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QUT
Influences of biodiesel chemical compositions and
physical properties on engine exhaust particle emissions
Md. Mostafuur Rahman
A thesis by publication submitted in partial fulfilment of the requirements for the degree of
Doctor of Philosophy (PhD)
International Laboratory of Air Quality and Health (ILAQH)
School of Chemistry Physics and Mechanical Engineering (CPME)
Science and Engineering Faculty (SEF)
Queensland University of Technology (QUT)
2015
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“Yesterday I was clever, so I wanted to change the world.
Today I am wise, so I am changing myself.” – Rumi
“Indeed, your lord will not change the condition of people,
until they change what is in themselves.”-Al-Quran
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Abstract Particulate matter is considered to be the most harmful substance among all compression
ignition (CI) engine exhaust pollutants. It is responsible for numerous adverse health
effects and contributes to climate change by altering global radiative forcing. Therefore,
there is widespread research interest about how to mitigate these emissions. Among many
others, alternative fuels, especially biodiesels, are considered as a potential way to reduce
these emissions to some extent. The work presented in this thesis investigated particle
emissions characteristics of biodiesel from different feedstocks, including conventional
biodiesels and biodiesels with controlled chemical composition, as well as microalgae
biodiesel. Several experimental measurements were conducted using a common rail
engine, in order to explore the effect of the biodiesels’ chemical composition and physical
properties on engine exhaust particle emissions. In addition, bioethanol and synthetic
diesel were also investigated. This thesis has made a significant contribution to our
understanding of how the chemical composition and physical properties of biodiesel can
influence diesel engine exhaust particle emissions.
Experimental measurements revealed that particle emissions could be reasonably different
among different biodiesel feedstocks, depending upon their fatty acid methyl ester (FAME)
composition. Particle emissions increase with biodiesel carbon chain length and degree of
unsaturation, however very high unsaturation with almost the same carbon chain length
could slightly reduce particle emissions. FAME carbon chain length and unsaturation also
affects particle median size. Short chain saturated biodiesel, which has the lowest PM and
PN emissions, also emitted particles with a smaller median diameter. In addition, it was
found that NOx emissions from biodiesel could actually be reduced by the careful control
of biodiesel FAME composition, particularly in the case of saturated biodiesel, with a
FAME carbon chain length of 8 to 14.
Particle emissions also decreased linearly with the oxygen content of fuel/fuel mix,
regardless of the type and percentage of biodiesel in diesel-biodiesel blends. However,
since fuel oxygen content increases with decreasing carbon chain length, it is not clear
which of these factors drive these lower particle emissions. Further, particle emissions
decreased linearly with reductions in the viscosity and surface tension of fuel/fuel mix, but
only for specific blends, with significantly different particle emissions observed for various
blends which had the same viscosity and surface tension.
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The volatile contents of particles, which are an indicator to their oxidative capacity, also
depend on the biodiesel FAME composition. Particles emitted from short chain saturated
biodiesel combustion were found to have a higher fraction of volatile organic substances
adsorbed or condensed onto them and therefore, reactive oxygen species (ROS) emissions
were also found to be high. Both overall volatility (OV) and ROS correlated exponentially
with fuel oxygen content. The increase in OV and ROS could be due to either the decrease
in available non-volatile soot, onto which the volatiles condense, or an actual increase in
the amount of volatile material. However, the exponential relationship observed between
OV and ROS suggests a combination of both effects, being a reduction in the mass of soot
and an increase in the fuel derived volatile (organic) material.
In addition, the microalgae biodiesel used in this work was composed mostly of very long
carbon chain (22 carbon atoms), polyunsaturated FAME. Therefore, average carbon chain
length and unsaturation was significantly higher than any other conventional biodiesel
feedstocks. The boiling point and volatility of this microalgae biodiesel was also higher
than diesel and other biodiesels, and its combustion resulted in almost the same total
particulate matter (TPM) emissions as diesel fuel, especially at a higher blend (i.e. B50). In
this particular case, biodiesel oxygen content could not effectively suppress particle mass
(PM) emissions. Rather, such a composition in biodiesel triggered a significant increase in
nucleation mode particle emissions. Perhaps the very low volatility and high boiling point
of microalgae biodiesel was the driving force here, in conjunction with other properties
(i.e. high density, viscosity and surface tension). Moreover, ethanol in the form of
fumigation and synthetic diesel was also found to be effective in reducing particle
emissions.
