United States Air and Radiation EPA420-P-01-001 

Environmental Protection July 2001 

Agency 

Strategies and Issues in 

Correlating Diesel Fuel 

Properties with Emissions

Staff Discussion Document
 

 Printed on Recycled Paper 
EPA420-P-01-001 

July 2001 

Strategies and Issues in Correlating
 

Diesel Fuel Properties with Emissions
 

Staff Discussion Document 

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 which
 

may form the basis for a final EPA decision, position, or regulatory action.
 
Table of Contents
 

Section I Introduction ...................................................... 1 
 

A. Regulatory Context ................................................ 1 
 

1. Federal and State Regulation of Diesel Fuel Parameters .............. 1 
 

2. The Texas Low Emission Diesel Rule ............................ 2 
 

B. Objectives and Scope of Research ..................................... 3 
 

C. Interaction with Stakeholders ......................................... 4 
 

D. Request for Comments .............................................. 4 
 

Section II What Data Was Used? .............................................. 6 
 

A. Criteria for choosing data sources ..................................... 6 
 

B. Preparation of database ............................................. 7 
 

1. Database structure ........................................... 7 
 

2. Entering data ............................................... 8 
 

3. Adjustments to database ..................................... 11 
 

C. Technology groupings ............................................. 12 
 

D. Test cycles ...................................................... 14 
 

1. Transient Cycles ............................................ 14 
 

2. Steady-State Cycles ......................................... 15 
 

3. Choice of Test Cycles ....................................... 16 
 

E. Summary statistics of data .......................................... 17 
 

1. Fuel properties ............................................. 17 
 

2. Test cycles ................................................ 20 
 

3. Model years ............................................... 21 
 

Section III How Was The Data Analyzed? ...................................... 23 
 

A. Fuel terms permitted in model ....................................... 23 
 

B. Regression approach .............................................. 24 
 

1. Principle Components Regressions ............................. 26 
 

2. Technology-group stepwise models ............................. 27 
 

3. Unified model ............................................. 28 
 

C. Sensitivity analyses ............................................... 35 
 

1. Monoaromatic versus polyaromatic effects ....................... 35 
 

2. Correlating additized cetane effects with baseline natural cetane ...... 36 
 

D. Incorporation of baseline fuel ....................................... 36 
 

E. Extrapolation and valid ranges ....................................... 39 
 

F. Summary of emission effects exhibited by equations ..................... 44 
 

G. Comparisons to other emission models ................................ 45 
 

Section IV How Should The Model Be Used? ................................... 49 
 

A. Technology group weightings ....................................... 49 
 

B. Application to heavy-duty highway fleet ............................... 51 
 

C. Biodiesel ....................................................... 54 
 
 

D. Other oxygenates ................................................. 55 
 

E. Application to CI nonroad fleet ...................................... 56 
 

Section V Diesel Fuel Property Effects On Toxics ................................ 58 
 

A. Introduction ..................................................... 58 
 

B. Studies Which Measured Emissions of Toxics .......................... 59 
 

1. CARB Report .............................................. 59 
 

2. Arco Chemical Company Cetane Improvement Additive Studies ..... 62 
 

3. EPEFE Light Duty Diesel Study ............................... 66 
 

C. Conclusions and Next Steps ........................................ 66 
 

Section VI Diesel Fuel Effects In Light-Duty Vehicles ............................. 68 
 

A. Introduction ..................................................... 68 
 

B. Individual Studies ................................................ 68 
 

1. EPEFE Study .............................................. 68 
 

2. Lange Study ............................................... 70 
 

3. Bertoli Study .............................................. 71 
 

C. Results and Discussion ............................................ 72 
 

1. EPEFE Study .............................................. 72 
 

2. Lange Study ............................................... 73 
 

3. Bertoli Study .............................................. 74 
 

4. Summary of Studies ......................................... 75 
 

D. Effects of Vehicle Technology and Operation ........................... 77 
 

1. DI and IDI Engines ......................................... 77 
 

2. Sensitivity of Vehicle Response to Engine Parameters .............. 78 
 

a. Engine Operating Conditions ............................ 78 
 

b. Engine Calibration Systems ............................. 78 
 

E. Conclusions ..................................................... 79 
 

Section VII What Additional Issues Should Be Addressed? .......................... 81 
 

A. The need for further testing and research ............................... 81 
 

1. Alternative fuels for heavy-duty applications ..................... 81 
 

2. Additional test data ......................................... 81 
 

B. Technical issues in model development ............................... 82 
 

1. Natural cetane and additized cetane ............................. 83 
 

a. Review of studies ..................................... 83 
 

b. Evaluation of Unified Model approach .................... 85 
 

2. Engine sensitivity to cetane ................................... 86 
 

3. Fuel term elimination process in PCR ........................... 87 
 

4. Model-year analysis ......................................... 88 
 

5. Uncertainty analysis ......................................... 89 
 

6. Fuel effects on nonroad engines ................................ 91 
 

7. Monoaromatic versus polyaromatic effects ....................... 92 
 

Appendices .................................................................. 94 
 
Appendix A - Data Sources ............................................... 94 
 

Appendix B - Database Structure .......................................... 102 
 

Appendix C - Aromatics Conversion Equations .............................. 108 
 

Appendix D - Technology Groups ......................................... 111 
 

References ................................................................. 114 
 
 

 

 

 

Section I Introduction 

This staff discussion document describes technical issues related to an assessment of the 

effect of changes in diesel fuel parameters on the emissions of hydrocarbons (HC), particulate matter 

(PM) and oxides of nitrogen (NOx). It is intended as a starting place for discussion and comment. 

It is not a final technical report. 

A. Regulatory Context 

1. Federal and State Regulation of Diesel Fuel Parameters 

EPA’s desire to quantify the emission effects of diesel fuel parameter changes stems from 

growing state interest in reducing criteria pollutant emissions by regulating diesel fuel properties. 

Federal law and regulations control sulfur and aromatic content and the cetane index of highway 

diesel fuel introduced into commerce as of October 1, 1993.1 Except for California,2 no state had 

regulated similar aspects of diesel fuel until April 2000, when Texas adopted its Low Emission 

Diesel (LED) rule for the Dallas metropolitan area,3 and later amended the same rule to expand the 

geographic scope of the covered area and to further restrict sulfur levels.4 Like the California rule 

(implemented in October, 1993) the Texas rule (to be implemented in April, 2005a

) controls sulfur 

and aromatic hydrocarbon content of diesel fuel for both highway and nonroad engines; Texas also 

controls the cetane number of diesel fuel.b 

The Ozone Transport Commission (OTC) also in 2000 began considering a model rule that 

could be adopted by its member states wanting to regulate the content of diesel fuel.c The OTC 

model rule would have required a high cetane number in diesel fuel for both highway and nonroad 

engines. The OTC states were interested in potential emission reductions of both NOx and HC. 

We also are aware of additional states that have considered or are considering controls of 

diesel fuel parameters. While there are a number of issues related to state fuel regulations, including 

fuel supply, actual use of the required fuel, and cost, one important unanswered question has been 

the amount of emission benefits in vehicles using the alternate fuel. As a result of the substantial 

a Although the rule as currently adopted requires implementation by May, 2002, Texas has proposed revising 

the rule to delay implementation until April, 2005, and has requested that EPA “parallel process” this proposed revision. 

See text of revisions to 30 Tex. Admin. Code, Chapter 114, Section 114.319, proposed by TNRCC on May 10, 2001, at 

the following website: http://www.tnrcc.state.tx.us/oprd/sips/houston.html. This proposed revision implements 

legislation adopted by the Texas Legislature (HB 2912) prohibiting implementation of this low emission diesel rule until 

February, 2005. 

b Although California does not set a regulatory standard for cetane number, it does require use of a reference 

fuel with a specific cetane number (identical to the Texas regulatory standard) in determining whether alternative 

formulations (which do not meet the 10% aromatics content standard) have equivalent emissions reductions. Alternative 

fuel formulations with equivalent emissions reductions can meet the California diesel fuel requirements. 

c To date, OTC has not adopted a model rule for diesel fuel control, and is no longer actively considering it. 

1
 
 

 

 

 

 

recent interest in diesel fuel control and the lack of comprehensive information on the benefits of 

such fuel, in November 2000, EPA initiated an effort to evaluate the emission benefits of varying 

diesel fuel parameters. 

2. The Texas Low Emission Diesel Rule 

In particular, EPA must respond to the Texas proposed State Implementation Plan (SIP) 

revisions seeking emissions reduction credit for its LED rule. Texas submitted to EPA its LED rule 

as one of many control measures to be included in its SIP for meeting the National Ambient Air 

Quality Standards for ozone in the Dallas and Houston areas. Because we must complete rulemaking 

action on the proposed Houston SIP revisions by October 15, 2001, in order to meet a court-ordered 

deadline, we needed to know as soon as possible whether our analysis supported approval of the 

NOx reductions claimed for the LED SIP revision. The analysis described in this staff discussion 

document was designed to help us answer this question, as well as help provide a consistent response 

to future questions on the emission benefits of diesel fuel controls. 

In its proposed SIP revisions, Texas claims the LED rule will provide significant reductions 

in emissions of oxides of nitrogen (NOx). In developing the NOx emission reduction estimates, 

Texas assumed its LED fuel would be similar to California diesel fuel. For highway engines with 

electronic controls (i.e., 1990 and later models for the most part), Texas estimated NOx reductions 

at 5.7%, based on regression equations in the Heavy Duty Engine Working Group (HDEWG) report, 

a project of the Coordinating Research Council (CRC). For pre-1990 highway engines and for all 

non-road diesel engines, Texas estimated NOx reductions at 7%, based on California Air Resources 

Board (CARB) test data from 1988.5 

Given the absence of a publicly reviewed emissions model for diesel fuel parameters, we 

were concerned about the accuracy, magnitude, and consistency of estimates of the emission 

reduction benefits of increasing cetane and limiting aromatics in diesel fuel used in the current fleet. 

Many studies of the emissions effects of diesel fuel parameters have been done in the past several 

years that were not included in the Texas analysis. 

In particular, the HDEWG report, which is included in our list of data sources (Appendix A,) 

examined, among other things, the effects of cetane number and aromatic content on emissions from 

1998 and 2004 prototype heavy duty diesel engines. The 5.7% estimate being used by Texas is 

drawn from the testing done on an engine meeting the 1998 standards and presumes that all 

technologies and model years from 1990 forward will respond similarly to changes in fuel properties. 

Also, for the older highway engines (i.e., pre-1990 model years), and for the entire nonroad 

diesel engine fleet, Texas relied on CARB “test data from 1988” as support for its estimate of 7% 

NOx reductions. CARB’s Staff Report and Technical Support Document for its proposed diesel fuel 

regulation (both dated October 1988) indicate the source of this data is CARB’s analysis of the 

preliminary results of the CRC test program for the Phase 1 VE-1 project.6

 As of the time of the 

2
 
 

 

 

 

1988 CARB Report, testing in the VE-1 project had been completed for two engines, a Cummins 

NTCC 400 engine and a Detroit Diesel DDAD 60 engine. After CARB’s final determination of the 

benefits of its diesel fuel control program in 1988, the Phase 1 VE-1 project was completed, 

including testing on a third engine, and the report was published in 1989. CRC went on to complete 

a Phase 2 to the VE-1 test program, and eventually a VE-10 test program. Numerous other studies 

have also been completed since the 1988 CARB Report. 

While the engines tested in the Phase 1 VE-1 study represent some pre-1990 technologies, 

additional test data is available on this portion of the fleet. In addition, it is not necessarily 

appropriate to use the Phase 1 VE-1 results alone to estimate NOx reductions in all nonroad diesel 

engines. We believe that a more thorough review of the existing data, including studies completed 

since 1988, is an important element of estimating the benefits of diesel fuel controls on the in-use 

fleet. 

B. Objectives and Scope of Research 

The primary goal of this EPA project is to provide an objective estimate of the effect of 

changes in diesel fuel parameters on emissions of NOx, HC and PM. As such, our objective is to 

provide equations for calculating fuel-based emissions changes of these pollutants for various 

commonly-used diesel engine technologies. 

This research project has been subject to tight time constraints, due to the need to meet the 

deadlines for EPA rulemaking on the Houston SIP. For this reason, we have not done any vehicle 

testing; instead we developed a regression model from existing test data. 

Our study has focused on the fuel parameters, vehicles, and pollutants that are currently of 

greatest concern in State Implementation Plans (SIPs). Thus, the study was primarily concerned with 

the NOx, HC and PM benefits of cetane and aromatics on emissions from engines commonly used 

in heavy-duty highway trucks. However, many of the studies included information on CO, HC and 

BSFC, so we also collected data on these parameters and may develop regression equations for them 

in the future. Data on toxic emissions was quite limited, so our analysis of these emissions was 

qualitative. Similarly, there were few data available on the effects of diesel fuel parameters in light 

duty vehicles. Since light duty diesel vehicles comprise a very small fraction of the current fleet, we 

conducted only a literature survey of the relevant light duty emissions studies. And, while the 

effects of diesel fuel changes on nonroad equipment such as construction and farming equipment are 

of considerable interest due to their contribution to in-use emission inventories, lack of data made 

it necessary for us to extrapolate our findings from highway vehicles to nonroad equipment. 

Where data was available, we used a regression model approach to analyze our results and 

to develop a quantitative relationship between fuel parameters and emissions changes. We 

considered the benefits of both a traditional regression approach and a principal component analysis 

approach that uses eigenvectors to eliminate colinearity between independent variables.7 

3
 
 

 

 

 

 

C. Interaction with Stakeholders 

To assure that our analysis benefitted from data and expertise outside the EPA, we have made 

a deliberate effort to work with external stakeholders from the beginning of our work on this topic. 

In order to inform stakeholders of the work that EPA would be doing to look at the effects of diesel 

fuels on emissions, a presentation was made to the Federal Advisory Committee Act’s Mobile 

Sources Technical Review Sub-committee and informational letters were sent to stakeholders. The 

first letter was a general notice to stakeholders, the second was a request for any pertinent data that 

could be included in our study. We established an e-mail list for individuals known to have an 

interest in our work, with periodic messages sent to inform members of the status of our work. We 

also developed a web site (http://www.epa.gov/otaq/models/analysis.htm) to share our plans and 

intermediate work products. 

In addition to general interaction with stakeholders, we have also worked particularly closely 

with the California Air Resources Board (CARB) and the Department of Energy (DOE). CARB 

staff has significant interest and experience in fuel emissions modeling (e.g. their Predictive Model 

for gasoline vehicle emissions). DOE staff and their consultants also have interest and expertise in 

this area and have an independent project to develop a principal component analysis for quantifying 

the relationship between fuel parameters and emissions. 

D. Request for Comments 

While more time and additional research could improve our analysis, within the time limits 

of the Houston SIP rulemaking, we performed a comprehensive review and analysis of all pertinent, 

available data, and produced what we believe is a reasonable model of the percent reductions of the 

relevant emissions as a function of fuel parameter changes. 

However, in this process, a number of issues have been raised , both regarding the details of 

the regression analysis and regarding its possible application. To assure that our model represents 

the best current scientific understanding of these emission effects, we are planning a workshop in 

August 2001 to discuss technical issues relating to our analysis of the emission effects of varying 

diesel fuel parameters. For information on this workshop, please see our website 

(http://www.epa.gov/otaq/models/analysis.htm). Comments on this staff discussion document and 

our analysis may also be sent to EPA in writing prior to the workshop date. Written comments can 

be submitted to Tia Sutton at sutton.tia@epa.gov, or through regular mail to: 

Tia Sutton
 

U.S. EPA National Vehicle and Fuel Emissions Laboratory
 

2000 Traverwood Drive
 

Ann Arbor, MI 48105
 

4
 
At the conclusion of the workshop, EPA will consider the comments received and will revise our 

analysis in response to comments. We then plan to publish a technical report that summarizes our 

work and conclusions. 

5
 
 

 

 

 

 

 

Section II What Data Was Used? 

We began the process of assembling data for use in developing a fuel property/emissions 

model by conducting literature searches, reviewing lists of relevant data sources that had been 

assembled by other researchers for use in similar analyses, and making requests from stakeholders 

for data that may not have been public. Given the short timeframe permitted for developing a model, 

we set a deadline of February 15, 2001 for receiving data from stakeholders. We received no 

additional data from stakeholders. 

Regarding lists of data sources from other researchers, we reviewed the bibliographies of 

Sierra Research's Maricopa County study8

 and the Oak Ridge National Laboratory's eigenvector 

model report9

. We also reviewed lists of suggested data sources provided by DaimlerChrysler and 

the Georgia Department of Natural Resources. Once we had assembled a complete list of 

prospective data sources, we reviewed each study to verify that it contained the actual raw data that 

the report or study described. If the raw data was not provided, we made attempts to contact the 

authors. Only one such attempt was successful, for SAE paper number 922214, and so this study 

was included in our analysis. The remaining papers without raw data which are excluded from this 

analysis are listed in Appendix A. 

We reviewed the studies to verify that they met certain criteria consistent with the goals of 

the project. These criteria are described in Section II.A below. As a result of this review, only 35 

of the full set of 70 studies were retained for our analysis. We then entered the data into a database 

specifically designed for this project, making adjustments to ensure consistency in units, corrections 

for emissions drift over time, and balancing of repeat measurements. The engines were then each 

assigned to a technology group designed specifically for this project as described in Section II.C 

below. Finally, the different test cycles used to collect emissions data were assessed to determine 

how well they represented the Federal Test Procedure (FTP), which is the current best representation 

of the operation of in-use heavy-duty highway engines among the available test data. All of these 

steps are described in the remaining portions of this Section. Additional details can be found in the 

final report from Southwest Research Institute10. 

A. Criteria for choosing data sources 

As described in Section I, the model we have developed is intended to represent conventional 

diesel fuel effects on emission from heavy-duty highway compression-ignition enginesd

. The data 

that we considered for use in the development of the model was screened to ensure that it met certain 

criteria consistent with this goal. 

d Although essentially all available data was collected on highway engines, the models can also be applied to 

the nonroad fleet as described in Section IV.E. 

6
 
 

 

 

 

 

To begin with, we limited our analysis to No. 1 and No. 2 diesel fuel and related blends that 

can be used in a typical heavy-duty diesel engine without engine modifications. As a result we 

excluded all emulsions and oxygenated blends with more than 20 vol% oxygenate. We also 

excluded fuels that were made entirely from pure chemicals rather than refinery streams. We did not 

specifically exclude Fischer-Tropsch fuels, nor did we limited ourselves to diesel fuels containing 

less than 500 ppm sulfur. However, some fuels were excluded as being not representative of current 

or potential future in-use fuels as described in more detail in Section B.3 below. 

We also limited this study to engines that had already been sold commercially or had a high 

probability of being sold in the future. Engines with experimental technologies that had no 

immediate plans for commercialization, such as those with innovative combustion chamber 

geometries, were excluded. Likewise, single-cylinder research engines were also excluded from 

consideration even though the associated full-size parent engine might have been appropriately 

included in the database had it been tested. Single-cylinder engines do not appear in heavy-duty 

applications. By definition they have lower total horsepower and displacement, both of which are 

parameters in our technology group definitions as described in more detail in Section II.C below. 

Unless we were to make the assumption that single cylinder engines respond in the same way as their 

parent engine to changes in fuel properties, we would have to define new technology groups specific 

to single-cylinder engines. Light-duty engine and vehicle data was separated for an independent 

analysis (see Section VI). Nonroad engines were not specifically excluded from the analysis, but the 

paucity of nonroad engine data made it necessary for us to evaluate nonroad engine fuel effects 

separately, as described in Section IV.E. 

The type of testing also played a role in determining if a given study should be included in 

our analysis. For instance, since we were primarily interested in fuel effects on emissions, we 

excluded all studies that did not test at least two different fuels on the same engine. Chassis tests 

were not specifically excluded from the database, but since the vast majority of heavy-duty testing 

is done on engines instead of chassis, the inconsequential amount of chassis test data was not 

included in our model development process. 

The complete list of data sources that we considered for our analysis is given in Appendix 

A. Studies that were excluded from our analysis are separated and categorized according to the 

reason for their exclusion. 

B. Preparation of database 

1. Database structure 

In designing the structure of the database and the fields that would be included, it was our 

intention to include all information that had any potential for helping us to quantify the relationship 

between diesel fuel properties and emissions. In addition, we also wanted to ensure that a wide 

variety of issues could be investigated once the database was assembled, including issues which were 

7
 
 

not immediately germane to our primary goal of correlating diesel fuel properties with emissions. 

This secondary goal is of broad and continuing interest to the EPA as we continue our efforts to 

understand and control pollution from diesel-powered engines and vehicles. Towards these ends, 

we selected a wide variety of fuel, engine, and test parameters to include in the database. 

The database was divided into three separate files: 

fbat_ad	 File containing a complete description of every fuel, including compositional, 

chemical, and combustion characteristics. 

equip_ad	 File containing a complete description of every engine, include both engine 

design characteristics and elements that may have been changed subsequent 

to production, such as aftertreatment and EGR 

etest_ad	 File containing individual test descriptions and emission results 

Data source IDs were used to link specific fuels, engines, and emission estimates across the three 

files. We also designed a fourth file in which modal data could be recorded. However, since so few 

studies included the raw modal data and we did not have sufficient time to investigate modal effects, 

no data was entered into this fourth file. A complete description of the fields for all three database 

files is given in Appendix B. 

2. Entering data 

The primary concern as data was being entered into the database was consistency of units. 

For the most part these conversions were straightforward, and are described in more detail in the 

SwRI final report10. In some cases, however, the fuel property unit conversions were not 

straightforward due to ambiguity in either a given study or the database structure itself. In these 

cases decisions were made that were intended to maximize the useful amount of data. These 

decisions are summarized below: 

Viscosity - The viscosity of a fuel can be measured at different temperatures. In cases where 

more than one temperature was used, the measurement closest to 40 o

C was entered into the 

database. If only one viscosity measurement was made, it was entered into the database 

without regard to test temperature. 

Biodiesel - Biodiesel blends were grouped with all other oxygenates, with its corresponding 

wt% oxygen level being entered into the database. An attempt was made to determine if the 

oxygenate type had an effect on emissions that was separate from the oxygen effect. This 

assessment is discussed in more detail in Section IV.D. 

8
 
 

 

 

 

 

Oxygen - If an oxygenate was not added to a fuel and the oxygen level was not measured, it 

was assumed to be zero. If oxygen was measured, we used the measured value even if doing 

so included the contribution of, for instance, cetane improver additives to the oxygen content 

of the fuel. 

Properties of cetane-enhanced fuels - If the properties of a fuel were measured before a 

cetane improver was added to the fuel but not afterward, the properties of the base fuel were 

considered to be applicable to the additized fuel as well, with the exception of cetane 

number. 

Concentration of cetane improver additives - Our database required that the concentration 

of cetane improver additives be entered as vol%. If a study provided the concentrations in 

terms of wt%, the conversions were made using the following equation: 

vol% = wt% X fuel specific gravity / b 

where b is the specific gravity of the cetane improver additivee

. 

Cetane increase due to additives - If the increase in cetane number which resulted from the 

addition of a cetane improver additive was not given in the study, it was estimated from a 

correlation given in SAE paper number 972901. This correlations is: 

CNI = a × CN0.36 × G0.57 × C0.032 × ln(1 + 17.5 × C) 

Where: 

CNI = Predicted cetane number increase due to an additive 

a = 0.16 for 2-ethylhexylnitrate and 0.119 for di-tertiary butylperoxide 

CN = Base cetane number 

G = Fuel API gravity 

C = concentration of additive in vol% 

Cetane index - If the cetane number of a fuel was not measured, the cetane index was used 

as a surrogate for cetane number. This applied to all fuels in two studies, for a total of 

thirteen test fuels (out of 300 in the database). 

Aromatics test methods - The database required total aromatics to be entered in units of vol% 

as established from an FIA test method (ASTM D 1319 or the equivalent). If total aromatics 

was derived using supercritical fluid chromatography (SFC, from ASTM D 5186 or its 

e For 2-ethylhexylnitrate (EHN) this value is 0.964 according to an Ethyl data sheet on their HiTEC Cetane 

Improver Additive (composed of 99% 2-EHN). For di-tertiary butylperoxide (DTBP), the value of b is 0.794 according 

to the CRC Handbook of Chemistry and Physics. 

9
 
 

 

equivalent), which produces measurements in wt%, the conversion was made using an 

equation derived from the California Code of Regulations, Title 13, Section 2282(c)(1): 

vol% (by FIA) = 0.916 × wt% (by SFC) + 1.33 

If total aromatics content was not measured by an SFC test method, then alternative 

conversion equations were used. These conversion equations were derived specifically for 

this analysis and are described in Appendix C. 

Total, mono, and polyaromatics - Total aromatics content is the sum of mono and 

polyaromatics. Thus if a study provided measurements for only two of these three properties, 

the third was estimated based on this relationship. 

The database required mono and polyaromatics to be entered in units of wt% as established 

using an SFC test method. If total aromatics was not measured by an SFC test method, then 

the conversion to wt% by SFC was made using equations derived specifically for this 

analysis. These equations are described in Appendix C. 

There were also situations in which aspects of the data not related to fuel properties were 

ambiguous. In these situations we again made decisions that were intended to maximize the 

usefulness of the database in the context of developing an emissions model. The primary decisions 

are listed below: 

Hot-start versus composite FTP - If the heavy-duty transient Federal Test Procedure was 

used to produce composite emission measurements, these were entered into the database as 

UDDS cycle values. If the FTP was used to produce separate hot and cold-start emission 

measurements and no composite results were presented, then the hot and cold-start results 

were weighted at 6/7 and 1/7, respectively, to produce composite results which were then 

entered into the database as UDDS cycle values. If the FTP was used to produce only hotstart emission measurements, then these results were entered into the database as UDDSH 

cycle values. See Section II.E.2 for a more detailed discussion of test cycles. 

