characteristics of engine emissions from different …...characteristics of engine emissions from...
TRANSCRIPT
Characteristics of Engine Emissions from Different Biodiesel Blends
by
Curtis Alan Wan
A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science
Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto
© Copyright by Curtis Alan Wan 2011
ii
Characteristics of Engine Emissions from
Different Biodiesel Blends
Curtis Alan Wan
Masters of Applied Science
Department of Chemical Engineering and Applied Chemistry
University of Toronto
2011
Abstract
Engine exhaust characteristics from two different biodiesel blends, formulated from soy and
animal fat biodiesel blended with ultra-low sulphur diesel, were tested during two different test
programs with similar operating conditions. Engine exhaust was measured in real-time for
nitrogen oxides, total hydrocarbons, particle-bound polyaromatic hydrocarbons, and particle size
distribution. Diesel particulate matter was collected on filters and subsequently analyzed for
organic carbon, elemental carbon, soluble organic fraction, cations, and anions. The use of
biodiesel was found to increase nitrogen oxide emissions, but decrease total hydrocarbons and
particulate matter emissions. The most significant impact on emissions was the difference
between the engine operating conditions rather than the fuel type. Minor differences were found
between the soy and animal fat biodiesel blends through speciation of the diesel particulate
matter.
iii
Acknowledgments
I would like to acknowledge my primary supervisor Professor Greg Evans for bringing me into
the field of aerosol science and providing the opportunity to research at the University of Toronto
with the Southern Ontario Centre for Atmospheric Aerosol Research (SOCAAR). I’d also like to
thank my co-supervisor Professor Jim Wallace for introducing me into the world of engines and
spending the extra time needed during critical engine repairs and maintenance. I am thankful to
both my supervisors for their continued guidance and supervision throughout this project. I am
also grateful for the partnership of General Electric Canada and the Sustainable Development
Technology Canada (SDTC) for providing the funding for this research program. Finally, I
would like to thank the Ontario Graduate Scholarship (OGS) and the Ontario Graduate
Scholarship in Science and Technology (OGSST) for their funding support.
Many thanks to my colleagues at SOCAAR for their support and also being there to discuss any
ideas and problems I encountered, namely: Andrew Knox, Colin Lee, Deanna Mendolia, Joel
Corbin, Kelly Sabaliauskas, Maygan Mcguire, Natalia Myhkaylova, Peter Rehbein, Umme
Akhtar, and Yun-seok Jun. Special thanks to Neeraj Rastogi for showing me how to be an
analytical chemist, and Cheol-Heon Jeong for his expertise in particle instrumentation.
I also would like to thank the members of the Engine Design and Research Laboratory (EDRL)
for their technical experience and graciously spending their spare evenings and weekends in
order to complete the engine testing, namely: Charles Habbaky, Mark Tadrous, and Silvio
Memme. A special thanks to Justin Ketterer for being my engine test cell partner throughout the
majority of the project, and for designing and building invaluable pieces of the engine
experimental setup.
Finally, I would like to acknowledge my mother, father, and brother for supporting me
throughout my life and my decision to study at the University of Toronto.
iv
Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Nomenclature ................................................................................................................................ xii
1 Introduction ................................................................................................................................ 1
2 Literature Review....................................................................................................................... 3
2.1 Diesel Engines .................................................................................................................... 3
2.1.1 Gaseous Emissions.................................................................................................. 3
2.1.2 Diesel Particulate Matter......................................................................................... 5
2.1.3 Influence of Dilution Conditions ............................................................................ 7
2.1.4 Environmental and Health Impacts......................................................................... 8
2.1.5 Emissions Regulations .......................................................................................... 10
2.1.6 Aftertreatment Systems......................................................................................... 11
2.2 Biodiesel in Diesel Engines .............................................................................................. 12
2.2.1 Biodiesel Overview............................................................................................... 12
2.2.2 Biodiesel Effect on Engine Performance .............................................................. 13
2.2.3 Biodiesel Effect on Engine Emissions.................................................................. 14
2.2.3.1 Carbon Monoxide Emissions ................................................................. 14
2.2.3.2 Nitrogen Oxide Emissions...................................................................... 15
2.2.3.3 Total Hydrocarbon Emissions ................................................................ 16
2.2.3.4 Diesel Particulate Matter Emissions....................................................... 16
2.2.3.5 Particle Size Distribution........................................................................ 17
2.2.4 Impact on Aftertreatment Systems........................................................................ 18
v
2.2.5 Impact of Source Feedstock on Biodiesel............................................................. 18
3 Experimental Setup .................................................................................................................. 20
3.1 Engine Experimentation.................................................................................................... 20
3.2 Engine Operating Conditions............................................................................................ 21
3.3 Fuel Type / Lubricating Oil .............................................................................................. 22
3.4 Sampling Equipment......................................................................................................... 23
3.4.1 Overall Sampling Train......................................................................................... 23
3.4.2 Horiba Raw Gas Analyzer .................................................................................... 25
3.4.3 Dekati Dilution System......................................................................................... 26
3.4.4 Scanning Mobility Particle Sizer / Thermodenuder.............................................. 28
3.4.5 p-PAH Analyzers .................................................................................................. 32
3.4.6 47mm Filter Sampling Train................................................................................. 32
3.5 Filter Analysis ................................................................................................................... 35
3.5.1 Gravimetric ........................................................................................................... 35
3.5.2 Elemental Carbon / Organic Carbon..................................................................... 36
3.5.3 Soluble Organic Fraction ...................................................................................... 38
3.5.4 Ion Chromatography ............................................................................................. 39
3.5.5 Water Soluble Organic Carbon / Nitrogen............................................................ 39
3.6 Operating Procedure ......................................................................................................... 40
3.6.1 Warm-up Procedure .............................................................................................. 40
3.6.2 Diluter Conditions................................................................................................. 40
3.6.3 Fuel Flow Rate Measurement ............................................................................... 41
3.6.4 Experimental Test Matrix ..................................................................................... 41
4 Results and Discussion............................................................................................................. 42
4.1 Biodiesel Characterization Results ................................................................................... 42
4.2 Engine Operating Conditions............................................................................................ 44
vi
4.3 Effect of Biodiesel on Engine Exhaust Emissions............................................................ 47
4.3.1 Gaseous Emissions................................................................................................ 47
4.3.2 Particulate Matter.................................................................................................. 51
4.3.3 Organic / Elemental / Total Carbon ...................................................................... 53
4.3.4 p-PAH’s ................................................................................................................ 58
4.3.5 Particle Size Distribution ...................................................................................... 59
4.3.6 Anions / Cations.................................................................................................... 63
4.4 Chemical Correlations ...................................................................................................... 67
4.4.1 PM Mass Reconstruction ...................................................................................... 67
4.4.2 Water Soluble Carbon to Organic Carbon Ratio .................................................. 68
4.4.3 Chemical Correlation Regression Analysis .......................................................... 70
4.4.4 NO3- / SO4
2- as a Biodiesel Marker....................................................................... 71
4.5 Particulate Matter Volatility ............................................................................................. 72
4.5.1 Organic Carbon / Total Carbon vs Soluble Organic Fraction............................... 73
4.5.2 Nonvolatile Fraction versus Particle Diameter ..................................................... 76
5 Conclusions and Recommendations ........................................................................................ 78
5.1 Conclusions....................................................................................................................... 78
5.2 Recommendations............................................................................................................. 79
References..................................................................................................................................... 81
Appendix A: Result and Discussion Appendix ............................................................................ 92
Appendix B: Filter Listing – General Electric Testing............................................................... 101
Appendix C: External Lab Results ............................................................................................. 104
vii
List of Tables
Table 1: Diesel Engine Specifications .......................................................................................... 20
Table 2: Summary of Test Conditions .......................................................................................... 21
Table 3: Summary of Fuel Matrix ................................................................................................ 22
Table 4: Summary of Lubricating Oil Specifications ................................................................... 22
Table 5: Horiba Calibration Information ...................................................................................... 26
Table 6: University of Toronto Test Program............................................................................... 41
Table 7: General Electric Test Program........................................................................................ 41
Table 8: AI-TF Biodiesel Blend Verification by ASTM D7371 .................................................. 42
Table 9: AI-TF Biodiesel Characterization by ASTM D6751...................................................... 43
Table 10: University of Toronto Testing - Mode 9 Engine Operating Conditions. ± Values
Represent 95% Confidence Intervals............................................................................................ 44
Table 11: University of Toronto Testing - Mode 2 Engine Operating Conditions. ± Values
Represent 95% Confidence Intervals............................................................................................ 44
Table 12: General Electric Testing - Mode 9x Engine Operating Conditions. ± Values Represent
95% Confidence Intervals............................................................................................................. 45
Table 13: General Electric Testing - Mode 2x Engine Operating Conditions. ± Values Represent
95% Confidence Intervals............................................................................................................. 45
Table 14: Brake Specific p-PAH Emissions Percentages Change Relative to ULSD Values –
University of Toronto Testing. ±Values Represent 95% Confidence Intervals ........................... 58
Table 15: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
University of Toronto Testing Mode 9. ±Values Represent 95% Confidence Intervals .............. 64
Table 16: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
University of Toronto Testing Mode 2. ±Values Represent 95% Confidence Intervals .............. 64
Table 17: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
General Electric Testing Mode 9. ±Values Represent 95% Confidence Intervals ....................... 65
Table 18: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
General Electric Testing Mode 2. ±Values Represent 95% Confidence Intervals ....................... 65
Table 19: Pearson Correlation Coefficient (R2) between Chemical PM Constituents ................. 70
viii
List of Figures
Figure 1: PM and NOx Regulations for the Cummins B3.9 Engine ............................................ 10
Figure 2: Engine and Diluter Sampling Train............................................................................... 23
Figure 3: University of Toronto Testing Sampling Train ............................................................. 24
Figure 4: General Electric Testing Sampling Train...................................................................... 25
Figure 5: Schematic of Porous Tube Diluter ................................................................................ 26
Figure 6: Schematic of an Ejector Diluter .................................................................................... 27
Figure 7: Differential Mobility Analyzer Operation Principle ..................................................... 29
Figure 8: Ultra-fine Water Based Condensation Particle Counter................................................ 30
Figure 9: Thermodenuder Operation Principle ............................................................................. 31
Figure 10: Photoelectric Aerosol Sensor Operating Principle ...................................................... 32
Figure 11: Filter Sampling System ............................................................................................... 33
Figure 12: University of Toronto Testing – Quartz Filter Breakdown......................................... 34
Figure 13: General Electric Testing – Quartz Filter Breakdown.................................................. 34
Figure 14: Filter Weighing Apparatus .......................................................................................... 35
Figure 15: Representative EC/OC Thermogram.......................................................................... 37
Figure 16: EC/OC Validation Experiments .................................................................................. 38
Figure 17: NOx Percentage Change Relative to Ultra-Low Sulphur Diesel Fuel – General Electric
Testing........................................................................................................................................... 48
Figure 18: THC Percentage Change Relative to Ultra-Low Sulphur Diesel Fuel – General
Electric Testing ............................................................................................................................. 49
Figure 19: Brake Specific Oxygen Emissions versus Fuel Type – General Electric Testing....... 50
Figure 20: Brake Specific Carbon Dioxide Emissions versus Fuel Type – General Electric
Testing........................................................................................................................................... 50
Figure 21: PM Percentage Change Relative to Ultra-Low Sulphur Diesel Fuel – General Electric
Testing........................................................................................................................................... 51
Figure 22: PM Mass: Ratio of Backup to Primary Filters – General Electric Testing ................. 52
ix
Figure 23: Organic Carbon: Ratio of Backup to Primary Filter OC............................................. 54
Figure 24: Correlation Plot between Backup/Primary Ratios of Quartz and Teflon Filters......... 55
Figure 25: General Electric Testing Partitioning of OC1, OC2, OC3, and OC4 – Mode 9x ....... 56
Figure 26: General Electric Testing Partitioning of OC1, OC2, OC3, and OC4 – Mode 2x ....... 56
Figure 27: Brake Specific Organic, Elemental, Total Carbon Emissions – General Electric
Testing Mode 9x ........................................................................................................................... 57
Figure 28: Brake Specific Organic, Elemental, Total Carbon Emissions – General Electric
Testing Mode 2x ........................................................................................................................... 57
Figure 29: ULSD Mode 9 versus Mode 2 Particle Size Distributions – University of Toronto
Testing........................................................................................................................................... 59
Figure 30: ULSD Mode 9x and Mode 2x Particle Size Distributions – General Electric Testing60
Figure 31: Mode 9x Particle Size Distributions versus Fuel Type – General Electric Testing .... 61
Figure 32: Mode 2x Particle Size Distributions versus Fuel Type – General Electric Testing .... 62
Figure 33: Representative Diesel Engine Exhaust Anion Chromatograph................................... 63
Figure 34: Representative Diesel Engine Exhaust Cation Chromatograph .................................. 63
Figure 35: Percentage of PM Mass Reconstruction - Mode 9x General Electric Testing............ 67
Figure 36: Percentage of PM Mass Reconstruction - Mode 2x General Electric Testing............ 67
Figure 37: Water Soluble Organic Carbon to Organic Carbon Ratio – University of Toronto
Testing........................................................................................................................................... 69
Figure 38: Ratio of Nitrate to Sulphate – University of Toronto Testing..................................... 71
Figure 39: Ratio of Nitrate to Sulphate – General Electric Testing.............................................. 72
Figure 40: OC/TC Ratio versus Biodiesel Blend % - General Electric Testing........................... 73
Figure 41: SOF versus Biodiesel Blend % - General Electric Testing ......................................... 74
Figure 42: Comparison of EC/OC and SOF Results .................................................................... 75
Figure 43: Nonvolatile Fraction versus Particle Diameter – University of Toronto Testing ....... 77
Figure A - 1: Brake Specific Nitrogen Oxide Emissions versus Fuel Type – General Electric
Testing........................................................................................................................................... 92
Figure A - 2: Brake Specific Total Hydrocarbon Emissions versus Fuel Type – General Electric
Testing........................................................................................................................................... 92
x
Figure A - 3: Brake Specific Particulate Matter Emissions versus Fuel Type – General Electric
Testing........................................................................................................................................... 92
Figure A - 4: Brake Specific Nitrogen Oxide Emissions versus Fuel Type – University of
Toronto Testing............................................................................................................................. 93
Figure A - 5: Brake Specific Total Hydrocarbon Emissions versus Fuel Type – University of
Toronto Testing............................................................................................................................. 93
Figure A - 6: Brake Specific Oxygen Gas Emissions versus Fuel Type – University of Toronto
Testing........................................................................................................................................... 93
Figure A - 7: Brake Specific Carbon Dioxide Emissions versus Fuel Type – University of
Toronto Testing............................................................................................................................. 94
Figure A - 8: Particulate Matter Emissions versus Fuel Type – University of Toronto Testing .. 94
Figure A - 9: Percentage of OC1/OC2/OC3/OC4 versus Fuel Type – Mode 9 University of
Toronto Testing............................................................................................................................. 94
Figure A - 10: Percentage of OC1/OC2/OC3/OC4 versus Fuel Type – Mode 2 University of
Toronto Testing............................................................................................................................. 95
Figure A - 11: Brake Specific Organic / Elemental / Total Carbon Emissions versus Fuel Type –
Mode 9 University of Toronto Testing ......................................................................................... 95
Figure A - 12: Brake Specfic Organic / Elemental / Total Carbon Emissions versus Fuel Type –
Mode 2 University of Toronto Testing ......................................................................................... 95
Figure A - 13: Brake Specific p-PAH Emissions versus Fuel Type – University of Toronto
Testing........................................................................................................................................... 96
Figure A - 14 : Brake Specific Anion and Cation Emissions – University of Toronto Testing
Mode 9 .......................................................................................................................................... 96
Figure A - 15: Brake Specific Anion and Cation Emissions – University of Toronto Testing
Mode 2 .......................................................................................................................................... 96
Figure A - 16: Brake Specific Anion and Cation Emissions – General Electric Testing Mode 9x
....................................................................................................................................................... 97
Figure A - 17: Brake Specific Anion and Cation Emissions – General Electric Testing Mode 2x
....................................................................................................................................................... 97
Figure A - 18: Mass Reconstruction of PM – Mode 9 and 2 University of Toronto Testing....... 98
Figure A - 19: Ratio of Potassium to Sulphate – University of Toronto Testing ......................... 98
Figure A - 20: Ratio of Potassium to Sulphate – General Electric Testing .................................. 99
xi
Figure A - 21: Biodiesel Effect on Nonvolatile Fraction – Mode 9 University of Toronto Testing
....................................................................................................................................................... 99
Figure A - 22: Biodiesel Effect on Nonvolatile Fraction – Mode 2 University of Toronto Testing
..................................................................................................................................................... 100
xii
Nomenclature
Acronym Definition
AF B’X’ Animal Fat Biodiesel Blend ‘X’%
AFR Air to Fuel Ratio
AI-TF Alberta Innovates – Technologies Futures
ASTM American Society for Testing and Materials
BMEP Brake Mean Effective Pressure
BPT Balance Point Temperature
BSFC Brake Specific Fuel Consumption
CANMET-MMSL Canadian Centre for Mineral and Energy Technology –
Mining and Mineral Sciences Laboratory
CC Carbonates
CDL Clean Diesel Locomotive
CPC Condensation Particle Counter
DR Dilution Ratio
EC Elemental Carbon
EC/OC Elemental Carbon / Organic Carbon
EGT Exhaust Gas Temperature
FID Flame Ionization Detector
ISO International Organization for Standardization
NDIR non-Dispersive Infrared Detector
nDMA nano-Differential Mobility Analyzer
NIOSH National Institute for Occupational Safety and Health
NIST National Institute of Standards and Technology
NO Nitrogen Monoxide
NO2 Nitrogen Dioxide
xiii
NPOC non-Purgable Organic Carbon
NRC Natural Resources Canada
OC Organic Carbon
OC1/OC2/OC3/OC4 Organic Carbon 1/2/3/4
OC/TC Organic Carbon to Total Carbon
OM Organic Mass
p-PAH Particle Bound Polyaromatic Hydrocarbons
PAH Polyaromatic Hydrocarbons
PAS Photoelectric Aerosol Sensor
PC Pyrolyzed Carbon
PSD Particle Size Distribution
PSL Polystyrene Latex
PTFE Polytetrafluoroethylene
rpm Revolutions Per Minute
SAPS Sulphated Ash, Phosphorus and Sulphur
SLPM Standard Litres per Minute
SMPS Scanning Mobility Particle Sizer
SOF Soluble Organic Fraction
Soy B’X’ Soy Biodiesel ‘X’% Blend
SWRI Southwestern Research Institute
TC Total Carbon
ULSD Ultra-low Sulphur diesel
WSN Water-Soluble Nitrogen
WSOC Water-Soluble Organic Carbon
WSOC/OC Water-Soluble Organic Carbon to Organic Carbon
1
1 Introduction
The scarcity of conventional fossil fuels, along with their increasing costs has prompted
governments to consider the use of alternative fuels. A suitable alternative to diesel fuel, the
main workhorse of the transportation industry, is biodiesel. Some of the advantages of biodiesel
over petroleum diesel fuel include the potential reduction of greenhouse gases and availability. In
addition, in recent years with growing technical feasibility, biodiesel is becoming more
economically competitive (Demirbas, 2009). Biodiesel has been mandated to be used throughout
Canada, as for 2012 the government has set a 2% biodiesel requirement in all heating oil and
diesel fuel sold (TTnews, 2011), and the province of Manitoba has already set this requirement
within the province since November 2009 (CBC, 2009). However, the decision for increased
usage of biodiesel from many governments has met resistance from car manufacturers, local
administrations, and private users. One of the reasons for this resistance is because the effects of
biodiesel on diesel engines are not fully understood. Although the emission characteristics of
diesel engines have been well characterized, biodiesel emissions have not. Not only does this
have an impact on meeting regulatory emissions, a thorough characterization of biodiesel
exhaust emissions is necessary to properly design aftertreatment systems, such as diesel
particulate filters. It is imperative to fully understand how upcoming changes in fuel sources will
change vehicular emissions, which will ultimately have an impact on overall human health and
the environment.
The characteristics of diesel and various biodiesel emissions have been well described in
the literature; however what has not been very well studied is the impact of biodiesel source.
Therefore the main questions that this thesis addresses include describing the impacts of
biodiesel fuelling on emissions from a heavy-duty diesel engine, understanding how these results
differ at distinct engine operating conditions, and differentiating between soy and animal-fat
biodiesel blends emissions.
In this study, animal-fat and soy based biodiesel blends were created through blending
with certification ultra-low sulphur diesel fuel. These blends were used to fuel a Cummins B3.9
direct injection diesel engine driven by a dynamometer, followed by an array of particle and
gaseous instrumentation in order to study the engine exhaust emissions. The results were
2
analyzed to study the impact of biodiesel blending, fuel type, and engine operating condition on
exhaust emissions.
This thesis is organized into three main sections: the first being a thorough literature
review which describes the characteristics of diesel engine exhaust, followed by the impact of
biodiesel fuelling on diesel engines. The second section outlines the experimental setup and all
the relevant components utilized throughout the study. The last major section, the results and
discussion, illustrates the major impacts of biodiesel fuelling on engine exhaust emissions and
assesses the implications.
3
2 Literature Review
This section summarizes relevant research in order to orient the reader with an overview of the
characteristics of diesel engine exhaust. Included are reviews of the environmental and health
impacts, regulations, and various exhaust aftertreatment systems used to minimize diesel
emissions. The second component of the literature review involves an overview of biodiesel as a
fuel, followed by an assessment of the various impacts of biodiesel on engine performance and
emissions.
2.1 Diesel Engines
The original diesel engine design can be attributed to Rudolph Diesel and his treatise on the topic
of designing a heat engine to replace steam and gasoline engines (Diesel, 1887). The concept of a
diesel engine, otherwise known as a compression ignition engine, involves spraying liquid fuel
directly into the combustion chamber and using compression alone to initiate combustion,
instead of a spark ignition. The diesel engine allows for much greater efficiencies compared to
gasoline engines due to the higher compression ratios involved. However, the diesel engines
inherently have greater emissions than spark ignitions engines as the fuel-air combustion is non-
premixed, where the existence of fuel rich and lean regions create a greater amount of
incomplete combustion products.
2.1.1 Gaseous Emissions
Diesel engines emit a variety of gaseous emissions which have been very well studied, namely:
nitrogen oxides (NOx), sulphur oxides (SOx), carbon monoxide (CO), carbon dioxide (CO2),
unburned total hydrocarbons (THC), as well as a series of other pollutants.
NOx is created through three major mechanisms: fuel NOx, thermal NOx (Zeldovich
mechanism), and prompt NOx (Fenimore mechanism). Fuel NOx simply involves the formation
of NOx from fuel bound nitrogen; although fuel bound nitrogen is typically not found in diesel
4
fuels. Thermal NOx consists of the formation of NO from nitrogen and oxygen gas, as given by
the following reaction:
½ N2 + ½ O2 = NO (1)
The Zeldovich mechanism can be mostly described by three major reactions, which are as
follows:
O + N2 ↔ NO + N (2)
N + O2 ↔ NO + O (3)
N + OH ↔ NO + H (4)
The Zeldovich mechanism for NOx is usually referred to as thermal NOx as it is
sensitive to temperature, due to the very high activation energy required for equation 2 to
proceed. As the reaction is kinetically controlled, NOx values do not typically reach their
equilibrium values as the residence times for these reactions are slower than the reaction time
required for hydrocarbon combustion. Therefore, thermal NOx formation can be controlled by
limiting the temperature and residence time. The third mechanism of NOx formation, prompt
NOx, involves a series of mechanisms where N≡N bonds undergo scission by flame radicals at
flame fronts.
Sulphur dioxides are formed from the presence of fuel sulphur, which is typically
higher for diesel fuel compared to gasoline fuel. Only recently have regulations called for the
use of ultra-low sulphur diesel, where the maximum allowable concentration of fuel sulphur is
15 ppm. During hydrocarbon combustion, the fuel bound sulphur will oxidize and appear both
as SO2 and SO3 in the diesel exhaust and the combination of the two is noted as SOx.
During the compression ignition combustion process, there are several regions where
partially oxidized hydrocarbons and CO will be formed as a result of incomplete combustion.
