United States Air and Radiation EPA420-P-02-001
Environmental Protection October 2002
Agency
vxEPA A Comprehensive
Analysis of Biodiesel
Impacts on Exhaust
Emissions
Draft Technical Report
> Printed on Recycled Paper
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EPA420-P-02-001
October 2002
A of
on
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.
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Nature and Purpose of This Technical Report
This Report presents a technical analysis of the effect of biodiesel on exhaust emissions
from diesel-powered vehicles. It analyzes pre-existing data from various emissions test programs
to investigate these effects. The conclusions drawn in this Technical Report represent the current
understanding of this specific technical issue, and are subject to re-evaluation at any time.
The purpose of this Technical Report is to provide information to interested parties who
may be evaluating the value, effectiveness, and appropriateness of the use of biodiesel. This
Report informs any interested party as to the potential air emission impacts of biodiesel. It is
being provided to the public in draft form so that interested parties will have an opportunity to
review the methodology, assumptions, and conclusions. The Agency will also be requesting
independent peer reviews on this draft Technical Report from experts outside the Agency.
This Technical Report is not a rulemaking, and does not establish any legal rights or
obligations for any party. It is not intended to act as a model rule for any State or other party.
This Report is by its nature limited to the technical analysis included, and is not designed to
address the wide variety of additional factors that could be considered by a State when initiating
a fuel control rulemaking. For example, this Report does not consider isues such as air quality
need, cost, cost effectiveness, technical feasibility, fuel distribution and supply impacts, regional
fleet composition, and other potentially relevant factors.
State or local controls on motor vehicle fuels are limited under the Clean Air Act (CAA) -
certain state fuel controls are prohibited under the Clean Air Act, for example where the state
control applies to a fuel characteristic or component that EPA has regulated (see CAA Section
21 l(c)(4)). This prohibition is waived if EPA approves the State fuel control into the State
Implementation Plan (SIP). EPA has issued guidance describing the criteria for SIP approval of
an otherwise preempted fuel control. See "Guidance on the Use of Opt-in to RFG and Low RVP
Requirements in Ozone SIPs," (August, 1997) at: http://www.epa.gov/otaq/volatility.htm.
The SIP approval process, a notice and comment rulemaking, would also consider a
variety of technical and other issues in determining whether to approve the State fuel control and
what emissions credits to allow. An EPA Technical Report like this one can be of value in such
a rulemaking, but the SIP rulemaking would need to consider a variety of factors specific to the
area, such as fleet make-up, refueling patterns, program enforcement and any other relevant
factors. Additional evidence on emissions effects that might be available could also be
considered. The determination of emissions credits would be made when the SIP rulemaking is
concluded, after considering all relevant information. While a Technical Report such as this may
be a factor in such a rulemaking, the Technical Report is not intended to be a determination of
SIP credits for a State fuel program.
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Executive Summary
Due to the increasing interest in the use of biodiesel, the Environmental Protection
Agency has conducted a comprehensive analysis of the emission impacts of biodiesel using
publicly available data. This investigation made use of statistical regression analysis to correlate
the concentration of biodiesel in conventional diesel fuel with changes in regulated and
unregulated pollutants. Since the majority of available data was collected on heavy-duty
highway engines, this data formed the basis of the analysis. The average effects are shown in
Figure ES-A.
Figure ES-A
Average emission impacts of biodiesel for heavy-duty highway engines
CO
o
CO
E
CD
C
CD
O)
CO
O
-i—'
CD
CD
CL
-60%
-70%
-80%
20
40 60
Percent biodiesel
80
100
One of the most common blends of biodiesel contains 20 volume percent biodiesel and
80 volume percent conventional diesel. For soybean-based biodiesel at this concentration, the
estimated emission impacts for the current fleet are shown in Table ES-A.
11
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Table ES-A
Emission impacts of 20 vol% biodiesel
for soybean-based biodiesel added to an average base fuel
NOx
PM
HC
CO
Percent change in emissions
+ 2.0 %
- 10.1%
-21.1%
-11.0%
Biodiesel is also predicted to reduce fuel economy by 1-2 percent for a 20 volume percent
biodiesel blend. Aggregate toxics are predicted to be reduced, but the impacts differ from one
toxic compound to another. We were not able to identify an unambiguous difference in exhaust
CO2 emissions between biodiesel and conventional diesel. However, it should be noted that the
CO2 benefits commonly attributed to biodiesel are the result of the renewability of the biodiesel
itself, not the comparative exhaust CO2 emissions. An investigation into the renewability of
biodiesel was beyond the scope of this report.
We have high confidence in these estimates for the current fleet. However, the database
contained no engines equipped with exhaust gas recirculation (EGR), NOx adsorbers, or PM
traps. In addition, approximately 98% of the data was collected on 1997 or earlier model year
engines. We made an attempt to estimate the impacts that biodiesel might have on EGR-
equipped engines by investigating cetane effects of biodiesel, and we have no reason to believe
that biodiesel will have substantially different impacts on emissions from the effects shown
above for engines having NOx adsorbers or PM traps. Still, our estimates of biodiesel impacts
on emissions may be less accurate for future fleets than they are for the current fleet.
The investigation also discovered that biodiesel impacts on emissions varied depending
on the type of biodiesel (soybean, rapeseed, or animal fats) and on the type of conventional diesel
to which the biodiesel was added. With one minor exception, emission impacts of biodiesel did
not appear to differ by engine model year.
The highway engine-based correlations between biodiesel concentration and emissions
were also compared to data collected on nonroad engines and light-duty vehicles. On the basis of
this comparison, we could not say with confidence that either of these groups responded to
biodiesel in the same way that heavy-duty highway engines do. Thus we cannot make any
predictions concerning the impacts of biodiesel use on emissions from light-duty diesel vehicles
or diesel-powered nonroad equipment.
in
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Table of Contents
Nature and Purpose of This Technical Report i
Executive Summary ii
Section I: Introduction 1
A. Regulatory Context 1
B. Objectives and Scope of Research 1
C. Interaction with Stakeholders 2
Section H: What Data Was Used? 4
A. Criteria for choosing data sources 4
B. Preparation of database 6
1. Database structure 6
2. Entering data 7
3. Adjustments to database 9
C. Emission standards groups 11
D. Test cycles 13
E. Summary statistics of data 14
1. Fuel properties 14
2. Test cycles 18
3. Standards groups 19
Section HI: How Was The Data Analyzed? 20
A. Overview of curve-fitting approach 20
1. Independent variables 20
2. Dependent variables 21
3. Curve fitting approach 22
B. Treatment of different types of diesel equipment 22
C. Inclusion of second-order and adjustment terms 24
1. Minimum data criteria 24
2. Curve-fitting approach for specific terms 27
a. Squared biodiesel term 27
b. Test cycle effects 28
c. Biodiesel source effects 29
d. Effects of engine standards groups 30
e. Base fuel effects 31
f. Cetane effects 34
Section IV: Biodiesel Effects on Heavy-Duty Highway Engines 36
A. Basic correlations 36
IV
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1. Regulated pollutants 36
2. Fuel economy impacts of biodiesel use 42
a. Via fuel energy content 42
b. Via correlations with fuel consumption 44
3. CO2 impacts of biodiesel use 46
B. Investigation of adjustment terms for regulated pollutants 50
1. Test cycle effects 50
2. Biodiesel source effects 51
3. Engine standards groups 55
4. Base fuel effects 56
5. Cetane effects 60
6. Composite correlations 61
C. Comparison of vehicle data to engine data 65
D. Use of virgin oils as biodiesel 66
E. Comparisons to other emission correlations 68
F. Applying the correlations to the in-use fleet 71
Section V: Biodiesel Effects on Light-Duty Vehicles and Nonroad 75
A. Methodology 75
B. Effects of biodiesel on nonroad engines 77
C. Effects of biodiesel on light-duty highway vehicles 83
Section VI: Biodiesel Effects On Gaseous Toxics 85
A. Toxic Pollutants Evaluated 85
B. Analytical Approach 86
C. Conclusions for individual toxics 93
1. Tier 1 toxics 94
2. Tier 2 toxics 96
3. Tier 3 toxics 97
Section VH: What Additional Issues Should Be Addressed? 99
A. Data gaps 99
1. Newer highway engines 99
2. Nonroad engines 99
3. Biodiesel properties 100
B. Mitigating NOx increases 100
C. Base fuel effects 100
D. Methyl versus ethyl esters 101
E. Minimum data criteria 101
Appendices 103
Appendix A - Data Sources 104
Appendix B - Field descriptions for database Ill
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Appendix C - Assignments for biodiesel source groups 115
Appendix D - Studies used in toxics analysis 116
Appendix E - Aromatics Conversion Equations 117
References 118
VI
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Section I: Introduction
There has been increasing interest in recent years in the use of biodiesel as a substitute for
petroleum-based diesel fuel. This draft technical report describes an assessment of the effect of
biodiesel on exhaust emissions of regulated and unregulated pollutants for vehicles and engines
that have not been specifically modified to use biodiesel. This draft report is intended as a
starting place for discussion and comment. By analyzing the emission impacts of biodiesel, we
have drawn no conclusions regarding the appropriateness of its use for any particular purpose or
in any particular context. Rather, this technical assessment of emissions impacts is intended only
to inform parties that are considering the use of biodiesel.
A. Regulatory Context
As States review their programmatic options for meeting air quality goals, biodiesel is
being considered more frequently. Several municipalities and States are considering mandating
the use of low levels of biodiesel in diesel fuel on the basis of several studies which have found
hydrocarbon (HC) and particulate matter (PM) benefits from the use of biodiesel. Biodiesel may
be appealing for other reasons as well. Renewed concern about national energy security has
heightened interest in the use of biodiesel as a domestically-produced diesel fuel substitute.
There is also strong evidence that biodiesel can reduce emissions of greenhouse gases,
particularly when emissions generated during its full production-to-consumption lifecycle are
taken into account.
Unfortunately, the studies which have examined biodiesel emission effects have not been
entirely consistent in their conclusions, and some studies also suggest that the use of biodiesel
may produce small increases in emissions of oxides of nitrogen (NOx) concurrent with
reductions in other pollutants. For States wishing to account for any potential air quality benefits
of biodiesel use, this presents a dilemma for U.S. EPA reviewers of State Implementation Plans.
As a result of the substantial recent interest in biodiesel and the lack of comprehensive
information on its emission impacts, in August 2001, the Environmental Protection Agency
(EPA) initiated an effort to evaluate the emission benefits of biodiesel for diesel engines which
had not been specifically modified to be used with biodiesel.
B. Objectives and Scope of Research
The primary goal of this EPA project is to provide an objective estimate of the effect of
biodiesel use on emissions of regulated and unregulated pollutants using existing data. As such,
our objective is to provide correlations between the concentration of biodiesel in conventional
diesel fuel and the percent change in emitted levels of different categories of pollutants, as well
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as fuel economy. The program is not intended to include investigations of other aspects of
biodiesel use, such as:
Engine durability Renewability/full fuel lifecycle emissions
Materials compatibility Biodiesel production feedstocks or costs
Fuel storage stability Cold flow properties
Lubricity Cost
As a result, the tentative conclusions reached in this report have some limitations. For instance,
the data on which we based our analyses was collected before the biodiesel samples had an
opportunity to degrade due to excessive storage time. Therefore, our estimates of emissions
impacts for biodiesel use would not be applicable to cases in which the biodiesel has experienced
some degradation. Likewise our estimates would not apply to situations in which cold flow may
be a problem. We also make no claims in this technical report regarding the comparative health
effects of PM emissions from biodiesel versus PM emissions from conventional diesel, other
than to estimate changes in total PM mass emissions.
We did investigate the degree to which other factors influenced the relationship between
biodiesel and emissions, including:
Engine/vehicle technology Base fuel to which biodiesel is added
Highway versus nonroad engines Light versus heavy-duty
Test cycle Type of biodiesel (soybean, rapeseed, grease)
Data on fuel economy, carbon dioxide (CO2) emissions, and toxics were much more
limited than the data on NOx, HC, PM, and carbon monoxide (CO). In particular, correlating
biodiesel with toxics required a more limited statistical approach than that used to evaluate the
other pollutants, and did not permit us to investigate many of the additional factors described
above. Data on light-duty vehicles and nonroad engines was also quite limited. Therefore, we
used data on light-duty and nonroad as validation sets to determine if correlations based on
heavy-duty highway engines could be used as an appropriate predictor of emissions from light-
duty or nonroad fueled with biodiesel.
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 biodiesel on emissions, informational letters were sent to stakeholders. We also
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/biodsl.htm) to share our plans and intermediate work products.
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Comments on this draft technical report and our analysis should be sent by December 31,
2002 to David Korotney at korotney.david@epa.gov, or through regular mail to:
David Korotney
U.S. EPA National Vehicle and Fuel Emissions Laboratory
2000 Traverwood Drive
Ann Arbor, MI 48105
To assure that our correlations represent the best current scientific understanding of the emission
impacts of biodiesel, we also intend to conduct a workshop subsequent to the comment period on
this draft technical report to discuss technical issues related to our analysis. For information on
this workshop, please see our website. At the conclusion of the workshop, EPA will consider all
the comments received and will revise our analysis in response to those comments. We then
plan to publish a final technical report that summarizes our work and conclusions.
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Section II: What Data Was Used?
We began the process of assembling data for use in correlating biodiesel concentrations
with emissions by conducting literature searches and reviewing lists of relevant data sources that
had been assembled by other researchers for use in similar analyses. 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. 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.
We reviewed the studies to verify that they met certain criteria consistent with the goals
of the project. These criteria are described in Section HA below. As a result of this review, only
39 of the full set of 80 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
and corrections for emissions drift over time. All of these steps are described in the remaining
portions of this Section.
A. Criteria for choosing data sources
The data that we considered for use in this analysis was screened to ensure that it met
certain criteria. For instance, 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 non-biodiesel 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.
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
may influence the way in which biodiesel impacts emissions. 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 vehicle and nonroad data was not specifically excluded from the
analysis, but the paucity of this data made it necessary for us to evaluate their effects separately.
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We also excluded data that was collected under test cycles that were unique or in some
way unrepresentative of the Federal Test Procedure (FTP). For instance, a number of studies
tested an engine only under a single steady-state mode, while others used two or three
nonstandard modes for testing. However, there were a number of studies that used atypical test
cycles which were comprehensive enough in their number and/or selection of modes, or in the
design of their transient speed-load traces, that the resulting emission measurements may still be
informative. These latter observations were identified in the database as having the generic test
cycle label TRANSIENT or STEADY-STATE as applicable, and were analyzed separately from
the rest of the data.
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 biodiesel impacts on
emissions, we excluded all studies that did not test at least two different fuels on the same engine
at two different biodiesel concentrations (one of which could be 0% biodiesel). Also, we
considered only studies in which the base fuel to which a biodiesel blend was compared was the
same conventional diesel fuel used to create the biodiesel/diesel blend.
There were a number of cases in which data from one study was repeated in another
study. This might occur if the authors published the same data set in multiple scientific journals
to maximize exposure, or if the authors presented a previously-published set of data in a new
publication for the purpose of comparing the two datasets. Table II. A-l lists the cases in which
repeat publications were excluded from our database.
Table H.A-l
Exclusion of duplicate datasets from the database
Retained Study
Excluded Study
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 paper no. 961166
Colorado Institute for Fuels and High Altitude Engine
Research, "Emissions from Biodiesel Blends and Neat
Biodiesel from a 1991 Model Series 60 Engine Operating
at High Altitude," Final Report to National Renewable
Energy Laboratory, September 1994
Note: CO2 values were not duplicative
Peterson, C.L., "Truck-In-The-Park Biodiesel
Demonstration with Yellowstone National Park,"
University of Idaho, August 1999.
Peterson, C.L., D.L. Reece, "Emissions Testing with
Blends of Esters of Rapeseed Oil Fuel With and Without
a Catalytic Converter," SAE paper no. 961114
Note: Only Table 6 data is duplicative
Manicom, B., C. Green, W. Goetz, "Methyl Soyate
Evaluation of Various Diesel Blends in a DDC 6V-92 TA
Engine," Ortech International, April 21, 1993
Schumacher, L.G., S.C. Borgelt, W.G. Hires, D. Fosseen,
W. Goetz, "Fueling Diesel Engines with Blends of
Methyl Ester Soybean Oil and Diesel Fuel," University of
Missouri
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Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of
Biodiesel Fuels on transient Emissions from Modern
Diesel Engines, Part I Regulated Emissions and
Performance," SAE paper no. 2000-01-1967
Sharp, C.A., "Characterization of Biodiesel Exhaust
Emissions for EPA 21 l(b)," Final Report on Cummins
N14 Engine, prepared for National Biodiesel Board,
January 1998
Peterson, C.L., "Truck-In-The-Park Biodiesel
Demonstration with Yellowstone National Park,"
University of Idaho, August 1999.
Taberski, J.S., C.L. Peterson, "Dynamometer Emissions
test Comparisons on a 5.9L Direct Injected Diesel
Powered Pickup," BioEnergy '98: Expanding BioEnergy
Partnerships
Taberski, J.S., C.L. Peterson, J. Thompson, H. Haines,
"Using Biodiesel in Yellowstone National Park - Final
Report of the Truck in the Park Project," SAE paper no.
1999-01-2798
Graboski, M.S., R.L. McCormick, T.L. Alleman, A.M.
Herring, "The Effect of Biodiesel Composition on Engine
Emissions from a DDC Series 60 Diesel Engine,"
Colorado School of Mines, Final Report to National
Renewable Energy Laboratory, June 8, 2000
McCormick, R.L., M.S. Graboski, T.L. Alleman, A.M.
Herring, "Impact of Biodiesel Source Material and
Chemical Structure on Emissions of Criteria Pollutants
from a Heavy-Duty Engine," Environmental Science and
Technology, 2001, 35, 1742-1747
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 biodiesel concentration 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 not immediately germane to our primary goal of correlating
biodiesel concentration 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:
Fuels.xls
Equipment.xls
File containing a complete description of every fuel, including
physical, compositional, and chemical characteristics.
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
Emissions.xls
File containing individual test descriptions and emission results
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Data source IDs were used to link specific fuels, engines, and emission estimates across the three
files. 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. 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 °C was
entered into the database. If only one viscosity measurement was made, it was entered
into the database without regard to test temperature.
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 oxygen contribution of, for instance, cetane improver additives.
Properties of cetane-enhancedfuels - 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 additive".
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 correlation is:
CNI = a x CN036 x G°'57 x C°-032 x ln(l + 17.5 x Q
For 2-ethylhexyInitiate (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.
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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 to estimate the cetane number. An analysis of unadditized fuels in the survey
database collected by the Alliance of Automobile Manufacturers 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 for
fuels in which no cetane improver additives were used:
Natural cetane number = 1.154 x Cetane index - 9.231
This equation was used to estimate the natural cetane number is cases where only the
cetane index was given and the fuel contained no cetane improver additives.
Aromatics test methods - The database required total aromatics content to be entered in
units of vol% as established from an FIA test method (ASTM D 1319 or the equivalent).
If total aromatics content was derived using supercritical fluid chromatography (SFC,
from ASTM D 5186 or its 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)(l):
vol% (by FIA) = 0.916 x 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 are described in Appendix
E.
Total, mono, and poly aromatics - 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. Mono and polyaromatics
was entered as weight percent.
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 described in
Appendix C of the July 2001 Staff Discussion Document.
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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 correlations between biodiesel
concentration and emissions. 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 labeled as "UDDS" cycle
values in the database (for the Urban Driving Dynamometer Schedule, the schedule on
which the FTP is based). 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. In this process, all
available hot-start tests were averaged before calculating the composite value. If the FTP
was used to produce only hot-start emission measurements, then these results were
entered into the database as UDDSH cycle values.
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.
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, some adjustments were made to ensure that the database was best suited for
our analysis.
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
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plotted against time (engine hours, date, or test run number) to determine if drift occurred. If
engine drift was evident, the authors may have chosen to add a time parameter to the regression
equations that were developed using the study data instead of correcting the data itself. This
option was not available in our correlation 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.
For the particular studies in our database, we found it was not necessary to use the
reference fuel in a given study to generate a correlation between emissions and time. Instead,
some studies included adjusted data that had already been corrected for time drift. For other
studies, time drift appeared more as a step change than as a continuous function. In these latter
cases it was possible to divide all the reference fuel emission measurements into independent
groups, and then use each group as the reference for biodiesel emission measurements collected
in the same timeframe. Table II.B.3-1 lists the studies affected and the type of correction that
was made to account for engine drift.
Table H.B.3-1
Studies corrected for engine drift
Study
SAE paper no. 961 166
Colorado School of Mines 1994
Fosseen 1994b
Graboski 2000
McCormick2001
Engine drift correct
Time-drift adjusted data provided in paper
Time-drift adjusted data provided in paper
Time-drift adjusted data provided in paper
Base fuel tests were found in four different
groups, each of which was statistically
distinct from the others. These four groups
were used as separate reference fuels in lieu
of implementing a time-drift correction
equation
Base fuel tests after Feb. 16, 2001 were
treated as a separate group to account for time
drift
There were also some special cases in which data was not entered into the database in
exactly the same form that it was presented in the study. For instance, if multiple hot-start
measurements were taken under the FTP in a given study, but only one of those hot-start
measurements was used to calculate the composite (at 1/7 weighting for cold-start and 6/7
weighting for hot-start), the composite value was recalculated by first averaging all available hot-
start measurements. In this way all available data was used, though the composite value in the
database may not be exactly the same as the composite value calculated by the study's authors.
There were also cases in which we rejected cold-start data altogether. This occurred in cases
10
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where cold start measurements may have been taken on a reference fuel but not on the biodiesel
blend, or vice-versa. Since the assessment of biodiesel impacts on emissions requires that all
other variables remain constant when comparing a reference fuel to a biodiesel blend, we chose
to exclude the cold-start data in such cases to maintain consistency in test cycle. These types of
data exclusions occurred with the following two studies:
•McCormick2001
• Graboski 2000
C. Emission standards groups
Engines with different technologies may respond differently to the use of biodiesel. It
therefore seemed prudent to examine ways in which the database could be subdivided to capture
any technology-specific effects that might exist. Unfortunately, the engine parameter data
available in the studies that comprised our database was rather limited, and did not permit the
precise assignment of individual engines to particular technology groups.
We took several alternative approaches to investigating the impact that engine technology
on the correlation between biodiesel use and emissions. One approach was to categorize engines
by the emission standards they were designed to meet as a surrogate for engine technology
groupings. Since the model year was available for nearly every engine in the database, the
engines were simply grouped by those model years that were required to meet a single set of
emission standards. These model year groups are shown in Table n.C-1
11
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Table E.C-1
Engine standards groups for heavy-duty highway diesel engines
Standards
group
B
C
D
E
F
G
H
I
Model years
2002 - 2006
1998-2001
1994 - 1997
1991 - 1993
1990
1988- 1989
1984 - 19877
- 19836
Federal emission standards, g/bhp-hr
HC
-
1.3
1.3
1.3
1.3
1.3
1.3
1.5
CO
15.5
15.5
15.5
15.5
15.5
15.5
15.5
25
NOx
-
4.0
5.0
5.0
6.0
10.7
10.7
-
HC + NOx
2.4a
-
-
-
-
-
-
10
PM
0.10P
0.10P
0.10P
0.25P
0.60
0.60
-
-
a Non-methane HC. Manufacturers have an option of meeting a 2.5 g/bhp-hr standard with a 0.5 g/bhp-hr cap on NMHC
P
Standard for urban buses is lower.