Engine adjustments - If adjustments were made to an engine, such as changes in injection 

timing, addition or removal of aftertreatment, etc. these were treated as unique engines and 

entered into the database as such. Thus each engine value in the database refers to a set of 

emissions data from a single engine whose operating parameters and physical characteristics 

did not change during the course of testing. 

Repeat measurements - There were many cases in which the same fuel was tested on the 

same engine multiple times. All such repeat measurements were entered into the database. 

Our model development approach used average repeat measurements in the term-selection 

stage of the analysis, and all individual repeat measurements in the coefficient estimation 

stage. This approach is described more fully in Section III.A.3. 

10
 
 

 

 

 

Averaged emissions - If the study presented only averaged emissions resulting from multiple 

repeat tests of a single fuel on a single engine, the average values were entered into the 

database the same number of times as the number of repeat tests on which the average was 

based. If the number of repeat tests was unknown, it was assumed to be two. 

3. Adjustments to database 

Once all the data had been entered into the database and it had been reviewed for errors and 

inconsistencies, two different adjustments were made to ensure that the database was best suited for 

model development. 

The first adjustment involved correcting for engine drift over time. This correction was 

necessary for cases in which the emissions from an engine appeared to drift upwards or downwards 

over the course of the study. Cases in which this drift was evident were those in which the study 

authors specifically looked for it by testing a single fuel - usually a reference fuel - at multiple times 

throughout the test program. The emissions from this fuel could then be plotted against time (usually 

engine hours) to determine if drift occurred. If engine drift was evident, the authors may have 

chosen not to correct the data itself but instead add a time parameter to the regression equations that 

were developed using the data in that study. This option was not available in our model because so 

few studies included time measurements. Thus it was necessary for us to correct the data for those 

studies in which engine drift was investigated and found to be significant. 

Engine drift was expected to be proportional to the absolute level of emissions. In other 

words, the percent change in emissions for the reference fuel over time was assumed to also apply 

to all other emission measurements. The first step was to correlate reference fuel emissions with 

time to produce an equation of the following form: 

Emissions (g/bhp-hr) = a × t + b 

where t is the time in engine hours and a and b are regression coefficients. This equation represents 

the average change in emissions for the reference fuel over time. From this equation it is evident that 

the emissions at time zero are equal to the constant b. The correction to the existing data is then 

carried out using the following equation: 

(Emissions)measured × [b/(a × t + b)] = (Emissions)corrected 

Thus if the reference fuel indicates an upward trend in emissions over time, the correction should 

change all emission measurements by an amount inversely proportional to this emission increase. 

The studies that included time-drift corrections in our database included: 

• SAE 2000-01-2890 

• VE-1 (Phase I) 

11
 
 

 

 

• VE-1 (Phase II) 

• VE-10 

The second database adjustment involved reviewing the distribution of fuel properties to 

ensure that they were representative of current or expected future fuels. Although we intended to 

rely in part on the statistical analyses to help us identify fuels that could be considered outliers, it 

seemed prudent to identify those fuels in the database prior to the analysis which were significantly 

different than any in-use fuel or which were unlikely to arise in the future. Since such fuels lie at the 

edges of the fuel property ranges, they have the potential for highly influencing the regression 

coefficients in ways that may not be representative of the in-use diesel pool. 

The distribution of fuel properties in the database was compared to distributions found in 

recent fuel surveys from the Alliance of Automobile Manufacturers to determine how well the 

database fuels represented in-use fuels. In addition, the database fuels were also compared to the 

Worldwide Fuels Charter proposed by manufacturers to determine whether they might be considered 

valid fuels in the future. Fuels that were neither representative of current fuels nor potential future 

fuels were identified as candidates for exclusion from the database. Particular attention was paid to 

heavier fuels, since the trend appears to be towards lighter fuels with lower density and higher 

cetane. Finally, if one of the candidate fuels appeared to be of a sort that could potentially produce 

rough engine operation (e.g. very low cetane), it was also highlighted for exclusion. Fischer-Tropsch 

fuels were not specifically excluded from the database since there are small markets where they 

could play a significant role. As a result, six fuels were deleted from the database, out of a total of 

306. Details of the deleted fuels can be found in the final report from Southwest Research Institute10. 

C. Technology groupings 

We expected that engines with different technologies would respond differently to changes 

in fuel properties. It therefore seemed prudent to examine ways in which the database could be 

subdivided to capture any technology-specific effects that might exist. Although dividing the data 

by model years or model year groups would be most convenient from the standpoint of correlating 

the results of our modeling with the in-use fleet, it became clear that engines of a given model year 

can have widely varying technologies, and some specific engine technologies span many model 

years. Thus grouping engines in our database by model years could potentially mask the true effects 

of changes in diesel fuel properties on emissions as effects for one type of technology were blended 

together with effects for another type of technology. We determined that a more precise model 

would result if we defined categories of engine technologies within which fuel effects on emissions 

would be expected to be consistent. 

We began by identifying ten engine characteristics that could interact with fuels and thus 

produce distinct effects on emissions. These characteristics are shown in Table II.C-1. 

12
 
 

Table II.C-1 - Engine Characteristics Used in Defining Technology Groups 

Rated speed
 

Injector type
 

Aspiration
 

Horsepower
 

Displacement
 

Oxy catalyst
 

Injection control
 

Type of injection
 

Cycle
 

EGR
 

The type of injector (inline, rotary, or unit) was used as a surrogate for injection pressure. From this 

selection of ten engine characteristics, we produced 56 technology groups intended to represent the 

most likely distribution of in-use engine types. The complete list of these technology groups is given 

in Appendix D. 

The database contained a total of 75 engines (some of which were actually duplicate engines 

run with different injection timings). When these engines were categorized by technology group, 

only 17 of the original 56 technology groups contained data. The number of engines and the percent 

of emission observations falling into each of these 17 technology groups are shown in Table II.C-2. 

Table II.C-2
 

Data in each technology group
 

Tech Group Number of engines Percent of emissions data 

B 4 2 

F 13 18 

G 2 3 

H 3 1 

I 1 6 

L 4 4 

P 3 4 

Q 4 6 

R 3 1 

T 22 42 

V 3 4 

X 1 2 

DD 1 1 

NN 2 2 

OO 3 3 

ZZ 4 2 

13
 
 

 

 

 

 

 

 

D. Test cycles 

The studies that we reviewed contained data generated from many diesel engine test cycles. 

The following is a description of the various test cycles, both transient and steady-state, that we 

evaluated throughout this work and a description of the test cycles that were chosen for the model. 

We then describe how we selected specific test cycles for inclusion in our model. In doing so, we 

aimed at selecting those cycles that were most representative of in-use operations. 

1. Transient Cycles 

The only transient test cycle used in the fuel studies being considered for inclusion in our 

database is the EPA transient test cycle that is used to certify on-highway heavy-duty diesel engines 

in the U.S. Because nearly all of the engines tested in these studies were on-highway engines and 

transient conditions can significantly affect particulate emissions, this is the preferred test cycle for 

inclusion in our model. 

The EPA transient test cycle is commonly referred to as the Federal Test Procedure (FTP)f, 

as it is the test cycle used for official emissions testing of diesel engines in the US. 

The heavy-duty, on-highway FTP consists of four phases and a variety of different speeds and loads 

that are sequenced to simulate the urban operation running of the vehicle that corresponds to the 

engine being tested. The average load factor of the heavy-duty FTP cycle is roughly 20 to 25 percent 

of the maximum engine horsepower available at a given speed. The cycle weighting factors signify 

medium to high exhaust gas temperatures. In our database, we refer to the EPA transient test as the 

UDDS (urban dynamometer driving schedule). The EPA transient cycle run with a hot start only is 

referred to as UDDSH.g 

2. Steady-State Cycles 

The U.S. 13-mode duty cycle, as defined in 40 CFR § 336, consists of 13 sequential steadystate operating regimes with defined minimum sampling times of 4.5 minutes and maximum 

sampling times of 6 minutes. The speed for each mode must be held within +/- 50 rpm for each 

mode and the load for each mode must be within +/- 2% of the maximum available torque for each 

mode. The test cycle consists of three idle sample points, as well as intermediate speed, and rated 

speed sample modes. The intermediate speed is typically defined as a peak torque speed. The rated 

speed is defined as a maximum measured, full power speed. The loads correspond to: 2%, 25%, 

50%, 75%, and 100% of maximum available torque at a given test speed. 

f CFR Title 40, Part 86.1333. 

g EPA’s Mobile Source Observation Database (MSOD), the database of in-use vehicle test result data, 

uses UDDS as a test procedure value. However, this may also tend to be referred to by others as an FTP test cycle. 

Our model did draw from MSOD on the data design, with a key distinction being that MSOD distinguishes between 

test procedures and schedules while this model does not. 

14
 
 

 

The ECE R49 cycle (also called the EEC 88/77 cycle) is the 13-mode steady-state test cycle 

for heavy-duty diesel engines which was used for certification of heavy-duty engines in Europe until 

October 2000. The test cycle is similar to the US 13-mode cycle, as both cycles have identical 

running conditions. However, the R49 has different weighting factors at the idle speeds and is 

characterized by high engine loads. 

The Japanese 13-Mode test cycle is a steady state test cycle that replaces the Japanese 6-mode 

cycle for testing heavy-duty engines. This test cycle places importance on low speed/ low power 

conditions, made evident by the fact that the cycle weight percentages are relatively low at high 

power. For example, for modes with loads of 80-95 percent, the weights vary from 0.037 to 0.055. 

In contrast, for the European and US 13-mode tests, the weights at high power (loads of 75-100%) 

are considerably higher, 0.08 and 0.08 to 0.25, respectively. 

The AVL 8-mode is a steady-state engine test procedure for heavy-duty diesel engines 

consisting of eight sequential engine operating points. The cycle was developed to simulate the FTP 

transient cycle for heavy-duty engines for pollutants other than PM, so the exhaust emission results 

are closely correlated for the two test cycles for HC and NOx. 

The ISO 8178 procedure is a collection of various steady state test cycles for non-road 

applications; with type C1 being an 8-mode, nonroad heavy-duty diesel cycle. While this is a steadystate test cycle, it is commonly referred to as the nonroad FTP cycle. 

3. Choice of Test Cycles 

In selecting data to include in our models, the choice of test cycle was considered to be very 

important. Data generated from UDDS transient cycle was preferred, as this cycle most closely 

represents in-use conditions. When a specific study tested an engine-fuel combination using the 

EPA transient test, that data was used to developed our models and data for that engine-fuel 

combination using all other test cycles was excluded. Transient cycle data represents the majority 

of the data in the database, 1195 observations. A number of studies only measured hot-start transient 

emissions. When this was the case, we included this data in our database and considered this data 

to be satisfactory when developing our models and preferred over steady-state data, if the latter were 

also available. As hot-start results comprise 6/7 of the composite value, we concluded that fuel 

effects measured using the hot-start transient test could be considered representative of composite 

results. 

The use of steady-state data was considered in the development of the HC and NOx emission 

models when transient data was not available. In the case of PM emissions, only transient test data 

was used to develop the relationships between fuel properties and emissions. Steady-state data was 

not used to develop the PM models due to the importance of transients in particulate formation in 

diesel engines. 

15
 
 

 

 

 

 

With respect to HC and NOx emission data, we included data from a steady-state cycle if the 

cycle contained a wide variety of operating modes, including those at high loads, and the weighting 

factors for the high load mode were similar to those of the EPA 13-mode cycle or the ECE R49 

cycle. This led us to include data from the R49 and the AVL 8-mode steady-state test cycles into the 

final model. Practically, as most of the studies which utilized one or more of the steady-state cycles 

listed in the database also utilized the UDDS, much of the steady-state data was not included in the 

development of the NOx and HC models. In all, 74 observations were excluded from the model due 

to test cycle. 

Our decision to include certain steady-state NOx and HC emission data in the model is 

confirmed by a previous study that found that fuel modifications produce similar changes in 

emissions over the R49 and the heavy-duty FTP tests.h This study concluded that the effects of fuel 

property changes on emissions were similar and that general extrapolations of effects from one data 

set to another are reasonable. 

E. Summary statistics of data 

This Section provides information on the data in our database, including distribution of fuel 

properties, test cycles, and model years. This information can be used to assess the degree to which 

the data used to develop our model is representative of in-use fuels and engines. For instance, we 

have made use of the distribution of fuel properties to determine the valid range limits of our model 

(see Section III.C.3). The summaries in this Section include all data in the database, i.e. no outliers 

identified during the analysis or observations with incomplete data have been excluded in these 

summaries, unless specified otherwise. 

1. Fuel properties 

We plotted the distribution of a selection of fuel properties for fuels in our database against 

the same distribution for AAM surveys. In general, the database included a wider range of fuel 

properties than the surveys with a tendency towards cleaner fuels (i.e. lower aromatics, lower 

density, higher cetane). This result is advantageous for our model because the model will most often 

be used to predict the benefits of cleaner-than-average fuels. 

h R. Lee, J. Pedley, C. Hobbs, "Fuel Quality Impact on Heavy Duty Diesel Emissions: -A Literature Review", SAE 

982649. 

16
 
Figure II.E.1-1
 

Distribution of aromatics
 

Percent of observations Percent of observations

40
 

30
 

20
 

10
 

0 

30
 

25
 

20
 

15
 

10
 

5
 

0
 

Database 

AAM surveys 

0 5 10 15 20 25 30 35 40 45 50 55 60
 

Aromatics, vol% 

Figure II.E.1-2
 

Distribution of total cetane number
 

Database 

AAM surveys 

37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67
 

Total cetane number 

17
 
Figure II.E.1-3
 

Distribution of specific gravity
 

Percent of observations Percent of observations

50 

40 

30 

20 

10 

0 

Database 

AAM surveys 

0.78 0.79 0.8 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 

Specific gravity 

50 

Figure II.E.1-4 

Distribution of T50 

40 

30 Database 

AAM surveys 

20 

10 

0 

400 420 440 460 480 500 520 540 560 580 600 

T50, oF 

18
 
40 

Figure II.E.1-5
 

Distribution of sulfur
 

Percent of observations

79% 

Database 

30 AAM surveys 

20 

10 

0 

0 400 800 1200 1600 2000 2400 2800 

200 600 1000 1400 1800 2200 2600 3000 

Sulfur, ppm 

2. Test cycles 

When collecting data for use in our modeling effort for input into our database, we did not 

exclude data collected on any test cycle. The determination concerning which test cycles to include 

in which models was made subsequent to database construction. Table II.E.2-1 summarizes the 

number of observations in our database for each of the test cycles included in our modeling. Only 

observations with a measured NOx value are included in this table, though some of the observations 

may be missing measurements for one or more fuel property. 

Table II.E.2-1
 

Database observations by test cycle
 

Test cycle Observations % of observations 

FTP composite 401 25 

FTP hot start 762 48 

R49 13-mode 350 22 

AVL 8-mode 87 5 

All cycles 1600 100 

19
 
 

3. Model years 

Categorizing the data in our database according to the model year of the engines might 

provide a convenient means for applying a resulting regression model to the in-use fleet. However, 

we determined that defining technology groups based on such engine parameters as type of injection, 

rated speed, and injection control was a more appropriate way to investigate the impact of fuel 

properties on emissions. Still, it is instructive to examine the distribution of model years in our 

database and to compare them to the expected distribution of model years for a current (2002) fleet. 

Table II.E.3-1 provides a summary of the model years in our database and the expected in-use 

distribution based on MOBILE6 input data. 

Table II.E.3-1
 

Model year distribution
 

Model year Engines in 

database 

Percent of engines 

in database 

Percent of engines 

for 2002 fleet 

1983 & prior 2 2.7 4.2 

1984 - 1987 4 5.5 4.4

1988 4 5.5 1.6

1989 1 1.4 1.9

1990 1 1.4 2.3

1991 15 20.5 2.7

1992 0 0.0 3.2

1993 7 9.6 3.7

1994 12 16.4 4.4

1995 5 6.8 5.2

1996 17 23.3 6.1

1997 0 0.0 7.2

1998 1 1.4 8.5

1999 0 0.0 10.1

2000 0 0.0 11.9

2001 0 0.0 14.1

2002† 4 5.5 8.4 

† All EGR-equipped engines are assumed to represent 2002 and later model years. 

It is also helpful to see how our technology groups correlate with model years. Figure II.E.3-

1 provides this comparison. 

20
 
21
 

Technology group 

BB B B

ZZZZZZZZ 

G GXTTT TTTT TTTTT TTTT TTTTT TRRR 

FFFFF FFFF FFFF

I 

DD

OOOOOO

NNNN 

PPPQQQQHH HVVV 

LLLL 

1977 

1979 

1981 

1983 

1985 

1987 

1989 

1991 

1993 

1995 

1997 

1999 

2001 

2003 

Engine model year 

Figure II.E.3-1
 

Model years for each technology group
 
 

 

Section III How Was The Data Analyzed? 

As mentioned previously, the goal of this study is to produce a set of correlations which 

predict the relative change in diesel engine emissions as a function of diesel fuel properties. We also 

desired to use as much of the relevant data as possible. To accomplish these goals, we applied a 

wide variety of regression techniques to the data described in the previous section. In this section, 

we first describe the fuel properties we investigated, followed by a description of the various 

analytical techniques which were applied to the data. Then, we present the final model for each 

pollutant, and show the predicted effects of changes in specific fuel properties on emissions of these 

pollutants. 

A. Fuel terms permitted in model 

There are a wide variety of fuel properties that can be used to describe diesel fuel. Some are 

compositional (e.g. aromatics or oxygen content), some are physical (e.g. distillation properties), and 

some are combustion/chemical in nature (e.g. cetane). Many of these fuel properties are interrelated, 

as when changes in composition also affect the physical or chemical properties of the fuel. One 

possible list of diesel fuel properties is given in Table III.A-1. 

Table III.A-1
 

Diesel fuel properties
 

Cetane number Copper strip corrosivity Total aromatics 

Cetane index Density Monoaromatics 

Cetane improver type Viscosity Polyaromatics 

Additives (defoamers, etc.) H/C ratio Ash (insolubles) 

IBP, T10 - T90, EP Sulfur Carbon residue 

Flash point Nitrogen Chloride 

Cloud point Oxygen Olefins 

Pour point Oxygenate type Saturates 

Aniline point Water 

For the purposes of generating a model correlating diesel fuel properties with emissions of 

regulated pollutants, ideally one would choose the smallest set of fuel properties which provides the 

most precise correlation. The smaller the set of fuel properties, the greater the chance that a given 

study will have included them all. This is important because we want to maximize the useable data 

in our database, and not all studies measured all relevant fuel properties. Only those fuels in the 

database which include all the fuel properties that are being investigated will be included in any 

curve-fitting process. 

As a first step in our modeling effort, we reviewed the studies listed in Appendix A to 

determine which fuel properties have been determined by past investigators to have the largest effect 

22
 
 

 

 

on emissions. As a result we developed a set of nine fuel properties that not only had a high 

likelihood for a significant correlation with emissions, but also had been measured with some 

regularity in the studies included in our database. The final set of fuel properties that we investigated 

in our modeling effort are list in Table III.A-2. 

Table III.A-2
 

Fuel properties included in modeling effort
 

Natural cetane
 

Additized cetane
 

Total aromatics
 

Sulfur
 

Specific gravity
 

Oxygen
 

T10
 

T50
 

T90
 

One property that we believe would have been very useful is the H/C ratio. Unfortunately, 

very few studies in our database measured this compositional property. It may also have been useful 

to study mono and polyaromatics instead of total aromatics. However, as described more fully in 

Section III.C.1, use of these fuel properties in our modeling effort would also have significantly 

restricted the amount of useable data. We rejected the initial boiling point (IBP) and end point (EP) 

due to the less precise nature of these measurements, and with the expectation that T10, T50, and 

T90 together comprise a sufficient description of the distillation properties of diesel fuel. Viscosity 

was investigated in some studies, but did not often have a strong correlation with emissions. Indeed 

in the vector-based analysis conducted for the Department of Energy (discussed in more detail in 

Section III.B.1 below), viscosity contributed a negligible amount to the model sum of squares, and 

so was dropped as a model term for both NOx and PM. 

B. Regression approach 

The first step of the analysis was to determine what form the equations would take. We 

examined the distribution of emission values in our database, testing for normality. We also 

investigated the heteroscedastic nature of the data, examining the degree to which the variability in 

measurements is correlated with the magnitude of those measurements. Both investigations 

suggested that the use of a (natural) logarithmic transform of the emission values would help assure 

the applicability of the statistical methods we intended to use. The use of a log transform has been 

used commonly in previous fuel-focused statistical analyses conducted by other researchers. The 

use of a log transform also provides a benefit in terms of model simplification, since the intercept 

terms can be dropped when the goal of the regression is to predict the percent change in emissions 

in comparison to a baseline fuel. See further description in Section III.D.1 below. 

23
 
 

 

 

 

 

 

We also determined that the fuel variables should be standardized in our analysis. 

Standardization involves calculating the mean and standard deviation for each fuel property using 

all of the individual values in the database that are used in the regression, subtracting the mean from 

every individual value of the associated fuel parameter, and then dividing this difference by the 

standard deviation. After the regressions have been completed, it is a trivial matter to convert back 

into the original fuel variables. Standardization removes the scale differences between fuel terms, 

allowing a more straightforward comparison of the magnitude and relative importance of the 

estimated fuel variable coefficients throughout the analysis. It also remove nonessential correlations 

between fuel terms, leading to reductions in the variances of the coefficient estimates. 

We know from past experience that the effects of fuel properties on emissions are much 

smaller than the differences in emissions from engine to engine. To separate out the effects of fuel 

properties, therefore, it was necessary to include engine terms in each of our regressions (since data 

for only one test cycle was included in the database for any given engine). These were introduced 

as categorical variables in all of our models. We also included engine × fuel interaction terms for 

those regressions that represented fuel properties as fixed effects and engines as random effects (socalled "mixed" models based on restricted maximum likelihood). Doing so more properly accounted 

for the engine-by-engine variability in fuel property effects on emissions. This approach results 

Instead of treating all model terms as fixed effects, we determined that it was more 

appropriate to represent all engine terms (whether categorical engine or engine × fuel terms) as 

random effects in our modeling, while continuing to treat fuel properties as fixed effects. Doing so 

produces regression models which are more predictive than explanatory, i.e. they can more 

appropriately be applied to the in-use population of diesel engines, rather than just providing an 

explanation of the fuel effects for the specific engines in the database. For all cases in which we 

were able, then, we used the proc_mix procedure in SAS in developing our final models. This 

procedure uses restricted maximum likelihood in place of the least-squares regressions that form the 

basis of "fixed" models. 

Finally, we determined that only those test cycles that could be considered to be 

representative of the Federal Test Procedure for highway diesel engines would be included in our 

modeling. Test cycles were discussed in more detail in Section II.D. For the NOx and HC models, 

the test cycles included in our modeling were the FTP composite, FTP hot-start, Europe's R49 

steady-state cycle, and the AVL 8-mode cycle. For PM, we made use of only the FTP composite and 

FTP hot-start data. 

We examined a number of different approaches to the regression analysis. The following 

subsections summarize those analyses. 

1. Principle Components Regressions 

24
 
 

 

 

 

 

 

 

 

 

Principle Components Regressions (PCR) involve the conversion of the set of fuels in our 

database to a set of eigenvectors which, when properly weighted together, yield the original fuel set. 

The eigenvectors are mutually orthogonal, unlike the original fuel set, and thus provide a means for 

correlating individual fuel properties with emissions in such a way that important colinearities 

between fuel properties are preserved. If no colinearities exist in a set of data, PCR offers no 

advantages over more traditional regression analysis. 

A recent report sponsored by the Department of Energy11 and authored by McAdams, 

Crawford, and Hadder (hereafter the "McAdams analysis") promotes the use of PCR as an effective 

way to approach regression analysis for diesel fuel effects on emissions. When significant 

colinearities exist in a set of data, PCR may lead to a substantial reduction in the standard errors of 

the estimated fuel property coefficients, leading to a better solution. The McAdams analysis 

produced a model which contained only a subset of the original fuel variables considered, 

presumably those fuel variables which were both significant and which were correlated with one 

another. Since McAdams has used PCR with some success, we investigated the utility of this 

approach for our database. 

To assess the theoretical benefit of PCR over traditional regression analysis, we calculated 

the Condition Number for the overall database as a measure of the colinearities it possesses. The 

Condition Number is equal to the square root of the largest eigenvalue divided by the smallest 

eigenvalue. If the Condition Number is on the order of 15, some important colinearities may exist 

in the data. If the Condition Number is 30 or more, severe colinearities likely exist in the data. In 

the database used in the McAdams analysis, the Condition Number was 13. In our current database, 

the overall Condition Number is 5 (though the Condition Numbers for some of the smaller 

technology groups were much higher). This suggests that PCR may be less advantageous for our 

purposes than it was for the more limited database used in McAdams' analysis. 

We conducted a full principle components regression with our database for NOx, in general 

following the steps outlined in the DOE report (see the SwRI report10 for details). We did this both 

for the database as a whole and for several technology groups that appeared to have severe 

colinearity problems (for instance, the data associated with technology group H had a Condition 

Number of 42, and the data associated with technology group B had a Condition Number of 163). 

After ranking the eigenvectors on the basis of statistical significance and contribution to the model 

sum of squares, we used the McAdams criteria of p = 0.05 significance and 1% contribution to the 

sum of squares to eliminate several eigenvectors. As a result, two eigenvectors were eliminated from 

the overall NOx model and three were eliminated from the overall PM model. Larger numbers of 

eigenvectors were eliminated from some of the technology group-specific models. We then 

converted back to the original set of fuel variables. Unfortunately, at this point nearly all fuel 

variables contributed more than 1 percent to the model sum of squares, suggesting that all fuel terms 

be retained in the model, according to the procedure outlined in the DOE report. Thus the PCR 

approach did not assist in eliminating unimportant fuel terms, and therefore did not appear to offer 

an advantage over more traditional regression analyses. 

