The Effects of Ultra-Low Sulfur 


Gasoline on Emissions from Tier 2 


Vehicles in the In-Use Fleet 


Final Report 
The Effects of Ultra-Low Sulfur 


Gasoline on Emissions from Tier 2 


Vehicles in the In-Use Fleet 


Final Report
 

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

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

are currently available. The purpose in the release of such reports is to facilitate the exchange of technical information and to inform the public of technical developments. 

EPA-420-R-14-002 

March 2014 
Table of Contents 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Appendix A. Details of On-Road Mileage Accumulation Route
 

Appendix B. Emission Concentration Plots
 

1 
Appendix C. Additional Information on Emission Measurements 

Appendix D. Discussion of Univariate and Multivariate Analysis of Variance 

Appendix E. Plots of All Pollutants and Bags 

Appendix F. Sensitivity Analysis of Influential Vehicles 

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

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

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

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

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

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

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

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

4 
 

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

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

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

ambient temperature of 75°F. 

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

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

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

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

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

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

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

Fuel 

NOx THC CO NMHC CH4 PM 

(p-value) (p-value) (p-value) (p-value) (p-value) (p-value) 

Bag 1 – – 6.0% 

(0.0151) – – 15.4% (< 0.0001) 

Bag 2 31.4% 

(0.0003) 

14.9% 

(0.0118) – 18.7% (0.0131) 

14.4% 

(0.0019) – 

Bag 3 35.4% 

(<0.0001) 

20.4% 

(<0.0001) 

21.5% 

(0.0001) 

27.7% 

(<0.0001) 

10.3% 

(<0.0001) 

24.5% 

(<0.0001) 

FTP 11.4% 3.8% 6.8% 3.5% 6.0% 13.7% 

Composite (0.0002) (0.0249) (0.0107) (0.0498) (0.0011) (<0.0001) 

Bag 1 – Bag 3 – – 7.2% 

(0.0656) – – – 

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

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

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

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

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

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

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

5 
 

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

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

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

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

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

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

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

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

NOx 

(p-value) 

THC 

(p-value) 

CO 

(p-value) 

NMHC 

(p-value) 

CH4 

(p-value) PM 

Bag 1 5.3% 

(0.0513) 

6.8% 

(0.0053) 

6.2% 

(0.0083) 

5.7% 

(0.0276) 

14.0% 

(<0.0001) – 

Bag 2 34.4% 

(0.0036) 

33.9% 

(<0.0001) – 26.4% (0.0420) 

49.4% 

(<0.0001) – 

Bag 3 42.5% 

(<0.0001) 

36.9% 

(<0.0001) 

14.7% 

(0.0041) 

51.7% 

(<0.0001) 

28.5% 

(<0.0001) – 

FTP Composite 15.0% 

(0.0002) 

13.3% 

(<0.0001) 

8.5% 

(0.0050) 

10.9% 

(0.0012) 

23.6% 

(<0.0001) – 

Bag 1 – Bag 3 – – – – – – 

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

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

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

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

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

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

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

immediately and continued as miles were accumulated. 

6 
 

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

Tier 2 Vehicles
 

NOx 

(p-value) 

THC 

(p-value) 

CO 

(p-value) 

NMHC 

(p-value) 

CH4 

(p-value) 

NOx+NMOG 

(p-value) PMa 

Bag 1 7.1% 

(0.0216) 

9.2% 

(0.0002) 

6.7% 

(0.0131) 

8.1% 

(0.0017) 

16.6% 

(< 0.0001) N/A – 

Bag 2 51.9% 

(< 0.0001) 

43.3% 

(< 0.0001) – a 42.7% (0.0003) 

51.8% 

(< 0.0001) N/A – 

Bag 3 47.8% 

(< 0.0001) 

40.2% 

(< 0.0001) 

15.9% 

(0.0003) 

54.7% 

(< 0.0001) 

29.2% 

(< 0.0001) N/A – 

FTP 

Composite 

14.1% 

(0.0008) 

15.3% 

(< 0.0001) 

9.5% 

(<0.0001) 

12.4% 

(< 0.0001) 

29.3% 

(< 0.0001) 

14.4% 

(<0.0001) – 

Bag 1 - Bag 3 – a 5.9% 

(0.0074) – a – b – b N/A – 

a Sulfur level not significant at α = 0.10. 

b Inconclusive because the mixed model did not converge. 

Major findings from this study include: 

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

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

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

when operating on higher sulfur fuel. 

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

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

broad range of common in-use Tier 2 vehicles. 

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

sensitivity analyses examining the impact of influential vehicles and measurement 

uncertainty at low emission levels. 

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

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

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

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

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

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

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

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.4 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 (this is often referred to as sulfur poisoning). 

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

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

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

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

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

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

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

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

challenges for emissions control and fuel economy. 

8 
Figure 2-1 Catalyst-Sulfur Interaction by Temperature and A/F Ratio (Adapted from
 

Information from Ford Motor Company)
 

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 SO 2, H2S 

SO2 adsorption & 

reduction to elemental S 

(reversible poisoning) 

SO2 adsorption & 

oxidation to SO 3, SO4 

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

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

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

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

life of a vehicle. 

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

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

compliant vehicles (referred to as the Mobile Source Air Toxics or MSAT study7

). The fuel 

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

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

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. 

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

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

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

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

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

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

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

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

Council.8 

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

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

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

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

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

with further reductions in gasoline sulfur level. 