In summary, alternative fuels, especially biodiesels were found to be useful for reducing
diesel engine exhaust particle emissions. With a higher saturation and lower carbon chain
length, the careful regulation of biodiesel FAME composition could reduce total particle
emissions, but not necessarily their toxicological effects. Rather, these emissions have been
observed to be more toxic in terms of OV and ROS emission. In addition, biodiesel fuels
are generally more costly, with a higher specific fuel consumption, and in exchange, they
provide the lowest specific power output compared to diesel fuels. On the other hand, a
long FAME carbon chain length almost eliminates the particle emission benefits from low
FAME carbon chain length. Therefore, based on the desired output, the FAME
composition of biodiesel could be regulated, with short chain saturated FAME serving to
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reduce NOx, PM and PN emissions, and long chain unsaturated FAME serving to increase
engine power and specific fuel consumption benefits. Alternatively, a compromise between
emissions (i.e. PM, PN, ROS, NOx etc.) and engine power output and specific fuel
consumption is also possible. In this particular case, it is recommended to regulate
biodiesel FAME composition within 16 to 18 carbon atoms, with a higher degree of
unsaturation. In particular, the species selection criterion for new emerging biodiesel
feedstock microalgae should actively consider their FAME composition, in addition to the
yield percentages, growth rates, land requirements and so on. This will almost certainly
improve biodiesel quality, which will ultimately enhance biodiesel performance and
emission benefits, including particle emissions. Overall, it is evident from this work that
the chemical composition of biodiesel may be more important than its physical properties
in controlling exhaust particle emissions.
Keywords Diesel Particulate Matter (DPM), , Particle Number (PN), Particle Size Distribution (PSD),
Biodiesel, Fatty Acid Methyl Ester (FAME), Biodiesel Feedstocks, Microalgae Biodiesel,
Chemical Composition, Physical Properties, Oxygen Content, Viscosity, Surface Tension,
Carbon Chain Length, Unsaturation, Accumulation Mode, Nucleation Mode, Scanning
Mobility Particle Sizer (SMPS), Differential Mobility Spectrometer (DMS), Volatilisation
Tandem Differential Mobility Analyser (V-TDMA), Volatility, Count Median Diameter
(CMD), DustTrak, Diesel Particulate Filter (DPF), Diesel Oxidation Catalyst (DOC),
Reactive Oxygen Species (ROS), Organic Content (OC), Soot
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List of publications M.M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski, “Influence of different alternative
fuels on particle emission from a turbocharged common-rail diesel engine”, Procedia
Engineering, 56 (2013) 381-386.
P.X. Pham, T.A. Bodisco, S. Stevanovic, M.D. Rahman, H. Wang, Z.D. Ristovski, R.J.
Brown, A.R. Masri, “Engine performance characteristics for biodiesels of different degrees
of saturation and carbon chain lengths”, SAE Int. J. Fuels Lubr., 6 (2013) 188-198.
M.M. Rahman, A. Pourkhesalian, M. Jahirul, S. Stevanovic, P. Pham, H. Wang, A. Masri,
R. Brown, Z. Ristovski, “Particle emissions from biodiesels with different physical
properties and chemical composition”, Fuel, (2014), pages 201-208.
A.M. Pourkhesalian, S. Stevanovic, F. Salimi, M.M. Rahman, H. Wang, X.P. Pham, S.E.
Bottle, A. R. Masri, R. J. Brown, Z.D. Ristovski, “The influence of fuel molecular
structure on the volatility and oxidative potential of biodiesel particulate matter”, Accepted
in Environmental Science & Technology, doi: 10.1021/es503160m.
M.M. Rahman, Muhammad Aminul Islam, S. Stevanovic, Md. Nurun Nabi
, George
Thomas, Bo Feng, R. J. Brown, Z. D. Ristovski, “Particle emissions from microalgae
biodiesels and their relative oxidative potential”, Submitted to Fuel
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Acknowledgements I would like to acknowledge and sincerely thank my principal supervisor Prof. Zoran
Ristovski and associate supervisors A/Prof. Richard Brown and Prof. Lidia Morawska
for their valuable support during my PhD journey here at QUT. They have taught me many
things, from understanding the very fundamentals of research to conducting scientific
investigations at their most advance level. They have supported me in ways which are not
possible for me to properly acknowledge here. Not only have they provided me with much
direction and many opportunities, but they have been there to provide very sound scientific
judgement and personal counsel throughout all stages of my candidature. Without their
continuous help, support and guidance, it would not have been possible for me get this
thesis submitted. I would also like to thank Prof. Assad Masri from the University of
Sydney and Dr. Bo Feng from the University of Queensland (UQ) for providing the
biodiesel fuels and engine test facilities, respectively, and for making such a successful
collaboration with us. In addition, I would also like to thank Dr. Svetlana Stevanovic, Dr.