25
 
 

 

 

There are several other reasons we have chosen not to use PCR as the basis of our model at 

this time. First, the elimination of eigenvectors introduces some bias into the fuel property 

coefficients. For this reason PCR is sometimes referred to as a "biased regression approach" and is 

sometimes advocated primarily as a means for reducing dimensionality rather than for estimating 

coefficients. Second, the criteria advocated in the McAdams analysis for eliminating eigenvectors 

and fuel properties from the regressions is arbitrary and is not yet based on a consensus among 

statisticians working on these types of issues. Third, the steps for including engine variables as 

random effects in the context of PCR have not been developed. There are a number of other issues 

with PCR that, if resolved, might make PCR a more valuable tool in the future. See the SwRI 

report10 for a more detailed discussion of the advantages and disadvantages of PCR. 

2. Technology-group stepwise models 

Because engines have such a strong effect on emissions, we felt it appropriate to consider 

how different types of engine technologies impact the effect of fuels on emissions. The first step was 

to define categories of engine technologies as described in Section II.C above. 

We began our traditional regression analysis by assuming that different technologies are 

likely to exhibit different fuel/emission relationships. This led us to generate entirely independent 

models for different technology groups. In other words, we separated the data by technology group 

and developed regression models for the data in each group. This approach has the advantage of 

ensuring that any impacts that engine technology might have on the relationship between fuel 

properties and emissions are captured, no matter how subtle those impacts. We also determined that 

a forward stepwise approach to adding fuel terms into the model was appropriate in this case, since 

it ensured that those terms best suited to describe the data are actually included in the models. We 

used a p = 0.05 criterion for adding terms. 

We encountered some limitations in the data as we developed these stepwise models. When 

the database was separated by technology group, some groups contained very little data. As a result, 

there was some suspicion that some statistically significant correlations between fuel properties and 

emissions for these smaller technology groups were spurious, the result of biased measurements for 

single test programs, the limited degrees of freedom, or some other reason. We were also faced with 

the fact that coefficients of zero assigned to some fuel property terms would more likely be the result 

of limited studies which simply did not investigate those fuel properties as opposed to having 

investigated them and found them to be unimportant. This meant that the smaller technology groups 

had fewer terms than the larger technology groups even though other non-zero fuel property 

coefficients might also important. 

The technology groups with smaller databases also tended to have the highest condition 

numbers, suggestive of significant colinearities between fuel properties. In the context of a forward 

stepwise regression, these colinearities might reduce the descriptive power of the correlations for 

these smaller technology groups. For instance, the model could choose to include only one of two 

26
 
 

 

 

 

 

 

 

correlated fuel properties and provide essentially the same explanatory power as if it had chosen the 

other fuel property. Both models in this example would loose the link between the two collinear fuel 

properties. The result is that the models for these smaller technology groups might permit a user to 

adjusting the value of a fuel property that is not represented in the model even though it might have 

a significant impact on emissions. 

Once the models were completed for those technology groups with sufficient data, we 

discovered that many of the fuel property coefficients were similar across technology groups. In the 

NOx models, for instance, the coefficients for distillation properties, sulfur, and oxygen were all at 

or near zero, while the coefficients for aromatics, natural cetane, and additized cetane exhibited only 

very small differences from one another. These differences were generally less than the uncertainty 

in the coefficients. As a result, we questioned the need for independently-generated technology 

group models, since this approach appeared to increase model complexity without improving its 

predictive power, with the additional problem that the models were more difficult to apply to the inuse fleet. We therefore decided that an approach which assumed that all technology groups exhibit 

the same fuel effects unless proven otherwise would be more appropriate. 

3.	 Unified model 

In an effort to promote model simplicity while still permitting engine technology to play a 

role in correlations between fuel properties and emissions, we developed a "unified" approach to the 

regressions. In this approach, forward stepwise regressions were carried out on the database as a 

whole, but technology group-specific fuel effects were also permitted to enter the model if 

significant. We also made efforts to more properly account for engine variability and the impact that 

such variability should have on the statistical significance of fuel property coefficients. The resulting 

Unified Model is the model that we are proposing in this staff discussion document as a means for 

predicting the impact of changes in diesel fuel properties on emissions. 

We separated the process of selecting model terms from the process of estimating coefficients 

for those terms deemed statistically significant. The first stage was aimed at generating a collection 

of fuel terms that had a strong likelihood of being important elements in the final correlations. We 

used a fixed model in SAS because this permitted us to automate the process. To make the selection 

of fuel terms, we first averaged all repeat emission measurements to avoid overweighting those 

fuel/engine combinations that had many repeat measurements. We then used a fixed model with 

categorical variables for the engines to conduct a sequential forward stepwise regression, consisting 

of five phases. In all phases, the criterion for significance was p = 0.05. The five phases are 

described below: 

1.	 Conduct a forward stepwise regression, allowing as candidates for
 

entry only the nine linear fuel terms.
 

27
 
 

 

 

 

2.	 Starting with the model from Step 1, conduct another forward
 

stepwise regression, allowing as candidates for entry only the nine
 

squared fuel terms.
 

3.	 Starting with the model from Step 2, conduct another forward
 

stepwise regression, allowing as candidates for entry only the thirtysix fuel×fuel interaction terms.
 

4.	 Starting with the model from Step 3, conduct another forward stepwise regression, 

allowing as candidates for entry only the nine technology group × linear fuel terms. 

5.	 Starting with the model from Step 4, conduct another forward stepwise regression, 

allowing as candidates for entry only the nine technology group × squared fuel terms. 

Up to this point, no terms were removed from the model once they were added. After phase 

5, we conducted three other checks to make sure we continued into the second stage of the Unified 

Model with only those terms which were truly necessary. First, some terms that had been forced into 

the model in phase 5, which earlier had been statistically significant, ceased to be significant by the 

end of phase 5. These terms were dropped all at once. Second, we reviewed Mallow's Cp criterion 

and determined that we had begun to overfit the model in phase 5. We therefore eliminated several 

terms, beginning with those added in phase 5 and working backwards, until Cp was equal to the 

number of terms (indicating a balance between over and under-fitting). Finally, we reviewed the 

variance inflation factors for terms in the model to determine if any of the terms might be 

problematic. We used a criterion of 100, which would indicate that a given term exhibited an 

extreme correlation with other model terms and was therefore unnecessary. Once this first stage of 

the Unified Model was completed, we were assured that the resulting model would only be as 

complex as necessary to include whatever nonlinear or technology group-specific effects were 

important. 

We did not place any restraints on whether a technology group-specific term could enter the 

model in the context of the sequential stepwise regression, other than statistical significance. 

However, it may have been appropriate to consider the amount of data in each technology group in 

this process. For instance, a number of technology groups consisted of only a single engine. It may 

be appropriate to establish some type of criteria based on the amount of data in a given technology 

group which would determine whether our Unified Model approach would permit that technology 

group to have its own fuel effects. We did not investigate such an approach, but we request 

comment on how such criteria could be established and used. 

The second stage of the Unified Model made use of the proc_mix procedure in SAS. Since 

this procedure does not produce estimates of the r2

 values for the models, we can only provide r2 

values for the models resulting from the sequential stepwise regression. These values are shown in 

Table III.B.3-1. 

28
 
 

 

 

 

 

 

Table III.B.3-1
 

R squared values after sequential stepwise regressions
 

Model R2 value 

NOx 0.996 

PM 0.991 

HC 0.953 

In the second stage of the Unified Model, we more fully accounted for engine variability. 

We were then able to determine if the fuel terms surviving the first stage of the Unified Model 

remained statistically significant, and we could estimate the coefficients for those fuel terms. In this 

stage, the full dataset, including all repeat measurements, is used in the SAS procedure proc_mix 

with all the terms identified in the first stage of the analysis. Engine × linear fuel interaction terms 

are added to the model in addition to the engine intercept terms, all of which are identified as random 

effect terms. Technology group intercepts are also added for those technology groups actually 

present as specific terms in the model. These intercepts were forced into the model regardless of 

their statistical significance to maintain hierarchy. After the proc_mix procedure was run, terms that 

were not significant at the p = 0.05 level were dropped, beginning with technology group terms and 

working back towards the linear common fuel terms. After each set of insignificant terms was 

dropped, the coefficients and significance were recalculated and the process repeated until all terms 

in the model were significant. This approach again ensures that the model will be as simple as 

possible while still permitting technology group-specific terms to remain in the model if they are 

important. Once this backwards stepwise regression was completed, the regressions were complete. 

The final Unified Models for NOx, PM, and HC were in standardized variable form, and 

contained all technology group terms as adjustments from the common linear and squared fuel terms. 

To make the equations more user-friendly, the fuel variables were first unstandardized. The common 

terms (applicable to all technology groups) and adjustments for specific technology groups are given 

in Tables III.B.3-2, III.B.3-3, and III.B.3-4 for NOx, PM, and HC respectively. 

We then generated independent equations for each technology group by adding the 

technology group adjustments to the common fuel terms. This process resulted in a polynomial 

function of fuel variables for each of the technology group-specific models and for the default model. 

Since the emission measurements were rendered as the natural log of emissions during the regression 

analysis, the equations were converted into a more useable form byrendering emissions as a function 

of the exponential of the polynomials. The final result of these manipulations was a set of equations 

for each pollutant which provided emissions in g/bhp-hr as a function of the exponential of a 

polynomial function of fuel properties. The coefficients for the fuel properties are given in Tables 

III.B.3-5, III.B.3-6, and III.B.3-7 for NOx, PM, and HC respectively. Note that the "Default" model 

applies to all technology groups except for those that have their own technology group-specific 

model. 

29
 
Table III.B.3-2 Unstandardized coefficients for NOx model with technology group adjustmen

Tech group B Tech group L Tech group R Tech group 

Common terms adjustments adjustments adjustments adjustment

Intercept 0.50628 0.46541 -0.64011 -0.05788 5.2757 

Natural cetane 0 0.005553 0 0 0 

Cetane difference -0.002779 0.007378 0.003951 0 0 

Aromatics, vol% 0.002922 0 0 0 0 

Sulfur, ppm 0 0 0 0.0001018 0 

Specific gravity 1.3966 0 0 0 0 

T50, oF -0.0004023 0 0 0 -0.0214077 

T50 squared 0 0 0 0 0.00002139 

Table III.B.3-3 Unstandardized coefficients for PM model with technology group adjustmen

Common terms 

Tech group X 

adjustments 

Tech group ZZ 

adjustments 

Intercept Natural cetane Natural cetane squared Cetane difference Aromatics, vol% Sulfur, ppm Specific gravity Oxygen, wt% T90, oF 

Natural cetane × cetane difference 

-3.75781 

-0.004521 

0 

-0.04825 

0.002157 

0.00008386 

2.3708 

-0.07193 

0 

0.001009 

3.44171 

-0.122579 

0.001206 

0 0 0 0 0 0 0 

-3.77928 

0 0 0 0 0 0 0 

0.007480 

0 

30
Table III.B.3-4 Unstandardized coefficients for HC model 

Common terms 

Intercept Natural cetane Natural cetane squared Cetane difference T10, oF T50, oF Natural cetane × cetane difference 

5.32059 

-0.1875 

0.001571 

-0.1880 

-0.0009809 

-0.002448 

0.003507 

Table III.B.3-5 Final coefficients for NOx model

NOx (g/bhp-hr) = exp(intercept + a × natural cetane + b × cetane difference + ··· )

Default Tech group B Tech group L Tech group R Tech group 

Intercept 0.50628 0.97169 -0.13383 0.44840 5.78198 

Natural cetane 0 0.005553 0 0 0 

Cetane difference -0.002779 0.004599 0.001172 -0.002779 -0.002779 

Aromatics, vol% 0.002922 0.002922 0.002922 0.002922 0.002922 Sulfur, ppm 0 0 0 0.0001018 0 

Specific gravity 1.3966 1.3966 1.3966 1.3966 1.3966 T50, oF -0.0004023 -0.0004023 -0.0004023 -0.0004023 -0.02181 

T50 squared 0 0 0 0 0.00002139 

31
Table III.B.3-6 Final coefficients for PM model

PM (g/bhp-hr) = exp(intercept + a × natural cetane + b × cetane difference + ··· )

Default Tech group X Tech group ZZ 

Intercept Natural cetane Natural cetane squared Cetane difference Aromatics, vol% Sulfur, ppm Specific gravity Oxygen, wt% T90, oF 

Natural cetane × cetane difference 

-3.75781 

-0.004521 

0 

-0.04825 

0.002157 

0.00008386 

2.3708 

-0.07193 

0 

0.001009 

-0.31610 

-0.1271 

0.001206 

-0.04825 

0.002157 

0.00008386 

2.3708 

-0.07193 

0 

0.001009 

-7.53709 

-0.004521 

0 

-0.04825 

0.002157 

0.00008386 

2.3708 

-0.07193 

0.007480 0.001009 

Table III.B.3-7 Final coefficients for HC model

HC (g/bhp-hr) = exp(intercept + a × natural cetane + b × cetane difference + ··· )

Default 

Intercept Natural cetane Natural cetane squared Cetane difference T10, oF 

T50, oF Natural cetane × cetane difference 

5.32059 

-0.1875 

0.001571 

-0.1880 

-0.0009809 

-0.002448 

0.003507 

32
 

 

 

 

 

 

 

Note that the default NOx model contains no term for natural cetane. This is an unexpected 

result, and warrants further attention. There was also some expectation that natural cetane and 

additized cetane would produce more similar effects on emissions, based on several studies that 

examined this particular issue. These issues are discussed more fully in Section VII.B. 

C. Sensitivity analyses 

There are many different fuel properties that could be chosen as independent variables when 

investigating potential correlations between fuel properties and emissions. There are also many 

different fuel term forms that can be investigated, such as squared and interactive fuel terms. The 

terms and term forms we used in our model development were chosen on the basis of a preliminary 

review of the studies from which the data in our database was derived. There are alternatives that 

we investigated or intended to investigate that might be important elements in a model correlating 

diesel fuel properties with emissions. This section addresses some of those alternatives. 

1. Monoaromatic versus polyaromatic effects 

A number of studies investigated the emission impacts of subcategories of aromatic 

compounds. In these studies, the most typical approach was to separate monoaromatic compounds 

(hydrocarbons containing a single benzene ring) from polyaromatics (hydrocarbons containing more 

than one benzene ring). A smaller set of studies made further distinctions between mono, di-, and 

tri-aromatic compounds. In the studies that actually measured these subcategories of aromatics, 

some actually made efforts to control the test fuel levels of one subcategory of aromatics separately 

from another subcategory of aromatics. In most cases, the polyaromatics were specifically controlled 

while the monoaromatics were uncontrolled. 

These studies offered evidence that different types of aromatic compounds may have different 

impacts on emissions, particularly for PM. Some studies, such as the ACEA study, also concluded 

that mono and polyaromatic compounds may exhibit different effects for NOx. On this basis, then, 

it would have been reasonable to investigate these potential effects in our modeling effort by 

including monoaromatic and polyaromatic terms instead of the single total aromatics term. 

In our modeling approach, the first phase involves a sequential stepwise regression in the 

context of the SAS procedure proc_reg. When we specify the selection of fuel properties that are 

candidates for entry into the model during the stepwise regression, this procedure makes use of only 

that data that contains all of the candidate fuel properties. For instance, since we determined that 

T50 would be a candidate for entry into the model, SAS made use only of data that included a nonblank T50 value, resulting in a loss of approximately 10% of the data in our database. If we had 

included monoaromatic and polyaromatic terms in our stepwise regression, we would have lost over 

50% of the data in our database. This is a significant amount of data to lose, and the model could 

potentially have exhibited different fuel property/emissions correlations as a result. Thus we 

33
 
 

 

 

 

 

 

determined that it was more reasonable to include only a total aromatic term for our draft model than 

to lose 50% of the available data. 

Ideally, the emission effects of the sum of mono and polyaromatics would be based on the 

entire database, while the separate effects for mono and polyaromatics would be based on the subset 

of the data that actually contains measurements for these two fuel properties. Unfortunately, a 

methodology for doing this is currently unavailable. We request comment on whether and how to 

include the separate effects of monoaromatics and polyaromatics in our modeling approach. 

2. Correlating additized cetane effects with baseline natural cetane 

There was some expectation during our model development that the effects of additized 

cetane on emissions would diminish as the base cetane number increased for the fuel to which cetane 

improver was added. In other words, for a fixed cetane difference value, the impact on emissions 

would be a function of the natural cetane value of the fuel. The most straightforward way to account 

for the possibility of this effect is to include an interactive term of the form natural cetane × cetane 

difference. Since our Unified Model included all possible fuel-by-fuel interactions as candidates in 

the sequential stepwise stage of model development, no special effort was necessary to account for 

this particular interactive effect. 

As can be seen by the coefficients in Tables III.B.3-5 through III.B.3-7, the PM and HC 

models do in fact include natural cetane × cetane difference terms. However, no such term arose in 

the NOx model. Earlier investigations of technology group-specific stepwise models indicated that 

this interactive term was not significant for the largest technology group T, but was significant in the 

technology group F+DD model (a model based on the combination of data from technology groups 

F and DD, which differ from one another only in the existence or absence of an oxy catalyst). But 

for the database as a whole, it appears that a natural cetane × cetane difference term is not an 

important element in the way in which additized cetane affects NOx emissions. 

D. Incorporation of baseline fuel 

The model presented in this staff discussion document must be used in connection with 

MOBILE model output in order to estimate the inventory impacts of changes to diesel fuel. The 

most straightforward way to do this is to calculate a percent change in emissions based on a given 

change to diesel fuel properties using the model presented in this discussion document, and then to 

apply this percent change to the emissions estimated by MOBILE. This approach requires that we 

define a baseline fuel from which changes can be assessed. 

The baseline fuel incorporated into our model represents the current nationwide, annual 

average highway diesel fuel. We used data from recent surveys conducted by the Alliance of 

Automobile Manufacturers (AAM). We averaged the annual average fuel properties across years 

34
 
 

 

 

1995 through 2000. Since our model makes use of natural cetane and cetane difference rather than 

total cetane, we needed to estimate the portion of the total cetane number that resulted from the use 

of cetane improver additives. To do this, we used two independent approaches: 

Approach #1: The AAM surveys include estimates of the amount of cetane improver 

additive in the fuels tested. We used the following correlation from SAE paper number 

972901 to convert these amounts into an equivalent increase in cetane number: 

CNI = 0.16 × CN0.36 × G0.57 × C0.032 × ln(1 + 17.5 × C) 

Where: 

CNI = Predicted cetane number increase due to an additive 

CN = Base cetane number 

G = Fuel API gravity 

C = concentration of additive in vol% 

Approach #2: The AAM surveys also include calculated cetane index values. Cetane index 

values are often used as surrogates for cetane number if the latter is missing. However, 

cetane index values cannot account for the existence of a cetane improver additive, and so 

can only be used to estimate unadditized or "base fuel" cetane number values. 

Recent analyses by Ethyl corporation, confirmed by our own analysis of unadditized fuels 

in the AAM database, indicates that cetane index does not have a 1:1 correlation with natural 

cetane number as formerly believed. Instead, the following equation appears to provide a 

much more precise relationship: 

Natural cetane number = 1.154 × Cetane index - 9.231 

The AAM survey results included measurements of both cetane index and cetane number. 

The existence of a cetane improver additive will cause cetane index and cetane number to 

differ by an amount greater than the difference suggested by the above equation. This 

difference is indicative of the increase in cetane number due to the existence of the additive. 

Thus in association with the equation above, we were able to calculate the increase in cetane 

number that was due to the addition of a cetane improver additive. 

The results for approaches 1 and 2 were very similar. We therefore averaged the results from these 

two approaches to generate baseline natural cetane and cetane difference values. The final baseline 

fuel properties for highway diesel fuel are shown in Table III.D-1. 

35
 
 

 

Table III.D-1
 

Baseline Fuel Properties
 

Property Value 

Natural cetane number 

Cetane number increase due to additives 

Aromatics, vol% 

Specific gravity 

Sulfur, ppm 

Oxygen, wt% 

T10, oF 

T50, o

F 

T90, oF 

44.1 

0.8 

34.4 

0.85 

333 

0 

422 

505 

603 

We modified the model equations so that they would automatically produce estimates of the 

percent change in emissions based on a change from the baseline fuel. Since the equations were 

based on regressions on the natural log of emissions, they could be represented in the following 

form: 

Emissions (g/bhp-hr) = exp(f(cetane, aromatics, etc.)) 

where f is a polynomial function of diesel fuel properties. The percent change in emissions is 

therefore 

% change in emissions = [exp(f(target fuel) - exp(f(baseline fuel)] / exp(f(baseline fuel) × 100% 

where the target fuel is the fuel whose properties are under consideration, and the baseline fuel is that 

given in Table III.C.1-1. Note that any constants in the function f will cancel out of the above 

equation. This equation can be rearranged to: 

% change in emissions = [100 / exp(f(baseline fuel)] × exp(f(target fuel) - 100 

The incorporation of the baseline fuel properties into the above equation need be done only once, 

so that the above equation can be represented as: 

% change in emissions = C × exp(f(target fuel)) - 100 

The constant C is called a “transformation constant” since it transforms the original equations, giving 

emissions in terms of g/bhp-hr, into equations that give the percent change in emissions with respect 

to the baseline fuel. The function f is simply that given in Tables III.B.3-5, III.B.3-6, and III.B.3-7 

for NOx, PM, and HC, respectively. However, we can ignore the intercepts for the regression 

equations since they cancel out when one is calculating a percent change in emissions. We 

calculated the constant C for every equation in our model (minus the intercepts) using the baseline 

36
 
 

 

 

 

 

 

 

fuel properties in Table III.D-1. These transformation constants are shown in Table III.D-2 for the 

Unified Model equations. 

Table III.D-2
 

Model Transformation Constants
 

Model 

Constant C 

NOx PM HC 

Default 33.883 14.735 98035 

Tech group B 26.366 n/a n/a 

Tech group L 33.776 n/a n/a 

Tech group R 32.753 n/a n/a 

Tech group X 7175.0 314.57 n/a 

Tech group H 21.710 n/a n/a 

Tech group ZZ n/a 0.16198 n/a 

The model equations with the above transformation constants provide a means for estimating 

the impact of fuel property changes on emissions for all vehicles in the current fleet. 

E. Extrapolation and valid ranges 

The applicability of any model to in-use fuels is limited by the distribution of fuel properties 

in the database on which the model is based. It is also important to take into account expected future 

fuels which may be cleaner than current fuels. Extrapolation can be used to extend model equations 

into regions of the multi-dimensional fuel property space that are not well represented by the 

database. If extrapolation cannot be justified, valid range limits may need to be assigned to the 

models. Valid range limits define the range of fuel properties within which the model equations can 

be used, and outside of which the predictions offered by the model equations are considered 

speculative and therefore not trustworthy. 

Section II.E presented ranges of fuel properties for fuels in our database through distribution 

plots. Included in those figures were summaries of in-use fuels surveys. For the most part, the data 

in our database does provide significant overlap with in-use fuels data, suggesting that our model 

can be used to evaluate most in-use fuels. In addition, the database actually contains many fuels 

which are generally cleaner than those found in-use. Since the primary use of the model will be to 

evaluate the emission benefits of cleaner fuels, this fact is an advantage for our model. 

We can delineate the region within which the predictions from our model can be considered 

trustworthy by examining the range of fuel property values in our database. As one approaches the 

edges of the fuel property dataset (e.g. very high or very low aromatics values), the confidence we 

have in the model's predictions decreases. Thus one way to define the valid range limits of the 

37
 
 

 

model is to determine high and low values for each fuel property that encompass a majority of the 

data. 

Using the subset of the database upon which the NOx model was based, we determined the 

two limiting values for each fuel property that encompassed 98 percent of the data. Thus 1 percent 

of the data lay above the high limit, and 1 percent of the data lay below the low limit; the valid range 

limits are thus defined by the percentile criteria 1 and 99. These results are shown in Table III.E-1. 

Table III.E-1
 

Valid range limits
 

Fuel property Lower limit Upper limit 

Natural cetane 38 66 

Cetane difference 0 17 

Aromatics, vol% 3 48 

Sulfur, ppm 0 3000 

Specific gravity 0.78 0.88 

Oxygen, wt% 0 3.5 

T10, o

F 340 525 

T50, oF 425 585 

T90, o

F 515 685 

We believe that the Unified Model should not be used to evaluate fuels outside the range of 

fuel properties given in Table III.E-1, and one should use caution when evaluating fuels near the 

valid range limits. Note that the oxygen limit of 3.5 wt% should also be combined with a 20 vol% 

limit on fuel oxygenate content, consistent with the limit we used in assembling the database. 

We do not believe it is appropriate to extrapolate the model into regions of the fuel property 

space that lie outside the valid range limits. The ranges defined by the values in Table III.E-1 are 

at least as wide as current in-use fuels, and in some cases are actually wider, so that fuels whose 

properties lie outside of our valid range limits would be unlikely. In addition, our percentile criteria 

of 1 and 99 percent capture the greatest possible amount of data, with the result that the model 

equations could easily be unrepresentative of fuel property effects on emissions if used outside the 

valid range limits. Even so, there may be cases in which a fuel with extremely high or low fuel 

properties must be evaluated for emission impact trends, if not absolute emission effects. For these 

cases we recommend that the equations be "flat-lined" outside the valid range limits. In other words, 

the emission effects predicted by the model at the valid range limits should be used for any cases 

where a fuel property exceeds the valid range limit. 

38
 
 

 

Note that, notwithstanding the valid range limits, the Unified Model should not be used to 

evaluate biodiesel, diesel/water emulsions, or other alternative fuels. Fischer-Tropsch fuels are not 

categorically excluded from the model (indeed the database contained several Fischer-Tropsch fuels), 

but they should still be considered in the context of the valid range limits. 