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

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

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

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

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

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

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

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

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

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 the effects of aging 

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

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

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

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

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

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

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

coming years due to LEV III and Tier 3 requirements. 

3. Study Design 

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

follow in the testing section. 

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

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

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

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

11 
Instead, the behavior of emissions during various test procedures was used as a proxy for sulfur 

loading. 

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

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

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

emission standards (see Table 5-1).i 

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

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

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

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

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

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

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

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

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

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

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

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

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

selected for use in this test program. 

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

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

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

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

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

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

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

choice. 

12 
materials in the catalyst may be relatively insensitive to sulfur loading regardless of driver 

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

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

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

3.2 Effect of Sulfur Level 

Following vehicle operation that raises catalyst temperature and removes sulfur, 

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

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

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

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

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

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

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

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

lower sulfur fuel. 

4. Test Vehicle Selection, Recruitment, and Delivery 

4.1 Choice of Makes and Models 

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

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

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

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

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

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

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

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

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

13 
The main and largest group of vehicles was intended to conform on average to the Tier 2 

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

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

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

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

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

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

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

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

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

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

Table 4-1 Test Vehicles Targeted for Recruitment 

Make Brand Model Engine 

Size Engine Family Tier 2 Cert Bin 

GM Chevrolet Cobalt 2.2L I4 8GMXV02.4025 5 

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

GM Saturn Outlook 3.6L V6 8GMXT03.6151 5 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

14 
newest, lowest-emission technologies that could represent a portion of the future in-use fleet. A 

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

4.2 Vehicle Recruitment Criteria 

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

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

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

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

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

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

for testing. 

4.3 Initial Checks and Test Vehicle Delivery 

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

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

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

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

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

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

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

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

documented. 

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

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

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

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

15 
ppm annual refinery average with an 80 ppm per-gallon cap. Figure 4-1 shows the distribution 

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

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

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

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

0 

5 

10 

15 

20 

25 

30 

35 

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

Number of Vehicles 

Fuel Sulfur Level (ppm) 

5. Test Fuel Specs and Procurement 

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

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

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

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

16 
the as-received 5 ppm to 28 ppm. Due to the small volume of sulfur agent added, changes in 

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

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

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

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

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

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

Table 5-1 Test Fuel Properties 

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

Sulfur D2622 5 ppm 28 ppm 

Benzene D5769 0.34 Vol. % 0.34 Vol. % 

Total Aromatics D5769 31.2 Vol. % 31.2 Vol. % 

Olefins D1319 0.5 Vol. % 0.5 Vol. % 

Saturates D1319 68.3 Vol. % 68.3 Vol. % 

Oxygenates D5599 0.0 Vol. % 0.0 Vol. % 

T50 D86 221°F 221°F 

T90 D86 317°F 317°F 

RVP D5191 9.0 psi 9.0 psi 

†

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

the base fuel given the small amount of dopant added. 

6. Test Procedures 

6.1 Initial Fuel Exchange and Vehicle Prep 

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

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

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

17 
learning procedures the vehicle may have.ii Next, the vehicle was filled to 50 percent capacity 

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

fuel system had been flushed fully prior to testing. 

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

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

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

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

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

6.2 Test Procedure Description 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

18 
1 

2 

3 

4 

5 

6 

1 

2 

3 

4 

5 

6 

1 

2 

3 

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

Fuel chang e to high-sulf ur (28 pp m) test f uel 

Pre-clean-out (as-received ) replicate 

FTPs at 28 p pm S 

Sulf ur clean-o ut cycle, 2 x US06 at 28 p pm S 

Po st-clean-o ut rep licate FTPs at 28 pp m S 

Ad d 'l replicates f o r long -pro ced ure vehicle at 28 p pm 

S, alternating co ld start FTPs f or d ata co llection 

(numbered circles) with unsampled ho t start FTPs 

used f o r mileag e accumulation (b lank circles) 

Fuel chang e to lo w-sulf ur (5 p pm) test f uel 

Sulf ur clean-o ut cycle, 2 x US06 at 5 p p m S 

Data collectio n at 5 p pm S, alternating co ld and ho t 

start FTPs as ab ove 

LONG PROCEDURE CONTINUES INTO THESE TESTS SHORT PROCEDURE 

Data used f or "clean-o ut ef f ect" 

Data used f or "sulf ur level ef f ect" 

19 
All recruited vehicles were tested over three cold-start FTPs with high sulfur fuel to 

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

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

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

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

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

returned to its owner. 

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

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

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

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

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

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

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

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

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

fuel use starting from a “clean” catalyst. 

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

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

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

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

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

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

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

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

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

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

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

mileage accumulation and did not have emissions sampled. 

20 
additional test cell time. A flowchart of the modified Long procedure (L/M) is shown in Figure 

6-2. 