Since diesel engines typically operate with very fuel lean conditions, overly lean regions will
be created in the combustion chamber that do not support rapid combustion, creating partial
oxidation products. Fuel-rich regions also cause incomplete combustion as insufficient oxygen
will be available to facilitate combustion and fuel pyrolysis can occur. Furthermore, within the
combustion cylinder, not all the fuel will fully react due to wall quenching effects, as well as
5
voids between the injector and combustion chamber where fuel can be trapped and not react
(Turns, 2006).
There are numerous species of unburned hydrocarbons created during the combustion
process, including: volatile organic gases, semi-volatile organic compounds, and particle-
phase organic compounds (Schauer et al., 1999). Other pollutants include metals and ions,
although these may originate from lubricating oil or engine component wear rather than the
combustion process.
2.1.2 Diesel Particulate Matter
In addition to numerous gaseous pollutants, diesel engines are also known to be heavy emitters
of particulate matter. As the diesel particulate matter can appear in a variety of shapes and sizes,
an important parameter is the particle size distribution (PSD), which describes the concentration
of particles as a function of diameter.
Kittelson (1998) describes an idealized diesel exhaust particle size distribution of being
both trimodal and lognormal in form, consisting of a nucleation, accumulation, and coarse mode.
The nucleation mode, which accounts for the majority of the total number of particles, typically
consists of particles less than 50 nm comprised primarily of volatile organic and sulphur
compounds (Burtscher, 2005). The accumulation mode, which includes particles typically from
100 to 1000 nm and accounts for the majority of the particle mass, mostly consists of solid
carbon and metal compounds. The coarse mode consists of larger particles, typically ones that
are from 2.5 to 10 µm in diameter (Kittelson, 1998).
The accumulation mode, otherwise known as soot particles, consists mainly of highly
agglomerated solid carbonaceous material, sulphur containing compounds, and ash originating
from incomplete combustion. Soot particles are composed of aggregates formed of 10 – 80 nm
spherules bound together in clusters and chains (Heywood, 1988). Work by Jiang et al. (2011)
has speculated that these spherules are composed of elemental carbon aggregates derived from
fullerenes. Coarse mode particles have been found to consist of accumulation mode particles that
have been deposited on walls and surfaces of exhaust systems that are later re-entrained
(Kittelson, 1998).
6
The formation of nucleation mode particles, sometimes known as nanoparticles due to
their size range, is not clearly understood. It is a complex process where the nucleation rate is
strongly dependent on factors such as ambient conditions, exhaust dilution, engine operating
conditions, and exhaust aftertreatment systems. There have been numerous theories that have
been proposed for the nucleation of these nanoparticles. Yu (2001) proposed that the main source
of nanoparticles is from chemiions, produced from combustion processes where high
temperature positive ions and electrons are generated. Ma et al. (2008) however, found that ionic
nucleation does not play a significant role in diesel exhaust nucleation due to the low
concentration of ions. Shi et al. (1999) simulated nanoparticle formation using homogenous
nucleation of sulphuric acid and water; however, the simulated nucleation rate underpredicted
the observed formation of nanoparticles. Furthermore, Schneider et al. (2005) also found
evidence that if nucleation occurs, sulphuric acid and water are the nucleating agents. Finally,
Tobias et al. (2001) suggested that ternary nucleation, between sulphuric acid, ammonia, and
water creates ammonium sulphate particles.
Although the majority of authors agree that the nucleation mode particles are semi-
volatile in nature (Maricq et al., 2002; Vaaraslahti et al., 2004), studies involving a
thermodenuder suggest that nucleation mode particles with a solid core are formed during
combustion (Filippo & Maricq, 2008; Kirchner et al., 2009). Other authors speculate that solid
nucleation mode particles may form due to ash residues from lubricating oil and fuel additives
used for filter regeneration (Skillas et al., 2000). Conversely, a time-of-flight secondary ion mass
spectrometry (TOF-SIMS) and metal-assisted secondary ion mass spectrometry (MetA-SIMS)
analysis conducted by Inoue et al. (2006) showed that hydrocarbons from lubricating oil acted as
nucleation materials for volatile nanoparticles. Additionally Abdul-Khalek et al. (1998) also
showed that nucleation can occur as metals from the lubricating oil are volatilized and undergo
gas-to-particle conversion.
Experimentally, the concentration of fuel sulphur has been shown to have a significant
impact on nanoparticle formation. Many recent laboratory experiments have shown that the fuel
sulphur content has a profound impact on vehicle-emitted nanoparticles (Schneider et al., 2005;
Du & Yu, 2006). However, there have been many reports that number concentrations of
nanoparticles of engines running on ultra-low sulphur fuel are unexpectedly high (Kittelson et
al., 2004; Arnold et al., 2006; Rönkkö et al., 2007), which may be due to the sulphur originating
7
from lubricating oil. Kittelson et al. (2008) found that nanoparticle emissions were minimized by
the use of ultra-low sulphur fuels and specially formulated low sulphur lubricating oil.
2.1.3 Influence of Dilution Conditions
Diesel engine exhaust undergoes rapid dilution after being emitted from tailpipes or stacks, and
dilution was found to be the dominant mechanism that changes particle number concentration
(Shi & Harrison, 1999; Zhu et al., 2002), as the concentration of nucleation mode particles
increases with sharp decreases in exhaust gas temperature (Kawai et al., 2004). This can be
attributed to the higher temperatures which slow down the nucleation rate and particle growth
rate considerably, due to increases in vapour pressure of volatile species (Abdul-Khalek et al.,
1999). Zhang & Wexler (2004) proposed that diesel exhaust undergoes two distinct dilution
stages after emission from the tailpipe: the first stage of dilution is induced by traffic turbulence,
reaching a dilution ratio of 1000:1 in 1 – 3 seconds; while second stage dilution is dependent on
atmospheric turbulence, reaching a dilution ratio of 10:1 between 3 – 10 minutes.
In order to replicate atmospheric dilution within the laboratory, various dilution systems
exist to dilute the raw exhaust using clean filtered air. Laboratory dilution is also used to lower
the concentration of particulates to within the detection range of particle instrumentation.
Another advantage of laboratory dilution is that dilution ratios and parameters can be strictly
controlled in order to study how particle size distribution is affected by phenomena such as
nucleation, condensation, and coagulation. Laboratory experiments can also be conducted with
stand alone engines instead of full sized vehicles, giving the advantage of greater flexibility and
less expensive experimentation, where the alternative is to conduct chase experiments involving
diesel engine vehicles driven on the road followed by another vehicle equipped with
instrumentation.
With laboratory dilution, there are a number of parameters that can influence the particle
size distribution, and the parameter which has the largest effect on the particle size distribution is
the primary dilution ratio. Researchers have found that with increasing primary dilution ratio,
lower concentrations of nucleation mode particles are produced (Mathis et al., 2004; Rönkkö et
al., 2006). This is because for isothermal processes, increasing the primary dilution ratio reduces
8
the vapour-phase concentration of all exhaust species, weakening nucleation and driving forces
for particle growth (Abdul-Khalek et al., 1999). Therefore, a higher primary dilution ratio should
theoretically decrease the amount of nanoparticles.
However, some researchers have observed instances where an increase in primary
dilution ratio increased the number of nanoparticles produced (Shi & Harrison, 1999; Liu et al.,
2007a). This increase in nanoparticles is believed to be a result from longer residence times
associated with higher dilution ratios, resulting in lower temperatures that create conditions
favoring nucleation (Shi & Harrison, 1999). In addition, Liu et al. (2007b) found that certain
combinations of high fuel sulphur and dilution ratios promoted optimal conditions for nucleation
mode particle formation due to the cooler diluted gas temperatures.
The relative humidity of the dilution air has also been noted to influence formation of
nanoparticles. Abdul-Khlalek et al. (1999) noted that an increase from 15% to 40% relative
humidity increased the nanoparticle concentration by 30%. An optimal relative humidity value
has been identified by researchers to be roughly 40%, where higher or lower values were
observed to decrease formation of nanoparticulates (Casati et al., 2007). However, for
standardization of laboratory dilution practices, dry dilution air can be used to maintain a
constant relative humidity, and nucleation mode particles can still observed (Mathis et al., 2004;
Ntziachristos et al., 2005).
2.1.4 Environmental and Health Impacts
As diesel exhaust emissions contain a wide variety of pollutants, there are also numerous
environmental and health impacts associated with each individual pollutant. Of the oxides of
NOx, NO2 has been found to be most important in terms of human health impacts (Yang &
Omaye, 2009). The presence of NO2 has been shown to act as an acute irritant and potentially be
related to chronic obstructive pulmonary disease (Wark et al., 1997). Exposure to 150 – 220 ppm
NO2 can produce broncholities fibrosa obiterans which can be fatal within several weeks and
exposure to 500 ppm NO2 or greater has been shown to result in acute pulmonary edema (Aviado
& Salem, 1968). NO2 also interacts with other oxidizing agents in the atmosphere such as ozone
(O3) to cause photochemical oxidation products, such as smog. SOx has been fairly well studied
9
for generations, and Greenwald (1954) indicates that sulphur dioxide acts as a irritating, pungent,
suffocating gas during moderate exposure. Humans exposed to 10 minute 1 – 13 ppm SO2 acute
exposures exhibited rapid bronchronconstrictive response (Lewis et al., 1969), while greater
exposures of 12 – 15 ppm SO2 resulted in nasal mucosa irritation (Yang & Omaye, 2009).
Additionally, atmospheric SOx can undergo chemical transformations to create acid rain, which
in history has devastated vegetation, water bodies, and city structures. CO is a colourless and
odorless gas, and has been linked to pathological and physiological changes within the human
body potentially causing death at high exposures. Acute CO exposure has also been shown to
produce myocardial and neurological injury. Lastly, for the wide spectrum of THC species,
certain hydrocarbon species such as polyaromatic hydrocarbons are recognized to be known
human carcinogens.
Diesel particulate matter, being a complex mixture of soot, sulphates, metals, and ash, has
been linked to higher incidence rates of cancer, respiratory diseases and symptoms (Vouitsis et
al., 2003; El-Zein et al., 2007). Diesel particulate matter has also been recognized by the United
States Environmental Protection Agency (USEPA) as a likely human carcinogen (USEPA,
2011). Ultrafine particles, which encompass the diesel nucleation mode particles, are defined by
the United States Environmental Protection Agency (USEPA) as particles with less than 100 nm
in diameter. The primary sources of these ultrafine particles originate from combustion sources
and nucleation events (Weber et al., 1999), which diesel engines are a significant source.
The significance of ultrafine particles on human health is that in general, smaller particles
are more toxic to human health. This is due to the higher probability of inhalation, higher surface
area, and higher capacity to adsorb organic compounds that may be toxic (Seigneur et al., 2009).
Furthermore, ultrafine particles have the ability to penetrate directly into the respiratory system,
potentially causing vascular or pulmonary diseases (Li et al., 2009; Seigneur et al., 2009). Past
epidemiological studies suggest correlations between ultrafine particle exposure at high
concentrations and adverse health effects (Davidson et al., 2005).
10
2.1.5 Emissions Regulations
In order to minimize the environmental and health impacts caused by diesel exhaust, a number of
regulations exist to help mitigate diesel emissions. For Canadian diesel regulations, the general
approach has been to follow and harmonize with standards set forth by the USEPA. Non-road
engines of lower horsepower typically have higher brake specific emissions standards, while the
corollary is true for non-road engines of higher horsepower. Diesel engines used on-road also
have more stringent requirements compared to non-road engines. For the Cummins B3.9 engine
used during this research, an 87 kW non-road diesel engine a summary of an evolution of the
USEPA regulations from Tiers 1 to 4 is shown below in figure 1.
Figure 1: PM and NOx Regulations for the Cummins B3.9 Engine
(Jääskeläinen et al., 2006)
As seen above in figure 1, emission regulations have changed drastically throughout the
tiers starting with Tier 1 (1997), Tier 2 (2003), Tier 3 (2007), to upcoming Tier 4 regulations
(2012-2014). It should also be noted that Tier 2 and 3 emission regulations specify the sum of
11
NOx and non-methane hydrocarbons (NMHC) emissions, while this is not the case for Tiers 1
and 4. Previously Tier 1 did not have any PM regulations, while Tier 4 stipulates PM regulations
of 0.02 g/kW-hr. Similarly Tier 4 will require NOx reductions from 9.2 to 0.40 g/kW-hr
compared to Tier 1 regulations. Therefore in order to meet these upcoming stringent Tier 4
regulations, diesel aftertreatment systems will certainly be required.
Although Tier 4 regulations require a significant reduction of particle mass emissions,
this may not proportionally reduce the toxicity of diesel particulate matter. Various authors have
showed that PM mass emission reductions are not equally translated into ecotoxicity reductions
(Vouitsis et al., 2009), as even if the larger accumulation and coarse mode particles are removed,
the majority of nucleation mode particles may be left behind which do not contribute much to the
overall PM mass. This has prompted governments to consider changing PM emission regulations
to be based on particle number concentrations, rather than particulate mass based regulations that
do not target nanoparticles. Recently, the United Nation’s Economic Commissions for Europe-
Group of Experts on Pollution and Energy (UN-ECE-GRPE) has been working on creating
particle number based regulations to supplement those based on particle mass through the
Particulate Measurement Programme (PMP). The PMP is currently validating a sampling system
designed to count solid particles above a particle size of 23 nanometers at 50% counting
efficiency (Johnson et al., 2009). Subsequently, an objective of the PMP is to develop
standardized dilution conditions for diesel engine exhaust sampling. Through strict requirements
for dilution systems, the PMP has been successful at being able to measure particle number
emissions to within 15% across an emission range of four orders of magnitude from different
laboratories (Giechaskiel et al., 2008). However, one drawback of the PMP is that it employs hot
primary dilution which eliminates volatile components of diesel exhaust, effectively removing
the nucleation mode particles are postulated to have the highest toxicity in relation to human
health.
2.1.6 Aftertreatment Systems
In order to meet emission regulations, diesel engine manufacturers have made significant
improvements in engine design and technology. However, even further emissions reductions are
required to meet current USEPA and EURO legislations (Johnson, 2007) for NOx and PM. The
12
most common PM treatment systems involve the use of a diesel oxidation catalyst (DOC) and a
diesel particulate filter (DPF). These devices may be combined in devices such as the
continuously regenerating trap (CRT). Similarly, the catalyzed continuously regenerating trap
(CCRT) consists of the CRT except with the DPF having a catalyzed washcoat application. For
in-engine NOx reduction, exhaust gas recirculation (EGR) can be applied to reduce the
combustion temperature to minimize thermal NOx formation. Various NOx aftertreatment
systems exist including selective catalytic reduction (SCR) devices or lean NOx traps (LNT)
(Setten et al., 2001). Currently, the effect of DPF on ultrafine and nanoparticles is under
investigation. Vaaraslahti et al. (2004) found that without the use a CCRT, nucleation mode
particles formed at a low load are likely from hydrocarbons, and with the use of a CCRT,
nucleation mode particles formed at a high load are likely from lubricating oil sulphur. In a
comparison between the CRT and CCRT, Kittelson et al. (2006c) demonstrated that with the use
of low sulphur fuel and lubricating oil the CRT and CCRT were both able to effectively remove
particulate matter, however the CRT produced large quantities of nuclei mode particles due to a
lack of accumulation mode particles to scavenge the nucleation mode particles. Finally, Frank et
al. (2007) showed that the effect of after treatment method on ultrafine particle size distributions
was greater than the effect of fuel characteristics.
2.2 Biodiesel in Diesel Engines
2.2.1 Biodiesel Overview
Biodiesel is considered a renewable biofuel, composed of fatty acid methyl or ethyl esters made
from various sources such as vegetable oils or animal fats. The most common vegetable oils used
are soybean oils within the United States, and rapeseed oils in Europe (Rakopoulos et al., 2007).
In Canada, Canola oil is more commonly used to create biodiesel. Although vegetable oils can be
used directly in fuel engines, there are problems with poor fuel atomization, incomplete
combustion, and carbon deposition causing serious engine fouling due to the high viscosity
(Ramadhas et al., 2004). In order to reduce the viscosity of biodiesel and improve other
characteristics, the process of transesterification is utilized. Transesterification is a chemical
reaction between triglycerides and alcohol in a presence of a catalyst, where three ester
molecules are produced from one molecule of triglyceride. The by-product glycerol is also
13
produced and has a commercial value. Transesterification allows production of biodiesel from
used cooking oil and animal fats.
The advantages of biodiesel over conventional diesel include: minimal aromatic and
sulphur contact, higher cetane number, higher flashpoint, higher lubricity, and enhanced
biodegradability and non-toxicity (Speidel et al., 2000; Knothe et al., 2006). The disadvantages
include: higher pour point, lower calorific value, lower volatility, higher viscosity, and lower
oxidation stability (Demirbas, 2007). Blends of standard diesel fuel with up to 20% by volume of
biodiesel can generally be used in current existing diesel engines without modification.
2.2.2 Biodiesel Effect on Engine Performance
The brake specific fuel consumption (BSFC) is the rate of fuel consumption divided by the
power produced. The BSFC allows the fuel efficiencies of different engine types to be directly
compared, and is inversely proportional to the thermal efficiency. The rate of fuel consumption
for biodiesel fuels is expected to increase in comparison to petroleum fuel, as biodiesel has a
lower heating value and more fuel must be burned to compensate. This lower heating value of
biodiesel is associated with the oxygenated nature of the fuel.
The majority of the studies found in the literature support that increased fuel consumption
is correlated with the lower heating value. Kim & Choi (2010) reported a 1 – 2% increase in fuel
consumption rate using 20% blends of biodiesel in diesel fuel. Rakopoulos et al. (2006) observed
an increase of 2 – 3% BSFC using biodiesel derived from a variety of vegetable oils. In a later
study, increases of BSFC of up to 10% were seen when using 100% Cottonseed biodiesel at
medium and high engine load conditions (Rakopoulos et al., 2007). Using a 6-cylinder urban bus
engine, Turrio-Baldassarri et al. (2004) measured an average 2.95% BSFC increase using 20%
blends of rapeseed biodiesel. The USEPA (2002) conducted a comprehensive study of 39 papers
from literature of only heavy-duty diesel engines with no treatment technologies, and observed
an average of 8.53% increase BSFC using 100% biodiesel.
An additional measure of engine performance is the brake mean effective pressure
(BMEP), which is proportional to the effective torque of the engine. A reduction in BMEP is
expected, due to the lowered heating value of biodiesel. For example, Kaplan et al. (2006)
14
experienced a loss of 5 – 10% BMEP at different engine speeds in a 2.5L Peugeot engine. Lin et
al. (2006) using palm-oil biodiesel obtained a loss of 3.5% BMEP using pure biodiesel, and 1%
BMEP with a 20% biodiesel blend. Other authors attribute the losses in BMEP and BSFC due to:
the higher viscosity of biodiesel causing an advanced injection (Usta et al., 2005) and less fuel
delivered (Monyem et al., 2001), greater friction losses due to higher lubricity (Ramadhas et al.,
2005), and higher bulk modulus (Boehman et al., 2005). In contrast, some studies have shown
increases in BMEP while using biodiesel. For example, Usta (2005) observed increases in
BMEP despite lower heating values of biodiesel. Additionally, in a literature review conducted
by Lapuerta et al. (2008), they concluded that biodiesel does not cause any loss of power unless
the maximum power is demanded, because a surplus in fuel consumption would compensate for
the difference in BMEP.
Another parameter that is important to understand is the effect of biodiesel on engine
tribology characteristics, as biodiesel may influence: lubrication parameters, wear of various
engine components, and carbon deposits (Pehan et al., 2009). The enhanced lubricity of the
biodiesel has shown to decrease the wear of various vital parts up to 30% (Agarwal, 2007). A
durability study conducted by Thornton et al. (2009) showed that no moving components showed
signs of excessive wear or deterioration as a result of extended biodiesel operation.
2.2.3 Biodiesel Effect on Engine Emissions
2.2.3.1 Carbon Monoxide Emissions
The general trend observed in the literature is a noticeable decrease in CO emissions when
biodiesel is used to substitute diesel fuel. Using a 20% soybean based biodiesel blend, an 11%
reduction in CO emissions was observed (Sharma et al., 2008). Similarly, Kim & Choi (2010)
observed a 12% decrease in CO emissions when using 20% biodiesel from soybean methyl
esters. Some authors reported a more significant reduction in CO, where the use of neat biodiesel
reduced CO emissions by over 50% in comparison to petroleum diesel CO emissions (Dorado et
al., 2003; Fontaras et al., 2009). Lue et al. (2001) reported a decrease of over 60% CO emissions
when using 20% blended biodiesel at high engine loads. Conversely, some studies have shown
15
that the use of biodiesel blending had little to no impact on CO emissions (Rakopoulos et al.,
2006; Yang et al., 2007).
There have been many reasons formulated to explain the general CO decrease when
substituting conventional diesel for biodiesel. One reason is that the oxygenated nature of
biodiesel allows for a more complete combustion of the fuel, reducing CO emissions (Pinto et
al., 2005). Another reason is the increased biodiesel cetane number (Shi et al., 2005), which
decreases the formation of fuel-rich zones, usually related to CO emissions (Graboski &
McCormick, 1998).
2.2.3.2 Nitrogen Oxide Emissions
The majority of the literature reviewed shows slight increases of NOx emissions when using
biodiesel fuel. For instance, studies have shown that the combustion of neat biodiesel in diesel
engines results in increases of NOx emissions over 12% (Song et al., 2002; Hess et al., 2005).
20% blended soybean oil methyl esters exhibited minimal increases in NOx emissions from as
low as 4% (Hess et al., 2007), to as high as 30% (Lue et al., 2001). On the other hand, some
authors did not find any significant increase in NOx emissions while using biodiesel (Wang et al.,
2000), and a few papers have even found a decrease in NOx emissions of up to 10% (Graboski &
McCormick, 1998).
There have been various explanations formulated to explain the general increase in NOx
emissions when using biodiesel fuels. Two commonly discussed arguments include the increased
cetane number of biodiesel which shortens the ignition delay (Schmidt & Gerpen, 1996), and the
higher oxygen content of the biodiesel (Kim & Choi, 2010). However, the most widely accepted
explanation found in the literature for increased NOx emissions is due to the advanced injection
start, where the biodiesel fuel is sprayed into the combustion chamber quicker compared to
petroleum diesel due to the greater viscosity and lower compressibility of biodiesel (Cardone et
al., 2002). Other authors support this explanation having found good correlations between NOx
emissions and the start of fuel injection (Szybist et al., 2005). Furthermore, authors such as Fang
& Lee (2009) were able to reduce NOx emissions using biodiesel by employing advanced
injection strategies.
16
2.2.3.3 Total Hydrocarbon Emissions
Most researchers observe a sharp decrease in THC emissions when substituting conventional
diesel fuel with biodiesel (Turrio-Baldassarri et al., 2004). For instance, Nwafor (2004) tested
several blends of rapeseed biodiesel and found a reduction of 60% THC emissions while using
pure biodiesel. A 75% reduction of THC emissions was found using biodiesel originating from
soybean oil (Last et al., 1995). However, some authors reported no changes in THC emissions or
even slight increases. For example, 20% biodiesel from soybean methyl esters resulted in a
negligible increase of 0.6% compared to ULSD emissions (Moser et al., 2009).
Several reasons have been proposed to explain the decrease in THC emissions when
using biodiesel, including the higher oxygen content in the biodiesel, allowing for a more
complete combustion (Lue et al., 2001; Payri et al., 2009). Other factors noted are the higher
cetane of the biodiesel reducing combustion delay (Abd-Alla et al., 2001), and the lack of
branched hydrocarbons and aromatics in biodiesel fuel which do not combust as readily as
straight-chained methyl esters (Knothe et al., 2006). In addition, some authors have noticed that
due to the higher final distillation points of diesel fuel, there exists a heavier fraction of diesel
that is less likely to be combusted and end up in exhaust emissions, increasing overall THC
emissions in comparison to biodiesel (Turrio-Baldassarri et al., 2004; Murillo et al., 2007).
2.2.3.4 Diesel Particulate Matter Emissions
The majority of researchers found a reduction of particulate matter (PM) mass when substituting
diesel fuel with biodiesel. For example, Moser et al. (2009) observed decreases of 27.9% and
22.5% when using 20% soybean biodiesel and partially hydrogenated soybean biodiesel,
respectively. Dwivedi et al. (2006) also observed a 30% decrease in PM mass emissions by using
a 20% biodiesel blend. Some authors have shown that the reduction in PM emissions is more
effective with lower biodiesel concentration in the blends, as opposed to higher concentrations
(Haas et al., 2001). For instance, Lapuerta et al. (2002) found the greatest relative reductions in
PM mass emissions with a 25% biodiesel compared to 50%, 75%, and 100% biodiesel blend.
