The Effects of Gasoline Sulfur Level 

on Emissions from Tier 2 Vehicles 

in the In-Use Fleet 

Draft
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 Gasoline Sulfur Level 

on Emissions from Tier 2 Vehicles 

in the In-Use Fleet

Draft

EPA-420-D-13-003

April 2013
1 

Table of Contents

Acronyms ........................................................................................................................................ 2

1. Executive Summary ................................................................................................................. 3

2. Introduction ............................................................................................................................. 7

2.1. Background ...................................................................................................................... 7

2.2 Motivation for this Study ............................................................................................... 10

3. Study Design.......................................................................................................................... 10

3.1. Measurement of Reversible In-Use Loading (Clean-Out Effect) .................................. 11

3.2 Effect of Sulfur Level ..................................................................................................... 12

4. Test Vehicle Selection, Recruitment, and Delivery .............................................................. 12

4.1 Choice of Makes and Models ......................................................................................... 12

4.2 Vehicle Recruitment Criteria ......................................................................................... 13

4.3 Initial Checks and Test Vehicle Delivery ...................................................................... 14

5. Test Fuel Specs and Procurement .......................................................................................... 15

6. Test Procedures...................................................................................................................... 16

6.1 Initial fuel exchange and vehicle prep ............................................................................ 16

6.2 Test procedure description ............................................................................................. 17

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

7.1. Data Preparation ............................................................................................................. 23

7.1.1. Imputation of measurements with low concentration ............................................. 23

7.1.2. Detection of outliers ................................................................................................ 26

7.2. Modeling Methodology .................................................................................................. 27

7.3. Statistical Analysis and Results ..................................................................................... 29

7.3.1. Effect of clean-out at 28 ppm.................................................................................. 29

7.3.2. Effect of clean-out at 5 ppm.................................................................................... 34

7.3.3. Effect of Sulfur level ............................................................................................... 38

7.3.4. Sensitivity Analysis ................................................................................................ 53

8. Summary and Conclusions .................................................................................................... 56

9. References ............................................................................................................................. 59

Appendix A. Details of On-Road Mileage Accumulation Route ................................................. 62

Appendix B. Emission Concentration Plots.................................................................................. 64

Appendix C. Additional Information on Emission Measurements ............................................... 67

Appendix D. Discussion of Univariate and Multivariate Analysis of Variance ........................... 68

Appendix E. Plots of All Pollutants and Bags .............................................................................. 69
2 

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 
3 

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.

2

 In 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 study sample described in this analysis consisted of 81 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. Test fuels were two 

non-ethanol 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. 
4 

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

Table ES-1 Average clean-out effect on in-use emissions 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 – – 4.7% 

(0.0737) – – 15.4%(< 0.0001) 

Bag 2 31.9% 

(0.0009) 

16.5% 

(0.0024) – 17.8% (0.0181) 

15.3% 

(0.0015) – 

Bag 3 38.3% 

(<0.0001) 

21.4% 

(<0.0001) 

19.5% 

(0.0011) 

27.8% 

(<0.0001) 

12.0% 

(<0.0001) 

24.5% 

(<0.0001) 

FTP Composite 11.4% 

(<0.0001)

4.1% 

(0.0187)

7.6% 

(0.0008)

3.0% 

(0.0751)

6.9% 

(0.0003)

13.7% 

(<0.0001)

Bag 1 – Bag 3 – – 4.2%

(0.0714) – – – 

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, a representative subset of 

vehicles was kept to conduct testing 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 cleanout procedure when done using 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 relative to the 28 

ppm fuel immediately after this clean-out; for example, Bag 2 NOx emissions were 47 percent 

lower after a clean-out on the 5 ppm fuel vs. following the clean-out on the 28 ppm fuel. This 

indicates that either the catalyst is not fully desulfurized after a clean out procedure as long as 

there is sulfur in the fuel, or that there is an instantaneous effect of sulfur concentration in the 

exhaust gas on the catalyst’s activity.
5 

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 5.9%

(0.0896) 

5.4%

(0.0118) 

7.3%

(0.0023) 

4.6%

(0.0465) 

11.1%

(<0.0001) – 

Bag 2 47.3%

(0.0010) 

40.2%

(<0.0001) – 34.4%(0.0041) 

53.6%

(<0.0001) – 

Bag 3 51.2%

(<0.0001)

35.0%

(<0.0001)

10.1%

(0.0988)

45.0%

(<0.0001)

25.4%

(<0.0001) – 

FTP 

Composite

17.7%

(0.0001)

11.2%

(<0.0001)

8.3%

(0.0003)

8.8%

(0.0003)