7 For 1984 model years, manufacturers could opt to certify on the 13 mode steady-state cycle
Standards shown applied to 1979 - 1983 model years. However, earlier model years have been grouped with 1979 -
1983 model years for the purposes of this analysis.
Dividing the data by standards groups is also ideal from the standpoint of correlating the
results of our analysis with the in-use fleet, since emission inventories are currently determined
as a function of vehicle model year and age. Although engines of a given model year can have
widely varying technologies, and some specific engine technologies span many model years, we
believe that this approach is an appropriate alternative to technology groups. It is noteworthy
that a recent analysis of the impact of diesel fuel properties on emissions from heavy-duty
engines1 found that, except for a few unique cases, engine technology does not play a significant
role in the way that engines respond to changes in fuel properties.
There were several other ways that we investigated the impact of engine technology on
the correlation between biodiesel and exhaust emissions. One was to exclude from our curve-
fitting analyses observations that were based on technologies with potentially different biodiesel
effects compared to heavy-duty highway diesel engines. This included nonroad engines and
light-duty vehicles. After the curve-fitting analyses were completed, data for these excluded
engines/vehicles was compared to the correlations between biodiesel use and emissions to
determine if these excluded engines/vehicles responded the same way to biodiesel as heavy-duty
highway engines. We also examined plots of the data to determine if certain groupings of
observations could be attributed to a consistent collection of engine technologies. These analyses
are described in more detail in the Sections IV and V of this report.
12
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D. Test cycles
The studies that we reviewed contained data generated from several different test cycles.
The following is a description of the various test cycles, both transient and steady-state, that we
evaluated throughout this work. We also provide a description of the test cycles that were chosen
for evaluating the effects of biodiesel on emissions. In selecting test cycles, we aimed at
selecting those cycles that were most representative of in-use operations.
There were several transient test cycles used in the studies included in our database. By
far the most prominent was the Urban Driving Dynamometer Schedule (UDDS). This test cycle
forms the basis of the Federal Test Procedure (FTP) used for engine certification, and we have
considered the FTP to be the most representative of in-use operation, especially for particulate
emissions. The heavy-duty, on-highway FTP consists of 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. In our database, we refer
to the EPA transient test as the UDDS. The EPA transient cycle run with a hot start only is
referred to as UDDSH.b
There were also some studies that used transient test cycles which were different than the
FTP. These other transient test cycles represented only about 5 percent of all the transient data in
the database. Emission measurements made under these alternative transient test cycles were
identified in the database as having the generic test cycle label TRANSIENT, and were analyzed
separately from the rest of the data.
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. Nevertheless, it is considered to be an appropriate
representation of some types of in-use engine operation, at least for NOx and HC. We have
therefore grouped R49 data with FTP data during our analyses for these two pollutants. See
Section IV.B. 1 for a comparison of R49 and FTP impacts on the relationship between biodiesel
concentration and emissions of regulated pollutants.
In selecting data to include in our correlations, the choice of test cycle was considered to
be very important. Data generated from UDDS (FTP) transient cycle was preferred, as this cycle
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 cycle is often referred to by others as a FTP test cycle. Our correlation
did draw from MSOD on the data design, with a key distinction being that MSOD distinguishes between test procedures
and schedules while this correlation does not.
13
-------
most closely represents in-use conditions. 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 correlations between biodiesel use and emissions.
As hot-start results comprise 6/7 of the composite value, we assumed that fuel effects measured
using the hot-start transient test could be considered representative of composite results. We
tested this assumption during our analysis and concluded that it was reasonable. See Section
IV.B.l for details.
Our decision to include certain steady-state NOx and HC emission data in the correlation
is confirmed by a previous study that found that fuel modifications produce similar changes in
emissions over the R49 and the heavy-duty FTP tests2. This study concluded that the effects of
fuel property changes on emissions were similar and that general extrapolations of effects from
steady-state data to transient operation 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 correlations are representative of in-use fuels and engines.
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
Because the analysis was intended to assess how the use of biodiesel affects emissions,
we began by investigating the properties of biodiesel and comparing those properties to
conventional diesel fuel. Of the 31 neat biodiesels in our database, 12 included a full
complement of measured fuel properties, while nearly all included measurements for natural
cetane and specific gravity. We determined average fuel properties for the neat biodiesels and
compared them to average fuel properties for conventional diesel fuel sold outside of California
(based on survey data from the Alliance of Automobile Manufacturers). The results are shown in
Table II.E. 1-1
14
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Table HE. 1-1
Comparison between biodiesel and diesel fuel outside of California
Natural cetane number
Sulfur, ppm
Nitrogen, ppm
Aromatics, vol%
T10, °F
T50, °F
T90, °F
Specific gravity
Viscosity, cSt at 40 °F
Average biodiesel
55
54
18
0
628
649
666
0.88
6.0
Average diesel
44
333
114
34
422
505
603
0.85
2.6
The neat biodiesels used to calculate the above average values can be subdivided into
several broad groups. These include virgin oils versus their transesterified counterparts, and
plant versus animal-based biodiesels. The plant-based biodiesels in the database are derived
from soybean, rapeseed, and canola oils, while the animal-based biodiesels are derived from
tallow, grease, and lard. A more detailed discussion of how we subdivided the plant and animal-
based biodiesels can be found in Section ni.C.2.c.
The largest group is the plant-based esters, comprising nearly 80% of all the biodiesel
blends in the database. Animal-based esters comprise most of the remaining biodiesel blends.
The database contains only two virgin oils, and these appear to have significantly different fuel
properties from the esters. As a result we removed the virgin oil biodiesels from our curve-
fitting, and analyzed them separately (see Section IV.D).
To illustrate how biodiesel and conventional diesel differ, we also examined the
distribution of specific gravity and natural cetane, since these two fuel properties were measured
for nearly every one of the neat biodiesels in the database. Figure HE. 1-1 shows the results.
15
-------
Figure HE. 1-1
Natural cetane and specific gravity of biodiesel and conventional diesel
0.94
0.92
0.9
0.88
2
o
o 0.86
Q.
C/5
0.84
0.82
0.8
O
A O °
o oooo 0
U(DO u r
0
•+- Diesel
° Biodiesel
30 35 40 45 50 55 60 65 70
Natural cetane
In this figure, the biodiesel with the lowest natural cetane (and correspondingly highest specific
gravity) is a virgin oil. The remaining neat biodiesels have relatively constant specific gravity,
but widely varying natural cetane. Although not shown here, there was little variation in the
other fuel properties for neat biodiesel.
The wide variation in natural cetane prompted two additional investigations. The first
was to determine if the natural cetane number of biodiesel was an important component in the
relationship between biodiesel concentration and emissions. This effort is described in more
detail in Section IV.B.5. The second investigation was aimed at determining if natural cetane
could be correlated with either plant or animal-based biodiesel categories. To do this, we plotted
the distribution of natural cetane values separately for plant and animal-based neat biodiesel, and
compared the two distributions. The results are shown in Figure HE. 1-2.
16
-------
Figure HE. 1-2
Distribution of natural cetane for plant and animal-based neat biodiesel
35
30
CO
I "
I 20
o
8
Q_
15
10
I
40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70
Natural cetane number
• Plant-based biodiesel D Animal-based biodiesel
Based on a two-tailed t-test, the probability that plant and animal-based neat biodiesels in the
database have different natural cetane values is 98%. Therefore, we have investigated whether
the correlations between biodiesel concentration and emissions ought to be derived separately for
animal and plant-based biodiesels.
There has also been some discussion in the literature about whether methyl esters differ
from ethyl esters in terms of fuel properties and, ultimately, emission impacts. However, the
studies included in our database often did not always specify whether the biodiesels in question
were methyl or ethyl esters. A review of our database indicated that the cases where a clear
distinction could be made were very small. Therefore, we determined that an investigation of the
differences between methyl and ethyl esters was not possible in our analysis.
Biodiesel can be blended into conventional diesel fuel at any concentration. This fact is
reflected in the distribution of biodiesel concentrations in the database, which is shown in Figure
II.E.1-3.
17
-------
Figure HE. 1-3
Distribution of biodiesel concentrations in database
tn
CD
50
40
30
CD
.Q
E 20
10
10 20 30 40 50 60 70 80
Volume percent biodiesel
90 100
Although 20 vol% biodiesel is the most common blend level among in-use biodiesel programs,
the fact that biodiesel can be blended at any level suggests that the most useful analysis of
biodiesel impacts on emissions would be to use biodiesel concentration as the independent
variable in a traditional curve-fitting process. This approach would permit us to use all the
available data in the analysis, and would provide a means for estimating the impact of biodiesel
on emissions for any biodiesel concentration. Our curve-fitting approach is described in more
detail in Section HI.
2. Test cycles
When collecting data for input into our database, we excluded data that was collected on
tests cycles that only contained a few nonstandard modes or which were otherwise deemed not
representative of in-use operation. Table HE.2-1 summarizes the number of observations in our
database for each of the test cycles included in our analysis. The values in parentheses are the
percent of total observations. "TRANSIENT" and "STEADY-STATE" tests cycles refer to all
those cycles which were unique in some fashion, but which represented valuable data that could
be used as validation sets.
18
-------
Table H.E.2-1
Database observations by test cycle
Test cycle
FTP composite
FTP hot start
R49 13 -mode
Nonroad 8 mode
"STEADY-STATE"
"TRANSIENT"
All cycles
HC
295 (36)a
380 (46)
57(7)
14(2)
36(4)
40(5)
822
CO
295 (36)
385 (47)
57(7)
14(2)
36(4)
40(5)
827
NOx
295 (34)
422 (49)
57(7)
14(2)
36(4)
40(5)
864
PM
294 (35)
422(51)
34(4)
8(1)
36(4)
40(5)
834
CO2
175 (32)
322 (59)
0
0
6(1)
40(7)
543
BSFC
167 (73)
51 (22)
0
0
10(4)
0
228
Values in parentheses are percent of total observations
3. Standards groups
As described in Section HC above, categorizing the data in our database according to the
emission certification standards that the engines were designed to meet might provide a
convenient means for applying regression correlations to the in-use fleet. Table II.E.3-1 provides
a summary of the number of engines and observations in our database.
Table H.E.3-1
Amount of data by engine standards groups for heavy-duty highway diesel engines
Standards group
B
C
D
E
F
G
H
I
Model years
2002 - 2006
1998-2001
1994 - 1997
1991 - 1993
1990
1988- 1989
1984- 1987
- 1983
FID highway engines
0
2
10
16
O
8
2
2
NOx observations
0
14 (2)a
152(19)
394 (50)
87(11)
112(14)
16(2)
10(1)
Values in parentheses are percent of total observations
19
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Section III: How Was The Data Analyzed?
As mentioned previously, the goal of this study is to determine how diesel engine exhaust
emissions are affected by the use of biodiesel. Although the most common biodiesel
concentration is 20 volume percent, it can also be used at concentrations varying from 1 to 100
percent. As a result, we determined that a statistical regression correlation would be appropriate,
offering a means for predicting the percent change in exhaust emissions as a function of the
concentration of biodiesel in conventional diesel fuel.
This Section describes our statistical approach to estimating the effect of biodiesel on
emissions of regulated pollutants. The results of applying these statistical approaches to the data
in our database are described in the following two Sections for heavy-duty highway vehicles, and
light-duty vehicles and nonroad engines. The impact of biodiesel on emissions of toxic
pollutants is then presented separately in Section VI.
A. Overview of curve-fitting approach
1. Independent variables
We intended to correlate emissions as a function primarily of biodiesel concentration.
Although it may have been ideal to include other fuel properties for biodiesel or the base fuel as
independent variables, few of the studies which comprised our database included measurements
of all relevant fuel properties. However, the category of "biodiesel" itself was considered to be a
reasonable surrogate for the missing biodiesel fuel properties in light of the fact that biodiesel
properties appeared to be largely constant across the studies in our database. In addition, we
were able to investigate the impacts of base fuel properties in a general fashion based on a
qualitative scale of "cleanliness," described more fully in Section ni.C.2.e below.
While biodiesel fuel properties generally fell within a narrow range, natural cetane
number was an exception; it varied significantly from one batch of biodiesel to another. Cetane
number was measured for nearly every biodiesel and base fuel, providing a means for its
inclusion in our analysis. However, since in the field the cetane number of a given batch of
biodiesel or the base fuel to which biodiesel is added is not always known, correlations which
include cetane number as an independent variable may not be the most user friendly. Therefore,
although cetane number was not included in our final correlations, it was used in several other
aspects of our analysis, such as:
• Establishing differences between animal and plant based biodiesel, as described in
SectionH.E.l
• Along with other fuel properties, categorizing base fuels as either "Clean" or
"Average" emitting, as described more fully in Section in.C.2.e
20
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• Determining whether EGR-equipped engines are likely to respond to biodiesel in
a similar fashion to engines not equipped with EGR, as described in Section
IV.B.5
One common technique in multivariable regression analysis is to standardize the
independent variables. Standardization involves subtracting the mean from every observation,
and then dividing the result by the standard deviation. It is useful for comparing the regression
coefficients of the different independent variables to determine relative importance. However, in
our approach we included only a single independent variable, the biodiesel concentration. Also,
through preliminary regressions we determined that a squared biodiesel term was not necessary.
As a result, we did not standardize the independent variable in our analysis.
2. Dependent variables
In reviewing the instances of repeat emissions data in our database (cases in which the
same fuel was tested on the same engine multiple times), it appeared that the variability in
emissions measurements was a function of the mean emissions measurements. In other words,
lower emission levels tended to exhibit smaller variability than higher emission levels. As a
result, we determined that the use of a log transform for emissions would be appropriate, since its
use tends to make the dependent variable's data in a regression equation homoscedastic.
The use of a log transform has another advantage. Our analysis was intended to produce
correlations that predict the percent change in emissions resulting from the use of a given
concentration of biodiesel. The analysis was not intended to permit the estimation of absolute
emission levels (g/mile or g/bhp-hr). Given the relative nature of the correlations, the intercept
terms produced during the regressions can be eliminated. This result is shown mathematically
below.
The regression equation can be expressed as:
log(Emissions) = a x (vol% biodiesel) + b (1)
where a and b are determined through the statistical curve-fitting process. In practice, absolute
emissions would be estimated from:
Emissions = exp[a x (vol% biodiesel) + b] (2)
= exp[a x (vol% biodiesel)] x exp(b) (3)
The percent change in emissions due to the use of biodiesel is calculated generally from:
% change in emissions = (Emissions)with biodiesel - (EmissionsWthoutbiodiesel x 100 (4)
EmiSSlOnSwithout biodiesel
21
-------
Equation (3) can be combined with (4) to produce:
% change in emissions = lexp[a * (vol% biodiesel)] * exp(b) - exp[a x 0] x exp(b)l x 100 (5)
exp[a x 0] x exp(b)
% change in emissions = lexp[a x (vol% biodiesel)] - II x expfb) x IQO (6)
exp(b)
% change in emissions = (exp[a x (vol% biodiesel)] - 1} x 100 (7)
Equation (7) does not contain the constant b, and can be used to predict the percent change in
emissions for a given concentration of biodiesel.
3. Curve fitting approach
Rather than using a least-squares type of regression, we opted to use a maximum
likelihood approach to curve fitting. In SAS, this approach is employed with the procedure
proc_mix. This procedure is less prone than least-squares to being influenced by large numbers
of repeat measurements. It can also treat some variables as fixed effects and others as random
effects. For instance, the primary independent variable that we intended to include in the
correlations, percent biodiesel, was represented as a fixed effect. Other variables, such as
engines and base fuels, were based on data that is a sampling from a wider population. As such,
they are best represented as random effects. Our curve-fitting effort, therefore, included engine
intercepts, engine x percent biodiesel, and engine x base fuel terms as random effects.
As the analysis progressed, we used several criteria for determining when candidate fixed
terms should be included in the correlations. The first was a screening tool that ensured that a
minimum amount of data was available for any adjustment term considered for inclusion in the
correlations. This screening tool is described in more detail in Section ni.C.l below. We also
used a significance criterion of p = 0.05 for all runs. We did not make use of Mallow's Cp
criterion to balance over-fitting and under-fitting, since this criterion cannot be calculated in a
mixed effects correlation. However, the number of adjustment terms added to our final
correlations was quite small and would be unlikely to cause significant overfitting.
B. Treatment of different types of diesel equipment
The database includes emissions data on a variety of diesel equipment types. These
include highway and nonroad, light-duty and heavy-duty, and both engines and vehicles. There
was no biodiesel data available for stationary source engines. The distribution of NOx
observations between these various categories is given in Table ni.B-1.
22
-------
Table HI.B-1
Distribution of NOx observations by diesel equipment category
Heavy-duty highway engines
Heavy-duty highway vehicles
Heavy-duty nonroad engines
Light-duty highway engines
NOx observations
658
143
14
6
Percent of observations
80
17
2
1
We decided to focus our initial curve-fitting efforts on the data from heavy-duty highway
engines, since this category contained the most data. We did not include the heavy-duty highway
vehicle data in this initial analysis, since we did not want to presume that biodiesel effects on
emissions would be the same for engines and vehicles, given differences in the associated test
cycles. Instead, once we had completed our investigations of heavy-duty highway engines, we
compared the resulting correlations to the data from the remaining three categories of diesel
equipment to determine how well the correlations represented these three other categories. Later
sections of this report describe these comparisons.
We also intended to examine the possibility that engine technology might be an important
factor in correlating biodiesel use with emissions. However, the engine descriptions in the
studies that comprise our database were often incomplete, making it difficult to assign each
engine to any but the broadest of technology groupings. This was not deemed a significant
detriment to our analysis for two reasons:
1.
A previous analysis of the impacts of diesel fuel properties on emissions3
concluded that adjustment terms representing different engine technologies were
rarely necessary.
Comments on our previous analysis had suggested that grouping engines by
model year might be more appropriate than grouping them by technology type.
As a result, we grouped all engines into one of seven "standards groups," based on their model
year which was available for nearly every engine in our database. These standards groups are
described in Section n.C, and the distribution of NOx data among these groups is described in
SectionH.E.S.
There was one important group of engine technologies which was notably missing from
our database: engines equipped with exhaust gas recirculation (EGR), designed to meet the 2004
heavy-duty engine certification standards. Because these engines will comprise a larger and
larger fraction of the in-use fleet in the coming years, their absence from our database raises the
question of how and to what degree our correlations should apply to the in-use fleet. However,
we note that in a previous analysis it was primarily cetane effects that were different for EGR-
equipped engines as compared to non-EGR engines. Specifically, EGR-equipped engines
23
-------
appeared to exhibit no NOx response to changes in cetane number, whereas non-EGR engines
exhibited reductions in NOx when cetane number increased. Thus, although no EGR-equipped
engines were in our biodiesel database, we designed an analytical approach that provided insight
into how NOx emissions from EGR-equipped engines might respond to the use of biodiesel.
This analysis is described in Section in.C.2.f
C. Inclusion of second-order and adjustment terms
We investigated the need for additional terms in our correlations in order to increase their
explanatory power. These additional terms included a squared term for biodiesel concentration
and adjustment terms for test cycle, biodiesel source, engine standards groups, and the
conventional diesel fuel to which biodiesel was added. We also investigated whether cetane
number was an important element in the correlation of biodiesel concentration with emissions.
Our approach to investigating the need for these additional terms is described in this section.
In response to previous work correlating diesel fuel properties with emissions,
stakeholders suggested that we develop criteria that establish the minimum amount of data that
would be necessary before a given adjustment term should even be considered for inclusion in
statistical correlations. Such criteria would provide some insurance against statistically
significant adjustment terms entering the correlations as a result of a limited amount of data that
just happens to exhibit a spurious emissions effect. Therefore, Section in.C.l describes the
development and application of these minimum data criteria. Section ni.C.2 will describe the
various specific adjustment terms that we investigated.
1. Minimum data criteria
The statistical significance of any potential adjustment term that we could add to our
correlations depends in part on the number of observations that represent that adjustment term.
For a small sample, the potential exists for the mean effect to significantly differ from the true
population mean. For instance, there is a 13% probability that every observation in a sample of
four observations would fall on one side of a normal distribution curve. For a sample of eight
observations, the probability of this occurring drops to less than 1%. Similarly, a data set that is
too small may lead to the conclusion that an adjustment term in our correlations is statistically
significant when in fact a larger sample would prove otherwise. Thus it seemed prudent to
establish some minimum number of observations below which we would not consider including
a potential adjustment term in our correlations.
We investigated criteria for the minimum amount of data that would be needed to
estimate a population mean within some confidence interval. The formula4 for this calculation is:
24
-------
[ Z«/2X
-------
emissions for 20% biodiesel for HC, CO, NOx, and PM were -20, -10, +2, and -10, respectively.
We arbitrarily chose an interval of ± 25% around each of these percent change values to define
the confidence interval E(percent). Thus, for instance, the confidence interval for HC would be
-15% to -25%, and this range of biodiesel effects on HC was converted into a value of E in units
of g/bhp-hr using equation (9). The mean emissions values for each pollutant were drawn from
the same database used to estimate the population standard deviations. Table ni.C. 1-1
summarizes the calculation of E(g/bhp-hr) for all four pollutants.
Table m.C. 1-1
Calculation of confidence interval for minimum data criteria
% change for B20
E (percent)
Mean emissions (g/bhp-hr)
E (g/bhp-hr)
HC
-20
± 25 %
0.25
±0.012
CO
-10
± 25 %
0.96
±0.024
NOx
±2
± 25 %
4.87
±0.024
PM
-10
± 25 %
0.11
±0.0029
The calculation of the minimum number of observations then follows equation (8). The
results are given in Table ni.C. 1-2.
Table m.C. 1-2
Minimum number of observations
Population standard
deviation (g/bhp-hr)
E (g/bhp-hr)
n
HC
0.024
0.012
10
CO
0.052
0.024
12
NOx
0.072
0.024
23
PM
0.0058
0.0029
11
The values for n in Table ni.C. 1-2 represent reasonable lower limits for the number of
observations that would be necessary in order to have some confidence that statistically
significant adjustment terms are legitimate. However, we note that the estimated value of n for
NOx is considerably higher than that for the other pollutants, owing primarily to the smaller
impact that biodiesel has on NOx emissions. Rather than have separate minimum data criteria
for each pollutant, and recognizing that alternative values of n could reasonably be estimated
with different inputs to equation (8), we have decided to use a single value of 20 observations as
our minimum data criterion.
26
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We made two modifications to the use of a minimum data criterion of 20 in our curve-
fitting effort. First, because our analysis was intended to permit estimation of the percent change
in emissions resulting from the use of biodiesel, a minimum of two observations are required to
establish each point estimate: the base fuel, and the biodiesel blend. Therefore, we applied our
minimum data criteria to point estimates, i.e. pairs of observations consisting of a base fuel and a
biodiesel blend. In practice, this meant counting only biodiesel blends when making a
determination as to whether the minimum data criterion had been met.
Second, we determined that any subset of data being considered as the basis for an
adjustment term should contain at least two engines. This additional criterion reduced the
chances that an engine with unique responses to biodiesel would by itself form the basis of an
adjustment term in any of our correlations.
2. Curve-fitting approach for specific terms
This section describes the various adjustment terms that we considered adding to our
correlations, including the application of our minimum data criteria. The analytical approaches
taken are described here, while results of the analyses are described in Section IV. All of these
analyses were done only for heavy-duty highway engines, after which we made comparisons of
the resulting correlations to data for other types of diesel equipment.