Among all the default models, there are only two cases in which nonlinear fuel terms produce 

turnover effects within the valid range limits. Both involve the natural and additized cetane terms, 

and both are cases in which we believe the model should be amended to prevent counterintuitive 

emission effect predictions which are simply artifacts of our selection of fuel terms. To amend the 

models, we have implemented flat-line extrapolation. 

In the default HC model, the existence of a squared term for natural cetane means that HC 

emissions are predicted to decrease with increasing natural cetane, until a minimum (mathematical 

extrema) is reached, after which the model predicts that HC emissions will increase with increasing 

natural cetane. In addition, the default HC model includes an interactive term for natural cetane × 

cetane difference, which forces the minimum point to vary. The effects of these nonlinear terms can 

be seen in Figure III.E-1. The arrows indicate the location of the minimum emissions values. 

Figure III.E-1
 

Floating turnovers in default HC model
 

3 

2.5 

2 

1.5 

1 

0.5 

0 

g/bhp-hr 

Cet diff = 0 

Cet diff = 5 

Cet diff = 10 

Cet diff = 17 Cet diff = 15

35 40 45 50 55 60 65 70 

Natural cetane 

39
 
Based to our stepwise modeling for individual technology groups, we would expect HC 

emissions to decrease as natural cetane increases. Therefore, we have determined that the predicted 

HC emissions should be held constant for any increases in natural cetane number above the 

mathematical extrema. Setting the first partial derivative for natural cetane equal to zero in the 

default HC model, we have derived an equation that calculates the location of the mathematical 

extrema as a function of the cetane difference. This equation is: 

"Turnover" point for natural cetane = -1.11598 × CETDIFF + 59.6493 

In practice, the natural cetane value that is entered into the default HC model should never be higher 

than the value calculated with the above equation. 

The default PM model also exhibits a turnover problem for natural cetane, but in this case 

the turnover in question is associated with the slope of the entire curve, not just one point on the 

curve. The problem is illustrated in Figure III.E-2. 

Figure III.E-2
 

Turnover effects in default PM model
 

g/bhp-hr 

0.2 

0.19 

0.18 

0.17 

0.16 

0.15 

0.14 

0.13 

35 40 45 50 55 60 65 70 

Cet diff = 17 

Cet diff = 15 

Cet diff = 10 

Cet diff = 5 

Cet diff = 0 

Natural cetane 

A similar graph can be shown for cetane difference, in which the slope of the curve is a function of 

the natural cetane number. 

40
 
 

 

 

 

 

 

Once again we believe, based on stepwise regressions with individual technology groups, that 

the primary trend should be for PM emissions to decrease as natural cetane or cetane difference 

increases. Therefore, we have determined that: 

1.	 The predicted effect of a change in natural cetane on PM emissions should be zero 

whenever the cetane difference value exceeds 4.48 (the value beyond which the 

natural cetane slope changes from negative to positive). 

2.	 The predicted effect of a change in cetane difference on PM emissions should be zero 

whenever the natural cetane value exceeds 47.81 (the value beyond which the cetane 

difference slope changes from negative to positive). 

In practice, this means that the cetane difference value that is entered into the default PM model 

should be 4.48 if the actual cetane difference value is higher than 4.48 and the actual natural cetane 

value is higher than 47.81. Likewise the natural cetane value that is entered into the default PM 

model should be 47.81 if the actual natural cetane value is higher than 47.81 and the actual cetane 

difference value is higher than 4.48. 

F.	 Summary of emission effects exhibited by equations 

The original intention of developing a model correlating diesel fuel properties with emissions 

was to ensure that the specific benefits claims for clean diesel fuel in the Texas State Implementation 

Plan were accurate. Given the nature of Texas’ clean diesel fuel program, we expect that fuel 

currently being sold in California is the best representation of what fuel in Houston and Dallas is 

likely to look like under their clean diesel fuel program. Therefore, we can examine the impacts that 

current California diesel fuel has on emissions according to our model as a way of estimating the 

benefits of Texas’ program. 

The average properties of current California diesel fuel were taken from surveys conducted 

by the Alliance of Automobile Manufacturers from 1995 through 2000, to be consistent with those 

for the baseline fuel as described in Section III.D-1 above. We were forced to use the survey results 

from one city, Los Angeles, since this is the only Californian city sampled in the AAM surveys. 

Also similar to the baseline fuel, we were forced to estimate the contribution that cetane improver 

additives make to the total cetane number of the average Californian diesel fuel. The results are 

shown in Table III.F-1. 

41
 
 

Table III.F-1
 

Average California Fuel Properties
 

Property Value 

Natural cetane number 

Cetane number increase due to additives 

Aromatics, vol% 

Specific gravity 

Sulfur, ppm 

Oxygen, wt% 

T10, oF 

T50, o

F 

T90, oF 

47.9 

4.4 

21.9 

0.837 

130 

0 

418 

502 

613 

We can input the fuel properties from Table III.F-1 into our default models to predict the 

impacts that the Texas clean diesel fuel program is likely to have on emissions from the heavy-duty 

fleet. These results are shown in Table III.F-2 for a "current" fleet (i.e. assuming EGR-equipped 

engines have not yet entered the fleet). 

Table III.F-2
 

Percent reduction in emissions for California diesel fuel
 

NOx PM HC 

6.2 8.5 19.4 

We can also use our equations to estimate the emissions impacts of discrete changes in 

specific diesel fuel properties. We have done this for each of the fuel properties that are represented 

in our model. In each case, the change was made to only one fuel property at a time, and was made 

relative to the baseline fuel defined in Table III.D-1. Colinearities between fuel properties were not 

taken into account. The results of these changes are shown in Table III.F-3 for a "current" fleet. 

42
 
Table III.F-3
 

Predicted percent emission reductions for discrete changes in fuel properties
 

(all values relative to baseline fuel)
 

NOx PM HC 

Increase natural cetane by 5 numbers 

Increase additized cetane by 5 numbers 

Decrease aromatics by 10 vol% 

Decrease specific gravity by 0.05 

Decrease sulfur by 100 ppm 

Increase oxygen by 1 wt% 

Decrease T10 by 10 oF 

Decrease T50 by 10 oF 

Decrease T90 by 10 oF 

0 

1.4 

2.9 

6.7 

0 

0 

0 

-0.4 

0 

1.8 

1.9 

2.1 

11.2 

0.8 

6.9 

0 

0 

0 

17.4 

15.3 

0 

0 

0 

0 

-1.0 

-2.5 

0 

G. Comparisons to other emission models 

As described in Section III.A, the different approaches that we investigated for generating 

regression equations did not appear to produce dramatically different results. But this fact may not 

be sufficient to conclude that our Unified Model provides reasonable predictions of fuel property 

effects on emissions. Therefore, it seemed prudent to compare predictions from our model to those 

from independent models created and published previous to this staff discussion document. 

We used the survey results for fuel sold in Los Angeles to make this comparison (Table III.F1). We input these fuel properties into a number of regression models for NOx developed by the 

authors of several different studies. Note that data from all of these studies were used in the 

development of the Unified Model. Figure III.G-1 shows the result. 

43
 
 

 

 

Figure III.G-1
 

Comparative NOx predictive effects for Los Angeles diesel fuel
 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

Percent reduction in NOx 

SAE 961074

SAE 950250, 1

SAE 950250, 2

VE-1, Phase I, 1

VE-10, DDC 60, 1

SAE 950251

VE-1, Phase I, 2

SAE 932800

VE-10, DDC 60, 2

Unified Model

VE-1, Phase I, 3

SAE 922267SAE 902172 

As this Figure shows, the predictions from our Unified Model compare favorably with those based 

on equations from other sources. Note that most of the other models do not contain a full 

complement of fuel properties, and many contain only two. Since the NOx benefits of fuel sold in 

Los Angeles arise from several different fuel properties, models with fewer terms might not predict 

the full benefit. In addition, the above Figure gives no indication of the amount of data each study 

contributed to our database, so drawing quantitative conclusions about the reliability of our model 

from this Figure would be premature. 

In 1998, MathPro Inc. and Energy and Environmental Analysis Inc. jointly produced a report 

for the State of Arizona in which they summarized their analysis of the impacts of changes in diesel 

fuel on emissions12 ("the MathPro report"). In similar fashion to our own analysis, the authors 

collected data from several different studies and conducted a regression analysis. Their database 

included 15 studies (in contrast to the 35 studies in our database), all of which contained transient 

cycle (FTP) test data. Although not as comprehensive as our own database, the MathPro model 

should provide a point of comparison to ensure that our own model is reasonable. 

We chose a selection of changes in fuel properties consistent with those used in Table III.F-3 

to determine how the predicted effects from our Unified Model and the MathPro model compared. 

The results are shown in Table III.G-1. 

44
 
Table III.G-1

Comparison of Unified Model and MathPro model

% reduction in NOx % reduction in PM % red

Unified Model MathPro model Unified Model† MathPro model Unified Mode

Increase natural cetane by 5 points 

0 1.4 1.8 1.8 17.4 

Increase additized cetane by 5 points 

1.4 1.4 1.9 1.8 15.3 

Lower aromatics by 10 vol% 

2.9 0.2 2.1 5.2 0 

Lower specific gravity by 0.05 

6.7 0 11.2 10.3 0 

Lower sulfur by 100 ppm 

0 0.2 0.8 0.3 0 

† "Best engineering models" were from the Mathpro report were used in this analysis 

45
 

 

 

In every case, the Unified Model and the MathPro model predict consistent directional 

changes in emissions for a given change in fuel properties. In most cases, the magnitudes of the 

predicted effects are also very similar. There are, however, some differences that could not be 

explained without a more thorough comparison of the two databases. For instance, the Unified 

Model appears to predict larger changes in NOx for reductions in aromatics and specific gravity. 

Even if the colinearities between these and other fuel properties are taken into account, the MathPro 

model still predicts NOx benefits one-third the size of the benefits predicted by the Unified Model. 

The Mathpro model also counter intuitively predicts an increase in HC emissions when aromatics 

are lowered, a fact that is addressed directly in the MathPro report. 

We request comment on the Unified Model in comparison to the MathPro model. 

Specifically, whether any of the following had a significant influence on the model coefficients: 

• The inclusion of steady-state data in our database 

• Differences in statistical methodology 

• Differences in the selection of fuel property terms to permit in the models 

• The distribution of engine technologies in the two databases 

• The degree to which studies in each database decorrelated fuel properties 

46
 
 

 

Section IV How Should The Model Be Used? 

A. Technology group weightings 

Our final model resulted from a regression approach that yielded one “default” model for 

each pollutant which applies to most of the fleet, and several technology group-specific models. To 

apply the fuel/emission effects exhibited by our final model to the entire in-use fleet, therefore, each 

of the technology group-specific models should be weighted according to the fraction of the in-use 

fleet that is represented by those technology groups. Ideally, this would require a detailed description 

of the in-use fleet that would include all of the engine parameters listed in Table II.C-1. 

Unfortunately, this type of description is not readily available. However, we did compile recent 

certification data that included rated speed, type of aspiration, horsepower, engine displacement, and 

the type of injection control. From this information we were able to determine which of our 

technology group-specific models would be expected to be important additions to the fleet-wide 

effects predicted by the default models. 

We used 1998-1999 certification data for diesel engines in this analysis, as this information 

was readily available. We had separate databases available for highway and nonroad engines. The 

certification data available did not include sales, but only listed certifications by engine family. Thus 

this information enabled us only to roughly estimate the percent of recent engine sales which would 

be expected to fall into specific technology groups. This analysis yielded the results shown in Table 

IV.A-1 for those technology groups having their own model. 

Table IV.A-1
 

Percent of selected technology groups in certification database
 

Technology Highway Nonroad engines 

group engines 

B 0 5 

H 0 1 

L 0 0 

R 0 0 

X 9 16 

ZZ† 0 0 

† All naturally aspirated engines also had indirect injection, and so 

were categorized as technology group X. 

Two-stroke engines (technology group B) are not separated from 4-stroke engines in either 

the highway or nonroad certification database. However, we were able to use the current version of 

EPA’s NONROAD model to estimate that only 5% of the current nonroad fleet is 2-stroke engines. 

For highway vehicles, we know that the only 2-stroke engines sold in recent years were produced 

by Detroit Diesel Corporation, who stopped production of these engines in 1997. 

47
 
 

 

 

From this recent data, it is clear that the technology groups which have their own model 

represent a rather small portion of current engine certifications and presumably sales. Although we 

don't have a straightforward means for estimating the contribution that these technology groups make 

to the full in-use fleet, we can surmise from this certification data that the fraction is small and will 

continue to decline over time. This conclusion may not apply to technology group X (indirect 

engines), since they appear to be a not insignificant portion of current sales, or technology group L 

(EGR-equipped engines), which are expected to increases in numbers in the future. Aside from these 

two exceptions, discussed more fully below, it appears appropriate to apply the effects exhibited by 

our default models for NOx, PM, and HC to the in-use fleet and to assume that the emission effects 

exhibited by technology group-specific models can be dismissed until the in-use fleets can be better 

characterized. 

As can be seen from the values in Table IV.A-1, indirect engines may represent a potentially 

not insignificant portion of the in-use fleet for both highway and nonroad. In our Unified Model, 

there is an independent equation for technology group X for both NOx and PM. However, our final 

NOx model for technology group X differs from the default model only in terms of the effect of T50 

on emissions. The slope of the T50 effects on NOx in the region of national average fuel properties 

is essentially identical for the default and technology group X models. As a result, we believe we 

can safely dismiss the technology group X model for NOx and instead use only the default NOx 

model when making estimates of emissions impacts for the both the highway and nonroad in-use 

fleets. 

For PM, the technology group X model differs from the default PM model in the natural 

cetane effect. Natural cetane has only a small impact on PM emissions in the default PM model 

relative to the effects of other fuel properties; specific gravity has by far the dominant impact. Thus 

any weighting of the default and technology group X models for PM is unlikely to differ 

substantially from the effects of the default model alone. For instance, if a weighting factor of 0.09 

was applied to the technology group X equation and a weighting factor of 0.91 was applied to the 

default equation consistent with the highway certification results in Table IV.A-1, California-like 

fueli

 would produce the PM reductions shown in Table IV.A-2. 

Table IV.A-2
 

Impacts of technology group X on percent reduction in PM emissions
 

Default model Default and technology group X models 

8.5 8.8 

Even though we used a weighting based on certification data in this example, we do not currently 

believe that we can confidently establish a weighting factor for technology group X in the PM model 

for either the highway or nonroad in-use fleets. We will continue to investigate assigning a 

i Based on fuel properties in Table III.F-1 

48
 
 

 

weighting factor to the technology group X model for PM. In the meantime, we propose that the 

default PM model be used. 

Finally, we expect EGR-equipped engines to become a larger portion of the highway fleet 

in the near future, and thus it seems prudent to include the NOx emission effects predicted by 

technology group L in any estimate of fleet-wide effects. For highway engines, we made the 

assumption that all future engines will be equipped with EGR beginning with the 2002 model year. 

We then estimated the fractional contribution that 2002 and later model year engines are expected 

to make to the heavy-duty diesel NOx inventory. The results are shown in Table IV.A-3. 

Table IV.A-3
 

Fraction of NOx inventory from EGR-equipped engines
 

Calender year Fraction of 

highway inventory 

2002 0.05 

2003 0.13 

2004 0.22 

2005 0.30 

2006 0.38 

2007 0.45 

2008 0.51 

2009 0.57 

2010 0.63 

The remainder of the highway NOx inventory would come from engines that do not have EGR. 

These fractions can be used to weight the results of the default and technology group L models to 

derive an estimate for the entire in-use fleet of highway engines. 

For nonroad engines, EGR may play a role in meeting future emission standards. However, 

EGR is unlikely to be used prior to 2006, and in fact manufacturers may choose to use aftertreatment 

instead. Given EGR's uncertain future in nonroad engines, we have not estimated the contribution 

that EGR-equipped engines may make to the nonroad inventory. Until such an estimate can be made 

with greater certainty, we propose that the default NOx model be used to represent all nonroad 

engines. 

B. Application to heavy-duty highway fleet 

The Unified Model can be used to evaluate the impacts of changes in diesel fuel on emissions 

of NOx, PM, and HC for the current fleet of heavy-duty highway diesel vehicles. It cannot be 

applied to light-duty diesel vehicles because we do not have sufficient information to determine if 

49
 
 

 

 

 

 

 

 

 

 

 

light-duty vehicles respond in the same way to changes in diesel fuel properties as heavy-duty 

engines (see further discussion of light-duty in Section VI). We have determined that the Unified 

Model can be applied to heavy-duty nonroad diesel engines with appropriate caveats and distinctions 

(see Section IV.E below). 

In using the Unified Model, it is the percent change in emissions that is most relevant. The 

equations as described in Section III.A.3 do permit calculation of emissions in g/bhp-hr, but these 

emission estimates should not be used as the basis of inventory estimates. Instead, the model 

equations should be used to calculate the percent change in emissions resulting from a change from 

one fuel to another. This percent change can then be applied to the inventory estimates for a 

particular region that have been determined separately according to accepted procedures. Note that 

for those parties wanting to evaluate the inventory impacts of changes to nonroad diesel fuel, or 

changes to highway diesel fuel sold in a particular area, it may be necessary to generate an alternative 

baseline fuel rather than use the nationwide average fuel summarized in Table III.D-1. 

The calculation of a percent change in emissions requires that a baseline fuel be established. 

Under most circumstances the most appropriate baseline fuel would be the national average highway 

diesel fuel. This baseline is discussed in Section III.C.1, and the model transformation constants in 

Table III.C.1-2 convert the equations in g/bhp-hr into equations in terms of percent change. The 

Unified Model can also be used with an alternative baseline if one can be established with sufficient 

precision. 

The predictive capabilities of the Unified Model will be a function of whether the candidate 

fuel represents a real, in-use fuel. If the model is being used to evaluate a fuel that already exists and 

whose properties have been measured, or to evaluate a fuel which represents the average of a 

selection of in-use fuels, this criterion is obviously fulfilled. If the fuel being evaluated with the 

model is instead conceptual, care must be taken to ensure that the fuel represents something the 

refiners would or could actually produce. 

As discussed in Section IV.A above, we determined that the default models for NOx, PM, 

and HC should be sufficient for representing the current in-use fleet of heavy-duty highway diesel 

vehicles. The lack of precise information on the fraction of the fleet represented by specific 

technology groups and the fact that the inclusion of emission impacts from technology group-specific 

equations would not substantially affect the predictions for the current fleet led us to this conclusion. 

However, technology group L is an exception for NOx because EGR-equipped engines are expected 

to become an increasing fraction of the fleet in the future, and the additized cetane effect for EGRequipped engines appears to run opposite to that for the rest of the fleet. Therefore, we propose that 

the emission impacts for changes in diesel fuel properties should be based on both the default and 

the technology group L models for NOx, and on the default models for PM and HC. 

As described in Section IV.A above, we have estimated the contribution that EGR-equipped 

engines make to the fleet-wide NOx inventory based on the assumption that all 2002 model year and 

later heavy-duty diesel vehicles will have EGR. This approach provides us with a means of 

50
 
weighting the predicted percent change effects from the default NOx model and the technology 

group L NOx model. The weighting factors are shown in Table IV.B-1. The constants "a" and "b" 

appear in the final NOx equation below. 

Table IV.B-1
 

Highway weighting factors for NOx model percent change predictions
 

Calender year 

Default NOx Model 

Constant "a" 

Tech group L NOx model 

Constant "b" 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

0.95 

0.87 

0.78 

0.70 

0.62 

0.55 

0.49 

0.43 

0.37 

0.05 

0.13 

0.22 

0.30 

0.38 

0.45 

0.51 

0.57 

0.63 

The final NOx model for highway vehicles is:
 

% change in NOx emissions = a × 33.883 × exp( - 0.002779 × cetane difference
 

+ 0.002922 × aromatics, vol% 

+ 1.3966 × specific gravity 

- 0.0004023 × T50, o

F ) 

+ b × 33.776 × exp( + 0.001172 × cetane difference 

+ 0.002922 × aromatics, vol% 

+ 1.3966 × specific gravity 

- 0.0004023 × T50, o

F ) - 100 

The final PM model for highway vehicles is:
 

% change in PM emissions = 14.735 × exp( - 0.004521 × natural cetane
 

- 0.04825 × cetane difference 

+ 0.001009 × natural cetane × cetane difference 

+ 0.002157 × aromatics, vol% 

+ 0.00008386 × sulfur, ppm 

+ 2.3708 

- 0.07193 

× specific gravity 

× oxygen, wt% ) - 100 

The final HC model for highway vehicles is: 

% change in HC emissions = 98035 × exp( - 0.1875 

+ 0.001571 

- 0.1880 

× natural cetane 

× natural cetane2 

× cetane difference 

51
 
 

 

 

 

 

 

+ 0.003507 × natural cetane × cetane difference 

- 0.0009809 × T10, oF 

- 0.002448 × T50, oF ) - 100 

Note that these equations must be used in the context of the valid range limits and extrapolations 

described in Section III.D. 

C. Biodiesel 

There has been increasing interest in recent years in the use of biodiesel, soy or animal 

fat-based esters that can be used as a substitute for petroleum-based diesel fuel. Several studies have 

found HC and PM benefits from the use of biodiesel, and its lubricity characteristics and 

renewability are also motivators for its use. Several municipalities and States are considering 

mandating the use of low levels of biodiesel in diesel fuel. Having an estimate of the emission 

benefits of biodiesel would be a valuable element in any decision to promote or mandate the use of 

biodiesel. 

In reviewing data for inclusion in our database, we found several studies that examined 

oxygenated fuels, including biodiesel. Many of these studies did not include the detailed fuel 

property or emissions measurements that would have been necessary for use in our modeling effort. 

Of those studies that did contain sufficient data for inclusion in our database, only three studies 

included biodiesel testing. These three studies contributed only three separate biodiesel blends to 

the database, out of 300 total fuels. 

Given the fuel properties that we selected for inclusion in our model, all three biodiesel fuels 

in our database were missing at least one of those fuel properties. For instance, two of the biodiesel 

fuels contained all fuel property data except distillation measurements. The third biodiesel included 

all fuel property data except the oxygen content (we did not make estimates of missing fuel property 

measurements). As a result, given the approach we took to model development, none of the three 

biodiesel blends in our database was actually included in the regressions. Thus, our model cannot 

be used to evaluate the emission effects of biodiesel. 

Even if we had chosen an alternative set of fuel properties on which to base our regression 

in order to include all three biodiesel fuels in the regressions, it may still not have been appropriate 

to permit use of our model for evaluating biodiesel. Biodiesel can be blended into petroleum-based 

diesel fuel at any concentration, and it is as yet unclear if the emission effects of such addition would 

correlate linearly with the biodiesel blend fraction. Since we have only two biodiesel fuels with 

oxygen measurements in our database, nonlinear effects cannot be captured in our model. We also 

do not have sufficient information to determine if different types of biodiesel (from different 

feedstock sources or processing) have different effects on emissions. 

52
 
 

 

 

 

 

Despite that fact that our model cannot be used to evaluate biodiesel blends, having estimates 

of the emission benefits of biodiesel would be invaluable to anyone considering its use. We have 

therefore decided to conduct an independent study of biodiesel emission effects. Although this study 

is unlikely to generate a model of the sort presented in this discussion document, it will include a 

thorough investigation of currently available data on biodiesel emission effects with the intention 

of assessing its sufficiency for estimating emission impacts for the current fleet. This study will be 

conducted over the next few months, with a draft report expected by late in 2001. 

D. Other oxygenates 

In constructing our database, we included any studies that investigated oxygenated diesel 

fuels, making no restrictions on the types of oxygenates that would be considered. As a result, a total 

of 21 oxygenated test fuels were included in our database. However, 15 of these fuels were missing 

measurements for one or more fuel properties, and so were effectively excluded from our model 

development. Of the remaining six fuels, only three types of oxygenates were represented, as shown 

in Table IV.D-1. 

Table IV.D-1
 

Oxygenated fuels arising in regressions
 

Study Fuel ID Oxygenate Oxygen, wt% 

VE-10 

VE-10 

VE-10 

VE-10 

VE-10 

SAE 972898 

D 

E 

F 

G 

I 

K 

Monoglyme 

Monoglyme 

Diglyme 

Diglyme 

Diglyme 

C11 heavy alcohols 

2.09 

3.64 

2.24 

4.02 

4.19 

0.3 

As we conducted our regression analyses, we permitted oxygen terms (in weight percent 

oxygen) to appear in the model. We determined that there was insufficient data to permit oxygenatespecific terms. Thus the use of our model to represent oxygenated diesel fuels must be conducted 

on a generic basis with no specific reference to the type of oxygenate. 

As the table shows, the types of oxygenates actually represented in the regressions that 

produced our models are quite limited, in comparison to the broad array of oxygenates available for 

blending into diesel fuel. As a result we determined that our models should only be used to represent 

fuels containing the types of oxygenates that actually played a role in the regressions. In addition, 

the use of unspecific "C11 heavy alcohols" in SAE paper number 972898 is unlikely to have had any 

appreciable influence on the correlation of oxygen content with emission due to the low 

concentration of this oxygenate. We do not believe, therefore, that alcohols should be evaluated with 

our model, either. 

53
 
 

 

 

 

The only oxygenates that we believe should be evaluated with our model are glycol ethers 

similar to monoglyme and diglyme (proper names: ethylene glycol dimethyl ether and diethylene 

glycol dimethyl ether). The emission impacts of other oxygenated diesel fuels should be evaluated 

based on data which is specific to the oxygenate in question. 

E. Application to CI nonroad fleet 

Nonroad compression-ignition engines are an important portion of the diesel fleet and an 

important contributor to inventories of regulated pollutants. Therefore, in addition to understanding 

the correlation of diesel fuel parameters with emissions from highway engines, it is important to 

understand this correlation in nonroad engines. 

However, there are very few studies of fuel effects in nonroad engines and it was not possible 

to develop a separate model for nonroad. For this reason, we considered options for applying the 

results of the Unified Model for highway vehicles to the nonroad fleet. There are several issues 

associated with this type of extrapolation, some of which are discussed below and in Section VII.B.6. 