21.4%

(<0.0001) – 

Bag 1 – Bag 3 – – 5.8%

(0.0412) – – – 

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 59 percent 

between 28 ppm and 5 ppm. For most pollutants, the analysis suggested the effect of sulfur level 

didn’t depend on miles driven after the fuel change, and therefore the emission benefit of lower 

fuel sulfur occurred immediately and continued as miles were accumulated. Some results, such 

as Bag 1 hydrocarbons, did show a significant miles-by-sulfur interaction. In this case, 

determination of a sulfur level effect for the in-use fleet required estimation of a miles-equivalent 

level of sulfur loading, which can be gleaned from the cleanout results obtained from the 

baseline testing on the vehicles as-received. 
6 

Table ES-3 Summary of mixed model results for emission reductions from 28 to 5 ppm 

sulfur, adjusted for in-use sulfur loading (mileage accumulation) where appropriate

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

NOx+NMOG

(p-value) PM

Bag 1 10.7%

(0.0033)

8.5%†

(0.0382)

7.5%†

(0.0552)

7.5%

(< 0.0001)

13.9%†

(< 0.0001) N/A – 

Bag 2 59.2%

(< 0.0001)

48.8%

(< 0.0001) – 44.8%†(0.0260)

49.9%

(< 0.0001) N/A – 

Bag 3 62.1%

(< 0.0001)

40.2%

(< 0.0001)

20.1%

(< 0.0001)

49.9%

(< 0.0001)

29.2%

(< 0.0001) N/A – 

FTP 

Composite

23.0%†

(0.0180)

13.0%†

(0.0027)

11.9%†

(0.0378)

10.6%†

(0.0032)

25.8%†

(< 0.0001)

17.3%

(0.0140) – 

Bag 1 – Bag 3 – 5.2%

(0.0063)

4.3%

(0.0689)

5.1%

(0.0107)

4.6%

(0.0514) N/A – 

† Model with significant sulfur and mileage interaction term. The effect of fuel sulfur level is not significant at α = 

0.10 when no reduction estimate is provided. For THC bag 1 and CH4 bag 1, because the effect of clean-out was 

not statistically significant, the reduction estimates are based on the estimates of least squares means.

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 fuel sulfur levels from 28 to 5 ppm produces significant reductions in 

emissions of NOx, hydrocarbons, and other pollutants of interest from a broad range of 

in-use Tier 2 vehicles. 

• Bag 2 NOx effects, which are relatively large but based on low emission levels, were 

found to be robust to sensitivity analyses related to influential vehicles and measurement 

uncertainty at low emission levels.

 
7 

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.3 The 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 (sulfur contamination or 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 concentration of sulfur 

oxides and the air-to-fuel ratio of the exhaust gas.4,5

 Modern vehicle engines operate with richlean 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. 
8 

Figure 2-1 Catalyst-sulfur interaction by temperature and A/F ratio (Adapted from 

information from Ford Motor Company)

 Additionally, it is not always possible to maintain these catalyst temperatures (e.g., cold 

weather, idles), and the rich air-to-fuel ratios necessary 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 study6). 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-Rich Cold-Lean

900°C

500°C

Rich Air/Fuel

Sulfur oxidation & sulfate 

decomposition

SO2 release & 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
9 

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 

containing a sulfur compound. 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 broken-in 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. Tests conducted on high-sulfur fuel were 

preceded by a low-speed cruise 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.7

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). 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. 

The study used two test fuels, at 3 and 33 ppm sulfur, combined with FTP, US06, and a high 

speed/load clean-out cycle similar to one in the MSAT study described above, to examine the 

impact of sulfur on the underfloor catalyst efficiency during repeated FTP tests. 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.8 
10 

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 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. 

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. 

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

loading. 
11 

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

30 ppm chosen to match the current average sulfur limit promulgated along with the Tier 2 

vehicle standards. All other fuel properties were controlled to be representative of conventional 

commercial gasoline (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.9 

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 be 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 

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 several 

 

i

At the time this study was designed, a non-oxygenated gasoline blend was selected as being representative of inuse fuel as ethanol blends were not as ubiquitous as they are today. However, since sulfur’s effect on (non-sulfur) 

emissions is understood to occur solely in the catalyst, we do not anticipate meaningful interaction with ethanol. 

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.
12 

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 toward a higher equilibrium level 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 following a 

cleanout 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 loading (stable level of 

emissions) were to be reached after many miles of driving, this procedure investigates 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 on emission inventories by 

observing the aggregate behavior of a representative fleet of vehicles.
13 

In terms of emissions standards, the test fleet was to conform on average to Tier 2 Bin 5 

exhaust levels and employ a variety of emission control technologies. These goals were

achieved by including a range of vehicle sizes, engine displacements, and manufacturers. Engine 

family sales data obtained from EPA certification and Wards databases were analyzed to 

generate a list of high-sales vehicles as candidates for inclusion. 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 Recruited

Make Brand Model Program 

ID

Engine 

Size Engine Family Tier 2 Cert Bin

GM Chevrolet Cobalt CCOB 2.2L I4 8GMXV02.4025 5

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

GM Saturn Outlook SOUT 3.6L V6 8GMXT03.6151 5

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

4.2 Vehicle Recruitment Criteria 

 

The aforementioned 19 makes/models selected for recruitment were targeted to have a 

mileage between 12,000 and 40,000 miles and an age of 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. Solicitations were sent to owners of 
14 

candidate vehicles to find willing participants who would allow their vehicles to be held and 

tested for up to six weeks, in exchange for cash and/or loaner vehicle. If an owner agreed to 

participate, the vehicle was scheduled for testing. 