These findings are in agreement with the model created by the USEPA from surveying the work
17
of 37 independent authors studying biodiesel effects on heavy-duty diesel engines (USEPA,
2002).
Various reasons have been formulated to explain the reduction of particulate matter when
substituting diesel fuel with biodiesel. One explanation is the higher oxygen content of biodiesel,
which enables a more complete combustion and promotes the oxidation of soot (Graboski &
McCormick, 1998; Wang et al., 2000; Rakopoulos et al., 2008). Biodiesel fuels also have an
absence of aromatics compounds, which are considered soot precursors contributing to
particulate matter (Wang et al., 2000; Sharma et al., 2008). Additionally, the use of biodiesel is
also known to increase the proportion of soluble organic fraction of the PM (Di et al., 2009b),
which is more easily combusted.
2.2.3.5 Particle Size Distribution
In terms of the particle size distribution, the majority of studies have shown that the effect of
biodiesel fuelling has a reduction of larger size particles and increased concentration of
nucleation mode particles (Krahl et al., 2005; J. Jung et al., 2006). Xinling & Zhen (2009)
rationalized that the increase of nucleation mode particles was due to a lack of accumulation
mode particles, which promoted the nucleation and condensation of semi-volatile compounds in
the exhaust gas. Similarly, some studies have shown no differences in soot morphology and
change in mean particle diameter with biodiesel fuelling (Turrio-Baldassarri et al., 2004). In
contrast, other authors have observed a decrease in mean particle diameter which they speculate
may be due to changes in the soot morphology of biodiesel exhaust (H. Jung et al., 2006a;
Ballesteros et al., 2008; Lapuerta et al., 2009). Furthermore, Fontaras et al. (2009) demonstrated
that the use of biodiesel reduced solid particle population, and shifted particle sizes to smaller
diameters and increased the total particle number count.
18
2.2.4 Impact on Aftertreatment Systems
The use of biodiesel lowers the balance point temperature (BPT) of the DPF (Williams et al.,
2006), which is defined as the DPF inlet temperature at which the rate of particle collection is
equivalent to the rate of particle oxidation. This may be attributed to the increase of soluble
organic fraction (SOF) which provides more reactive hydrocarbons for catalytic oxidation in the
DPF, and the more amorphous soot structure enhances the rate of soot oxidation (Boehman et al.,
2005). Furthermore, Kapetanović et al. (2009) found that large amounts of nanoparticles were
formed during DPF regeneration with biodiesel fuelling as opposed to petroleum diesel, which is
speculated to be due to higher volatilization of biodiesel generated soot and greater desorption of
nanoparticles from a diesel particulate filter with petroleum diesel fuels.
2.2.5 Impact of Source Feedstock on Biodiesel
The feedstock of the biodiesel source material can have an impact on the diesel emissions, as
studies have shown that the molecular structure of biodiesel can have a substantial impact on
diesel emissions (McCormick et al., 2001). Schmidt & Gerpen (1996) tested a number of
biodiesel fuels from different sources, and found that the reduction in particulate matter due to
biodiesel fuelling can be related to the oxygen content, in addition to the absence of aromatic and
shorter chain paraffin hydrocarbons. Increases in biodiesel oxygen content can also attribute to
increased carbonyl emissions, as well as increased emissions of the majority of polyaromatic
hydrocarbon (PAH) compounds (Karavalakis et al., 2010). Moreover, McCormick et al. (2001)
found increased NOx emissions with decreasing fuel cetane number, increasing fuel density, and
iodine numbers; where the iodine number represents the number of double bonds or degree of
saturation.
When comparing the two biodiesels studied in this project, animal fat and soy based
biodiesel, animal fat biodiesels are generally more saturated than soy based biodiesels (Lapuerta
et al., 2009), which represents a lower number of double bonds. Therefore, animal fat biodiesels
should exhibit lower NOx emissions compared to soy biodiesel due to the lower number of
double bonds. Furthermore, less saturated fuels have been found to have greater PM emissions,
likely due to the double bonds in unsaturated fuels which offer a more direct pathway to the
19
formation of ethene and ethyne, which are known to be precursors of soot (Schönborn et al.,
2009). Therefore, soybean biodiesels are expected to produce greater PM emissions than animal
fat biodiesels. These findings are supported by a comprehensive literature survey by the USEPA
(2002), where comparing soybean and animal based biodiesels, they found decreased NOx
emissions, and greater PM and CO reductions when comparing animal fat biodiesels to soy and
rapeseed based biodiesels.
20
3 Experimental Setup
3.1 Engine Experimentation
The engine used during experimentation was an off-road 1997 Cummins B3.9-C direct injection
engine. Fuel was introduced to the engine using a Bosch in-line injection pump. The engine was
connected to a Siemens DC dynamometer, which has the capability to load or motor the engine
through a Lebow rotary torque transducer (1605-5K). The injector nozzles were replaced prior to
experimentation to ensure an optimal combustion environment. A series of controllers were used
to regulate oil pressure and coolant temperature during engine operation. Table 1 below lists a
summary of the engine specifications.
Table 1: Diesel Engine Specifications
Engine Model 1997 Cummins B3.9-C, off-road
Family VCE239R6DTRB
Date of Manufacture 02/04/1997
Serial Number 45505257
Displacement 239 in3/3.9 litres
Rated Power 116 hp/ 87 kW @ 2500 rpm
Peak Torque 312 ft-lbs/423 N-m @ 1500 rpm
Fuel Rate 83 mm3/stroke @ rated power
Low Idle Speed 950 rpm
Injection Timing 18° BTDC
Bosch in-line injection pump:
PES 4A95D320/3RS2880
Fuel Injection Pump
S/N 76335194
Bosch Governor:
RSV475…1250AOC892R
Governor
KD-NR.: 392 8602
HC 0.3 g/kW-hr CO 0.6 g/kW-hr EPA Certification
NOx 8.3 g/kW-hr PM 0.12 g/kW-hr
Pressure and temperature transducers were placed throughout the engine apparatus for
monitoring operating conditions. The transducers were connected to a data acquisition setupand
the measured values were recorded during experiments using a Labview program module.
21
3.2 Engine Operating Conditions
Two separate test programs were performed throughout this research. The first test program
entitled, “University of Toronto (U of T) Testing,” utilized two different engine operating
conditions based on the ISO 8178 emission test cycle, an international standard designed for
non-road engine applications (Dieselnet, 2011). ISO 8178 modes 9 and 2 were used for the U of
T Testing, where mode 9 represents 25% of maximum torque (100 Nm) at intermediate speed
(1400 rpm), and mode 2 represents 75% of maximum torque (254 Nm) at rated speed (2500
rpm). These two modes were chosen as results can be compared to previous work by
Kapetanović et al. (2009) and Jääskeläinen et al. (2006) which used these engine operating
conditions on the same experimental setup. Also, these two ISO 8178 modes represent operating
conditions which have exhaust gas temperatures below and above the balance point temperature
of a typical catalyzed diesel particulate filter (Jääskeläinen et al., 2006).
In addition to the U of T Testing, an additional test program entitled, “General Electric
(GE) Testing,” was conducted as part of the Sustainable Development Technology Canada
(SDTC) Clean Diesel Locomotive (CDL) Program. As GE was primarily interested in
locomotive diesel emissions which use significantly larger GEVO locomotive engines, ISO 8178
modes 9 and 2 were adapted in order to best represent standard locomotive test cycles by
matching size independent engine parameters. These parameters were the brake mean effective
pressure (BMEP), exhaust gas temperature (EGT), and mean piston speed. Additional airflow
was necessary for the GE testing in order to match the EGT of the GEVO engine, and this was
provided by supplying compressed air to the turbocharger inlet. A summary of the two test
programs is shown below in table 2:
Table 2: Summary of Test Conditions
University of Toronto
(U of T) Testing
General Electric
(GE) Testing
Mode 9 Mode 2 Mode 9x Mode 2x
Power Output (hp) 20.8 87 33.1 87
Torque (Nm) 105.8 254 153 254
BMEP (kPa) 339 799 491 799
Exhaust Temperature (°C) 236 434 269 380
Speed (rpm) 1400 2500 1550 2500
Mean Piston Speed (m/s) 5.6 10 6.2 10
22
3.3 Fuel Type / Lubricating Oil
Two different types of biodiesels were studied in this project. Soy NEXSOL BD-0100 Biodiesel
was obtained from Peter Cremer North America. Animal fat biodiesel obtained from Rothsay
Biodiesel and is a blend of feedstocks, approximately 75% of which consisted of various waste
animal fats from meat processing facilities. The remaining 25% consisted of used cooking oil.
The base fuel used for blending was a 2007 ultra low sulphur diesel (ULSD) certification fuel
produced by Chevron Phillips Chemical Company LP, a well characterized base diesel fuel.
Reference specification sheets for each of the fuels are attached in the appendix, and a summary
of the fuel blends used in each test series is shown below in table 3:
Table 3: Summary of Fuel Matrix
University of Toronto
(U of T) Testing
General Electric
(GE) Testing
Fuel Source Biodiesel Blend (%)*
Animal Fat 0, 5, 20 0, 5, 10, 20, 30
Soy 0, 5, 20 0, 5, 10, 20, 30
*Base fuel is ultra low sulphur diesel (ULSD)
A CJ-4 lubricating oil which meets API CJ-4 specifications and also meets or exceeds the
requirements of the Cummins engine was used. CJ-4 oils were developed to work with diesel
particulate filters and low/ultra-low sulphur diesel fuel. A key requirement of CJ-4 is a low
SAPS (Sulphated Ash, Phosphorus and Sulphur) level with a sulphated ash limit of 1.0% mass
maximum (by ASTM D874), a phosphorus limit of 0.12% mass maximum (by ASTM D4941)
and a sulphur limit of 0.4% by mass maximum (by ASTM D4941). A summary of the lubricating
oil specifications is shown below in table 4.
Table 4: Summary of Lubricating Oil Specifications
CJ-4 Oil
15W-40
Sulphated Ash 1.00%
Total Base Number 10 typical
Sulphur Content (ASTM D4294) 0.40% max.
23
3.4 Sampling Equipment
3.4.1 Overall Sampling Train
Fuel and air are introduced into the engine to drive the combustion reaction within the cylinder
pistons, controlled by the dynamometer which has the capability to either load or motor the
engine to simulate different driving conditions. The combustion products, or raw exhaust gases,
flow through an exhaust manifold and a portion is sampled using the Horiba EXSA-1500
Exhaust Gas Analyzer. Another fraction of the raw exhaust is sampled using the Dekati FPS-
4000 Ejector Diluter in order to lower the temperature, humidity, and concentration of the raw
exhaust products for the sensitive particle instrument analyzers, also in an attempt to simulate
atmospheric dilution conditions. A flow diagram of the foremost section of the exhaust manifold
is shown below in figure 2:
Figure 2: Engine and Diluter Sampling Train
For the University of Toronto testing, the diluted exhaust was then sampled using a series
of instruments connected through 3/8” Stainless Steel tubing. For filter measurements, a single
Teflon filter and two quartz filters in series were used to collect diesel particulate matter for
further off-line analysis. Two identical p-PAH monitors placed in parallel were used to study the
particle bound polyaromatic hydrocarbons (PAH) concentrations of the diluted exhaust. Finally,
particle size distributions were determined using a particle sizer and a three-way valve to direct
24
the incoming flow either through or bypassing a thermodenuder. A flow chart of the diluted
exhaust for the University of Toronto testing is shown below in figure 3:
Figure 3: University of Toronto Testing Sampling Train
For the General Electric testing, the sampling train employed was similar with a few
differences in instrumentation. First, neither the thermodenuder nor the p-PAH monitors were
utilized during this set of experiments. Secondly, for the filter collection, two filters in series
were used for both quartz and Teflon filters. A flow chart of the diluted exhaust for the General
Electric testing is illustrated below in figure 4:
25
Diluted
Exhaust Secondary
Quartz Filter
Primary
Quartz Filter
Q Q
Primary
Teflon Filter
T
Flow
Controller
Vacuum
Pump
Flow
Controller
Vacuum
Pump
SMPS
Secondary
Teflon Filter
T
Figure 4: General Electric Testing Sampling Train
3.4.2 Horiba Raw Gas Analyzer
A Horiba EXSA-1500 Exhaust Gas analyzer was used to analyze raw engine exhaust emissions.
A heated sampling line was connected directly from the exhaust manifold to the Horiba gas
analyzer in order to minimize condensation of gas products in the sampling line. The Horiba
system has a variety of analyzers to study various chemical species: NOx using a
chemiluminescent analyzer, THC using a flame ionization detector (FID), CO/CO2 using a non-
dispersive infrared (NDIR) detector, and O2 using a magnetic pressure analyzer. The sample
lines for CO, CO2, and O2 were diverted through a thermoelectric chiller within the Horiba unit
to condense water vapour. Therefore, the measurements for CO, CO2, and O2 were made on a
dry basis, while the NOx and THC measurements were conducted on a wet basis. All of the
analyzers were zeroed using emissions grade nitrogen gas. Throughout the study there were
issues with the CO detector and the results were deemed unreliable for the purposes of this
research project. Although the NOx analyzer features the ability to differentiate between NO and
NO2 gases by enabling or disabling the catalytic conversion of NO to NO2, for this study only
NOx (NO + NO2) was studied. A summary of the Horiba calibration gases is shown below in
table 5:
26
Table 5: Horiba Calibration Information
Gas Analyzer Measurement Range Span Gas Concentration
O2 0 - 25% 20.95% O2 (air)
NOx 0 - 5000 ppm 1014 ppm NO in N2
THC 0 - 5000 ppm 1488 ppm CH4 in N2
CO 0 - 5000 ppm 497 ppm CO in N2
CO2 0 - 20% 8.96% CO2
3.4.3 Dekati Dilution System
A Dekati FPS-4000 Diluter was used to dilute the raw exhaust using dry, filtered air. The Dekati
FPS-4000 features two diluters in series, the first being a porous tube diluter, where the sample
flows through a porous tube in which dilution gas is mixed into the exhaust gases. A schematic
of a porous tube diluter is shown below in figure 5:
Figure 5: Schematic of Porous Tube Diluter
(Lyyränen et al., 2004) (© Taylor and Francis)†
The second diluter downstream of the porous tube diluter was an ejector diluter, which
operates on the principle of a Venturi nozzle in which dry, filtered pressurized air is combined
† This image is used with the permission of Taylor and Francis
27
with raw exhaust in a mixing chamber. The advantage of an ejector diluter is that they can be
used for particles ranging in size from of nanometers to several micrometers without noticeable
losses. Giechaskiel et al. (2009) studied the effect of using ejector diluters on engine exhaust and
concluded particle size distribution characteristics were not altered for typical use. The dilution
ratio of ejector dilution systems are lower than that for rotary disk diluters, typically on the order
of 1:10 depending on nozzle design. Higher dilution ratios can be achieved by placing ejector
diluters in series. A schematic of the ejector diluter is shown below in figure 6:
Figure 6: Schematic of an Ejector Diluter
(Burtscher, 2005) (© Elseivier)‡
In order to represent atmospheric dilution conditions, the following dilution parameters
were utilized and kept constant whenever possible:
• Primary Dilution Ratio ~ 12-13
• Total Dilution Ratio ~ 90-100
• Residence Time ~ 1-3 seconds
• Dry, Filtered Dilution Air ~ 25-30 °C
These parameters fall within the suggested dilution factors for a partial-flow two-stage
dilution system recommended by Kittelson et al. (2002) in order to create representative and
‡ This image is used with the permission of Elsevier
28
repeatable laboratory dilution methods. Other researchers have also used similar dilution
conditions in their research (Vaaraslahti et al., 2004; Ntziachristos et al., 2005; Grose et al.,
2006; Kittelson et al., 2006b). Additionally, these parameters are also similar to those employed
by Kapetanović et al. (2009) using the same experimental setup in order to promote nucleation in
diesel engine exhaust.
A number of problems were encountered using the Dekati FPS-4000 diluter. Any slight
leak within the diluter setup would cause the dilution ratio (DR) to vary dramatically from its
intended DR. In addition, the ejector critical orifice would get clogged frequently and the DR
would be affected correspondingly. For these reasons, the DR from the Dekati FPS-4000
calculated from pressure and temperature readings was deemed unreliable. Therefore, the DR
was monitored constantly throughout experimentation using the concentration of NOx upstream
and downstream of the diluter measured using the Horiba EXSA-1500 and a three-way valve.
3.4.4 Scanning Mobility Particle Sizer / Thermodenuder
A scanning mobility particle sizer (SMPS) (Wang & Flagan, 1990) was used to obtain particle
size distributions. The SMPS consisted of a TSI model 3080 electrostatic classifier, TSI model
3085 nano-differential mobility analyzer (nDMA), and a TSI model 3786 ultrafine water-based
condensation particle counter (CPC). The TSI 3080 electrostatic classifier uses a KR-85 bipolar
charger to neutralize the charge on particles, and then the TSI 3085 nDMA utilizes a variable
electric field in order to separate particles according to their electrical mobility equivalent
diameter. The working principle of an nDMA is shown below in figure 7:
29
Figure 7: Differential Mobility Analyzer Operation Principle
(Chen et al., 1998) (© Elsevier)‡
These size-separated particles downstream of the nDMA, otherwise known as
monodispersed aerosol, are fed into the CPC which counts the number of particles by first
growing them using a saturated atmosphere of water until the particles grows to a size, detectable
by conventional optical techniques. A diagram of the CPC is shown below in figure 8:
‡ This image is used with the permission of Elsevier
30
Figure 8: Ultra-fine Water Based Condensation Particle Counter
(Hering et al., 2005) (© Taylor and Francis)†
The SMPS upscan and downscan time was set to 160 and 15s, respectively. A 0.071 cm
virtual impactor was placed before the classifier to remove large particulates that may damage
the instrument. Using a sheath flow rate of 6.0 SLPM and an aerosol flowrate of 0.6 SLPM, the
SMPS measured a particle size distribution of range 3.16 to 107.5 nm.
In order to differentiate between the volatile and nonvolatile components from the diesel
particulate matter, a Dekati Thermodenuder was utilized in order to remove volatiles eliminating
possible sample transformations by heating the diluted exhaust prior to removal via activated
charcoal. During the University of Toronto test program, diluted exhaust either bypassed or
flowed directly through the thermodenuder before the SMPS with the use of a three-way valve.
The thermodenuder temperature was set to 265°C, which is similar to the studies of other authors
(Vaaraslahti et al., 2004; Rönkkö et al., 2007; Heikkilä et al., 2009).
Although attempts were made to correct for the thermophoretic and diffusion losses
involved with the thermodenuder, losses reported using the same Dekati thermodenuder
† This image is used with the permission of Taylor and Francis
31
observed from other authors ranged from an average of 35% (Vaaraslahti et al., 2004), 28 – 95%
losses for particles from 6 to 30 nm (Rönkkö et al., 2007), and ~30% over the 20 – 250 nm range
(Filippo & Maricq, 2008). Attempts to characterize the thermodenuder losses using polystyrene
latex (PSL) particles, as well as theoretical diffusion and thermophoretic calculations, predicted
the losses to be from 55 – 40% over the 15 – 100 nm range. These predicted differences greatly
overcorrected the maximum differences in volatility observed while using the thermodenuder.
This is likely due to the differences in physical and chemical characteristics between diesel
particulate matter and PSL, and the high concentration of diesel exhaust. Therefore no
corrections were applied, as it was not clear what losses were applicable to this particular
experimental setup. A diagram of a thermodenuder operation is shown below in figure 9:
Figure 9: Thermodenuder Operation Principle
(Burtcher et al., 2001) (© Elsevier)‡
‡ This image is used with the permission of Elsevier
32
3.4.5 p-PAH Analyzers
Two identical Ecochem Photoelectric Aerosol Sensor (PAS) 2000CE analyzers were employed
to estimate the total particle bound PAH’s in the diluted exhaust. The instrument operates by
using UV radiation that causes photoionization of carbonaceous particles prior to collection on
an electrically insulated filter, where the current is constantly measured. The PAS 2000 CE was
set to a time resolution of 10 seconds. A schematic of the working principle is shown below in
figure 10:
Figure 10: Photoelectric Aerosol Sensor Operating Principle
(Arnott et al., 2005) (© ACS Publications)£
3.4.6 47mm Filter Sampling Train
Filters were sampled using a Model 6186 FRM Exhaust Filter Holder System supplied by
Thermo Electron Corporation. A pre-classifier was placed upstream of the filter holding system,
a cyclone designed to remove particles greater than 10 µm in diameter with greater than 50%
efficiency. A “Y” shaped fork then splits the flow into two separate filter holders. 47 mm filters
were placed on a circular stainless steel mesh prior to being enclosed in polycarbonate filter
cassettes. These cassettes allow the 47 mm filters to have a stain diameter of 38 mm. The
£ This image is used with the permission of ACS Publications
33
cassettes were then placed in a filter body, constructed of 300 series stainless steel with a tapered
inlet and outlet to provide an even flow of particulate matter across the filter.
The two parallel filter holders were then connected individually to two separate Hastings
mass flow controllers capable of 0 – 35 SLPM flow, and a vacuum pump. A solenoid valve was
also used to control sampling through the filters from either the diluted exhaust stream or
ambient air. The set point of the flow controllers were set to 20 SLPM, as greater values caused
issues due to insufficient flow from the Dekati diluter for certain operating conditions. Since the
USEPA engine test procedure section 1065.170 specifications for filter sampling stipulates the
face velocity to be less than 100 cm/s, there were no issues of exceeding this velocity using a 20
SLPM flowrate. Figure 11 below shows the sampling system schematic.
Figure 11: Filter Sampling System
Three different types of 47 mm filters were used throughout the research: Pall Teflo, Pall
Emfab, and Pall Tissuquartz filters. Pall Teflo 2µm Polytetrafluoroethylene (PTFE) ethylene
with polymethylpentene (PMP) support rings were used for the gravimetric analysis for the
University of Toronto testing. Pall Emfab filters, which are composed of borosilicate glass
microfibers reinforced with woven glass cloth and bonded with PTFE, were used for the General
Electric testing for SOF analysis following gravimetric measurements. For both test programs,
Pall Tissuquartz filters were used, where the filter media consisted of pure quartz material with
no binder. All of these filters feature typical aerosol retention efficiencies in excess of 99.9%
following ASTM D 2986-95A 0.3µm at 32 L/min/100cm2 filter media. The quartz filters were
used for the chemical speciation of the particulate matter, and a breakdown of the chemical
Solenoid
Vacuum
34
analyses conducted on the quartz filters is shown below in figures 12 and 13 for the U of T and
GE testing, respectively.
Figure 12: University of Toronto Testing – Quartz Filter Breakdown
Figure 13: General Electric Testing – Quartz Filter Breakdown
Primary Quartz Filter Secondary Quartz Filter
EC/OC Analysis (NRCan) EC/OC Analysis (NRCan)
Water Soluble Anions, Cations
Primary Quartz Filter Secondary Quartz Filter
EC/OC Analysis (In-house) EC/OC Analysis (In-house)
Future Analyses Unused
Water Soluble Organic Carbon, Anions, Cations and Nitrogen
35
3.5 Filter Analysis
3.5.1 Gravimetric
A Sartorious model SE-2F microbalance was used for gravimetric measurement. This balance
has a precision of 0.1µg and was placed on a Newport Benchtop Vibration Isolation System
along with a Sartorious ionization beam, which minimizes static electrical interferences. These
components were enclosed in a custom designed humidity controlled chamber constructed of
3/8” acrylic material. A sample pump circulated the inside air through an Erlenmeyer flask
containing water in equilibrium with K2CO3(s). This solution controls the humidity of the
chamber to approximately 43% at 25°C (Rockland, 1960). An air conditioner was placed in the
same room as the chamber to remove humidity from the room and help maintain the ambient
temperature at 20 to 24°C within the chamber. Two National Institute of Standards and
Technology (NIST) traceable weights with masses of 2 and 200mg were used to validate the
precision of the balance. Two iris openings were installed in the chamber to allow the operator to
weigh filters with minimal mixing of the chamber air with ambient air. Figure 14 below shows a
picture of the weighing setup:
Figure 14: Filter Weighing Apparatus
36
In accordance with USEPA engine test procedure section 1065.190 specifications, filters
used for gravimetric measurement were equilibrated in the chamber before and after collection of
PM, to ensure consistent humidity and temperature effects throughout the gravimetric
measurements.