We welcome comments on the degree to which our model can be applied to nonroad. Note that a 

new study that does look at fuel effects in nonroad engines will become available soon and offers 

an opportunity to test our assumptions that our Unified Model can be appropriately applied to 

nonroad. We will be evaluating this data in the coming months. 

Most nonroad engines use technologies similar to those found in highway vehicle engines, 

although in a given year, the highway vehicle technology is generally more advanced. Thus, 

subgroups of nonroad engines can be mapped to appropriate subgroups of highway vehicle engines.

 Furthermore, our Unified Model suggests that most technologies exhibited a similar response to 

variations in fuel parameters. Thus, in most cases, the distinctions between nonroad and highway 

vehicle technologies may not be important for the purpose of evaluating relative emission effects of 

fuel changes. On the basis of technology, then, we believe it is appropriate to apply the Unified 

Model to nonroad. 

There are some concerns that the type of operation that nonroad engines experience may be 

sufficiently different from the operation of highway vehicles that our Unified Model, based on test 

cycles designed to represent highway driving, may not be applicable to nonroad. However, there are 

a variety of test cycles which could represent nonroad applications which are currently being 

evaluated. The current body of data on nonroad engine cycles is insufficient to indicate whether the 

effect of changes in diesel fuel properties will affect emissions differently for nonroad engines than 

for highway engines. On the basis of the information we currently have, then, we believe that the 

relative effects exhibited by the Unified Model are applicable to nonroad. See the additional 

discussion of this issue in Section VII.B.6. 

As for application to highway engines, we propose that the default models we developed 

using our Unified Approach be applied to nonroad without accounting for the technology group54
 
 

specific models that are likely to represent only a small portion of the fleet (see Section IV.A). For
 

highway applications, we made one exception for EGR-equipped engines based on the expectation
 

that EGR will play a more prominent role in the future. However, as discussed in Section IV.A, we
 

do not have the same level of confidence that EGR will play a prominent role in nonroad engines.
 

Therefore, we do not believe it appropriate to introduce weighting factors for technology group L
 

into the final equations when they are used to represent nonroad. 

The final NOx model for nonroad engines is:
 

% change in NOx emissions = 33.883 × exp( - 0.002779 × cetane difference
 

+ 0.002922 × aromatics, vol% 

+ 1.3966 × specific gravity 

- 0.0004023 × T50, o

F ) - 100 

The final PM model for nonroad engines is:
 

% change in PM emissions = 14.735 × exp( - 0.004521 × natural cetane
 

- 0.04825 × cetane difference 

+ 0.001009 × natural cetane × cetane difference 

+ 0.002157 × aromatics, vol% 

+ 0.00008386 × sulfur, ppm 

+ 2.3708 × specific gravity 

- 0.07193 × oxygen, wt% ) - 100 

The final HC model for nonroad engines is: 

% change in HC emissions = 98035 × exp( - 0.1875 × natural cetane 

+ 0.001571 × natural cetane2 

- 0.1880 × cetane difference 

+ 0.003507 × natural cetane × cetane difference 

- 0.0009809 × T10, o

F 

- 0.002448 × T50, o

F ) - 100 

Note that these equations must be used in the context of the valid range limits and extrapolations 

described in Section III.D. 

55
 
 

 

Section V Diesel Fuel Property Effects On Toxics 

A. Introduction 

This section considers the impact of diesel fuel properties and qualities on toxics emissions. 

Toxics emissions can be reduced via control of criteria pollutants such as oxides of nitrogen (NOx), 

volatile organic compounds (VOCs), carbon monoxide (CO), and particulate matter (PM). For 

instance, our recently promulgated diesel fuel rule [66 FR 5002, January 18, 2001] limits the 

maximum sulfur level of on-highway diesel to 15ppm. This enables the use of aftertreatment 

technology for the control of PM and NOx emissions (e.g., traps and NOx adsorbers). As a result, 

certain types of toxics emissions are also expected to be reduced. However, our focus here is to 

predict the change in toxics emissions solely through specific fuel quality changes. 

EPA’s Mobile Source Air Toxics (MSAT) list specifies 21 compounds emitted from motor 

vehicles that are known or suspected to cause cancer or other serious health effects. The list includes 

VOCs, metals, diesel particulate (DPM) and diesel exhaust organic gases (DEOG). The specific 

compounds are shown in Table V.A.1. The selection process for including, adding and removing 

compounds from the list is described in the recently promulgated rule, “Control of Emissions of 

Hazardous Air Pollutants from Mobile Sources” [66 FR 17230, March 29, 2001]. While that rule 

focused on toxic compounds emitted from gasoline-fueled vehicles, many of the same compounds 

are found in diesel exhaust organic gases. In that rule, the toxics regulated (as a group) are benzene, 

1,3-butadiene, formaldehyde, acetaldehyde and polycyclic organic matter (POM). 

Table V.A-1
 

List of Mobile Source Air Toxics (MSAT)
 

1. Acetaldehyde 8. Dioxin/Furans 15. MTBE 

2. Acrolein 9. Ethylbenzene 16. Naphthalene 

3. Arsenic Compounds 10. Formaldehyde 17. Nickel Compounds 

4. Benzene 11. n-Hexane 18. POM 

5. 1,3 - Butadiene 12. Lead Compounds 19. Styrene 

6. Chromium Compounds 13. Manganese Compounds 20. Toluene 

7. Diesel Particulate Matter 14. Mercury Compounds 21. Xylene 

+ Diesel Exhaust Organic 

Gases (DPM + DEOG) 

As will be seen in the results of the studies reviewed here, many of these compounds are also 

found in diesel emissions. 

Few studies of the effects of diesel fuel quality on toxics emissions exist. Most studies which 

evaluate diesel fuel quality changes have only measured their impact on criteria pollutants. Only a 

few studies report emissions of carbonyls (aldehydes and ketones) or/and gas phase toxic 

hydrocarbons such as benzene and 1,3-butadiene. Still, the limited information available can be 

useful for directionally indicating the likely outcome of a diesel fuel quality change on toxics., and 

56
 
 

 

also for directing researchers to future work in this area. Three studies which evaluated diesel fuel 

quality changes and also reported emissions of certain toxic compounds are discussed below. 

B. Studies Which Measured Emissions of Toxics 

Of the studies which were included in the development of the NOx, PM, and HC models 

above, only three measured toxic emissions. One was a comprehensive analysis performed for the 

California Air Resources Board (CARB). The other two studies were performed by Arco Chemical 

Company and focused on the impact of cetane additives on emissions. These studies are described 

below, along with a summary of their findings. 

The European Programmes on Emissions, Fuels and Engine Technologies (EPEFE) - Light 

Duty Diesel Study (SAE 961073) also measured certain toxics emissions, and the results are 

discussed briefly. However, because the speciation measurements were made only for a single test 

of each fuel/vehicle combination, they concluded that “a statistical analysis...was not feasible.” 

1. CARB Report13 

The study performed for CARB tested three diesel fuels in a Cummins L10 engine. The three 

fuels included a pre-1993 diesel fuel (beginning in 1993, CARB regulations limited diesel sulfur to 

5 ppm, minimum cetane index of 40 and maximum aromatic content to 10 vol%), a low aromatic 

fuel, and an alternative formulation which should achieve the same emissions reductions as the low 

aromatic fuel. The fuel specification ranges are shown in Table V.B.1-1. 

Table V.B.1-1
 

Diesel Fuel Specification Ranges
 

Pre-1993 Low Aromatic Reformulated 

Aromatics (vol%) 33 10 max 20-25 

Sulfur (ppm) <5000 500 max 100-300 

Cetane number >40 48 min 50-55 

PAH (wt%) 8 1.4 max 2-5 

Nitrogen (ppm) 300-600 10 max 40-500 

API gravity measurements of the fuels were 32.8, 37.2 and 37.1, respectively. 

Total hydrocarbon, NOx and PM emissions were all reduced for both the low aromatic fuel 

and the reformulated fuel compared to the Pre-1993 fuel (Table V.B.1-2). However, only the total 

57
 
hydrocarbon and PM emissions changes from the Pre-1993 fuel were deemed statistically significantj

. 

Table V.B.1-2 

Mean Weighted Total Emissionsk 

NOx THC PM 

gm/bhp-hr %l gm/bhp-hr % gm/bhp-hr % 

Pre-1993 4.77 --- 0.53 --- 0.224 ---

Low Aromatic 4.44 -7.1 0.47 -11 0.183 -18 

Reformulated 4.64 -2.7 0.5 -5.7 0.186 -17 

Though carbonyls increased in all cases compared to the Pre-1993 fuel (Table V.B.1-3), only 

the acetaldehyde results for both the low aromatic and reformulated fuels, and the acrolein results 

for the low aromatic fuel were deemed statistically significant. 

Table V.B.1-3
 

Carbonyls
 

Mean Weighted Total Emissions 

Formaldehyde Acetaldehyde Acrolein Propionaldehyde 

mg/bhphr 

% mg/bhphr % mg/bhphr % mg/bhphr % 

Pre-1993 57.12 --- 18.15 --- 2.14 --- 3.69 ---

Low Aromatic 58.75 2.8 19.1 5.2 5.79 171 3.92 6.2 

Reformulated 59.83 4.7 19.93 9.8 2.42 14 4.13 12 

The direction of the changes in gas phase emissions of specific hydrocarbons were mixed. 

The low aromatic fuel showed significant increases (in terms of percent change) for benzene, 1,3-

butadiene, toluene and styrene emissions. This fuel also significantly reduced ethylbenzene, oxylene and m&p-xylene (Tables V.B.1-4A, B, C). The results of the low aromatic fuel were 

statistically significant only for benzene, toluene, and m&p-xylene. There were no statistically 

significant differences between the Pre-1993 and reformulated fuel gas phase hydrocarbon emissions. 

j Significant at 95% confidence limit using Fisher’s Protected Least Significant Difference Test.
 k Weighting of cold start and hot start emissions, 1/7 and 6/7, respectively.
 l Percent change from the Pre-1993 fuel.
 

58
 
Table V.B.1-4A
 

Gas Phase Hydrocarbons - A
 

Mean Weighted Total Emissions 

Benzene 1,3-Butadiene 

mg/bhp-hr % mg/bhp-hr % 

Pre-1993 5.9 --- 1.8 ---

Low Aromatic 8.03 36 2.46 37 

Reformulated 5.81 -1.5 1.84 2.9 

Table V.B.1-4B
 

Gas Phase Hydrocarbons - B
 

Mean Weighted Total Emissions 

Toluene Ethylbenzene O-Xylene m&p-Xylene 

mg/bhphr 

% mg/bhphr % mg/bhphr % mg/bhphr % 

Pre-1993 1.93 --- 1.22 --- 0.78 --- 2.09 ---

Low Aromatic 2.26 17 0.67 -45 0.61 -21 1.24 -40 

Reformulated 1.86 -3.6 1.18 -3.6 0.88 12 2.14 2.5 

Table V.B.1-4C
 

Gas Phase Hydrocarbons - C
 

Mean Weighted Total Emissions 

Styrene Naphthalene 

mg/bhp-hr % mg/bhp-hr % 

Pre-1993 1.27 — 1.69 ---

Low Aromatic 1.58 24 1.74 2.9 

Reformulated 1.45 14 1.27 -24 

An overall decrease was seen in particle-bound Polycyclic Aromatic Hydrocarbon (PAH) 

emissions based on hot start sampling only. Nitro-PAH hot-start emissions changes compared to 

59 
 

emissions from the Pre-1993 fuel were mixed directionally. For specific compounds, the percent 

change in emissions from the Pre-1993 fuel ranged from -54% to +35% for the low aromatic fuel 

and -39% to +27% for the reformulated fuel. In half the cases, due to analytical limits, no 

changes were noted between the Pre-1993 fuel and the other two fuels. Results of single hot start 

sampling showed reductions in vapor phase PAH emissions; the reductions for the low aromatic 

fuel were particularly large, for several compounds over 85%. 

2. Arco Chemical Company Cetane Improvement Additive Studies 

In two related studies, Arco Chemical Company compared the emissions of a base fuel 

with the emissions of a fuel which contained either a peroxide-based cetane improvement 

additive or a conventional cetane improvement additive, 2-ethylhexyl nitrate. The minimum 

cetane number increase was nine, and the emission measurements were based on hot-start cycles 

only. The first study14 looked at a single base fuel (compared to additized fuels); the second 

study15 looked at three different levels of each additive. A single engine, Detroit Diesel Corp., 

1991 Prototype Series 60, was used for all tests. 

For a given base fuel, the additized fuels produced similar hydrocarbon reductions. These 

reductions ranged from 40-75%. Speciated toxics emissions, including benzene, 1,3-butadiene, 

aldehydes and ketones, were also significantly reduced with the cetane-improved fuels compared 

to the corresponding base fuels. A summary of the toxics emission results is shown in Table 

V.B.2-1. The percent reductions from the corresponding base fuel emissions are shown in Table 

V.B.2-2. These reductions are in line with the reductions in hydrocarbon emissions. . 

60
 
Table V.B.2-1

Emission Results for Additized Fuels

0.40% ethylhexyl nitrate 

0.55% ethylhexyl nitrate 

0.65% ethylhexyl nitrate 

0.70% ethylhexyl nitrate 

0.50% peroxide 

0.70% peroxide 

0.75% perox

Cetane number 56 52 49 57 57 53 4

Emissions (mg/bhp-hr) 

Acetaldehyde 11 9 18 12 14 10 1

Acetone 11 3.7 7 4 18 5.8 Acrolein 3 3 5 2 3 3 Benzaldehyde 1 0.9 0.5 0 2 0.7 1Benzene 0.8 --- 1.6 1.7 1.4 --- 01,3-Butadiene 1.7 --- 2 1.4 1.4 --- 1Crotonaldehyde 3 1.2 5 2 4 1.0 

Formaldehyde 25 23 47 25 26 24 3

Hexanaldehyde 2 0.7 8 2 2 0.6 

Isobutyraldehyde + MEK 

1 1.2 2 0.4 2 1.0 

Propionaldehyde 3 2 9 3 4 1 

61
Table V.B.2-2

Changes from Base Fuel

0.40% ethylhexyl nitrate 

0.55% ethylhexyl nitrate 

0.65% ethylhexyl nitrate 

0.70% ethylhexyl nitrate 

0.50% peroxide 

0.70% peroxide 

0.75% perox

Cetane number (absolute change) 

+10 +9 +10 +15 +11 +10 +

% Change in Emissions 

Acetaldehyde -70 -62 -66 -68 -62 -58 -

Acetone -68 -63 -63 -69 -47 -42 -

Acrolein -70 -57 -50 -78 -70 -57 -

Benzaldehyde -67 -55 -92 -100 -33 -65 -Benzene -62 ---- -73 -39 -33 --- -

1,3-Butadiene -29 ---- -66 -61 -41 --- -Crotonaldehyde -57 -57 -75 -83 -43 -64 -Formaldehyde -44 -62 -70 -67 -42 -60 -

Hexanaldehyde -71 -82 -80 -50 -71 -84 -

Isobutyraldehyde + MEK 

-83 -74 -80 -90 -67 -79 -

Propionaldehyde -73 -71 -83 -70 -64 -86 -

62
3. EPEFE Light Duty Diesel Study 

As mentioned above, the toxics measurements described in the light-duty study 

conducted by the European Programme on Emissions, Fuels and Engine Technologies (EPEFE)16 

were not sufficient to allow statistical analysis. Nonetheless, the summary of their findings is 

informative and would provide a helpful start when comparing results of other test programs or 

looking for starting points for future test programs. 

Table V.B.3-1
 

EPEFE Toxics Summary
 

Parameter Effect on emissions 

Engine technology vs. Fuel quality “...engine technology has a greater effect on air toxic 

emissions than changes in fuel quality.” 

Density and polyaromatics a. Decreasing density decreased benzene and 1,3 -

butadiene in line with THC 

b. Decreasing polyaromatics increased benzene 

c. Decreasing density and polyaromatics decreased 

formaldehyde 

d. Decreasing density (at low polyaromatics) or 

decreasing polyaromatics (at low density) decreased 

acetaldehyde 

Cetane number a. Increasing cetane number decreased benzene and 

1,3 - butadiene in line with THC 

b. Increasing cetane number decreased acetaldehyde 

Back end distillation (T95) a. Decreasing T95 may increase benzene 

b. Decreasing T95 increased formaldehyde 

C. Conclusions and Next Steps 

The results of the studies evaluated for this staff discussion document show that changes 

in certain diesel fuel qualities, such as cetane number, may significantly affect toxic emissions 

from diesel engines. For other diesel fuel qualities, such as sulfur or aromatic content, the results 

are mixed (a given change in fuel quality increases one type of emission while decreasing 

another) or are not statistically significant. Clearly more testing of diesel fuels of varied 

composition is needed. Such testing should further address which fuel properties are most 

important for controlling individual toxic compounds, and whether conclusions can be drawn for 

specific fuel formulations rather than individual fuel properties. 

63
 
 

Section VI Diesel Fuel Effects In Light-Duty Vehicles 

A.	 Introduction 

The available data on fuel effects in light-duty diesels are quite limited. As a result, it 

was not possible to develop a model for light-duty vehicles. Instead, we reviewed the data for 

consistent trends in fuel effects. The studies17,18,19,20,21 examined the effects of diesel fuel 

properties on exhaust emissions (i.e., PM, NOx, HC, and CO) for light-duty vehicles/engines. 

The researchers focused mainly on four key properties in their investigations of diesel fuel 

effects: density, aromatics content, cetane number, and distillation range. Even though the effect 

of sulfur on diesel particulate formation is well established, some investigators also examined 

how sulfur interacted with oxidation catalysts and various test cycles to affect PM formation (See 

references 18, 20, 21). 

The studies tested light-duty vehicles or trucks made in the 1990s. These 

vehicles/engines encompassed a combination of the following technologies: 

- Electronically or mechanically controlled fuel injection system 

- Naturally aspirated (NA) or turbocharged (TC) engines, some with intercooling 

- Direct injection (DI) or pre or swirl chambers indirect injection (IDI) combustion 

chambers 

- Exhaust Gas Recirculation (EGR) - electronically and mechanically controlled 

- Oxidation Catalysts 

The test cycles used in the light-duty studies included the European MVEG test cycles 

(ECE15+EUDC), the European ECE R49, and the U.S. FTP. For the purpose of our light-duty 

analysis, we focused mainly on vehicle testing results, although comparisons were also made 

with some results obtained from engine testing. Table VI.A-1 lists the ranges of diesel fuel 

properties examined across various studies. 

Table VI.A-1
 

Ranges of Fuel Properties Examined
 

Property density 

(kg/m3

) 

poly-aromatics 

(wt%) 

cetane 

number 

T95 (oC) viscosity 

cSt @40oC 

Range 805-857 0-11 45-70 248-391 1.3-3.9 

B.	 Individual Studies 

1.	 EPEFE Study 

64 
During the mid-1990s, an initiative was carried out by the European Automobile and Oil 

Industries - the European Programme on Emissions, Fuels and Engine Technology (EPEFE). 

This program examined a fleet of 19 vehicles (17 passenger cars, 2 light duty trucks), all fitted 

with oxidation catalysts. All testing were done against the MVEG test cycle (ECE15+EUDC). 

The objective of this program was to focus on density, poly-aromatics, cetane number, and T95. 

The investigators studied the poly-aromatics content but did not report the total aromatics 

content. They examined how these fuel variables affected PM, NOx, HC, and CO emissions. 

Table VI.B.1-1 shows the eleven fuels in the fuel matrix. The fuels were designed to decorrelate 

the individual effects of density, poly-aromatics content, cetane number, T95, and especially 

density and poly-aromatics content which are closely intercorrelated in market fuels. 

Table VI.B.1-1
 

EPEFE Fuel Properties
 

Fuel No. Density 

kg/m3 

Poly-aromatics % Cetane Number T95 

Celsius 

EPD1 829.2 1.0 51.0 344 

EPD2 828.8 7.7 50.2 349 

EPD3 857.0 1.1 50.0 348 

EPD4 855.1 7.4 50.3 344 

EPD5 828.8 7.1 50.6 346 

EPD6 855.5 7.6 50.2 371 

EPD7 826.9 1 49.5 326 

EPD8 855.1 7.3 54.8 345 

EPD9 855.4 8 59.1 344 

EPD10 826.6 1.1 58.0 347 

EPD11 827 0.9 57.1 329 

The investigators performed extensive data analysis that included pairwise comparisons, 

submatrix analysis, and full regression analysis. Specifically, they separated the fuel matrix into 

three individual subsets of fuels, allowing comparisons of fuels by varying in one specified 

property only: 

Matrix (1): EPD1-5 to investigate the effect of density and poly-aromatics 

Matrix (2): EPD1,7,10,11 at low density and EPD 4,8,9 at high density to investigate 

the effects of cetane number 

65
 
Matrix (3):	 EPD 1,7,10,11 at low density and EPD 4,6 at high density to investigate 

the effects of T95 

2. Lange Study 

Lange et al.18 tested a Mercedes-Benz 250 D (2.5 liter) engine typical of the 1991-1993 

model years. This passenger vehicle had a 5 cylinder naturally aspirated IDI engine, and it was 

equipped with an EGR and an oxidation catalyst, enabling it to meet 91/441/EEC emissions 

standards. The researchers reported the effect of fuel changes on PM and NOx 

emissions. They designed three sets of diesel fuels (12 fuels total) for vehicle emission testing 

over the ECE15+EUDC cycle. The fuel properties and chemical composition are shown in Table 

VI.B.2-1. 

Table VI.B.2-1
 

Fuel Properties and Chemical Composition
 

Unit SET 1 SET 2 SET 3 

Fuels 1 2 3 4 5 6 7 8 9 10 11 12 

Density g/ml 0.826 0.826 0.826 0.826 0.826 0.837 0.807 0.814 0.834 0.844 0.838 0.842 

Distillation 

T10E 

T50E 

T90E 

T95E 

FBP 

o

C 

o

C 

o

C 

o

C 

o

C 

228 

278 

323 

335 

347 

228 

272 

324 

335 

350 

227 

272 

325 

337 

351 

229 

281 

326 

338 

353 

228 

270 

326 

338 

354 

219 

269 

326 

348 

368 

234 

296 

346 

355 

364 

213 

231 

269 

279 

293 

235 

288 

345 

368 

380 

224 

280 

339 

358 

381 

220 

279 

350 

371 

389 

223 

281 

344 

364 

389 

Cetane no. 56.4 56.4 56.5 56.4 56.1 50.0 70 54 59 48 51 50 

Viscosity 

@ 40o

C 

mm2

/s 3.12 3.09 3.08 2.90 2.88 2.82 3.90 1.93 3.85 3.11 3.26 3.35 

Sulfur 

content 

ppm m <10 220 450 960 1800 500 <10 10 45 680 450 430 

Aromatics 

Mono 

DI 

TRI+ 

Poly 

Total 

m% 

m% 

m% 

m% 

m% 

7.95 

0.05 

0.02 

0.07 

8.02 

9.74 

0.20 

0.46 

0.66 

10.39 

11.61 

0.56 

0.73 

1.29 

12.90 

16.57 

2.04 

1.21 

3.25 

19.82 

16.72 

2.09 

1.04 

3.13 

19.84 

24.9 

1.9 

1.4 

3.3 

28.3 

<0.05 

<0.05 

<0.05 

<0.05 

<0.05 

9.7 

0.1 

<0.05 

0.1 

9.8 

4.1 

0.1 

<0.05 

<0.1 

4.2 

23.4 

2.7 

3.0 

5.7 

29.1 

22.4 

1.7 

1.3 

3.0 

25.4 

22.4 

1.7 

1.3 

3.0 

25.4 

The three sets of fuels were designed for studying the effect of sulfur, mono- and polyaromatics content, density, cetane number, and distillation properties on PM and NOx emissions. 

The objectives for individual sets of fuels are described in the following: 

Set (1): Fuels 1-5 were designed to decorrelate fuel sulfur content from other 

properties. 

66
 
Set (2):Fuels 6-9 were to decorrelate fuel density from aromatics (i.e., fuel pairs 6 and 9 

or 7 and 8 were similar in density, but significantly different total aromatics 

content.) No attempt was made to control the variation in cetane number and 

distillation in this fuel set. 

Set (3):Fuels 10-12 focused on the poly-aromatics/distillation properties. 

The authors studied fuel effects by making pairwise comparisons as well as regression 

analyses across all 12 fuels in order to identify important fuel properties. However, the fuels 

were not designed for such a pooled analysis across the three sets and, therefore, significant 

intercorrelations existed when the individual fuel sets were pooled together. 

3. Bertoli Study 

In another study, Bertoli et al.19 studied a matrix of 14 fuels on a passenger vehicle that 

met the ECE 15:04 regulations. In this study, they tested a turbocharged, direct injection 2.5 liter 

displacement engine over a cycle representative of the ECE 15 cycle. They measured PM, NOx, 

HC, and CO emissions. Table VI.B.3-1 lists the fuel properties examined. 

Table VI.B.3-1
 

Fuel Properties
 

Fuel 1 2 3 4 5 6 7 8 9 10 11 12 13 14 

Density 

[g/ml] 

0.829 0.836 0.806 0.827 0.820 0.811 0.821 0.829 0.827 0.841 0.814 0.817 0.814 0.818 

Sulfur 

[ppm] 

1300 9420 50 445 2 1 542 1050 1200 2320 2 1 1 1 

Distill. 