4.3 Initial Checks and Test Vehicle Delivery 

 Vehicles were driven to the test facility either by their owners or NVFEL personnel sent 

to retrieve them. Drivers were instructed to avoid hard accelerations and high speeds in an effort 

to preserve the “as-received” state of the catalyst. Once in custody, vehicles were subjected to 

visual inspection for obvious damage or malfunction. The condition of the MIL (malfunction 

indicator, or “check engine”, lamp) was also noted, and if illuminated, the vehicle was not 

accepted for testing. 

Upon receipt of the vehicle at NVFEL, the existing fuel was drained and a small sample 

was analyzed for its sulfur content to screen for vehicles that had been operating on unusually 

high or low sulfur levels. Federal fuel sulfur standards applicable in the recruiting area specify a 

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

distribution of fuel sulfur levels found in the tanks of test vehicles “as received”. The mean of 

the fuel samples for which data were available was 25 ppm with standard deviation of 11.0. Fuel 

in the recruiting area typically contains 10% ethanol. 

All vehicles were inspected for minor fuel and exhaust leaks, and were repaired, as 

needed. However, damage to the catalyst, muffler, or sections of exhaust pipe disqualified the 

vehicle from inclusion in the program. Fuel leaks were not repaired unless they presented a 

safety issue. All repairs were documented. Gas caps and other fuel systems components were

visually inspected.
15 

Figure 4-1 Fuel sulfur levels for test vehicles as received

5. Test Fuel Specs and Procurement 

A non-ethanol, low-sulfur, certification-grade gasoline was purchased in bulk from a 

commercial fuel blender (Haltermann Solutions, Houston, TX) and split into two underground 

fuel 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 the as-received 5 

ppm to 28 ppm. Due to the small volume of sulfur agent added, changes in other fuel properties 

were assumed to be 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). 
16 

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 

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 

 

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.
17 

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. 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 vehicle undergoing the Long procedure was selected 

at random from willing participants in a given vehicle class. The Long and Short procedures are 

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

the first six emission tests. 
18 

Figure 6-1 Original Short (S) and Long (L) Procedure flow chart LONG PROCEDURE CONTINUES INTO THESE TESTS SHORT PROCEDURE

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
19 

All 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-to-back 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, collected on all vehicles, 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 short procedure vehicle was complete at this point and the vehicle was returned to 

its owner. 

Long procedure vehicles continued testing for an additional length of time to determine 

the long term effects of both high and low sulfur fuel on emissions. 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 in-between) 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 an “unloaded” 

catalyst. 

Vehicles tested on the Long procedure were run for approximately 100 miles of operation

on each fuel following a cleanout. Mid-way through the test program, however, we were 

concerned that 100 miles on each fuel may not be a sufficient amount of time to fully re-load the 

catalyst to a level that is representative of relatively mild in-use driving. Therefore, we modified 

the Long procedure to incorporate several 50-mile on-road mileage accumulation intervals inbetween emission tests on each fuel. This modified Long procedure, called the L/M procedure, 

allowed us to accumulate an additional 100 miles on each fuel without increasing the number of 

 

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.
20 

sampled emission tests. A flowchart of the modified Long procedure (L/M) is shown in Figure 

6-2. 

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

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

driving route using the same fuel used for emissions testing (either high or low sulfur). The 

route used was selected for its resemblance to the FTP in terms of speed and load distribution, LONG PROCEDURE CONTINUES INTO THESE TESTS SHORT PROCEDURE 

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
21 

and was used as a way to rapidly accumulate miles over a representative test cycle without 

occupying a chassis dynamometer. Refer to Appendix A for more details on this drive route. 

Approximately halfway through the data collection, the Short procedure was extended to 

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

This change was made after a mid-point analysis of available data suggested that sulfur level had 

an effect on emissions starting immediately after the cleanout for some vehicles.v

 Figure 6-3

shows the modified Short procedure containing two fuel sulfur levels, and the change in the 

number of vehicles providing the “sulfur level” data can be seen in Table 7-7 starting with

Family ID N513. 

Figure 6-3 Modified Short procedure

 

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

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
22 

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 

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 bag, but after the vehicle has been turned off for only 10 minutes). The ‘Bag 1 minus Bag 

3’ emission 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 

 

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. 
23 

approaches and 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 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 
24 

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. 

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

0.1

1

10

100

1000

ppb NOx

Bag 2 NOx concentration Background Sample, High Sulfur Sample, Low Sulfur M501-0023 M502-0026 M513-0089 M513-0178 M514-0010 M515-0006 M511-0179 M515-0165 M520-0022 M512-0107 M500-0003 M503-0194 M504-0021 M505-0031 M506-0123 M507-0148 M508-0075 M509-0046 M510-0264 M514-0101 M515-0007 M515-0074 M520-0011
25 

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 

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,10 they were imputed in the current analysis. 

