of cold start FTPs at 28 ppm S

Low sulfur replicates of cold start FTPs at 5 ppm S

Sulfur clean-out cycle, 2 x US06 at 28 ppm S

Sulfur clean-out cycle, 2 x US06 at 5 ppm S

1

2

3

6

1

2

1

2

3 Data used for "clean-out effect"

Data used for "sulfur level effect"

~50 Miles on-road mileage accumulation

Add'l replicates of cold start FTPs at 28 ppm S

Low sulfur replicates of cold start FTPs at 5 ppm S

3

4

Low sulfur replicates of cold start FTPs at 5 ppm S

5

6

~50 Miles on-road mileage accumulation

~50 Miles on-road mileage accumulation

~50 Miles on-road mileage accumulation

Fuel change to low-sulfur (5 ppm) test fuel

Fuel change to high-sulfur (28 ppm) test fuel
22

Following the mid-point data review the Short procedure was also modified to include a

second clean-out and subsequent set of emission tests performed on low sulfur fuel. This change

was made based on the observation that sulfur level had an effect on emissions immediately after

the cleanout for some vehicles.vFigure 6-3 shows the modified Short procedure containing two

fuel sulfur levels, which was implemented beginning with vehicle Family ID N513.

Figure 6-3 Modified Short Procedure

7. Data Analysis and Results

The data generated by this test program will be discussed as three distinct but overlapping

datasets: “clean-out at 28 ppm” data, “clean-out at 5 ppm” data, and “sulfur level” data. The

“clean-out at 28 ppm” data consists of the measurements from original and modified ‘short’

procedures; “clean-out at 5 ppm” data consists of a subset of original and modified ‘long’

procedures and modified ‘short procedures; “sulfur level” data consists of the complete data

v This information became the basis for the “clean-out at 5ppm” conclusions.

EXTE

NSIO

N S P

R

O

CE

D

URE

Pre-clean-out (as-received) replicate

FTPs at 28 ppm S

Post-clean-out replicate FTPs at 28 ppm S

Low-sulfur replicates for modified S procedure, FTPs

at 5 ppm S

Sulfur clean-out cycle, 2 x US06 at 28 ppm S

1

2

3

1

2

3

1

2

3

Data used for "clean-out effect"

Data used for "sulfur level effect" Sulfur clean-out cycle, 2 x US06 at 5 ppm S

Fuel change to low-sulfur (5 ppm) test fuel

Fuel change to high-sulfur (28 ppm) test fuel
23

from original and modified ‘long’ procedures and modified ‘short’ procedures. All vehicles

included in the “sulfur level” dataset had a subset of their tests included in the “clean-out”

datasets, by design.

Pollutants included in the analysis were total hydrocarbons (THC) as reported by the FID

analyzer, carbon monoxide (CO), oxides of nitrogen (NOx), methane (CH4), as well as

particulate matter (PM) mass. The following calculated emissions were also included: nonmethane hydrocarbons (NMHC, defined as THC minus methane) and oxides of nitrogen plus

non-methane organic gases (NOx + NMOG), a measurement relevant in regulatory contexts.vi

Each bag, ‘Bag 1 minus Bag 3’, and the composites from the FTP test cycle were analyzed

separately. The first bag captures the initial “cold start”, meaning the emissions produced when

the vehicle is started after cooling to room temperature overnight. The “hot stabilized” operation

is captured in the second bag (a portion of the test that begins after approximately 8 minutes of

driving), and the emissions from “hot-restart” are measured in the third bag (a repeat of the cold

start drive cycle, but after the vehicle has been turned off for only 10 minutes). The ‘Bag 1

minus Bag 3’ emission value was used to isolate cold-start emissions for each FTP test.

The statistical methodologies described in the following section were applied consistently in

the analysis of all pollutants and all bags. However, the analysis of nitrogen oxides (NOx) from

Bag 2 is presented in greater detail to assist the reader in understanding the analytical approaches

and to illustrate the statistical methods being used. The final model results from all other

pollutants are also presented in this report.

The goals of the statistical analyses of the “clean-out” and “sulfur level” data were to assess

the reversible sulfur loading in the catalysts of the in-use fleet, to examine the differences in the

effectiveness of the clean-out cycle by comparing emissions immediately following the clean-out

vi NMOG refers to non-methane organic gases. The flame ionization analyzer used to produce the THC result

produces an inaccurate measurement of oxygenated emission species, which can be corrected using speciated results

for those compounds along with their analyzer response factors. The ratio of NMOG/NMHC can be predicted with

good accuracy using the fuel ethanol content (zero in this case), which was the approach used here to generate the

NMOG results. This method is described in Sluder S.C, West B.H., “NMOG Emissions Characterizations and

Estimation for Vehicles Using Ethanol-Blended Fuels”. Oak Ridge National Laboratories, 2011. Publication

number ORNL/TM-2011/461.
24

procedure at two sulfur levels, and to characterize the effects of fuel sulfur level on emissions as

mileage was accumulated.

7.1. Data Preparation

Prior to proceeding with the statistical analyses, the issues associated with the dataset,

namely, very low emission measurements and outliers were examined. The following sections

describe how these issues were addressed.

7.1.1. Imputation of Measurements with Low Concentration

The graphical examination of both the “clean-out” and the “sulfur level” dataset revealed

the presence of very low emission measurements from some pollutants and bags including NOx

Bag 2. Since uncertainty associated with these low measurements could potentially affect the

outcome of the analysis, it was important to understand the measurement process and evaluate

the impact of any uncertainty.

During emission testing, the vehicle exhaust stream is collected and diluted with

background air to avoid water condensation and other stability problems. A small sample of this

mixture flows into a collection bag for analysis after the test. The concentration of emission

species in the bag is determined by flowing the contents through a properly-calibrated gas

analyzer and reading the result. This method provides a time-weighted result via physical

integration of the emission stream produced over the course of a transient driving cycle.

The measurement process has uncertainty associated with it as a result of the physics of

mixing and sampling from a gas stream as well as noise in analyzer components such as

optoelectronic detectors and signal amplifiers. This means that repeated measurements taken

under identical process conditions will produce a range of results, their average being the true

(intended) response of the instrument and the range around it representing the measurement

variability.
25

For the analyzers used in this program, the size of the measurement error is expected to

increase relative to the measured value as the concentration decreases. Moreover, the dilute bag

method used here requires measurement of concentrations in both the sample and background

bags, followed by a subtraction between the two, such that the net result contains variability from

two measurements. To assess whether these issues may affect this dataset, we examined plots of

the measured concentrations for each test by vehicle by pollutant and bag. Figure 7-1 shows the

Bag 2 NOx dataset for the vehicles providing the “sulfur level” data, which contains a number of

very low values, as well as tests where sample and background are of similar magnitude (the

vehicle codes refer to the Family IDs listed in Table 7-7; additional plots are available in

Appendix B). Given these findings, we performed sensitivity analyses to evaluate the impact of

these low emission measurements on the study results (presented in Section 7.3.4).

Figure 7-1 Bag 2 NOx Concentration Measurements by Vehicle

When a dilute emission measurement is lower than the measured background level, the

net result is reported as zero (this calculation is performed on a test-by-test basis). However,

since it is unlikely that tailpipe emissions are truly zero during a test, it was assumed that a zero

0.0001

0.001

0.01

0.1

1

parts per million

Bag 2 NOx concentration Background Sample, High Sulfur Sample, Low Sulfur

M500-0003M501-0023M502-0026M503-0194M504-0021M505-0031M506-0123M507-0148M508-0075M509-0046

N510-0264N511-0179N512-0107N513-0089N513-0178N514-0010N514-0101N514-0104N515-0006N515-0007N515-0074N515-0165N520-0011N520-0022N520-0026N521-0131N521-0162N521-0179N521-0280N521-0329

P522-0009P522-0011P522-0039P522-0045P522-0146
26

is a result of the actual emission level being smaller than the sum of the measurement errors

occurring on the sample and background measurements. The emission level was thus considered

to be below the limit of quantification (LOQ), a level below which we are not confident in the

accuracy of quantitative value.