[

o

C] 

10% 200 221 179 217 214 211 212 213 210 210 213 213 208 206 

50% 274 283 242 279 272 268 265 269 256 254 260 261 252 245 

95% 387 391 381 386 382 380 380 380 378 385 349 352 348 347 

Cetane 

number 

57.1 54 52.7 57 59 62.3 56.8 57.4 52.6 47 61 60.1 58.7 56 

Aromatics 

[m%] 

mono- 10.7 14.54 5.67 18.10 14.80 6.1 4.1 4.1 7.8 9.8 5.1 4.9 4.2 3.9 

di- 5.5 6.6 1.04 5.60 2.00 0.5 2.4 6.6 6.9 10.7 1 0.1 0.4 0.2 

tri- 0.7 1.0 0 0.50 0.10 0 1.0 3.0 0.1 0.1 0 0 0 0 

total 16.9 22.1 6.7 24.2 16.90 6.6 7.5 13.7 14.8 20.6 6.1 5.0 4.6 4.1 

S-arom 0.9 5.0 0.3 0.3 0 0 0.3 0.7 1.3 1.4 0 0 0 0 

They analyzed the correlation matrix of the fuel variables and found a strong correlation 

between density and the sum of di- and tri-aromatics content. The researchers decided to treat 

them as a single variable and regressed on � di-tri-aromatic compounds in their analysis. In 

67
 
 

addition, the investigators focused on the cetane number, sulfur, T95, and total aromatics content. 

They carried out a linear regression analysis with these parameters as independent variables and 

emissions as the dependent variables. 

C. Results and Discussion 

All three studies found that an increase in either the poly- (or � di- and tri-) aromatics 

content or density resulted in higher PM emissions. However, variations in other fuel properties 

led to inconsistent changes in emissions. Factors such as engine design or vehicle technologies, 

engine operating conditions, and the test cycle all played important interactive roles with fuel 

properties in influencing pollutant emissions. Due to a scarcity of available data, it was not 

possible to separate all these variables in order to isolate the fuel effects on emissions. 

Nevertheless, some additional conclusions could be drawn regarding fuel effects on pollutant 

emissions. A summary of the magnitude and/or directional changes of emissions from varying 

fuel properties in each study is given in this section. 

1. EPEFE Study 

Results from the EPEFE study17 indicated that while individual vehicles responded to 

variations in certain fuel properties consistently, different vehicles showed significantly different 

responses to changes in other fuel properties, both in magnitude and direction. Upon averaging 

the emission results across all 15 vehicle models, the EPEFE researchers observed the emission 

changes summarized in Table VI.C.1-1. 

Table VI.C.1-1
 

EPEFE Averaged Percentage Changes in Emission over Combined ECE15+EUDC Cycles
 

Emission effects due to 

increasing parameters indicated: PM NOx HC CO 

density: 827 to 855 kg/m3 +19% -2% +18% +17% 

poly-aromatics: 1 to 8 %m/m +5% +3% -5.5% -4% 

cetane: 50 to 58 +5% +1% -26% -25% 

T95: 325 to 370o

C +7% -5% -3% +2% 

The investigators noted that the magnitude of the density effect on NOx emissions was 

highly dependent on the engine design. We discuss engine technologies interactions with fuel 

effects in Section VI.D. 

68
 
The EPEFE study also examined the amount of sulfate formed over the fleet of light duty 

vehicles, all equipped with oxidation catalysts. Although the fuels explored in this study were 

not designed to vary the sulfur level, the investigators found differences ranging from 5-10% in 

sulfate formation for the individual vehicle tested. They noted that the vehicle with the highest 

sulfate formation was one of the light-duty trucks. The investigators suggested that the high 

sulfate formation rate in the truck was probably due to its heavier weight compared to a 

passenger car and the resulting higher engine loads in the EUDC (extra-urban driving cycle) 

which resulted in higher temperatures, and thus more sulfate production over the catalyst. 

2. Lange Study 

Lange et al.18 focused primarily on fuel effects on NOx and PM emissions. They found 

no fuel effects with respect to NOx emissions. For PM emissions only, the investigators made 

pairwise comparisons over fuel properties. They found that the mono-aromatics content, cetane 

number, and distillation did not affect PM emissions. They also concluded that total aromatics 

were little use in describing fuel effects on emissions, whereas the sulfur content, density, and 

poly-aromatics content affected PM emissions. Table VI.C.2-1 lists the trends observed in 

pairwise comparisons. 

Table VI.C.2-1
 

Fuel Effects on PM Emissions over ECE+EUDC Cycles (Lange et al.) 

Fuel effects due to increasing 

parameters indicated: 

PM Emissions 

(g/km); before 

PM emissions 

(g/km); after 

Percentage 

Change 

density a

: 814 to 834 kg/m3 0.06 0.07 +15% 

poly-aromatics: 3.3 to 5.7 %m/m 0.085 0.10 +15% 

cetane b: 54 to 70 0.06 0.06 0 

T90: 269 to 350oC NC NC N/A 

sulfur level: 960 to 1800 ppm 0.085 0.1 +15%

 a: 814 kg/m3 (9.7 mass% mono-aromatics) to 834 kg/m3 (4.1 mass% mono-aromatics)

 b: 54 (10 mass% mono-aromatics) to 70 (negligible mono-aromatics)

 NC: no correlations found

 N/A: not applicable
 

As shown in Table VI.C.2-1, the results from changing density and cetane indicated that 

the mono-aromatics content did not affect particulate emissions. This was based on the 

collective findings that a reduction of mono-aromatics content by 5-6 mass% did not reduce PM 

emissions, while an increase of mono-aromatics content by 10 mass% did not affect particulate 

emissions although there was a substantial difference in the cetane number. 

69
 
 

With regard to effects due to sulfur level on PM emissions, Lange et al. designed fuels 

with a diesel sulfur level ranging from <10 to1800 ppm. Apart from the sulfur quantity in fuels, 

they observed that PM emissions are highly dependent on the driving conditions. They found the 

level of dependence increases significantly from the least severe cycle to the high speed steadystate cycle when the sulfur level is above 500 ppm: FTP75 < ECE+EUDC < 120 km/h constant 

speed. Moreover, when they tested the Mercedes over ECE R49 on a diesel fuel containing 

about 10 ppm sulfur, they noticed that the amount of sulfate produced was greater than that seen 

at the engine-out level using 450 ppm sulfur fuel. Although the oxidation catalyst was able to 

reduce the soluble organic fraction of particulate matter, the overall PM emissions at engine-out 

(using 450 ppm sulfur) and post-catalyst (using 10 ppm sulfur) were similar due to trade off of 

increased sulfate formation over the vehicle equipped with the oxidation catalyst. 

3. Bertoli Study 

Bertoli et al.19 found similar trends as those observed by Lange et al. in that density and 

di-tri aromatics correlated well with PM emissions. As mentioned previously, density and di-tri 

aromatics were closely intercorrelated. Their results also showed that the sum of two and threering aromatics was far better correlated to PM emissions than the total aromatics content. 

Furthermore, unlike EPEFE, they found no correlations between T95 and emissions of any 

pollutant among the fuels they examined. They also found that a reduction of cetane number 

resulted in increased emissions of NOx, HC, and CO. Because the authors did not provide 

numerical results as those studies presented in Sections VI.C.1 and VI.C.2, we summarize the 

trends obtained from their correlation analysis in Table VI.C.3-1. 

Table VI.C.3-1 

Correlation Trends Observed by Bertoli et al. (ref.)
 

Fuel Properties PM NOx HC CO 

I di- and triaromatics t + NR NR NR 

cetane number t NC + + + 

T95 NC NC NC NC

 NC: no correlations found 

NR: not reported 

4. Summary of Studies 

70
 
All three studies presented in the previous section found a consistent correlation between 

increasing fuel density and PM emissions. A similar correlation was found between poly- (or the 

sum of two and three-ring) aromatics content and PM emissions. However, there were variations 

among the observations of fuel effects on NOx emissions. Table VI.C.4-1 summarizes various 

correlations found by these investigators on PM and NOx emissions. 

Table VI.C.4-1
 

Fuel Effects on PM and NOx Emissions 

PM Emissions NOx Emissions 

Fuel properties EPEFEa Lange et 

al.(ref.)

 Bertoli et 

al. (ref) EPEFEa Lange et al.(ref.) 

Bertoli et 

al. (ref) 

density t + + + -

b,c NC NR 

cetane number t + NC NR NCb NC -

� di-tri, or polyaromatics t

+ + + + NC NR 

distillation 

temperature t + NC NC - NC NC

 NC: no correlations found 

NR: not reported

 a: fleet averaged results

 b: individual vehicle responses varied widely with engine technologies

 c: very slight averaged reduction 

While the effect due to fuel density on PM emissions has been consistent across various 

findings, the magnitude of the density effect on PM emissions is related to the engine design and 

technologies22,20. The impact of cetane number on NOx emissions also appears to depend on 

engine technology. Further discussions on the interactions between fuel properties and engine 

technologies are provided in Section VI.D. 

There is very limited data set on fuel effects on HC and CO emissions. Table VI.C.4-2 

summarizes these experimental findings. The two research groups that reported fuel effects on 

HC and CO emissions agreed that an increase in the cetane number resulted in a reduction of 

both HC and CO emissions. However, Bertoli found no correlations between the distillation 

temperature and HC or CO emissions, while correlations were obtained in the EPEFE study. 

71
 
Table VI.C.4-2
 

Fuel Effects on HC and CO Emissions 

HC Emissions CO Emissions 

Fuel properties EPEFEa Bertoli et 

al. (ref) 

EPEFEa Bertoli et 

al. (ref) 

density t + NR + NR 

cetane number t - - - -

� di-tri, or polyaromatics t - NR - NR 

distillation 

temperature t - NC + NC

 NC: no correlations found 

NR: not reported

 a: fleet averaged results 

D. Effects of Vehicle Technology and Operation 

As mentioned previously, results from various research groups demonstrated that the 

magnitude of any diesel fuel property alone was generally not a good indicator for projecting the 

amount of pollutant emissions. This was especially true for determining NOx emissions. The 

results showed that diesel fuel properties, engine technologies, and driving cycle all played 

interactive roles in determining the amount of pollutants emitted. 

1. DI and IDI Engines 

In the EPEFE study17, an increase in density resulted in a slight reduction of fleet 

averaged NOx emissions, shown in Table VI.C.1-1. However, individual vehicle responses to 

density increase were not consistent directionally, even though this group of light-duty vehicles 

was tested under the same protocol and fuels. They also varied considerably in magnitude. 

When the density was reduced, emissions data from individual vehicle showed that the half of 

the fleet with electronic injection responded with increased NOx emissions, while the opposite 

effect was seen with the remaining half of the fleet. This varying behavior from the light-duty 

fleet was also seen with NOx emissions when the cetane number of the fuel was varied. As the 

cetane number was increased, the NOx emissions reduced for DI (mostly electronically 

controlled) fleet, while the NOx emissions increased for the IDI (mostly mechanically controlled) 

fleet. The investigators reported that DI vehicles were primarily tuned to control NOx with 

72
 
resulting trade off of the other emissions (e.g., PM, HC, and CO). Consequently, vehicles with 

electronically controlled injection generally showed higher levels of PM, HC, and CO emissions 

than mechanically controlled vehicles. Because the engine technologies played such an integral 

part in how fuel properties would affect emissions, the fuel property should not be taken alone in 

determining its impact on the pollutant emission levels. 

Although the magnitude changes due to fuel effects were generally of the same order 

between the DI and IDI fleets in the EPEFE study, the DI and IDI fleets displayed a very different 

sensitivity in cetane number effects on PM emissions. The investigators observed that from PM 

emissions DI vehicles were about four times more sensitive than those from IDI vehicles, 

percentage wise. Therefore, their study indicated that under certain circumstances, vehicle 

technology changes may play an even more significant role than fuel property changes in 

affecting the amount of pollutant emissions. 

2. Sensitivity of Vehicle Response to Engine Parameters 

This chapter has thus far focused on fuel parameter studies with little discussions on 

engine effects such as changes to engine calibration or operating conditions. However, two 

studies that focus on these effects offer important insights for interpreting the previously 

discussed studies. 

a. Engine Operating Conditions 

Beatrice et al carried out an engine study over a 2 liter, turbocharged, DI engine equipped 

with an EGR system23. The fuel matrix examined consisted of 12 different fuels. Focusing on 

the engine sensitivity to fuel quality in their steady-state testing at various operating (e.g., load, 

speed, and ambient temperature) conditions, they indicated that the engine sensitivity to fuel 

quality changes was very different depending on both the operating conditions and the individual 

pollutant emission under examination. They noticed the sensitivity to fuel quality changes 

increased at low load and speed, especially for HC emissions. With respect to PM emissions, all 

test conditions were found to be relevant, while particularly higher sensitivity was noted at 

retarded timings and during cold operation. However, this was not true for NOx whose behavior 

was quite flat over varying test conditions. Their study stressed the importance of the interplay 

between the engine operating conditions and fuel properties on pollutant emissions. 

b. Engine Calibration Systems 

Another study by Mann et al. focused on how fuel properties influenced the engine 

management system, thus affecting pollutant emissions22. Mann et al. examined the effect of 

diesel fuels on an electronically controlled, 2 liter DI passenger car engine, equipped with an 

EGR. Both the injection timing and EGR were controlled by closed-loop strategies. This engine 

73
 
was tested under the European 14 mode test conditions. Mann et al. studied the interplay 

between the fuel properties and various engine control systems. Specifically, when the density 

was increased, they noted an increased EGR rate and an advanced injection timing. Advancing 

injection timing was for controlling PM (trade off for NOx), while the EGR was for controlling 

NOx emissions (trade off for PM), and these strategies would contribute to competing effects in 

emission reduction. With this particular engine, the EGR rate dominated, thus resulting in lower 

NOx emissions. This study demonstrated how the fuel property had led to changes in engine 

operation settings of competing consequences. Table VI.D.2.b-1 compares the PM and NOx 

emissions obtained before and after engine calibration modifications due to fuel density change. 

Table VI.D.2.b-1
 

Effect of Engine Calibration Changes on Emissions 

Engine Fuel density PM emissions NOx emissions 

Calibration (kg/m3

) (g/km) (g/km) 

Before 829 0.075 0.76 

After 857 0.084 0.69 

This study clearly illustrated the complex relationships between various engine 

management components that could impact pollutant emissions. Even though advanced injection 

timing should lead to higher NOx emissions, the net effect due to an increase in fuel density was 

NOx reduction by the co-existence of the more dominant EGR effect. Thus, all aspects of the 

engine systems need to be taken together to assess fuel effects on emissions. 

E. Conclusions 

We noted a consistent trend across several studies that showed an increase in either 

density or the poly- (or I di- and tri-) aromatics content results in higher PM emissions. 

Investigators who also examined effects due to both mono- and poly-aromatics content found no 

correlations between the mono-aromatics content and PM emissions. For other fuel properties, 

the results indicated a wide variation in diesel fuel effects on NOx emissions. Some 

investigators found a correlation between cetane number and NOx emissions, while other 

research groups found no significant fuel effects for NOx emissions over a wide variation in fuel 

composition. In addition to fuel effects on PM and NOx emissions, several investigators 

observed that an increase in the cetane number resulted in a reduction of both HC and CO 

emissions. 

The studies also showed that engines with different technologies would respond 

differently to changes in fuel properties. The varied engine responses may have partly attributed 

to inconsistencies among various findings in fuel effects on pollutant emissions. The EPEFE 

74
 
study demonstrated that fuel properties such as density or cetane number on the extent of NOx 

emissions clearly depended on the engine design: DI engine fleets (mostly electronically 

controlled) had responded in the opposite direction compared to the IDI (mostly mechanically 

controlled) engines. The investigators also presented results indicating that the amount of 

pollutant emissions would, in some instances, strongly depend on the engine technologies on the 

vehicle. 

Unlike our results for heavy-duty vehicles, these results collectively suggest the difficulty 

of projecting changes in light-duty vehicle emissions as a function of diesel fuel parameters. 

Nevertheless, there is clearly some PM benefit associated with reducing density/poly-aromatics, 

and HC and CO benefit with an increase in the cetane number. However, the magnitude of 

emissions reduction is highly uncertain without a full understanding of the specific vehicle 

design and configurations, and such assessment would require further analysis. Diesel fuel 

properties, along with existing engine design or vehicle technologies, operating conditions (load, 

speed, ambient conditions) as well as the driving cycles all play interactive roles in influencing 

the amount of pollutant emissions. 

75
 
Section VII What Additional Issues Should Be Addressed? 

To assure that our analysis represents the best technical analysis possible at this time, we 

are making this staff discussion document available in for review and comment. The comment 

period continues through September, 2001. At the end of the comment period, we will consider 

the comments received and we will revise our discussion document and our analysis to take those 

comments into account. We will then finalize a report and make it publically available as an EPA 

technical report. 

Eventually, we plan to incorporate the results of this analysis into EPA modeling tools 

such as the MOBILE and NONROAD models or their successor models. This will allow 

emission modelers to better apply the results of this analysis to their inventory estimates and to 

estimate emissions for a variety of “what-if” scenarios. 

In addition to the work EPA plans in finishing this analysis, there are a number of areas 

that could benefit from further study. While we believe the analysis in our discussion document 

is sufficient for estimating the emission impacts of changes to diesel fuel within the ranges 

described in Section III, there are a number of areas that we think could particularly benefit from 

more test data and analysis. This section summarizes those issues we believe are of the highest 

importance. We welcome comments on these or any other issues. 

A. The need for further testing and research 

1. Alternative fuels for heavy-duty applications 

EPA plans to conduct a detailed analysis into the emission benefits of biodiesel fuels. A 

number of states are considering requirements for biodiesel fuel, and others have programs to 

promote the use of this fuel. The use of biodiesel is expected to have substantial benefits in 

reducing carbon dioxide emissions and it may also reduce particulate emissions. For these 

reasons, it is important that we have a better understanding of the emissions effects of varying 

concentrations of biodiesel, and a better understanding of the effects of biodiesel on engine 

durability. 

Several states are also considering emission reduction programs that make use of 

emulsions of diesel fuel and water. Use of these emulsions is expected to reduce NOx and 

particulate emissions. EPA intends to review the emissions and engine durability data currently 

available on such emulsions to determine if enough data exists to draw conclusions about these 

topics. 

2. Additional test data 

76
 
The graphs in Section II.E.1 provide a comparison between the fuels in our database and 

in-use fuels in terms of distributions of fuel property values. These graphs suggest that our 

database is a good representation of in-use fuels as well as fuels that tend to be cleaner (i.e. lower 

emitting) than typical in-use fuels. The sulfur distribution of fuels in our database includes those 

with sulfur levels in the thousands of ppm, which is indicative of current nonroad diesel sulfur 

levels. In general, then, it does not appear that there are significant gaps in our fuels database, at 

least for fuels used in current highway CI engines. 

However, our database could benefit from additional data on the newest highway engine 

technologies, nonroad engines, and light-duty vehicles. For instance, injection rate-shaping is 

becoming a more prominent form of combustion control, and there is some evidence that it will 

affect the way in which cetane affects emissions (see Section VII.B.2 below). Our database 

contains only a single engine meeting the 1998 standards, and this was a pre-production engine. 

Also, future heavy-duty emission standards may drive the use of particulate traps and NOx 

adsorbers. Engines with this type of aftertreatment have not yet been tested in a carefully 

controlled program to determine fuel effects on emissions. There are also some technology types 

that exist in the current fleet of highway vehicles that are not represented well in our database. 

These include light-heavy duty engines having a total displacement of less than 9.4 liters and 

which have relatively slow rated speeds of 2100 rpm or less. Although the results of our Unified 

approach to model development suggests that engine technology type only has a unique impact 

on fuel/emission relationships in a limited number of cases, the representativeness of the Unified 

Model for these engine technologies could be verified by collecting additional data. 

There is very limited data on nonroad engines and test cycles, and we were therefore 

unable to derive an independent model applicable to nonroad engines. As discussed in more 

detail in Section IV.E, we believe that the default equations for our Unified Model can be applied 

to nonroad engines. However, it would be useful to collect additional data on nonroad engines to 

verify that the emission effects exhibited by the equations presented in Section IV.E are in fact 

representative of nonroad engines under typical nonroad operating conditions. Additional 

nonroad testing should include not only a wide distribution of diesel fuel properties and engines, 

but also different test cycles. 

Finally, our review of available data indicated that information on toxics impacts of 

changes in diesel fuel and diesel fuel effects on light-duty vehicles is very limited. These are both 

areas is which additional test data would be very useful. Until substantial additional test data is 

collected, we can draw only general conclusions about the way that diesel fuel properties affect 

toxics emissions from heavy-duty engines and emissions of regulated pollutants from light-duty 

vehicles. 

B. Technical issues in model development 

77
 
Throughout our analysis, a number of technical issues arose that warrant further 

investigation. We would welcome comment on these or any other issues. 

1. Natural cetane and additized cetane 

Our current modeling efforts utilize two distinct cetane terms: natural cetane and delta, or 

additized, cetane. This was done for a number of reasons, which were discussed in Section II 

above. There are a number of reasons why one would expect that both natural and additized 

cetane would impact diesel engine emissions in the same fashion. One, cetane is an indication of 

ignition delay. If ignition delay affects emissions, then changing ignition delay, whether through 

modifying the chemical composition of the hydrocarbons in the fuel (natural cetane) or by adding 

a cetane improver, might be expected to have the same emission impact. Two, several fuelemission studies have evaluated the effect of changes in both natural and additized cetane on 

emissions and concluded that they are similar, particularly with respect to NOx and PM 

emissions. 

The Unified Model for PM emissions contains coefficients for both natural and additized 

cetane and these coefficients are very similar in magnitude. Thus, the general expectation 

described above is met for PM emissions. However, this is not the case with the NOx emissions 

model. The Unified NOx emission model only contains a coefficient for delta (additized) cetane. 

The coefficient for natural cetane was dropped due to the fact that it did not meet the 5% 

significance level. In fact, it was highly non-significant after the final mixed model regression, 

with a p-value of 0.88. This dramatic difference in the impact of natural and additized cetane in 

the Unified NOx model is an issue which we wish to highlight. 

a. Review of studies 

The first question in addressing this issue is how robust were the conclusions in various 

studies that natural and additized cetane had the same impact on NOx emissions. We reviewed a 

number of studies which made this conclusion and our reviews are summarized below. 

One study, by Navistar International and Amoco Oil Co., measured the emission impacts 

of natural cetane and additized cetane, as well as changes in total aromatics and polynuclear 

aromatics.24 Four statistical NOx emissions models were developed just for this study’s data, two 

with total aromatics as one of the fuel variables and two with PNAs as one of the fuel variables. 

The models with total aromatics produced much better correlation, so they will be the focus here. 

One of the two models with total aromatics also included delta, or additized cetane, while 

the other included total cetane (natural plus delta cetane). The cetane term was statistically 

significant in both cases and the coefficients were essentially identical. However, the model with 

final cetane showed a 20% lower impact of aromatics on NOx emissions than the model with 

delta cetane. Neither emission model included specific gravity. We reviewed the fuels tested and 

found that specific gravity changed substantially between fuels and the change was highly 

78
 
correlated with total aromatics. Natural cetane was also quite correlated with aromatics and 

specific gravity. 

It appears that there are at least two difficulties in using the results of this study to 

determine that natural and additized cetane have the same impact on NOx emissions. One, natural 

cetane was never used as a variable in any of the models. Two, natural cetane did not vary 

independently of other important fuel variables. The fact that total cetane and additized cetane 

have the same impact in the two models is not surprising, given the high degree of correlation in 

the study’s fuels between aromatics, specific gravity and natural cetane. The difference in the 

predicted impact of aromatics on NOx emissions with a change in the cetane term is probably due 

to this correlation. Given that the changes in delta cetane were done in isolation of changes to 

other fuel parameters, the statistical model found it most efficient to develop the final cetane 

coefficient almost exclusively from the delta cetane effect. The changes in natural cetane then 

reduced the changes in NOx emissions assignable to aromatics, so the effect of aromatics 

decreased. 

This study also directly measured the impact of fuels on a number of combustion related 

parameters, such as ignition delay, premix combustion fraction, and rate of pressure rise. In 

general, these combustion parameters were highly correlated with cetane, either natural or 

additized. Thus, at least in terms of engine operation, it does not appear to matter whether fuel 

cetane comes from the primary fuel hydrocarbons or additives. 

A second study by Navistar and Amoco tested a second set of twelve fuels. Natural cetane 

and aromatics were less correlated in this set. Natural cetane and specific gravity were also not 

strongly correlated. The best correlations of NOx emissions with fuel properties showed that the 

effect of both natural and additized cetane were statistically significant. Both aromatics and API 

gravity were included in this correlation. The effect of natural cetane was roughly 80% that for 

additized cetane. It seems likely that the confidence intervals for the two cetane coefficients 

overlap. Thus, the conclusions of this second Navistar-Amoco study appear to more strongly 

support an equivalent natural-additized cetane effect on NOx emissions. 

The CRC VE-1 test program also attempted to estimate the impact of natural and additized 

cetane on emissions. SwRI, for CRC, developed a number of statistical models for the 

relationship between fuel properties and NOx emissions. A number of these models included 

terms for both the logarithm of natural and additized cetane. Both cetane terms were statistically 

significant and the effect of natural cetane was roughly 50% larger than that for additized cetane. 

However, like the first Navistar-Amoco study, natural cetane was highly correlated with 

specific gravity (Pearson coefficient of -0.80) and specific gravity was not included in the model. 

Thus, the coefficient for natural cetane likely included a portion of the effect of lower specific 

gravity and a direct comparison of the two cetane terms cannot be made. 

Given the fact that our models show that specific gravity affects NOx and PM emissions 

substantially, this means that a significant fuel effect was not included in the statistical model and 

79
 
 

 

likely affected the calculated emission effects assigned to the other fuel parameters, including the 

two cetane measures. The changes in specific gravity were most closely associated with total 

aromatics. The changes in specific gravity were not very associated with changes in natural 

cetane and totally unrelated to changes in additized cetane. Thus, the absence of specific gravity 

in this study’s emission model is not likely to have affected the calculated emission effects for 

natural and additized cetane and we found no reason to qualify this conclusion. 