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

4 

5 

1 

2 

3 

6 

1 

2 

1 

2 

3 

3 

4 

5 

6 

Fuel chang e to hig h-sulf ur (28 p p m) test f uel 

Pre-clean-o ut ("as-recieved ") rep licate FTPs 

at 28 p p m S 

LONG PROCEDURE CONTINUES INTO THESE TESTS SHORT PROCEDURE 

Data used f or "clean-o ut ef f ect" 

Sulf ur clean-out cycle, 2 x US06 at 28 p p m S 

Po st-clean-o ut replicates at 28 p p m S 

~50 Miles on-road mileage accumulation 

Ad d'l replicates of co ld start FTPs at 28 p p m S 

~50 Miles on-road mileage accumulation 

Ad d'l replicates of co ld start FTPs at 28 p p m S 

Fuel chang e to lo w-sulf ur (5 pp m) test f uel 

Sulf ur clean-out cycle, 2 x US06 at 5 pp m S 

Data used f or "sulf ur level ef f ect" 

Low sulf ur rep licates of co ld start FTPs at 5 pp m S 

~50 Miles on-road mileage accumulation 

Lo w sulf ur rep licates of cold start FTPs at 5 p p m S 

~50 Miles on-road mileage accumulation 

Low sulf ur rep licates of co ld start FTPs at 5 pp m S 

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

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

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

21 
Following the mid-point data review the Short procedure was also modified to include a 

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

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

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

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

Figure 6-3 Modified Short Procedure 

1 

2 

3 

1 

2 

3 

1 

2 

3 

Fuel chang e to high-sulf ur (28 p p m) test f uel 

Pre-clean-out (as-received ) rep licate 

FTPs at 28 p p m S 

EXTENSION S PROCEDURE 

Data used f o r "clean-o ut ef f ect" Sulf ur clean-o ut cycle, 2 x US06 at 28 ppm S 

Po st-clean-o ut replicate FTPs at 28 ppm S 

Fuel chang e to low-sulf ur (5 pp m) test f uel Data used f o r "sulf ur level ef f ect" Sulf ur clean-o ut cycle, 2 x US06 at 5 ppm S 

Lo w-sulf ur rep licates f o r modif ied S p roced ure, FTPs 

at 5 ppm S 

7. Data Analysis and Results 

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

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

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

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

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

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

22 
from original and modified ‘long’ procedures and modified ‘short’ procedures. All vehicles 

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

datasets, by design. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

pollutants are also presented in this report. 

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

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

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

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

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

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

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

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

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

number ORNL/TM-2011/461. 

23 
procedure at two sulfur levels, and to characterize the effects of fuel sulfur level on emissions as 

mileage was accumulated. 

7.1. Data Preparation 

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

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

describe how these issues were addressed. 

7.1.1. Imputation of Measurements with Low Concentration 

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

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

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

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

the impact of any uncertainty. 

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

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

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

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

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

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

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

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

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

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

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

variability. 

24 
0.0001 parts per million 0.01 

0.001 

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 

1 

0.1 

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

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

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

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

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 

25 
is a result of the actual emission level being smaller than the sum of the measurement errors 

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

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

accuracy of quantitative value. 

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

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

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

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

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

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

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

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

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

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

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

value. 

Although using vehicle-specific imputation minimizes the likelihood of artificially 

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

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

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

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

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

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

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

7.3.4. 

26 
Table 7-1 Number of Measurements With Zero Values 

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

NOx THC CO NMHC CH4 PM 

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

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

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

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

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

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

NOx THC CO NMHC CH4 PM 

Bag 1 0 0 0 0 0 0 

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

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

FTP Composite 0 0 0 0 0 0 

Bag 1 – Bag 3 0 0 0 15 (8.2%) 0 0 

Sulfur level data (N = 322) † 

NOx THC CO NMHC CH4 PM 

Bag 1 0 0 0 0 0 0 

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

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

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

FTP Composite 0 0 0 0 0 0 

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

† The sulfur level data for PM had 232 measurements. 

7.1.2. Detection of Outliers 

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

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

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

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

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

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

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

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

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

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

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 = 541) 

NOx THC CO NMHC CH4 PM 

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

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

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

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

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

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

NOx THC CO NMHC CH4 PM 

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

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

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

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

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

Sulfur level data (N = 322)† 

NOx THC CO NMHC CH4 PM 

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

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

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

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

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

† The sulfur level data for PM had 232 measurements. 

7.2. Modeling Methodology 

The following section describes the statistical approaches and the model fitting 

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

5 ppm”, and “sulfur level” data. 

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

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

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

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

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

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

emissions data.15,16,17 

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

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

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

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

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

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

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

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

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

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

including vehicles with missing data and handling irregularly spaced measurements. 

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

mixed model is represented in Equation 7-1 as: 

 7-1 Equation ࢏ࢿ ൅࢏࢛࢏ࢆ ൅ ࢼ࢏ࢄ ൌ ࢏ࢅ

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

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

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

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

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

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

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

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

29 
In developing the mixed model, a top-down model fitting strategy, similar to previously 

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

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

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

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

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

the final model. 

7.3. Statistical Analysis and Results 

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

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

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

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

7.3.1. Effect of Clean-Out at 28 ppm 

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

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

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

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

Section 7.3.3. 

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

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

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

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

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

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

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

30 
A total of 541 measurements were taken – 275 pre-cleanout and 266 post-cleanout 

measurements. 