In this situation, the data point can be left as zeroes, deleted, or replaced with an imputed

value. However, because it was necessary to apply log-transformation, as described in more

detail in Section 7.2, the zero values could not be left as is in the data. Table 7-1 summarizes the

number of measurements with zero values and the percentages in parenthesis for each dataset.

Given that observations below the LOQ appear to be randomly distributed across sulfur levels

and vehicles, and since excluding such observations would result in reduced sample size, less

statistical power, and larger standard errors,12 they were imputed in the analysis.

Since an imputation method involving each vehicle’s own longitudinal data would be

superior to methods using no information about the vehicle,13 a commonly-used singleimputation method, using half the minimum of a valid measurement from a given mileage bin for

the vehicle with zero values, was performed. This imputation method recognized the fact that

emission measurements below the limit of quantification must be smaller than any quantified

value.

Although using vehicle-specific imputation minimizes the likelihood of artificially

reducing the natural variance of the data, there is a potential to inflate the reliability estimates as

the number of imputed values increase. However, since the number of measurements with

imputed values are less than 20 percent (Table 7-1), we can expect good estimates of the

reliability of measurements.14 Nonetheless, it is important to determine the effect of these

imputed values on the resulting test statistics and corresponding conclusions. Thus, the results

from the statistical analysis with and without the imputed values were compared once the model

was finalized to assess the potential for introducing bias. This is discussed further in Section

7.3.4.
27

Table 7-1 Number of Measurements With Zero Values

Clean-out at 28 ppm data (N = 541)

NOx THC CO NMHC CH4 PM

Bag 1 0 1 (0.2%) 0 1 (0.2%) 1 (0.2%) 1 (0.2%)

Bag 2 32 (5.9%) 11 (2.0 %) 33 (6.1%) 48 (8.9%) 4 (0.7%) 2 (0.4%)

Bag 3 0 1 (0.2%) 21 (3.9%) 38 (7.0%) 1 (0.2%) 1 (0.2%)

FTP Composite 0 1 (0.2%) 0 1 (0.2%) 1 (0.2%) 2 (0.4%)

Bag 1 – Bag 3 0 1 (0.2%) 0 1 (0.2%) 1 (0.2%) 1 (0.2%)

Clean-out at 5 ppm data (N = 183)

NOx THC CO NMHC CH4 PM

Bag 1 0 0 0 0 0 0

Bag 2 15 (8.2%) 7 (3.8%) 4 (2.2%) 5 (3.8%) 4 (2.2%) 0

Bag 3 2 (1.1%) 0 2 (1.1%) 17 (9.3%) 0 0

FTP Composite 0 0 0 0 0 0

Bag 1 – Bag 3 0 0 0 15 (8.2%) 0 0

Sulfur level data (N = 322) †

NOx THC CO NMHC CH4 PM

Bag 1 0 0 0 0 0 0

Bag 2 21 (6.5%) 14 (4.3%) 10 (3.1%) 33

(10.2%) 5 (1.5%) 2 (0.9%)

Bag 3 3 (0.9%) 0 4 (1.2%) 28 (8.6%) 0 0

FTP Composite 0 0 0 0 0 0

Bag 1 – Bag 3 7 (2.2%) 0 1 (0.3%) 0 0 15 (6.5%)

† The sulfur level data for PM had 232 measurements.

7.1.2. Detection of Outliers

Next, before proceeding to the full analysis, preliminary models were fit to detect

outliers. The residual plots were visually inspected for outlying observations and the outliers

were identified using the screening criterion value of ±3.5 for the externally studentized

residuals. Generally, one can expect about 95% of the externally studentized residuals to be

within ±3.5 standard deviations, and applying the ±3.5 criterion to detect potential outliers has

been widely used in statistics. When the outlying observation represented an actual

measurement, it was examined to assess its validity. Since none of the outliers representing

actual measurements showed a clear indication of measurement error, it was assumed that the

outlying observations were real and thus they were included in the dataset for the analysis.

However, there were instances where a very low imputed value was identified as an outlier. In
28

such instances, the imputed values were removed from the dataset. Table 7-2 summarizes the

number of outliers as well as the number of imputed measurements that were removed in

parenthesis.

Table 7-2 Number of Outliers

Clean-out at 28 ppm data (N = 541)

NOx THC CO NMHC CH4 PM

Bag 1 2 (0) 4 (0) 2 (0) 5 (0) 2 (1) 2 (0)

Bag 2 3 (1) 7 (4) 5 (2) 3 (0) 6 (1) 5 (0)

Bag 3 3 (0) 2 (0) 5 (2) 5 (4) 1 (0) 2 (0)

FTP Composite 3 (0) 3 (0) 2 (0) 3 (0) 0 (0) 1 (0)

Bag 1 – Bag 3 13 (4) 5 (0) 1 (0) 6 (0) 5 (0) 7 (3)

Clean-out at 5 ppm data (N = 183)

NOx THC CO NMHC CH4 PM

Bag 1 0 (0) 1 (0) 2 (0) 1 (0) 1 (0) 1 (0)

Bag 2 1 (0) 0 (0) 1 (1) 1 (0) 2 (0) 1 (0)

Bag 3 3 (0) 2 (0) 1 (0) 2 (1) 0 (0) 1 (0)

FTP Composite 2 (0) 1 (0) 0 (0) 1 (0) 0 (0) 2 (0)

Bag 1 – Bag 3 3 (2) 1 (0) 1 (1) 2 (0) 3 (0) 3 (0)

Sulfur level data (N = 322)†

NOx THC CO NMHC CH4 PM

Bag 1 0 (0) 4 (0) 4 (0) 3 (0) 3 (0) 2 (0)

Bag 2 0 (0) 1 (1) 4 (1) 3 (0) 3 (0) 1 (0)

Bag 3 1 (0) 4 (0) 3 (0) 2 (0) 0 (0) 1 (0)

FTP Composite 4 (0) 3 (0) 3 (0) 3 (0) 1 (0) 2 (0)

Bag 1 – Bag 3 2 (0) 2 (0) 6 (1) 2 (0) 1 (0) 4 (0)

† The sulfur level data for PM had 232 measurements.

7.2. Modeling Methodology

The following section describes the statistical approaches and the model fitting

methodologies applied in the analysis of all three datasets – “clean-out at 28 ppm”, “clean-out at

5 ppm”, and “sulfur level” data.

First, the emission measurements were log-transformed. In the current study, the

distributions of emissions exhibited positive skewness (log-normal), and thus, transforming

emission measurements by the natural logarithm was necessary to stabilize the variance, to
29

obtain a linear relationship between the mean of the dependent variable and the fixed and

random effects, and to normalize the distribution of the residual. The log-transformation of

emission measurements has been well-established in previous studies analyzing vehicle

emissions data.15,16,17

Both the “clean-out” and “sulfur level” data are a classic example of a “repeated

measures data” where multiple measurements were taken from a single vehicle at different

accumulated mileages. The conventional methods for analyzing “repeated measures data” are

the univariate and multivariate analysis of variance (discussed further in Appendix D).

However, the linear mixed model was selected for the analyses of both “clean-out” and “sulfur

level” data for the following reasons: The mixed model approach uses generalized least squares

to estimate the fixed effects, which is considered superior to the ordinary least squares used by

the univariate and multivariate procedure.17 It is a more robust and flexible procedure in

modeling the covariance structures for repeated measurements data and better accounts for

within-vehicle mileage-dependent correlations.15,16 In addition, the mixed model is capable of

including vehicles with missing data and handling irregularly spaced measurements.