A follow-on CRC study, VE-10m, evaluated the effect of aromatics, natural cetane and 

additized cetane on two engines, both of which were tested at two NOx emission calibrations. 

The changes in aromatics, specific gravity, natural cetane and additized cetane were intended to 

be independent of the others, though there was still significant correlation between aromatics and 

natural cetane (Pearson coefficient of 0.68). While the potential for comparing the impacts of the 

two cetane types on NOx emissions exists, the final report for this study did not present NOx 

emission correlations with natural cetane, additized cetane, and aromatics at the same time. Thus, 

it is not possible to determine if this study found similar NOx emission impacts for the two types 

of cetane when the effect of aromatics is separately accounted for. 

Some of the most convincing conclusions regarding the relative NOx impact of natural 

and additized cetane come from the Heavy-Duty Engines Workgoup (HDEWG). In their test 

program, two sets of two fuels were tested where the pairs of fuels truly differed by only the 

existence of a cetane improver additive; the aromatics, density, and other fuel properties were 

nearly identical for each fuel pair. Only two repeat tests were performed on each fuel/engine 

combination, making the statistical analyses "extremely limited." Even so, the report's authors 

concluded that natural and additized cetane produce indistinguishable emission effects. We note 

that the cetane index values were also nearly identical for the pairs of fuels, as were the hydrogento-carbon ratios. If NOx is driven by the adiabatic flame temperature as suggested by one 

stakeholder (see Section VII.B.2), we would expect these two fuels to combust similarly and thus 

produce the same impact on emissions. The limited dataset produced by the HDEWG does not 

permit us to prove that natural and additized cetane have different impacts on emissions. 

b. Evaluation of Unified Model approach 

The second question which could be asked is why our Unified NOx emission model did 

not find the same NOx emission effects for the two cetane terms. We reviewed the impact of the 

two cetane terms on NOx emissions in all of the various types of models which we generated. 

The NOx models for individual technology groups showed a variety of results for the two cetane 

terms. The mixed NOx models for the two technology groups containing the largest amount of 

data, F-DD and T, show very similar coefficients for natural and additized cetane. Thus, two 

large subsets of the database indicate a similar NOx impact for the two types of cetane. However, 

m This program was also summarized in SAE papers 941020, 950250, and 950251. The statements made here 

also apply to the analyses presented in these SAE papers. 

80
 
these mixed models did not include engine-by-fuel interactive terms, while the Unified Models 

were developed including these terms.. 

The Unified Model was not developed without engine-by-fuel interactive terms, however 

the technology group T model was also developed with these interactive terms. When these terms 

were included in the mixed NOx model for technology group T, the coefficients for additized 

cetane and additized cetane squared were statistically significant. However, neither the natural 

cetane, nor natural cetane squared terms were statistically significant. Of the two, the natural 

cetane squared term was the more significant (p=0.07), but indicated that increasing cetane 

increased NOx emissions. Thus, at least for technology group T representing greater than half of 

the data in the database, the model structure of the mixed model had a significant impact on the 

predicted effect of natural cetane on NOx emissions. 

The NOx model developed using the eigenvector technique found natural and additized 

cetane effects which were similar to those found by the fixed effect Unified Model. Again, this 

may not be surprising given that the final modeling step in the eigenvector technique did not 

include the engine-fuel interactive terms. 

While a Unified NOx model was not developed without the engine-by-fuel interactive 

terms, the fixed effects model which was used to develop a set of candidate fuel-related terms for 

the mixed model was developed without engine-by-fuel interactive terms. This fixed effects NOx 

model found that the coefficients for both natural and additized cetane were statistically 

significant, with the coefficient for additized cetane being about twice as large as that for natural 

cetane. When the statistically significant terms contained in the fixed effects model were input 

into a mixed effects model, the additized cetane term was essentially unaffected, while the 

coefficient for natural cetane dropped in magnitude substantially and ceased to be statistically 

significant. 

It seems clear that the structure of the statistical model used to develop the NOx emission 

model has a significant impact on the emission effects assigned to natural cetane. The differing 

projections for the effect of natural cetane may be due to differences in the way the various 

models separate the effects of natural cetane, specific gravity and aromatics, which are somewhat 

correlated in the fuels which have been tested to date. We request comment on the issue of 

relative NOx impacts of natural versus additized cetane, and on the impact that the regression 

modeling approach may have on these relative effects. 

2. Engine sensitivity to cetane 

Historically, cetane number has been considered to be one of the most important diesel 

fuel properties in terms of its impact on engine performance and emissions. Since it is a measure 

of the combustion properties of the fuel (rather than a strict measure of the fuel's composition or 

physical properties), there is an expectation that it will interact with engine technology in terms of 

81
 
the impact on emissions. In fact, of all the fuel property effects in our Unified Model which were 

technology group-specific, cetane arose most frequently. 

Our database included data collected on a single engine equipped with exhaust gas 

recirculation (EGR). This engine was tested as part of the Heavy-Duty Engines Workgroup in an 

effort to determine how changes in fuel properties would affect emissions from an engine 

designed to meet the 2004 standards. That study concluded that the EGR-equipped engine 

actually exhibited a small increase in NOx emissions with increased cetane. We therefore felt it 

important to create a technology group for EGR-equipped engines (technology group L) so that 

any effects of cetane on emissions which were significantly different from those for other engine 

technologies would be captured in our modeling process. In fact, technology group L did have its 

own adjustment term for additized cetane in our final Unified Model, confirming the findings of 

the Heavy-Duty Engines Workgroup. 

Recently, one stakeholder suggested that the increase in NOx associated with higher 

cetane levels as observed by the Heavy-Duty Engines Workgroup may not be associated with 

EGR specifically, but rather with the predominance of diffusion burning that is more 

characteristic of newer engines. The more frequent use of injection rate-shaping in recent model 

years to control the combustion process may result in less pre-mix burning, which would raise the 

adiabatic flame temperature. The result would be that increases in cetane would actually increase 

NOx emissions. In support of this possible explanation for the unique cetane effects we observed 

for technology group L, the stakeholder directed us to a combustion simulation model developed 

by Southwest Research Institute called ALAMO_ENGINE25. According to the stakeholder, this 

model shows no effect of cetane on NOx emissions for engines using rate-shaping. 

There is currently insufficient information to determine conclusively whether this 

explanation is accurate. Aside from additional testing, we could more thoroughly investigate the 

engines in our database to determine which ones included rate-shaping, and adjust our technology 

group definitions accordingly. Information about rate-shaping is unlikely to have been presented 

in the studies from which we derived the data for our database, so this process might require us to 

contact manufacturers. We request comment on the possibility that rate-shaping, rather than 

EGR, is the reason that technology group L exhibits a different NOx response to cetane than other 

technology groups, and on approaches we could take to resolve the issue. 

3. Fuel term elimination process in PCR 

In applying principle components regression (PCR) to our database, we followed the 

description given in the DOE report. This analysis is described in Section III.B.1. However, 

recent communications from the authors suggest that the procedure for eliminating or "pruning" 

eigenvectors and individual fuel terms which was summarized in the report may not be the actual 

procedure that the authors actually used. If a change in the pruning procedure were made, it might 

affect the results of our own PCR analysis. 

82
 
 

According to the report's authors, the revised pruning procedure differs from the example 

in the report in that the eigenvector contributions are summed before squaring, rather than the 

reverse process shown in the example, thereby retaining the contributions from cross-product 

terms. Thus while both approaches take the same model sums of squares and apportion part to 

each fuel term, the apportionment differs between the methods. The revised method uses the 

squares and cross-product terms of the eigenvector values in determining the individual fuel sums 

of squares, while the first method uses only the squares of the eigenvector values. We will be 

investigating the effect that this change would have on our own analysis. In the meantime, we 

would welcome comments on this issue. 

4. Model-year analysis 

As discussed in Section II.C, we subdivided our database into technology groups that we 

believed had the potential for exhibiting different fuel property/emission effects. We chose not to 

separate the data according to the model year of the engines, because this approach would be less 

efficient in capturing the different types of engine technologies. In addition, our technology group 

definitions did correlate roughly with model year, as described in Section II.E.3. 

Even so, one stakeholder suggested that categorizing our database by model year might 

provide a emissions model that is more representative of the in-use fleet. Unfortunately, the 

selection of engines on our database is not comprehensive enough to permit a model year by 

model year analysis. There are some model years entirely missing from our database (e.g. 1992, 

1997), and other model years consist of only a single engine. Given that so few technology 

groups had adjustment terms in our Unified Model, we concluded that most engines respond in a 

similar fashion to changes in diesel fuel properties, and thus we do not consider the absence of 

data on a few model years to be problematic. 

Another reason to avoid a model-year analysis of our database is that there is some 

ambiguity in how the model years for specific engines are assigned. Many test programs used 

engines that were designed to meet standards that applied in a particular timeframe (for instance, 

an engine was designed to meet the standards which apply to 1991 - 1993 model years). An 

engine described in this way could be assigned to the 1991, 1992, or 1993 model year. Other test 

engines were actually modified to meet a more stringent set of standards, such that the actual 

model year of the engine was not really descriptive of the intended model year. 

Still, there is some belief that the emission standards to which an engine was certified may 

provide a unique description of how engines respond to changes in diesel fuel properties. Even 

though there is a significant distribution of technologies within any given model year, they all 

have the common attribute of being designed to meet the same emission standardsn

. Thus it may 

be possible to group engines together according to the emissions standards they were designed to 

n Phase-in of emission standards not withstanding. 

83
 
meet, and to re-apply our Unified approach to this alternatively subdivided database. We request 

comment on this potential approach and on the expected representativeness of the resulting 

model. 

5. Uncertainty analysis 

After the development of any regression-based emissions model, it is prudent to further 

evaluate the uncertainty associated with the model coefficients. There are a number of ways that 

such an evaluation can be made. One way would be to determine how well the model can predict 

fuel effects on emissions for an independent data set. Since we used all available data in the 

development of our model, this was not possible. An alternative approach could have been to 

develop the model on the basis of a randomly-chosen subset of the database, and then use the 

remaining observations to evaluate the model. This approach means that the model will not be 

based on all available data, and the coefficients could potentially vary depending on the randomly 

selected observations that are used to construct the model. However, this remains a valid 

approach to evaluating regression-based emission models, and we request comment on its use for 

our Unified Model. 

Another approach would be to assign error bounds to the regressions equations. In the 

context of the multivariable mixed model regression that we carried out, determining such error 

bounds would be very difficult. At best, an estimate of the error bounds could be constructed. 

However, given that the terms in our final model are there because they are statistically 

significant, such estimated error bounds are unlikely to indicate that a term should be dropped 

from the model. Estimated error bounds could, however, provide a degree of confidence that the 

true effect of changes in fuel properties on emissions lies somewhere within those bounds. In 

terms of predicted emission effects, then, estimated error bounds will permit one to estimate the 

likely minimum and maximum emission effects for a given set of fuel properties. We request 

comment on the need for estimated error bounds and the best way to make use of them in 

predicting emission effects for the in-use fleet. 

Another approach is to carry out a residuals analysis. A residuals analysis provides 

information on how well the model predicts emission effects for the data on which the model was 

based. Predicted emissions are compared to observed emissions to ensure that the comparison 

averages out to 1:1 and that scatter is at a minimum. Since the engine has a much more 

significant effect on emissions than fuel properties, care must be taken to separate out the engine 

effects in this process. The most straightforward way to do this is to use the version of the final 

model that includes intercepts for every engine. SwRI did such an analysis for our final Unified 

Model and confirmed that the predicted and observed emissions were very similar. See the SwRI 

report10 for further details. 

Since we intend to use our Unified Model to predict emission effects for the entire fleet 

rather than for individual engines, we generally ignore the engine intercepts terms. However, 

84
 
doing so may raise the question of how well our Unified Model, sans engine intercepts, predicts 

the data in our database. Thus it seemed prudent to us to conduct an uncertainty analysis akin to a 

more traditional residuals analysis, but using percent changes in emissions rather than absolute 

change in g/bhp-hr. In this analysis, a baseline fuel was chosen from among those tested on each 

engine in our database, and the percent change in emissions was calculated for the remaining fuels 

tested on that engine. This approach effectively eliminates the engine effect on emissions, and 

resembles the manner in which we intend our Unified Model to be used. We then used the same 

baseline fuel for each engine to calculate the predicted percent change in emissions for each 

remaining fuel according to our Unified Model sans engine intercepts. The difference between 

the predicted percent change values from our model and the calculated percent change values 

from the database was then determined. The results of this analysis are shown in Table VII.B.5-1 

(details can be found in the SwRI report10). 

Table VII.B.5-1
 

Unified Model predicted versus calculated percent change values
 

Maximum difference between 

predicted and calculated 

percent change values 

Cumulative percent of database observations 

NOx PM HC HC limited† 

2 

4 

6 

20 

30 

100 

63 

86 

95 

100 

100 

100 

23 

41 

60 

98 

100 

100 

8 

16 

24 

63 

82 

99 

11 

21 

29 

74 

87 

100 

† The "HC limited" analysis was intended to look more closely at predicted versus 

observed HC results by limiting the observed values to those that were between -20 

and +20 percent change from the baseline fuel 

For our NOx model, 86 percent of the observations in the database exhibited a percent change 

from baseline fuel that differed from the change predicted by our Unified Model by 4 percent or 

less. Our PM model was less accurate at predicting the effects in the database, but still came 

within 20 percent of the measured effect 98 percent of the time. Our HC model was a rather poor 

predictor of the effects in our database. 

Note that only the default models from our Unified Model were used in this analysis. 

Presumably if the individual technology group models had been included, the differences between 

predicted and calculated percent change values would have been smaller. Also, the fact that the 

PM model appears to be less accurate than the NOx model, and the HC model less accurate than 

the PM model, can be partially explained by the relative magnitude of the predicted effects 

between the three pollutant models for a single fuel. For instance, the default PM model will on 

average predict percent change effects that are two times larger than those predicted by the default 

NOx model for the same set of fuel properties. The HC model will on average predict percent 

85
 
change effects that are five times larger than those predicted by the default NOx model. The 

larger the predicted effects, the wider the error can be while maintaining a statistically significant 

result. We request comment on this analysis and on the uncertainty associated with predictions 

made by the Unified Model. 

6. Fuel effects on nonroad engines 

Although we believe that our Unified Model can provide reasonable predictions of fuel 

effects for nonroad CI engines, there are several issues that we believe should be addressed. 

These include differences in the distribution of technology between highway and nonroad engines, 

and cycle effects. Both of these issues are discussed below. We welcome comments on these or 

any other issues related to the application of our Unified Model to the nonroad CI fleet. 

As discussed in Section IV.E, the Unified Model contained only a few technology group 

adjustment terms out of the approximately 140 possible. This result lead us to conclude that, with 

rare exceptions, engine technology does not play a significant role in the relationship between fuel 

properties and emissions. As a result it seemed appropriate to apply the Unified Model to 

nonroad engines, since the technology groups we defined in Appendix D could be used to 

categorize nonroad engines as well as highway engines. 

However, we did not take into account the degree to which the distribution of technologies 

among the nonroad fleet may be different than that for the highway fleet. For instance, the 

nonroad fleet has a higher proportion of mechanically-controlled engines and indirectly-injected 

engines than there is in the highway fleet. The range of rated horsepower is also considerably 

wider for nonroad CI engines. In addition, although there are nonroad engines which exactly 

mimic highway heavy duty counterparts, some nonroad engines have niche applications that 

operate under extreme conditions and so have been calibrated to function differently. We have 

not evaluated the degree to which these types of differences in engine technology between 

nonroad and highway engines could affect correlations between diesel fuel properties and 

emissions. In large part this is due to the fact that the available data on fuel effects for nonroad 

engines is extremely limited. In the coming months we will be attempting to validate the use of 

the Unified Model for nonroad using recently collected data on nonroad engines. In the interim, 

because nonroad engine technology tends to follow highway engine technology, though lagging 

by a few model years, and our Unified Model did not appear to have a strong technology 

component, we have proposed that the Unified Model for NOx and HC be used to represent 

nonroad as well as highway engines. 

A potentially more significant difference between highway and nonroad engines is the way 

in which they are operated, and thus the duty cycles that are most representative. In our current 

effort for highway vehicles, we have based our models on emission data collected with test cycles 

that are intended to represent highway operation. In general these included both transient and 

steady-state cycles, though the PM model was based only on testing with the transient highway 

86
 
FTP (as explained in Section II.D.3). As described above, there was simply insufficient data on 

nonroad engines using nonroad engine cycles. However, we suggest that highway cycles can 

serve as a basis for comparison and extrapolation to nonroad applications for relative effects of 

fuel property changes on emissions. We believe this is appropriate because the Unified Model is 

based on both transient and steady-state test data, and the variety of cycles being evaluated for 

nonroad include both transient and steady-state. To appropriately characterize PM impacts it may 

be necessary to build up a database which incorporates additional transient nonroad cycle data 

once a sufficiently robust data set becomes available. 

EPA has a steady-state certification test for nonroad engines. Given the paucity of 

nonroad data, we do not believe that this fact is sufficient to conclude that the Unified Model, 

which includes transient test data, is not representative of nonroad. However, we could later 

revise our Unified Model for application to nonroad if sufficient additional data is collected. We 

welcome comment on whether the potential cycle differences between highway and nonroad 

would affect the applicability of the Unified Model to nonroad. 

7. Monoaromatic versus polyaromatic effects 

As discussed more fully in Section III.C.1, we chose to use total aromatics in our modeling 

effort instead of monoaromatics and polyaromatics. If we had included monoaromatic and 

polyaromatic terms in our stepwise regression, we would have lost over 50% of the data in our 

database. This is a significant amount of data to lose, and the model could potentially have 

exhibited different fuel property/emissions correlations as a result. Thus we determined that it 

was more reasonable to include only a total aromatic term for our draft model than to lose 50% of 

the available data. However, there may be statistical methods, such as correlation partialling, that 

would permit the development of a regression model that bases the effects of mono and 

polyaromatics on a subset of the database, while the remaining fuel properties are based on the 

entire database. We have not investigated this type of approach. We request comment on 

whether and how to include the separate effects of monoaromatics and polyaromatics in our 

modeling. 

87
 
Appendices 

Appendix A - Data Sources 

Data sources which meet all basic criteria and which were included in the database 

1.	 Mitchell, K., "Effects of Fuel Properties and Source on Emissions from Five Different 

Heavy Duty Diesel Engines," SAE 2000-01-2890 

2.	 Cheng, A. S., R. W. Dibble, “Emissions Performance of Oxygenate-in-Diesel Blends and 

Fischer-Tropsch Diesel in a Compression Ignition Engine,” SAE 1999-01-3606. 

3.	 Schwab, Scott D., G. H. Guinther, T. J. Henly, K. T. Miller, “The Effects of 2-Ethylhexyl 

Nitrate and Di-Tertiary-Butyl Peroxide on the Exhaust Emissions from a Heavy-Duty 

Diesel Engine,” SAE 1999-01-1478. 

4.	 Clark, Nigel N., C. M. Atkinson, G. J. Thompson, R. D. Nine, “Transient Emissions 

Comparisons of Alternative Compression Ignition Fuels,” SAE1999-01-1117. 

5.	 Starr, Michael E., “Influence on Transient Emissions at Various Injection Timings, Using 

Cetane Improvers, Bio-Diesel, and Low Aromatic Fuels,” SAE 972904. 

6.	 Schabert, Paul W., Ian S. Myburgh, Jacobus J. Botha, Piet N. Roets, Carl L. Viljeon, Luis 

P. Dancuart, Michael E. Starr, “Diesel Exhaust Emissions Using Sasol Slurry Phase 

Distillate Process Fuels,” SAE 972898. 

7.	 Lange, W.W., J.A. Cooke, P. Gadd, H.J. Zurner, H. Schlogl, and K. Richter., “Influence of 

fuel Properties on Exhaust Emissions from Advanced heavy-Duty Engines considering the 

Effect of Natural and Additive Enhanced Cetane Number,” SAE 972894. 

8.	 Stradling, Richard, Paul Gadd, Meinrad Signer, Claudio Operti, “The Influence of fuel 

Properties and Injection Timing on the Exhaust Emissions and fuel Consumption of an 

Iveco Heavy-Duty Diesel Engine,” SAE 971635. 

9.	 Tamanouchi, Mitsuo, Jiroki Morihisa, Shigehisa yamada, Jihei Lida, Takanobu Sasaki, 

and Harufusa Sue, “Effects of Fuel Properties on Exhaust Emissions for Diesel Engines 

With and Without Oxidation Catalyst and High Pressure Injection,” SAE 970758. 

10.	 Daniels, Teresa L., Robert L. McCormick, Michael S. Graboski, Philip N. Carlson, 

Venkatesh Rao, and Gary W. Rich, “The Effect of diesel Sulfur Content and Oxidation 

Catalysts on Transient Emissions at High Altitude from a 1995 Detroit diesel Series 50 

Urban Bus Engine,” SAE 961974. 

88
 
11.	 Geiman, Richard A., Patrick B. Cullen, Peter R. Chant, Philip N. Carlson and Venkatesh 

Rao, “Emission Effects of Shell LOW NOX Fuel on a 1990 Model Year Heavy HeavyDuty Diesel Engine,” SAE 961973. 

12.	 Signer, M., P. Heinze, R. Mercogliano, H.J. Stein, "European Programme on Emissions, 

Fuels and Engine Technologies (EPEFE) - Heavy-Duty Diesel Study," SAE 961074 

13.	 Mitchell, K., D.E. Steere, J.A. Taylor, B. Manicom, J.E. fisher, E.J. Sienicki, C. Chiu, P. 

Williams, “Impact of Diesel Fuel Aromatics on Particulate, PAH and Nitro-PAH 

Emissions,” SAE 942053. 

14.	 Nandi, Manish K., David C. Jacobs, Frank J. Liotta, Jr., H.S. Kesling, Jr., “The 

Performance of a Peroxide-Based Cetane Improvement Additive in Different Diesel 

Fuels,” SAE 942019. 

15.	 Rosenthal, M. Lori, Tracy Bendinsky, “The Effects of Fuel Properties and Chemistry on 

the Emissions and Heat Release of Low-Emission Heavy Duty Diesel Engines,” SAE 

932800. 

16.	 Liotta, Jr., Frank J., “A Peroxide Based Cetane improvement Additive with Favorable Fuel 

Blending Properties,” SAE 932767. 

17.	 Liotta, Jr., Frank J., Daniel M. Montaivo, “The Effect of Oxygenated Fuels on Emissions 

from a Modern Heavy-Duty Diesel Engine,” SAE 932734. 

18.	 Gonzalez D., Manuel A., Guillermo B. Rodriguez, Roberto Galiasso, Edilberto Rodriguez, 

“A Low Emission Diesel Fuel: Hydrocracking Production, C haracterization and Engine 

Evaluations,” SAE 932731. 

19.	 Lange, W.W., A. Schafer, A. Le’Jeune, D. Naber, A.A. Reglitzky, M. Gairing, “The 

influence of Fuel Properties on Exhaust Emissions from Advanced Mercedes Benz Diesel 

Engines,” SAE 932685. 

20.	 McCarthy, Christopher I., Warren J. Slodowske, Edward J. Sienicki, Richard E. Jass, 

“Diesel Fuel Property Effects on Exhaust Emissions from a Heavy Duty Diesel Engine 

that Meets 1994 Emissions Requirements, SAE 922267. 

21.	 Asaumi, Y., M. Shintani, Y. Watanabe, "Effects of Fuel Properties on Diesel Engine 

Exhaust Emission Characteristics," SAE 922214 (raw data provided by author) 

22.	 Lange, W.W., “The Effect of Fuel Properties on Particulates Emissions in Heavy-Duty 

Truck Engines Under Transient operating Conditions,” SAE 912425. 

89
 
23.	 Ullman, Terry L., David M. Human, “Fuel and Maladjustment Effects on Emissions from 

a Diesel Bus Engine,” SAE 910735. 

24.	 Cunningham, Lawrence J., Timothy J. Henly, Alexander M. Kulinowski, “The Effects of 

Diesel Ignition Improvers in Low-Sulfur Fuels on Heavy-Duty Diesel Emissions,” SAE 

902173. 

25.	 Sienocki, E., R.E. Jass, W.J. Slowdowske, C.I. McCarthy, A.L. Krodel, "Diesel Fuel 

Aromatic and Cetane Number Effects on Combustion and Emissions from a Prototype 

1991 Diesel Engine," SAE 902712 

26.	 Knuth, Hans Walter, Hellmut Garthe, “Future Diesel Fuel Compositions - Their Influence 

on Particulates.” SAE 881173. 

27.	 Barry, E.G., L.J. McCabe, D.H. Gerke, J.M. Perez, “Heavy-Duty Diesel Engine/Fuels 

Combustion Performance and Emissions - A Cooperative Research Program,” SAE 

852078. 

28.	 Hare, C.T., R.L. Bradow, "Characterization of heavy-Duty Diesel Gaseous Particulate 

Emissions, and Effects of Fuel Composition," SAE 790490 

29.	 Ullman, Terry L.,”Investigation of the Effects of Fuel Composition and Injection and 

Combustion System Type on Heavy-Duty Diesel Exhaust Emissions,” CRC Contract 

CAPE 32-80. Project VE-1. 

30.	 Ullman, Terry L., R. L. Mason, D. A. Montalvo, “Study Of Fuel Cetane Number And 

Aromatic Content Effects on Regulated Emissions From A Heavy-Duty Diesel Engine,” 

CRC Contract NO. VE-1. Project VE-1. 

31.	 Spreen, Kent B., T. L. Ullman, R. L. Mason, “Effects of Fuel Oxygenates, Cetane 

Number, and Aromatic Content on Emissions From 1994 and 1998 Prototype Heavy-Duty 

Diesel Engines,” CRC Contract No. VE-10. Project VE-10. 

32.	 Matheaus, Andrew C., T. W. Ryan III, R. Mason, G. Neely, R. Sobotowski, “Gaseous 

Emissions From A Caterpillar 3176 (With EGR) Using A Matrix of Diesel Fuels (Phase 

2),” Final Report under EPA Contract Number 68-C-98-169, September 1999. 