3.5.2 Elemental Carbon / Organic Carbon
The elemental and organic carbon content from quartz filters was measured using a Sunset
Laboratory Elemental Carbon / Organic Carbon (EC/OC) Instrument using the National Institute
for Occupational Safety and Health (NIOSH) 5040 protocol. There protocol had three main
stages: measurement of organic carbon, measurement of elemental carbon, and lastly a
calibration measurement. A 1.5 cm2
quartz filter punch was first placed in a quartz oven in a
completely oxygen-free helium atmosphere. This sample was heated sequentially to 310, 475,
615 and 870°C to first evolve the volatile organic carbon (OC), followed by cooling down to
550°C. 2% oxygen in helium was then injected into the quartz oven and the sample heated to
temperatures of 625, 700, 775, 840, and 880 °C to combust the elemental carbon (EC). The final
step of the EC/OC instrument was to inject a known quantity of methane gas in order to calibrate
the instrument after every sample. The evolved carbon was catalytically converted using a
manganese catalyst and detected with either a flame ionization detector (FID) or a non-dispersive
infrared detector (NDIR) for CH4 or CO2, respectively.
A helium-neon laser was constantly transmitted through the sample in order to detect the
presence of carbonates (CC) or pyrolyzed carbon (PC), and to detect the transition from organic
to elemental carbon. Carbonates and pyrolyzed carbon were not detected in diesel exhaust
samples, therefore, the total carbon (TC) was defined as the sum of the OC and EC detected in
the EC/OC instrument. A representative thermogram is shown below in figure 15:
37
Figure 15: Representative EC/OC Thermogram
(NIOSH, 1999)§
The EC/OC was conducted on both the primary and secondary quartz filters, in order to
provide a correction for adsorption of organic vapours by the quartz filter medium which could
positively bias the organic carbon result. Correcting for the secondary backup filter also provided
a blank correction for the EC/OC analysis. Samples were kept in tightly sealed polycarbonate
filter holders in double zip-locked bags at less than 0°C to prevent volatilization of organic
carbon prior to analysis.
EC/OC analyses were conducted both in-house and by Natural Resources Canada (NRC)
– Canadian Centre for Mineral and Energy Technology – Mining and Mineral Sciences
Laboratory (CANMET-MMSL) using a very similar Sunset Laboratory Organic
Carbon/Elemental Carbon analyzer. NRC used an older version of the instrument that used an
FID detector, while the in-house experiments conducted at SOCAAR used a newer NDIR
detector. For validation purposes, identical samples were tested on both the NRCan and
SOCAAR EC/OC instrument, and the results are shown below in figure 16:
§ U.S. Government Report (NIOSH) – Public Domain
38
y = 0.97x
R2 = 0.59
y = 1.00x
R2 = 0.85
y = 1.00x
R2 = 0.85
0
10
20
30
40
50
0 10 20 30 40 50
SOCAAR (mg/m3)
NRCan (mg/m
3)
OC
EC
TC
Linear (OC)
Linear (EC)
Linear (TC)
Figure 16: EC/OC Validation Experiments
As seen in figure 16, the NRCan and SOCCAR instruments corresponded extremely well
with each other, with R2 values of over 0.85 for EC and TC. In addition, the slopes for OC, EC,
and TC are all very close to 1. Further validation included analysis of reference EC/OC samples
obtained from KulTech Incorporated.
3.5.3 Soluble Organic Fraction
The soluble organic fraction (SOF) analysis was conducted by the Southwestern Research
Institute (SWRI), using methylene chloride as the solvent. A Soxhlet extraction apparatus was
used for extraction and filters were weighed before and after solvent extraction. The loss of mass
relative to the particulate matter loading constituted the SOF. The primary and backup filters
were extracted together to get an overall SOF as per recommendation of SWRI.
39
3.5.4 Ion Chromatography
Water-soluble anion and cation speciation was performed using ion chromatography, a technique
in which individual ions are separated and quantified based on their ionic interactions of the
solute and chromatography column. Samples were prepared by taking quartz filters sections and
placing them into 15 mL Falcon test tubes, prior to sonication with 10 mL of Milli-Q water for
30 minutes. This filter extract was then filtered through a 0.2 µm syringe filter prior to ion
chromatography in a Dionex ICS-2000 equipped with suppressor and concentrator columns.
Anions were calibrated for flouride (F-), acetate ([CH3COO]
-), formate (CHOO
-), chloride (Cl
-),
nitrite (NO2-), bromide (Br
-), nitrate (NO3
-), sulphate (SO4
2-), oxalate (C2O4
2-), and phosphate
(PO43-
). Cations were calibrated for lithium (Li+), sodium (Na
+), ammonium (NH4
+), potassium
(K+), magnesium (Mg
2+), and calcium (Ca
2+).
3.5.5 Water Soluble Organic Carbon / Nitrogen
Water-soluble organic carbon (WSOC) and water-soluble nitrogen (WSN) analysis was
conducted using a Shimadzu Total Organic Carbon Analyzer (TOC-VCP) equipped with a Total
Nitrogen Analyzer attachment (TNM-1). Samples were prepared in an identical fashion as for
ion chromatography described in the previous section. The non-purgable organic carbon (NPOC)
method was utilized for WSOC analysis, where the sample is first acidified to a pH of about 2 –
3 using hydrochloric acid. Sparge gas is then bubbled through the sample to eliminate the
inorganic carbon component. The remaining sample is introduced into a catalyzed combustion
tube where the organic carbon components are converted to CO2, which is subsequently detected
with an NDIR detector. WSN is detected by catalytically converting the nitrogen in the sample
into nitrogen monoxide (NO), prior to detection by chemiluminescence.
40
3.6 Operating Procedure
3.6.1 Warm-up Procedure
Prior to any engine testing, it was imperative for the engine to be operating at steady-state
conditions with a constant oil and coolant temperature. To achieve this, the engine was idled for
at least 10 minutes before running at either mode 9 or 9x for an additional 15 minutes, ensuring
that the coolant temperature had stabilized to the 85°C set point. If mode 2 or 2x was required,
the engine was allowed to warm up at mode 2 or 2x from mode 9 or 9x for an additional 10
minutes prior to any testing. An auxiliary oil pump and heat exchanger were necessary for mode
2 or 2x for additional cooling of the oil. In the case of modes 9x and 2x, additional airflow was
introduced into the system by pressurizing the turbocharger inlet with compressed air from an
external air compressor. The pressure in the turbocharger inlet manifold was kept constant to the
ULSD test conditions to ensure consistent airflow with the various biodiesel blends while using
the compressed air system.
3.6.2 Diluter Conditions
Once the engine stabilized at a steady-state condition, the next important consideration was to
maintain a consistent dilution ratio in the sampling system. The suggested dilution factors for a
partial-flow two-stage dilution system by Kittelson et al. (2002) were followed in order to create
representative and repeatable laboratory dilution method. The recommended dilution ratios are:
primary dilution ratio ~ 12-13, total dilution ratio ~ 90-100, residence time ~ 1-3 seconds, and
using dry, filtered dilution air at ~ 25-30 °C. However, as there the exhaust gas pressure and
temperature sometimes fluctuated during experiments, the dilution ratio would change
accordingly. The dilution ratio may have shifted between test series as it was deemed more
important to maintain a consistent dilution ratio during experiments rather than to adjust the
dilution ratio midway, especially between the different engine operating modes. Nevertheless,
the dilution ratio was verified between every test cycle and constantly throughout testing using
the ratio of the NOx concentration measured upstream and downstream of the diluter from the
Horiba gas analyzer.
41
3.6.3 Fuel Flow Rate Measurement
An important engine operating parameter that had a direct impact on the exhaust parameters is
the fuel flow rate. The fuel flow also allows a direct calculation of the air to fuel ratio (AFR)
rather than determination from combustion stoichiometry of the reactants and products. To
accomplish this, two fuel tanks were used to fuel the engine: a primary fuel tank and a
measurement fuel tank. Two three-way solenoid valves operated by a timer were able to switch
from one fuel tank to another instantaneously. As the measurement tank was placed on a balance
throughout the engine operation, the fuel flow rate could be calculated by dividing the difference
in mass over a fixed sampling time period.
3.6.4 Experimental Test Matrix
As mentioned previously, two different test programs were conducted throughout this project:
the University of Toronto and General Electric testing. Three repetitions were completed for
each fuel blend at each of the operating conditions. A summary of the test matrices conducted is
shown below in tables 6 and 7:
Table 6: University of Toronto Test Program Table 7: General Electric Test Program
Test Repetitions
Fuel Blend Mode 9x Mode 2x
ULSD 3 3
Soy B5 3 3
Soy B10 3 3
Soy B20 3 3
Soy B30 3 3
AF B5 3 3
AF B10 3 3
AF B20 3 3
AF B30 3 3
Test Repetitions
Fuel Blend Mode 9 Mode 2
ULSD 3 3
Soy B5 3 3
Soy B20 3 3
AF B5 3 3
AF B20 3 3
42
4 Results and Discussion
4.1 Biodiesel Characterization Results
In order to verify the biodiesel blend concentrations created for the General Electric test
program, an analysis for fatty acid methyl esters (ASTM D7371) was conducted by Alberta
Innovates – Technologies Futures (AI-TF) – Fuels and Lubricants Division (formally known as
Alberta Research Council). The results from AI-TF are shown below in table 8:
Table 8: AI-TF Biodiesel Blend Verification by ASTM D7371
Biodiesel Blend
(%Volume)
AI-TF Result
(%Volume) % Difference
Soy B5 4.92 1.6
Soy B10 10.12 1.2
Soy B20 20.58 2.9
Soy B30 30.69* 2.3
Animal Fat B5 5.29 5.8
Animal Fat B10 10.28 2.8
Animal Fat B20 20.38 1.9
Animal Fat B30 31.92* 6.4
*ASTM D7371 states “This test method covers the determination of the content of fatty acid methyl esters (FAME)
in diesel fuel oils. It is applicable to concentrations from 1.00 to 20 volume %. Using the proper ATR sample
accessory, the range maybe expanded from 1 to 100 volume %, however precision data is not available above 20
volume %.”
Looking at the percentage differences of the biodiesel blends compared to the AI-TF
results, the biodiesel blends are representative of their intended blend characteristics considering
experimental variation from the ASTM D7371 method. The greatest difference was 5.8% for the
Animal Fat B5 fuel excluding the B30 blend. The 6.4% difference for the B30 blend was
discounted as the test method ASTM D7371 had to be extrapolated in order to accommodate
biodiesel blends of 30%. The neat biodiesels for both soy and animal fat biodiesels were also
analyzed by AI-TF using ASTM D6751. A comparison between the two biodiesels is illustrated
below in table 9:
43
Table 9: AI-TF Biodiesel Characterization by ASTM D6751
Specifications Analysis Test Name
Minimum Maximum
Soy BD
Results
Animal Fat
BD Results Units
90% ASTM D1160 360 352 353 °C
Copper Corrosion -
Classification ASTM D130 3 1a 1a
Water and
Sediment ASTM D2709 0.05 <0.005 <0.005 %
Kinematic
Viscosity ASTM D445 1.9 6 3.954 4.512
mm2/s
(cSt)
Carbon Residue
B100 ASTM D4530 0.05 0.001 0.000 %
Total Sulphur ASTM D5453 15 1 6.8 mg/kg
Cloud Point ASTM D5773 -1.4 11 °C
Cetane Number ASTM D613 47 50.2 58.6
Water Content ASTM D6304 500 313 135 mg/kg
Free Glycerin ASTM D6584 0.02 0.012 0.008
Mass
%
Total Glycerin ASTM D6584 0.24 <0.050 0.099
Mass
%
Acid Number ASTM D664 0.5 0.15 0.29
mg
KOH/g
Flash Point-
Alcohol Control
STM D6751-
Alcohol
control 130 176 178.5 °C
Filtration Time
ASTM D6751-
Annex A1 360 108 120 sec
Ash Content ASTM D874 0.02 0 0.000
Mass
%
Corrected
Flash Point ASTM D93 93 176.005 178.305 °C
Oxidation Stability
@ 110°C EN 14112 3 5.7 4.4 hours
Ca and Mg,
combined EN 14538 5 <1.0 <1.0
ppm
(w/w)
Na and K,
combined EN 14538 5 <1.0 <1.0
ppm
(w/w)
Phosphorus
Content EN 14538 0.001 <0.0002 <0.0002
Mass
%
An important observation for both biodiesels is that they both meet the latest revision of
ASTM 6751. Comparing the two biodiesels, two differences that would have the greatest impact
on emissions characteristics include the higher kinematic viscosity and total sulphur content of
animal fat biodiesel.
44
4.2 Engine Operating Conditions
The engine operating conditions were kept as similar as possible for each of the biodiesel blends
by maintaining the same load and speed conditions for each of the tests. Tables 10 and 11 below
show the actual engine operating conditions used throughout the University of Toronto test
program. The average values and 95% confidence intervals reported were calculated from the
three engine test replicates.
Table 10: University of Toronto Testing - Mode 9 Engine Operating Conditions. ± Values
Represent 95% Confidence Intervals
ULSD Soy B5 Soy B20 AF B5 AF B20
Exhaust Gas
Temperature (°C)
300.71
± 1.61
260.18
± 0.76 -
254.08
± 0.77
261.77
± 1.44
Fuel Flow Rate (g/s) 1.04
± 0.02
1.05
± 0.01
1.04
± 0.02
1.01
± 0.06
1.05
± 0.02
Air Flow Rate (g/s) 49.83
± 0.01
49.71
± 0.01
49.83
± 0.01
49.58
± 0.01
49.33
± 0.01
Air to Fuel Ratio (AFR) 48.1
± 0.6
47.5
± 0.2
48.2
± 0.1
49.0
± 0.4
47.2
± 0.1
Dilution Ratio (DR) 46.4
± 1.4
88.1
± 6.6
88.5
± 2.5
92.3
± 5.9
96.6
± 10.1
Table 11: University of Toronto Testing - Mode 2 Engine Operating Conditions. ± Values
Represent 95% Confidence Intervals
ULSD Soy B5 Soy B20 AF B5 AF B20
Exhaust Gas
Temperature (°C)
554.08
± 6.43 - -
540.64
± 1.75 -
Fuel Flow Rate (g/s) 4.63
± 0.05
4.59
± 0.12
4.68
± 0.11
4.56
± 0.05
4.69
± 0.04
Air Flow Rate (g/s) 154.39
± 0.04
148.06
± 0.02
148.03
± 0.03
147.35
± 0.02
152.19
± 0.04
Air to Fuel Ratio (AFR) 33.3
± 0.4
32.3
± 0.1
31.6
± 0.1
32.3
± 0.5
32.5
± 0.1
Dilution Ratio (DR) 72.6
± 3.5
94.5
± 7.3
67.8
± 3.1
80.0
± 5.3
80.0
± 7.6
During the University of Toronto test program, the thermocouple that measured exhaust
gas temperature was not functioning at all times; therefore for some fuels the exhaust gas
temperature could not be measured. Also, the torque transducer had failed prior to the beginning
of the University of Toronto test program. Therefore, in order to control the load setting of the
45
engine, the same rack setting was applied to the servomechanism controlling the fuel flow rate
throughout testing. This rack setting was obtained from previous experimental data before the
torque transducer failed. However, this effectively controlled the fuel inflow rate to be constant
throughout the test sequence, as seen previously in tables 10 and 11. As the engine was operating
at quiescent conditions, the air flow rate and air to fuel ratio were also kept fairly constant
throughout testing. Furthermore, theoretical increases in fuel consumption due to a lower heating
value of biodiesel would not be observed. Lastly, the dilution ratio was maintained
approximately from 80 – 90, with some fluctuations.
Table 12: General Electric Testing - Mode 9x Engine Operating Conditions. ± Values
Represent 95% Confidence Intervals
ULSD Soy
B5
Soy
B10
Soy
B20
Soy
B30
AF
B5
AF
B10
AF
B20
AF
B30
Exhaust Gas
Temperature (°C)
272.79
± 3.32
267.10
± 2.90
272.80
± 2.14
269.45
± 2.58
256.92
± 1.94
250.50
± 2.41
261.33
± 1.62
265.90
± 3.63
266.54
± 1.67
Fuel Flow Rate (g/s) 1.58
± 0.01
1.57
± 0.02
1.53
± 0.03
1.58
± 0.02
1.50
± 0.02
1.39
± 0.02
1.58
± 0.02
1.59
± 0.02
1.57
± 0.01
Air Flow Rate (g/s) 76.33
± 2.03
84.00
± 1.90
73.38
± 0.56
75.20
± 2.29
78.78
± 1.70
74.74
± 2.26
80.69
± 2.50
83.96
± 2.11
78.52
± 1.87
Air to Fuel Ratio
(AFR)
48.4
± 1.3
53.5
± 1.2
48.0
± 0.4
47.7
± 1.5
52.6
± 1.1
53.7
± 1.6
51.1
± 1.6
52.7
± 1.3
49.9
± 1.2
Dilution Ratio (DR) 40.7
± 1.1
32.9
± 1.2
32.2
± 0.6
29.7
± 1.9
23.5
± 0.8
34.6
± 1.3
23.1
± 1.5
29.4
± 0.8
33.0
± 0.7
Table 13: General Electric Testing - Mode 2x Engine Operating Conditions. ± Values
Represent 95% Confidence Intervals
ULSD Soy
B5
Soy
B10
Soy
B20
Soy
B30
AF
B5
AF
B10
AF
B20
AF
B30
Exhaust Gas
Temperature (°C)
379.56
± 6.86
368.14
± 2.49
368.22
± 5.45
353.53
± 4.86
352.11
± 0.66
350.78
± 1.68
372.09
± 0.45
385.24
± 4.56
371.71
± 3.78
Fuel Flow Rate (g/s) 4.38
± 0.03
4.25
± 0.07
4.33
± 0.03
4.09
± 0.07
4.20
± 0.03
3.96
± 0.05
4.34
± 0.07
4.36
± 0.06
4.39
± 0.01
Air Flow Rate (g/s) 192.77
± 1.92
192.47
± 0.78
193.37
± 2.64
183.35
± 5.42
191.18
± 1.47
190.96
± 2.67
192.43
± 0.92
202.86
± 1.33
190.18
± 1.21
Air to Fuel Ratio
(AFR)
44.1
± 0.4
45.3
± 0.2
44.7
± 0.6
44.9
± 1.3
45.5
± 0.4
48.2
± 0.7
44.4
± 0.2
46.6
± 0.3
43.3
± 0.3
Dilution Ratio (DR) 50.9
± 3.3
41.2
± 2.5
38.5
± 3.3
48.2
± 3.0
48.2
± 1.5
42.8
± 1.5
67.6
± 10.5
54.7
± 1.1
80.2
± 3.3
46
The torque transducer was replaced prior to the General Electric test program, and the
respective engine operating conditions are shown above in tables 12 and 13. It should be noted
for both the University of Toronto and General Electric test programs, the three test repetitions
were run in series instead of a randomized fashion, where the repetitions were divided equally in
time. This was due to the difficulty of constantly starting and shutting down the engine, and also
in the interest of time. Therefore, the variation between test repetitions may not fully incorporate
the variability in engine operation, although the parameters of engine load, speed, were
attempted to be maintained constant as much as possible.
The 95% confidence intervals were determined from using the dynamometer values from
the three test replicates. The fuel flow rate was determined once or twice during each test
replicate, and the dilution ratio verified two to three times during each test. For the ULSD test
fuel, the exhaust gas temperature was stabilized to the set points of modes 9x and 2x
respectively, by controlling the compressed air inlet pressure which directly influenced the intake
air flow rate. The same air flow rate used for the ULSD test conditions would subsequently be
used for each biodiesel blend in order to keep test conditions as consistent as possible despite
using compressed air.
As seen above in tables 12 and 13, a trend in exhaust gas temperature was observed only
for the soy biodiesel at both modes 9x and 2x, where increasing the biodiesel concentration
decreased the exhaust gas temperature. Relative to the ULSD exhaust gas temperature, using Soy
B30, decreases of 15.9°C and 27.5°C were noted for mode 9x and 2x, respectively. Similar
results were observed by Di et al. (2009b) where a decreasing exhaust gas temperature was
observed with biodiesel blending. However, this trend was not observed for the animal fat
biodiesel blends, where decreases of only 6.3°C and 7.0°C were noted for AF B30 relative to the
ULSD exhaust gas temperature, for modes 9x and 2x respectively. Although slight increases in
fuel consumption were expected due to the decreased heating value of biodiesel blends (Last et
al., 1995; Rakopoulos et al., 2007), as seen in tables 12 and 13 there was a minimal variation in
fuel consumption for both modes with the exception of AF B5, which has a much lower fuel
flow rate compared to the other biodiesel blends; therefore the AF B5 results may be
questionable. No trends were observed in fuel consumption as they were likely not observed
apart from experimental variation, because the decrease in heating value would only be a few
percent even for the B30 blends.
47
Although the air flow rate was kept steady by maintaining the same inlet conditions as
the ULSD tests, due to the pressurized nature of the inlet air flowing through the turbocharger,
the system was more susceptible to ambient temperature and pressure fluctuations compared to
using quiescent air. This may also have had secondary repercussions on the exhaust gas
temperatures. In terms of the air to fuel ratio (AFR), this parameter remained fairly constant
throughout testing, hovering around ~50 and ~44 for modes 2x and 9x, respectively.
The dilution ratio was kept as constant as possible, however due to the finite
combinations of the solenoid valves within the diluter unit which control the dilution ratio; it was
not possible to replicate the exact same dilution ratio for each test. Moreover, slight changes in
exhaust conditions such as pressure and temperature had a direct impact on the diluter operation
and thus dilution ratio. In addition, the precision of the NOx gas analyzer also may have
contributed to the DR variation as the ratio of NOx was used to compute the dilution ratio. In
comparison to the University of Toronto testing, lower values of the dilution ratio were used
during the General Electric testing in order to accumulate more mass on the filters; high mass
was necessary for the SOF analysis.
4.3 Effect of Biodiesel on Engine Exhaust Emissions
4.3.1 Gaseous Emissions
As the University of Toronto test program controlled the fuel flow rate rather than the engine
load, the effect of biodiesel on engine exhaust emissions may not be representative of the true
biodiesel fuelling effects and the results are shown in the appendix. These problems did not
affect the results from the General Electric test program and thus are shown in the following
sections. All of the emissions have been converted to a brake specific basis, in which the
emission rate has been normalized with respect to engine work, which provides a close
approximation to the indicated work of the diesel engine (Heywood, 1988). Emissions in brake
specific terms are generally more useful for heavy-duty off-road engines as these engines are not
primarily built for exceptional mileage or speed, but rather to be able to produce torque or haul
cargo.
48
-5
0
5
10
15
20
25
Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30NO
x Percentage Change (%
) Mode 9x (low load)
Mode 2x (high load)
*Error bars represent 95% confidence intervals
Figure 17: NOx Percentage Change Relative to Ultra-Low Sulphur Diesel Fuel – General
Electric Testing
The absolute values for the brake specific NOx emissions results versus fuel type can be
seen in the appendix (Fig. A-1). The data was calculated from averaging the three test replicates,
and error bars generated from 95% confidence intervals. Figure 17 above, was produced by
normalizing the biodiesel results relative to ULSD fuel. As seen in the mode 9x test condition, an
increase in brake specific NOx emissions was noted with biodiesel blending as, although there
was no consistent trend with the percentage of biodiesel blended. The outlier to this trend was
the AF B5 fuel, which was removed from the graph as it was noted previously to have a lower
fuel flow rate compared to the other fuels. The percentage increase in NOx emissions with
biodiesel fuelling was greater in magnitude for mode 9x compared to mode 2x, although
increases ranging from 2.7 to 13.5% NOx were still observed at mode 2x. This is contrary to the
results of Li et al. (2008) who observed a greater increase of NOx with higher load conditions.
The most widely accepted theory for the increased NOx emissions with biodiesel as seen in the
literature, is due to the effect of the physical properties of biodiesel on injection advance timing,
where the pressure rise produced in pump-line-nozzle systems is quicker due to its lower
compressibility, creating an earlier injection into the combustion chamber allowing a greater
residence time for NOx formation (Krahl et al., 2007; Lapuerta et al., 2008).