The evaluation of every type of potential adjustment terms (test cycle effects, biodiesel
source effects, etc.) was initially done independently from all other types of potential adjustment
terms. In each case, the correlations were generated in three steps:
Step 1: Generate a correlation to identify outliers
Step 2: After dropping outliers, generate a correlation to identify statistical significance of
terms
Step 3: After dropping non-significant adjustment terms, generate a final correlation
Once all the important adjustment terms had been identified through this process, a single
correlation incorporating them all was developed. These 'composite correlations' are described in
Section IV.B.6.
a. Squared biodiesel term
There were a number of studies in our database that tested three or more different
biodiesel concentrations using a single base diesel fuel. We therefore felt it appropriate to
investigate whether a squared biodiesel concentration term should be added to our correlations.
For all pollutants, the squared biodiesel concentration term was not significant. Thus for all
subsequent analysis, only a linear biodiesel concentration term was included.
27
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b. Test cycle effects
The database contained emission measurements collected on a variety of test cycles, as
shown in Table HE.2-1. For our initial analysis of heavy-duty highway engines, we set aside all
data collected on generic transient or generic steady-state cycles. This left only composite FTP,
hot-start FTP, and R49 data. The distribution of biodiesel NOx observations for this data subset
is shown in Table ni.C.2.b-l.
Table m.C.2.b-l
Biodiesel NOx observations by cycle for heavy-duty highway engines
FTP composite
FTP hot start
R49 13 -mode
135
175
18
Although the number of R49 observations did not strictly meet our minimum data criterion of 20
observations, previous analyses suggested that test cycle may have an effect on emissions from
heavy-duty diesel engines, particularly for PM and CO. Since the R49 data was collected on
three separate engines, and comes close to our minimum data criterion, in this case we decided to
proceed with investigating the need for adjustment terms representing all three test cycles. Thus
we did not automatically exclude the steady-state R49 data from the PM and CO analyses, but
instead investigated whether or not it was appropriate to include this steady-state data in our
analysis of biodiesel effects on PM and CO emissions.
In addition to biodiesel concentration as a fixed effect in the SAS procedure proc_mix,
we introduced terms representing each of the three test procedures and interactions between them
and biodiesel concentration. The list of fixed effects included in this analysis are shown in Table
m.C.2.b-2.
Table m.C.2.b-2
Fixed terms used to investigate test cycle effects
Overall intercept
R49 intercept
HDDS intercept
UDDSH intercept
Percent biodiesel
Percent biodiesel * R49
Percent biodiesel * HDDS
Percent biodiesel x UDDSH
Because the number of interactive terms for which coefficients can be estimated in the fixed
28
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portion of proc_mix is limited to n-1, where n is the number of test cycle groups, no coefficient
could be estimated for one of the three interactive terms. In this case, no estimate was made for
the percent biodiesel x UDDSH interactive term. As a result, the overall percent biodiesel term
defaulted to representing the UDDSH cycle. The results of our investigation of test cycle effects
is described in Section IV.B.l.
c. Biodiesel source effects
Biodiesel can be produced from a wide variety of feedstocks. The studies that comprise
our database included only a portion of the many feedstocks possible, though they do represent
the most common feedstocks. The biodiesel feedstocks in our database are listed in Table
m.C.2.c-l.
Table m.C.2.c-l
Biodiesel feedstocks in database
Feedstock
Soybeans
Rapeseeds
Canola oil
Grease
Tallow
Lard
Number of biodiesel observations
232
41
3
23
9
O
From Table ni.C.2.c-l we see that at least three feedstock categories do not contain
sufficient data for an analysis of feedstock impacts on the correlation between biodiesel
concentration and emissions. We therefore investigated ways in which the feedstocks in Table
in.C.2.c-l could be combined into larger groups. We know from a review of pure biodiesel
cetane numbers that plant-based biodiesel (soybean, rapeseed, and canola) are distinct from
animal-based biodiesel (grease, tallow, and lard). We also know that canola oil is derived from a
rape plant offbreed, and so might be appropriately combined with rapeseed-based biodiesel. As a
result, we created three groups which are listed in Table in.C.2.c-2. Every biodiesel blend in our
database was placed into one of the three source groups shown in this table.
29
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Table IH.C.2.C-2
Biodiesel source groups
Feedstock
Soybeans
Rapeseeds/canola
All animal
Number of biodiesel observations
232
44
35
In addition to biodiesel concentration as a fixed effect in the SAS procedure proc_mix,
we introduced terms representing each of the three biodiesel source groups and interactions
between them and biodiesel concentration. The list of fixed effects included in this analysis are
shown in Table III.C.2.C-3.
Table IH.C.2.C-3
Fixed terms used to investigate biodiesel source effects
Overall intercept
Soybean intercept
Rape intercept
Animal intercept
Percent biodiesel
Percent biodiesel * soybean
Percent biodiesel x rape
percent biodiesel x animal
Because the number of interactive terms for which coefficients can be estimated in the fixed
portion of proc_mix is limited to n-1, where n is the number of biodiesel source groups, no
coefficient could be estimated for one of the three interactive terms. In this case, no estimate was
made for the percent biodiesel x soybean interactive term. As a result, the overall percent
biodiesel term defaulted to representing soybean-based biodiesel. The results of our
investigation of our investigation of biodiesel source groups is described in Section IV.B.2.
d. Effects of engine standards groups
As described in Sections II.C and in.B, we grouped all heavy-duty highway engines into
one of seven "standards groups," based on their model year which was available for nearly every
engine in our database. These standard groups were used as a surrogate for engine technology
for which data was largely missing. We then investigated whether the biodiesel effects on
emissions were significantly different between each of these standards groups. The number of
NOx observations for biodiesel blends is given in Table in.C.2.d-l.
30
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Table m.C.2.d-l
Number of biodiesel NOx observations for heavy-duty highway diesel engines
Standards group
C
D
E
F
G
H
I
Model years
1998-2001
1994 - 1997
1991 - 1993
1990
1988- 1989
1984- 1987
- 1983
NOx observations
0
113
149
15
44
2
4
This table shows that three of the standards groups, F, H, and I, do not meet our minimum data
criteria for the investigation of subgroup effects of biodiesel on emissions. We therefore only
investigated adjustment terms for engine standard groups D, E, and G.
In addition to biodiesel concentration as a fixed effect in the SAS procedure proc_mix,
we introduced terms representing each of the three engine standards groups and interactions
between them and biodiesel concentration. The list of fixed effects included in this analysis are
shown in Table ni.C.2.d-2. The results of our investigation of engine standard group effects is
described in Section IV.B.3.
Table m.C.2.d-2
Fixed terms used to investigate engine standards group effects
Overall intercept
Group D intercept
Group E intercept
Group G intercept
Percent biodiesel
Percent biodiesel * Group D
Percent biodiesel x Group E
Percent biodiesel x Group G
e. Base fuel effects
When biodiesel is added to a base fuel, we initially assumed that any impacts on
emissions would be strictly a function of the biodiesel concentration. That is, the impact that
31
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biodiesel has on emissions at a given concentration would be the same regardless of the
properties of the base fuel to which the biodiesel was added. However, it seemed prudent to test
this assumption.
Ideally, the base fuel would be taken into account in our curve-fitting process by adding
terms to the correlations representing the properties of the base fuel, in addition to the biodiesel
concentration term. The resulting correlations would then allow one to predict the impact that
biodiesel has on emissions both as a function of the biodiesel concentration and the properties of
the base fuel, if the terms for those base fuel properties were statistically significant.
Unfortunately, many of the studies which comprise our database did not include a full
complement of fuel property measurements for the base fuel. Of the 39 base fuels, only 21 had
measured values for all the primary fuel properties of interest (cetane, sulfur, aromatics, T10,
T50, T90, density). These fuel properties were identified in our July 2001 Staff Discussion
Document as being important measures of fuel property effects on emissions of regulated
pollutants.
In order to maximize the useable data when investigating base fuel effects, we opted to
divide all the base fuels into one of three groups: clean, average, and dirty. The assignments
were made using a combination of available measured fuel properties and the description of the
base fuel from the original study. Using a distribution of fuel properties from an in-use diesel
fuel survey, we determined that none of the base fuels in our database should be assigned a label
of "dirty." Many base fuels were designed to meet certification fuel standards, and these were
assigned to the "average" group. The complete assignments are given in Appendix C. Generally
speaking, the "clean" fuels had lower aromatics, higher cetane, lower density, and lower
distillation points than "average" fuels.
Based on the distribution of fuel properties for these two base fuel groups, we here
propose a set of conditions that might permit one to distinguish a "clean" base fuel from an
"average" base fuel when using the correlations presented in Section IV. These conditions are
given in Table UI.C.2.e-l.
32
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Table m.C.2.e-l
Base fuel emission group proposed definitions
A. All base fuels to which biodiesel is added are assigned to the
"average" emission category for the purposes of estimating
emission benefits of biodiesel using the correlations in this
report, unless
Bl.The base fuel in question meets the requirements for
highway diesel fuel sold in California or alternative
requirements that are substantially similar to those in
California, or
B2. The fuel in question meets all of the following conditions:
1. Total cetane number is greater than 52
2. Total aromatics content is less than 25 vol%
3. Specific gravity is less than 0.84
For fuels meeting conditions Bl or B2, the base fuel should be
assigned to the "clean" category.
In addition to biodiesel concentration as a fixed effect in the SAS procedure proc_mix,
we introduced terms representing each of the two base fuel emission groups and interactions
between them and biodiesel concentration. The list of fixed effects included in this analysis are
shown in Table m.C.2.e-2.
Table m.C.2.e-2
Fixed terms used to investigate base fuel effects
Overall intercept
Average intercept
Clean intercept
Percent biodiesel
Percent biodiesel * average
Percent biodiesel x clean
Because the number of terms for which coefficients can be estimated in the fixed portion of
proc_mix is limited to n-1, where n is the number of base fuel emission groups, no coefficient
could be estimated for one of the two interactive terms. In this case, no estimate was made for
the percent biodiesel x clean interactive term. As a result, the overall percent biodiesel term
defaulted to representing 'clean' base fuels. The results of our investigation of base fuel effects in
given in Section IV.B.4.
33
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f. Cetane effects
As shown in Table HE.3-1, our database does not contain any data on model year 2002+
heavy-duty highway engines. Heavy-duty highway engines produced to meet the 2004 standards
(and which, under a consent decree, may be produced as early as October of 2002) are expected
to make substantial use of exhaust gas recirculation (EGR). In a previous analysis5 we concluded
that EGR-equipped engines respond differently to the cetane number of diesel fuels, in terms of
NOx emissions, than engines without EGR. In short, cetane appears to have a significant role in
NOx emission effects for engines that do not have EGR, but a negligible role in NOx emission
effects for EGR-equipped engines.
As shown in Figure HE. 1-1, the cetane number of biodiesel varies widely. On average,
however, the natural cetane number of biodiesel is higher than that for conventional diesel fuel
(see Table HE. 1-1). If the cetane number of biodiesel plays a role in the impact that biodiesel
has on NOx emissions, it is possible that EGR-equipped engines may exhibit a different
biodiesel/NOx relationship than engines not equipped with EGR. In order to test this hypothesis,
we conducted an analysis to determine the importance of cetane in the correlation of biodiesel
concentration with NOx emissions. This analysis was not done in order to add a cetane term to
the NOx correlation, but rather to determine if it was legitimate to extrapolate the effects of the
NOx correlation to EGR-equipped engines.
Through previous work, we had developed a draff correlation between cetane number
and NOx emissions. This correlation is shown below:
NOx =exp(-0.004512 x CET_DIF + 0.0001458 x CET_DIF2 + 1.5497034)
The correlation was based on NOx differences between fuels containing a cetane improver
additive and their associated unadditized base fuel. The independent variable CET_DIF is the
difference between the cetane number of the additized fuel and the cetane number of the base
fuel. As a result, this correlation avoids the collinear effects that natural cetane often has with
aromatics and specific gravity.
In order to determine the degree to which cetane plays a role in the biodiesel/NOx
relationship, we used the above equation to separate cetane effects in the biodiesel database from
the measured NOx emissions. To do this, we first converted the above equation into an
equivalent form that could be applied to cetane number (as opposed to cetane difference). The
result is shown below:
This correlation was generated in preliminary analyses and would not be appropriate for use in other contexts.
EPA has released a draft technical report that contains our updated analysis on additized cetane effects on NOx
emissions: "The effect of cetane number increase due to additives on NOx emissions from heavy-duty highway engines,"
EPA Draft Technical Report EPA420-S-02-012, June 2002.
34
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NOx =exp(-0.004512 x (CETNUM - 45) + 0.0001458 x (CETNUM - 45)2 + 1.5497034)
where CETNUM is the total cetane number of any fuel. We then used this equation to predict
the NOx emissions for every fuel in our biodiesel database, and calculated the difference between
the predicted NOx and measured NOx for every observation. These "pseudo-residuals" should
therefore be independent of any cetane effects, and could be used as dependent variables in a
mixed model analysis of biodiesel concentration. Depending on the magnitude of the biodiesel
effect for this correlation and its associated statistical significance, we could determine the degree
to which cetane plays a role in the relationship between biodiesel concentration and NOx
emissions. The results of this analysis are described in Section IV.B.5.
35
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Section IV: Biodiesel Effects on Heavy-Duty Highway Engines
Our primary analyses were carried out only on heavy-duty highway engine data, since this
data comprised the majority of the database. In this context we evaluated the need for various
adjustment terms and produced a single correlation for each of the pollutants NOx, PM, CO, and
HC which included all those effects that were deemed significant. We also investigated fuel
economy impacts of biodiesel and the impact that biodiesel use would have on emissions of the
greenhouse gas carbon dioxide. The investigation of toxics impacts of biodiesel use followed an
alternative analytical approach, and so is covered separately in Section VII.
This section describes the results of our analysis for heavy-duty highway engines, and
shows how well on-highway heavy-duty vehicle emissions data represents engine impacts of
biodiesel use. In Section V we compare the correlations presented in this Section to data
collected on light-duty vehicles and nonroad engines.
A. Basic correlations
Before investigating the degree to which biodiesel effects differ due to engine technology,
type of biodiesel, type of base fuel, or test cycle, we first carried out more fundamental analyses
of biodiesel effects on regulated pollutants, fuel economy, and carbon dioxide. This Section
summarizes our analyses of these basic correlations, while Section IV.B provides a description of
the various adjustments we investigated.
1. Regulated pollutants
The first step in our analysis involved development of correlations between biodiesel
concentration and the percent change in emissions, without any adjustment terms'1, for heavy-
duty highway engines. All correlations were of the form:
% change in emissions = (exp[a x (vol% biodiesel)] - 1} x 100%
Table IV. A-1 gives the values for the coefficient "a" for each of the four pollutants, all of which
were statistically significant at the p = 0.05 level. Figure IV. A. 1-1 presents these basic
correlations graphically.
Subsequent analyses indicated that R49 data should not be included in curve-fitting for PM or CO. See
Section IV.B. 1. Therefore, R49 data was removed from the basic correlations for PM and CO.
36
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Table IV. A. 1-1
Coefficients for basic emission correlations
NOx
PM
HC
CO
Coefficient "a"
0.0009794
-0.006384
-0.011195
-0.006561
Figure IV. A. 1-1
Basic emission correlations
20%
-80%
20
40 60
Percent biodiesel
80
100
Because we used maximum likelihood curve-fitting instead of least-squares regressions to
develop our correlations, the coefficient of determination (r2) is not applicable and was not
provided by the SAS procedure proc_mix. As an alternative, we plotted the actual data on the
same graph as our correlations to provide a visual assessment of goodness of fit. To do this, we
needed to calculate the percent change values associated with every observation in our database.
Because the number of repeat tests on base fuel (0% biodiesel) were not always paired with an
equivalent number of tests on biodiesel, this process required that we first calculate the average
of repeat emission measurements made on every base fuel (0% biodiesel) for every engine/test
cycle combination. Percent change in emission values for every biodiesel blend were then
37
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calculated using this average base fuel. The comparisons are shown in Figures IV. A. 1-2, IV. A.I-
3, IV.A. 1-4, IV.A. 1-5 forNOx, PM, HC, and CO, respectively.
Figure IV. A. 1-2
Comparison of data to basic NOx correlation
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-10
20
40 60
Percent biodiesel
80
100
38
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Figure IV. A. 1-3
Comparison of data to basic PM correlation
40
20
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-------
Figure IV. A. 1-4
Comparison of data to basic HC correlation
E
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D)
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O
100
50
-50
-100
20
40 60
Percent biodiesel
80
100
Figure IV. A. 1-5
Comparison of data to basic CO correlation
30
20
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-50
-60
-70
20
40 60
Percent biodiesel
80
100
40
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Emissions of all pollutants except NOx appear to decrease when biodiesel is used. The
fact that NOx emissions increase with increasing biodiesel concentration could be a detriment in
areas that are out of attainment for ozone. Thus it seemed appropriate to consider the limited
conditions under which NOx might actually decrease with increasing biodiesel concentration. As
can be seen in Figure IV. A. 1-2, a number of observations in our database actually exhibit a
decrease in NOx when biodiesel is added to conventional diesel fuel. Some such results are
possible simply due to test measurement variability. For instance, using the values for mean
NOx emissions and the standard deviation from Tables lU.C.l-l and UI.C.1-2, we were able to
estimate that repeated measurements of a 20% biodiesel blend could actually show a decrease in
NOx emissions 10 percent of the time even though the mean effect might indicate a 2% increase
in NOx. Still, there might be other elements that might cause NOx emissions to decrease with
increasing biodiesel concentration. We therefore took a closer look at these observations to
determine if there was anything unique about them. Table IV. A. 1-2 summarizes our review of
these observations.
Table IV. A. 1-2
Review of observations for which NOx emissions decrease with increasing biodiesel
Conclusion
Implication
All observations were tested using
soy-based biodiesel.
None. 75% of the biodiesel
observations in the database are
soy-based.
Base fuels have an average cetane
number of 49.
The average is influenced by a
single Fischer-Tropsch base fuel.
The remaining six base fuels have
an average cetane number of 45,
equivalent to average in-use
conventional diesel fuel.
Only engine standard groups D and E
are represented.
None. 80% of the observations in
the database were collected on
standard groups D and E
Of the eight engines, various
manufacturers, engine sizes, and
injection control systems are
represented.
Nothing unique about the engines.
Based on this review, there is no consistent set of characteristics for these eight engines,
the type of biodiesel used, and the base fuels to which biodiesel was added that would explain
why NOx emissions decreased when biodiesel replaced conventional diesel. However, it is
notable that these engines all exhibited more beneficial emission effects due to biodiesel use for
41
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all pollutants in comparison to other engines in our database. This result implies that these eight
engines respond to biodiesel use in some fundamentally different, albeit subtle, way than other
engines even though the characteristic(s) that produce this result are not readily apparent. This
issue may warrant further investigation.
2. Fuel economy impacts of biodiesel use
Biodiesel use will generally reduce the number of miles that a vehicle can be driven on a
gallon of fuel due to its lower energy content in comparison to conventional diesel fuel. We used
two different approaches to quantify this reduction in fuel economy: differences in fuel energy
content between pure biodiesel and conventional diesel, and correlations between biodiesel
concentration and brake-specific fuel consumption.
a. Via fuel energy content
Our database contained 19 pure biodiesel fuels for which energy content was measured.
Generally energy content was reported in terms of net energy per unit mass in the studies. We
converted these units into Btu/gal using each fuel's measured specific gravity to best represent the
impact on fuel economy. We also used only the net (lower) heating value of the fuels, since this
is a better approximation of the available work that can be extracted from the fuel than the gross
(higher) heating value. The average values are shown in Table IV.A.2.a-l.
Table IV.A.2.a-l
Average energy content of 100% biodiesel
All biodiesels
Animal-based
Rapeseed/canola-based
Soybean-based
Rapeseed or soybean-based
Average net Btu/gal
118,296
115,720
119,208
119,224
119,216
A two-tailed t-test of the biodiesel energy content revealed that rapeseed and soybean-based
biodiesels cannot be distinguished from one another, but that the animal-based biodiesels can be
distinguished from the plant-based biodiesels (at a 99% confidence level).
In order to estimate fuel economy impacts using energy content, we needed a sampling of
energy contents from conventional diesel fuel. Unfortunately, most fuel surveys available to us
did not include measurements of energy content. We found only two sources from which to
draw energy contents, shown in Table IV.A.2.a-2.
42
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Table IV.A.2.a-2
Sources for diesel fuel energy content
Database on which EPA Staff Discussion
Document (July 2001) analyses were based
The Transportation Data Energy Book.,
Edition 17, 1997
Average
Average net Btu/gal
130,256
128,700
129,500
The first source provided 111 separate measurements of fuel energy content, but some of these
fuels might not be the best representation of in-use diesel fuels because they were designed for
use in engine test programs. The second source presented only the average for some unspecified
sampling of conventional fuels. Without a straightforward means for evaluating the relative
accuracy of the two estimates, we decided to use the average Btu/gal from the two sources.
Figure IV.A.2.a-l shows how the distribution of volumetric energy contents differs for
biodiesel and conventional diesel. The difference in energy content between pure biodiesel and
conventional diesel fuel is summarized in Table IV.A.2.a-3.
43
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Figure IV.A.2.a-l
Distribution of net energy content for biodiesel and conventional diesel
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Difference in energy content between biodiesel and conventional diesel fuel
Conventional diesel
Animal-based biodiesel
Percent difference
129,500 Btu/gal
11 5,720 Btu/gal
-10.6%
Conventional diesel
Plant-based biodiesel
Percent difference
129,500 Btu/gal
11 9,2 16 Btu/gal
-7.9 %
Since the energy content of two fuels is an excellent predictor of the relative fuel
economy and volumetric energy content is expected to blend linearly, the percent differences in
Table IV.A.2.a-3 can be applied directly to biodiesel blend fuel economy. Thus a 20 vol%
biodiesel blend would be expected to exhibit a 2.1% (10.6% x 20%) reduction in fuel economy
relative to the base fuel if the biodiesel in question was produced from animal fats, and a 1.6%
reduction in fuel economy if the biodiesel was produced from soybeans or rapeseeds.
b. Via correlations with fuel consumption
The number of observations in our database for brake-specific fuel consumption (BSFC)
was far less than that for the primary pollutants. For instance, the total number of NOx
44
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observations for heavy-duty highway engines that we used in our analysis was 626, while the
number of available fuel consumption observations was only 217. As a result, we did not
investigate all of the various adjustment terms for fuel consumption that we did for the pollutant
correlations. Based on our work with the pollutant correlations, we concluded a priori that a
squared biodiesel concentration term and adjustments for test cycle should not be included.
Also, since adjustments for engine standards groups were largely unnecessary in the pollutant
correlations (see Section IV.B.3), we did not investigate them for the fuel consumption
correlation.
We compared the number of biodiesel observations for fuel consumption to our minimum
data criteria to determine what other adjustment terms should be investigated. This analysis
showed that the only adjustment term that could be investigated was rapeseed-based biodiesel
source.