The MIXED procedure in the SAS 9.2 software package was used to fit the model. The

mixed model is represented in Equation 7-1 as:

7-1 Equation ࢏ࢿ ൅࢏࢛࢏ࢆ ൅ ࢼ࢏ࢄ ൌ ࢏ࢅ

where β and ui are fixed and random effects parameters, respectively, and εi is the random

residuals. The mixed model accounts for correlation in the data through the inclusion of random

effects and modeling of the covariance structure. β represents parameters that are the same for

all vehicles and ui represents parameters that are allowed to vary over vehicles reflecting the

natural heterogeneity in the vehicle fleet. In other words, the model considers the differences in

the effect of sulfur level on emission for each vehicle. The distributional assumptions for the

mixed model are: ui is normal with mean 0 and variance Gi; εi is normal with mean 0 and

variance Ri; the random components ui and εi are independent.
30

In developing the mixed model, a top-down model fitting strategy, similar to previously

established methods,18,19 was used. The first step was to start with a saturated model, including

all candidate fixed effects to ensure unbiasedness of the fixed effect estimates. Next, we selected

a model with the most optimal covariance structure, which specifies the variation between

vehicles as well as the covariation between emission measurements at different accumulated

mileages on the same vehicle. Finally, the fixed effects portion of the model was reduced to fit

the final model.

7.3. Statistical Analysis and Results

The statistical analyses were performed on all three datasets (“clean-out at 28 ppm”,

“clean-out at 5 ppm”, and “sulfur level” data) for the conventional Tier 2 vehicles. However, for

the “Tier 3-like” vehicles designed to supplement the Tier 2 vehicles, only the “sulfur level” data

was analyzed, considering the small sample size of the “Tier 3-like” vehicles.

7.3.1. Effect of Clean-Out at 28 ppm

The main objective of analyzing the “clean-out at 28 ppm” data was to assess the in-use

reversible sulfur loading in the catalysts at the fuel sulfur level of 28 ppm. In addition, the

results from the “clean-out” data were used to supplement the “sulfur level” data in estimating

the differences of in-use sulfur loading between high and low fuel sulfur levels, as described in

Section 7.3.3.

The “clean-out at 28 ppm” data consists of the as-received emission measurements (precleanout) and the measurements after the back-to-back US06 cycles (post-cleanout) at the fuel

sulfur level of 28 ppm from original and modified ‘short’ procedures. The change in emissions

between pre- and post-cleanout represents the amount of reversible in-use sulfur loading at 28

ppm. The “clean-out at 28 ppm” data at the time of the analysis comprised 19 vehicle families,

for a total of 93 unique vehicles. Vehicles from the same engine family had the same engine size,

vehicle configuration, and weight. The average starting odometer reading of the 93 unique

vehicles was 32,049 ± 6,136 miles. Additional details of this test fleet are shown in Table 7-3.
31

A total of 541 measurements were taken – 275 pre-cleanout and 266 post-cleanout

measurements.

Table 7-3 Description of Vehicles in the “Clean-Out at 28 ppm Data”

Vehicle

Family ID Make Model ModelYear

Tier 2

Cert Bin

Number of

Vehicles

Average Starting

Odometer

M500 Toyota Corolla 2007 5 5 32,578

M501 Ford Explorer 2007 4 5 33,605

M502 Dodge Caliber 2007 5 5 31,184

M503 Honda Odyssey 2007 5 5 35,954

M504 Saturn Outlook 2007 5 5 35,762

M505 Chevrolet Silverado 2007 5 5 37,401

M506 Nissan Altima 2007 5 5 32,283

M507 Ford Taurus 2007 5 5 29,442

M508 Dodge Caravan 2007 8 5 34,371

M509 Chevrolet Impala 2007 5 5 26,183

N510 Toyota Sienna 2007 5 5 30,996

N511 Chevrolet Cobalt 2008 5 6 29,023

N512 Jeep Liberty 2008 5 5 27,530

N513 Ford Focus 2008 4 5 26,843

N514 Honda Civic 2008 5 4 37,988

N515 Ford F150 2008 8 5 31,719

N520 Toyota Tacoma 2009 5 3 28,964

N521 Toyota Camry 2008 3 5 28,506

P522 Honda Accord 2008 3 5 29,601

The box-plot of the “clean-out at 28 ppm” data (Figure 7-2) for log-transformed NOx bag

2 shows the spread of the data for each vehicle family by clean-out status. The diamond and the

line inside the box represent the mean and the median, respectively. The box represents the

interquartile range between 25th and 75th percentile and the error bars show the full data range.

The box-plot suggests that the variance in the pre-cleanout group tends to be larger than

the variance observed in the post-cleanout group. Generally, there is a tendency for the precleanout measurements, representing the as-received sulfur level, to have higher NOx than the

post-cleanout measurements. In addition, the plot illustrates that the emission profiles are

substantially different between vehicle families. Some of the vehicle families also show the

presence of within-family variability, as demonstrated by each error bar, reflecting the influence
32

of individual driving patterns on in-use sulfur loading. These findings from the graphical

examination of the data assisted in selecting the appropriate statistical model for the analysis.

Figure 7-2 Box-Plot of Vehicle Families by Pre- and Post-Cleanout at 28ppm

(NOx Bag 2)

The dependent variable (Yi) of the mixed model (Equation 7-1) was the natural logarithm

of emissions. The fixed effect (Xi) included in the model was the cleanout status (pre- and postcleanout), vehicle type (car vs. trucks), and the interaction term. The random effects (Zi) were

each vehicle family in the study assuming that vehicles from the same vehicle family have

similar emission profiles. For the degrees of freedom, the Satterthwaite approximation was used

for tests of fixed effects.20

First, a test for the significance of the between-family variation was performed for

inclusion of the random intercept for each family using the likelihood ratio test. For all

pollutants and bags, the result was significant and the random intercept for each family was
33

included for all subsequent models. The significance of between family-variation was observed

graphically in Figure 7-2 and is also supported by recent automotive emissions studies.21,22

The covariance structure was estimated using restricted, or residual, maximum likelihood

(REML)23 and selected by comparing the Schwarz Bayesian Criterion (BIC) values for various

potential covariance structures. BIC, considered a consistent criterion, accounts for the total

number of observations in the dataset and has generally been known to perform relatively better

for small size datasets.24,25,26

In modeling the covariance structures, the variance associated with the pre-cleanout was

permitted to differ from the variance associated with the post-cleanout. As an initial step, a

saturated model with fixed effects was attempted to be fit with “unstructured” (UN) covariance,

which makes no assumptions regarding equal variances or correlations. One advantage of

starting with the unstructured covariance is the fact that it allows selection of the most optimal

covariance structure based on the data by visually examining the matrix for patterns. However,

the model failed to converge and it was not possible to fit an unstructured covariance matrix. It

may be explained by the fact that UN structure requires estimation of a large number of variance

and covariance parameters, resulting in computational problems.

Since the “clean-out” data involved three repeat tests of FTP for both pre- and postcleanout without mileage accumulation, the covariance matrix of compound symmetry (CS), in

Equation 7-2, was selected, which assumes that the measurements from the same vehicle have

the homogeneous variance and the correlation among measurements is constant regardless of

how far apart the measurements are.