Nurun Nabi, Dr. Hao Wang, Dr. Branka Miljevic and Dr. Nick Surawski for their help
with instrumentation, study design, measurements, data analysis and manuscript
preparation.
I also wish to praise the efforts of Mr. Noel Hartnett and Mr. Scott Abbett, technicians
in Biofuel Engine Research Facilities (BERF), who have provided valuable assistance
during all of the experimental measurements conducted at QUT.
I wish to extend my acknowledgement and thanks to all of my PhD colleagues at ILAQH
and BERF, for their assistance, co-operation, friendship, social interaction and occasional
humour. In particular, Muhammad Aminul Islam, Md. Jahirul Islam and Meisam Babaie
from BERF and Ali Mohammad Pourkhesalian, Marc Mallet, Luke Cravigan, Jana
Adamovska, Andjelija Milic, Farzaneh Hedayat and many others from ILAQH.
The dream of completing my PhD wouldn’t have been possible by any means without the
financial assistance I have received from QUT. I had the honour of receiving an
International Postgraduate Research Scholarship (IPRS), Australian Post-Graduate Award
(APA) and Deputy Vice-Chancellors Top-up Scholarship for my tuition fees and living
allowance, respectively. Therefore, I would like to express my deepest thanks to the
relevant departments and committee’s for making these scholarships available to me.
Moreover, my ultimate thanks also go to the Australian tax-payers, who contributed from
behind the scenes, to pay my scholarship.
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I would also like to thank and acknowledge my family members back home in Bangladesh
and all of my friends, either here in Australia or back in Bangladesh. Without their love,
sympathy and inspiration I wouldn’t have any pleasure in life, or enough motivation to
complete my PhD. Finally, I would like to thank my employer RUET back in Bangladesh
for giving me necessary leave and allowing me to pursue my PhD overseas.
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Dedication
This thesis is dedicated to my beloved mother Shahida Khatun. Without her immense
care, inspiration and sincere prayer, I may not have achieved my potential to complete my
PhD.
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Statement of original authorship The work in this thesis has not been previously submitted for a degree or diploma at any
other higher education institution. To the best of my knowledge and belief, the thesis
contains no material previously published or written by another person except where due
reference is made.
QUT Verified Signature
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Abbreviations Bxx -xx volume percentage of biodiesel in diesel-biodiesel blends
CMD - count median diameter
CPC- condensation particle counter
CSO – cotton seed oil
CO - carbon mono oxides
CO2 - carbon dioxides
CoV- coefficient of variation
C100- 100% canola
CN- cetane number
DOC - diesel oxidation catalyst
DPF - diesel particulate filter
DMS - differential mobility spectrometer
DMA – differential mobility analyzer
DPM - diesel particulate matter
EC - elemental carbon
EU- European union
Exx – xx percent of energy substitution by ethanol
FAME - fatty acid methyl ester
FT – Fischer-Tropsch
HC - hydrocarbon
IMEP- indicated mean effective pressure
ISFC- indicated specific fuel consumption
NOx - oxides of nitrogen
NHHR- neat heat release rate
OC - organic content
OV - overall volatility
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PM - particulate matter
PN - particle number
PSD - particle size distribution
PAH - poly aromatic hydrocarbons
PMP- particle measurement program
ROS - reactive oxygen species
SMPS - scanning mobility particle sizer
SOF- soluble organic fraction
TPM- total particulate matter
TDC- top dead center
T100- 100% tallow
VVF- volume of volatile fraction
V-TDMA - volatilisation tandem differential mobility analyser
WCO – waste cook oil
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Table of Contents
Abstract ................................................................................................................................. iii
Keywords ............................................................................................................................... v
List of publications ............................................................................................................... vi
Acknowledgements .............................................................................................................. vii
Statement of original authorship ............................................................................................ x
Abbreviations ........................................................................................................................ xi
List of tables ...................................................................................................................... xvii
List of figures .................................................................................................................... xviii
Chapter 1: Introduction .......................................................................................................... 1
1.1 The investigated research problem .............................................................................. 1
1.2 Study aim ..................................................................................................................... 3
1.3 Specific study objectives .............................................................................................. 3
1.4 Evidence of research progress linking the research papers .......................................... 4
1.5 References .................................................................................................................... 7
Chapter 2: Literature review ................................................................................................ 13
2.1 Introduction ................................................................................................................ 13
2.2 Emissions from diesel engines ................................................................................... 13
2.3 Importance of DPM emissions in respect to climate change ..................................... 17
2.4 Health effects of DPM ............................................................................................... 18
2.5 Measurement of DPM emissions ............................................................................... 21
2.5.1 Standardisation of measurement technique ......................................................... 21
2.5.2 Requirement of proper dilution system ............................................................... 21
2.5.3 Importance of exhaust conditioning prior to measurement ................................. 23
2.5.4 PM, BC or EC measurement ............................................................................... 24
2.5.5 Particle number and size distribution measurement ............................................ 26
2.5.6 Measurement of DPM composition .................................................................... 28
2.6 Measures and techniques for DPM mitigation ........................................................... 29