33.	 Fritz, S.G., "Diesel Fuel Effects on Locomotive Exhaust Emissions," Southwest Research 

Institute Final Report, prepared for California Air Resources Board in October, 2000. 

34.	 Kleinschek, G., K. Richter, A. Roj, M. Signer, H.J. Stein, "Influence of Diesel Fuel 

Quality on Heavy-Duty Diesel Engine Emissions," ACEA Heavy-Duty Diesel Truck 

Manufacturers, March 20, 1997, VE/ACEA/30. 

90
 
35.	 Treux, Timothy J., J.M. Norbeck, M.R. Smith, "Evaluation of Factors That Affect Diesel 

Exhaust Toxicity," report sponsored by the California Air Resources Board, July 24, 1998 

Data sources which met all criteria, but the data was not readily available 

1.	 Vertin, K. D., J. M. Ohi, D. W. Naegeli, K. H. Childress, G. P. Hagen, C. I. McCarthy, A. 

S. Cheng, R. W. Dibble, “Methylal and Methylal-Diesel Blended Fuels for Use in 

Compression-Ignition Engines,” SAE 1999-01-1508. 

2.	 Uchida, M., Y. Akasaka, "A Comparison of Emissions from Clean Diesel Fuels," SAE 

1999-01-1121 

3.	 Tamanouchi, M., H. Morihisa, H. Araki, S. Yamada, "Effects of Fuel Properties and 

Oxidation Catalyst on Exhaust Emissions for Heavy-Duty Diesel Engines and Diesel 

Passenger Cars," SAE 980530 

4.	 Akasaka, Y., T. Suzuki, Y. Sakurai, "Exhaust Emissions of a DI Diesel Engine Fueled 

with Blends of Biodiesel and Low Sulfur Diesel Fuel," SAE 972998 

5.	 Nylund, N., P. Aakko, S. Mikkonen, A. Niemi, "Effects of Physical and Chemical 

Properties of Diesel Fuel on NOx Emissions of Heavy-Duty Diesel Engines," SAE 972997 

6.	 Graboski, M.S., J.D. Ross, R.L. McCormick, "Transient Emissions from No. 2 Diesel and 

Biodiesel Blends in a DDC Series 60 Engine," SAE 961166 

7.	 Kobayashi, S., T. Nakajima, M. Hori, "Effect of Fuel Properties on Diesel Exhaust 

Emissions," SAE 945121 

8.	 Den Ouden, C.J.J., R.H. Clark, L.T. Crowley, R.J. Stradling, W.W. Lange, C. Maillard, 

"Fuel Quality Effects on Particulate Matter Emissions from Light- and Heavy-Duty Diesel 

Engines," SAE 942022 

9.	 Crowley, L.T., R.J. Stradling, J. Doyon, "The Influence of Composition and Properties of 

Diesel Fuel on Particulate Emissions from Heavy-Duty Engines," SAE 932732 

10.	 Likos, B., T.J. Callahan, C.A. Moses, "Performance and Emissions of Ethanol and 

Ethanol-Diesel Blends in Direct-Injected and Pre-Chamber Diesel Engines," SAE 821039 

11.	 Broering, L.C., L.W. Holtman, "Effect of Diesel Fuel Properties on Emissions and 

Performance," SAE 740692 

91
 
Data sources which were not used because the data was collected on a single-cylinder research 

engines in addition to other anomalies 

1.	 Neill, W.S., W.L. Chippior, O.L. Gulder, J. Cooley, E.K. Richardson, K. Mitchell, C. 

Fairbridge, "Influence of Fuel Aromatics Type on the Particulate Matter and NOx 

Emissions of a Heavy-Duty Diesel Engine," SAE 2000-01-1856 

2.	 Kidogucki, Y., C. Yang, K. Miwa, "Effects of Fuel Properties on Combustion and 

Emission Characteristics of a Direct-Injection Diesel Engine," SAE 2000-01-1851 

3.	 Hara, H., Y. Itoh, N. A. Henien, W. Bryzik, “ Effect of Cetane Number With and Without 

Additive on Cold Startability and White Smoke Emissions in a Diesel Engine,” SAE 

1999-01-1476. 

4.	 Kouremenos, D.A., D. T. Hountalas, A. D. Kouremenos, “Experimental Investigation of 

the Effects of Fuel Composition on the Formation of Pollutants in Direct Injection Diesel 

Engines,” SAE 1999-01-0189. 

5.	 Nakakita, K, S. Takasu, H. Ban, T. Ogawa, H. Naruse, Y. Tsukasaki, L. I. Yeh. “Effect of 

Hydrocarbon Molecular Structure on Diesel Exhaust Emissions,” SAE 982494. 

6.	 Li, X., W. L. Chippior, O. L. Gulder, “Effects of Fuel Properties on Exhaust Emissions of 

a Single Cylinder DI Diesel Engine,” SAE 962116. 

7.	 Akasaka, Y., Y. Sakurai, "Effects of Oxygenated Fuel and Cetane Improver on Exhaust 

Emission from Heavy-Duty DI Diesel Engine," SAE 942023 

8.	 Kajitani, S., H. Usisaki, E. Clausen, S. Campbell, K. T. Rhee, “MTBE for Improved 

Diesel Combustion and Emissions?,” SAE 941688 

9.	 Ryan, T.W., III, J. Erwin, “Diesel Fuel Composition Effects on Ignition and Emissions,” 

SAE 932735. 

10.	 Belardini, P., C. Bertoli, F. E. Corcione, G. Police, “ Effect of Fuel Quality on the 

Performance of High-Speed Direct Injection Diesel Engines,” SAE 852077. 

11.	 Erwin, J., T.W. Ryan, III, D. S. Moulton, “Diesel Fuel Component Contribution to Engine 

Emissions and Performance,” National Renewable Energy Laboratory, TP-425-6354, 

November 1994 

12.	 Ladommatos, N., M. Parsi, A. Knowles, "The Effect of Fuel Cetane Improver on Diesel 

Pollutant Emissions," Fuel Vo. 75 No. 1, pp. 8-14, 1996 

92
 
13.	 Karonis, D., E. Lois, S. Stournas, F. Zannikos, "Correlations of Exhaust Emissions from a 

Diesel Engine with Diesel Fuel Properties," Energy & Fuels 1998, 12, 230-238 

Data sources which were not used to develop the heavy-duty model because the data was collected 

on light-duty vehicles and/or engines 

1.	 Nic Mann, Frode Kvinge, Geoff Wilson, “Diesel Fuel Effects on Emissions: Towards a 

Better Understanding,” SAE 982486 

2.	 Xiaobin Li, Wallace L. Chippior, and Omer L. Gulder, “Effects of Cetane Enhancing 

Additives and Ignition Quality on Diesel Engine Emissions,” SAE 972968. 

3.	 Kevin Schmidt and Jon Van Gerpen, “The Effect of Biodiesel Fuel Composition on Diesel 

Combustion and Emissions,” SAE 961086 

4.	 R. H. Hammerie, D.A. Ketcher, and R. W. Horrocks, G. Lepperhoff, G. Huthwohl, and B. 

Luers, “Emissions from Current Diesel Vehicles,” SAE 942043. 

5.	 B. P. Pundir, S. K. Singal and A. K. Gondal, “Diesel Fuel Quality: Engine Performance 

and Emissions,” SAE 942020. 

6.	 Bertoli, C., N. Del Giacomo, B. Iorio, M.V. Prati, "The Influence of Fuel Composition on 

Particulate Emissions of DI Diesel Engines," SAE 932733 

7.	 Ari Juva, Paul Zelenka and Peter Tritthart, “Influences of Diesel Fuel Properties and 

Ambient Temperature on Engine Operation and Exhaust Emissions.” SAE 890012 

8.	 R. M. Olree, D. L. Lenane, “Diesel Combustion Cetane Number Effects,” SAE 840108. 

Data sources which were excluded for other reasons 

1.	 Becker, R. F., P. Ndiomu, D. H. Hoskin, “ Reduction in Particulate and Black Smoke in 

Diesel Exhaust Emissions,” SAE 972903. 

Reason: Fuel properties not provided for fuels which were tested 

2.	 David Y.Z. Chang and Jon H. Van Gerpen, “Fuel Properties and Engine Performance for 

Biodiesel Prepared from Modified Feedstocks,” SAE 971684 

Reason: Only one mode was used in testing 

93
 
3.	 Tadao Ogawa, Klyomi Nakakita, and Minoru Yamamoto, Masanori Okada and Yoshio 

Fujimoto, “Fuel Effects on Particulate Emissions from D.I. Engine - Relationship among 

Diesel Fuel, Exhaust Gas and Particulates,” SAE 971605 

Reason: Injection timing was changed for each mode 

4.	 Shigeyuki Tanaka, Masataka Morinaga, Haruhisa Yoshida, Haruo Takizawa, Kazuyoshi 

Sanse, and Hiromichi Ikebe, “Effects of Fuel Properties on Exhaust Emissions from DI 

Diesel Engines,” SAE 962114. 

Reason: Pure chemicals were used in developing test fuels 

5.	 Thomas W. Ryan III, Jimell Erwin, Robert L. Mason, and David S. Moulton, 

“Relationships Between Fuel Properties and Composition and Diesel Engine Combustion 

Performance and Emissions,” SAE 941018. 

Reason: Repeats SAE 932735 

6.	 Mitsuo Tamanouchi and Yukio Akasaka, “Effects of Fuel Composition on Exhaust Gas 

Emissions from DI and DI Impingement Diffusion Combustion Diesel Engines,” SAE 

941016. 

Reason: Pure chemicals were used in developing test fuels 

7.	 Cynthia A. Chaffin and Terry L. Ullman, “Effects of Increased Altitude on Heavy-Duty 

Diesel Engine Emissions,” SAE 940669. 

Reason: Study of altitude, not fuel effects 

8.	 Bower, G., R. Donahue, D. Shamis, D. Foster, "Emission Tests of Diesel Fuel with NOx 

Reduction Additives," SAE 932736 

Reason: Single-cylinder engine tested on only two modes 

9.	 Shigeru Tosaka, Yasuhiro Fujiwara, Tadashi Murayama, “The Effect of Fuel Properties on 

Diesel Engine Exhaust Particulate Formation,” SAE 890421. 

Reason: Thermal cracking study using a cracking bong 

10.	 Frisch, L.E., J.H. Johnson, D.G. Leddy, "Effects of Fuels and Dilution Ratio on Diesel 

Particulate Emissions," SAE 790417 

Reason: Only two modes tested. 

94
 
95
 
Appendix B - Database Structure 

Table B1 - File Definitions 

File Name Definition 

EQUIP_AD This table represents the procurement of both Equipment and Engine for the 

engine tests. 

ETEST_AD Any observation, measurement, or modification to a mobile source. This entity 

stores the results of an engine test performed on an engine dynamometer. 

FBAT_AD A particular batch of fuel that can be used to power mobile sources during 

emission tests. Instances of this entity represent a physical batch of fuel that has 

measured properties. 

Table B2 - Individual field definitions 

File Name Field Definition 

EQUIP_AD eng_ms_id Mobile source identifier. For engines, their serial number, 

probably in conjunction with their manufacturer code. 

study_id Identification number assigned to the analysis/paper/report of 

interest. 

veh_ms_id Mobile source identifier. For equipment this would be the serial 

number which best identifies the equipment as a whole. . 

vehclass vehicle class. Will have a translation table. Values defined by 

translation table for this field. 

vehcompany Vehicle manufacturer. Is designed to align with the MFR_ fields 

in CFEIS. Has extended translation table in which 

COMPANY_N will contain the same numeric code as CFEIS for 

this manufacturer. Values defined by Company translation table 

for this field. 

engcompany Engine manufacturer. Is designed to align with the MFR_ fields 

in CFEIS. Has extended translation table in which 

COMPANY_N will contain the same numeric code as CFEIS for 

this manufacturer. Values defined by Company translation table 

for this field. 

highway Yes if mobile source is intended for highway use. No for nonroad mobile sources. 

model_name model name 

model_yr If a prototype, enter representative model year. 

96
 
File Name Field Definition 

make Vehicle make e.g. Buick, as distinct from vehicle manufacturer, 

GM. Legal values defined by MAKE translation table. Values 

defined by translation table for this field. 

disp_liter Nominal engine displacement, expressed in liters. 

fi_type type of fuel injection PFI (port fuel injection) TBI (throttle body 

injection) INDIR (Indirect injection) DIRECT (direct fuel injection 

e.g. as for most diesel engines.) Values defined by translation 

table for this field. 

aspirated indicates how engine is aspirated. CHARGED if turbocharged 

or supercharged. NATURAL if not. Values defined by translation 

table for this field. 

cylinder Number of cylinders or rotors. 

cat_type What type catalyst, if any, is present on the mobile source. 

Values are: 3WAY Three-way catalyst OX3W Oxidation plus 

three-way catalyst OXID Oxidation Catalyst NONE No catalyst 

NULL Unknown Values defined by translation table for this field. 

egr_type Type of exhaust gas recirculation (EGR). Values defined by 

translation table. Values defined by translation table for this 

field. 

engseries Engine series or product line name. 

cooling Type of after_cooling. (Legal values defined by translation 

table.) Values defined by translation table for this field. 

fi_meth Method of fuel injection. ( Legal values defined by translation 

table.) 

fi_press Fuel injection pressure, expressed in kPa. 

parttrap Is particulate trap used? "YES", "NO", or "NUL". 

eng_cycle Engine cycle, 2 =. 2-stroke, 4 = 4-stroke. 0 = Unknown. Values 

defined by translation table for this field. 

ratedpower Rated horsepower of engine. 

ratedspeed Rated rpm of engine 

idle_rpm Idle rpm as declared by the oem. 

97
 
File Name Field Definition 

proc_odom Approximate odometer reading in miles at time of vehicle 

recruitment. 

hour_meter Hours of operation (usually available only for off-road mobile 

sources). Null value is 0. 

gvwr Gross vehicle weight rating in pounds. The value specified by 

the manufacturer as the loaded weight of a single vehicle. 

pk_torque Peak torque of the engine expressed in ft-lb. 

pk_t_speed Peak torque speed expressed in rpm. 

cyl_valves The number of valves per cylinder. 

stroke Piston stroke expressed in inches. (not ready to be stored in 

msod database at this time) 

bore The diameter of the cylinder expressed in inches. 

inj_ctrl Code of the Injection control type. Values defined by translation 

table for this field. 

inj_pcat Code of the injection equipment/pressure category. Values 

defined by translation table for this field. 

98
 
File Name Field Definition 

ETEST_AD test_id Identification number assigned to the engine test. 

study_id Identification number assigned to the analysis/paper/report of 

interest. 

fbatch_id Fuel batch identification. 

eng_ms_id Mobile source identifier. For engines, their serial number, 

probably in conjunction with their manufacturer code. 

test_proc Identifies the specific test procedure used. Values defined by 

translation table for this field. 

ms_type General kind of mobile source: 1 = Vehicle 2 = Engine. 

No_modes Number of test modes involved in this result. Data for individual 

chassis test modes is stored in the DYNOMODE table; data for 

individual engine dynamometer test modes is stored in the 

EMODE table. 

p_ch4 Methane emissions. Expressed in grams per bhp-hr. 

p_thc Total HC emissions. Expressed in grams per bhp-hr. 

p_co CO emissions. Expressed in grams per bhp-hr. 

p_nox NOx emissions. Expressed in grams per bhp-hr. 

p_pm Total particulate emissions. Expressed in grams per bhp-hr. 

total_work Total work performed in test. Expressed in bhp-hrs. 

bsfc_meas Measured brake-specific fuel consumption. Expressed in 

pounds per bhp-hr. 

99
 
File Name Field Definition 

FBAT_AD fbatch_id Fuel batch identification. 

study_id Identification number assigned to the analysis/paper/report of 

interest. 

cetane_num Cetane number of complete fuel. 

cetane_idx Cetane index of complete fuel. 

cetane_imp Amount of cetane improver added, expressed as percentage by 

volume 

cetane_typ Type of cetane improver used, e.g. "N" for nitrate type or "P" for 

peroxide type. Exact set of legal values defined and described 

by translation table for this field. 

sulfur Sulfur content, expressed in parts per million. 

nitrogen Nitrogen content, expressed in parts per million. 

tarom Total aromatics content of fuel, expressed as a percentage by 

volume. This is a measured value, as opposed as being 

calculated as the sum of the monoaromatics and polyaromatics 

fields. 

marom Monoaromatics content of fuel, expressed as a percentage by 

weight. This is a measured value, as opposed as being 

calculated as the difference of the total aromatics and 

polyaromatics fields. 

parom Polyaromatics content of fuel, expressed as a percentage by 

weight. This is a measured value, as opposed as being 

calculated as the difference of the total aromatics and 

monoaromatics fields. 

IBP Initial boiling point expressed in degrees F. 

T10 10% distillation boiling point, expressed in degrees Fahrenheit. 

T50 50% distillation boiling point, expressed in degrees Fahrenheit. 

T90 90% distillation boiling point, expressed in degrees Fahrenheit. 

T95 95% distillation boiling point, expressed in degrees Fahrenheit. 

EP End point of distillation curve, expressed in degrees Fahrenheit. 

spec_grav Specific gravity. 

viscosity Viscosity, expressed in centistokes @40 degrees F. 

hcratio Molecular ratio of hydrogen to carbon. 

oxygen Amount of oxygen in the fuel, expressed as a percentage by 

weight. 

100
 
File Name Field Definition 

oxy_type Type of oxygenate. "NONE" if no oxygenate was added to the 

base fuel. Values defined by translation table for this field. 

heat Net heating value of the fuel, expressed in btu/pound. 

ash Ash content of fuel, expressed as a percentage. 

cetane_dif This is the difference in cetane number between the described 

fuel (with additive) and a baseline fuel without additive. 

101
 
Appendix C - Aromatics Conversion Equations 

As data was copied from the studies we considered relevant into our database, we made 

conversions as necessary to ensure that the database represented a consistent set of units. For 

aromatics, the test method can be just as important as the units, since different aromatics test 

methods can produce measurements which are biased relative to other aromatics test methods. 

Thus it was necessary for aromatics values to be entered not only in a consistent set of units, but 

also representing consistent test methods. 

We determined that all total aromatics entries should represent a fluorescent indicator 

adsorption (FIA) test method such as ASTM D 1319, producing units of volume percent. For 

mono-aromatics and polyaromatics, we determined that all entries should represent a supercritical 

fluid chromatography (SFC) test method such as ASTM D 5186, with units of weight percent. 

These units and test methods were chosen to represent the most common approaches that refiners 

use to measure aromatics. 

As stated in Section II.B.2, we made use of a conversion equation for total aromatics that 

can be found in the California Code of Regulations, Title 13, Section 2282(c)(1) to convert wt% 

by SFC to vol% by FIA for total aromatics. However, there were a number of other cases in 

which a conversion needed to be made but no equation was readily available. For these cases, 

conversion equations were developed especially for this project. 

Throughout the studies that were used to develop our database, there were only two test 

methods that needed to be converted into units of vol% by FIA for total aromatics or wt% by SFC 

for mono and polyaromatics. These two test methods were mass spectrometry and high pressure 

liquid chromatography (HPLC). Available studies were reviewed for cases in which fuels were 

tested on two different test methods. This data was collected and used to develop correlations 

between test methods. A summary of the data sources used to develop the correlations is shown 

in Table C1. 

102
 
Table C1 - Data sources for aromatics conversion equations 

Aromatics 

component Conversion from Conversion to Data sources Observations 

Total wt% by mass spec vol% by FIA SAE 942053 

SAE 2000-01-2980 

VE-10 

VE-1 Phase 1 

VE-1 Phase 2 

32 

Total wt% by HPLC vol% by FIA SAE 2000-01-2980 

SAE 972968 

VE-1 Phase 1 

21 

Mono wt% by mass spec wt% by SFC VE-10 

SAE 942053 

HDEWG 

SAE 2000-01-2980 

26 

Mono wt% by HPLC wt% by SFC SAE 2000-01-2980 

SAE 972968 

11 

Poly wt% by mass spec wt% by SFC VE-10 

SAE 942053 

HDEWG 

SAE 2000-01-2980 

26 

Poly wt% by HPLC wt% by SFC SAE 2000-01-2980 

SAE 972968 

11 

Regression analysis was applied to the data described in Table C1 using linear terms for 

the aromatics measurements. Specific gravity was included if doing so improved significantly the 

fit and the data was available. The resulting correlations are shown in Tables C2, C3, and C4, 

along with the associated R2

 values. These correlations were used to convert the data from the 

studies we used into the units and test method bases we chose for our database. 

Table C2 - Correlations for total aromatics 

[vol% by FIA] = 0.777 X [wt% by mass spec] + 132.2 X [sp. gravity] - 105.0 R2 = 0.93 

[vol% by FIA] = 0.760 X [wt% by HPLC] + 178.0 X [sp. gravity] - 144.4 R2 = 0.96 

103
 
Table C3 - Correlations for mono aromatics 

[wt% by SFC] = 0.882 X [wt% by mass spec] + 2.37 R2

 = 0.91 

[wt% by SFC] = 0.885 X [wt% by HPLC] + 0.88 R2 = 0.99 

Table C4 - Correlations for poly aromatics 

[wt% by SFC] = 1.22 X [wt% by mass spec] + 0.33 R2 = 0.95 

[wt% by SFC] = 1.27 X [wt% by HPLC] + 0.69 R2 = 0.97 

104
 
Appendix D - Technology Groups 

Tech 

Group 

Governed 

speed (rpm) 

Injector 

type Aspiration Horsepower Displacement (L) Oxy catalyst? Injection control Injectio

type 

A any any Turbo any any No Mechanical Direct 

B any any Turbo any any No Mechanical Direct 

C >3000 unit Turbo any <=9.4 No Mechanical Direct 

D >3000 inline or rotary Turbo any <=9.4 No Mechanical Direct 

E <=3000 unit Turbo any <=9.4 No Mechanical Direct 

F <=3000 inline or rotary Turbo any <=9.4 No Mechanical Direct 

G 2101 - 2500 unit Turbo any >9.4 No Mechanical Direct 

H 2101 - 2500 inline or rotary Turbo any >9.4 No Mechanical Direct 

I <=2100 unit Turbo <500 hp >9.4 No Mechanical Direct 

J <=2100 unit Turbo >500 hp >9.4 No Mechanical Direct 

K <=2100 inline or rotary Turbo any >9.4 No Mechanical Direct 

L any any Turbo any any No Electronic Direct 

M any any Turbo any any No Electronic Direct 

N >3000 unit Turbo any <=9.4 No Electronic Direct O >3000 inline or rotary Turbo any <=9.4 No Electronic Direct 

P <=3000 unit Turbo any <=9.4 No Electronic Direct 

Q <=3000 inline or rotary Turbo any <=9.4 No Electronic Direct 

R 2101 - 2500 unit Turbo any >9.4 No Electronic Direct 

S 2101 - 2500 inline or rotary Turbo any >9.4 No Electronic Direct 

T <=2100 unit Turbo <500 hp >9.4 No Electronic Direct 

U <=2100 unit Turbo >500 hp >9.4 No Electronic Direct 

V <=2100 inline or rotary Turbo any >9.4 No Electronic Direct 

W any any any any any No Electronic Indirec

X any any any any any No Mechanical Indirec

Y any any Turbo any any Yes Mechanical Direct 

Z any any Turbo any any Yes Mechanical Direct 

AA >3000 unit Turbo any <=9.4 Yes Mechanical Direct 

BB >3000 inline or rotary Turbo any <=9.4 Yes Mechanical Direct 

105
CC <=3000 unit Turbo any <=9.4 Yes Mechanical Direct 

DD <=3000 inline or rotary Turbo any <=9.4 Yes Mechanical Direct 

EE 2101 - 2500 unit Turbo any >9.4 Yes Mechanical Direct 

FF 2101 - 2500 inline or rotary Turbo any >9.4 Yes Mechanical Direct 

GG <=2100 unit Turbo <500 hp >9.4 Yes Mechanical Direct 

HH <=2100 unit Turbo >500 hp >9.4 Yes Mechanical Direct 

II <=2100 inline or rotary Turbo any >9.4 Yes Mechanical Direct 

JJ any any Turbo any any Yes Electronic Direct 

KK any any Turbo any any Yes Electronic Direct 

LL >3000 unit Turbo any <=9.4 Yes Electronic Direct 

MM >3000 inline or rotary Turbo any <=9.4 Yes Electronic Direct 

NN <=3000 unit Turbo any <=9.4 Yes Electronic Direct OO <=3000 inline or rotary Turbo any <=9.4 Yes Electronic Direct 

PP 2101 - 2500 unit Turbo any >9.4 Yes Electronic Direct 

QQ 2101 - 2500 inline or rotary Turbo any >9.4 Yes Electronic Direct 

RR <=2100 unit Turbo <500 hp >9.4 Yes Electronic Direct 

SS <=2100 unit Turbo >500 hp >9.4 Yes Electronic Direct 

TT <=2100 inline or rotary Turbo any >9.4 Yes Electronic Direct 

UU any any any any any Yes Electronic IndirecVV any any any any any Yes Mechanical Indirec

WW >3000 unit Natural any any No Mechanical Direct 

XX >3000 inline or rotary Natural any any No Mechanical Direct 

YY <=3000 unit Natural any any No Mechanical Direct 

ZZ <=3000 inline or rotary Natural any any No Mechanical Direct 

AAA 2101 - 2500 unit Natural any any No Mechanical Direct 

BBB 2101 - 2500 inline or rotary Natural any any No Mechanical Direct CCC <=2100 unit Natural any any No Mechanical Direct 

DDD <=2100 inline or rotary Natural any any No Mechanical Direct 

106
107
References 

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2. See 13 Calif. Code of Regulations, Sections 2281- 2282. 

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109