࢏ࣕ)࢘ࢇࢂ ൌ࢏ࡾ

) =

⎣

⎢

⎢

⎢

⎡

࣌

૚࣌ ൅ ૛૚࣌ ૛૚࣌ ૛

૛

૚࣌

࣌ ૛

૚࣌ ൅ ૛૚࣌ ૛

૛

૚࣌

૛

૚࣌

૛

૚࣌

૛

૚࣌

૛

࣌

૚࣌ ൅ ૛

૛

૚࣌

૛

૚࣌

૛

૚࣌

૛

૚࣌

૛

࣌

૚࣌ ൅ ૛

૛

⎦

⎥

⎥

⎥

⎤

Equation 7-2

The covariance structure was modeled using a combination of compound symmetry

structure within families and a random effect between families. This combination structure

specifies an inter-vehicle random effect of differences between families, and a correlation
34

structure within families that are the same between emission measurements. It implies that the

only aspect of the covariance between repeated measures is due to the vehicle contribution.

Following the selection of structures for the random effects and the covariance structure

for the residuals, the fixed effects in the model were tested using the approximate F-test with the

Satterthwaite degrees of freedom. A non-significant effect was removed from the model and the

reduced model was re-fit and re-tested until all fixed effects in the model were significant. The

significance level of 10 percent (α = 0.1) was used to test the null hypothesis. Statistical

hierarchy was kept in removing insignificant fixed parameters – first order terms were included

when the second order term was significant.

We then performed the likelihood ratio test between the reference and the nested models

using Maximum Likelihood (ML)23,27 as the estimation method to examine whether the model

can be reduced further without influencing the model fit. To perform the likelihood ratio test, the

-2 Res Log Likelihood Scores from two separate models were considered and the chi square test

statistic was computed by subtracting the -2 Res Log Likelihood from the model with more

parameters from the model with fewer. The Chi-Square test statistic had the degrees of freedom

equal to the difference of the number of parameters. When this test statistic was significant, the

model with the greater number of parameters was chosen. However, when the test statistic was

not significant, there is no significant difference in the fit of the two models and the model with

the fewer number of parameters was selected. Once the fixed effects were finalized based on the

chi square test, no further simplification of the fixed-effects component of the model was

performed. For all pollutants and bags, only the cleanout effect was significant – the emissions

levels did not differ significantly between cars and trucks.

The change in emissions between pre- (as-received level) and post-cleanout at 28 ppm

was estimated using the differences of least squares means from the final model. The TukeyKramer adjustment was used in calculating the p-values for the least squares means. The

difference of least squares means between pre- and post-cleanout were reverse-transformed to

grams per mile space to estimate the percent reduction in emissions. Table 7-4 shows the

reductions in emissions for all pollutants between pre- and post-cleanout at the fuel sulfur level
35

of 28 ppm, reflecting the level of reversible sulfur loading in catalysts. The statistical analysis of

the “clean-out at 28 ppm” showed highly significant reductions in emissions for several

pollutants including NOx and hydrocarbons. The findings here suggest there is a degradation of

emission performance for Tier 2 vehicles in-use due to sulfur loading in the catalyst, and it has a

measureable effect on aftertreatment performance.

Table 7-4 Percent Reduction in In-Use Emissions After the Clean-Out Using 28 ppm Test

Fuel

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

PM

(p-value)

Bag 1 – –

6.0%

(0.0151) – –

15.4%

(< 0.0001)

Bag 2 31.4%

(0.0003)

14.9%

(0.0118)

– 18.7%

(0.0131)

14.4%

(0.0019) –

Bag 3 35.4%

(<0.0001)

20.4%

(<0.0001)

21.5%

(0.0001)

27.7%

(<0.0001)

10.3%

(<0.0001)

24.5%

(<0.0001)

FTP Composite 11.4%

(0.0002)

3.8%

(0.0249)

6.8%

(0.0107)

3.5%

(0.0498)

6.0%

(0.0011)

13.7%

(<0.0001)

Bag 1 – Bag 3 – –

7.2%

(0.0656) – – –

The clean-out effect is not significant at α = 0.10 when no reduction estimate is provided.

7.3.2. Effect of Clean-Out at 5 ppm

To study the differences in the effectiveness of the clean-out procedure between 28 ppm

and 5 ppm fuel sulfur levels, a dataset was constructed using the subset of “sulfur level” data

which include the measurements from a subset of original and modified ‘long’ procedures and

modified ‘short’ procedure with mileage accumulation less than 50 miles for both sulfur levels.

This dataset essentially represents the first three repeat tests of FTP from each sulfur level

following a high-speed/load “clean-out” procedure consisting of two back-to-back US06 cycles.

The dataset included 17 vehicle families, 35 individual vehicles with 183 observations –

88 measurements from clean-out at 28 ppm and 95 measurements from clean-out at 5 ppm. The
36

average starting odometer reading of 35 unique vehicles was 31,178 ± 6,351 miles. Additional

details of the test fleet are shown in Table 7-5.

Table 7-5 Description of Vehicles in the “Clean-Out at 5 ppm Data”

Vehicle

Family

ID

Vehicle ID Make Model Model

Year

Tier 2

Cert Bin

Number of

Vehicles

Average

Starting

Odometer

M500 0003 Toyota Corolla 2007 5 1 33,122

M501 0023 Ford Explorer 2007 4 1 27,562

M502 0026 Dodge Caliber 2007 5 1 29,097

M503 0194 Honda Odyssey 2007 5 1 35,816

M504 0021 Saturn Outlook 2007 5 1 43,733

M505 0031 Chevrolet Silverado 2007 5 1 27,891

M506 0123 Nissan Altima 2007 5 1 39,936

M507 0148 Ford Taurus 2007 5 1 28,802

M508 0075 Dodge Caravan 2007 8 1 41,117

M509 0046 Chevrolet Impala 2007 5 1 37,734

N510 0264 Toyota Sienna 2007 5 1 38,464

N511 0179 Chevrolet Cobalt 2008 5 1 38,722

N512 0107 Jeep Liberty 2008 5 1 24,614

N513 0089, 0178 Ford Focus 2008 4 2 24,726

N514 0010, 0101,

0104 Honda Civic 2008 5 3 32,931

N515 0006, 0007,

0074, 0165 Ford F150 2008 8 4 29,738

N520 0011, 0022,

0026, Toyota Tacoma 2009 5 3 28,964

N521

0131, 0162,

0179, 0280,

0329

Toyota Camry 2008 3 5 28,506

P522

0009, 0039,

0146, 0045,

0011

Honda Accord 2008 3 5 29,601

Figure 7-3 shows the box-plot of log-transformed emissions from Bag 2 NOx by vehicle

family and by clean-out sulfur level at 28 ppm and 5 ppm. The diamond and the line inside the

box represent the mean and the median, respectively. The box represents the interquartile range

between 25th and 75th percentile and the error bars show the full data range. The data generally

shows that the NOx emissions from clean-out at 28 ppm fuel sulfur are higher compared to the

emissions from clean-out at 5 ppm, suggesting that the fuel sulfur level affects the effectiveness
37

of the clean-out cycle in reducing sulfur loading in the catalyst. Furthermore, the data illustrates

that there are significant between- and within-vehicle family variability.

Figure 7-3 Box-Plot of Vehicle Families by Clean-Out Sulfur Level at 28 ppm and 5 ppm

(NOx Bag 2)

The statistical approach described in Section 7.3.1 was applied in modeling the fixed

effects and the covariance structure of this dataset, including modeling the vehicle family as a

random effect. Table 7-6 summarizes the percent reduction in emissions from 28 ppm to 5 ppm

fuel sulfur level only for the first three test replicates following the clean-out cycle. The results

from bag 2 NOx, for example, shows that immediately after the clean-out, a reduction of 34

percent was achieved by lowering the fuel sulfur level. This suggests that the effectiveness of

high-temperature and high-speed cleanout cycle to reduce catalyst sulfur loading is limited by
38

the level of sulfur in the fuel. Also of note here is the absence of any statistically significant PM

reductions for these procedures; there is more discussion of PM effects in 7.3.3.