Comparing the mode 9x and 2x results (Fig. A-1), the higher load (mode 2x) had much
lower NOx emissions in magnitude compared to the lower load (mode 9x). This is likely a result
of the greater fuel and air flow rate through the combustion chamber, roughly three times greater,
49
which decreases the residence time for NOx formation reactions. Contrasting the General Electric
results to the University of Toronto (Fig. A-1 vs. A-4), the University of Toronto NOx emissions
experienced slight decreases in contrast. For example, for the University of Toronto testing at
mode 2x, NOx emissions decreased by 5.3% and 0.7% for Soy B20 and AF B20, respectively.
-40
-30
-20
-10
0
10
20
30
Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
THC Percentage Change (%
)
Mode 9x (low load)
Mode 2x (high load)*Error bars represent 95% confidence intervals
Figure 18: THC Percentage Change Relative to Ultra-Low Sulphur Diesel Fuel – General
Electric Testing
Figure 18 above illustrates the percentage change in THC emissions relative to ULSD for
both modes 9x and 2x, where the absolute values of THC emissions versus fuel type can be seen
in the appendix (Fig. A-2). For the lower load condition, mode 9x, there was a general decrease
of brake specific THC emissions observed with increased biodiesel blending. A trend of greater
THC reduction with increasing biodiesel percentage was noted only with the animal fat biodiesel
fuel, where decreases of 19.4% THC emissions relative to ULSD emissions were seen for AF
B30. The exception was the AF B5 fuel which was previously noted to have questionable results,
and therefore removed from figure 18. The overall reduction in THC emissions is likely due to
the contribution of oxygen in the fuel (Payri et al., 2009). Moreover, Knothe et al. (2006)
observed similar results in THC reduction, and speculated this may be due to the branched
hydrocarbons and aromatics that are more prominent in petroleum diesel, which generate a less
complete combustion compared to straight chained hydrocarbons that predominantly comprise
biodiesel. Comparing the two modes, there was no trend observed for THC reduction at the
higher load condition (mode 2x), as the effect of biodiesel fuelling caused both increases and
decreases in THC emissions. The results from the University of Toronto testing were similar to
the trends of the General Electric testing (Fig. A-2 vs. A-5), with minor decreases in THC
50
emissions. For instance, for the University of Toronto testing, THC emissions decreased by 5.4%
and 9.7% with Soy B20 at modes 9x and 2x, respectively.
0
1000
2000
3000
4000
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
O2 (g/kW
-hr)
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure 19: Brake Specific Oxygen Emissions versus Fuel Type – General Electric Testing
0
200
400
600
800
1000
1200
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
CO2 (g/kW
-hr)
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure 20: Brake Specific Carbon Dioxide Emissions versus Fuel Type – General Electric
Testing
Figures 19 and 20 above show the results of brake specific oxygen and carbon dioxide
emissions versus fuel type for both modes. As the oxygen and carbon dioxide (CO2) emissions
depend greatly on combustion stoichiometry, a substantial difference would not be expected due
the use of biodiesel fuelling. Figure 19 shows that the oxygen levels remained fairly constant,
and the greatest difference noted was between the two engine operating modes. This is likely due
to the AFR ratio, as the lower load condition (mode 9x) with a greater AFR had higher brake
specific oxygen emissions in comparison to the higher load condition (mode 2x). These higher
oxygen emissions for the lower modes (mode 9 and 9x) are consistent with their higher NOx
emissions. The CO2 emissions seen in figure 20 were fairly consistent for the soy based
51
biodiesel, but relative to the ULSD emissions the animal fat biodiesel CO2 emissions decreased
substantially, which was not expected as the fuel input was minimally different for animal fat
biodiesel. In addition, these results are contrary to what other authors report in the literature,
where increases of CO2 from biodiesel fuelling were observed likely due to a greater
concentration of combustion products, as a result of higher fuel consumption (Dorado et al.,
2003; Fontaras et al., 2009).
4.3.2 Particulate Matter
Particulate matter (PM) emissions were characterized for each biodiesel fuel blend through
gravimetric analysis on Teflon coated Pall Emfab Filters.
-70
-60
-50
-40
-30
-20
-10
0
10
20
30
Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
PM Percen
tage Change (%
)
Mode 9x (low load)
Mode 2x (high load)
*Error bars represent 95% confidence intervals
Figure 21: PM Percentage Change Relative to Ultra-Low Sulphur Diesel Fuel – General
Electric Testing
Figure 21 shows the percentage change in PM relative to the ULSD fuel, and the absolute
brake specific PM emissions versus fuel type can be seen in the appendix (Fig. A-3). For mode
9x, there was no significant trend observed in PM emissions versus fuel type, as the 95%
confidence intervals were greater than the overall percentage change. The one exception noted is
the AF B5 fuel with an increase of 57.9% PM emissions, likely due to the lower exhaust gas
temperature observed resulting in a less complete combustion reaction and therefore greater PM
formation. On the other hand, mode 2x had a noticeable decrease in PM with biodiesel blending,
52
with a greater percentage reduction in soy biodiesel compared to the animal fat biodiesel. Similar
results were noted by Li et al. (2007) where greater decreases in PM were observed with higher
load conditions. Several factors may have contributed to this reduction in PM such as the
absence of aromatics (Schmidt & Gerpen, 1996), oxygenated nature of biodiesel (Lapuerta et al.,
2008), and the amorphous nature of biodiesel soot (Boehman et al., 2005). The effect of PM
reduction was likely noted only for mode 2x due to the elevated load, speed, and combustion
temperature, in comparison to the mode 9x.
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 10 20 30 40
Biodiesel Blend (%)
Back
up / Primary PM
Mode 9x - Soy BD
Mode 9x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 10 20 30 40
Biodiesel Blend (%)
Back
up / Primary PM
Mode 2x - Soy BD
Mode 2x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
Figure 22: PM Mass: Ratio of Backup to Primary Filters – General Electric Testing
As both the primary and secondary backup Teflon coated filters underwent gravimetric
analysis, the ratio of the backup PM to primary PM was calculated and illustrated above in figure
22, with the left graph corresponding to mode 9x, and the right graph corresponding to mode 2x.
A noteworthy trend seen in both modes 9x and 2x was an increase in the ratio of backup to
primary PM with greater biodiesel blend concentration. As the filtration efficiency of particulates
for these filters is greater than 99.5%, the backup filter mass is certainly due to the volatile
organic compounds which penetrate the primary filter but become adsorbed by the backup filter.
The increase of the backup to primary PM ratio suggests that a greater fraction of semi-volatile
53
organic compounds were present for the biodiesel fuels or that the retention of these compounds
on the primary filter was lower for the biodiesel. These compounds may have been gaseous
species that penetrated the primary filter and were then absorbed onto the backup filter, or
compounds that were released from the particulate retained on the primary filter. Furthermore,
the composition, morphology, or mass of particles retained on the primary filter would also
influence the tendency of this filter to adsorb semi-volatile organic compounds. This effect was
more prominently noted for animal fat biodiesel compared to soy biodiesel.
4.3.3 Organic / Elemental / Total Carbon
The elemental carbon / organic carbon (EC/OC) analyses were conducted both by National
Resources Canada (NRC) Canada Centre for Mineral and Energy Technology - Mining and
Mineral Sciences Laboratories (CANMET-MMSL) and in-house at the University of Toronto at
the SOCAAR laboratory, using similar Sunset EC/OC laboratory instruments. Two quartz filters
were placed in series to collect diesel exhaust emissions, where the backup filter was used to
correct for the positive organic artifact caused by volatile organic compounds. The results from
only the General Electric testing will be discussed as there is a greater certainty with regards to
engine operating conditions, while the results from the University of Toronto testing are included
in the appendix.
54
Figure 23: Organic Carbon: Ratio of Backup to Primary Filter OC
A plot of the ratio of backup OC to primary filter OC is shown above in figure 23, where
the left and right graphs illustrate mode 9x and 2x, respectively. This plot is very similar to the
ratio of backup to total PM shown previously in figure 22, where similar trends of increasing
backup to total OC with biodiesel blending were observed. Comparing the two modes however,
it appears that the effects were more noted in mode 2x compared to mode 9x, as the ratio in
backup OC to total OC increased by 30% for Soy B30 and 43% for AF B30 relative to the ULSD
test fuel, for mode 2x. However, the overall values were higher for mode 9x than mode 2x, with
the highest value for B30 for mode 9x at 0.50 backup to primary filter OC.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 10 20 30 40
Biodiesel Blend (%)
Back
up / Primary O
C...
Mode 9x - Soy BD
Mode 9x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 10 20 30 40
Biodiesel Blend (%)
Back
up / Primary O
C...
Mode 2x - Soy BD
Mode 2x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
55
y = 0.8158x + 0.0029
R2 = 0.6183
0.00
0.05
0.10
0.15
0.20
0.25
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Backup PM / Primary PM
Back
up O
C / Primary O
C + EC…
.
Figure 24: Correlation Plot between Backup/Primary Ratios of Quartz and Teflon Filters
A comparison between figures 22 and 23 can be made by including the EC in addition to
the OC to the primary quartz filter, in order to approximate the total PM as TC. The
corresponding correlation scatter plot is shown in figure 24, where the ratio of backup OC to
primary OC + EC (representing total carbon or PM) on quartz filters is 82% of the backup PM to
primary PM on the Teflon filters. This difference may be attributed to the other components that
constitute the remaining PM speciation such as sulphates, or different penetration efficiencies of
gaseous components of various molecular weights that get absorbed by the backup filter.
Furthermore, this difference may also be attributed to the 1.4 value that typically describes the
ratio organic carbon to organic mass (OM), which accounts for the oxygen and other non-carbon
atoms in the organic molecule (Russell, 2003).
During the OC segment of the EC/OC analysis, there were four different temperature
ramps used to evolve the OC off the quartz filters in an oxygen-free helium environment,
temperature steps of 310, 475, 615 and 870°C. Therefore, it was possible to segregate the OC
response from the FID detector for each of these particular temperature ramps: OC1, OC2, OC3,
and OC4 which constitutes the overall OC.
56
0%
10%
20%
30%
40%
50%
60%
70%
80%
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
OC1
OC2
OC3
OC4
Figure 25: General Electric Testing Partitioning of OC1, OC2, OC3, and OC4 – Mode 9x
0%
10%
20%
30%
40%
50%
60%
70%
80%
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
OC1
OC2
OC3
OC4
Figure 26: General Electric Testing Partitioning of OC1, OC2, OC3, and OC4 – Mode 2x
Figures 25 and 26 above illustrate the partitioning of OC1, OC2, OC3, and OC4 on the
primary filters for modes 9x and 2x, as a percentage of the overall organic carbon mass. There
was virtually no variance within the whole spectrum of biodiesel blends and load conditions. In
addition, the percentage of OC was greatest in the OC1 partition, indicating that the volatility of
OC collected on the quartz filters was mostly due to compounds released at less than 310 °C.
These results are comparable to those of Zhang et al. (2011) who noted the majority of their
volatile organic carbon species came from the OC1 partition, which was less than 450 °C for
their study. However, the 90% distillation temperature of the fuels is about 350 °C for both
biodiesels and petroleum diesel, indicating that that the majority of combustion products consist
largely of smaller hydrocarbons and intermediates from the combustion process which cannot be
differentiated from the OC partitioning.
57
0.00
0.02
0.04
0.06
0.08
0.10
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
OC, EC, TC (g/kW-hr)...
OC
EC
TC
*Error bars represent 95% confidence intervals
Figure 27: Brake Specific Organic, Elemental, Total Carbon
Emissions – General Electric Testing Mode 9x
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
OC, EC, TC (g/kW
-hr)...
OC
EC
TC
*Error bars represent 95% confidence intervals
Figure 28: Brake Specific Organic, Elemental, Total Carbon
Emissions – General Electric Testing Mode 2x
The brake specific results for OC, EC, and TC are illustrated above in figures 27 and 28
for both modes 9x and 2x. The OC and TC results were corrected for the positive organic artifact
by subtracting the results of the backup filter from the primary quartz filter. Implicit to this
correction method was the assumption that the organic carbon on the backup filter was due to the
gaseous compounds adsorbed by the filter and not due to desorption from the particles retained
on the primary filter. In figure 27, mode 9x, the patterns observed from the TC measurement are
similar to the PM trends in figure 4. Also, the overall magnitude of the PM and TC
concentrations are comparable as the majority of diesel PM is composed of EC and OC. A
decreasing trend in EC was observed with increasing biodiesel blend percentage, which again is
58
likely due to the oxygenated nature of the biodiesel fuels causing a more complete combustion
reaction. Similar results were not seen for OC, where no significant trend was observed. Results
from Cheung et al. (2009) show similar results where biodiesel fuelling decreased the EC
emissions by 82.8% while the OC emissions remained fairly constant, relative to ULSD
emissions. In figure 28, mode 2x, similar trends exhibited decreasing TC and EC emissions,
though there was no apparent trend noted for the animal fat biodiesel. Further correlation
between OC and EC speciation and PM will be seen in the mass reconstruction section.
4.3.4 p-PAH’s
Particle bound polyaromatic hydrocarbons (p-PAHs) were tested only during the University of
Toronto test program, where the average readings of two identical Ecochem PAS2000 units were
reported.
Table 14: Brake Specific p-PAH Emissions Percentages Change Relative to ULSD Values –
University of Toronto Testing. ±Values Represent 95% Confidence Intervals
Soy B5 Soy B20 AF B5 AF B20
Mode 9 7.00
± 0.45
14.70
± 0.45
6.74
± 0.47
5.54
± 0.57
Mode 2 -11.08
± 0.97
-20.94
± 1.03
-13.14
± 0.99
-20.96
± 1.01
Table 14 above shows the brake specific p-PAH emissions percentage decrease, relative
to ULSD emissions, and the absolute brake specific PM emissions versus fuel type can be seen
in the appendix (Fig. A-13). Positive values represent increases in p-PAH emissions, and
negatives values represent a decrease. As p-PAH emissions are highly correlated with black
carbon and soot emissions, it is not surprising that the p-PAH emissions decreased with the use
of biodiesel similarly to PM and TC (EcoChem Analytics, 2005). However, the results at mode 9
did not follow this trend as the p-PAH emissions increased slightly. Chien et al. (2009) observed
similar results to the mode 2 trends, where decreasing p-PAH emissions were noted with
biodiesel blending and they attribute this to the oxygenated nature of biodiesel causing a more
complete combustion. Comparable trends were also observed by others (Sharp et al., 2000;
59
Karavalakis et al., 2010), although for PAHs both in the particle and gaseous phase collectively,
which they speculate is due to the lack of aromatics in biodiesel fuel.
4.3.5 Particle Size Distribution
A scanning mobility particle sizer (SMPS) was used to determine the particle size distribution
(PSD) of the diesel engine exhaust, with a range of particle diameters from 3.16 to 104.5 nm.
The PSDs were normalized in order to facilitate comparison of data from different instruments,
which may use a different number of discrete particle size bins. The results have also been
transformed from particles per cubic centimeter to particles per kW-hr in a similar fashion to the
gaseous and PM emissions, in order to give results on a brake specific basis.
0.00E+00
1.00E+14
2.00E+14
3.00E+14
4.00E+14
5.00E+14
6.00E+14
7.00E+14
8.00E+14
0 20 40 60 80 100 120
Particle Diameter (nm)
Mode 2 dN/dP (# particles/kW
-hr)...
0.00E+00
5.00E+13
1.00E+14
1.50E+14
2.00E+14
2.50E+14
3.00E+14
Mode 9 dN/dP (# particles/kW
-hr)…
Mode 2
Mode 9
Mode = 40.0 nm
*Error bars represent 95% confidence intervals
Mode = 59.4 nm
Figure 29: ULSD Mode 9 versus Mode 2 Particle Size Distributions –
University of Toronto Testing
A comparison between modes 2 and 9 from the University of Toronto test program is
shown above in figure 29, where the two scatter plots have been placed on separate y-axes in
60
order to compare the shape of the curves. The overall number of particles for mode 2 is higher
than mode 9 as expected because of the greater PM emissions associated with mode 2. The
overall particle size distribution is highly dependent on the engine operating condition as seen by
others (Fontaras et al., 2009; Heikkilä et al., 2009; Zhang et al., 2011). The modal diameter for
mode 2, 40.0 nm, is smaller than the modal diameter for mode 9, 59.4 nm, also observed by other
authors (Chung et al., 2008; Di et al., 2009b).
0.00E+00
2.00E+14
4.00E+14
6.00E+14
8.00E+14
1.00E+15
1.20E+15
0 20 40 60 80 100 120
Particle Diameter (nm)
Mode 2x dN/dP (# particles/kW
-hr)......
0.00E+00
2.00E+13
4.00E+13
6.00E+13
8.00E+13
1.00E+14
1.20E+14
1.40E+14
1.60E+14
1.80E+14
Mode 9x dN/dP (# particles/kW
-hr)...…
Mode 2x
Mode 9xMode = 57.3 nmMode = 46.1 nm
*Error bars represent 95% confidence intervals
Figure 30: ULSD Mode 9x and Mode 2x Particle Size Distributions –
General Electric Testing
The resulting PSDs from the General Electric Test program are shown above in figure 30.
In comparison to the PSDs for the University of Toronto testing, the shape of the overall curves
are very similar, however the mode 2x has a slightly higher modal diameter of (46.1 vs. 40.0 nm)
while the modal diameters for mode 9 to mode 9x are similar, (57.3 vs. 59.4 nm).
Typically, diesel particulate matter emissions exhibit a tri-modal size distribution. These
three size modes are known as the nucleation mode, accumulation mode, and coarse mode
particles (Kittelson, 1998). The U of T Cummins engine does not appear to generate any
61
nucleation mode particles, generally particles with a diameter less than 50 nm, despite having
appropriate dilution conditions for nucleation mode formation. It is hypothesized that the
nucleation mode particles are being scavenged due to the large amount of carbonaceous soot
(Kittelson et al., 2006a), generated by the U of T Cummins engine as it only meets USEPA Tier
1 emission standards. The high soot concentrations in combination with the long residence time
in the exhaust tailpipe also allows the amorphous structure of soot particles, with an extremely
high surface area, to cause precursor gases to condense, or the nucleation mode particles to
coagulate onto the soot particles. A similar issue was encountered by Kirchner et al. (2009) who
observed nucleation mode particles only during cold start conditions and not after the engine was
warmed up.
The coarse mode particles, consisting of accumulation mode particles which deposited on
surfaces within the engine cylinder or exhaust system before being re-emitted (Kittelson et al.,
1998), were also not detected due to having a diameter of greater than several micrometers,
which is out of the range of the SMPS. Therefore the only particles measured were accumulation
mode particles, which are mainly carbonaceous agglomerates as well as materials which adsorb
onto the particles and also constitute the majority of PM mass.
0.E+00
4.E+16
8.E+16
1.E+17
2.E+17
2.E+17
0 20 40 60 80 100 120
Particle Diameter (nm)
dN/dlogdP (#particles/kW
-hr)..
ULSD
Soy B30
AF B30
Figure 31: Mode 9x Particle Size Distributions versus Fuel Type – General Electric Testing
62
0.E+00
2.E+17
4.E+17
6.E+17
8.E+17
1.E+18
1.E+18
0 20 40 60 80 100 120
Particle Diameter (nm)
dN/dlogdP (#particles/kW
-hr)..
ULSD
Soy B30
AF B30
Figure 32: Mode 2x Particle Size Distributions versus Fuel Type – General Electric Testing
The effect of biodiesel fuelling on the PSDs from the General Electric testing for modes
9x and 2x is shown above in figures 31 and 32, where only the 30% biodiesel blends are shown
for clarity. For mode 9x (figure 31), there was no clear correlation between increasing biodiesel
blend concentration and number of particles. In contrast, mode 2x (figure 32) shows a decrease
in the total number of particles compared to the ULSD test condition, where the decrease in
particles is greatest for soy biodiesel. As the PSDs primarily display the accumulation mode
particles, which are representative of soot particles that constitute the majority of diesel
particulate matter mass, these results correspond to the trends for PM shown previously in figure
21. However, the modal diameter does not display any relationship with increasing biodiesel
blend concentration, and thus is primarily a function of engine operating condition rather than
fuel type. Some authors have observed similar results with regards to the mean particle diameter
using biodiesel (Turrio-Baldassarri et al., 2004), while conversely other authors noticed a
decrease in the mean particle diameter with biodiesel fuelling which they speculate may be due
to changes in the soot morphology (H. Jung et al., 2006; Ballesteros et al., 2008; Lapuerta et al.,
2009).
63
4.3.6 Anions / Cations
Speciation of water-soluble anions and cations was conducted by ion chromatography, a
technique in which individual ions are separated and quantified based on their ionic interactions
of the solute and chromatography column. These tests were conducted in-house at the University
of Toronto using a Dionex ICS-2000 system with an auto-sampler.
5.0 6.3 7.5 8.8 10.0 11.3 12.5 13.8 15.0 16.3 17.5 18.8 20.0 21.3 22.5 23.8
1 - Fluoride - 5.385
2 - Acetate - 5.852
3 - Formate - 6.120
4 - Chloride - 9.890
5 - Nitrite - 11.343
6 - Nitrate - 16.307
7 - Sulphate - 21.115
8 - Oxalate - 21.650
Figure 33: Representative Diesel Engine Exhaust Anion Chromatograph
Figure 34: Representative Diesel Engine Exhaust Cation Chromatograph
Representative anion and cation chromatograph for water-soluble diesel exhaust extract
collected on quartz filters are shown above in figure 33 and 34. Although a number of ions were
64
detected using this method, after blank filter correction and comparison to laboratory standard
solutions, only nitrite, nitrate, sulphate, and ammonium ions were present in sufficient quantities
to be able to be quantified for both the University of Toronto and General Electric test programs.
Additionally, chloride, phosphate, and potassium ions could be identified for the University of
Toronto testing.
Table 15: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
University of Toronto Testing Mode 9. ±Values Represent 95% Confidence Intervals
Soy B5 Soy B20 AF B5 AF B20
Chloride -57.8
± 30.3
24.1
± 43.3 -69.6
± 52.8
-28.4
± 106
Nitrite -43.9
± 56.5
3.70
± 26.0
1.14
± 56.3 -73.3
± 49.2
Nitrate 11.5
± 25.6 53.2
± 9.97
88.6
± 19.3
101
± 35.0
Sulphate 66.0
± 117 -54.7
± 33.6
-17.6
± 30.8
-17.9
± 40.9
Phosphate -48.3
± 67.9
-22.8
± 98.1
44.1
± 117 -119
± 94.7
Ammonium 22.8
± 83.1
-3.0
± 16.8 34.1
± 33.7
7.37
± 18.5
Table 16: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
University of Toronto Testing Mode 2. ±Values Represent 95% Confidence Intervals
Soy B5 Soy B20 AF B5 AF B20
Chloride -61.6
± 73.1 -81.4
± 71.3
-84.7
± 75.6
-60.2
± 66.9
Nitrite -75.9
± 45.0
-87.9
± 37.4
-43.6
± 106 -79.9
± 56.5
Nitrate -30.2
± 35.4 77.5
± 51.9
61.7
± 36.0
-39.8
± 32.6
Sulphate -17.7
± 57.5
-13.7
± 47.2
57.8
± 63.2
-10.7
± 48.3
Phosphate -48.9
± 28.4
-39.5
± 40.6 -24.8
± 24.5
-85.8
± 21.4
Ammonium 20.2
± 127 -25.9
± 20.4
5.20
± 18.4 -60.8
± 20.0
65
Tables 15 and 16 above show that a few of the the anions and cations percentage
increased relative to ULSD emissions for the University of Toronto testing. Bolded values
represent results that were greater than the 95% confidence intervals. The overall magnitude of
the ion emissions can be seen in the appendix (Fig. A-14, A-15), where it was noted that the
dominant ion emissions were nitrate ions. For mode 9, a number of the biodiesel blends observed
increased compared to ULSD emissions. Mode 2 showed decreased chloride, nitrite, and
phosphate emissions for several of the biodiesel blends.