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

superior to methods using no information about the vehicle,11 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 measures.

12 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. 
26 

Table 7-1 Number of measurements with zero values

Clean-out at 28 ppm data (N = 479)

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 (6.7%) 6 (1.3%) 33 (6.9%) 32 (6.7%) 4 (0.8%) 2 (0.4%)

Bag 3 0 1 (0.2%) 21 (4.4%) 35 (7.3%) 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 = 132)

NOx THC CO NMHC CH4 PM

Bag 1 0 0 0 0 0 0 

Bag 2 14 (10.6%) 2 (1.5%) 3 (2.3%) 5 (3.8%) 3 (2.3%) 0 

Bag 3 2 0 1 (0.8%) 8 (6.1%) 0 0 

FTP Composite 0 0 0 0 0 0 

Bag 1 – Bag 3 0 0 0 0 0 0 

Sulfur level data (N = 228) †

NOx THC CO NMHC CH4 PM

Bag 1 0 0 0 0 0 0

Bag 2 18 (7.9%) 2 (0.9%) 8 (3.5%) 9 (3.9%) 3 (1.3%) 2 (0.9%)

Bag 3 3 (1.3%) 0 3 (1.3%) 6 (2.8%) 0 0

FTP Composite 0 0 0 0 0 0

Bag 1 – Bag 3 7 (3.1%) 0 1 (0.4%) 0 0 15 (6.6%)

† The sulfur level data for NMHC Bag 3 had 215 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 
27 

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 = 479)

NOx THC CO NMHC CH4 PM

Bag 1 2 (0) 6 (1) 2 (0) 6 (1) 2 (1) 2 (0)

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

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

FTP Composite 3 (0) 3 (0) 2 (0) 5 (0) 0 (0) 1 (0)

Bag 1 – Bag 3 10 (0) 5 (1) 2 (0) 6 (1) 5 (1) 7 (1)

Clean-out at 5 ppm data (N = 132)

NOx THC CO NMHC CH4 PM

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

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

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

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

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

Sulfur level data (N = 228)†

NOx THC CO NMHC CH4 PM

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

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

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

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

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

† The sulfur level data for NMHC Bag 3 had 215 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 

obtain a linear relationship between the mean of the dependent variable and the fixed and 
28 

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.13,14,15

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.

15 It is a more robust and flexible procedure in 

modeling the covariance structures for repeated measures data and better accounts for withinvehicle mileage-dependent correlations.

13,14 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: 

Yi = Xiβ + Ziui + εi Equation 7-1 

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.

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

established methods,16,17 was used. The first step was to start with a saturated model, including 
29 

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

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 17 vehicle families, 

for a total of 81 unique vehicles. Vehicles from the same engine family had the same engine size, 

vehicle configuration, and weight. The average starting odometer reading of 81 unique vehicles 

was 32,508 ± 6,164 miles. Additional details of this test fleet are shown in Table 7-3. A total of

479 measurements were taken – 242 pre-cleanout and 237 post-cleanout measurements. 
30 

Table 7-3 Description of vehicles in the “clean-out at 28 ppm data”

Vehicle 

Family ID Make Model Model Year

Tier 2 

Cert Bin

Number of 

Vehicles

Average Starting 

Odometer

M500 Toyota Corolla 2007 5 5 32,520

M501 Ford Explorer 2007 4 5 33,548

M502 Dodge Caliber 2007 5 5 31,131

M503 Honda Odyssey 2007 5 5 35,893

M504 Saturn Outlook 2007 5 5 35,700

M505 Chevrolet Silverado 2007 5 5 37,342

M506 Nissan Altima 2007 5 5 32,227

M507 Ford Taurus 2007 5 5 29,407

M508 Dodge Caravan 2007 8 5 34,317

M509 Chevrolet Impala 2007 5 5 26,126

N510 Toyota Sienna 2007 5 5 30,939

N511 Chevrolet Cobalt 2008 5 6 28,106

N512 Jeep Liberty 2008 5 5 27,530

N513 Ford Focus 2008 4 5 26,843

N514 Honda Civic 2008 5 3 32,869

N515 Ford F150 2008 8 5 31,662

N520 Toyota Tacoma 2009 5 2 28,837

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 

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. 
31 

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.18 

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 

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.19,20
32 

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

(REML)21 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 settings.22,23,24 

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. 