As for the individual pollutant correlations, we used proc_mix in SAS to determine how
biodiesel concentration varied with the natural log of brake-specific fuel consumption (in units of
Ib/bhp-hr). This analysis indicated that the rapeseed adjustment term was not significant, so it
was dropped and the correlation regenerated. The resulting correlation is shown below:
BSFC, Ib/hp-hr = exp[0.0008189 x (vol% biodiesel) - 0.855578]
This correlation indicates that fuel consumption increases as biodiesel is added to conventional
diesel fuel. This result is consistent with the observation in Section IV.A.2.a above that fuel
economy (proportional to the reciprocal of fuel consumption) decreases with increasing biodiesel
concentration.
In order to make a direct comparison between the above correlation for mass-based fuel
consumption and the volume-based fuel economy discussed in Section IV.A.2.a, we made
several modifications to the BSFC correlation. These included taking the inverse of the BSFC
correlation, adding a mass-to-volume units conversion using the specific gravities given in Table
HE. 1-1, and then converting the equation into a percent change format. The result is shown
below:
% change in fuel economy = { (exp[- 0.0008189 x (vol% biodiesel)]
x [0.88 x (vol% biodiesel/100) + 0.85 x (1 . Vol% biodiesel/100)]
-0.85}- l} x 100%
This version of the correlation between biodiesel concentration and percent change in fuel
economy predicts that 100% biodiesel will result in a 4.6% reduction in fuel economy. This
estimate is somewhat smaller than the values in Table IV.A.2.a-3. Since the 4.6% estimate is
based on actual measurements of fuel consumption whereas the values in Table IV.A.2.a-3 are
theoretical estimates based on energy content, we might have reason to place more confidence in
45
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the 4.6% estimate. However, the fact that it was necessary to make several modifications to the
BSFC correlation introduces some additional uncertainty into the 4.6% estimate. As a result, we
feel confident only in the range of possible impacts that biodiesel may have on fuel economy.
These ranges are shown in Table IV.A.2.b-l.
Table IV.A.2.b-l
Fuel economy impacts of biodiesel use
20% biodiesel
100% biodiesel
% reduction in miles/gallon
0.9-2.1
4.6- 10.6
3. CO2 impacts of biodiesel use
Since biodiesel is produced from plant oils or animal fats, it has been promoted as a
means for reducing emissions of carbon dioxide that would otherwise be produced from the
combustion of petroleum-based fuels. Carbon dioxide is considered by many to be an important
component in global warming, though other pollutants can also play a role. The total impact that
biodiesel could have on global warming would be a function not just of its combustion products,
but also of the emissions associated with the full biodiesel production and consumption lifecycle.
A study of the full lifecycle emission impacts of biodiesel is beyond the scope of this analysis,
and we defer to previous and ongoing studies on this issue6'7. For this analysis, we have focused
only on the calculation of CO2 emissions as a function of biodiesel end use.
The carbon dioxide emissions impacts of biodiesel use was analyzed in much the same
way that the regulated pollutants were analyzed. However, rather than first investigating each set
of adjustment terms separately (base fuels, biodiesel source, and engine standards groups as
discussed in Section IV.B below), we opted to only create a single correlation that examined
them all at once. This approach is no different than that taken at the end of our assessment of
regulated pollutants, described in Section IV.B.6 below. We also made the assumption, based on
our analyses of regulated pollutants, that test cycle effects and engine standards group effects
could be safely ignored during the analysis of carbon dioxide.
Before deciding which adjustment terms to include in our CO2 correlation, we first
examined the various categories of data and compared them to our minimum data criteria. As a
result we determined that adjustment terms could be investigated for clean base fuels, animal-
based biodiesel, and rapeseed-based biodiesel. In keeping with our approach to analyzing
regulated pollutant effects, we used a backwards elimination process to settle on a set of
statistically significant terms. The final correlation for CO2 is shown below:
% change in CO2 =
(exp[ +0.0000177 x (Vol% biodiesel)
46
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+ 0.0002664 x CLEAN x (vol% biodiesel)
- 0.0001266 x ANIMAL x (vol% biodiesel) ] - 1} x 100%
where
vol% biodiesel = Value from 0 to 100
CLEAN = 1 if the base fuel meets the conditions for "Clean" fuel given in Table
m.C.2.e-l; otherwise, CLEAN = 0
ANIMAL = 1 if the biodiesel is produced from animal fat, tallow, or lard as described
in Section m.C.2.c; otherwise, ANIMAL = 0
Note that the the overall vol% biodiesel term represents all biodiesel which is not animal-
based and which has not been added to a 'clean' base fuel. Thus the overall vol% biodiesel term
actually represents plant-based biodiesel added to an 'average' base fuel. The coefficient for this
overall vol% biodiesel term was not statistically significant, but we retained it in the above
equation to maintain hierarchy with the adjustment terms. Even so, the result is that this
correlation predicts almost no effect of biodiesel on emissions of carbon dioxide for plant-based
biodiesel added to an 'average' base fuel. The correlation does predict that the addition of
biodiesel to a clean base fuel causes a moderate increase in carbon dioxide emissions, while the
use of animal-based biodiesel with an average base fuel is predicted to produce a slight decrease.
These results are shown graphically in Figure IV.A.3-1.
47
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Figure IV. A.3-1
Biodiesel impacts on CO2 emissions
CO
_c
o
(U
o
'E
(D
(D
O) 1
0
-1
-2
0
Clean base fuels
Amma -based biodies
20 40 60
Vol% biodiesel
80
100
The correlations suggest that biodiesel will produce higher CO2 emissions if it is blended
with a clean base fuel instead of an average base fuel. This result appears to be inconsistent with
the correlations for HC and CO (see Figures IV.B.4-3 and IV.B.4-4), in that higher emissions of
HC and CO should be associated with lower, not higher, emissions of CO2. Similarly, the fact
that animal-based biodiesel is predicted to produce less CO (see Figure IV.B.2-3) than plant-
based biodiesel would suggest that animal-based biodiesel should produce higher, not lower, CO2
emissions than plant-based biodiesel. However, the difference in CO2 emissions impacts for
animal and plant-based biodiesel might also be the result of different carbon contents for these
two types of biodiesel. Therefore, we also examined the available data on carbon content of
biodiesel versus that of conventional diesel to determine if it suggested differences in total
carbon emitted.
Our database contained measures of Ft/C ratio and oxygen content for seventeen 100%
biodiesel fuels. From this data we calculated the carbon content in terms of wt% carbon. The
results are shown in Table IV.A.3-1, along with an estimate of the carbon content of
conventional diesel fuel (0% biodiesel) drawn from the same database.
48
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Table IV.A.3-1
Carbon content of biodiesel and conventional diesel (w/w %)
Biodiesel
All biodiesel
Plant-based
Animal-based
Conventional diesel
77.3 %
77.8 %
76.1 %
86.7 %
The difference between plant and animal-based biodiesel carbon content is small (approximately
2%) but statistically significant at the p = 0.1 level. This difference is directionally consistent
with that observed in Figure IV.A.3-1, where the "Overall" curve represents all plant-based
biodiesel.
We can convert the wt% carbon values from Table IV.A.3-1 into pounds of carbon per
gallon of fuel by multiplying them by the density (see Table HE. 1-1). Table IV.A.3-2 shows the
results.
Table IV.A.3-2
Carbon content of biodiesel and conventional diesel (Ib carbon / gal fuel)
Biodiesel
All biodiesel
Plant-based
Animal-based
Conventional diesel
5.69
5.75
5.57
6.15
Although it would appear from the values in Table IV.A.3-2 that biodiesel would have a
tendency to produce less CO2 emissions than conventional diesel fuel, biodiesel also contains
less energy per gallon. Thus more biodiesel must be consumed to propel a diesel vehicle a given
distance than conventional diesel fuel. In order to take the difference in energy content into
account, we calculated the carbon content per Btu by dividing the values in Table IV.A.3-2 by
the volumetric energy contents given in Tables IV.A.2.a-l and IV.A.2.a-2. The results are shown
inTableIV.A.3-3.
49
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Table IV.A.3-3
Carbon content of biodiesel and conventional diesel (Ib carbon / million Btu)
Biodiesel
All biodiesel
Plant-based
Animal-based
Conventional diesel
48.1
48.2
48.1
47.5
On an energy basis, the distinction between animal and plant-based biodiesel disappears. It also
appears that biodiesel may actually increase emissions of CO2 relative to conventional diesel
fuel. However, this potential increase is small (~ 1%), and it is unlikely to be discernable in-use
given the variability in each of the components (density, H/C ratio, and energy content). These
results suggest that there would likely be no measurable difference between biodiesel and
conventional diesel in terms of exhaust CO2 emissions. The CO2 correlations shown in Figure
IV.A.3-1 suggest that a difference between biodiesel and conventional diesel may exist, but those
correlations were also somewhat inconsistent with the correlations of HC and CO as described
above. Therefore, we cannot confidently conclude that biodiesel increases or decreases CO2
emissions based on these analyses. This issue warrants further investigation, as the impacts of
biodiesel on CO2 inventories is one reason for the heightened interest in biodiesel in recent years.
B. Investigation of adjustment terms for regulated pollutants
Because there were a number of different categories of potential adjustment terms, we
decided first to investigate each separately. Once we determined which adjustment terms were
important, we then developed a strategy for incorporating them all into a single correlation for
each pollutant. Each of the subsections below describes our results for the separate adjustment
term investigations, and Section IV.B.6 describes our composite correlations.
1.
Test cycle effects
For our initial analysis of heavy-duty highway engines, we set aside all data collected on
generic transient or steady-state cycles. This left only composite FTP, hot-start FTP, and R49
data. We introduced terms into the regression analysis representing the interaction of these test
cycle categorical variables with percent biodiesel to determine which ones might be statistically
significant. As described in Section ni.C.2.b, coefficients could be estimated for only two of the
three potential interactive terms because an overall % biodiesel term was also included. The p-
values for the % biodiesel terms are listed in Table IV.B. 1-1, while the estimated coefficients are
given in Table IV.B. 1-2.
50
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Table IV.B. 1-1
P-values for test cycle effects
% biodiesel
R49 x % biodiesel
HDDS x % biodiesel
UDDSH x % biodiesel
NOx
0.0002
0.4144
0.1782
NA
PM
0.0001
0.0256
0.7031
NA
HC
0.0001
0.3733
0.5709
NA
CO
0.0001
0.0016
0.2358
NA
Table IV.B. 1-2
Estimated coefficients for test cycle effects
% biodiesel
R49 x % biodiesel
HDDS x % biodiesel
UDDSH x o/o biodiesel
NOx
0.000855
0.000600
0.000131
0.000000
PM
-0.006292
0.005147
-0.000151
0.000000
HC
-0.011469
0.006599
-0.000654
0.000000
CO
-0.006863
0.005646
0.000335
0.000000
For the test cycle adjustment terms, p-values greater than 0.05 suggest that the effect in question
cannot be confidently distinguished from zero. In these cases the statistically significant overall
'% biodiesel' term would apply to the individual test cycle term as well, indicating that there is no
variation in the biodiesel effect on emissions as a function of test cycle. The two exceptions to
this conclusion are for PM and CO where R49 data appears to exhibit different biodiesel effects
than data collected on the FTP. This result is consistent with previous experience: steady-state
test cycles are generally not accurate predictors of the PM and CO emissions that would be
generated under transient conditions. Based on this result, we decided to exclude R49 data from
our final PM and CO correlations.
2.
Biodiesel source effects
All biodiesel blends in our database were placed into one of three biodiesel source
categories: soybean, rapeseed/canola, and animal. We introduced terms into the regression
analysis which represented the interaction of these biodiesel source categorical variables with
percent biodiesel to determine which ones might be statistically significant. As described in
Section ni.C.2.c, coefficients could be estimated for only two of the three potential interactive
terms because an overall % biodiesel term was also included. The p-values for the % biodiesel
terms are listed in Table IV.B.2-1, and the estimated coefficients are given in Table IV.B.2-2. P-
values lower than 0.05 were considered significant.
51
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Table IV.B.2-1
P-values for biodiesel source effects
% biodiesel
Animal x % biodiesel
Rape x % biodiesel
Soy x % biodiesel
NOx
0.0001
0.0001
0.0311
NA
PM
0.0001
0.0001
0.6316
NA
HC
0.0001
0.5525
0.9162
NA
CO
0.0003
0.0001
0.0164
NA
Table IV.B.2-2
Estimated coefficients for biodiesel source effects
% biodiesel
Animal x % biodiesel
Rape x % biodiesel
Soy x % biodiesel
NOx
0.001553
-0.001216
-0.000331
0.000000
PM
-0.000908
0.024410
0.007517
0.000000
HC
-0.001031
0.000022
0.000038
0.000000
CO
-0.000603
-0.000838
-0.000316
0.000000
For NOx and CO, the three biodiesel source categories appear to produce three different
correlations between biodiesel concentration and emissions. For PM, animal-based biodiesel
appears to differ from plant-based biodiesel. For HC, there is no discernable difference between
the three source categories. The biodiesel source effects for NOx, PM, and CO are shown in
Figures IV.B.2-1, IV.B.2-2, and IV.B.2-3, respectively.
52
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Figure IV.B.2-1
Biodiesel source effects for NOx
20
CO
c
•55 15
CO
E
o>
c
03
CD
Q.
10
Soybean-based biodiese
apeseed-based biodiesel
nimal-based biodiesel
20
40 60
Percent biodiesel
80
100
53
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Figure IV.B.2-2
Biodiesel source effects for PM
£ -10
o
'co
E -20
CD
|-30
03
^ -40
8
°- -50
Soybean or rapeseed-based biodiesel
Animal-based biodiesel
-60
20
40 60
Percent biodiesel
80
100
54
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Figure IV.B.2-3
Biodiesel source effects for CO
CO
c
o
'co
CO
E
CD
CD
O)
c
03
I
CD
Q.
-10
-20
Soybean-based biodiesel
-30
-40
-50
Rapeseed-based bioc
Animal-based biodiesel
20
40 60
Percent biodiesel
80
100
3. Engine standards groups
All biodiesel blends in our database were placed into one of several engine standards
groups. Of these, only three groups met our minimum data criteria: D, E, and G. We therefore
introduced terms into the regression analysis which represented the interaction of these three
engine standards groups (as categorical variables) with percent biodiesel to determine which ones
might be statistically significant. The p-values for the % biodiesel terms are listed in Table
IV.B.3-1, and the estimated coefficients are given in Table IV.B.3-2. P-values lower than 0.05
were considered significant.
Table IV.B.3-1
P-values for engine standards groups
% biodiesel
Group D x % biodiesel
Group E x % biodiesel
Group G x % biodiesel
NOx
0.0074
0.2598
0.1678
0.4056
PM
0.4005
0.3008
0.0147
0.6825
HC
0.2939
0.3383
0.3407
0.5549
CO
0.0066
0.5056
0.0676
0.2035
55
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Table IV.B.3-2
Estimated coefficients for engine standards groups
% biodiesel
Group D x % biodiesel
Group E x % biodiesel
Group G x % biodiesel
NOx
0.001382
-0.000720
-0.000844
0.000575
PM
-0.002260
-0.002979
-0.006973
-0.001240
HC
-0.006038
-0.006813
-0.006515
-0.004582
CO
-0.004081
-0.001235
-0.003257
-0.002567
There is only one case in which an engine standards group interactive term is statistically
significant: Standards Group E for PM, representing model years 1991 through 1993. In this
case, the adjustment terms for Groups D and G were dropped and the correlation regenerated,
after which both the overall'% biodiesel' term and the Group E adjustment terms were
statistically significant. Figure IV.B.3-1 shows the effect of the Group E adjustment term on PM.
Figure IV.B.3-1
Engine standard group effects for PM
to -10
c
o
.« -20
E
CD
£ -30
CD
O)
-40
§ -50
i_
0)
^-60
-70
All engine groups
other than group E
Engine group E
(model years 1991 -19
20
40 60
Percent biodiesel
80
100
4.
Base fuel effects
All biodiesel blends in our database were placed into one of two base fuel groups: clean
or average. We therefore introduced terms into the regression analysis representing the
interaction of these two categorical base fuel groups with percent biodiesel to determine which
56
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ones might be statistically significant. As described in Section in.C.2.e, coefficients could be
estimated for only one of the two potential interactive terms because an overall % biodiesel term
was also included. The p-values for the % biodiesel terms are listed in Table IV.B.4-1, and the
estimated coefficients are given in Table IV.B.4-2. P-values lower than 0.05 were considered
significant.
Table IV.B.4-1
P-values for base fuel groups
% biodiesel
Average x % biodiesel
Clean x % biodiesel
NOx
0.0001
0.0001
NA
PM
0.0001
0.0004
NA
HC
0.0048
0.0015
NA
CO
0.0001
0.0001
NA
Table IV.B.4-2
Estimated coefficients for base fuel groups
% biodiesel
Average x % biodiesel
Clean x % biodiesel
NOx
0.002467
-0.001706
0.000000
PM
-0.004355
-0.001753
0.000000
HC
-0.007087
-0.004757
0.000000
CO
-0.004544
-0.001820
0.000000
For all four pollutants, the base fuel appears to have a significant impact on the
correlation between biodiesel concentration and emissions. This result suggests that the base fuel
effects on the emission impacts of biodiesel blending should be investigated further. The base
fuel effects for NOx, PM, HC, and CO are shown in Figures IV.B.4-1, IV.B.4-2, IV.B.4-3, and
IV.B.4-4 respectively.
57
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Figure IV.B.4-1
Base fuel effects for NOx
30
co 25
c
o
1 20
CD
c
I 15
cc
o
•£ 10
CD
0-
5
0
Clean base fuel
20
40 60
Percent biodiesel
80
100
Figure IV.B.4-2
Base fuel effects for PM
I "1°
w
E
CD
-20
CD
D)
cc
^
o
CD
-30
S. -40
Clean base fuel
Average base fuel
-50
20
40 60
Percent biodiesel
80
100
58
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Figure IV.B.4-3
Base fuel effects for HC
-10
w
c
•i -20
w
E
"> -30
CD
CL
-40
CD
c
cc
" -50
-i-»
c
CD
-60
-70
-80
Clean base fuel
Average base fuel
20
40 60
Percent biodiesel
80
100
Figure IV.B.4-4
Base fuel effects for CO
.2 -10
w
E
CD
CD
D)
cc
•g
CD
O
-20
-30
-40
-50
Clean base fuel
Average base fuel
20
40 60
Percent biodiesel
80
100
59
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5.
Cetane effects
We conducted an analysis to determine the importance of cetane in the correlation of
biodiesel concentration with NOx emissions. This analysis was not done in order to add a cetane
term to the basic correlations in Section IV. A, but rather to determine if it was legitimate to
extrapolate the effects of the basic correlations to EGR-equipped engines as described in Section
m.c.2.f
In order to determine the degree to which cetane plays a role in the biodiesel/NOx
relationship, we separated cetane effects in the biodiesel database from the measured NOx
emissions. To do this, we used the following equation to predict the NOx emissions for every
fuel in our biodiesel database,
NOx =exp(-0.004512 x (CETNUM - 45) + 0.0001458 x (CETNUM - 45)2 + 1.5497034)
and then calculated the difference between the predicted NOx and measured NOx for every
observation. These "pseudo-residuals" were therefore independent of any cetane effects, and
could be used as dependent variables in a mixed model analysis of biodiesel concentration.
Depending on the magnitude of the biodiesel effect for this correlation and its associated
statistical significance, we could determine the degree to which cetane plays a role in the
relationship between biodiesel concentration and NOx emissions.
Table IV.B.5-1 gives the results of this analysis. Since the correlation made use of only
those observations for which the cetane number was available, the results are not directly
comparable to the basic NOx correlation presented in Section IV. A. Therefore, for comparison,
we reconstructed the basic NOx correlation using only observations that included a cetane
number measurement. This modified basic NOx correlation is also shown in Table IV.B.5-1.
Table IV.B.5-1
Mixed model results for cetane analysis
Dependent variable
ln(pseudo-residuals + l.F)
ln(NOx)
Coefficient for
% biodiesel
0.001027
0.000694
Intercept
0.474478
1.734796
P-value for %
biodiesel
0.5900
0.0008
The factor 1.1 was added to ensure that the natural log was always defined
The pseudo-residuals do not appear to be correlated with biodiesel concentration, given
the high P-value. This result suggests that cetane number is highly correlated with biodiesel
concentration, so that the removal of cetane effects on NOx from the NOx measurements leaves
little left to be explained by biodiesel concentration.
60
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This result does not imply that cetane number is a better predictor of biodiesel effects on
NOx than biodiesel concentration. Rather, it highlights a collinearity between the two, a
collinearity that could be inferred from the different average cetane numbers between biodiesel
and conventional diesel as shown in Table HE. 1-1. As a result of this collinearity, a wide variety
of correlations having equivalent explanatory power could be developed that have either cetane
number, biodiesel concentration, or some combination of the two. For our purposes, correlations
based strictly on biodiesel concentration are more practical because those who use biodiesel don't
always have a measurement of its cetane number. Note that the cetane number of biodiesel does
appear to differ between plant and animal-based biodiesel, as described in Section HE. 1. Thus
the cetane number of biodiesel is being taken into account in an indirect fashion through the
investigation of biodiesel source effects and base fuel effects, as described in Sections IV.B.2 and
IV.B.4, respectively.
The analysis summarized in Table IV.B.5-1 indicates that cetane number is an important
component of the impact that biodiesel has on NOx emissions, and we know from previous
analyses that EGR-equipped engines do not respond in any significant way to changes in cetane.
Therefore, it might seem reasonable not to apply the fleet-average biodiesel effects on NOx to
EGR-equipped engines. In practical terms, this would mean that our NOx correlations would not
apply to model year 2002 and later engines (engine standards group B, per Table II.C-1).
However, this approach would actually mean that the NOx detriment associated with biodiesel
would diminish as the fleet turns over. Since we do not have any actual data showing biodiesel
effects on EGR-equipped engines, we cannot verify that this result is accurate, and thus there is a
possibility that this approach would underestimate the true NOx impact of biodiesel for future
fleets. Therefore, we have determined that the more environmentally conservative approach is to
continue to allow our NOx correlations to apply to the entire fleet, include EGR-equipped
engines. We welcome comment on this approach and any alternative approaches for dealing
with the lack of biodiesel data on EGR-equipped engines.
6. Composite correlations
The analyses described above suggest that the correlation between biodiesel concentration
and emissions should include adjustments for biodiesel source, engine standards groups, and base
fuel. We therefore developed a single correlation for each pollutant that included any adjustment
terms that had been deemed important in the previous analyses.
We first noted that some potential adjustment terms were redundant, given that the
correlations would all include an overall'% biodiesel' term. This fact is reflected in the 'NA'
results in Tables IV.B.2-1 and IV.B.4-1. As a result we assigned a hierarchy to the adjustment
terms within each category to help determine which adjustment terms should be investigated in
the composite correlation. For base fuel effects, "Clean" was chosen as the adjustment term, so
that the unadjusted '% biodiesel' term would necessarily reflect "Average" base fuels. For
biodiesel source effects, "Animal" and "Rape" were chosen as the adjustment terms, so that the
61
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unadjusted '% biodiesel' term would necessarily reflect "Soy". Both of these choices reflect the
most common circumstances under which biodiesel is currently used.
We then made a list of all adjustment terms that we believed should be introduced into
each pollutant correlation, based on the previous analyses6. This list included not only the first-
order interactions between % biodiesel and the individual categorical variables, but also second-
order interactions between one categorical variable and another, in addition to their combined
interaction with % biodiesel. These second-order interactions needed to be considered because
the previous analyses provided no information on whether one type of adjustment is affected by
the presence of another type of adjustment. Table IV.B.6-1 lists the terms that we intended to
investigate in our composite correlation, based on previous analyses.