2.7 Why Biodiesel ............................................................................................................ 33
2.8 Emission behavior of biodiesel fuelled engines ......................................................... 37
2.9 Properties of biodiesel influential to combustion and emission ................................ 41
2.10 Effect of fatty acid profile on biodiesel properties .................................................. 42
2.11 The impact of biodiesel feedstocks on combustion and emissions .......................... 46
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2.12 Progress of microalgae as biodiesel feedstock ......................................................... 49
2.13 References ................................................................................................................ 51
Chapter 3: Influence of different alternative fuels on particle emissions from a turbo
charged commonrail diesel engine ....................................................................................... 68
3.1 Introduction ................................................................................................................ 69
3.2 Experimental Methods ............................................................................................... 71
3.3 Results and Discussions ............................................................................................. 72
3.3.1 PM 2.5 emission from different alternative fuels ................................................ 72
3.3.2 Particle number and size distribution form different alternative fuels ................ 73
3.3.3 Effect of different alternative fuels on specific NOx emission ........................... 75
3.4 Conclusions ................................................................................................................ 76
3.5 Acknowledgement ...................................................................................................... 77
3.6 References .................................................................................................................. 77
Chapter 4: Engine performance characteristics of biodiesels having different degree of
saturation and carbon chain length ....................................................................................... 79
4.1 Introduction ................................................................................................................ 81
4.2 Experimental Setup .................................................................................................... 83
4.2.1 Test Facility ......................................................................................................... 83
4.2.2 Fuel Selection ...................................................................................................... 86
4.3 Results and discussion ................................................................................................ 87
4.3.1 Engine Performance ............................................................................................. 87
4.3.2 Emission Characteristics ...................................................................................... 92
4.4 Conclusion .................................................................................................................. 99
4.5 References .................................................................................................................. 99
Chapter 5: Particle emissions from biodiesels with different physical properties and
chemical composition......................................................................................................... 103
5.1 Introduction .............................................................................................................. 105
5.2 Materials and methods .............................................................................................. 106
5.2.1 Engine and fuel specification ............................................................................. 106
5.2.2 Exhaust sampling and measurement system ...................................................... 110
5.3 Results and discussion .............................................................................................. 111
5.3.1 Specific PM emissions ....................................................................................... 111
5.3.2 Specific PN emissions ....................................................................................... 113
5.3.3 Particle number size distribution ....................................................................... 114
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5.3.4 Particle median size ........................................................................................... 115
5.3.5 Specific NOx emissions .................................................................................... 116
5.3.6 Influence of fuel physical properties and chemical composition on particle
emissions .................................................................................................................... 117
5.3.7 Comparison of engine performance and particle emissions among used
biodiesels .................................................................................................................... 119
5.4 Conclusions .............................................................................................................. 120
5.5 Acknowledgement ................................................................................................... 120
5.6 References ................................................................................................................ 120
5.7 Appendix .................................................................................................................. 124
Chapter 6: The influence of fuel molecular structure on the volatility and oxidative
potential of biodiesel particulate matter ............................................................................ 135
6.1 Introduction .............................................................................................................. 137
6.2 Experimental setup and methodology ...................................................................... 139
6.3 Fuel Selection ........................................................................................................... 140
6.4 Results and discussion ............................................................................................. 141
6.4.1 Volatility measurements .................................................................................... 141
6.4.2 BC Emissions .................................................................................................... 143
6.4.3 ROS measurements ........................................................................................... 144
6.5 Acknowledgments .................................................................................................... 150
6.6 References ................................................................................................................ 150