Ri = Var(εi) = 

⎣

⎢

⎢

⎢

⎡

 

σ2 + σ1

2 σ12 σ12

σ1

2 σ2 + σ12 σ12

σ1

2

σ1

2

σ1

2

σ1

2

σ2 + σ1

2

σ1

2

σ1

2

σ1

2

σ1

2

σ2 + σ1

2 ⎦

⎥

⎥

⎥

⎤

 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 

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. 
33 

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)

21,24 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 

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 

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 
34 

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 Average emission reduction post-cleanout vs. pre-cleanout 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 – – 4.7% 

(0.0737) – – 15.4%(< 0.0001)

Bag 2 31.9% 

(0.0009)

16.5% 

(0.0024) – 17.8% (0.0181)

15.3% 

(0.0015) – 

Bag 3 38.3% 

(<0.0001)

21.4% 

(<0.0001)

19.5% 

(0.0011)

27.8% 

(<0.0001)

12.0% 

(<0.0001)

24.5% 

(<0.0001)

FTP Composite 11.4% 

(<0.0001)

4.1% 

(0.0187)

7.6% 

(0.0008)

3.0% 

(0.0751)

6.9% 

(0.0003)

13.7% 

(<0.0001)

Bag 1 – Bag 3 – – 4.2%

(0.0714) – – – 

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, 23 individual vehicles with 132 observations – 

68 measurements from clean-out at 28 ppm and 64 measurements from clean-out at 5 ppm. The 

average starting odometer reading of 23 unique vehicles was 31,869 ± 6,572 miles. Additional 

details of the test fleet are shown in Table 7-5. 
35 

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 Honda Civic 2008 5 2 30,091

N515 0006, 0007, 

0074, 0165 Ford F150 2008 8 4 29,738 

N520 0011, 0022 Toyota Tacoma 2009 5 2 28,897

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

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 might affect the 

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

illustrates that there are significant between- and within-vehicle variability. 
36 

Figure 7-3 Box-plot of vehicle emissions 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. 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 cleanout cycle. The results from bag 2 NOx, for example, shows that immediately after the clean-out, 

a reduction of 47 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 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. 
37 

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 23 unique vehicles that received the 

cleanout at both 28 ppm and 5 ppm are included (rather than comparing to the pre-cleanout 

results from 81 vehicles in Section 7.3.1). 

Figure 7-4 Box-plot of vehicle emissions by pre-cleanout and 

post-cleanout at 28 ppm and 5 ppm (NOx Bag 2)
38 

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 5.9%

(0.0896)

5.4%

(0.0118)

7.3%

(0.0023)

4.6%

(0.0465)

11.1%

(<0.0001) – 

Bag 2 47.3%

(0.0010)

40.2%

(<0.0001) – ‡ 34.4%(0.0041)

53.6%

(<0.0001) – 

Bag 3 51.2%

(<0.0001)

35.0%

(<0.0001)

10.1%

(0.0988)

45.0%

(<0.0001)

25.4%

(<0.0001) – 

FTP 

Composite

17.7%

(0.0001)

11.2%

(<0.0001)

8.3%

(0.0003)

8.8%

(0.0003)

21.4%

(<0.0001) – 

Bag 1 – Bag 3 – ‡ – ‡ 5.8%

(0.0412) – ‡ – ‡ – 

‡ Sulfur level not significant at α = 0.10. 

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). The “sulfur level” data included all measurements from vehicles tested on both 

sulfur levels, for a total of 23 unique vehicles from 17 vehicle families (Table 7-7). The average

starting odometer of 23 unique vehicles was 31,869 ± 6,572 miles. A total of 228 measurements 

were taken from Long (S/L and L/M) and modified Short procedures – 114 measurements each 

for both high and low fuel sulfur levels. 
39 

Table 7-7 Description of 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 Honda Civic 2008 5 2 30,091

N515 0006, 0007, 

0074, 0165 Ford F150 2008 8 4 29,738 

N520 0011, 0022 Toyota Tacoma 2009 5 2 28,897

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 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. For example, Toyota Corolla, Ford Focus, and 

Toyota Tacoma clearly show a large effect of fuel sulfur level on emissions while the effect is 

more marginal for Nissan Altima and Toyota Sienna. Furthermore, the “sulfur level” data

illustrates the presence of substantial between-vehicle variability, and suggesting that each 

vehicle be considered as a random effect in constructing a statistical model. Although some of 

the vehicle families had multiple vehicles (i.e., vehicle family IDs N513, N514, N515, and 

N520), we did not consider each family as random effects since the vehicles from the same 
40 

family had markedly different emission profiles, suggestive of considerable between-vehicle

variability even within the same family. 

Also, 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. This is shown in Figure 7-6, which shows the log-transformed emissions from

individual vehicles by sulfur level. 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. 

Figure 7-5 Box-plot of individual vehicles by sulfur level (NOx Bag 2)
41 

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.

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 

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 
42 

terms. The random effects (Zi) were each vehicle 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 was included in the model. The significance of 

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

All of the measurements on the same vehicle will have the same between-vehicle errors; 

their within-vehicle errors will differ, and can be correlated within a vehicle. The measurements 

from the same vehicle are correlated simply because they share common characteristics. 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) 

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 
43 

the first-order autoregressive structure was 764.90. Based on the BIC value, the first-order 

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

Ri = Var(εi) = 

⎣

⎢

⎢

⎡

 

σ2 σ2ρ σ2ρ2

σ2ρ σ2 σ2ρ

⋮

σ2ρn−1

⋮

σ2ρn−2

⋮

σ2ρn−3

⋯ σ2ρn−1

... σ2ρn−2

⋱ ⋮ 

... σ2 ⎦

⎥

⎥

⎤

 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. 
44 

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 significant, we

concluded that accumulated mileage indeed has an effect on Bag 2 NOx, after controlling for 

sulfur level, and thus, model 4 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 (χ2)