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

Vehicle 

Family ID Make Model Model Year 

Tier 2 

Cert Bin 

Number of 

Vehicles 

Average Starting 

Odometer 

M500 Toyota Corolla 2007 5 5 32,578 

M501 Ford Explorer 2007 4 5 33,605 

M502 Dodge Caliber 2007 5 5 31,184 

M503 Honda Odyssey 2007 5 5 35,954 

M504 Saturn Outlook 2007 5 5 35,762 

M505 Chevrolet Silverado 2007 5 5 37,401 

M506 Nissan Altima 2007 5 5 32,283 

M507 Ford Taurus 2007 5 5 29,442 

M508 Dodge Caravan 2007 8 5 34,371 

M509 Chevrolet Impala 2007 5 5 26,183 

N510 Toyota Sienna 2007 5 5 30,996 

N511 Chevrolet Cobalt 2008 5 6 29,023 

N512 Jeep Liberty 2008 5 5 27,530 

N513 Ford Focus 2008 4 5 26,843 

N514 Honda Civic 2008 5 4 37,988 

N515 Ford F150 2008 8 5 31,719 

N520 Toyota Tacoma 2009 5 3 28,964 

N521 Toyota Camry 2008 3 5 28,506 

P522 Honda Accord 2008 3 5 29,601 

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

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

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

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

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

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

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

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

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

31 
of individual driving patterns on in-use sulfur loading. These findings from the graphical 

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

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

(NOx Bag 2) 

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

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

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

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

for tests of fixed effects.20 

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

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

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

32 
included for all subsequent models. The significance of between family-variation was observed 

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

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

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

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

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

for small size datasets.24,25,26 

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

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

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

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

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

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

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

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

and covariance parameters, resulting in computational problems. 

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

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

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

how far apart the measurements are. 

 = (࢏ࣕ)࢘ࢇࢂ ൌ࢏ࡾ

⎡

⎢

⎢

⎢

⎣

૚࣌ ൅ ૛࣌૚࣌ ૛૚࣌ ૛

૛

૚࣌૚࣌ ൅ ૛࣌ ૛૚࣌ ૛

૛

࣌

࣌

࣌

૚

૚

૚

૛

૛

࣌ ૛

࣌

૚

૚

૛

૛

࣌

࣌

૚

૚

૛

૛

૚࣌ ൅ ૛࣌

૛

૚࣌૚࣌ ൅ ૛࣌ ૛

૛

⎤

⎥

⎥

⎥

⎦

Equation 7-2 

The covariance structure was modeled using a combination of compound symmetry 

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

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

33 
 

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

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

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

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

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

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

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

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

when the second order term was significant. 

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

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

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

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

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

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

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

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

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

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

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

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

levels did not differ significantly between cars and trucks. 

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

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

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

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

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

34 
 

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

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

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

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

measureable effect on aftertreatment performance. 

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

Fuel
 

NOx 

(p-value) 

THC 

(p-value) 

CO 

(p-value) 

NMHC 

(p-value) 

CH4 

(p-value) 

PM 

(p-value) 

Bag 1 – – 6.0% 

(0.0151) – – 15.4% (< 0.0001) 

Bag 2 31.4% 

(0.0003) 

14.9% 

(0.0118) 

– 18.7% 

(0.0131) 

14.4% 

(0.0019) – 

Bag 3 35.4% 

(<0.0001) 

20.4% 

(<0.0001) 

21.5% 

(0.0001) 

27.7% 

(<0.0001) 

10.3% 

(<0.0001) 

24.5% 

(<0.0001) 

FTP Composite 11.4% 

(0.0002) 

3.8% 

(0.0249) 

6.8% 

(0.0107) 

3.5% 

(0.0498) 

6.0% 

(0.0011) 

13.7% 

(<0.0001) 

Bag 1 – Bag 3 – – 7.2% 

(0.0656) – – – 

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

7.3.2. Effect of Clean-Out at 5 ppm 

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

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

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

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

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

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

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

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

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

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

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

Vehicle 

Family 

ID 

Vehicle ID Make Model Model 

Year 

Tier 2 

Cert Bin 

Number of 

Vehicles 

Average 

Starting 

Odometer 

M500 0003 Toyota Corolla 2007 5 1 33,122 

M501 0023 Ford Explorer 2007 4 1 27,562 

M502 0026 Dodge Caliber 2007 5 1 29,097 

M503 0194 Honda Odyssey 2007 5 1 35,816 

M504 0021 Saturn Outlook 2007 5 1 43,733 

M505 0031 Chevrolet Silverado 2007 5 1 27,891 

M506 0123 Nissan Altima 2007 5 1 39,936 

M507 0148 Ford Taurus 2007 5 1 28,802 

M508 0075 Dodge Caravan 2007 8 1 41,117 

M509 0046 Chevrolet Impala 2007 5 1 37,734 

N510 0264 Toyota Sienna 2007 5 1 38,464 

N511 0179 Chevrolet Cobalt 2008 5 1 38,722 

N512 0107 Jeep Liberty 2008 5 1 24,614 

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

N514 0010, 0101, 

0104 Honda Civic 2008 5 3 32,931 

N515 0006, 0007, 

0074, 0165 Ford F150 2008 8 4 29,738 

N520 0011, 0022, 

0026, Toyota Tacoma 2009 5 3 28,964 

N521 

0131, 0162, 

0179, 0280, 

0329 

Toyota Camry 2008 3 5 28,506 

P522 

0009, 0039, 

0146, 0045, 

0011 

Honda Accord 2008 3 5 29,601 

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

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

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

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

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

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

36 
of the clean-out cycle in reducing sulfur loading in the catalyst. Furthermore, the data illustrates 

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

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

(NOx Bag 2) 

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

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

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

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

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

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

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

37 
the level of sulfur in the fuel. Also of note here is the absence of any statistically significant PM 