Furthermore, the reduction in emissions from cleanout shown in Table 7-4 would likely

be larger if the low sulfur test fuel at 5 ppm had been used for the cleanout procedure and the

tests that immediately followed the as-received baseline emissions. This is demonstrated in

Figure 7-4, which compares the pre-cleanout (as-received) emissions level to the emissions level

after the clean-out using 28 ppm and 5 ppm fuel sulfur. The data generally shows that the lower

fuel sulfur level increases the effectiveness of the cleanout cycle. In order to eliminate the

vehicle variability, only the pre-cleanout results from 35 unique vehicles that received the

cleanout at both 28 ppm and 5 ppm are included (rather than comparing these results on 35

vehicles to the pre-cleanout results from 93 vehicles in Section 7.3.1).

Figure 7-4 Box-Plot of Vehicle Families by Pre-Cleanout and

Post-Cleanout at 28 ppm and 5 ppm (NOx Bag 2)
39

Table 7-6 Percent Reduction in Emissions From 28 ppm to 5 ppm

for the First Three Repeat FTP Tests Immediately Following Clean-Out

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value) PM

Bag 1 5.3%

(0.0513)

6.8%

(0.0053)

6.2%

(0.0083)

5.7%

(0.0276)

14.0%

(<0.0001) –

Bag 2 34.4%

(0.0036)

33.9%

(<0.0001) –

26.4%

(0.0420)

49.4%

(<0.0001) –

Bag 3 42.5%

(<0.0001)

36.9%

(<0.0001)

14.7%

(0.0041)

51.7%

(<0.0001)

28.5%

(<0.0001) –

FTP

Composite

15.0%

(0.0002)

13.3%

(<0.0001)

8.5%

(0.0050)

10.9%

(0.0012)

23.6%

(<0.0001) –

Bag 1 – Bag 3 – – – – – –

The effectiveness of clean-out cycle is not significant at α = 0.10 when no reduction estimate is provided.

7.3.3. Effect of Sulfur level

The “sulfur level” data represents the emission measurements from the repeated FTP test

cycles following the clean-out. This dataset provided key information for assessing the in-use

effect of sulfur level on emissions over time as vehicles operate on fuels with different sulfur

levels. The dataset is comprised of emission measurements from two different fuel sulfur levels

at 28 ppm and 5 ppm, following vehicle operation that removed sulfur from the catalyst (cleanout procedure).

7.3.3.1. Tier 2 Vehicles

The “sulfur level” data for Tier 2 vehicles included all measurements from vehicles tested

on both sulfur levels, for a total of 35 vehicles from 19 vehicle families (Table 7-7). The average

starting odometer of the 35 vehicles was 31,178 ± 6,351 miles. A total of 322 measurements

were taken from Long (S/L and L/M) and modified Short procedures – 161 measurements each

for both high and low fuel sulfur levels.
40

Table 7-7 Description of Tier 2 Vehicles in the “Sulfur Level” Data

Vehicle

Family

ID

Vehicle ID Make Model Model

Year

Tier 2

Cert Bin

Number of

Vehicles

Average

Starting

Odometer

M500 0003 Toyota Corolla 2007 5 1 33,122

M501 0023 Ford Explorer 2007 4 1 27,562

M502 0026 Dodge Caliber 2007 5 1 29,097

M503 0194 Honda Odyssey 2007 5 1 35,816

M504 0021 Saturn Outlook 2007 5 1 43,733

M505 0031 Chevrolet Silverado 2007 5 1 27,891

M506 0123 Nissan Altima 2007 5 1 39,936

M507 0148 Ford Taurus 2007 5 1 28,802

M508 0075 Dodge Caravan 2007 8 1 41,117

M509 0046 Chevrolet Impala 2007 5 1 37,734

N510 0264 Toyota Sienna 2007 5 1 38,464

N511 0179 Chevrolet Cobalt 2008 5 1 38,722

N512 0107 Jeep Liberty 2008 5 1 24,614

N513 0089, 0178 Ford Focus 2008 4 2 24,726

N514 0010, 0101,

0104 Honda Civic 2008 5 3 32,931

N515 0006, 0007,

0074, 0165 Ford F150 2008 8 4 29,738

N520 0011, 0022,

0026, Toyota Tacoma 2009 5 3 28,964

N521

0131, 0162,

0179, 0280,

0329

Toyota Camry 2008 3 5 28,506

P522

0009, 0039,

0146, 0045,

0011

Honda Accord 2008 3 5 29,601

The box-plot of the log-transformed emissions from Bag 2 NOx “sulfur level” data

(Figure 7-5) shows the spread of the data for each vehicle family and sulfur level across all

mileages. The diamond and the line inside the box represent the mean and the median,

respectively. The box represents the interquartile range between 25th and 75th percentile and the

error bars show the full data range. Generally, there is a tendency for the vehicles running on

high sulfur fuel to emit more NOx than the vehicles running on low sulfur fuel. However, the

effect of operation on higher sulfur fuel certainly varies by vehicle family, suggesting the

presence of substantial between-vehicle family variability. For example, Toyota Corolla, Ford
41

Focus, and Chevrolet Cobalt clearly show a large effect of fuel sulfur level on emissions while

the effect is more marginal for the Nissan Altima and Honda Civic.

As a result of the changes in testing procedures described in Section 6.2, the number of

tested vehicles is not the same across vehicle families in the “sulfur level” data. Considering the

differences in number of unique vehicles in each vehicle family and the presence of variability

between vehicle families illustrated by Figure 7-5, each vehicle family was considered as a

random effect in constructing the statistical model, similar to the analyses done for the “clean-out

at 28 ppm” and “clean-out at 5 ppm”.

Figure 7-6 presents the log-transformed emissions from individual vehicles by sulfur

level. The plot shows that the increase in emissions as vehicles accumulate mileage for the high

sulfur level is more significant compared to the low sulfur level, contributing to the increased

variance for some vehicles and suggests that the rate of sulfur loading might be different for the

two fuel sulfur levels. Thus, the interaction between sulfur level and the accumulated mileage

was included in the statistical modeling of the data. These findings from the graphical

examination of the data assisted in selecting the statistical modeling approach discussed in this

section.
42

Figure 7-5 Box-Plot of Individual Vehicle Families by Sulfur Level (NOx Bag 2)

We refrained from looking at the simple descriptive statistics, such as the mean and the

standard deviation, to assess the relationship between the sulfur level and emissions even as a

preliminary step, because reaching any conclusion from them can potentially be very misleading

due to the presence of repeated measurements, and both between-vehicle and within-vehicle

variability. In addition, the mileage accumulations varied from vehicle to vehicle, and simple

descriptive statistics would not be able to accurately capture the significant amount of variability

inherent in the dataset.
43

Figure 7-6 Log-Transformed Emissions from Individual Vehicles

by Sulfur Level (NOx Bag 2)

In analyzing the “sulfur level” data, a similar top-down model fitting statistical approach

to that applied to the “clean-out” data, as described in Section 7.3.1, was applied to characterize

the effects of fuel sulfur level on emissions as a function of accumulated mileages since cleanout.

The dependent variable (Yi) was the natural logarithm of emissions. The fixed effects (Xi)

included in the model were sulfur level, accumulated mileage, vehicle type, and the interaction

terms. The random effects (Zi) were each vehicle family in the study. The likelihood ratio test

for the significance of between-vehicle variation was statistically significant for all pollutants

and bags, and thus, the random intercept for each vehicle family was included in the model. The

significance of the between-vehicle variation was observed graphically in Figure 7-5.
44

All of the measurements from the same vehicle family will have the same betweenvehicle family errors; their within-vehicle family errors will differ, and can be correlated within a

vehicle family. The measurements from the same vehicle family are assumed to be correlated

because they share common vehicle characteristics and have similar emission profiles. Also,

measurements on the same vehicle close in time are often more highly correlated than

measurements far apart in time as observed in Figure 7-6 – the covariation within vehicles. Both

within- and between-vehicle errors are assumed independent from vehicle to vehicle. Since

measurements on different vehicles are assumed independent, the structure refers to the

covariance pattern of measurements on the same subject. For most of these structures, the

covariance between two measurements on the same vehicle depends only on the differences in

mileage accumulation between measurements, and the variance is constant over mileage.