Table IV.B.6-1
Adjustment terms to consider in composite correlations
First-order
Second-order
NOx
Clean
Animal
Rape
Clean x Animal
Clean x Rape
PM
Clean
Animal
Group E
Clean x Animal
Clean x Group E
Animal x Group E
HC
Clean
CO
Clean
Animal
Rape
Clean x Animal
Clean x Rape
The next step was to determine if there was sufficient data in the database to investigate
all of the adjustment terms listed in Table IV.B.6-1. We therefore counted all heavy-duty
highway engine biodiesel observations for every pollutant and compared them to our minimum
data criteria. We determined that there was insufficient data to investigate any of the second-
order terms involving biodiesel source (i.e. Animal or Rape) and base fuel (i.e. Clean), but that
all of the first-order terms had sufficient data to be investigated.
We also discovered that there were several cases in which the same data could be used to
produce either a first order adjustment term or a second-order adjustment term involving engine
standards group E (model years 1991 to 1993). In these cases, all the relevant data was collected
on engines falling into engine standards group E. These redundant terms are listed in Table
IV.B.6-2.
We also conducted an all-possible-terms analysis using stepwise backwards elimination to verify that the final
selection of correlation terms was appropriate. This additional analysis is described at the end of this Section.
62
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Table IV.B.6-2
Redundant adjustment terms in composite correlation
NOx
PM
HC
CO
(Animal) and (Animal x Group E)
(Clean) and (Clean x Group E)
(Animal) and (Animal x Group E)
n/a
(Clean) and (Clean x Group E)
(Animal) and (Animal x Group E)
Since group E contains the most data of all engine standards groups, we could, for example,
legitimately apply the Animal adjustment term in the correlation for NOx to the entire fleet or
only to engine standards group E. We chose to use an environmentally conservative approach in
which we made this decision based on whether the adjustment term in question would increase or
decrease the emission effects of biodiesel. If one of the adjustment terms listed in Table IV.B.6-
2 would result in increased emissions, we chose to apply it to all engine groups. If instead it
would result in decreased emissions, we chose to apply it only to engine standards group E.
Given that our previous analyses showed that all the first-order adjustment terms listed in
Table IV.B.6-1 were important, we decided to use an approach to developing a composite
correlation that retained as many of these adjustment terms as possible. Thus we used a
backwards elimination approach that began with a correlation having all the first-order
adjustment terms listed in Table IV.B.6-1 and then eliminated statistically insignificant terms one
by one until all remaining terms were significant. The result was that few of the terms were
dropped. The resulting "composite" correlations are shown below:
% change in NOx =
(exp[ +0.0010375
+ 0.0012289
- 0.0002732
- 0.0009795
% change in PM =
(exp[ -0.0047395
+ 0.0010742
- 0.0045908
-0.0019343
% change in HC =
(exp[ -0.0118443
+ 0.0047569
x (vol% biodiesel)
x CLEAN x (vol% biodiesel)
x RAPE x (vol% biodiesel)
x ANIMAL x GROUP E x (vol% biodiesel)
x (vol% biodiesel)
x CLEAN x (vol% biodiesel)
x GROUP E x (vol% biodiesel)
x ANIMAL x GROUP E x (vol% biodiesel)
]- 1} x 100%
]- 1} x 100%
x (vol% biodiesel)
x CLEAN x (vol% biodiesel)
]- 1} x 100%
63
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% change in CO =
(exp[ -0.0058238
+ 0.0010853
+ 0.0017335
-0.0017116
where
x (vol% biodiesel)
x CLEAN x (vol% biodiesel)
x RAPE x (vol% biodiesel)
x ANIMAL x GROUP E x (vol% biodiesel)
]- 1} x 100%
vol% biodiesel =
CLEAN
ANIMAL
RAPE
GROUP E
Value from 0 to 100
= 1 if the base fuel meets the conditions for "Clean" fuel given in Table
m.C.2.e-l; otherwise, CLEAN = 0
= 1 if the biodiesel is produced from animal fat, tallow, or lard as described
in Section m.C.2.c; otherwise, ANIMAL = 0
1 if the biodiesel is produced from rapeseed oil or canola oil, as described in
Section m.C.2.c; otherwise, RAPE = 0
1 if the highway engines being evaluated are model years 1991 to 1993;
otherwise, GROUP E = 0
We also used an alternative approach to developing correlations between biodiesel
concentration and emission impacts. In this alternative approach, we did not limit the candidate
adjustment terms for each pollutant to those we identified as important in Sections IV.B.2,
IV.B.3, and IV.B.4. Instead, we permitted any term, including second-order terms, to be
included in the regression analysis if the available data for that term met our minimum data
criteria. Table IV.B.6-3 lists all the terms that met our minimum data criteria and thus were
included in this alternative analysis.
Table IV.B.6-3
All possible terms for composite correlations
NOx
Clean
Animal"
Rape
Group D
Group E
Group G
Rape x Group D
Clean x Group E
PM
Clean"
Animal"
Rape
Group D
Group E
Group G
Rape x Group D
HC
Clean
Animal"
Rape
Group D
Group E
Group G
Rape x Group D
Clean x Group E
CO
Clean"
Animal"
Rape
Group D
Group E
Group G
Rape x Group D
These terms can also be represented as an interaction with engine standards group
E, since all the data in question was collected on group E engines
As before, we used a (manual) stepwise backwards elimination process in SAS procedure
proc_mix to drop terms that were not significant. When all the remaining terms were significant,
we stopped. As a result of this "all possible terms" analysis, the PM, HC, and CO correlations
64
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were identical to the final composite correlations given above. The "all possible terms" NOx
correlation, however, included an additional adjustment term for Group G. Further review of the
"all possible terms" correlation for NOx indicated that the inclusion of this Group G term
lowered fleet-average estimated NOx emissions by an average of 0.2 percent. Thus not only are
the estimated effects of the "all possible terms" NOx correlation very similar to those from our
original analysis, but the original analysis predicts slightly larger increases in NOx due to the use
of biodiesel. We have chosen to use the original final composite correlation for NOx presented
above, since the slightly higher NOx emissions provide a more environmentally conservative
estimate of biodiesel impacts.
C. Comparison of vehicle data to engine data
Our database contained heavy-duty highway data collected on both engines and vehicles.
The amount of engine data far surpassed the amount of vehicle data, as shown in Table in.B-1.
Given that engines and vehicles have the potential for exhibiting different emission effects, we
opted to base our primary biodiesel correlations on engine data only. However, we wanted to
compare the vehicle data to predictions made by our engine-based correlations to determine if the
impact of biodiesel on emissions can be considered to be the same for vehicles and engines.
To make this comparison, we first converted all vehicle emissions data from g/mile to %
change in emissions. This process involved identifying the base fuel to which biodiesel had been
added for each vehicle/test cycle combination, and averaging any repeat measurements made on
this base fuel. The % change values for each biodiesel test were then calculated with respect to
the averaged emissions from the repeat base fuel measurements.
We used our composite correlations to estimate the % change in emissions of each
pollutant for the specific biodiesel concentration associated with each vehicle test. We then
compared the predicted and observed % change emission values in several different ways to
determine if our engine-based correlations could be said to represent vehicle effects of biodiesel.
These comparisons included graphical comparisons, paired t-tests of predicted versus observed
% change values, and comparisons of residuals for the engine and vehicle data. Details of how
these comparisons were done are summarized in Section V.
Figures IV.C-1 through IV.C-4 compare the predicted and observed values for each of the
four pollutants.
65
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Figure IV.C-1
Predicted versus observed NOx for vehicles
Figure IV.C-2
Predicted versus observed PM for vehicles
-10% 0% 10%
Predicted % change
100% 200%
Predicted % change
Figure IV.C-3
Predicted versus observed CO for vehicles
Figure IV.C-4
Predicted versus observed HC for vehicles
-40% -30% -20% -10%
Predicted % change
O -40%
-40% -20% 0%
Predicted % change
These figures suggest that the emission effects measured for heavy-duty vehicles exhibit a bias in
comparison to the estimates predicted using our composite correlations. A t-test of predicted
versus observed values confirms that the bias is statistically significant for all four pollutants. In
addition, this bias is not consistent. For PM and HC, the vehicle data appears to produce
emission benefits that are smaller than those predicted by the composite correlations. For NOx
the vehicle data appears on average to produce emission reductions whereas the composite
correlations predict emission increases. For CO, the vehicle data appears to produce larger
emission benefits than the composite correlation predictions. Based on this comparison, we do
not believe that the vehicle data can be used to represent the emission effects of biodiesel on
heavy-duty diesel engines.
D. Use of virgin oils as biodiesel
66
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In our analysis we excluded all biodiesels which were virgin oils rather than their
transesterified counterparts. From the literature it appears that biodiesel research has focused
primarily on esters due to their superior properties; virgin oils tend to have significantly lower
cetane and higher viscosity than ester biodiesels and conventional diesel fuel. On this basis we
would not expect virgin oils to play a significant role in biodiesel use in the future.
However, it seemed prudent to investigate whether the effects exhibited by our composite
correlations would reflect the data on virgin oils. Unfortunately, our database contains only two
unique measurements of virgin oil biodiesel collected on standard test cycles. We would not
expect conclusions drawn on the basis of these two emission measurements to be determinate,
but we offer the comparison of correlation predictions to virgin oil data nonetheless. Because the
properties of virgin oils are so different from those of esters, we would not expect virgin oil
biodiesel to produce the same emission effects as ester-based biodiesel. Unless a comparison of
predicted and observed values provided strong evidence to the contrary, we would assume that
our ester-based composite correlations would not be applicable to virgin oil biodiesels.
A graphical comparison does not suggest a 1:1 alignment of predicted and observed
values (see Figure IV.D-1). As a result, we do not believe that our composite correlations should
be applied to virgin oil biodiesels.
Figure IV.D-1
Comparison of virgin oils to ester-based biodiesel
c
CD
_c
o
_Q
O
20.0%
0.0%
-20.0%
-40.0%
-60.0%
-80.0%
-80%
HC
CO
NOx
PM
-60% -40% -20% 0%
Predicted % change
20%
67
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E. Comparisons to other emission correlations
We believe that the analyses presented in this report represent the most comprehensive
analyses done to date on the effects that biodiesel has on emissions of regulated pollutants.
However, since we used all available and relevant data, essentially none is left with which to
validate the correlation's predictive capabilities. We therefore compared our correlation's
predictions to those from previous analyses conducted by other analysts.
We are aware of three different previous analyses that attempted to compile data from
multiple test programs and draw conclusions regarding the impact that varying biodiesel
concentrations have on emissions of regulated pollutants:
1. Howell, S., "Emissions Summary for Biodiesel," MARC-IV Consulting, Inc.,
memorandum to Sam McCahon, December 8, 2000
2. Lindhjem et al, "Impact of Biodiesel Fuels on Air Quality," ENVIRON
International Corporation Task 1 Report, June 13, 2000
3. Sheehan et al, "Life Cycle Inventory of Biodiesel and Petroleum Diesel for Use in
an Urban Bus," National Renewable Energy Laboratory Final Report, May 1998
These researchers made no attempt to account for such factors as test cycle, base fuel properties,
biodiesel source effects, and engine technology. Therefore, a direct comparison of the
correlations developed by the above researchers to our composite correlations may not be
appropriate. Instead, we compared these other correlations to our basic correlations described in
Section IV.A. Figures IV.E-1, IV.E-2, IV.E-3, and IV.E-4 provide this comparison. Note that
the Lindhjem report includes average biodiesel impacts at only 20 vol% and 100 vol%. The
associated emission impacts were linearly interpolated for the purposes of graphical presentation.
68
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Figure IV.E-1
Comparison of basic EPA correlation to alternative correlations for NOx
EPA
Howell
Sheehan
Lindhjem
20 40 60
Percent biodiesel
80
100
Figure IV.E-2
Comparison of basic EPA correlation to alternative correlations for PM
— EPA
-o- Howell
-*- Sheehan
-*- Lindhjem
40 60
Percent biodiesel
80
100
69
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CO
c
o
CO
E
CD
CD
D)
cc
.c
o
CD
CD
CL
Figure IV.E-3
Comparison of basic EPA correlation to alternative correlations for HC
-10
-20
-30
-40
-50
-60
-70
-80
— EPA
-o- Howell
-*- Sheehan
-*- Lindhjem
20 40 60
Percent biodiesel
80
100
Figure IV.E-4
Comparison of basic EPA correlation to alternative correlations for CO
w -10
o
'w
w
E -20
^ -30
ro
.c
o
-40
(D
s
(U
CL
-50
-60
— EPA
-o- Howell
-^ Sheehan
-*- Lindhjem
20 40 60
Percent biodiesel
80
100
70
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All four graphs show reasonable agreement between the predicted effects from our basic
correlations and those from other researchers. For HC and CO, our basic correlations predict
slightly larger benefits than the other correlations. This result may be due to the fact that our
database contained more data, or because our analytical or statistical approach was different.
F. Applying the correlations to the in-use fleet
The composite correlations presented in Section IV.B.6 are the means through which we
propose that emission impacts of biodiesel use are estimated for heavy-duty highway engines.
Predictions for diesel-powered light-duty vehicles and nonroad engines are covered in Section V.
Three pollutant correlations include terms that are specific to engine standards group E
(i.e. model years 1991 - 1993). As a result, these correlations are functions of the calender year
for which emission benefit predictions are being made. Thus we need a means for determining
what fraction of the heavy-duty highway inventory is impacted by engine standards group E.
Using the inventory modeling done in the context of our rulemaking setting new standards for
heavy-duty engines beginning in 2007 [66 FR 5002], we determined how the inventories were
distributed among the various model years in the fleet. From this we were able to estimate the
fraction of the inventory produced from either engine standards group E for any calender year.
These fractions are given in Table IV.F-1.
71
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Table IV.F-1
Yearly weighting factors for composite correlations
Pollutant
Factor name
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Fraction of diesel highway inventory
which comes from standards group E
PM
kl
0.15
0.14
0.13
0.12
0.11
0.10
0.10
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
CO
k2
0.11
0.10
0.10
0.09
0.08
0.07
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.04
NOx
k3
0.13
0.11
0.10
0.09
0.08
0.08
0.07
0.06
0.06
0.06
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
The composite correlations for each pollutant can now be presented in a more useful
form, as functions not only of base fuel and biodiesel source, but also of calender year. The final
proposed composite correlations are shown below:
% change in NOx =
(1 - k3) x {exp[
k3
+ 0.0010375 x (vol% biodiesel)
+ 0.0012289 x CLEAN x (vol% biodiesel)
- 0.0002732 x RAPE x (vol% biodiesel) ] - 1} x 100%
(exp[ - 0.0009795 x ANIMAL x (vol% biodiesel) ] - 1} x 100%
72
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% change in PM =
(1 - kl) x (exp[ - 0.0047395 x (vol% biodiesel)
+ 0.0010742 x CLEAN x (vol% biodiesel) ] - 1} x 100%
+ kl x (exp[ - 0.0045908 x (vol% biodiesel)
- 0.0019343 x ANIMAL x (vol% biodiesel) ] - 1} x 100%
% change in HC = (exp[ - 0.0118443 x (vol% biodiesel)
+ 0.0047569 x CLEAN x (vol% biodiesel) ] - 1} x 100%
% change in CO =
(l-k2)x (exp[ -0.0058238
k2
x (vol% biodiesel)
+ 0.0010853 x CLEAN x (vol% biodiesel)
+ 0.0017335 x RAPE x (vol% biodiesel) ] - 1} x 100%
(exp[ - 0.0017116 x ANIMAL x (vol% biodiesel) ] - 1} x 100%
where
vol% biodiesel = Value from 0 to 100
kl = Fraction of diesel highway PM inventory which comes from engine
standards group E for the calender year being considered, per Table IV.F-1
k2 = Fraction of diesel highway CO inventory which comes from engine
standards group E for the calender year being considered, per Table IV.F-1
k3 = Fraction of diesel highway NOx inventory which comes from engine
standards group E for the calender year being considered, per Table IV.F-1
CLEAN = 1 if the base fuel meets the conditions for "Clean" fuel given in Table
m.C.2.e-l; otherwise, CLEAN = 0
ANIMAL = 1 if the biodiesel is produced from animal fat, tallow, or lard as described
in Section m.C.2.c; otherwise, ANIMAL = 0
RAPE = 1 if the biodiesel is produced from rapeseed oil or canola oil, as described in
Section m.C.2.c; otherwise, RAPE = 0
The categorical adjustment terms CLEAN, ANIMAL, and RAPE were chosen so that the
unadjusted correlation would represent soybean-based biodiesel added to an "average" base fuel.
Since soybeans are currently the largest single feedstock for biodiesel in the U.S.f and the
majority of in-use fuels used for blending with biodiesel can be categorized as "average," the
unadjusted correlation is the best representation of biodiesel effects on emissions when the actual
biodiesel feedstock and base fuel properties are unknown. This case is shown in Table IV.F-2 for
the year 2003 for a 20 vol% biodiesel blend.
According to the National Biodiesel Board, 80% of current biodiesel is derived from soybeans, 19% from
yellow grease, and the remaining 1% from other feedstocks.
73
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Table IV.F-2
Default emission effects of 20 vol% biodiesel in 2003
(assumes soybean-based biodiesel added to an "average" base fuel)
NOx
PM
HC
CO
Percent change in emissions
+ 2.0 %
- 10.1%
-21.1%
-11.0%
74
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Section V: Biodiesel Effects on Light-Duty Vehicles and Nonroad
Because the amount of heavy-duty highway engine data in our database far surpassed the
amount of data for other equipment types, we opted to base our primary analyses on heavy-duty
highway engine data only. However, nonroad diesel engines and, to a much lesser extent, light-
duty diesel vehicles, also contribute to the total mobile source inventories for regulated
pollutants. We therefore compared the emission impacts of biodiesel use estimated for heavy-
duty highway engines to data collected on nonroad engines and light-duty vehicles. Based on the
analyses described in this Section, we have concluded that there is insufficient support for
extrapolating heavy-duty highway engine effects to nonroad or light-duty.
A. Methodology
We first converted all nonroad and light-duty vehicle emissions data from absolute g/bhp-
hr or g/mile to percent change in emissions. This process began by identifying the base fuel to
which biodiesel had been added for each vehicle/test cycle combination, and averaging any
repeat measurements made on this base fuel. The percent change values for every emission
measurement were then calculated with respect to this average base fuel emissions measurement,
producing "observed" values. To calculate "predicted" values, we used our composite
correlations to estimate the percent change in emissions of each pollutant for the specific
biodiesel concentration associated with each nonroad or light-duty vehicle test.
We compared the predicted and observed percent change emission values in several
different ways to determine if our heavy-duty highway engine-based correlations could be
reasonably applied to either nonroad engines or light-duty vehicles. The different types of
comparisons are listed in Table V.A-1.
Table V. A-1
Methods for comparing nonroad and light-duty data to the composite correlations
Comparison
Graphical
Paired t-tests of predicted versus
observed percent change values
Ratio of mean absolute value
residuals
Purpose
Provided a qualitative assessment of the match
between predicted and observed values, as well as
scatter in the data and potential bias.
Provided a more quantitative assessment of potential
bias between the composite model and the data
Provided a quantitative assessment of scatter
75
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The ratio of residuals was calculated from the following formula:
rid
Ratio = —
rim
2.,\Rmj I
Urn
where:
Rd; = Residual for the nonroad or light-duty data being evaluated, for observation i
= Observed percent change for nonroad or light-duty observation i minus composite
correlation's predicted percent change associated with observation i
Rd; = Absolute value of Rd;
= Residual for the heavy-duty highway engine data on which the composite
correlation was based, for observation j
= Observed percent change for highway engine observation j minus composite
correlation predicted percent change for observation j
= Absolute value
•j
nd = Total number of biodiesel observations for the nonroad or light-duty data being
evaluated
nm = Total number of biodiesel observations for the heavy-duty highway engine data on
which the composite correlation was based
A ratio significantly higher than 1.0 indicated that the scatter in the nonroad and/or light-duty
vehicle data was significantly greater than that for the data on which the composite correlations
were based. Such a result would call into question whether the estimated emission effects for
heavy-duty highway engines could appropriately be applied to nonroad or light-duty. For
example, a ratio of 1.5 would suggest that the scatter in the nonroad or light-duty data about the
composite correlation was 50% greater than the scatter in the heavy-duty highway engine data on
which that composite correlation was based. A ratio of 2.0 would suggest that the scatter in the
nonroad or light-duty data about the composite correlation was twice that of the heavy-duty
highway engine data. For our purposes we considered a ratio of 1.5 to be noteworthy and 2.0 to
be substantial.
The primary purpose in conducting this analysis was to determine if the composite
correlations presented in Section IV.F for the heavy-duty highway fleet can be used to predict
emission impacts of biodiesel for nonroad engines or light-duty highway vehicles. However, the
composite correlations include multiplicative factors kl - k3 that complicate the analysis. These
factors account for the idiosyncratic effects of biodiesel on heavy-duty highway engines having
model years 1991 through 1993. This group of model years was identified as engine standards
group E during our derivation of the composite correlations, and represents one particular set of
heavy-duty highway engine certification standards as shown in Table HC-1. However, these
76
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emission standards are not directly relevant for nonroad engines or light-duty vehicles. Thus the
values for multiplicative factors kl - k3 as presented in Table IV.F-1 cannot be applied to
nonroad or light-duty vehicles. Among the various possible approaches for addressing this issue,
the most straightforward is to use the composite correlations presented in Section IV.B.6 instead
of those in Section IV.F (which were modified to more directly apply to the in-use fleet of heavy-
duty highway engines), and to set the variable "GROUP E" equal to zero. This approach
recognizes the fact that there is no group of model years among nonroad engines or light-duty
highway vehicles that is uniquely comparable to highway engine standards group E.
B. Effects of biodiesel on nonroad engines
Most large nonroad diesel engines use technologies similar to those found in heavy-duty
highway diesel engines, although in a given year, the highway engine technology is generally
more advanced. On this basis we might expect biodiesel to produce similar emission effects for
heavy-duty nonroad and heavy-duty highway engines. Additionally, in both this work and
previous analyses, we have found few distinctions between different highway engine
technologies in terms of fuel effects on emissions (i.e. very few engine standard group terms
were significant, as shown in Table IV.B.3-1). Given this result among different highway engine
technologies, one might expect that the distinctions between highway engine technology and
nonroad technology would similarly not be important in the context of evaluating the effects of
biodiesel on emissions. The analyses we conducted for heavy-duty nonroad engines were
intended to determine if this expectation was reasonable.
Our database contained only 8 biodiesel observations for heavy-duty nonroad engines,
which falls below our minimum data criteria. Thus it might not be reasonable to expect the
biodiesel effects exhibited by these nonroad engines to compare favorably with effects predicted
by the composite correlations, simply due to variability in emission measurements. In addition,
the nonroad engines were all tested on the 8-mode steady-state cycle used for nonroad
certification, whereas the heavy-duty highway engines were tested primarily on the FTP (and, to
a lesser extent, the European 13-mode steady-state test). The difference in test cycles may also
contribute to differences between predicted and observed values for nonroad engines. This is
especially true for PM and CO, which we determined in Section IV.B. 1 exhibited different
effects of biodiesel on emissions for transient versus steady-state operation.