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

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

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

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

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

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

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

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

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

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

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

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

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 5.3% 

(0.0513) 

6.8% 

(0.0053) 

6.2% 

(0.0083) 

5.7% 

(0.0276) 

14.0% 

(<0.0001) – 

Bag 2 34.4% 

(0.0036) 

33.9% 

(<0.0001) – 26.4% (0.0420) 

49.4% 

(<0.0001) – 

Bag 3 42.5% 

(<0.0001) 

36.9% 

(<0.0001) 

14.7% 

(0.0041) 

51.7% 

(<0.0001) 

28.5% 

(<0.0001) – 

FTP 

Composite 

15.0% 

(0.0002) 

13.3% 

(<0.0001) 

8.5% 

(0.0050) 

10.9% 

(0.0012) 

23.6% 

(<0.0001) – 

Bag 1 – Bag 3 – – – – – – 

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

7.3.3. Effect of Sulfur level 

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

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

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

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

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

7.3.3.1. Tier 2 Vehicles 

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

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

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

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

for both high and low fuel sulfur levels. 

39 
Table 7-7 Description of Tier 2 Vehicles in the “Sulfur Level” Data 

Vehicle 

Family 

ID 

Vehicle ID Make Model Model 

Year 

Tier 2 

Cert Bin 

Number of 

Vehicles 

Average 

Starting 

Odometer 

M500 0003 Toyota Corolla 2007 5 1 33,122 

M501 0023 Ford Explorer 2007 4 1 27,562 

M502 0026 Dodge Caliber 2007 5 1 29,097 

M503 0194 Honda Odyssey 2007 5 1 35,816 

M504 0021 Saturn Outlook 2007 5 1 43,733 

M505 0031 Chevrolet Silverado 2007 5 1 27,891 

M506 0123 Nissan Altima 2007 5 1 39,936 

M507 0148 Ford Taurus 2007 5 1 28,802 

M508 0075 Dodge Caravan 2007 8 1 41,117 

M509 0046 Chevrolet Impala 2007 5 1 37,734 

N510 0264 Toyota Sienna 2007 5 1 38,464 

N511 0179 Chevrolet Cobalt 2008 5 1 38,722 

N512 0107 Jeep Liberty 2008 5 1 24,614 

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

N514 0010, 0101, 

0104 Honda Civic 2008 5 3 32,931 

N515 0006, 0007, 

0074, 0165 Ford F150 2008 8 4 29,738 

N520 0011, 0022, 

0026, Toyota Tacoma 2009 5 3 28,964 

N521 

0131, 0162, 

0179, 0280, 

0329 

Toyota Camry 2008 3 5 28,506 

P522 

0009, 0039, 

0146, 0045, 

0011 

Honda Accord 2008 3 5 29,601 

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

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

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

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

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

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

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

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

40 
Focus, and Chevrolet Cobalt clearly show a large effect of fuel sulfur level on emissions while 

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

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

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

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

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

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

at 28 ppm” and “clean-out at 5 ppm”. 

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

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

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

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

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

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

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

section. 

41 
Figure 7-5 Box-Plot of Individual Vehicle Families by Sulfur Level (NOx Bag 2) 

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

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

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

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

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

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

inherent in the dataset. 

42 
Figure 7-6 Log-Transformed Emissions from Individual Vehicles 

by Sulfur Level (NOx Bag 2) 

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

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

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

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

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

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

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

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

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

43 
All of the measurements from the same vehicle family will have the same betweenvehicle family errors; their within-vehicle family errors will differ, and can be correlated within a 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

symmetry was 803.36. 

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

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

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

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

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

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

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

44 
 

 

૛࣋૛࣌ ࣋૛࣌ ૛࣌

࣋૛࣌

࣌ ڮ ૛࣋ ૚ି࢔

= (࢏ࣕ)࢘ࢇࢂ ൌ࢏ࡾ

⎡

⎢

⎢

⎣

૛ି࢔࣋૛࣌ ǥ ࣋૛࣌ ૛࣌

૛࣌ ǥ૜ ି࢔࣋૛࣌ ૛ି࢔࣋૛࣌ ૚ି࢔࣋૛࣌

⎤

⎥

⎥

⎦

⋮ ⋮ ⋮ ⋱ ⋮ Equation 7-3 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

keeping statistical hierarchy. 

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

Effect† Num DF Den DF F Value Pr > F‡ 

Model 1 

slevel 1 254 7.66 0.0061 

miles 1 271 0.10 0.7499 

vehclass 1 18.2 0.18 0.6761 

slevel * miles 1 170 0.79 0.3743 

miles * vehclass 1 280 1.20 0.2748 

Model 2 

slevel 1 259 7.63 0.0062 

miles 1 264 17.07 < 0.0001 

vehclass 1 17 0.40 0.5363 

slevel * miles 1 175 0.72 0.3982 

Model 3 slevel 1 259 7.66 0.0061 

miles 1 264 17.08 < 0.0001 

45 
slevel * miles 1 174 0.70 0.4028 

Model 4 slevel 1 219 18.28 < 0.0001 

miles 1 270 17.54 < 0.0001 

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

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

statistic;
 

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

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

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

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

thus, model 5 was selected as the final model. 

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

Fixed effects in model -2 Res Log Likelihood p-value (χ2

) 

Model 4 slevel, miles 991.6 0.1213 Model 5 slevel 994 

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

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

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

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

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

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

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

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

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

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

vehicle. 