The covariance structure was modeled by first fitting the “unstructured” (UN) covariance

matrix with a saturated model including all fixed effects. For similar reasons provided in the

analysis of the “clean-out” data, the unstructured model failed to converge. Next, since

emissions were measured irregularly, where the mileage intervals between measurements are

more or less unique to each vehicle, the spatial covariance structure, which allows for a

continuous representation of mileage, was fit. However, the model failed to converge for the

spatial covariance matrix as well. Thus, we proceeded to fit the compound symmetry (CS)

structure which specifies that measurements at all mileage have the same variance, and that all

measurements on the same vehicle have the same correlation. The BIC value for the compound

symmetry was 803.36.

Lastly, the first-order autoregressive structure was modeled. It assumes that the variances

are homogeneous and the correlations decline exponentially with time, i.e., the variability in

measured emissions is constant regardless of mileage for each vehicle and the measurements

next to each other are more correlated than the measurements further apart. The BIC value for

the first-order autoregressive structure was 764.90. Since the BIC value of the first-order

autoregressive structure was lower than the BIC value for the compound symmetry of 803.36,

the first-order autoregressive structure (Equation 7-3) was selected as the covariance matrix.
45

࢏ࣕ)࢘ࢇࢂ ൌ࢏ࡾ

) =

⎣

⎢

⎢

⎡

࣌࣌ ૛࣌ ࣋૛࣋૛

૛

࣌࣌ ࣋૛࣌ ૛࣋૛

⋮

࣌࣋૛

૚ି࢔

⋮

࣌࣋૛

૛ି࢔

⋮

࣌࣋૛

૜ି࢔

࣌ ڮ࣋૛

૚ି࢔

࣌ ǥ࣋૛

૛ି࢔

⋱ ⋮

࣌ ǥ

૛ ⎦

⎥

⎥

⎤

Equation 7-3

A combination of first-order autoregressive structure within vehicles and a random effect

between vehicles was used to model the covariance structure which specified an inter-vehicle

random effect of differences between vehicles, and a correlation structure within vehicles that

decreases with increasing mileage lag between emission measurements. Furthermore, the

variance associated with the low sulfur level was permitted to differ from the variance associated

with the high sulfur level. Since the first-order autoregressive structure was selected due to

limited available options, we acknowledge that there might be some limitations inherent in the

assumption of constant distance between two measurements. However, the estimates of fixed

effects, such as the differences between sulfur level means, may be the same for different

covariance structures, differing only in the standard errors of these estimates.

Once the structures for the random effects and the covariance structure for the residuals

were selected, the fixed effects in the model were tested using the approximate F-test with the

Satterthwaite degrees of freedom. The step-wise backward elimination approach was used to

remove any non-significant fixed effects (shown in red in Table 7-8), starting from the saturated

model. The significance level of 10% (α = 0.1) was used to test the null hypothesis while

keeping statistical hierarchy.
46

Table 7-8 Type 3 Tests of Fixed Effects (NOx Bag2)

†

slevel = sulfur level (high and low); miles = accumulated mileage since clean-out;

vehclass = vehicle types (car and truck); ‡Pr > F represents the p-value associated with the F

statistic;

Then, the likelihood ratio test using maximum likelihood was performed to examine if

the model can be reduced further without compromising the model fit. For example, comparing

model 4 and 5 (Table 7-9), since the result of the likelihood ratio test was not statistically

significant, we concluded that accumulated mileage does not have an effect on Bag 2 NOx, and

thus, model 5 was selected as the final model.

Table 7-9 Likelihood Ratio Test for Bag 2 NOx Model

Fixed effects in model -2 Res Log Likelihood p-value (χ

2

)

Model 4 slevel, miles 991.6 0.1213 Model 5 slevel 994

The final NOx Bag 2 model (model 5) had sulfur level as the fixed effects. Thus, the

model finds that there is a statistically significant difference in emissions from high and low fuel

sulfur levels that do not differ between vehicle types (car vs. truck) since the sulfur level and

vehicle type interaction term was not significant. Also, since the mileage term is not significant,

it can be concluded that the mileage accumulation after the clean-out does not increase emissions

independent of the fuel sulfur level. In addition, since the sulfur level and the accumulated

mileage interaction term was not significant, the model suggests that the rate of sulfur loading

Effect† Num DF Den DF F Value Pr > F‡

Model 1

slevel 1 254 7.66 0.0061

miles 1 271 0.10 0.7499

vehclass 1 18.2 0.18 0.6761

slevel * miles 1 170 0.79 0.3743

miles * vehclass 1 280 1.20 0.2748

Model 2

slevel 1 259 7.63 0.0062

miles 1 264 17.07 < 0.0001

vehclass 1 17 0.40 0.5363

slevel * miles 1 175 0.72 0.3982

Model 3

slevel 1 259 7.66 0.0061

miles 1 264 17.08 < 0.0001

slevel * miles 1 174 0.70 0.4028

Model 4 slevel 1 219 18.28 < 0.0001

miles 1 270 17.54 < 0.0001
47

does not vary by accumulated mileages after the clean-out between high and low fuel sulfur

levels. In other words, the effect of high fuel sulfur on Bag 2 NOx exists immediately after

clean-out and remains essentially constant on a percentage basis, during subsequent driving of a

vehicle.

Figure 7-7 shows the data vs. predicted plots based on the final model for NOx Bag 2.

There are two paired plots next to each other with the same vehicle ID showing emissions from

both high and low sulfur. There are some instances (e.g., VID M502) where the model

overestimates the effect of sulfur by over-predicting the emission levels of high sulfur and underpredicting the emission levels of low sulfur. In contrast, there are other instances (e.g., VID

M513) where the model underestimates the effect of sulfur by under-predicting the emission

levels of high sulfur and over-predicting the emission levels of low sulfur. However, this is to be

expected given the variability in the emission testing. In general, the model predictions are in

agreement with the data.
48

Figure 7-7 Data vs. Predicted (Log-Transformed Bag 2 NOx)
49

Furthermore, the one-to-one plot of data vs. model predictions in Figure 7-8 shows that

the points generally lie close to the 1:1 line and has the adjusted R-square of 0.71, demonstrating

reasonable accuracy in model predictions for Bag 2 NOx.

Figure 7-8 Data vs. Predicted (Log-Transformed NOx Bag 2)

Figure 7-9 presents the model predictions for individual vehicle by sulfur level for Bag 2

NOx in grams/mile. The model-predicted values depicted in this figure were generated from

Model 5 in Table 7-8 for NOx bag 2. The model for Bag 2 NOx shows that sulfur loading

resumes immediately after the clean-out (at or near zero accumulated mileage) with high fuel

sulfur level emitting significantly higher emissions compared to low fuel sulfur level. In

addition, the sulfur loading continues with vehicle operation, causing an increase in emissions.