We first developed graphical comparisons between our modeled effects for heavy-duty
highway engines and the nonroad data. The results are shown in Figures V.B-1 through V.B-4.
77
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Figure V.B-1
Predicted vs observed NOx for nonroad
Figure V.B-2
Predicted vs. observed PM for nonroad
-10% -5% 0% 5% 10%
Predicted % change
20.0%
10.0%
0.0%
0
D> -10.0%
CD
-C
" -20.0%
o^
¥ -30.0%
o
en
g -40.0%
-50.0%
-60.0%
-60% -50% -40% -30% -20% -10%
Predicted % change
Figure V.B-3 Figure V.B-4
Predicted vs observed CO for nonroadPredicted vs. observed HC for nonroad
-80% -60% -40%
Predicted % change
JS -20.0%
-40% -30% -20%
Predicted % change
-10%
0%
In order to conclude that nonroad effects of biodiesel use could be reasonably predicted
by the composite correlations based on heavy-duty highway engines, the data should exhibit
equal scatter about the diagonal line. In the figures above, it appears that there is bias between
the predicted and observed values. To verify that this bias is significant, we used paired t-tests to
compare predicted and observed percent change values. The results are shown in Table V.B-1.
78
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Table V.B-1
Paired t-tests for nonroad data
NOx
PM
CO
HC
Probability that predicted and
observed values are different
0.997
0.577
0.975
0.934
The values in Table V.B-1 confirm that the composite correlations do a poor job of representing
the nonroad engine effects in our database. PM may appear to be an exception. However, there
are only four observations for PM, raising the possibility that the overlap between predicted and
observed values in Figure V.B.2 may be a statistical anomaly. The overlap is also difficult to
interpret given the established differences between transient and steady-state results for PM as
described in Section IV.B.l.
We also examined the ratio of mean absolute residuals to determine if the scatter in the
nonroad data about the composite correlations was comparable to that exhibited by the highway
data on which those composite correlations were based. Table V.B-2 provides these ratios for
the nonroad data in our database.
Table V.B-2
Ratio of mean absolute value residuals for nonroad
NOx
PM
CO
HC
2.60
5.23
2.51
1.55
Since these ratios are significantly higher than 1.0, we conclude that the scatter in the nonroad
data around the composite correlations is substantial. This result might be expected given the
high degree of bias exhibited in Figures V.B-1 through V.B-4, since that bias contributes to this
particular measure of scatter. Therefore, we also calculated these ratios using an alternative
nonroad-only set of correlations. In this analysis, we used a simple linear least-squares
regression to produce correlations based on only the nonroad data. Although the available
nonroad data is too limited to produce correlations that could be used to represent the entire
nonroad fleet, these correlations do allow the bias exhibited in Figures V.B-1 through V.B-4 to
be eliminated. The ratios are then the mean absolute value residuals for the nonroad data around
the nonroad correlations, compared to the mean absolute value residuals for the highway data
around the highway composite correlations. The result is a set of ratios that might be considered
a more appropriate measure of relative scatter. The results are shown in Table V.B-3.
79
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Table V.B-3
Ratio of mean absolute value residuals for nonroad
using least-squares regression for nonroad data
NOx
PM
CO
HC
1.64
3.63
1.63
1.25
The values in Table V.B-3 still indicate a significantly higher amount of scatter in the nonroad
data relative to scatter in the highway data. Given the limited number of nonroad observations
and the higher level of scatter exhibited by that nonroad data, it would be reasonable to conclude
that there is insufficient information to determine whether the composite correlations based on
highway engines can be used to represent nonroad engine effects of biodiesel use.
We also investigated whether the bias exhibited in Figures V.B-1 through V.B-4 might be
a result of differences in test cycles. As stated above, the nonroad data was collected on an 8-
mode steady-state nonroad cycle, whereas most of the heavy-duty highway data was collected on
the transient FTP. Since there was some highway engine data collected on the European 13-
mode steady-state cycle ("R49"), we regenerated the basic correlations described in Section IV.A,
but using only the highway engine R49 data. The results are shown in Table V.B-4.
Table V.B-4
Coefficients for basic emission correlations using R49 data
% change = |exp[a x (vol% biodiesel)] - 1} x 1QQ%
NOx
PM
CO
HC
Coefficient 'a'
0.0014502
-0.001108
-0.00122
- 0.004832
P-value
0.0001
0.2990
0.4934
0.0402
The coefficients in Table V.B-4 are direct!onally consistent with those in Table IV. A-1. The fact
that the PM and CO correlations are not significant is consistent with our investigation of test
cycle effects as described in Section IV.B.l.
We used these R49-based highway engine correlations to compare predicted emission
impacts to those observed for nonroad engines. The results are shown in Figures V.B-5, V.B-6,
V.B-7, and V.B-8. Note that these graphical comparisons use the actual estimated coefficients
listed in Table V.B-4, rather than assigning a zero coefficient to the PM and CO correlations.
The corresponding paired t-tests and the ratio of mean absolute value residuals are given in Table
V.B-5.
80
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Figure V.B-5
Predicted vs observed NOx for nonroad
using R49 correlation
Figure V.B-6
Predicted vs observed PM for nonroad
using R49 correlation
20.0%
& 10.0%
ro
.c
o
1 0.0%
0
o
-10.0%
-10% 0% 10%
Predicted % change
20%
20.0%
10.0%
0.0%
-10.0%
-20.0%
-30.0%
-40.0%
-50.0%
-60.0%
-60% -40% -20% 0% 20%
-50% -30% -10% 10%
Predicted % change (not significant)
Figure V.B-7 Figure V.B-8
Predicted vs observed CO for nonroadPredicted vs observed HC for nonroad
using R49 correlation using R49 correlation
0.0%
-10.0%
I
o -20.0%
•o
| -30.0%
O
-40.0%
-40% -30% -20% -10%
Predicted % change (not significant)
0%
0.0%
-20.0%
I
o -40.0%
-a
| -60.0%
O
-80.0%
-80% -60% -40% -20%
Predicted % change
0%
81
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Table V.B-5
Evaluation of nonroad data using R49 correlation
NOx
PM
CO
HC
Paired t-tests: Probability that predicted and
observed values are different
Ignoring P-values
0.999
0.447
0.425
0.426
Zero effect for not
significant correlations
0.999
0.709
0.951
0.426
Ratio of mean
residuals
4.06
3.73
1.49
1.47
The paired t-tests indicate that the predicted and observed NOx values are clearly
different from one another. The best-fit CO correlation with a zero coefficient also produces a
statistically significant difference between predicted and observed values. Since this analysis
was intended to remove test cycle effects from the comparison of nonroad and highway
responses to biodiesel, we conclude that the bias exhibited in Figures V.B-1 and V.B-3 for NOx
and CO, respectively, is most likely due to factors other than test cycle differences, i.e. the results
appear to be due to differences in the way that nonroad and highway engines respond to
biodiesel. However, given the high amount of scatter in the nonroad data relative to highway
data, it is not clear that the true cause of the bias can be discerned from the data in our database.
As a result, we cannot draw a clear conclusion regarding the degree to which highway and
nonroad engines respond similarly to biodiesel, and there is some evidence that they respond
differently. On the basis of these analyses, then, we cannot say with confidence that the
composite correlations presented in Section IV.B.6 can provide accurate predictions of biodiesel
effects on emissions for nonroad engines.
There is an alternative analytical approach that could be used to produce a single set of
correlations representing both highway and nonroad engines. In this approach, the nonroad data
would be pooled with the highway data when the SAS procedure proc mix is run. Adjustment
terms for nonroad could then be permitted in these regression analyses. If none of the nonroad
adjustment terms were significant, then we might conclude that nonroad effects of biodiesel
cannot be distinguished from heavy-duty highway effects of biodiesel, and that the composite
correlations presented in Section IV.B.6 can be applied to heavy-duty nonroad engines. Such an
approach would be more appropriate for NOx and HC which appear to exhibit similar responses
to biodiesel for transient and steady-state cycles. It may be less appropriate to use this approach
for PM and CO. Regardless, given that the amount of nonroad data is substantially less than that
for highway, it is unclear if this approach would yield correlations (regardless of whether
adjustment terms for nonroad are significant) that would be representative of the nonroad fleet.
Finally, the preceding analyses applied only to heavy-duty nonroad engines. Light-duty
engines are sufficiently different from heavy-duty that we would not expect the effects of
82
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biodiesel on emissions to be the same for heavy-duty highway and light-duty nonroad. In this
case, we would propose that biodiesel emission effects estimated for heavy-duty highway engines
not be applicable to light-duty nonroad engines unless strong evidence to the contrary exists.
Given that our database contained no light-duty nonroad engines, no evaluation was possible, and
we therefore propose that our models not be applied to light-duty nonroad engines.
C. Effects of biodiesel on light-duty highway vehicles
As for light-duty nonroad engines, light-duty highway diesel engines are sufficiently
different from heavy-duty that we would not expect the effects of biodiesel on emissions to be
the same for heavy-duty highway and light-duty highway. Thus we would propose that biodiesel
emission effects estimated for heavy-duty highway engines not be applicable to light-duty
highway engines unless strong evidence to the contrary exists.
Our database contained only three biodiesel observations for light-duty vehicles. All
three observations were collected on the same biodiesel blend, engine, and test cycle. Given the
extreme paucity of light-duty data, we do not believe that a conclusive determination can be
reached regarding the appropriateness of using the highway composite correlations given in
Section IV.B.6 to represent biodiesel effects on light-duty vehicles. We therefore default to our
recommendation that the composite correlations not be used to represent light-duty vehicles.
However, in the interest of completeness, we present a comparison of observed and
predicted % change values for the three light-duty vehicle observations in our database. The
comparisons are shown graphically in Figures V.C-1 through V.C-4. Table V.C-1 presents the
accompanying t-tests and ratio of mean residuals.
Figure V.C-1
Predicted vs observed NOx for light-duty
Figure V.C-2
Predicted vs observed PM for light-duty
6.0%
4.0%
8,
§ 2.0%
.n
o
? 0.0%
0
I -2.0%
O
4.0%
4% -2% 0% 2%
Predicted % change
4%
6%
-5.0%
-10.0%
-15.0%
-15% -10%
Predicted % change
-5%
83
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Figure V.C-3
Predicted vs observed for CO
Figure V.C-4
Predicted vs observed for HC
-2.0%
4.0%
-6.0%
-8.0%
-10.0%
-12.0%
-14.0%
-14% -12% -10% -8% -6% -4% -2%
Predicted % change
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
-10.0%
-20.0%
-20% -10% 0% 10% 20% 30% 40% 50% 60%
Predicted % change
Table V.C-1
Evaluation of light-duty data
NOx
PM
CO
HC
Paired t-tests: Probability that predicted
and observed values are different
0.664
0.969
0.428
0.924
Ratio of mean
residuals
0.98
1.10
0.60
2.97
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Section VI: Biodiesel Effects On Gaseous Toxics
In addition to impacts on regulated pollutants, we also investigated the impact that
biodiesel has on emissions of unregulated hazardous air pollutants, hereafter referred to as toxics.
Most of the studies in our database focused only on the effects of regulated pollutants. The total
amount of data on toxics effects was much smaller than that for regulated pollutants, and as a
result we took a different approach to evaluating biodiesel effects on toxics. We treat our
conclusions regarding the effects of biodiesel on toxics as preliminary and only potentially
indicative of the true effects, due to the limited nature of the data.
A.
Toxic Pollutants Evaluated
Hydrocarbons include many different individual toxic compounds. For the purpose of
evaluating biodiesel effects on toxics, we focused on mobile source air toxics (MSATs) as
defined in a recent rulemaking8. MSATs are significant contributors to toxic emission
inventories, and are known or suspected to cause cancer or other serious health effects. There are
21 MSATs as shown in Table VI.A-1.
Table VI. A-1
List of 21 Mobile Source Air Toxics (MSATs)
Acetaldehyde
Acrolein
Arsenic Compounds1
Benzene
1,3-Butadiene
Chromium Compounds1
Diesel Particulate Matter + Diesel Exhaust
Organic Gases (DPM + DEOG)
Dioxin/Furans2
Ethylbenzene
Formaldehyde
1 Although the different metal compounds generally differ in
their toxicity, the onroad mobile source inventory contains
emissions estimates for total metal compounds (i.e., the sum
of all forms).
2 This entry refers to two large groups of chlorinated
compounds. In assessing their cancer risks, their quantitative
potencies are usually derived from that of the most toxic,
2,3,7,8-tetrachlorodibenzodioxin.
n-Hexane
Lead Compounds1
Manganese Compounds1
Mercury Compounds1
MTBE
Naphthalene
Nickel Compounds1
POM3
Styrene
Toluene
Xylene
3 Polycyclic Organic Matter includes organic compounds
with more than one benzene ring, and which have a boiling
point greater than or equal to 100 degrees centigrade. A
group of seven polynuclear aromatic hydrocarbons, which
have been identified by EPA as probable human carcinogens,
(benz(a)anthracene, benzo(b)fluoranthene,
benzo(k)fluoranthene, benzo(a)pyrene, chrysene, 7,12-
dimethylbenz(a)anthracene, and indeno(l,2,3-cd)pyrene) are
used here as surrogates for the larger group of POM
compounds.
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We reviewed the available biodiesel studies to determine which studies had actually
measured and reported any of the MSATs listed in Table VI. A-1. To be considered in this
analysis, the studies also had to conform to the criteria for choosing data sources, as described in
Section II. A. The resulting list of seven studies used for this toxics analysis is given in
Appendix D.
Of the 21 MSATs, six are metals. Since none of the studies we reviewed contained
measurements of metal emissions, we cannot directly determine the relationship between
biodiesel use and metal emissions. However, biodiesel is essentially metal-free, so we would
expect reduced emissions of metals for biodiesel blends, if indeed conventional diesel fuel or any
additives used in conventional diesel contains metals. Similarly, emissions of MTBE are not an
issue for diesel fuels since MTBE is only used as an additive for gasoline. Of the remaining
fourteen MSATs, we have emission measurements for eleven. Table VI.A-2 lists the eleven
MSATs that we therefore investigated for biodiesel effects.
Table VI.A-2
MSATs Investigated for Biodiesel Effects
acetaldehyde
acrolein
benzene
1,3-butadiene
ethylbenzene
formaldehyde
n-hexane
naphthalene
styrene
toluene
xylene
B. Analytical Approach
The available toxics data was collected on different engines, test cycles, and base fuels.
For our purposes, we focused on those occasions in which a biodiesel blend was formulated
using the same base fuel to which the blend was being compared. We then summarized the data
in a frequency table to determine how many toxic measurements were available. Table VI.B-1
lists the number of times a toxic compound was measured at specific biodiesel concentrations.
Every observation was a unique measurement of a given fuel on a given engine; there were no
repeat emission measurements.
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Table VLB-1
Number of toxic observations
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
Total
Percent biodiesel
0 % (base)
15
10
9
7
7
15
9
11
4
9
9
105
20%
15
10
9
7
7
15
9
9
4
9
9
103
50%
0
0
0
0
0
0
0
4
0
0
0
4
100%
7
7
7
5
5
7
7
9
2
7
7
70
Total
37
27
25
19
19
37
25
33
10
25
25
282
Before proceeding with an evaluation of each toxic separately, we first investigated the
degree to which the aggregated sum of all toxics were correlated with biodiesel concentration.
Although individual toxics vary from one another in terms of health effects and there is no reason
to believe that one toxic compound will respond to the presence of biodiesel identically to
another toxic compound, an analysis of the aggregated sum of toxics was appropriate for several
reasons. First, the amount of available data for total toxics was much larger than that for any
individual toxic compound, so that correlations are more likely to be statistically significant. As
a result, a correlation between biodiesel concentration and total toxics provides a means for
judging the overall toxics impacts of biodiesel without making statements regarding how
individual toxic compounds are affected by the presence of biodiesel. We recognize that
individual toxics may increase or decrease when biodiesel is blended with diesel fuel, and of
those that decrease, the magnitude of that decrease will vary from one toxic to another. Thus the
correlation of biodiesel concentration with total toxics is bound to be smaller in magnitude than
some individual toxics, and larger than others.
Second, since the toxics in Table VI.B-1 are components of total hydrocarbon emissions,
this approach allowed us to compare biodiesel effects on total toxics to biodiesel effects on total
hydrocarbons in order to determine if the combustion mechanisms could be said to differ
between the two (overlapping) groups. This latter issue is important because, in the absence of
sufficient or conclusive information on toxics emissions from diesel engines, we often make the
assumption that the ratio of toxics to total hydrocarbons remains constant.
There were no repeat measurements among the toxics data we evaluated. Therefore,
87
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there was no need to average repeat base fuel emission measurements. There did not appear to
be sufficient information to directly evaluate such issues as test cycle effects, engine technology,
base fuel, or type of biodiesel. However, these variables could still have an impact on the
response of toxics to biodiesel. In order to control for these variables as well as individual
engine effects, we first calculated the percentage change in emissions of each toxic compound for
every available toxic emissions measurement at a given non-zero biodiesel concentration, with
respect to the toxics measurements for the base fuel. This simplifying approach was deemed
appropriate for this preliminary analysis of biodiesel effects on toxics, since the limited amount
of toxics data may not be sufficient to draw specific, quantified conclusions applicable to the
entire in-use fleet. Rather, our analysis was intended to investigate directional effects and the
potential magnitude of those effects, with the understanding that a more comprehensive toxics
database may be necessary to produce more robust estimates.
We identified and eliminated outliers that exceeded four standard deviations from the
mean (two observations were eliminated thus), and then used linear least-squares regression to
correlate percent change in aggregated (total) toxics with biodiesel concentration. The resulting
correlation was statistically significant, and is shown below:
% change in total toxics = -0.001580 x (% biodiesel)
This correlation was compared to the basic model for total hydrocarbons given in Section IV. A.
The result is shown in Figure VI.B-1.
88
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Figure VLB-1
Comparison of aggregated toxics and total HC
0%
-10%
° -20%
to
(/5
E
CD
-30%
CD
O)
CO
.c
o
-40%
-50%
S -60%
CD
CL
-70%
-80%
Total HC
20 40 60 80
Percent biodiesel
100
Based on this analysis, we could reasonably conclude that total toxics are reduced when
biodiesel is added to conventional diesel fuel. This conclusion differs from the outcome that
would result if one assumed that the mass ratio of toxics to hydrocarbons was constant, i.e.
independent of biodiesel concentration. On the contrary, our analysis suggests that the mass ratio
of total toxics to total hydrocarbons actually increases with the addition of biodiesel, though
apparently not to the point of causing total toxics emissions to increase over baseline levels.
The above analysis of total toxics gives no indication of how individual toxics might
respond to the addition of biodiesel to conventional diesel fuel. In fact, it is possible that
emissions of one toxic compound might increase with increasing biodiesel concentration even
though the aggregate of all toxics decreases. It therefore seemed prudent to investigate individual
toxics despite the relative paucity of data in comparison to total toxics and total hydrocarbons.
We took several different approaches to evaluating data for individual toxics because the
data was so limited. Consideration of the results of all of these analytical approaches was used in
drawing conclusions about the effect of biodiesel on individual toxics. These approaches are
listed below:
a) Correlation of mass ratio of toxic/HC with biodiesel concentration
b) Correlation of % change in toxics emissions with biodiesel concentration
c) Binomial analysis of increases and decreases in toxics for biodiesel blends
d) Difference in average toxics effects at 20% biodiesel and 100% biodiesel
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The results of these analyses are shown in Tables VI.B-2, VI.B-3, VI.B-5, and VI.B-6. A
discussion of how the results were together considered to draw conclusions about individual
toxics is given in Section VI.C. Note that'% biodiesel' refers to values between 0 and 100.
Table VI.B-2
Correlation of toxic/HC ratios with % biodiesel
toxic/HC ratio = a * (% biodiesel) + b
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
a
0.000403
0.000142
0.000097
0.000015
-0.000032
0.000917
0.000011
0.000011
0.000021
0.000022
-0.000054
b
0.051071
0.026455
0.012679
0.014188
0.003687
0.129502
0.000673
0.001104
0.000694
0.009908
0.006798
P-value for a
0.03
0.06
0.01
0.33
0.02
0.02
0.05
0.01
0.02
0.26
0.02
Table VI.B-3
Correlation of % change in toxics with % biodiesel
% change in toxics = c x (% biodiesel) x 100%
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
c
-0.001606
-0.000846
0.000390
-0.000132
-0.006970
-0.001696
-0.002381
-0.002847
0.003501
0.001750
-0.004078
P-value for c
0.05
0.21
0.40
0.48
0.00
0.00
0.12
0.04
0.05
0.19
0.01
Note that most of the values for 'c' in Table VI.B-3 are negative, indicating that emissions
of the toxic compound are generally reduced when biodiesel is added to conventional diesel fuel.
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Of the three positive values, two were not statistically significant. This result is consistent with
the observation that total toxics appear to be reduced with increasing biodiesel concentration, as
shown in Figure VI.B-1.
The correlations in Table VI.B-2 can also be used in combination with the basic HC
correlation from Section IV. A. 1 to estimate the impact that biodiesel has on toxics emissions.
This approach yields a means for checking that the sign of the coefficients in Table VI.B-3 make
sense. To do this, we first assumed a basic HC emission rate of 1.0 g/bhp-hr (although the
specific value has no impact on the calculation). The basic HC correlation from Table IV.A. 1-1
was then converted into one giving emissions of HC in g/bhp-hr as follows:
HC emissions (g/bhp-hr) = 1 + 1 x {exp[-0.011195 x (vol% biodiesel)] - 1}
This equation was then combined with each of the correlations in Table VI.B-2 to yield an
equation giving emissions of each toxic compound in g/bhp-hr as a function of vol% biodiesel:
Toxic (g/bhp-hr) =
(a x (o/0 biodiesel) + b} x (l + l x {exp[-0.011195 x (vol% biodiesel)] - 1}}
Finally, this equation was converted from g/bhp-hr to percent change. The resulting equation can
be rearranged to the following form:
% change in toxics =
{{a/b x (o/o biodiesel) + 1} x exp[-0.011195 x (vol% biodiesel)] - 1} x 100%
where the coefficients 'a' and 'b' are drawn from Table VI.B-2. We were able to use this equation
to estimate directional effects for each toxic (i.e. increase or decrease when biodiesel is added to
diesel fuel), and then to compare these directional effects with the coefficients for 'c' in Table
VI.B-3 for consistency. The results are shown in Table VI.B-4, where "Table VI.B-2
correlations" refers to the equation above. Note that we did not regard statistical significance of
the regressions for this comparison.
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Table VI.B-4
Comparison of directional effects for toxics using two different approaches
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
Toxics emissions effect with increasing biodiesel for...
Table VI.B-2 correlations
Decrease
Decrease
Decrease
Decrease
Decrease
Decrease
Decrease
Decrease
Increase
Decrease
Decrease
Table VI.B-3 correlations
Decrease
Decrease
Increase
Decrease
Decrease
Decrease
Decrease
Decrease
Increase
Increase
Decrease
The comparison in Table VI.B-4 indicates an inconsistency for benzene and toluene. The
implications are discussed in more detail in Section VI.C.3 below.
Table VI.B-5
Binomial distributions of toxic increases versus decreases
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
Number of biodiesel observations of
% change from baseline which are...