Model 4 slevel, miles 740.0 0.04713 Model 5 slevel 737.2

The final NOx Bag 2 model (model 4) had sulfur level and mileage terms 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 did not differ between vehicle types (car vs. truck) since 

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

significant, it can be concluded that the mileage accumulation after the clean-out increases 

emissions independent of the fuel sulfur level. However, since the sulfur level and the 

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

Effect† Num DF Den DF F Value Pr > F‡

Model 1

slevel 1 93.8 5.32 0.0232

miles 1 197 0.85 0.3579

vehclass 1 23.1 0.11 0.7443

slevel * miles 1 85.3 2.36 0.1279

miles * vehclass 1 199 0.00 0.9591

Model 2

slevel 1 94.4 5.35 0.0229

miles 1 161 7.67 0.0063

vehclass 1 21 0.11 0.7468

slevel * miles 1 85.9 2.40 0.1247

Model 3

slevel 1 94.5 5.38 0.0226

miles 1 161 7.69 0.0062

slevel * miles 1 86.1 2.38 0.1263

Model 4 slevel 1 68 19.25 < 0.0001

miles 1 167 8.31 0.0045
45 

sulfur loading 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., vehicle ID 0046) where the model 

overestimates the effect of sulfur by over-predicting high sulfur and under-predicting low sulfur. 

In contrast, there are other instances (e.g., vehicle ID 0178) where the model underestimates the 

effect of sulfur by under-predicting high sulfur and over-predicting 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.
46 

Figure 7-7 Data vs. predicted (log-transformed Bag 2 NOx)
47 

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 0.72, 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 4 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. 
48 

Figure 7-9 Model predictions for individual vehicle by sulfur level (NOx Bag 2)
49 

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 models without the 

sulfur level and mileage interaction term, 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.
50 

Table 7-10 Final models of all pollutants

Pollutant Bag Fixed Effects†

NOx

Bag 1 slevel, miles

Bag 2 slevel, miles

Bag 3 slevel, miles

FTP Composite slevel, miles, slevel * miles

Bag 1 – Bag 3 slevel

THC

Bag 1 slevel, miles, slevel * miles

Bag 2 slevel, miles

Bag 3 slevel, miles

FTP Composite slevel, miles, slevel * miles

Bag 1 – Bag 3 slevel

CO

Bag 1 slevel, miles, slevel * miles

Bag 2 slevel

Bag 3 slevel, miles

FTP Composite slevel, miles, slevel * miles

Bag 1 – Bag 3 slevel

NMHC

Bag 1 slevel, miles

Bag 2 slevel, miles, slevel * miles

Bag 3 slevel, miles

FTP Composite slevel, miles, slevel * miles

Bag 1 – Bag 3 slevel

CH4

Bag 1 slevel, miles, slevel * miles

Bag 2 slevel, miles

Bag 3 slevel, miles

FTP Composite slevel, miles, slevel * miles

Bag 1 – Bag 3 slevel, miles

PM

Bag 1 –

Bag 2 –

Bag 3 –

FTP Composite –

Bag 1 – Bag 3 miles

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

However, for models with significant sulfur level and mileage interaction term, the sulfur 

effect does depend on mileage accumulation and the rate of sulfur loading is different for high 

and low sulfur levels. Thus, the in-use emission level upon arrival prior to clean-out was 

necessary to accurately estimate the percent differences in emissions between high and low fuel 

sulfur levels for in-use operation. Without comparing the high sulfur loading curve to the level 

of as-received loading, the calculated emissions difference between high and low sulfur may be 

arbitrarily high or low based on the point at which the comparison is made. Comparing 
51 

emissions immediately after clean-out, for example, may not accurately represent the role of 

sulfur accumulation in the real world. Conversely, comparing emissions after a large number of 

miles accumulated on the FTP cycle (relatively mild driving) may overestimate the effect of 

loading. Comparing just the average results of high sulfur to the average results of low sulfur 

would bias the results depending on the number of tests included in the analysis. 

Therefore, for cases where the sulfur level and mileage interaction term (slevel * miles) 

was significant, the in-use emission level upon arrival (pre-cleanout) from the “clean-out” 

dataset was projected out to the sulfur level curve and the differences in emissions at the mileage 

where the two lines intersect (in-use equivalent loading) were estimated as the in-use sulfur level 

effect. For example, the NOx FTP composite model had a significant sulfur level and mileage

interaction term and thus, the sulfur level curves diverge as the vehicle accumulates mileage. 