The rate of sulfur loading was not statistically different between high and low sulfur levels on a

percentage basis.
50

Figure 7-9 Model Predictions for Individual Vehicle by Sulfur Level (NOx Bag 2)

Table 7-10 summarizes the final models that were selected for all pollutants and bags,

applying the same statistical methodology described for Bag 2 NOx. For all models, the sulfur
51

level and mileage interaction term was not significant, and the change in emissions from

reducing the fuel sulfur from 28 ppm to 5 ppm was estimated using the differences of least

squares means from the final model, adjusting for other effects in the model. The Tukey-Kramer

adjustment was used in calculating the p-values for the least squares means. The differences of

least squares means between high and low fuel sulfur level were reverse-transformed to estimate

the percent reduction in emissions (Table 7-11). When the sulfur level and mileage interaction

term is not significant, the percent differences in emissions between high and low fuel sulfur

levels stay constant across accumulated mileage after clean-out (the sulfur loading curves for

high and low sulfur are parallel) and thus, using the least squares means to quantify the reduction

in emissions without considering the as-received in-use sulfur loading was sufficient.
52

Table 7-10 Final Models of All Pollutants

Pollutant Bag Fixed Effects†

NOx

Bag 1 slevel, miles

Bag 2 slevel

Bag 3 slevel, miles

FTP Composite slevel, miles

Bag 1 – Bag 3 –

THC

Bag 1 slevel, miles

Bag 2 slevel, miles

Bag 3 slevel, miles

FTP Composite slevel, miles

Bag 1 – Bag 3 slevel

CO

Bag 1 slevel, miles

Bag 2 –

Bag 3 slevel, miles

FTP Composite slevel, miles

Bag 1 – Bag 3 –

NMHC

Bag 1 slevel

Bag 2 slevel, miles

Bag 3 slevel, miles

FTP Composite slevel, miles

Bag 1 – Bag 3 –

CH4

Bag 1 slevel, miles

Bag 2 slevel, miles

Bag 3 slevel, miles

FTP Composite slevel, miles

Bag 1 – Bag 3 –

PM

Bag 1 –

Bag 2 –

Bag 3 –

FTP Composite –

Bag 1 – Bag 3 –

†

slevel = sulfur level (high and low); miles = accumulated mileage since clean-out;

Table 7-11 summarizes the percent reduction in emissions from 28 ppm to 5 ppm fuel

sulfur for all pollutants and all bags. The results suggest that significant reductions in emissions

can be achieved by reducing the fuel sulfur levels from 28 to 5 ppm in the in-use fleet of Tier 2

vehicles. Unlike the gaseous pollutants, there was no effect of sulfur level found for PM. A

potential explanation is that the majority of PM mass as measured in this program (that is, from

normal-emitting Tier 2 vehicles operated at low and moderate loads) was likely soot produced

shortly after cold start (bag 1).28 Once formed in the combustion chamber, oxidation of soot
53

requires residence time at high temperature with lean air-fuel conditions. Since modern gasoline

engines are calibrated to operate at or very near a stoichiometric fuel/air mixture over all

operating modes, minimal destruction of soot occurs in the catalyst regardless of its relative

efficiency. As a result, sulfur would not be expected to have a significant effect on directlyemitted PM (other than the very small amounts of sulfate), which is consistent with these results.

A clean-out effect on PM was observed in the initial portion of the procedures but not later,

suggesting it may not have been an actual effect of sulfur on catalyst efficiency but something

else related to release and accumulation of material typically measured as PM. Since there were

no analyses of PM composition in this program, we are not able to draw more definitive

conclusions.

Table 7-11 Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur

on In-Use Tier 2 Vehicles

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

NOx+NMOG

(p-value) PMa

Bag 1 7.1%

(0.0216)

9.2%

(0.0002)

6.7%

(0.0131)

8.1%

(0.0017)

16.6%

(< 0.0001) N/A –

Bag 2 51.9%

(< 0.0001)

43.3%

(< 0.0001) –

a 42.7%

(0.0003)

51.8%

(< 0.0001) N/A –

Bag 3 47.8%

(< 0.0001)

40.2%

(< 0.0001)

15.9%

(0.0003)

54.7%

(< 0.0001)

29.2%

(< 0.0001) N/A –

FTP

Composite

14.1%

(0.0008)

15.3%

(< 0.0001)

9.5%

(< 0.0001)

12.4%

(< 0.0001)

29.3%

(< 0.0001)

14.4%

(<0.0001) –

Bag 1 - Bag 3 –

a 5.9%

(0.0074) –

a

–

b

–

b N/A –

a

Sulfur level not significant at α = 0.10.

b

Inconclusive because the mixed model did not converge.

7.3.3.2. Tier 3-Like Vehicles

Following the main test program of Tier 2 vehicles, a set of vehicles meeting lower “Tier

3-like” emissions standards were tested to evaluate the effect of sulfur on these newer and

cleaner vehicles. These vehicles were tested using the same fuel and test procedures described in

Chapters 5 and 6. The “sulfur level” data for “Tier 3-like” vehicles consisted of all

measurements from the five vehicles tested on both 28 and 5 ppm sulfur fuels. A total of 64

measurements were taken from Long (L/M) and modified Short procedures – 33 measurements
54

from high fuel sulfur levels and 31 measurements from low fuel sulfur levels. The description of

the vehicles tested in the supplemental program is shown in Table 7-12.

Table 7-12 Description of “Tier 3-like” Vehicles in the “Sulfur Level” Data

Vehicle

Family

ID

Vehicle

ID Make Model ModelYear

Emission

Standards

Starting

Odometer Vehicle Origin

P528 0001L Honda Crosstour 2011 ULEV 12,827 Recruited

P530 0001 Chevy Malibu 2010 SULEV 10,285 Manufacturer a

P531 0001L Subaru Outback 2008 SULEV 36,635 Recruited

R532 0001L Ford Focus 2010 SULEV 28,673 EPA-owned

P532 0001L Chevy Silverado 2011 T2 B4 714 EPA-owned

a This vehicle was loaned by Umicore Autocat USA, and is the same vehicle used in their 2011 study.

The box-plot of the log-transformed emissions from Bag 2 NOx “sulfur level” data

(Figure 7-10) shows the spread of the data for each vehicle and sulfur level across all mileages.

The diamond and the line inside the box represent the mean and the median, respectively. The

box represents the interquartile range between 25th and 75th percentile and the error bars show the

full data range. Generally, there is a tendency for the vehicles running on high sulfur fuel to emit

more NOx than the vehicles running on low sulfur fuel. However, the effect of operation on

higher sulfur fuel certainly varies by each vehicle.
55

Figure 7-10 Box-Plot of “Tier 3-Like” Vehicles by Sulfur Level (NOx Bag 2)

In analyzing the “sulfur level” data for “Tier 3-like” vehicles, a similar top-down model

fitting statistical approach to that described in Section 7.3.1, was applied to characterize the

effects of fuel sulfur level on emissions as a function of accumulated mileages since cleanout.

The dependent variable (Yi) was the natural logarithm of emissions. The fixed effects (Xi)

included in the model were sulfur level, accumulated mileage, vehicle type, and the interaction

terms. The random effects (Zi) were each vehicle in the study. A combination of first-order

autoregressive structure within vehicles and a random effect between vehicles was used to model

the covariance structure which specified an inter-vehicle random effect of differences between

vehicles, and a correlation structure within vehicles that decreases with increasing mileage lag

between emission measurements. The same statistical methodologies utilized for evaluating the

sulfur level effects for Tier 2 vehicles were applied to “Tier 3-like” vehicles.
56

Table 7-14 summarizes the percent reduction in emissions from 28 ppm to 5 ppm fuel

sulfur for all pollutants and all bags. The results suggest that significant reductions in emissions

can be achieved by reducing the fuel sulfur levels from 28 to 5 ppm in the in-use fleet of “Tier 3-

like” vehicles.

As indicated in the analysis, the cleaner “Tier 3-like” vehicles are impacted more

significantly in Bag 1 NOx and THC (and composite as a result) than what was observed in the

analysis of the Tier 2 vehicles. This is not unexpected since the cleaner vehicles tend to rely

more on efficient catalyst activity sooner in the operation of the vehicle following the cold start.

The sulfur hinders the catalyst from performing at expected efficiency levels early in the

operation, resulting in a larger penalty to these cleaner vehicles that rely more heavily on the

catalyst to meet the lower emission standards. Overall, we expect lower-emitting Tier 3 vehicles

to show similar or greater sensitivity to the fuel sulfur levels compared to the conventional Tier 2

vehicles.