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

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

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

overestimates the effect of sulfur by over-predicting the emission levels of high sulfur and under46 
predicting the emission levels of low sulfur. In contrast, there are other instances (e.g., VID 

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

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

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

agreement with the data. 

47 
Figure 7-7 Data vs. Predicted (Log-Transformed Bag 2 NOx) 

48 
Furthermore, the one-to-one plot of data vs. model predictions in Figure 7-8 shows that 

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

reasonable accuracy in model predictions for Bag 2 NOx. 

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

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

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

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

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

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

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

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

percentage basis. 

49 
Figure 7-9 Model Predictions for Individual Vehicle by Sulfur Level (NOx Bag 2) 

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

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

50 
level and mileage interaction term was not significant, and the change in emissions from 

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

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

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

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

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

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

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

high and low sulfur are parallel) and thus, using the least squares means to quantify the reduction 

in emissions without considering the as-received in-use sulfur loading was sufficient. 

51 
Table 7-10 Final Models of All Pollutants 

Pollutant Bag Fixed Effects† 

NOx 

Bag 1 slevel, miles 

Bag 2 slevel 

Bag 3 slevel, miles 

FTP Composite slevel, miles 

Bag 1 – Bag 3 – 

THC 

Bag 1 slevel, miles 

Bag 2 slevel, miles 

Bag 3 slevel, miles 

FTP Composite slevel, miles 

Bag 1 – Bag 3 slevel 

CO 

Bag 1 slevel, miles 

Bag 2 – 

Bag 3 slevel, miles 

FTP Composite slevel, miles 

Bag 1 – Bag 3 – 

NMHC 

Bag 1 slevel 

Bag 2 slevel, miles 

Bag 3 slevel, miles 

FTP Composite slevel, miles 

Bag 1 – Bag 3 – 

CH4 

Bag 1 slevel, miles 

Bag 2 slevel, miles 

Bag 3 slevel, miles 

FTP Composite slevel, miles 

Bag 1 – Bag 3 – 

PM 

Bag 1 – 

Bag 2 – 

Bag 3 – 

FTP Composite – 

Bag 1 – Bag 3 – † slevel = sulfur level (high and low); miles = accumulated mileage since clean-out; 

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

sulfur for all pollutants and all bags. The results suggest that significant reductions in emissions 

can be achieved by reducing the fuel sulfur levels from 28 to 5 ppm in the in-use fleet of Tier 2 

vehicles. Unlike the gaseous pollutants, there was no effect of sulfur level found for PM. A 

potential explanation is that the majority of PM mass as measured in this program (that is, from 

normal-emitting Tier 2 vehicles operated at low and moderate loads) was likely soot produced 

shortly after cold start (bag 1).28 Once formed in the combustion chamber, oxidation of soot 

52 
 

requires residence time at high temperature with lean air-fuel conditions. Since modern gasoline 

engines are calibrated to operate at or very near a stoichiometric fuel/air mixture over all 

operating modes, minimal destruction of soot occurs in the catalyst regardless of its relative 

efficiency. As a result, sulfur would not be expected to have a significant effect on directlyemitted PM (other than the very small amounts of sulfate), which is consistent with these results. 

A clean-out effect on PM was observed in the initial portion of the procedures but not later, 

suggesting it may not have been an actual effect of sulfur on catalyst efficiency but something 

else related to release and accumulation of material typically measured as PM. Since there were 

no analyses of PM composition in this program, we are not able to draw more definitive 

conclusions. 

Table 7-11 Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur 

on In-Use Tier 2 Vehicles 

NOx 

(p-value) 

THC 

(p-value) 

CO 

(p-value) 

NMHC 

(p-value) 

CH4 

(p-value) 

NOx+NMOG 

(p-value) PM a 

Bag 1 7.1% 

(0.0216) 

9.2% 

(0.0002) 

6.7% 

(0.0131) 

8.1% 

(0.0017) 

16.6% 

(< 0.0001) N/A – 

Bag 2 51.9% 

(< 0.0001) 

43.3% 

(< 0.0001) – a 42.7% (0.0003) 

51.8% 

(< 0.0001) N/A – 

Bag 3 47.8% 

(< 0.0001) 

40.2% 

(< 0.0001) 

15.9% 

(0.0003) 

54.7% 

(< 0.0001) 

29.2% 

(< 0.0001) N/A – 

FTP 

Composite 

14.1% 

(0.0008) 

15.3% 

(< 0.0001) 

9.5% 

(< 0.0001) 

12.4% 

(< 0.0001) 

29.3% 

(< 0.0001) 

14.4% 

(<0.0001) – 

Bag 1 - Bag 3 – a 5.9% 

(0.0074) – a – b – b N/A – 

a Sulfur level not significant at α = 0.10. 

b Inconclusive because the mixed model did not converge. 

7.3.3.2. Tier 3-Like Vehicles 

Following the main test program of Tier 2 vehicles, a set of vehicles meeting lower “Tier 

3-like” emissions standards were tested to evaluate the effect of sulfur on these newer and 

cleaner vehicles. These vehicles were tested using the same fuel and test procedures described in 

Chapters 5 and 6. The “sulfur level” data for “Tier 3-like” vehicles consisted of all 

measurements from the five vehicles tested on both 28 and 5 ppm sulfur fuels. A total of 64 

measurements were taken from Long (L/M) and modified Short procedures – 33 measurements 

53 
from high fuel sulfur levels and 31 measurements from low fuel sulfur levels. The description of 

the vehicles tested in the supplemental program is shown in Table 7-12. 