Positive
7
7
7
7
2
8
3
6
2
8
2
Negative
15
10
5
5
10
14
8
12
2
8
10
Probability that a random
distribution would
produce this result
0.13
0.63
0.77
0.77
0.04
0.29
0.23
0.24
n/a
n/a
0.04
The probabilities in Table VI.B-5 give an indication of how likely it is that the direction
(not the magnitude) of the true effect can be discerned from the direction of the individual
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observations. Normally a value of 0.10 (corresponding to a 90% confidence interval) would
indicate a statistically significant result. Given the small amount of data and the notorious
variability in toxics measurements, we have used the binomial distribution results in a less
rigorous fashion as a check on the correlations given in Table VI.B-3. This comparison is
discussed for each individual toxic in Section VI.C below.
Table VI.B-6
Difference in average toxics effects at two biodiesel blend levels
Acetaldehyde
Acrolein
Benzene
1,3 -Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
Average % change compared to base fuel
20% biodiesel
-7.1 %
-1.5%
16.5 %
39.0 %
-44.9 %
-7.8 %
-48.7 %
-13.8%
-3.7%
19.9%
-12.3 %
100% biodiesel
-14.4 %
-8.5 %
-0.8 %
-12.3 %
-61.0%
-15.1%
-12.1%
-26.7 %
39.3 %
13.3 %
-39.5 %
Consistent trend?
Yes
Yes
No
No
Yes
Yes
?
Yes
No
?
Yes
For n-hexane and toluene in Table VI.B-6, the directional effects are consistent between
the 20% and 100% biodiesel blends, but the magnitude of those effects are not consistent with
expectations (we would expect the magnitude of the effect at 100% biodiesel to be larger than
that at 20% biodiesel). Such results could easily be an outgrowth of the paucity of data.
Regardless, the trend for these two toxics is not wholly inconsistent, a fact taken into account in
our discussions in the next Section.
C.
Conclusions for individual toxics
Since the amount of available data on toxic impacts of biodiesel use is so limited, we treat
our conclusions regarding the effects of biodiesel on toxics as preliminary and only potentially
indicative of the true effects. We have some confidence that the impact of biodiesel on total
toxics is beneficial, i.e. total toxics are on average reduced when biodiesel is added to
conventional diesel fuel. We have less confidence in our conclusions for individual toxic
compounds. It is our hope that additional toxics data will be generated by independent
researchers in the near future, allowing a more robust and definitive analysis of biodiesel effects
on toxics.
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Because the available toxics data was so limited, we determined that a tiered approach to
drawing conclusions about biodiesel effects on toxics would be appropriate. We have defined
three tiers corresponding to three different qualitative levels of confidence in the conclusions.
They are described below:
Tier 1: Includes those toxic compounds for which the analyses described in
Section VLB above appear to be largely consistent with one another. The effect
of biodiesel on toxics can be quantified with reasonable confidence.
Tier 2: Includes those toxic compounds for which the analyses may not be
entirely consistent with one another, or not statistically significant. However, the
effect of biodiesel on toxics can still be estimated qualitatively in terms of
directional effects.
Tier 3: Includes those toxic compounds for which the analyses are significantly in
conflict with one another. No clear conclusions can be drawn.
The eleven toxic compounds evaluated in this analysis were assigned to one of the three tiers, as
shown in Table VI.C-1. Each of the following three subsections describes our conclusions for
the three tiers of toxics.
Table VI.C-1
Tier assignments for toxics
Acetaldehyde
Acrolein
Benzene
1,3-Butadiene
Ethylbenzene
Formaldehyde
n-Hexane
Naphthalene
Styrene
Toluene
Xylene
Tier 1
Tier 2
Tier3
Tier3
Tier 1
Tier 1
Tier 2
Tier 1
Tier 2
TierS
Tier 1
1.
Tier 1 toxics
Tier 1 toxics include those toxic compounds for which the analyses described in Section
VLB above appear to be largely consistent with one another. The effect of biodiesel on toxics
can be quantified with reasonable confidence. The toxics which we have determined fall into
this tier include acetaldehyde, ethylbenzene, formaldehyde, naphthalene, and xylene. Table
VI.C. 1-1 repeats the correlations presented in Table VI.B-3 for these five toxics. Note that'%
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biodiesel' is given as a value between 0 and 100. Limited discussion of the analyses leading to
the conclusion that these five toxics can be quantified is given below.
Table VI.C. 1-1
Toxics which can be correlated with biodiesel concentration
Acetaldehyde
Ethylbenzene
Formaldehyde
Naphthalene
Xylene
% change in emissions = -0.001606 x (% biodiesel) x 100%
% change in emissions = -0.006970 x (% biodiesel) x 100%
% change in emissions = -0.001696 x (% biodiesel) x 100%
% change in emissions = -0.002847 x (% biodiesel) x 100%
% change in emissions = -0.004078 x (% biodiesel) x 100%
Acetaldehyde: All analyses suggest a statistically significant reduction in
acetaldehyde emissions with increasing biodiesel concentration. The effect is
considerably smaller in magnitude than the total HC effect, which would suggest
that the mass ratio of acetaldehyde to HC would increase with biodiesel
concentration. This expectation is confirmed with the results in Table VI.B-2.
Ethylbenzene: All analyses suggest a statistically significant reduction in
ethylbenzene emissions with increasing biodiesel concentration. The effect is
slightly larger in magnitude than the total HC effect, which would suggest that the
mass ratio of ethylbenzene to HC would decrease with biodiesel concentration.
This expectation is confirmed with the results in Table VI.B-2.
Formaldehyde: All analyses suggest a statistically significant reduction in
formaldehyde emissions with increasing biodiesel concentration. The directional
result of the binomial analysis is consistent with the direction of the highly
significant correlation in Table VI.B-3 even though the binomial analysis was not
statistically significant.
Naphthalene: All analyses suggest a statistically significant reduction in
naphthalene emissions with increasing biodiesel concentration. The directional
result of the binomial analysis is consistent with the direction of the highly
significant correlation in Table VI.B-3 even though the binomial analysis was not
statistically significant.
Xylene: All analyses suggest a statistically significant reduction in naphthalene
emissions with increasing biodiesel concentration. The effect is smaller in
magnitude than the total HC effect, which would suggest that the mass ratio of
ethylbenzene to HC would increase with biodiesel concentration. This
expectation is not confirmed with the results in Table VI.B-2, which instead
shows that the mass ratio decreases with increasing biodiesel concentration. This
contrary result suggests that xylene emissions might actually exhibit reductions
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that are larger than the total HC reductions. However, the binomial analysis is
strongly significant, indicating that xylene emissions would be expected to
decrease with increasing biodiesel concentration, regardless of whether that
decrease was larger or smaller in magnitude than the total HC effect. As a result,
it would appear that the environmentally conservative (lowest benefit) estimate of
xylene emission impacts of biodiesel use would be the correlation from Table
VI.B-3.
2. Tier 2 toxics
Tier 2 toxics include those toxic compounds for which the analyses described in Section
VLB above may not be entirely consistent with one another or are not statistically significant.
The effect of biodiesel on toxics cannot be quantified with confidence, but it can be estimated
qualitatively in terms of directional effects. The toxics which we have determined fall into this
tier include acrolein, n-hexane, and styrene. Table VI.C.2-1 gives our qualitative conclusions for
these three toxics. A discussion of the analyses for these tier 2 toxics is given below.
Table VI.C.2-1
Qualitative toxic effects of biodiesel use
Acrolein
n-Hexane
Styrene
Likely small reduction
Likely small reduction
Possible increase
Acrolein: The correlation between % change in acrolein emissions and %
biodiesel in Table VI.B-3 was not statistically significant, suggesting that acrolein
emissions are not affected by the addition of biodiesel to conventional diesel fuel.
The calculated probability in the binomial analysis would support this conclusion.
However, all means for determining the direction of the effect consistently give
the same answer: biodiesel reduces acrolein emissions. This includes the analyses
summarized in Tables VI.B-3, VI.B-5, and VI.B-6. Table VI.B-4 shows that the
combination of the acrolein/HC correlation from Table VI.B-2 with the basic HC
model from Section IV.A.I also indicates that acrolein emissions decrease with
increasing biodiesel concentration. Based on these analyses we conclude that the
reduction in acrolein emissions with increasing biodiesel concentration is real, but
that the effect was simply too small to be captured in a statistically significant way
in the correlation from Table VI.B-3.
n-Hexane: The correlation between % change in n-hexane emissions and %
biodiesel in Table VI.B-3 was not statistically significant, suggesting that n-
hexane emissions are not affected by the addition of biodiesel to conventional
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diesel fuel. Although the mean % change effects at 20% biodiesel and 100%
biodiesel were both negative (Table VI.B-6), the value at 20% biodiesel was
substantially larger in magnitude than the value at 100% biodiesel. This result
may have contributed to the not significant result in Table VI.B-3. Direct!onally,
all analyses indicate that n-hexane emissions should decrease with increasing
biodiesel concentration. Table VI.B-4 shows that the combination of the n-
hexane/HC correlation from Table VI.B-2 with the basic HC model from Section
IV. A. 1 also indicates that n-hexane emissions decrease with increasing biodiesel
concentration. Based on these analyses we conclude that the reduction in n-
hexane emissions with increasing biodiesel concentration is real, but that the
effect was simply too small to be captured in a statistically significant way in the
correlation from Table VI.B-3.
Styrene: The correlation between % change in styrene emissions and % biodiesel
in Table VI.B-3 was statistically significant, suggesting that styrene emissions are
increased by the addition of biodiesel to conventional diesel fuel. However, the
number of observations was extremely small, and the binomial analysis indicated
no clear direction. We therefore conclude that the correlation in Table VI.B-3 can
at best be used to suggest the likely direction of the effect.
3. Tier 3 toxics
Tier 3 toxics include those toxic compounds for which the analyses described in Section
VLB above are significantly in conflict with one another. The effect of biodiesel on toxics
cannot be determined quantitatively or qualitatively. The toxics which we have determined fall
into this tier include benzene, 1,3-butadiene, and toluene. A discussion of the analyses for these
tier 3 toxics is given below.
Benzene: The correlation between % change in benzene emissions and %
biodiesel in Table VI.B-3 was not statistically significant, suggesting that benzene
emission are not affected by the addition of biodiesel to conventional diesel fuel.
Ignoring statistical significance, this correlation suggests a small increase in
benzene emissions with increasing biodiesel concentration, consistent with the
(highly non-significant) binomial analysis. Table VI.B-4 shows that the
combination of the benzene/HC correlation from Table VI.B-2 with the basic HC
model from Section IV.A.I indicates that benzene emissions decrease with
increasing biodiesel concentration, a result that is inconsistent with the trends
suggested in Tables VI.B-3 and VI.B-5. In addition, the mean effect at 20%
biodiesel is direct!onally opposite to the mean effect at 100% biodiesel (Table
VI.B-6). As a result, we cannot conclude with confidence that benzene emissions
are unaffected by biodiesel addition, nor can we discern a clear direction for the
effect.
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1,3-Butadiene: The correlation between % change in 1,3-butadiene emissions and
% biodiesel in Table VI.B-3 was not statistically significant, suggesting that 1,3-
butadiene emissions are not affected by the addition of biodiesel to conventional
diesel fuel. Ignoring statistical significance, this correlation suggests a small
decrease in 1,3-butadiene emissions with increasing biodiesel concentration,
which is inconsistent with the (highly non-significant) binomial analysis. The
correlation between the mass ratio of 1,3-butadiene to HC and % biodiesel was
not significant, suggesting that the mass ratio should remain constant and
therefore that the correlation between % change in 1,3-butadiene emissions and %
biodiesel should not only have been statistically significant, but of a magnitude
close to that for the basic HC model from Section IV.A. This was, however, not
the case. With such conflicting results, we cannot conclude with confidence that
there is no effect of biodiesel on emissions of 1,3-butadiene, nor can we infer a
direction for any potential impact.
Toluene: The correlation between % change in toluene emissions and % biodiesel
in Table VI.B-3 was not statistically significant, suggesting that toluene emissions
are not affected by the addition of biodiesel to conventional diesel fuel. The
binomial analysis would support this conclusion. However, the mean % change
values at 20% biodiesel and 100% biodiesel are both positive (Table VI.B-6),
suggesting that toluene emissions should increase with increasing biodiesel
concentration. The correlation between the mass ratio of toluene to HC and %
biodiesel was not significant, suggesting that the mass ratio should remain
constant and therefore that the correlation between % change in toluene emissions
and % biodiesel should not only have been statistically significant, but also
negative. Table VI.B-4 shows that the combination of the toluene/HC correlation
from Table VI.B-2 with the basic HC model from Section IV.A. 1 indicates that
toluene emissions decrease with increasing biodiesel concentration, a result that is
inconsistent with the trends suggested in Table VI.B-3.
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Section VII: What Additional Issues Should Be Addressed?
After reviewing the available data on emission effects of biodiesel and making efforts to
quantify those effects in a robust manner, we identified those areas that could benefit from more
focused attention. This Section lists those areas related to exhaust emission effects where
additional work would be of value. As this list is not meant to be exhaustive, reviewers of this
draft technical report are invited to suggest other areas for EPA or other stakeholders to
investigate.
A. Data gaps
1. Newer highway engines
Our database contains no data on heavy-duty highway engines meeting the 2004
standards. Not only will these engines be a significant portion of the in-use fleet in the near
future, but there is evidence that they may respond differently than older engines to changes in
fuel properties due to the expected predominance of exhaust gas recirculation and injection rate-
shaping in these newer engines. Although we attempted to evaluate how these engines might
respond to biodiesel in Section IV.B.5, we cannot know for certain how these engines will
respond to biodiesel without actual emissions data.
There is also some indication that injection rate-shaping has been used more commonly in
engines produced in recent years. It is not known whether rate-shaping would cause engines to
respond differently to biodiesel, but a combustion simulation model developed by Southwest
Research Institute called ALAMO_ENGINE reportedly shows no effect of cetane on NOx
emissions for engines using rate-shaping. Unfortunately, the few observations in our database
representing 1998+ model years (standards group C) were collected on unique steady-state
cycles, and so were not included in our analysis of heavy-duty highway engines. A preliminary
review of the data on these engines does not indicate that they are likely to exhibit different
responses to biodiesel than older model years. However, given the limited number of
observations and the fact that the data was collected on unique steady-state test cycles, a firm
conclusion cannot be reached until more data is collected.
2. Nonroad engines
Most large nonroad diesel engines use technologies similar to those found in heavy-duty
highway diesel engines. On this basis we might expect biodiesel to produce similar emission
effects for heavy-duty nonroad and heavy-duty highway engines. Unfortunately, as discussed in
Section V.B, there were only 8 biodiesel observations collected on nonroad engines in our
database. Despite the various analytical approaches we took, we could not draw a clear
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conclusion regarding the degree to which highway and nonroad engines respond similarly to
biodiesel. In fact, there was some evidence that they respond differently. Therefore, we believe
that additional data on nonroad engines should be collected before conclusions can be drawn
about their emission responses to biodiesel. This would apply both to large and smaller (< 50 hp)
engines.
We believe that nonroad engines very often operate in transient modes, and so may not be
ideally represented by the current steady-state federal test procedure used for certification. As a
result, the Agency has been in the process of developing a transient nonroad cycle in recent years.
If additional biodiesel data is collected on nonroad engines, we believe that emission
measurements on a transient cycle would be preferable, particularly for PM and CO.
3. Biodiesel properties
In order to more accurately determine the emission impacts of biodiesel, it would be
useful to correlate actual fuel property measurements of biodiesel with emissions rather than
simply using broad source categorizations as described in Section ni.C.2.c. Unfortunately, as
described in Section II.E. 1, few of the biodiesels in our database included a full complement of
compositional and physical property measurements. And yet, some properties, such as cetane
number, vary considerably from one batch of biodiesel to another, while others such as specific
gravity, remain relatively constant (see Figure HE. 1-1). We therefore recommend that further
testing of biodiesel include more detailed property measurements of biodiesel. These properties
should at minimum include cetane number, distillation, specific gravity, sulfur, aromatics,
oxygen, and cloud point. Measurements of other properties would also be useful, such as
lubricity characteristics or specific molecular compounds.
B. Mitigating NOx increases
One potential drawback to the use of biodiesel is the increase in NOx emissions.
Although the increase is small in comparison to the reductions in other regulated pollutants, such
NOx increases may be problematic for ozone nonattainment or maintenance areas. Thus
additional research on ways to mitigate the NOx increase would be valuable. Various strategies
might include using a lower-emitting base fuel for blending, adding a cetane improver additive to
the biodiesel blend, or determining what source or properties of biodiesel can be modified to
lower NOx emissions. Some of these fuel strategies have been investigated in a recent SAE
paper by Robert McCormick et al9. Other strategies might be hardware-related, such as changing
injection timing or adding a lean NOx catalyst, and additional studies of these strategies in
concert with biodiesel would be valuable.
C. Base fuel effects
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As discussed in Section IV.B.4, the base fuel to which biodiesel is added does appear to
have an impact on the degree to which biodiesel affects exhaust emissions of regulated
pollutants. There is no obvious reason for this outcome, and thus it deserves closer attention. If
both the base fuels and the biodiesels in our database had a full complement of measured fuel
properties, analytical means of explaining this result might be possible. Thus it would be helpful
to have additional data in which the same engine is run on one batch of biodiesel blended into
several different base fuels, with full speciation and property measurements for all fuels.
However, a true understanding of this phenomenon might require a more in-depth study of the
such things as spray atomization, diffusion burning, etc.
D. Methyl versus ethyl esters
The studies that comprised our database usually did not specify whether the biodiesel was
a methyl ester or an ethyl ester. It is not clear whether this distinction is an important one, since
we were unable to investigate it. It would be valuable to have information on how different
transesterification processes on the same batch of plant or animal oil may lead to biodiesels
exhibiting different emissions impacts.
E. Minimum data criteria
There are several aspects of our calculation of the minimum data criteria in Section
ffi.C.l that may warrant further investigation, since these criteria necessarily preclude the
investigation of some adjustment terms in the correlations. For instance, in gasoline emissions
test programs conducted by the Auto/Oil Air Quality Improvement Research Program, a
determination was made that the number of vehicles was more important than the number of
repeat tests on a given vehicle for accurately estimating emissions effects for the fleet. Thus it
might be reasonable for this biodiesel analysis to formulate minimum data criteria that reflect
engine-to-engine variability.
It might also be reasonable to use an alternative approach to calculating values for E
(percent) in Table ni.C. 1-1 that are absolute instead of relative. In this alternative approach,
equation (9) would change to the following:
E(g/bhp-hr) = E(percent) x Mean emissions (g/bhp-hr)
where
E(g/bhp-hr) = One-half the width of the confidence interval, used in equation
(8)
E(percent) = One-half the width of the confidence interval as a fixed fraction
of emissions
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Mean emissions = Average g/bhp-hr for conventional diesel fuel
This alternative approach would avoid the problem that the minimum data criteria for NOx were
calculated to be significantly more stringent than for the other pollutants, due to the smaller
relative effect of biodiesel on NOx emissions.
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Appendices
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Appendix A - Data Sources
Studies that were included in database
Bouche, T., M. Hinz, R. Pitterman, "Optimising Tractor CI Engines for Biodiesel Operation,"
SAE paper no. 2000-01-1969
Callahan, T.J., C.A. Sharp, "Evaluation of Methyl Soyate/Diesel Fuel Blends as a Fuel for Diesel
Engines," Southwest Research Institute Final Report to the American Biofuels Association,
December 1993
Clark, N.N., C.M. Atkinson, GJ. Thompson, R.D. Nine, "Transient Emissions Comparisons of
Alternative Compression Ignition Fuels," SAE paper no. 1999-01-1117
Durbin, T.D., J.R. Collins, J.M. Norbeck, M.R. Smith, "Evalution of the Effects of Alternative
Diesel Fuel Formulations on Exhaust Emissions Rates and Reactivity," Final Report from the
Center for Environmental Research and Technology, University of California, April 1999
Fosseen, D., "DDC 6V-71N Emission Testing on Diesel and Biodiesel Blend," Fosseen
Manufacturing and Development, Ltd., report no. NSDB4F15, July 14, 1994
Fosseen, D., "DDC 6V-92TA MU1 Coach Upgrade Emission Optimization on 20%/80%
Soy/Diesel Blend," Fosseen Manufactuering & Development, Ltd., report no. 260-2 and 24 1-1,
September 30, 1994
Goetz, W., "Evaluation of Methyl Soyate/Diesel Blend in a DDC 6V-92TA Engine:
Optimization of NOx Emissions," Ortech International, Report No. 93-E14-36, July 20, 1993
Goetz, W., "Evaluation of a Tallow/Diesel Blend in a DDC 6V-92TA Engine," Ortech
International report no. 93-E14-37, July 21, 1993
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 paper no. 961166
Graboski, M.S., R.L. McCormick, T.L. Alleman, A.M. Herring, "The Effect of Biodiesel
Composition on Engine Emissions from a DDC Series 60 Diesel Engine," Colorado School of
Mines, Final Report to National Renewable Energy Laboratory, June 8, 2000
Hansen, K.F., M.G. Jensen, "Chemical and Biological Characteristics of Exhaust Emissions from
a DI Diesel Engine Fuelled with Rapeseed Oil Methyl Ester (RME)," SAE paper no. 971689
Howes, P., G. Rideout, "Evaluation of Biodiesel in an Urban Transit Bus Powered by a 1988
DDECII6V92 TA Engine," National Biodiesel Board, MSED Report # 96-26743-1, June 1995
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Howes, P., G. Rideout, "Evaluation of Biodiesel in an Urban Transit Bus Powered by a 1981
DDCC8V71 Engine," National Biodiesel Board, MSED Report # 95-26743-2
Liotta, F.J., D.M. Montalvo, "The Effect of Oxygenated Fuels on Emissions from a Modern
Heavy-Duty Diesel Engine," SAE paper no. 932734
Manicom, B., C. Green, W. Goetz, "Methyl Soyate Evaluation of Various Diesel Blends in a
DDC 6V-92 TA Engine," Ortech International, April 21, 1993
Marshall, W., L.G. Schumacher, S. Howell, "Engine Exhaust Emissions Evaluation of a
Cummins L10E When Fueled With a Biodiesel Blend," University of Missouri.
McCormick, R.L., J.D. Ross, M.S. Graboski, "Effect of Several Oxygenates on Regulated
Emissions from Heavy-Duty Diesel Engines," Environmental Science and Technology., 1997, 31,
1144-1150
McCormick, R.L., J.R. Alvarez, M.S. Graboski, "NOx Solutions for Biodiesel," Colorado School
of Mines, August 31, 2001
McDonald, J.F., D.L. Purcell, B.T. McClure, D.B. Kittelson, "Emissions Characteristics of Soy
Methyl Ester Fuels in an IDI Compression Ignition Engine," SAE paper no. 950400
Peterson, C., D. Reece, J. Thompson, S. Beck, C. Chase, "Development of Biodiesel for Use in
High Speed Diesel Engines," University of Idaho, presentation at Sixth National Bioenergy
Conference, Oct. 2-6, 1994.
Peterson, C.L., D.L. Reece, "Emissions Tests with an On-Road Vehicle Fueled with Methyl and
Ethyl Esters of Rapeseed Oil," presented at the 1994 ASAE International Winter Meeting, ASAE
paper no. 946532.