Table 7-13 Percent Reduction in In-Use Emissions from 28 ppm to 5 ppm Fuel Sulfur

on “Tier 3-Like” Vehicles

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

NOx+NMOG

(p-value) PM

Bag 1 15.4%

(0.0731)

13.5%

(0.0388)

17.7%

(<0.0001) –

19.7%

(0.0003) N/A –

Bag 2 40.9%

( 0.0074) – – –

27.5%

(0.0021) N/A –

Bag 3 –

30.1%

(0.0294)

28.0%

(0.0587) –

29.2%

(0.0024) N/A –

FTP

Composite

23.9%

(0.0203)

14.6%

(0.0312)

21.0% (<

0.0001) –

24.8%

(0.0002)

13.9%

(< 0.0001) –

Bag 1 - Bag 3 – – – – – N/A –

Sulfur level not significant at α = 0.10 when no reduction estimate is provided.

7.3.4. Sensitivity Analysis

A series of sensitivity analyses of the “sulfur level” data was performed to address some

of the issues that might affect the mixed model results. They include the impacts of: low

concentration measurements, censoring of measurements with zero values, and influential
57

vehicles. The sensitivity analyses were conducted only for Bag 2 NOx, since above mentioned

issues pertain the most to Bag 2 NOx. For example, Bag 2 NOx showed a higher percentage of

measurements with zero values than most other pollutant and bag combinations, as illustrated in

Table 7-1.

Effect of Low Concentration Measurements

The issue of measurements with very low concentration from Bag 2 NOx has been

discussed in Section 7.1.1. To address uncertainty of measurements from these very lowemitting vehicles, we performed sensitivity analyses using two measurement concentration

screening levels: 100 ppb (based on the lower end of the instrument manufacturer’s stated

calibration range for the emission analyzer), and 50 ppb (chosen at half the former limit). In

each analysis, vehicles with all sample measurements falling below the screening level were

removed, and models were refit. Results of these sensitivity analyses are provided in Table 7-14.

Table 7-14 Results of sensitivity modeling analysis (NOx Bag 2)

Model Description Number of

Vehicles Observations Model Estimate ofBag 2 NOx Reduction

Final NOx bag 2 model 35 322 51.9%

50 ppb vehicle screen 28 263 48.4%

100 ppb vehicle screen 19 191 48.2%

In each of these sensitivity analyses, the sulfur level effect remained highly significant

with p-value < 0.004, suggesting a meaningful sulfur effect exists regardless of removal of the

lowest-emitting vehicles. Thus, we conclude that the sulfur effect is considerably larger than the

uncertainty or error associated with the measurements.

Effect of Use of Imputed Values

In order to assess the impact of replacing censored values, models with and without

imputed values for Bag 2 NOx were compared. For the model without imputed values, the mixed

model was re-fit using a new dataset with all imputed values removed, consisting only of actual
58

measurements. Based on the examination of the estimates of fixed effects and the standard

errors from both models, we concluded that the imputed values did not significantly bias the

results. The percent reduction in emissions from 28 ppm to 5 ppm fuel sulfur level was changed

from 51.9% in model with imputed values to 50.0% in model without them. The sulfur level

effect remained highly significant with p-value <.0001 for the model with and without the

imputed values.

Effect of influential vehicles

As an additional test of robustness, we also looked at the impact of removing the

influential vehicles from the dataset. Influence can be broadly defined as the ability of a single or

multiple vehicles to affect the resulting outcome through the presence or absence in the data.

The influential vehicles can be identified by examining the restricted likelihood distance (RLD),

which is calculated after an iterative process of refitting the model with and without each vehicle.

Figure 7-11 shows the restricted likelihood distance from the influential diagnostics

where vehicle family IDs N515, N520, and N521 can be considered influential vehicles affecting

both the fixed effects and covariance parameter estimates based on Cook’s D and COVRATIO

estimates. Although we do not have specific grounds for excluding these vehicles from the

mixed model analysis since the measurements from these vehicles did not fall into the category

of neither low concentration measurements nor the outlying observations, these influential

vehicles were removed and the model for Bag 2 NOx was refit to examine the impacts from these

vehicles.
59

Figure 7-11 Influence Diagnostics for NOx Bag 2

The resulting model showed that the percent reduction in emissions from 28 ppm to 5

ppm was 52.1 percent, compared to the reduction of 51.9 percent from the final model. This

analysis demonstrated that even when the influential vehicles are removed from the analysis, the

reduction in emissions from reducing the fuel sulfur level from 28 ppm to 5 ppm is still highly

significant with p-value <0.0001. The sensitivity analyses examining the influential vehicles for

all pollutants and bags are presented in Appendix F.

8. Summary and Conclusions

This study assessed the emission reductions expected from in-use Tier 2 light duty

vehicles with a reduction in gasoline sulfur content. The test fleet consisted of light-duty cars
60

and trucks chosen to be representative of high sales models covering a range of types and sizes.

Test fuels were two non-ethanol gasolines with properties typical of certification fuel, one at a

sulfur content of 5 ppm and the other at 28 ppm.

Using the high-sulfur test fuel, emissions data were collected from vehicles in their asreceived state, and then following a high-speed/load “clean-out” procedure consisting of two

back-to-back US06 cycles to examine the existence of reversible sulfur loading in the in-use

fleet. In addition, the differences in the effectiveness of the clean-out procedure at reducing

emissions between the two fuel sulfur levels were assessed. Lastly, a representative subset of

vehicles performed additional test replicates alternated with mileage accumulation on both high

and low sulfur test fuels. This dataset was used to assess the differences in emission

performance after clean-out as a function of fuel sulfur level. Major findings from this study

include:

 Reversible sulfur loading is occurring in the in-use fleet of Tier 2 vehicles and has a

measureable effect on emissions of NOx and other pollutants of interest. For example, by

performing a clean-out cycle on 28 ppm fuel, FTP composite NOx was reduced by 11%,

NMHC by 4%. A PM reduction of 14% was found, but it is not clear that it was a sulfur

effect given other results in the program.

 The effectiveness of high speed/load procedures in restoring catalyst efficiency is limited

when using higher sulfur fuel. Comparing emissions immediately following the cleanout procedure on 5 vs. 28 ppm fuel, FTP composite NOx emissions were 15% lower,

NMHC 11% lower, and CO 9% lower.

 Reducing fuel sulfur levels from 28 to 5 ppm is expected to achieve significant reductions

in emissions of NOx, hydrocarbons, and other pollutants of interest in the in-use fleet.

For example, FTP composite NOx was 14% lower, NOx+NMOG 14% lower, and CO

10% lower. Several sensitivity analyses were performed for Bag 2 NOx and suggested

the magnitude and statistical significance of the results are robust.

 Lower-emitting “Tier 3-like” vehicles are expected to show similar or greater sensitivity

to the fuel sulfur levels compared to the conventional Tier 2 vehicles in-use.
61

The overall results of this study are in agreement with other studies conducted using low

sulfur gasoline in Tier 2 vehicles. The magnitude of NOx and HC reductions found in this study

when switching from 28 ppm to 5 ppm fuel are consistent with those found in other studies done

by the US EPA and automobile and catalyst manufacturers.29,30,31

A draft version of this report underwent an independent peer review covering the design,

analysis methods, and results. This process was conducted according to guidelines described in

EPA’s Peer Review Handbook, and did not produce any significant adverse findings. A detailed

description of the process and results is available on the EPA Science Inventory website.3
62

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2

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3

Four Peer Reviews in Support of the Tier 3 Rulemaking: Fuel Sulfur Effects Analysis Draft

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Warrendale, PA.

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