Table 7-12 Description of “Tier 3-like” Vehicles in the “Sulfur Level” Data 

Vehicle 

Family 

ID 

Vehicle 

ID Make Model Model Year 

Emission 

Standards 

Starting 

Odometer Vehicle Origin 

P528 0001L Honda Crosstour 2011 ULEV 12,827 Recruited 

P530 0001 Chevy Malibu 2010 SULEV 10,285 Manufacturer a 

P531 0001L Subaru Outback 2008 SULEV 36,635 Recruited 

R532 0001L Ford Focus 2010 SULEV 28,673 EPA-owned 

P532 0001L Chevy Silverado 2011 T2 B4 714 EPA-owned a This vehicle was loaned by Umicore Autocat USA, and is the same vehicle used in their 2011 study. 

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

(Figure 7-10) shows the spread of the data for each vehicle and sulfur level across all mileages. 

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

box represents the interquartile range between 25th and 75th percentile and the error bars show the 

full data range. Generally, there is a tendency for the vehicles running on high sulfur fuel to emit 

more NOx than the vehicles running on low sulfur fuel. However, the effect of operation on 

higher sulfur fuel certainly varies by each vehicle. 

54 
Figure 7-10 Box-Plot of “Tier 3-Like” Vehicles by Sulfur Level (NOx Bag 2) 

In analyzing the “sulfur level” data for “Tier 3-like” vehicles, a similar top-down model 

fitting statistical approach to that described in Section 7.3.1, was applied to characterize the 

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

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

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

terms. The random effects (Zi) were each vehicle in the study. A combination of first-order 

autoregressive structure within vehicles and a random effect between vehicles was used to model 

the covariance structure which specified an inter-vehicle random effect of differences between 

vehicles, and a correlation structure within vehicles that decreases with increasing mileage lag 

between emission measurements. The same statistical methodologies utilized for evaluating the 

sulfur level effects for Tier 2 vehicles were applied to “Tier 3-like” vehicles. 

55 
 

Table 7-14 summarizes the percent reduction in emissions from 28 ppm to 5 ppm fuel 

sulfur for all pollutants and all bags. The results suggest that significant reductions in emissions 

can be achieved by reducing the fuel sulfur levels from 28 to 5 ppm in the in-use fleet of “Tier 3­

like” vehicles. 

As indicated in the analysis, the cleaner “Tier 3-like” vehicles are impacted more 

significantly in Bag 1 NOx and THC (and composite as a result) than what was observed in the 

analysis of the Tier 2 vehicles. This is not unexpected since the cleaner vehicles tend to rely 

more on efficient catalyst activity sooner in the operation of the vehicle following the cold start. 

The sulfur hinders the catalyst from performing at expected efficiency levels early in the 

operation, resulting in a larger penalty to these cleaner vehicles that rely more heavily on the 

catalyst to meet the lower emission standards. Overall, we expect lower-emitting Tier 3 vehicles 

to show similar or greater sensitivity to the fuel sulfur levels compared to the conventional Tier 2 

vehicles. 

Table 7-13 Percent Reduction in In-Use Emissions from 28 ppm to 5 ppm Fuel Sulfur 

on “Tier 3-Like” Vehicles 

NOx 

(p-value) 

THC 

(p-value) 

CO 

(p-value) 

NMHC 

(p-value) 

CH4 

(p-value) 

NOx+NMOG 

(p-value) PM 

Bag 1 15.4% 

(0.0731) 

13.5% 

(0.0388) 

17.7% 

(<0.0001) – 19.7% (0.0003) N/A – 

Bag 2 40.9% 

( 0.0074) – – – 27.5% (0.0021) N/A – 

Bag 3 – 30.1% 

(0.0294) 

28.0% 

(0.0587) – 29.2% (0.0024) N/A – 

FTP 23.9% 14.6% 21.0% (< – 24.8% 13.9% – Composite (0.0203) (0.0312) 0.0001) (0.0002) (< 0.0001) 

Bag 1 - Bag 3 – – – – – N/A – 

Sulfur level not significant at α = 0.10 when no reduction estimate is provided. 

7.3.4. Sensitivity Analysis 

A series of sensitivity analyses of the “sulfur level” data was performed to address some 

of the issues that might affect the mixed model results. They include the impacts of: low 

concentration measurements, censoring of measurements with zero values, and influential 

56 
vehicles. The sensitivity analyses were conducted only for Bag 2 NOx, since above mentioned 

issues pertain the most to Bag 2 NOx. For example, Bag 2 NOx showed a higher percentage of 

measurements with zero values than most other pollutant and bag combinations, as illustrated in 

Table 7-1. 

Effect of Low Concentration Measurements 

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

discussed in Section 7.1.1. To address uncertainty of measurements from these very lowemitting vehicles, we performed sensitivity analyses using two measurement concentration 

screening levels: 100 ppb (based on the lower end of the instrument manufacturer’s stated 

calibration range for the emission analyzer), and 50 ppb (chosen at half the former limit). In 

each analysis, vehicles with all sample measurements falling below the screening level were 

removed, and models were refit. Results of these sensitivity analyses are provided in Table 7-14. 