The mileage at which the pre-cleanout level intersects the high sulfur curve was used to estimate 

the percent reduction in emissions from high to low fuel sulfur levels as illustrated in Figure 

 7-10. 
52 

Figure 7-10 Estimating in-use sulfur effect based on clean-out (NOx FTP composite)

Table 7-11 summarizes the percent reduction in emissions from 28 ppm to 5 ppm fuel

sulfur for all pollutants and all bags. For all models except CO Bag 1, CO Bag1-Bag3, CH4 

Bag1-Bag3, and PM, the reduction estimates are statistically significant at α = 0.05; CO Bag 1, 

CO Bag1-Bag3, and CH4 Bag1-Bag3 are significant at α = 0.10. The results suggest that 

significant reduction 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 likely 

explanation is that the majority of PM as measured in this program (that is, from normal-emitting 

Tier 2 vehicles operated at low and moderate loads) was soot produced shortly after cold start

(bag 1).

25 Once formed in the combustion chamber, oxidation of soot requires high 

temperatures, lean air-fuel conditions, and residence time. Modern gasoline vehicles are 

calibrated to operate at or very near a stoichiometric fuel/air mixture over all operating modes, 

and therefore destruction of soot by the catalyst is minimal regardless of its relative efficiency. 
53 

As a result, sulfur would not be expected to have a significant effect on directly-emitted PM 

(other than very small amounts of sulfate). The clean-out effect on PM was observed in the 

initial portion of the procedures but not later, suggesting again that it was not likely an 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 definite conclusions here.

Table 7-11 Estimated reduction in in-use emissions from 28 ppm to 5 ppm fuel sulfur

NOx 

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

NOx+NMOG

(p-value) PM‡

Bag 1 10.7%

(0.0033)

8.5%†

(0.0382)

7.5%†

(0.0552)

7.5%

(< 0.0001)

13.9%†

(< 0.0001) N/A – 

Bag 2 59.2%

(< 0.0001)

48.8%

(< 0.0001) – ‡ 44.8%†(0.0260)

49.9%

(< 0.0001) N/A – 

Bag 3 62.1%

(< 0.0001)

40.2%

(< 0.0001)

20.1%

(< 0.0001)

49.9%

(< 0.0001)

29.2%

(< 0.0001) N/A – 

FTP 

Composite

23.0%†

(0.0180)

13.0%†

(0.0027)

11.9%†

(0.0378)

10.6%†

(0.0032)

25.8%†

(< 0.0001)

17.3%

(0.0140) – 

Bag 1 – Bag 3 – ‡ 5.2%

(0.0063)

4.3%

(0.0689)

5.1%

(0.0107)

4.6%

(0.0514) N/A – 

† Model with significant sulfur and mileage interaction term. ‡ Sulfur level not significant at α = 0.10. For THC bag 1 and CH4 

bag 1, because the effect of clean-out was not statistically significant, the reduction estimates are based on the estimates of least 

squares means.

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 

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. 
54 

Effect of low concentration measurements

The issue of measurements with very low concentration from Bag 2 NOx has been

discussed in 7.1.1. To address uncertainty of measurements from these very low-emitting 

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 7-12. 

Table 7-12 Results of sensitivity modeling analysis (NOx Bag 2)

Model Description Vehicles Observations Model Estimate of

Bag 2 NOx Reduction 

Final NOx bag 2 model 23 228 59.2%

50 ppb vehicle screen 17 174 60.5%

100 ppb vehicle screen 11 120 70.2%

In each of these sensitivity analyses, the sulfur level effect remained highly significant 

with p-value <0.0001, 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 in Table 7-13. 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 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 59.2% in model with imputed values to 57.8% in model without 

them.
55 

Table 7-13 Impact of imputed values on final model (NOx Bag 2)

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 IDs 0007, 0046, and 0178 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. 

 

Effect Estimate StdErr DF tValue p-value

Model with imputed values slevel -0.8953 0.2040 68 -4.39 < 0.0001

miles 0.0047 0.0016 167 2.88 0.0045

Model without imputed values slevel -0.8618 0.2001 64.1 -4.31 < 0.0001

miles 0.0046 0.0016 157 2.82 0.0055
56 

Figure 7-11 Influential diagnostics for NOx bag 2

The resulting model showed that the percent reduction in emissions from 28 ppm to 5 

ppm was 50.9 percent, compared to the reduction of 59.2 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. 

8. Summary and Conclusions

This study assessed the emission reductions expected from in-use Tier 2 light duty 

vehicles with reduction in gasoline sulfur content. The test fleet consisted of light-duty cars 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. 
57 

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 the rate of sulfur 

reloading 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 3%. 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 18% lower, 

NMHC 9% lower, and CO 8% 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, controlling for sulfur loading effects by mileage where appropriate, FTP 

composite NOx was 23% lower, NOx+NMOG 17% lower, and CO 12% lower. The 

following sensitivity analyses were performed for Bag 2 NOx and suggested the 

magnitude and statistical significance of the results are robust: 

o With and without data from very-low-emitting vehicles, which may contain larger 

measurement error than data from vehicles with higher emission levels

o With and without inclusion of imputed values used in place of zero measurements 

o With and without data from vehicles determined to be most influential on model 

fitting
58 

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.26,27,28 
59 

9. References

1. The Regulatory Impact Analysis for the Tier2/Sulfur Final Rule, Chapter III and 

Appendix B, EPA 420-R-99-023. 