Table 17: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
General Electric Testing Mode 9. ±Values Represent 95% Confidence Intervals
Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
Nitrite -64.6
± 95.4
-0.83
± 141
-40.9
± 98.4
-26.7
± 77.6
23.6
± 125
-25.1
± 78.0
3.56
± 113
27.1
± 163
Nitrate 5.54
± 45.3 -47.4
± 27.2
26.5
± 27.9
35.6
± 39.5
30.2
± 59.3
31.8
± 33.8
23.5
± 66.8 57.1
± 23.4
Sulphate 22.8
± 53.4
-9.59
± 8.20
-10.9
± 18.0 -20.5
± 16.1
35.8
± 29.7
-6.62
± 38.6 -31.0
± 27.8
3.23
± 14.1
Ammonium -31.3
± 52.3
-46.5
± 56.4
-5.30
± 44.6
-2.01
± 49.5
30.3
± 82.0
-7.25
± 44.0
9.04
± 44.8
9.10
± 44.4
Table 18: Brake Specific Anion and Cation Change Percentages Relative to ULSD Values –
General Electric Testing Mode 2. ±Values Represent 95% Confidence Intervals
Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
Nitrite -9.89
± 82.1
-10.1
± 82.9
25.8
± 138
7.23
± 97.9
-84.6
± 65.5
87.5
± 340
-70.9
± 66.7
-49.8
± 96.7
Nitrate 19.0
± 27.8
19.2
± 20.6 35.9
± 36.7
78.4
± 24.8
61.2
± 22.3
107
± 28.0
-36.9
± 19.0
98.6
± 28.0
Sulphate 4.12
± 24.0
-5.42
± 13.7
-4.44
± 32.7 21.7
± 16.4
47.1
± 14.7
64.1
± 42.3
-38.5
± 14.6
80.8
± 39.2
Ammonium 4.89
± 16.6
14.6
± 54.2
57.6
± 78.2 45.9
± 27.0
32.9
± 16.0
87.0
± 36.8
24.2
± 23.3
70.8
± 39.4
Tables 17 and 18 above show the anion and cation emission results from the General
Electric test program, as a percentage decrease relative to ULSD emissions. Bolded values
represent results that were greater than the 95% confidence intervals. The absolute ion emissions
versus fuel type can be seen through the appendix (Fig. A-16, A-17). Comparing the magnitude
of the University of Toronto and General Electric test program ion emissions which can be seen
66
in the appendix, the overall magnitude of the concentrations are lower for the General Electric
testing unexpectedly, noticeably for the nitrate ions. Table 17 indicates that emissions of nitrates
generally increased with biodiesel fuelling, similar to the University of Toronto testing results,
however only one values was statistically significant. This increase may be attributed to the
higher levels of NOx also observed with biodiesel fuelling, but a similar trend was not observed
for nitrites. Comparing the results with the work of others (Cheung et al., 2009), similar results
were seen with respect to increasing nitrate emissions with biodiesel. However, their
measurements (Cheung et al., 2009) revealed increases in sulphate and decreases in ammonium
which were not observed during the University of Toronto test program
Table 18, which illustrates the results from mode 2x, exhibited similar trends seen in
mode 9x with increased nitrate emission for several of the fuel blends. Comparing mode 9x and
2x ion emissions (Fig. A-16, A-17), the main difference was the magnitude of the ion emissions
which reflect the PM emission rates.
Although the sulphate emissions were expected to decrease as biodiesel contain virtually
no fuel sulphur, previous studies by Liang et al. (2000) have found that almost all of the sulphate
content in fuel appears as SO2 in the exhaust. Thus, in the absence of a catalyst to produce SO3
and subsequently sulphate, the ultra low levels of sulphur in the fuels would not be expected to
contribute much to sulphate emissions, and therefore lubricating oil should be the primary source
of sulphate in the exhaust. Additionally, Kapetanović et al. (2009) found that the use of soy B20
blends in this same engine increased the amount of sulphate in undiluted exhaust. The increase
was attributed to an increase in lubricating oil consumption caused by interactions of the
biodiesel fuel spray with the lubricant film on the cylinder wall. The difference between the
results of Kapetanović et al. (2009) and the current project may also be due to the much lower
sulphur content of the CJ-4 lubricating oil used in the present tests.
67
4.4 Chemical Correlations
4.4.1 PM Mass Reconstruction
In order to qualify the results generated during this project, a mass balance was performed by
comparing the summation of the PM speciation with PM mass gravimetrically. A mass
reconstruction was also performed on the University of Toronto testing, but as the reliability of
the PM measurements at the time was questionable, the results are not discussed here and are
placed in the appendix.
0%
20%
40%
60%
80%
100%
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
Percentage of PM (%
)
EC
OC
Calcium
Potassiuum
Ammonium
Sodium
Oxalate
Sulphate
Nitrate
Nitrite
Chlroide
Formate
Acetate
Average = 77.55% PM Mass Reconstruction
Figure 35: Percentage of PM Mass Reconstruction - Mode 9x General Electric Testing
0%
20%
40%
60%
80%
100%
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
Percentage of PM (%)
EC
OC
Calcium
Potassiuum
Ammonium
Sodium
Oxalate
Sulphate
Nitrate
Nitrite
Chlroide
Formate
Acetate
Average = 90.08% PM Mass Reconstruction
Figure 36: Percentage of PM Mass Reconstruction - Mode 2x General Electric Testing
68
Figures 35 and 36 above shows the PM mass reconstruction for modes 9x and 2x of the
General Electric test program, constructed by summing the EC, OC, anion, and cation results and
normalizing with respect to the PM emissions. Mode 9x, shown in figure 35, shows an average
mass reconstruction of 77.55%, with the majority of the mass reconstruction originating from
OC and EC detected by the Sunset OC/EC analyzer. The anions and cations constituted less than
a few percent of the total PM mass. The mode 2x results as seen in figure 36 have a greater
percentage of PM mass reconstruction, an average of 90.08% over the span of the biodiesel
blends. This is likely due to a greater fraction of EC in the diesel exhaust at the higher load
condition. The results indicate that there are 22.45% of species for mode 9x that were not
detected and constitute the rest of the PM and 9.98% for mode 2x. The undetected species may
be partially attributed to the difference in OC and organic matter (OM), where OM accounts for
the oxygen and other non-carbon atoms in the organic molecule. 100% mass balance was
achieved by multiplying OC values by OC:OM correction factors of 1.84 and 1.27, for modes 9x
and 2x respectively. The typical OC:OM ratio for atmospheric particles is 1.4 (Russell, 2003).
Similar results were seen from previous mass balances conducted by Schauer et al.
(1999), who reconstructed 30.4% OC, 30.8% EC, 1.0% sulphate ions, 0.73% ammonium ions,
along with detectable amounts of iron, silicon and zinc, and 31% that could not be identified. A
more recent study by Liu et al. (2010) with engines equipped with newer technology, found 48%
EC, 44% OM, and 8% inorganic ions / metallic species from a 2004 engine, and 21% EC, 35%
OM, 44% inorganic ions / metallic species from a newer 2007 engine.
4.4.2 Water Soluble Carbon to Organic Carbon Ratio
The water soluble organic carbon to organic carbon ratio (WSOC/OC) was conducted by first
analyzing the WSOC content using a Shimadzu Total Organic Carbon analyzer from the quartz
filter extracts. These values were divided by the organic carbon generated using a different
portion of the same quartz filter with the thermal-optical Sunset EC/OC instrument, to yield the
fraction of WSOC/OC.
69
0%
20%
40%
60%
80%
100%
ULSD Soy B5 Soy B20 AF B5 AF B20
WSOC/O
C (%
)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure 37: Water Soluble Organic Carbon to Organic Carbon Ratio –
University of Toronto Testing
The WSOC/OC was conducted only for the University of Toronto testing, and the results
can be seen in figure 37. The ratio of WSOC/OC decreased with biodiesel fuelling, which was
likely due to the tighter distillation curves of the neat biodiesel fuels compared to petroleum
diesel fuel, as the biodiesel fuels are primarily composed of methyl esters that are straight
chained and have little branched or aromatic compounds (Basha et al., 2009). Therefore, the
biodiesel fuels will have a smaller fraction hydrophobic and a greater fraction of hydrophilic
species. The WSOC/OC ratios observed in this study are similar to those of vehicles with
uncatalyzed diesel particulate filters, where a study by Biswas et al. (2009b) found WSOC/OC
ratios of 60 ~ 100%. The same study indicated that retrofitted vehicles with catalyzed filters
reduced the OC solubility, causing a WSOC/OC reduction to 8 – 25 % (Biswas et al., 2009b;
Cheung et al., 2009).
70
4.4.3 Chemical Correlation Regression Analysis
A statistical correlation between emission factors of the major chemical PM constituents, PM
emissions, and exhaust gas temperature was performed. Coefficients of statistical determination
(R2) were generated and are tabulated below in table 19, where coefficients bolded in black
indicate coefficients greater than 0.80.
Table 19: Pearson Correlation Coefficient (R2) between Chemical PM Constituents
EGT PM NO2- NO3
- SO4
2- PO4
3- NH4
+ WSOC WSTN
p-
PAH OC EC
EGT 1.00
PM 0.66 1.00
NO2- 0.65 0.19 1.00
NO3- 0.57 0.25 0.42 1.00
SO42- 0.42 0.35 0.39 0.62 1.00
PO43- 0.94 0.87 0.60 0.43 -0.02 1.00
NH4+ 0.75 0.46 0.57 0.74 0.70 0.75 1.00
WSOC 0.61 0.63 0.36 -0.08 -0.07 0.63 0.75 1.00
WSTN 0.76 0.70 0.44 0.61 0.28 0.67 0.30 0.31 1.00
p-PAH 0.98 0.93 0.22 0.25 -0.15 0.81 0.62 0.64 0.45 1.00
OC 0.59 0.94 0.11 0.31 0.57 0.28 0.53 0.47 0.64 0.62 1.00
EC 0.81 0.92 0.30 0.55 0.74 0.89 0.80 0.70 0.95 0.89 0.86 1.00
The major products of combustion, EC, OC, p-PAHs, and PM correlated very well with
each other, as expected as these factors are mostly influenced by the overall PM emission rate. In
addition, emissions of phosphate ions also correlated fairly well (R2
= 0.87) with PM emissions.
A lack of a high correlation coefficient would symbolize a dependence that is not simply
influenced by engine operating condition, but rather by fuel type. Similar statistical correlation
analyses conducted by other researchers, found similar good correlations of EC with light PAHs
such as naphthalene, phenanthrene, pyrene (Geller et al., 2006), and sulphate and ammonium
ions (Biswas et al., 2009a). Strong correlations found between WSOC and OC by others (Biswas
et al., 2009a), were not found to be as significant in this study (R2 = 0.47).
71
4.4.4 NO3- / SO4
2- as a Biodiesel Marker
From the previous regression correlation analyses and ion emission rates versus fuel type,
attempts were made to find correlations between chemical constituents that varied differently due
to biodiesel blending. The pairings of chemical species that gave the greatest difference with the
use of biodiesel blending were the ratios of NO3- to SO4
2- and K
+ to SO4
2-. These ratios are
indicative of biodiesel emissions, as NO3- and K
+ emissions both increased as a result of
biodiesel blending, which is speculated to be from biodiesel processing. The SO42-
emissions
however, decreased slightly likely due to the lower fuel sulphur levels compared to petroleum
diesel fuel. However, the ratio of K+ to SO4
2- graphs were not included in this section due to the
high experimental variation associated with the results. These graphs and can be seen in the
appendix (Fig. A-19, A-20).
0
5
10
15
20
25
ULSD Soy B5 Soy B20 AF B5 AF B20
NO
3- / SO
42-
Mode 9
Mode 2
*Error bars reprsent 95% confidence intervals
Figure 38: Ratio of Nitrate to Sulphate – University of Toronto Testing
72
0
2
4
6
8
10
12
14
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
NO
3- / SO
42-
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure 39: Ratio of Nitrate to Sulphate – General Electric Testing
The plots of the ratio of NO3- to SO4
2- are shown above in figures 38 and 39, for the
University of Toronto and General Electric testing, respectively. The ratio of NO3- to SO4
2-
increased quite significantly for the University of Toronto testing seen in figure 38, especially in
the case for mode 2x with soy biodiesel in comparison to animal fat biodiesel. For the lower load
condition (mode 9x), both the animal fat and soy biodiesel ratios of NO3- to SO4
2- increased
compared to the baseline ULSD test fuel. The General Electric test results as seen above in
figure 39 showed a similar increase in the ratio of NO3- to SO4
2- with the use of biodiesel;
however the overall increases were not as great especially considering experimental variation.
4.5 Particulate Matter Volatility
Three different methods were used as indicators for particulate matter volatility throughout this
project. The first two which attempt to describe similar characteristics of the particulate matter,
are the ratio of OC/TC and the SOF which were performed off-line from PM collected on filters.
However, the OC/TC and SOF detection procedures are very different fundamentally as the
OC/TC ratio employs a thermal-optical method while the SOF uses solvent extraction. The third
method of measuring particle volatility were the measurements with a thermodenuder conducted
in real-time during sampling of exhaust from the diesel engine. Only the SOF results are
73
available for the General Electric testing, while the thermodenuder results are only available for
the University of Toronto testing.
4.5.1 Organic Carbon / Total Carbon vs Soluble Organic Fraction
Another result generated from the EC/OC analysis was the ratio of organic carbon to total carbon
(OC/TC).
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 10 20 30 40
Biodiesel Blend (%)
OC / TC
Mode 9x - Soy BD
Mode 9x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 10 20 30 40
Biodiesel Blend (%)
OC / TC
Mode 9x - Soy BD
Mode 9x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
Figure 40: OC/TC Ratio versus Biodiesel Blend % - General Electric Testing
As seen above in figure 40, the OC/TC ratio does not exhibit any clear trend throughout
the biodiesel blends in both modes 9x and 2x. This is slightly unexpected; theoretically the
enhanced combustion of biodiesel should cause a greater decrease in EC, or soot, than in OC,
thereby increasing the OC/TC ratio. However, as the OC results are corrected for using a
secondary backup quartz filter and the backup filter OC concentration is much higher with
biodiesel blends as seen before in figure 23, this may counteract the potential increase in OC/TC.
In contrast, Zhang et al. (2011) found a dramatic increase in OC/EC from 3 to 8 times in their
74
study, however the authors acknowledged that their OC results may be artificially high as they
did not apply a backup filter in their study. Furthermore, Chung et al. (2008) also observed a
greater OC/EC ratio with biodiesel in comparison to diesel fuel at three different load conditions.
The soluble organic fraction (SOF) was determined by performing a methylene chloride
Soxhlet extraction using on the loaded Teflon filters, and the percentage of PM mass that was
removed is the SOF. The SOF testing was conducted by the Southwest Research Institute
(SWRI).
Figure 41: SOF versus Biodiesel Blend % - General Electric Testing
Figure 41 above illustrates the SOF versus biodiesel blend concentration, where the left
graph shows the mode 9x results, and the right graph shows the mode 2x results. For the left
graph (mode 9x), the overall trend for both biodiesel fuel types is an increasing SOF with
increasing biodiesel blend concentration. The right graph (mode 2x) shows a much higher
increase in SOF with the use of biodiesel fuel, which again was similar for both biodiesel fuel
types, although a greater SOF increase with the low concentrations of animal fat biodiesel was
observed compared to soy biodiesel. Contrasting the two load conditions, the 30% increase in
SOF seen in mode 2x was much greater than the 10% increase in SOF for mode 9x, both results
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0 10 20 30 40
Biodiesel Blend (%)
SOF (%
)
Mode 2x - Soy BD
Mode 2x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0 10 20 30 40
Biodiesel Blend (%)
SOF (%)
Mode 9x - Soy BD
Mode 9x - AF BD
*Error bars represent 95% confidence intervals
(ULSD)
75
comparing B30 and ULSD fuels. Likewise, other authors have noted increasing SOF of PM with
biodiesel fuelling (Turrio-Baldassarri et al., 2004; Knothe et al., 2006; Arapaki et al., 2007). The
increase in SOF has been linked to the oxygen content of the fuel, which is greater for biodiesels
(Di et al., 2009a).
The SOF analysis was conducted on both the primary and backup Teflon filters
simultaneously in an attempt to give an indication of the SOF of the gas and particle phases of
diesel exhaust, although with only one backup filter there will be some penetration of gaseous
compounds through the filters. Therefore in order to conduct a fair comparison of SOF and the
EC/OC results, the backup filter OC was added to the TC collected on the primary filter in order
to account for the gaseous components, where conversely the backup filter was subtracted from
the primary TC to remove interference from organic carbon vapours for the EC/OC results.
y = 0.41x + 0.50
R2 = 0.43
y = 0.64x + 0.40
R2 = 0.14
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
SOF (%)
OC / (TC + Backup OC)...
Mode 9x
Mode 2x
Linear (Mode 9x)
Linear (Mode 2x)
.
Figure 42: Comparison of EC/OC and SOF Results
A scatter plot comparing the EC/OC and SOF is shown above in figure 47, separated into
both modes 9x and 2x. The OC to TC plus backup OC ratio is much higher than the SOF at both
engine load conditions. There is also a dependence on load, as seen from the slopes of the linear
regression analysis, the SOF measures 40.9% (for mode 9x) and 63.5% (for mode 2x) of the
value calculated by the OC to TC plus backup OC ratio. Although the EC/OC and the SOF
76
analysis strive to give similar information about the speciation of the diesel PM, the fundamental
differences in the measurement methodology may cause discrepancies in the results. In addition,
the filter material was composed of dissimilar materials; moreover, the SOF extraction efficiency
greatly depends on the solvent used, as other extraction solvents such as toluene and ethanol may
have been used in place of methylene chloride.
4.5.2 Nonvolatile Fraction versus Particle Diameter
The final volatility results were generated from the fraction of nonvolatile particles using the
thermodenuder, which was only conducted during the University of Toronto testing. The
thermodenuder temperature was set to 265 °C which as been found to remove virtually all the
volatile species (Kittelson et al., 2006a). The effect of thermodenuder temperature on particle
size distribution from 200 to 293 °C was studied by Maricq et al. (2002), who found that the
temperature had little effect on accumulation mode particles, and that the nucleation mode
particles were most affected. Nucleation mode particles were not detected during these
experiments. Although the nonvolatile fraction of particles was calculated for all 5 biodiesel
blends during the University of Toronto testing, there was no variance observed considering fuel
type and the resulting charts can be seen in the appendix (Fig. A-21, A-22). This is contrary to
the work of other authors, who noted that rapeseed methyl ester biodiesel has a higher fraction of
nonvolatile particles (Heikkilä et al., 2009).
77
0%
10%
20%
30%
40%
50%
60%
70%
80%
0 20 40 60 80 100 120Diameter (nm)
Nonvolatile Fraction (%
)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure 43: Nonvolatile Fraction versus Particle Diameter – University of Toronto Testing
The results from the different fuel types were averaged, and 95% confidence intervals
were generated from the variance from the fuel type, seen above in figure 43. The engine
operation condition had a significant impact on the fraction of nonvolatile particles. For mode 9
(lower load), there was a greater nonvolatile fraction of particles across all particle diameters.
Changes in the nonvolatile fraction of particles as a result of engine operating conditions were
also noted by other authors (Biswas et al., 2008).
78
5 Conclusions and Recommendations
5.1 Conclusions
In this research study, the effect of biodiesel fuelling from animal fat and soy based biodiesel
was conducted on two separate test programs with similar operating conditions, the University of
Toronto test program, and the General Electric test program. A fuel characterization was
conducted on both neat soy and animal fat biodiesels. Two parameters which may have
influenced the emissions between the biodiesels include the higher kinematic viscosity and total
suphur content of the animal fat biodiesel.
From the General Electric test program results, brake specific THC emissions decreased
with increasing biodiesel blend percentage for mode 9, while NOx emissions experienced a slight
increase, more prominently noted at mode 9x than mode 2x. PM emissions decreased with the
use of biodiesel for the high load condition (mode 2x), but not at low load condition (mode 9x).
The soy biodiesel also had a greater PM reduction when compared to the animal fat biodiesel.
For both PM and OC, the ratio of backup to primary filter concentrations increased with
the use of biodiesel, potentially indicating a greater gas-particle partitioning towards the gaseous
phase. Partitioning of the OC1, OC2, OC3, and OC4 revealed that the majority of the organic
carbon was in the most volatile fraction that released at less than 310°C. With the use of
biodiesel fuelling, the organic carbon emissions stayed fairly constant, while elemental and total
carbon emissions decreased. The OC/TC did not experience any noticeable variation when
altering biodiesel blend concentration; however, the SOF increased with higher biodiesel blend
concentrations similarly for both biodiesel fuel types.
Emissions of water-soluble nitrites, nitrates, and sulphate concentrations were also found
to be influenced by biodiesel fuelling. The ratio of WSOC/OC decreased with the use of
biodiesel, and the ratio of NO3- to SO4
2- had a noticeable increase with biodiesel, however only
during the University of Toronto testing and not the General Electric Testing. A comparison of
the particle size distributions of the diesel engine exhaust showed no dependence on fuel type,
but the overall particle number concentration and modal diameter was found to vary with engine
79
operation condition. The volatility of particles measured using a thermodenuder was not affected
by the biodiesel fueling as well.
This research project has shown that biodiesel fuelling in diesel engines can help achieve
substantial decreases in PM, THC, and other regulated pollutants, and that the biodiesel fuel
source has a definite impact on emissions. While the present tests showed slight increases in NOx
emissions, there are many researchers working to mitigate this effect by altering the injection
timing when using biodiesel. Reductions in these emissions can help produce cleaner emissions
from diesel engines, in order to meet future stringent diesel emission standards. However, as
future emission regulations are most likely to require the use of aftertreatment systems; further
research is required to understand the implications of biodiesel fuelling on these aftertreatment
systems.
5.2 Recommendations
It is recommended that further research into the use of biodiesel fuelling in diesel engines study
changes in gaseous emissions and PM speciation upstream and downstream of aftertreatment
devices such as diesel oxidative catalysts and particulate filters which will be necessary for diesel
engines to fulfill future stringent emission standards. This research program has shown that the
biodiesel exhaust emissions alter the characteristics of PM, and therefore the efficiencies and
durability of these aftertreatment systems may be different for biodiesel exhaust as they are
currently designed for petroleum diesel emissions. Furthermore, previous research at the
University of Toronto has shown that biodiesel use decreases the balance point temperature of a
diesel particulate filter (Jääskeläinen et al., 2006), which would benefit active regeneration of
diesel particulate filters if used.
Further PM and gaseous speciation may also merit study. Collecting hydrocarbon
emissions on sorbent tubes for analysis by gas chromatography would give an indication of the
organic compounds which compose diesel engine exhaust, which are likely to vary drastically
using biodiesel. Additional consideration may also be paid towards unregulated constituents such
as polyaromatic hydrocarbons (PAHs) and nitro-PAHs, some which are known to be human
carcinogens. It would also be interesting to include methods to study the toxicity of the PM both
80
in vitro and in vivo, to assess directly how the use of biodiesel would affect human health.
Lastly, if engine aftertreatment systems are to be studied, the effect on the particle size
distribution would be interesting to study up and downstream, especially with upcoming trends
to regulate particle number concentration rather than particle mass.
81
References
Abd-Alla, G. H., Soliman, H. A., Badr, O. A., & Abd-Rabbo, M. F. (2001). Effects of diluent
admissions and intake air temperature in exhaust gas recirculation on the emissions of an
indirect injection dual fuel engine. Energy Conversion and Management, 42(8), 1033-
1045.
Abdul-Khalek, I., Kittelson, D. B., & Brear, F. (1998). Diesel trap performance: Particle size
measurements and trends. SAE International, (982599).
Abdul-Khalek, I., Kittelson, D. B., & Brear, F. (1999). The Influence of Dilution Conditions on
Diesel Exhaust Particle Size Distribution Measurements. SAE International, (1999-01-
1142).
Agarwal, A. K. (2007). Biofuels (alcohols and biodiesel) applications as fuels for internal
combustion engines. Progress in energy and combustion science, 33(3), 233-271.
Arapaki, N., Bakeas, E., Karavalakis, G., Tzirakis, E., Stournas, S., & Zannikos, F. (2007).
Regulated and Unregulated Emissions Characteristics of a Diesel Vehicle Operating with
Diesel/Biodiesel Blends. SAE International, (2007-01-0071).
Arnold, F., Pirjola, L., Aufmhoff, H., Schuck, T., Lähde, T., & Hämeri, K. (2006). First gaseous
sulfuric acid measurements in automobile exhaust: Implications for volatile nanoparticle
formation. Atmospheric Environment, 40(37), 7097-7105.