Table 7-14 Results of sensitivity modeling analysis (NOx Bag 2) 

Model Description Number of 

Vehicles Observations Model Estimate of Bag 2 NOx Reduction 

Final NOx bag 2 model 35 322 51.9% 

50 ppb vehicle screen 28 263 48.4% 

100 ppb vehicle screen 19 191 48.2% 

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

with p-value < 0.004, suggesting a meaningful sulfur effect exists regardless of removal of the 

lowest-emitting vehicles. Thus, we conclude that the sulfur effect is considerably larger than the 

uncertainty or error associated with the measurements. 

Effect of Use of Imputed Values 

In order to assess the impact of replacing censored values, models with and without 

imputed values for Bag 2 NOx were compared. For the model without imputed values, the mixed 

model was re-fit using a new dataset with all imputed values removed, consisting only of actual 

57 
measurements. Based on the examination of the estimates of fixed effects and the standard 

errors from both models, we concluded that the imputed values did not significantly bias the 

results. The percent reduction in emissions from 28 ppm to 5 ppm fuel sulfur level was changed 

from 51.9% in model with imputed values to 50.0% in model without them. The sulfur level 

effect remained highly significant with p-value <.0001 for the model with and without the 

imputed values. 

Effect of influential vehicles 

As an additional test of robustness, we also looked at the impact of removing the 

influential vehicles from the dataset. Influence can be broadly defined as the ability of a single or 

multiple vehicles to affect the resulting outcome through the presence or absence in the data. 

The influential vehicles can be identified by examining the restricted likelihood distance (RLD), 

which is calculated after an iterative process of refitting the model with and without each vehicle. 

Figure 7-11 shows the restricted likelihood distance from the influential diagnostics 

where vehicle family IDs N515, N520, and N521 can be considered influential vehicles affecting 

both the fixed effects and covariance parameter estimates based on Cook’s D and COVRATIO 

estimates. Although we do not have specific grounds for excluding these vehicles from the 

mixed model analysis since the measurements from these vehicles did not fall into the category 

of neither low concentration measurements nor the outlying observations, these influential 

vehicles were removed and the model for Bag 2 NOx was refit to examine the impacts from these 

vehicles. 

58 
Figure 7-11 Influence Diagnostics for NOx Bag 2 

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

ppm was 52.1 percent, compared to the reduction of 51.9 percent from the final model. This 

analysis demonstrated that even when the influential vehicles are removed from the analysis, the 

reduction in emissions from reducing the fuel sulfur level from 28 ppm to 5 ppm is still highly 

significant with p-value <0.0001. The sensitivity analyses examining the influential vehicles for 

all pollutants and bags are presented in Appendix F. 

8. Summary and Conclusions 

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

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

59 
and trucks chosen to be representative of high sales models covering a range of types and sizes. 

Test fuels were two non-ethanol gasolines with properties typical of certification fuel, one at a 

sulfur content of 5 ppm and the other at 28 ppm. 

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

back-to-back US06 cycles to examine the existence of reversible sulfur loading in the in-use 

fleet. In addition, the differences in the effectiveness of the clean-out procedure at reducing 

emissions between the two fuel sulfur levels were assessed. Lastly, a representative subset of 

vehicles performed additional test replicates alternated with mileage accumulation on both high 

and low sulfur test fuels. This dataset was used to assess the differences in emission 

performance after clean-out as a function of fuel sulfur level. Major findings from this study 

include: 

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

measureable effect on emissions of NOx and other pollutants of interest. For example, by 

performing a clean-out cycle on 28 ppm fuel, FTP composite NOx was reduced by 11%, 

NMHC by 4%. A PM reduction of 14% was found, but it is not clear that it was a sulfur 

effect given other results in the program. 

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

when using higher sulfur fuel. Comparing emissions immediately following the cleanout procedure on 5 vs. 28 ppm fuel, FTP composite NOx emissions were 15% lower, 

NMHC 11% lower, and CO 9% lower. 

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

in emissions of NOx, hydrocarbons, and other pollutants of interest in the in-use fleet. 

For example, FTP composite NOx was 14% lower, NOx+NMOG 14% lower, and CO 

10% lower. Several sensitivity analyses were performed for Bag 2 NOx and suggested 

the magnitude and statistical significance of the results are robust. 

	 Lower-emitting “Tier 3-like” vehicles are expected to show similar or greater sensitivity 

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

60 
The overall results of this study are in agreement with other studies conducted using low 

sulfur gasoline in Tier 2 vehicles. The magnitude of NOx and HC reductions found in this study 

when switching from 28 ppm to 5 ppm fuel are consistent with those found in other studies done 

by the US EPA and automobile and catalyst manufacturers.29,30,31 

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

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

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

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

61 
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 Four Peer Reviews in Support of the Tier 3 Rulemaking: Fuel Sulfur Effects Analysis Draft 

Report. U.S. Environmental Protection Agency, Washington, D.C. Documents available at 

http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=240145. 

4 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. 

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

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7 The Regulatory Impact Analysis for the Tier2/Sulfur Final Rule, Chapter III and Appendix B, 

EPA 420-R-99-023. 

8 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. 

9 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. 

10 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. 

11 EPAct/V2/E-89: Assessing the Effect of Five Gasoline Properties on Exhaust Emissions from 

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

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

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63 
31 

28 “Analysis of Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas City” 

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

29 Regulatory Impact Analysis for the Control of Hazardous Air Pollutants from Mobile Sources 

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64