Peterson, C.L., D.L. Reece, "Emissions Testing with Blends of Esters of Rapeseed Oil Fuel With
and Without a Catalystic Converter," SAE paper no. 961114
Peterson, C.L., "Truck-In-The-Park Biodiesel Demonstration with Yellowstone National Park,"
University of Idaho, August 1999.
Rantanen, L., S. Mikkonen, L. Nylund, P. Kociba, M. Lappi, N. Nylund, "Effect of Fuel on the
Regulated, Unregulated and Mutagenic Emissions of DI Diesel Engines," SAE paper no. 932686
Schumacher, L.G., S.C. Borgelt, W.G. Hires, D. Fosseen, W. Goetz, "Fueling Diesel Engines
with Blends of Methyl Ester Soybean Oil and Diesel Fuel," University of Missouri. See
http://web.missouri.edu/~pavt0689/ASAED94.htm.
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Schumacher, L., S.C. Borgelt, W.G. Hires, W. Wetherell, A. Nevils, "100,000 Miles of Fueling
5.9L Cummins Engines with 100% biodiesel," SAE paper no. 962233
Sharp, C.A., "Transient Emissions Testing of Biodiesel in a DDC 6V-92TA DDEC Engine,"
Southwest Research Institute, Final Report to the National Biodiesel Board, October 1994
Sharp, C.A., "Transient Emissions Testing of Biodiesel and Other Additives in a DDC Series 60
Engine," Southwest Research Institute, Final Report to the National Biodiesel Board, December
1994
Sharp, C.A., "Emissions and Lubricity Evaluation of Rapeseed Derived Biodiesel Fuels,"
Southwest Research Institute, Final Report for Montana Department of Environmental Quality,
November 1996
Sharp, C.A., "Characterization of Biodiesel Exhaust Emissions for EPA 21 l(b)," Southwest
Research Institute report no. 08-1039A, January 1998
Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of Biodiesel Fuels on transient Emissions from
Modern Diesel Engines, Part I Regulated Emissions and Performance," SAE paper no. 2000-01-
1967
Simian, M.B., E.G. Owens, K.A. Whitney, "Emissions Comparison of Alternative Fuels in an
Advanced Automotive Diesel Engine," Southwest Research Institute, AD A353968/PAA,
November 1998
Smith, J.A., D.L. Endicott, R.R. Graze, "Biodiesel Engine Performance and Emissions Testing,"
Caterpillar Technical Center, May 1998
Spataru, A., C. Romig, "Emissions and Engine Performance from Blends of Soya and Canola
Methyl Ester with ARE #2 Diesel in a DDC 6V92TA MUI Engine," SAE paper no. 952388
Starr, M.E., "Influence on Transient Emissions at Various Injection Timings, Using Cetane
Improvers, Bio-Diesel, and Low Aromatic Fuels," SAE paper no. 972904
Stotler, R.W., D.M. Human, "Transient Emission Evaluation of Biodiesel Fuel Blend in a 1987
Cummins L10 and DDC 6V-92-TA," Engineering Test Services, Report No. ETS-95-128, Nov.
30, 1995
Ullman, T.L., C.T. Hare, T.M. Baines, "Heavy-Duty Diesel Emissions as a Function of Alternate
Fuels," SAE paper no. 830377
"Emissions from Biodiesel Blends and Neat Biodiesel from a 1991 Model Series 60 Engine
Operating at High Altitude," Colorado Institute for Fuels and High Altitude Engine Research,
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Colorado School of Mines, September 1994.
"Effects of Methyl Esters of Tallow and Grease on Exhaust Emissions and Performance of a
Cummins L10 Engine," National Institute for Petroleum and Energy Research, September 16,
1993
Studies that were not included in the database because the data was collected on an engine that
was single cylinder or otherwise experimental
Murayama, T., Y. Oh, A. Kido, T. Chikahisa, N. Miyamoto, K. Itow, "Effects of Super Heating
of Heavy Fuels on Combustion and Performance in DI Diesel Engines," SAE paper no. 860306
Murayama, T., Y. Oh, N. Miyamoto, T. Chikahisa, N. Takagi, K. Itow, "Low Carbon Flower
Buildup, Low Smoke, and Efficient Diesel Operation with Vegetable Oils by Conversion to
Mono-Esters and Blending with Diesel Oil or Alcohols," SAE paper no. 841161
Shaheed, A., E. Swain, "Performance and Exhaust Emission Evaluation of a Small Diesel Engine
Fuelled with Coconut Oil Methyl Esters," SAE paper no. 981156
Suda, K.I, "Vegetable Oil or Diesel Fuel - A Flexible Option," SAE paper no. 840004
Ziejewski, M., HJ. Goettler, "Comparative Analysis of the Exhaust Emissions for Vegetable Oil
Based Alternative Fuels," SAE paper no. 920195
Studies that were not included in the database because the emissions data was not readily
available
Alfuso. S., M. Auriemma, G. Police, M.V. Prati, "The Effect of Methyl-Ester of Rapeseed Oil on
Combustion and Emissions of DI Diesel Engines," SAE paper no. 932801
Hemmerlain, N., V. Korte, H. Richter, G. Schroder, "Performance, Exhaust Emissions and
Durability of Modern Diesel Engines Running on Rapeseed Oil," SAE paper no. 910848
Humke, A.L., NJ. Barsic, "Performance and Emissions Characteristics of a Naturally Aspirated
Diesel Engine with Vegetable Oil Fuels - (Part 2)," SAE paper no. 810955
Last, R.J., M. Kriiger, M. Diirnholz, "Emissions and Performance Characteristics of a 4-Stroke,
Direct Injected Diesel Engine Fueled with Blend of Biodiesel and Low Sulfur Diesel Fuel," SAE
paper no. 950054
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Ma, W., L.I. Leviticus, F.G. Ullman, "On-Line Measurement of Formaldehyde in Tailpipe
Emissions by Tunable Diode Laser Spectroscopy," SAE paper no. 941702
Martin, B., P. Aakko, D. Beckman, N. D. Giacomo, F. Giavazzi, "Influence of Future Fuel
Formulations on Diesel Engine Emissions - A Joint European Study," SAE paper no. 972966
McDonald, J.F., "Evaluation of a Yellow Grease Methyl Ester and Petroleum Diesel Fuel Blend,"
University of Minnesota Final Report to the Agricultural Utilization Research Institute, August
11, 1997
Montagne, X., "Introduction of Rapeseed Methyl Ester in Diesel Fuel - The French National
Program," SAE paper no. 962065
Reece, D.L., C.L. Peterson, "Biodiesel Testing in Two On-Road Pickups," SAE paper no.
952757
Staat, F., P. Gateau, "The Effects of Rapeseed Oil Methyl Ester on Diesel Engine Performance,
Exhaust Emissions and Long-Term Behavior - A Summary of Three Years of Experimentation,"
SAE paper no. 950053
Wang, W.G., D.W. Lyons, N.N. Clark, M. Gautam, "Emissions from Nine Heavy Trucks Fueled
by Diesel and Biodiesel Blend without Engine Modification," Environmental Science &
Technology, 2000, 34, 933-939
Studies that were not included in the database because the test cycle only included a small
number of nonstandard modes or was otherwise unrepresentive of the FTP
Adelman, A.J., "Emission Evaluation Test Report for a Comparative Analysis of Emissions
Among Petroleum Diesel and Biodiesel Blends fired in a Large Diesel Engine," AirNova, Inc.,
January 1998
Akasaka, Y., T. Suzuki, Y. Sakurai, "Exhaust Emissions of a DI Diesel Engine Fueled with
Blends of Biodiesel and Low Sulfur Diesel Fuel," SAE paper no. 972998
Bagley, S.T., L.D. Gratz, J.H. Johnson, J.F. McDonald, "Effects of an Oxidation Catalytic
Converter and a Biodiesel Fuel on the Chemical, Biological, and Particulate Size Characteristics
of Emissions from an IDI Diesel Engine," Michigan Technological University, December 1995
Chang, D. Y.Z., J.H. Van Gerpen, "Fuel Properties and Engine Performance for Biodiesel
Prepared from Modified Feedstocks," SAE paper no. 971684
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Chang, D.Y., J.H. Van Gerpen, "Determination of Particulate and Unburned Hydrocarbon
Emissions from Diesel Engines Fueled with Biodiesel," SAE paper no. 982527
Choi, C.Y., G.R. Bower, R.D. Reitz, "Effects of Biodiesel Blended Fuels and Multiple Injections
on D.I. Diesel Engines," SAE paper no. 970218
Czerwinski, J., "Performance of HD-DI-Diesel Engine with Addition of Ethanol and Rapeseed
Oil," SAE paper no. 940545
Desantes, J.M., J. Arregle, S. Ruiz, A. Delage, "Characterization of the Injection-Combustion
Process in a D.I. Diesel Engine Running with Rape Oil Methyl Ester," SAE paper no. 1999-01-
1497
Fort, E.F., P.N. Blumberg, H.E. Staph, J.J. Staudt, "Evaluation of Cottonseed Oil as Diesel Fuel,"
SAE paper no. 820317
Jacobus, M.J., S.M. Geyer, S.S. Lestz, W.D. Taylor, T.H. Risby, "Single-Cylinder Diesel Engine
Study of Four Vegetable Oils," SAE paper no. 831743
Masjuki, H., M.Z. Abdulmuin, H.S. Sii, "Investigations on Pre-Heated Palm Oil Methyl Esters in
the Diesel Engine," EVIechE 1996, Part A: Journal of Power and Energy
Needham, J.R., D.M. Doyle, "The Combustion and Ignition Quality of Alternative Fuels in Light
Duty Diesels," SAE paper no. 852101
Schmidt, K., J. Van Gerpen, "The Effect of Biodiesel Fuel Composition on Diesel Combustion
and Emissions," SAE paper no. 961086
Scholl, K.W., S.C. Sorenson, "Combustion of Soybean Oil Methyl Ester in a Direct Injection
Diesel Engine," SAE paper no. 930934
Schramm, J., I. Foldager, N. Olsen, L. Gratz, "Emissions from a Diesel VehicleOperated on
Alternative Fuels in Copenhagen," SAE paper no. 1999-01-3603
Schroder, O., J. Krahl, A. Munack, J. Krahl, J. Biinger, "Environmental and Health Effects
Caused By the Use of Biodiesel," SAE paper no. 1999-01-3561
Senatore, A., M. Cardone, V. Rocco, M.V. Prati, "A Comparative Analysis of Combustion
Process in D.I. Diesel Engine Fueled with Biodiesel and Diesel Fuel," SAE paper no.
2000-01-0691
Uchida, M., Y. Akasaka, "A Comparison of Emissions from Clean Diesel Fuels," SAE paper no.
1999-01-1121
109
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Zhang, Y., J.H. Van Gerpen, "Combustion Analysis of Esters of Soybean Oil in a Diesel
Engine," SAE paper no. 960765
Studies that were not included in the database because they did not contain any new emissions
data for regulated pollutants
Culshaw, F.A., "The Potential of'Biodiesel' from Oilseed Rape," IMechE 1993, Part A: Journal
of Power and Energy
Durbin, T.D., J.R. Collins, H. Galdamez, J.M. Norbeck, M.R. Smith, R.D. Wilson, T.
Younglove, "Evaluation of the Effects of Biodiesel Fuel on Emissions from Heavy-Duty Non-
Road Engines," Univerity of California Final Report submitted to South Coast Air Quality
Management District, May 2000
Peterson, C.L., D.L. Auld, "Technical Overview of Vegetable Oil as a Transportation Fuel,"
University of Idaho, 1991
Sharp, C.A., S.A. Howell, J. Jobe, "The Effect of Biodiesel Fuels on Transient Emissions from
Modern Diesel Engines, Part II Unregulated Emissions and Chemical Characterization," SAE
paper no. 2000-01-1968
Studies that were not included in the database for other reasons
Watts, W.F., M. Spears, J Johnson, "Evaluation of Biodiesel Fuel and Oxidation Catalyst in an
Underground Metal Mine," University of Minnesota, September 24, 1998
Reason: Emission measurements were made in the field with portable emission
measurement devices
Yoshimoto, Y., M. Onodera, H. Tamaki, "Reduction of NOx, Smoke, and BSFC in a Diesel
Engine Fueled by Biodiesel Emulsion with Used Frying Oil," SAE paper no. 1999-01-3598
Reason: Biodiesel blend included water in an emulsion
110
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Appendix B - Field descriptions for database
Table
EQUIP_AD
Field
3quip_id
study_id
Class
;quip_type
Company
lighway
nodel name
nodel_yr
nake
dispjiter
Fi_type
aspirated
cylinder
;at_type
3gr_type
3ngseries
iooling
Definition
Unique mobile source identifier. Serial number is ideal.
Otherwise some variant of the study_id.
dentification number assigned to the analysis/paper/report of
nterest.
Light-duty or heavy-duty
Vehicle or engine
Vehicle or engine manufacturer.
'Yes" if mobile source is intended for highway use. "No" for
non-road mobile sources.
model name
f a prototype, enter representative model year.
Vehicle make e.g. Buick, as distinct from vehicle manufacturer,
GM.
Nominal engine displacement, expressed in liters.
ype of fuel injection PFI (port fuel injection) TBI (throttle body
njection) INDIR (Indirect injection) DIRECT (direct fuel
njection e.g. as for most diesel engines.) Values defined by
ranslation table for this field.
ndicates how engine is aspirated. CHARGED if turbocharged
or supercharged. NATURAL if not. Values defined by
ranslation table for this field.
Number of cylinders or rotors.
What type catalyst, if any, is present on the mobile source.
Values are: 3WAY Three-way catalyst OX3W Oxidation plus
hree-way catalyst OXID Oxidation Catalyst NONE No catalyst
NULL Unknown Values defined by translation table for this
ield.
Type of exhaust gas recirculation (EGR). Values defined by
ranslation table. Values defined by translation table for this
ield.
Engine series or product line name.
Type of after_cooling. (Legal values defined by translation
able.) Values defined by translation table for this field.
Ill
-------
ETEST AD
Fi_meth
:i_press
Darttrap
3ng_cycle
•atedpower
•atedspeed
dle_rpm
3roc_odom
iour_meter
3\A/vr
Dk_torque
Dk_t_speed
;yl_valves
stroke
Dore
nj_ctrl
nj_pcat
est id
study_id
batch id
3quip_id
test_proc
Mo modes
Method of fuel injection. ( Legal values defined by translation
able.)
Fuel injection pressure, expressed in kPa.
s particulate trap used? "YES", "NO", or "NUL".
Engine cycle, 2 =. 2-stroke, 4 = 4-stroke. 0 = Unknown. Values
defined by translation table for this field.
Rated horsepower of engine.
Rated rpm of engine
die rpm as declared by the oem.
Approximate odometer reading in miles at time of vehicle
recruitment.
Hours of operation (usually available only for off-road mobile
sources). Null value is 0.
Gross vehicle weight rating in pounds. The value specified by
he manufacturer as the loaded weight of a single vehicle.
Peak torque of the engine expressed in ft-lb.
Peak torque speed expressed in rpm.
The number of valves per cylinder.
Piston stroke expressed in inches, (not ready to be stored in
msod database at this time)
The diameter of the cylinder expressed in inches.
Code of the Injection control type. Values defined by
ranslation table for this field.
Code of the injection equipment/pressure category. Values
defined by translation table for this field.
dentification number assigned to the engine test.
dentification number assigned to the analysis/paper/report of
nterest.
Fuel batch identification.
Unique mobile source identifier. Serial number is ideal.
Otherwise some variant of the study_id.
dentifies the specific test procedure/cycle used. Values
defined by translation table for this field. "Steady- State" if a
unique steady-state cycle, "Transient" for unique transient cycle.
For steady-state tests, the number of modes in the test
112
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rBAT AD
sh4
he
SO
nox
pm
otal work
bsfc_meas
Fbatch id
fbatch_base
study_id
setane num
setane idx
setane_imp
setane_typ
setane dif
setane nat
sulfur
nitrogen
arom
marom
Methane emissions. Expressed in grams per bhp-hrfor
engines, and g/mi for vehicles.
Total HC emissions. Expressed in grams per bhp-hrfor
engines, and g/mi for vehicles.
CO emissions. Expressed in grams per bhp-hrfor engines, and
g/mi for vehicles.
NOx emissions. Expressed in grams per bhp-hr for engines,
and g/mi for vehicles.
Total particulate emissions. Expressed in grams per bhp-hrfor
engines, and g/mi for vehicles.
Total work performed in test. Expressed in bhp-hrs.
Measured brake-specific fuel consumption. Expressed in
grams per bhp-hr for engines, and g/mi for vehicles.
Unique fuel batch identification.
Unique identification linking all batches that are intended to be
compared to one another.
dentification number assigned to the analysis/paper/report of
nterest.
Total cetane number of complete fuel.
Cetane index of complete fuel.
Amount of cetane improver added, expressed as percentage
by volume
Type of cetane improver used, e.g. "N" for nitrate type or "P"
or peroxide type. Exact set of legal values defined and
described by translation table for this field.
This is the difference in cetane number between the described
uel (with additive) and a baseline fuel without additive.
Natural cetane number of fuel.
Sulfer content, expressed in parts per million.
Nitrogen content, expressed in parts per million.
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
ields.
Monoaromatics content of fuel, expressed as a percentage by
weight. This is a measured value, as opposed as being
113
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parom
BP
no
T50
T90
T95
EP
spec grav
viscosity
hcratio
oxygen
bio_source
)io_type
)io_cat
>er bio
heat
3miss_cat
3io source gr
p
calculated as the difference of the total aromatics and
polyaromatics fields.
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.
nitial boiling point expressed in degrees F.
10% distillation boiling point, expressed in degrees Fahrenheit.
50% distillation boiling point, expressed in degrees Fahrenheit.
90% distillation boiling point, expressed in degrees Fahrenheit.
95% distillation boiling point, expressed in degrees Fahrenheit.
End point of distillation curve, expressed in degrees
Fahrenheit.
Specific gravity.
Viscosity, expressed in centistokes @40 degrees F.
Molecular ratio of hydrogen to carbon.
Amount of oxygen in the fuel, expressed as a percentage by
weight.
Source of biodiesel. "NONE" if no biodiesel was added.
Values defined by translation table for this field.
Type of biodiesel. "Oil" or "Ester"
Category of biodiesel. "Plant" or "animal"
Volume percent biodiesel in the blend
Net heating value of the fuel, expressed in btu/pound.
Emission category applicable to base fuel. "Clean" or "average"
iiodiesel source group. "Fat", "soy", or "rape". "Base" applies to
)ase fuels.
114
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Appendix C - Assignments for biodiesel source groups
All base fuels were assigned to the "average" emissions category, except for those in the
following table which were assigned to the "clean" category.
Study
SAE 932686 (Rantanen 1993)
SAE 971689 (Hansen 1997)
Durbin 1999
Manicom 1993
McCormick2001
McCormick2001
McCormick2001
Simian 1998
Fuel
SF2
Ultra-light diesel
RFD
Esso diesel #1
10 vol% aromatics
Fischer-Tropsch
No. 1 diesel
Low sulfur
EMISS_CAT assignment
Clean
Clean
Clean
Clean
Clean
Clean
Clean
Clean
115
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Appendix D - Studies used in toxics analysis
Durbin, T., J. Collins, J. Norbeck, and M. Smith. "Evaluation of the Effects of Alternative
Diesel Fuel Formulations on Exhaust Emission Rates and Reactivity," Center for Environmental
Research and Technology, University of California. April 1999
Howes, P. and G. Rideout. "Evaluation of Biodiesel in an Urban Transit Bus Powered by a 1981
DDC8V71 Engine," MSED Report #95-26743-2. 1981.
Howes, P. and G. Rideout. "Evaluation of Biodiesel in an Urban Transit Bus Powered by a 988
DDECII6V92 TA Engine," MSED Report #95-26743-1. 1988.
Sharp, C. "Transient Emissions Testing of Biodiesel and Other Additives in a DDC Series 60
Engine," Prepared for National Biodiesel Board by Southwest Research Institute. December
1994.
Sharp, C. "Emissions and Lubricity Evaluation of Rapeseed Derived Biodiesel Fuels," Prepared
for Montana Department of Environmental Quality by Southwest Research Institute. November
1996.
Sharp, C. Characterization of Biodiesel Exhaust Emissions for EPA 21 l(b). Prepared for
National Biodiesel Board by Southwest Research Institute. January 1998.
Sharp, C., S. Howell, and J. Jobe. "The Effect of Biodiesel Fuels on Transient Emissions from
Modern Diesel Engines, Part II Unregulated Emissions and Chemical Characterization," 2000.
Staat, F. and P. Gateau. "The Effects of rapeseed Oil Methyl Ester on Diesel Engine
Performance, Exhaust Emissions and Long-Term Behavior - A Summary of Three Years of
Experimentation," No date.
116
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Appendix E - Aromatics Conversion Equations
The conversion equation described in this appendix were originally derived and presented in
Appendix C of the July 2001 Staff Discussion Document. The reader is refered to that document
for details of the derivations.
Table El - Correlations for total aromatics
[vol%
[vol%
by FIA]
byFIA]
= 0
= 0
777 >
760 >
< [wt%
< [wt%
by mass spec] +
byHPLC] + 178
132
Ox
2x
[sp
[sp. gravity] -
gravity] - 144
105.0
.4
R2 =
R2 =
0.93
0.96
Table E2 - Correlations for mono aromatics
[wt%
[wt%
by
by
SFC]
SFC]
= 0
= 0
882 >
885 >
< [wt%
< [wt%
by mass spec]
by HPLC] + 0
+ 2.37
88
R2
R2
= 0
= 0
91
99
Table E3 - Correlations for poly aromatics
[wt%
[wt%
by
by
SFC] =
SFC] =
1.22
1.27
X
X
[wt%
[wt%
by mass spec]
by HPLC] + 0
+ 0.33
69
R
R
2 _
2 _
0
0
95
97
117
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References
1. "Strategies and Issues in Correlating Diesel Fuel Properties with Emissions," Staff Discussion
Document, EPA document number EPA420-P-01-001, July 2001
2. R. Lee, J. Pedley, C. Hobbs, "Fuel Quality Impact on Heavy Duty Diesel Emissions: -A
Literature Review", SAE 982649.
3. "Strategies and Issues in Correlating Diesel Fuel Properties with Emissions," Staff Discussion
Document, EPA document number EPA420-P-01-001, July 2001
4. Statistics Today: A Comprehensive Introduction, Donald R. Byrkit, 1987, The
Benjamin/Cummings Publishing Company, Inc. Page 321
5. "Strategies and Issues in Correlating Diesel Fuel Properties with Emissions," Staff Discussion
Document, EPA document number EPA420-P-01-001, July 2001
6. Rickeard, D.J., N.D. Thompson, "A Review of the Potential for Bio-Fuels as Transportation
Fuels," SAE paper number 932778
7. Sheehan, J., V. Camobreco, J. Duffield, M. Graboski, H. Shapouri, "Life Cycle Inventory of
Biodiesel and Petroleum Diesel for Use in an Urban Bus," Final Report, National Renewable
Energy Laboratory, May 1998, NREL/SR-580-24089
8."Control of Emissions of Hazardous Air Pollutants from Mobile Sources," 66 FR 17230,
March 29, 2001
9. McCormick, R.L., J.R. Alvarez, M.S. Graboski, K.S. Tyson, K. Vertin, "Fuel additive and
blending approaches to reducing NOx emissions from biodiesel," SAE paper no. 2002-01-1658.
118
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