Arnott, W. P., Zielinska, B., Rogers, C. F., Sagebiel, J., Park, K., Chow, J., Moosmüller, H., et
al. (2005). Evaluation of 1047-nm Photoacoustic Instruments and Photoelectric Aerosol
Sensors in Source-Sampling of Black Carbon Aerosol and Particle-Bound PAHs from
Gasoline and Diesel Powered Vehicles. Environmental Science & Technology, 39(14),
5398-5406.
Aviado, D. M., & Salem, H. (1968). Acute effects of air pollutants on the lungs. Archives of
Environmental Health, 16(6), 903-907.
Ballesteros, R., Hernández, J. J., Lyons, L. L., Cabañas, B., & Tapia, A. (2008). Speciation of the
semivolatile hydrocarbon engine emissions from sunflower biodiesel. Fuel, 87(10-11),
1835-1843.
Basha, S. A., Gopal, K. R., & Jebaraj, S. (2009). A review on biodiesel production, combustion,
emissions and performance. Renewable and Sustainable Energy Reviews, 13(6-7), 1628–
1634.
Biswas, S., Hu, S., Verma, V., Herner, J. D., Robertson, W. H., Ayala, A., & Sioutas, C. (2008).
Physical properties of particulate matter (PM) from late model heavy-duty diesel vehicles
operating with advanced PM and NOx emission control technologies. Atmospheric
Environment, 42(22), 5622-5634.
Biswas, S., Verma, V., Schauer, J. J., Cassee, F. R., Cho, A. K., & Sioutas, C. (2009a). Oxidative
Potential of Semi-Volatile and Non Volatile Particulate Matter (PM) from Heavy-Duty
Vehicles Retrofitted with Emission Control Technologies. Environmental Science &
Technology, 43(10), 3905-3912.
82
Biswas, S., Verma, V., Schauer, J. J., & Sioutas, C. (2009b). Chemical speciation of PM
emissions from heavy-duty diesel vehicles equipped with diesel particulate filter (DPF)
and selective catalytic reduction (SCR) retrofits. Atmospheric Environment, 43(11),
1917-1925.
Boehman, A. L., Song, J., & Alam, M. (2005). Impact of Biodiesel Blending on Diesel Soot and
the Regeneration of Particulate Filters. Energy & Fuels, 19(5), 1857-1864.
Burtscher, H., Baltensperger, U., Bukowiecki, N., Cohn, P., Hüglin, C., Mohr, M., Matter, U., et
al. (2001). Separation of volatile and non-volatile aerosol fractions by thermodesorption:
instrumental development and applications. Journal of Aerosol Science, 32(4), 427-442.
Burtscher, H. (2005). Physical characterization of particulate emissions from diesel engines: a
review. Journal of Aerosol Science, 36(7), 896-932.
CBC. (2009). Manitoba to mandate biodiesel blends - Technology & Science - CBC News. CBC
News. Retrieved March 31, 2011, from
http://www.cbc.ca/news/technology/story/2009/09/10/mb-biodiesel-fuel-manitoba.html
Cardone, M., Prati, M. V., Rocco, V., Seggiani, M., Senatore, A., & Vitolo, S. (2002). Brassica
carinata as an Alternative Oil Crop for the Production of Biodiesel in Italy: Engine
Performance and Regulated and Unregulated Exhaust Emissions. Environmental Science
& Technology, 36(21), 4656-4662.
Casati, R., Scheer, V., Vogt, R., & Benter, T. (2007). Measurement of nucleation and soot mode
particle emission from a diesel passenger car in real world and laboratory in situ dilution.
Atmospheric Environment, 41(10), 2125-2135.
Chen, D.-R., Pui, D. Y. H., Hummes, D., Fissan, H., Quant, F. R., & Sem, G. J. (1998). Design
and evaluation of a nanometer aerosol differential mobility analyzer (Nano-DMA).
Journal of Aerosol Science, 29(5-6), 497-509.
Cheung, K. L., Polidori, A., Ntziachristos, L., Tzamkiozis, T., Samaras, Z., Cassee, F. R.,
Gerlofs, M., et al. (2009). Chemical Characteristics and Oxidative Potential of Particulate
Matter Emissions from Gasoline, Diesel, and Biodiesel Cars. Environmental Science &
Technology, 43(16), 6334-6340.
Chien, S. M., Huang, Y. J., Chuang, S. C., & Yang, H. H. (2009). Effects of Biodiesel Blending
on Particulate and Polycyclic Aromatic Hydrocarbon Emissions in
Nano/Ultrafine/Fine/Coarse Ranges from Diesel Engine. Aerosol and Air Quality
Research, 9(1), 18-31.
Chung, A., Lall, A. A., & Paulson, S. E. (2008). Particulate emissions by a small non-road diesel
engine: Biodiesel and diesel characterization and mass measurements using the extended
idealized aggregates theory. Atmospheric Environment, 42(9), 2129-2140.
Davidson, C. I., Phalen, R. F., & Solomon, P. A. (2005). Airborne particulate matter and human
health: A review. Aerosol Science and Technology, 39(8), 737-749.
Demirbas, A. (2007). Importance of biodiesel as transportation fuel. Energy Policy, 35(9), 4661-
4670.
Demirbas, A. (2009). Political, economic and environmental impacts of biofuels: A review.
Applied Energy, 86(S1), S108-S117.
83
Di, Y., Cheung, C. S., & Huang, Z. (2009a). Comparison of the Effect of Biodiesel-Diesel and
Ethanol-Diesel on the Particulate Emissions of a Direct Injection Diesel Engine. Aerosol
Science and Technology, 43(5), 455-465.
Di, Y., Cheung, C. S., & Huang, Z. (2009b). Experimental investigation on regulated and
unregulated emissions of a diesel engine fueled with ultra-low sulfur diesel fuel blended
with biodiesel from waste cooking oil. Science of The Total Environment, 407(2), 835-
846.
Diesel, R. (1887). Theorie und Konstruktion eines rationellen Wärmemotors zum Ersatz der
Dampfmaschinen. Und der heute bekannten Verbrennungsmotoren [Theory and
Construction of a Rational Heat-engine to Replace the Steam Engine and Combustion
Engines Known Today]. VDI, Ddf.
Dieselnet. (2011). Emission Test Cycles ISO 8178. Retrieved March 15, 2011, from
http://www.dieselnet.com/standards/cycles/iso8178.html
Dorado, M. P., Ballesteros, E., Arnal, J. M., Gomez, J., & Lopez, F. J. (2003). Exhaust emissions
from a Diesel engine fueled with transesterified waste olive oil. Fuel, 82(11), 1311-1315.
Du, H., & Yu, F. (2006). Role of the binary H2SO4-H2O homogeneous nucleation in the
formation of volatile nanoparticles in the vehicular exhaust. Atmospheric Environment,
40(39), 7579-7588.
Dwivedi, D., Agarwal, Avinash Kumar, & Sharma, M. (2006). Particulate emission
characterization of a biodiesel vs diesel-fuelled compression ignition transport engine: A
comparative study. Atmospheric Environment, 40(29), 5586-5595.
EcoChem Analytics. (2005). User’s Guide. Photoelectric Aerosol Sensor (PAS 2000CE)
Diffusion Charger (DC 2000CE).
El-Zein, A., Nuwayhid, I., El-Fadel, M., & Mroueh, S. (2007). Did a ban on diesel-fuel reduce
emergency respiratory admissions for children? The Science of the Total Environment,
384(1-3), 134-140.
Fang, T., & Lee, C. F. (2009). Bio-diesel effects on combustion processes in an HSDI diesel
engine using advanced injection strategies. Proceedings of the Combustion Institute,
32(2), 2785-2792.
Filippo, A. D., & Maricq, M. M. (2008). Diesel Nucleation Mode Particles: Semivolatile or
Solid? Environmental Science & Technology, 42(21), 7957-7962.
Fontaras, G., Karavalakis, G., Kousoulidou, M., Tzamkiozis, T., Ntziachristos, L., Bakeas, E.,
Stournas, S., et al. (2009). Effects of biodiesel on passenger car fuel consumption,
regulated and non-regulated pollutant emissions over legislated and real-world driving
cycles. Fuel, 88(9), 1608-1617.
Frank, B. P., Tang, S., Lanni, T., Grygas, J., Rideout, G., Meyer, N., & Beregszaszy, C. (2007).
The Effect of Fuel Type and Aftertreatment Method on Ultrafine Particle Emissions from
a Heavy-Duty Diesel Engine. Aerosol Science and Technology, 41(11), 1029.
Geller, M. D., Ntziachristos, L.s, Mamakos, A., Samaras, Z., Schmitz, D. A., Froines, J. R., &
Sioutas, C. (2006). Physicochemical and redox characteristics of particulate matter (PM)
emitted from gasoline and diesel passenger cars. Atmospheric Environment, 40(36),
6988-7004.
84
Giechaskiel, B., Dilara, P., Sandbach, E., & Andersson, J. (2008). Particle measurement
programme (PMP) light-duty inter-laboratory exercise: comparison of different particle
number measurement systems. Meas. Sci. Technol, 19(095401), 095401.
Giechaskiel, B., Ntziachristos, L., & Samaras, Z. (2009). Effect of ejector dilutors on
measurements of automotive exhaust gas aerosol size distributions. Measurement Science
and Technology, 20(4), 045703.
Graboski, M. S., & McCormick, R. L. (1998). Combustion of fat and vegetable oil derived fuels
in diesel engines. Progress in Energy and Combustion Science, 24(2), 125-164.
Greenwald, I. (1954). Effects of inhalation of low concentrations of sulfur dioxide upon man and
other mammals. A.M.A. Archives of Industrial Health, 10(6), 455-475.
Grose, M., Sakurai, H., Savstrom, J., Stolzenburg, M. R., Watts Jr, W. F., Morgan, C. G.,
Murray, I. P., et al. (2006). Chemical and physical properties of ultrafine diesel exhaust
particles sampled downstream of a catalytic trap. Environ. Sci. Technol, 40(17), 5502-
5507.
Haas, M. J., Scott, K. M., Alleman, T. L., & McCormick, R. L. (2001). Engine Performance of
Biodiesel Fuel Prepared from Soybean Soapstock: A High Quality Renewable Fuel
Produced from a Waste Feedstock. Energy & Fuels, 15(5), 1207-1212.
Heikkilä, J., Virtanen, A., Rönkkö, T., Keskinen, J., Aakko-Saksa, P., & Murtonen, T. (2009).
Nanoparticle Emissions from a Heavy-Duty Engine Running on Alternative Diesel Fuels.
Environmental Science & Technology, 43(24), 9501-9506.
Hess, M. A., Haas, M. J., Foglia, T. A., & Marmer, W. N. (2005). Effect of Antioxidant Addition
on NOx Emissions from Biodiesel. Energy & Fuels, 19(4), 1749-1754.
Hess, M. A., Haas, M. J., & Foglia, T. A. (2007). Attempts to reduce NOx exhaust emissions by
using reformulated biodiesel. Fuel Processing Technology, 88(7), 693-699.
Hering, S. V., Stolzenburg, M. R., Quant, F. R., Oberreit, D. R., & Keady, P. B. (2005). A
Laminar-Flow, Water-Based Condensation Particle Counter (WCPC). Aerosol Science
and Technology, 39(7), 659.
Heywood, J. (1988). Internal Combustion Engine Fundamentals (1st ed.). McGraw-Hill
Science/Engineering/Math.
Inoue, M., Murase, A., Yamamoto, M., & Kubo, S. (2006). Analysis of volatile nanoparticles
emitted from diesel engine using TOF-SIMS and metal-assisted SIMS (MetA-SIMS).
Applied Surface Science, 252(19), 7014–7017.
Jääskeläinen, H., Wallace, J. S., Graham, L., & Baas, C. (2006). The Effect of Biodiesel Fuel
Blends on Diesel Particulate Filter Operation. Presented at the Mining Diesel Emissions
Conference (MDEC 2006).
Jiang, M. Y., Li, J. Q., Wu, Y. Q., Lin, N. T., Wang, X. M., & Fu, F. F. (2011). Chemical
characterization of nanometer-sized elemental carbon particles emitted from diesel
vehicles. Journal of Aerosol Science, 42(6), 365-371.
Johnson, T. V. (2007). Diesel Emission Control in Review. SAE International, (2007-01-0233).
Johnson, K. C., Durbin, T. D., Jung, H., Chaudhary, A., Cocker, D. R., Herner, Jorn D.,
Robertson, William H., et al. (2009). Evaluation of the European PMP - Methodologies
85
during On-Road and Chassis Dynamometer Testing for DPF - Equipped Heavy-Duty
Diesel Vehicles. Aerosol Science and Technology, 43(10), 962.
Jung, H., Kittelson, D. B., & Zachariah, M. R. (2006). Characteristics of SME Biodiesel-Fueled
Diesel Particle Emissions and the Kinetics of Oxidation. Environmental Science &
Technology, 40(16), 4949-4955.
Jung, J., Adams, P. J., & Pandis, S. N. (2006). Simulating the size distribution and chemical
composition of ultrafine particles during nucleation events. Atmospheric Environment,
40(13), 2248-2259.
Kapetanović, S., Wallace, J. S., & Evans, G. (2009). Investigation of Sulfate Nanoparticulate
Formation from a Catalyzed Diesel Particulate Filter on an Engine Fueled with ULSD
and a Biodiesel Blend. SAE International, (2009-01-0691).
Kaplan, C., Arslan, R., & Sürmen, A. (2006). Performance Characteristics of Sunflower Methyl
Esters as Biodiesel. Energy Sources, Part A: Recovery, Utilization, and Environmental
Effects, 28(8), 751.
Karavalakis, G., Bakeas, E., & Stournas, S. (2010). Influence of Oxidized Biodiesel Blends on
Regulated and Unregulated Emissions from a Diesel Passenger Car. Environmental
Science & Technology, 44(13), 5306-5312.
Kawai, T., Goto, Y., & Odaka, M. (2004). Influence of Dilution Process on Engine Exhaust
Nano-Particles. SAE International, (2004-01-0963).
Kim, H., & Choi, B. (2010). The effect of biodiesel and bioethanol blended diesel fuel on
nanoparticles and exhaust emissions from CRDI diesel engine. Renewable Energy, 35(1),
157-163.
Kirchner, U., Scheer, V., Vogt, R., & Kägi, R. (2009). TEM study on volatility and potential
presence of solid cores in nucleation mode particles from diesel powered passenger cars.
Journal of Aerosol Science, 40(1), 55-64.
Kittelson, D. B. (1998). Engines and nanoparticles a review. Journal of Aerosol Science, 29(5-6),
575-588.
Kittelson, D. B., Arnold, M., & Watts, W. F. (1998). Review of Diesel Particulate Matter
Sampling Methods: Final Report. University of Minnesota,
http://www.umn.edu/centers/cdr/Proj_EPA.htm.
Kittelson, D. B., Watts, W., & Johnson, J. (2002). Diesel Aerosol Sampling Methodology–CRC
E-43. University of Minnesota - Department of Mechanical Engineering.
Kittelson, D. B., Watts, W. F., Johnson, J. P., Remerowki, M. L., Ische, E. E., Oberdörster, G.,
Gelein, R. M., et al. (2004). On-Road Exposure to Highway Aerosols. 1. Aerosol and Gas
Measurements. Inhalation Toxicology, 16(s1), 31.
Kittelson, D. B., Watts, W. F., & Johnson, J. P. (2006a). On-road and laboratory evaluation of
combustion aerosols--Part 1: Summary of diesel engine results. Journal of Aerosol
Science, 37(8), 913-930.
Kittelson, D. B., Watts, W. F., Johnson, J. P., Rowntree, C. J., Goodier, S. P., Payne, M. J.,
Preston, W. H., et al. (2006b). Driving Down On-Highway Particulate Emissions. SAE
International, (2006-01-0916).
86
Kittelson, D. B., Watts, W. F., Johnson, J. P., Rowntree, C., Payne, M., Goodier, S., Warrens, C.,
et al. (2006c). On-road evaluation of two Diesel exhaust aftertreatment devices. Journal
of Aerosol Science, 37(9), 1140-1151.
Kittelson, D. B., Watts, W. F., Johnson, J. P., Thorne, C., Higham, C., Payne, M., Goodier, S., et
al. (2008). Effect of Fuel and Lube Oil Sulfur on the Performance of a Diesel Exhaust
Gas Continuously Regenerating Trap. Environmental Science & Technology, 42(24),
9276-9282.
Knothe, G., Sharp, C. A., & Ryan, T. W. (2006). Exhaust Emissions of Biodiesel, Petrodiesel,
Neat Methyl Esters, and Alkanes in a New Technology Engine. Energy & Fuels, 20(1),
403-408.
Krahl, J., Munack, A., Schroder, O., Stein, H., Herbst, L., Kaufmann, A., & Bunger, J. (2005).
Fuel Design as Constructional Element with the Example of Biogenic and Fossil Diesel
Fuels. Agricultural Engineering International: the CIGR Journal of Scientific Research
and Development, VII(EE 04 008).
Krahl, J., Munack, A., Grope, N., Ruschel, Y., Schröder, O., & Bünger, J. (2007). Biodiesel,
Rapeseed Oil, Gas-To-Liquid, and a Premium Diesel Fuel in Heavy Duty Diesel Engines:
Endurance, Emissions and Health Effects. Clean - Soil, Air, Water, 35(5), 417-426.
Lapuerta, M., Armas, O., Ballesteros, R., Carmona, M. (2000). Fuel formulation effects on
passenger car diesel engine particulate emissions and composition. SAE International,
(2000-01-1850).
Lapuerta, M., Armas, O., & Rodríguez-Fernándeza, J. (2008). Effect of biodiesel fuels on diesel
engine emissions. Progress in Energy and Combustion Science, 34(2), 198-223.
Lapuerta, M., Rodríguez-Fernández, J., Oliva, F., & Canoira, L. (2009). Biodiesel from Low-
Grade Animal Fats: Diesel Engine Performance and Emissions. Energy & Fuels, 23(1),
121-129.
Last, R. J., Kruger, M., & Durnholz, M. (1995). Emissions and performance characteristics of a
4-stroke, direct injected diesel engine fueled with blends of biodiesel and low sulfur
diesel fuel. SAE International, (950054).
Lewis, T. R., Campbell, K. I., & Vaughan, T. R., Jr. (1969). Effects on canine pulmonary
function via induced NO2 impairment, particulate interaction, and subsequent SOx.
Archives of Environmental Health, 18(4), 596-601.
Li, H., Andrews, G. E., & Balsevich-Prieto, J. L. (2007). Study of Emission and Combustion
Characteristics of RME B100 Biodiesel from a Heavy Duty DI Diesel Engine. SAE
International, (2007-01-0074).
Li, H., Lea-Langton, A., Andrews, G. E., Thompson, M., & Musungo, C. (2008). Comparison of
Exhaust Emissions and Particulate Size Distribution for Diesel, Biodiesel and Cooking
Oil from a Heavy Duty DI Diesel Engine. SAE International, (2008-01-0076).
Li, Rongsong, Ning, Z., Cui, J., Khalsa, B., Ai, L., Takabe, W., Beebe, T., et al. (2009). Ultrafine
particles from diesel engines induce vascular oxidative stress via JNK activation. Free
Radical Biology and Medicine, 46(6), 775-782.
Liang, C. Y., Baumgard, K. J., Gorse, R. A., Jr., Orban, J. E., Storey, J. M. E., Tan, J. C., Thoss,
J. E., et al. (2000). Effects of Diesel Fuel Sulfur Level on Performance of a Continuously
87
Regenerating Diesel Particulate Filter and a Catalyzed Particulate Filter. SAE
International, (2000-01-1876).
Lin, Y.-C., Lee, W.-J., Wu, T.-S., & Wang, C.-T. (2006). Comparison of PAH and regulated
harmful matter emissions from biodiesel blends and paraffinic fuel blends on engine
accumulated mileage test. Fuel, 85(17-18), 2516-2523.
Liu, Z. G., Vasys, V. N., & Kittelson, D. B. (2007a). Nuclei-mode particulate emissions and their
response to fuel sulfur content and primary dilution during transient operations of old and
modern diesel engines. Environmental Science & Technology, 41(18), 6479-6483.
Liu, Z. G., Vasys, V. N., Swor, T. A., & Kittelson, D. B. (2007b). Significance of Fuel Sulfur
Content and Dilution Conditions on Particle Emissions from a Heavily-Used Diesel
Engine During Transient Operation. SAE International, (2007-01-0319).
Liu, Z. G., Berg, D. R., Vasys, V. N., Dettmann, M. E., Zielinska, B., & Schauer, J. J. (2010).
Analysis of C1, C2, and C10 through C33 particle-phase and semi-volatile organic
compound emissions from heavy-duty diesel engines. Atmospheric Environment, 44(8),
1108-1115.
Lue, Y.-F., Yeh, Y.-Y., & Wu, C.-H. (2001). The Emission Characteristics of a Small D.I. Diesel
Engine Using Biodiesel Blended Fuels. Journal of Environmental Science and Health,
Part A, 36(5), 845-859.
Lyyränen, J., Jokiniemi, J., Kauppinen, E., Backman, U., & Vesala, H. (2004). Comparison of
Different Dilution Methods for Measuring Diesel Particle Emissions. Aerosol Science
and Technology, 38, 12-23.
Ma, H., Jung, H., & Kittelson, D. (2008). Investigation of Diesel Nanoparticle Nucleation
Mechanisms. Aerosol Science and Technology, 42(5), 335-342.
Maricq, M. M., Chase, R. E., Xu, N., & Laing, P. M. (2002). The Effects of the Catalytic
Converter and Fuel Sulfur Level on Motor Vehicle Particulate Matter Emissions: Light
Duty Diesel Vehicles. Environmental Science & Technology, 36(2), 283-289.
Mathis, U., Ristimäki, J., Mohr, M., Keskinen, J., Ntziachristos, L., Samaras, Z., & Mikkanen, P.
(2004). Sampling Conditions for the Measurement of Nucleation Mode Particles in the
Exhaust of a Diesel Vehicle. Aerosol Science & Technology, 38(12), 1149-1160.
McCormick, R. L., Graboski, M. S., Alleman, T. L., Herring, A. M., & Tyson, K. S. (2001).
Impact of Biodiesel Source Material and Chemical Structure on Emissions of Criteria
Pollutants from a Heavy-Duty Engine. Environmental Science & Technology, 35(9),
1742-1747.
Monyem, A., Van Gerpen, J. H., & Canakei, M. (2001). The effect of timing and oxidation on
emissions from biodiesel-fueled engines. Transactions of the ASAE, 44(1), 35-42.
Moser, B. R., Williams, A., Haas, M. J., & McCormick, R. L. (2009). Exhaust emissions and
fuel properties of partially hydrogenated soybean oil methyl esters blended with ultra low
sulfur diesel fuel. Fuel Processing Technology, 90(9), 1122-1128.
Murillo, S., Míguez, J. L., Porteiro, J., Granada, E., & Morán, J. C. (2007). Performance and
exhaust emissions in the use of biodiesel in outboard diesel engines. Fuel, 86(12-13),
1765-1771.
88
NIOSH. (1999). Elemental Carbon (Diesel Particulate), Method 5040. NIOSH Manual of
Analytical Methods (NMAM), Fourth Edition.
Ntziachristos, L., Samaras, Z., Zervas, E., & Dorlhene, P. (2005). Effects of a catalysed and an
additized particle filter on the emissions of a diesel passenger car operating on low
sulphur fuels. Atmospheric Environment, 39(27), 4925-4936.
Nwafor, O. M. I. (2004). Emission characteristics of diesel engine operating on rapeseed methyl
ester. Renewable Energy, 29(1), 119-129.
Payri, F., Bermúdez, V. R., Tormos, B., & Linares, W. G. (2009). Hydrocarbon emissions
speciation in diesel and biodiesel exhausts. Atmospheric Environment, 43(6), 1273–1279.
Pehan, S., Jerman, M. S., Kegl, M., & Kegl, B. (2009). Biodiesel influence on tribology
characteristics of a diesel engine. Fuel, 88(6), 970–979.
Pinto, A. C., Guarieiro, L. L. N., Rezende, M. J. C., Ribeiro, N. M., Torres, E. A., Lopes, W. A.,
Pereira, P. A. P., et al. (2005). Biodiesel: an overview. Journal of the Brazilian Chemical
Society, 16(6B), 1313-1330.