2. The Regulatory Impact Analysis for the Control of Hazardous Air Pollutants from Mobile 

Sources Final Rule, Chapter 6, EPA 420-R-07-002. 

3. Hochhauser A.M. (2008). Review of Prior Studies of Fuel Effects on Vehicle Emissions, 

Report number E-84. Coordinating Research Council, Alpharetta, GA. Available at 

www.crcao.org. 

4. Heck R., Farrauto R.J., Gulati, S.T. (2009). Catalytic Air Pollution Control: Commercial 

Technology (3rd Ed.). John Wiley & Sons: Hoboken, NJ. 

5. Eastwood P. (2000) Critical Topics in Exhaust Gas Aftertreatment (Engineering Design).

Research Studies Press Ltd: Hertfordshire, England.

6. The Regulatory Impact Analysis for the Tier2/Sulfur Final Rule, Chapter III and 

Appendix B, EPA 420-R-99-023. 

7. Durbin T.D., et al. (2003). The Effect of Fuel Sulfur on NH3 and Other Emissions from 

2000-2001 Model Year Vehicles, Appendix C. Report number E-60. Coordinating 

Research Council, Alpharetta, GA. Available at www.crcao.org.

8. Ball D., Clark D., Moser D. (2011). Effects of Fuel Sulfur on FTP NOx Emissions from a 

PZEV 4 Cylinder Application. SAE 2011 World Congress Paper 2011-01-0300. SAE 

International: Warrendale, PA.

9. Durbin T.D., et al. (2003). The Effect of Fuel Sulfur on NH3 and Other Emissions from 

2000-2001 Model Year Vehicles, Report number E-60. Coordinating Research Council, 

Alpharetta, GA. Available at www.crcao.org.

10. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the 

behavioral sciences (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum. 

11. Engels, J.M.; Diehr, P. (2003). Imputation of missing longitudinal data: a comparison of 

methods. Journal of Clinical Epidemiology, 56 (2003), 968–976.

12. Downey R.G., & King C.V. (1998). Missing data in Likert ratings: a comparison of 

replacement methods. J Gen Psychol. 125:175–191. 

13. Stephens, R.D. (1994). Remote sensing data and a potential model of vehicle exhaust 

emissions. Journal of the Air Waste Management Association, 44:1284-1292. 
60 

14. Holmen, B.A., & Niemeier, D.A. (1998). Characterizing the effects of driver variability 

on real-world vehicle emissions. Transportation Research Part D: Transport and 

Environment, 3(2), 117-128. 

15. Beydoun, M., & Guldmann, J. (2006). Vehicle characteristics and emissions: Logit and 

regression analyses of I/M data from Massachusetts, Maryland, and Illinois. 

Transportation Research Part D: Transport and Environment, 11(1), 59-76. 

16. Diggle P.J. (1988). An approach to the analysis of repeated measures. Biometrics,

44:959-971. 

17. Wolfinger, R.D. (1993). Covariance structure selection in general mixed models. 

Communications in Statistics, Simulation and Computation, 22(4):1079-1106. 

18. Keselman, H.J., Algina, J., Kowalchuk, R.K., & Wolfinger, R.D. (1999). A comparison 

of recent approaches to the analysis of repeated measurements. British Journal of 

Mathematical and Statistical Psychology, 52:63-78. 

19. Deaton, M.I., & Winebrake, J.J. (2000). The Use of Mixed Effects ANCOVA to 

Characterize Vehicle Emissions Profiles. J. Transp. Stat. 3:49-64. 

20. Winebrake, J.J., Deaton, M.L., Coburn, T.C., & Kelly, K.J. (2000). Statistical Analysis of 

Emissions and Deterioration Rates from In-Use, High Mileage CNG and Gasoline Ford 

Crown Victoria Taxicabs; SAE Technical Paper Series No. 2000-01-1959; Society of 

Automotive Engineers: Warrendale, PA. 

21. Verbeke, G., & Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data, 

New York: Springer-Verlag.

22. Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): the 

general theory and its analytical extensions. Psychometrika, 52:345-370. 

23. Little, R.C., Milliken, G.A., Stroup, W.W. & Wolfinger, R.D. (1996). SAS system for 

mixed models, Cary, NC: SAS Institute.

24. Wolfinger, R.D. (1993). Covariance structure selection in general mixed models. 

Communications in Statistics, Simulation and Computation, 22(4):1079-1106. 

25. “Analysis of Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas 

City” (2008), Chapter 8. EPA420-R-08-010. 

26. Regulatory Impact Analysis for the Control of Hazardous Air Pollutants from Mobile 

Sources Final Rule, Chapter 6. EPA 420-R-07-002. 
61 

27. Ball D., Clark D., Moser D. (2011). Effects of Fuel Sulfur on FTP NOx Emissions from a 

PZEV 4 Cylinder Application. SAE 2011 World Congress Paper 2011-01-0300. SAE 

International: Warrendale, PA.

28. Shapiro, E. (2009). National Clean Gasoline, An Investigation of Costs and Benefits.

Published by the Alliance of Automobile Manufacturers, Washington, DC.