Rönkkö, T., Virtanen, A., Vaaraslahti, K., Keskinen, J., Pirjola, L., & Lappi, M. (2006). Effect of
dilution conditions and driving parameters on nucleation mode particles in diesel exhaust:
Laboratory and on-road study. Atmospheric Environment, 40(16), 2893-2901.
Rönkkö, T., Virtanen, A., Kannosto, J., Keskinen, J., Lappi, M., & Pirjola, L. (2007). Nucleation
Mode Particles with a Nonvolatile Core in the Exhaust of a Heavy Duty Diesel Vehicle.
Environmental Science & Technology, 41(18), 6384-6389.
Rakopoulos, C. D., Antonopoulos, K. A., Rakopoulos, D. C., Hountalas, D. T., & Giakoumis, E.
G. (2006). Comparative performance and emissions study of a direct injection diesel
engine using blends of diesel fuel with vegetable oils or bio-diesels of various origins.
Energy conversion and management, 47(18-19), 3272-3287.
Rakopoulos, C. D., Antonopoulos, K. A., Rakopoulos, D. C., & Kakaras, E. C. (2007).
Characteristics of the performance and emissions of a HSDI diesel engine running with
cottonseed oil or its methyl ester and their blends with diesel fuel. International Journal
of Vehicle Design, 45(1), 200-221.
Rakopoulos, C. D., Rakopoulos, D. C., Hountalas, D. T., Giakoumis, E. G., & Andritsakis, E. C.
(2008). Performance and emissions of bus engine using blends of diesel fuel with bio-
diesel of sunflower or cottonseed oils derived from Greek feedstock. Fuel, 87(2), 147-
157.
Ramadhas, A. S., Jayaraj, S., & Muraleedharan, C. (2004). Use of vegetable oils as IC engine
fuels—a review. Renewable Energy, 29(5), 727-742.
Ramadhas, A. S., Muraleedharan, C., & Jayaraj, S. (2005). Performance and emission evaluation
of a diesel engine fueled with methyl esters of rubber seed oil. Renewable Energy,
30(12), 1789-1800.
Rockland, L. B. (1960). Saturated Salt Solutions for Static Control of Relative Humidity between
5° and 40° C. Analytical Chemistry, 32(10), 1375-1376.
Russell, L. M. (2003). Aerosol Organic-Mass-to-Organic-Carbon Ratio Measurements.
Environmental Science & Technology, 37(13), 2982-2987.
89
Schönborn, A., Ladommatos, N., Williams, J., Allan, R., & Rogerson, J. (2009). The influence of
molecular structure of fatty acid monoalkyl esters on diesel combustion. Combustion and
Flame, 156(7), 1396-1412.
Schauer, J. J., Kleeman, M. J., Cass, G. R., & Simoneit, B. R. T. (1999). Measurement of
Emissions from Air Pollution Sources. 2. C1 through C30 Organic Compounds from
Medium Duty Diesel Trucks. Environmental Science & Technology, 33(10), 1578-1587.
Schmidt, K., & Van Gerpen, J. H. (1996). The effect of biodiesel fuel composition on diesel
combustion and emissions. SAE International, (961086).
Schneider, J., Hock, N., Weimer, S., Borrmann, S., Kirchner, U., Vogt, R., & Scheer, V. (2005).
Nucleation particles in diesel exhaust: Composition inferred from in situ mass
spectrometric analysis. Environmental science & technology, 39(16), 6153-6161.
Seigneur, C., Nuszkowski, J., Clark, N. N., Spencer, T. K., Carder, D. K., Gautam, M., Balon, T.
H., et al. (2009). Current Understanding of Ultrafine Particulate Matter Emitted from
Mobile Sources. Journal of the Air & Waste Management Association (1995), 59(1), 3.
van Setten, B., Makkee, M., & Moulijn, J. (2001). Science and technology of catalytic diesel
particulate filters. Catalysis Reviews, 43(4), 489-564.
Sharma, Y. C., Singh, B., & Upadhyay, S. N. (2008). Advancements in development and
characterization of biodiesel: A review. Fuel, 87(12), 2355-2373.
Sharp, C. A., Howell, S. A., & Jobe, J. (2000). The Effect of Biodiesel Fuels on Transient
Emissions from Modern Diesel Engines, Part II Unregulated Emissions and Chemical
Characterization. SAE International, (2000-01-0968).
Shi, J. P., & Harrison, R. M. (1999). Investigation of Ultrafine Particle Formation during Diesel
Exhaust Dilution. Environmental Science & Technology, 33(21), 3730-3736.
Shi, J. P., Khan, A. A., & Harrison, R. M. (1999). Measurements of ultrafine particle
concentration and size distribution in the urban atmosphere. The Science of The Total
Environment, 235(1-3), 51-64.
Shi, X., Yu, Y., He, H., Shuai, S., Wang, J., & Li, R. (2005). Emission characteristics using
methyl soyate-ethanol-diesel fuel blends on a diesel engine. Fuel, 84(12-13), 1543-1549.
Skillas, G., Qian, Z., Baltensperger, U., Matter, U., & Burtscher, H. (2000). The Influence of
Additives on the Size Distribution and Composition of Particles Produced by Diesel
Engines. Combustion Science and Technology, 154(1), 259-273.
Song, J., Cheenkachorn, K., Wang, J., Perez, J., Boehman, A. L., Young, P. J., & Waller, F. J.
(2002). Effect of Oxygenated Fuel on Combustion and Emissions in a Light-Duty Turbo
Diesel Engine. Energy & Fuels, 16(2), 294-301.
Speidel, H. K., Lightner, R. L., & Ahmed, I. (2000). Biodegradability of new engineered fuels
compared to conventional petroleum fuels and alternative fuels in current use. Applied
biochemistry and biotechnology, 84-86, 879-897.
Szybist, J. P., Boehman, A. L., Taylor, J. D., & McCormick, R. L. (2005). Evaluation of
formulation strategies to eliminate the biodiesel NOx effect. Fuel Processing Technology,
86(10), 1109-1126.
90
TTnews. (2011). Canada Sets 2% Biodiesel Mandate | Transport Topics Online | Trucking,
Freight Transportation and Logistics News. Retrieved March 31, 2011, from
http://www.ttnews.com/articles/basetemplate.aspx?storyid=26114
Thornton, M. J., Alleman, T. L., Luecke, J., & McCormick, R. L. (2009). Impacts of Biodiesel
Fuel Blends Oil Dilution on Light-Duty Diesel Engine Operation. SAE International,
(2009-01-1790).
Tobias, H. J., Beving, D. E., Ziemann, P. J., Sakurai, H., Zuk, M., McMurry, P. H., Zarling, D.,
et al. (2001). Chemical Analysis of Diesel Engine Nanoparticles Using a Nano-
DMA/Thermal Desorption Particle Beam Mass Spectrometer. Environmental Science &
Technology, 35(11), 2233-2243.
Turns, S. R. (2006). An Introduction to Combustion: Concepts and Applications w/Software (2nd
ed.). McGraw-Hill Science/Engineering/Math.
Turrio-Baldassarri, L., Battistelli, C. L., Conti, L., Crebelli, R., De Berardis, B., Iamiceli, A. L.,
Gambino, M., et al. (2004). Emission comparison of urban bus engine fueled with diesel
oil and “biodiesel” blend. Science of The Total Environment, 327(1-3), 147-162.
USEPA. (2002). A Comprehensive Analysis of Biodiesel Impacts on Engine Emissions. U.S.
Environmental Protection Agency.
USEPA. (2011). Diesel Exhaust in New England | US EPA. Retrieved March 31, 2011, from
http://www.epa.gov/ne/eco/diesel/
Usta, N. (2005). An experimental study on performance and exhaust emissions of a diesel engine
fuelled with tobacco seed oil methyl ester. Energy Conversion and Management, 46(15-
16), 2373-2386.
Vaaraslahti, K., Virtanen, A., Ristimaki, J., & Keskinen, J. (2004). Nucleation Mode Formation
in Heavy-Duty Diesel Exhaust with and without a Particulate Filter. Environmental
Science & Technology, 38(18), 4884-4890.
Vouitsis, E., Ntziachristos, L., & Samaras, Z.. (2003). Particulate matter mass measurements for
low emitting diesel powered vehicles: what’s next? Progress in Energy and Combustion
Science, 29(6), 635-672.
Vouitsis, E., Ntziachristos, L., Pistikopoulos, P., Samaras, Z., Chrysikou, L., Samara, C.,
Papadimitriou, C., et al. (2009). An investigation on the physical, chemical and
ecotoxicological characteristics of particulate matter emitted from light-duty vehicles.
Environmental Pollution (Barking, Essex: 1987), 157(8-9), 2320-2327.
Wang, S. C., & Flagan, R. C. (1990). Scanning electrical mobility spectrometer. Aerosol Science
and Technology, 13(2), 230-240.
Wang, W. G., Lyons, D. W., Clark, N. N., Gautam, M., & Norton, P. M. (2000). Emissions from
Nine Heavy Trucks Fueled by Diesel and Biodiesel Blend without Engine Modification.
Environmental Science & Technology, 34(6), 933-939.
Wark, K., Warner, C. F., & Davis, W. T. (1997). Air Pollution: Its Origin and Control (3rd ed.).
Prentice Hall.
Weber, R. J., McMurry, P. H., III, R. L. M., Tanner, D. J., Eisele, F. L., Clarke, A. D., &
Kapustin, V. N. (1999). New Particle Formation in the Remote Troposphere: A
91
Comparison of Observations at Various Sites. Geophysical Research Letters, 26(3), 307–
310.
Williams, A., McCormick, R. L., Hayes, R., Ireland, J., & Fang, H. L. (2006). Effect of biodiesel
blends on diesel particulate filter performance. National Renewable Energy Laboratory.
Xinling, L., & Zhen, H. (2009). Emission reduction potential of using gas-to-liquid and dimethyl
ether fuels on a turbocharged diesel engine. Science of the Total Environment, 407(7),
2234-44.
Yang, H.-H., Chien, S.-M., Lo, M.-Y., Lan, J. C.-W., Lu, W.-C., & Ku, Y.-Y. (2007). Effects of
biodiesel on emissions of regulated air pollutants and polycyclic aromatic hydrocarbons
under engine durability testing. Atmospheric Environment, 41(34), 7232-7240.
Yang, W., & Omaye, S. T. (2009). Air pollutants, oxidative stress and human health. Mutation
Research/Genetic Toxicology and Environmental Mutagenesis, 674(1-2), 45-54.
Yu, F. (2001). Chemiions and nanoparticle formation in diesel engine exhaust. Geophysical
Research Letters, 28(22), 4191-4194.
Zhang, K. M., & Wexler, A. S. (2004). Evolution of particle number distribution near roadways.
Part I: analysis of aerosol dynamics and its implications for engine emission
measurement. Atmospheric Environment, 38(38), 6643-6653.
Zhang, J., He, K., Shi, X., & Zhao, Y. (2011). Comparison of particle emissions from an engine
operating on biodiesel and petroleum diesel. Fuel, 90(6), 2089-2097.
Zhu, Y., Hinds, W. C., Kim, S., & Sioutas, C. (2002). Concentration and size distribution of
ultrafine particles near a major highway. Journal of the Air & Waste Management
Association, 52(9), 1032-1042.
92
Appendix A: Result and Discussion Appendix
0
5
10
15
20
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
NO
x (g/kW
-hr)
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure A - 1: Brake Specific Nitrogen Oxide Emissions versus Fuel Type –
General Electric Testing
0.0
0.2
0.4
0.6
0.8
1.0
1.2
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
THC (g/kW
-hr)
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure A - 2: Brake Specific Total Hydrocarbon Emissions versus Fuel
Type – General Electric Testing
0.00
0.10
0.20
0.30
0.40
0.50
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
PM (g/kW
-hr)
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure A - 3: Brake Specific Particulate Matter Emissions versus Fuel Type –
General Electric Testing
93
0
5
10
15
20
ULSD Soy B5 Soy B20 AF B5 AF B20
NO
x (g/kW
-hr)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure A - 4: Brake Specific Nitrogen Oxide Emissions versus Fuel Type –
University of Toronto Testing
0.0
0.5
1.0
1.5
ULSD Soy B5 Soy B20 AF B5 AF B20
THC (g/kW
-hr)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure A - 5: Brake Specific Total Hydrocarbon Emissions versus Fuel Type –
University of Toronto Testing
0
500
1000
1500
2000
2500
3000
3500
ULSD Soy B5 Soy B20 AF B5 AF B20
O2 (g/kW
-hr)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure A - 6: Brake Specific Oxygen Gas Emissions versus Fuel Type –
University of Toronto Testing
94
0
200
400
600
800
1000
1200
ULSD Soy B5 Soy B20 AF B5 AF B20
CO
2 (g/kW
-hr)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure A - 7: Brake Specific Carbon Dioxide Emissions versus Fuel Type –
University of Toronto Testing
0.00
0.05
0.10
0.15
0.20
0.25
0.30
ULSD Soy B5 Soy B20 AF B5 AF B20
PM (g/kW
-hr) Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure A - 8: Particulate Matter Emissions versus Fuel Type –
University of Toronto Testing
0%
20%
40%
60%
80%
100%
ULSD Soy B5 Soy B20 AF B5 AF B20
OC1
OC2
OC3
OC4
Figure A - 9: Percentage of OC1/OC2/OC3/OC4 versus Fuel Type – Mode 9
University of Toronto Testing
95
0%
20%
40%
60%
80%
100%
ULSD Soy B5 Soy B20 AF B5 AF B20
OC1
OC2
OC3
OC4
Figure A - 10: Percentage of OC1/OC2/OC3/OC4 versus Fuel Type – Mode 2
University of Toronto Testing
0.00
0.05
0.10
0.15
ULSD Soy B20 AF B20
OC/EC/TC (g/kW
-hr).. OC
EC
TC
*Error bars represent 95% confidence intervals
Figure A - 11: Brake Specific Organic / Elemental / Total Carbon Emissions
versus Fuel Type – Mode 9 University of Toronto Testing
0.00
0.05
0.10
0.15
0.20
0.25
ULSD Soy B20 AF B20
OC/EC/TC (g/kW
-hr).. OC
EC
TC
*Error bars represent 95% confidence intervals
Figure A - 12: Brake Specfic Organic / Elemental / Total Carbon Emissions
versus Fuel Type – Mode 2 University of Toronto Testing
96
0.0000
0.0005
0.0010
0.0015
0.0020
0.0025
0.0030
ULSD Soy B5 Soy B20 AF B5 AF B20
p-PAHs (g/kW
-hr)
Mode 9
Mode 2
*Error bars represent 95% confidence intervals
Figure A - 13: Brake Specific p-PAH Emissions versus Fuel Type –
University of Toronto Testing
0.0
1.0
2.0
3.0
4.0
5.0
6.0
ULSD Soy B5 Soy B20 AF B5 AF B20
mg/kW
-hr
Chloride
Nitrite
Nitrate
Sulphate
Phosphate
Ammonium
*Error bars represent 95% confidence intervals
Figure A - 14 : Brake Specific Anion and Cation Emissions –
University of Toronto Testing Mode 9
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
ULSD Soy B5 Soy B20 AF B5 AF B20
mg/kW-hr
Chloride
Nitrite
Nitrate
Sulphate
Phosphate
Ammonium
*Error bars represent 95% confidence intervals
Figure A - 15: Brake Specific Anion and Cation Emissions –
University of Toronto Testing Mode 2
97
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
mg/kW-hr
Nitrite
Nitrate
Sulphate
Ammonium
*Error bars represent 95% confidence intervals
Figure A - 16: Brake Specific Anion and Cation Emissions –
General Electric Testing Mode 9x
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
mg/kW-hr
Nitrite
Nitrate
Sulphate
Ammonium
*Error bars represent 95% confidence intervals
Figure A - 17: Brake Specific Anion and Cation Emissions –
General Electric Testing Mode 2x
98
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
Mode 9 Mode 9 Mode 9 Mode 2 Mode 2 Mode 2
ULSD Soy B20 AF B20 ULSD Soy B20 AF B20
Percentage of PM (%
)
EC
OC
PAH
Calcium
Magneisum
Potassiuum
Ammonium
Sodium
Phosphate
Oxalate
Sulphate
Nitrate
Nitrite
Chlroide
Formate
Acetate
Figure A - 18: Mass Reconstruction of PM – Mode 9 and 2 University of Toronto Testing
-0.6
-0.3
0.0
0.3
0.6
0.9
1.2
1.5
ULSD Soy B5 Soy B20 AF B5 AF B20
K+ / SO
42-
Mode 9
Mode 2
*Error bars reprsent 95% confidence intervals
Figure A - 19: Ratio of Potassium to Sulphate – University of Toronto Testing
99
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
ULSD Soy B5 Soy B10 Soy B20 Soy B30 AF B5 AF B10 AF B20 AF B30
K+ / SO
42-
Mode 9x
Mode 2x
*Error bars represent 95% confidence intervals
Figure A - 20: Ratio of Potassium to Sulphate – General Electric Testing
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0 20 40 60 80 100 120
Diameter (nm)
Nonvolatile Fraction (%
)
ULSD
Soy B5
Soy B20
AF B5
AF B20
*Error bars represent 95% confidence intervals
Figure A - 21: Biodiesel Effect on Nonvolatile Fraction – Mode 9
University of Toronto Testing
100
0%
10%
20%
30%
40%
50%
60%
70%
80%
0 20 40 60 80 100 120
Diameter (nm)
Nonvolatile Fraction (%
)
ULSD
Soy B5
Soy B20
AF B5
AF B20
*Error bars represent 95% confidence intervals
Figure A - 22: Biodiesel Effect on Nonvolatile Fraction – Mode 2
University of Toronto Testing
101
Appendix B: Filter Listing – General Electric Testing
Fuel
Type
BD Blend
% Mode Trial
Filter
Position Filter ID
Filter
Position Filter ID
ULSD 0 9 1 A GE-Q001 C GE-T001
ULSD 0 9 1 B GE-Q002 D GE-T002
ULSD 0 9 2 A GE-Q003 C GE-T003
ULSD 0 9 2 B GE-Q004 D GE-T004
ULSD 0 9 3 A GE-Q005 C GE-T005
ULSD 0 9 3 B GE-Q006 D GE-T006
ULSD 0 2 1 A GE-Q007 C GE-T007
ULSD 0 2 1 B GE-Q008 D GE-T008
ULSD 0 2 2 A GE-Q009 C GE-T009
ULSD 0 2 2 B GE-Q010 D GE-T010
ULSD 0 2 3 A GE-Q011 C GE-T011
ULSD 0 2 3 B GE-Q012 D GE-T012
Soy 5 9 1 A GE-Q013 C GE-T013
Soy 5 9 1 B GE-Q014 D GE-T014
Soy 5 9 2 A GE-Q015 C GE-T015
Soy 5 9 2 B GE-Q016 D GE-T016
Soy 5 9 3 A GE-Q017 C GE-T017
Soy 5 9 3 B GE-Q018 D GE-T018
Soy 5 2 1 A GE-Q019 C GE-T019
Soy 5 2 1 B GE-Q020 D GE-T020
Soy 5 2 2 A GE-Q021 C GE-T021
Soy 5 2 2 B GE-Q022 D GE-T022
Soy 5 2 3 A GE-Q023 C GE-T023
Soy 5 2 3 B GE-Q024 D GE-T024
Soy 10 2 1 A GE-Q025 C GE-T025
Soy 10 2 1 B GE-Q026 D GE-T026
Soy 10 2 2 A GE-Q027 C GE-T027
Soy 10 2 2 B GE-Q028 D GE-T028
Soy 10 2 3 A GE-Q029 C GE-T029
Soy 10 2 3 B GE-Q030 D GE-T030
Soy 10 9 1 A GE-Q031 C GE-T031
Soy 10 9 1 B GE-Q032 D GE-T032
Soy 10 9 2 A GE-Q033 C GE-T033
Soy 10 9 2 B GE-Q034 D GE-T034
Soy 10 9 3 A GE-Q035 C GE-T035
102
Soy 10 9 3 B GE-Q036 D GE-T036
Soy 20 9 1 A GE-Q037 C GE-T037
Soy 20 9 1 B GE-Q038 D GE-T038
Soy 20 9 2 A GE-Q039 C GE-T039
Soy 20 9 2 B GE-Q040 D GE-T040
Soy 20 9 3 A GE-Q041 C GE-T041
Soy 20 9 3 B GE-Q042 D GE-T042
Soy 20 2 1 A GE-Q043 C GE-T043
Soy 20 2 1 B GE-Q044 D GE-T044
Soy 20 2 2 A GE-Q045 C GE-T045
Soy 20 2 2 B GE-Q046 D GE-T046
Soy 20 2 3 A GE-Q047 C GE-T047
Soy 20 2 3 B GE-Q048 D GE-T048
Soy 30 9 1 A GE-Q049 C GE-T049
Soy 30 9 1 B GE-Q050 D GE-T050
Soy 30 9 2 A GE-Q051 C GE-T051
Soy 30 9 2 B GE-Q052 D GE-T052
Soy 30 9 3 A GE-Q053 C GE-T053
Soy 30 9 3 B GE-Q054 D GE-T054
Soy 30 2 1 A GE-Q055 C GE-T055
Soy 30 2 1 B GE-Q056 D GE-T056
Soy 30 2 2 A GE-Q057 C GE-T057
Soy 30 2 2 B GE-Q058 D GE-T058
Soy 30 2 3 A GE-Q059 C GE-T059
Soy 30 2 3 B GE-Q060 D GE-T060
Animal 5 2 1 A GE-Q061 C GE-T061
Animal 5 2 1 B GE-Q062 D GE-T062
Animal 5 2 2 A GE-Q063 C GE-T063
Animal 5 2 2 B GE-Q064 D GE-T064
Animal 5 2 3 A GE-Q065 C GE-T065
Animal 5 2 3 B GE-Q066 D GE-T066
Animal 5 9 1 A GE-Q067 C GE-T067
Animal 5 9 1 B GE-Q068 D GE-T068
Animal 5 9 2 A GE-Q069 C GE-T069
Animal 5 9 2 B GE-Q070 D GE-T070
Animal 5 9 3 A GE-Q071 C GE-T071
Animal 10 9 1 B GE-Q072 D GE-T072
Animal 10 9 1 A GE-Q073 C GE-T073
Animal 10 9 2 B GE-Q074 D GE-T074
Animal 10 9 2 A GE-Q075 C GE-T075
103
Animal 10 9 3 B GE-Q076 D GE-T076
Animal 10 9 3 A GE-Q077 C GE-T077
Animal 10 9 3 B GE-Q078 D GE-T078
Animal 10 2 1 A GE-Q079 C GE-T079
Animal 10 2 1 B GE-Q080 D GE-T080
Animal 10 2 2 A GE-Q081 C GE-T081
Animal 10 2 2 B GE-Q082 D GE-T082
Animal 10 2 3 A GE-Q083 C GE-T083
Animal 10 2 3 B GE-Q084 D GE-T084
Animal 20 9 1 A GE-Q085 C GE-T085
Animal 20 9 1 B GE-Q086 D GE-T086
Animal 20 9 2 A GE-Q087 C GE-T087
Animal 20 9 2 B GE-Q088 D GE-T088
Animal 20 9 3 A GE-Q089 C GE-T089
Animal 20 9 3 B GE-Q090 D GE-T090
Animal 20 2 1 A GE-Q091 C GE-T091
Animal 20 2 1 B GE-Q092 D GE-T092
Animal 20 2 2 A GE-Q093 C GE-T093
Animal 20 2 2 B GE-Q094 D GE-T094
Animal 20 2 3 A GE-Q095 C GE-T095
Animal 20 2 3 B GE-Q096 D GE-T096
Animal 30 9 1 A GE-Q097 C GE-T097
Animal 30 9 1 B GE-Q098 D GE-T098
Animal 30 9 2 A GE-Q099 C GE-T099
Animal 30 9 2 B GE-Q100 D GE-T100
Animal 30 9 3 A GE-Q101 C GE-T101
Animal 30 9 3 B GE-Q102 D GE-T102
Animal 30 2 1 A GE-Q103 C GE-T103
Animal 30 2 1 B GE-Q104 D GE-T104
Animal 30 2 2 A GE-Q105 C GE-T105
Animal 30 2 2 B GE-Q106 D GE-T106
Animal 30 2 3 A GE-Q107 C GE-T107
Animal 30 2 3 B GE-Q108 D GE-T108
104
Appendix C: External Lab Results