6.7 Supporting information (SI) ..................................................................................... 155
Chapter 7: The role of microalgal biodiesel composition on diesel engine exhaust particle
emissions and their oxidative potential .............................................................................. 159
7.1 Introduction .............................................................................................................. 161
7.2 Materials and Methods ............................................................................................. 162
7.3 Results and Discussion............................................................................................. 166
7.3.1 Specific particulate matter (PM) emissions ....................................................... 166
7.3.2 Particle number (PN) emission .......................................................................... 169
7.3.3 Particle number size distribution ....................................................................... 170
7.3.4 Relationship between fuel oxygen content and particle emissions ................... 172
7.3.5 Influence of microalgae biodiesel on nucleation mode particle formation ....... 174
7.3.6 Oxidative potential of particles emitted from microalgae biodiesel blends ...... 175
7.4 Conclusion ............................................................................................................... 177
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7.5 Acknowledgement .................................................................................................... 177
7.6 References ................................................................................................................ 178
Chapter 8: Conclusion and further research ....................................................................... 187
8.1 Conclusion arising from this thesis .......................................................................... 187
8.2 Future research direction .......................................................................................... 190
8.3 References ............................................................................................................................ 193
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List of tables Table 3-1: Engine specification .......................................................................................... 71
Table 4-1: Test engine specifications .................................................................................. 84
Table 4-2: Chemical composition and physical properties of used fuels ........................... 86
Table 5-1: Test engine specification ................................................................................. 107
Table 5-2: Fatty acid profile of used biodiesels ................................................................ 108
Table 5-3: Important physicochemical properties of tested fuels ..................................... 109
Table 7-1: Test engine specifications ............................................................................... 163
Table 7-2: Elemental compositions and important physical properties of the biodiesel
blends used for engine testing ............................................................................................ 166
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List of figures
Figure 1-1: Flow chart representing the publications arising from the research work and
contributing to thesis .............................................................................................................. 4
Figure 2-1: An engineer’s depiction of DPM ([8]) ............................................................. 14
Figure 2-2: Diesel particulate matter (DPM) size distributions [11, 22] ............................ 16
Figure 2-3: Change in DPM emission standards in the EU and US over last two
decades[89] .......................................................................................................................... 30
Figure 2-4: The total reserves of coal, natural gas and crude oil and their predicted
diminishing trends[124] ....................................................................................................... 34
Figure 2-5: IEA prediction of biofuel demand in 2050 ...................................................... 35
Figure 2-6: Existing/required production capacity of biodiesel in the EU [126]................ 37
Figure 2-7: Variation of biodiesel properties with FAME chemical structure (Notes:
Length of arrow indicates relative magnitude of effect, thickness of arrow indicates
consistency of effect, symbol “–” indicates highly uncertain effect, blank box indicates that
no relevant information was found.) [156] .......................................................................... 44
Figure 2-8: Fatty acid composition of some common biodiesel feedstocks [196] ............. 46
Figure 3-1: Schematic diagram of experimental set up ...................................................... 71
Figure 3-2: Brake specific PM2.5 emissions for different alternative fuels at 2000 rpm
engine speed ......................................................................................................................... 73
Figure 3-3: Brake specific particle number emissions for different alternative fuels at 2000
rpm engine speed.................................................................................................................. 74
Figure 3-4: Particle size distribution for canola biodiesel .................................................. 74
Figure 3-5: Particle size distribution for different alternative fuels at 2000 rpm engine
speed ..................................................................................................................................... 74
Figure 3-6: Brake specific NOx emissions for different alternative fuels at 2000 rpm
engine speed ......................................................................................................................... 76
Figure 4-1: Experimental set up schematics ....................................................................... 84
Figure 4-2: p-V indicator diagrams of fossil diesel and biodiesels at 1500 rpm, full load . 88
Figure 4-3: p-V indicator diagrams of fossil diesel and biodiesels at 2000 rpm, full load . 88
Figure 4-4: p-θ indicator diagrams of fossil diesel and biodiesels at 1500 rpm, full load .. 88
Figure 4-5: p-θ indicator diagrams of fossil diesel and biodiesels at 2000 rpm, full load .. 89
Figure 4-6: Indicated mean of effective pressure of fossil diesel and biodiesels at 2000
rpm, full load ........................................................................................................................ 89
Figure 4-7: CoV of IMEP of fossil diesel and biodiesels at2000 rpm, full load ................ 90
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Figure 4-8: NHRR at 1500 rpm, 25% of full load .............................................................. 91
Figure 4-9: NHRR at 1500 rpm, full load .......................................................................... 91
Figure 4-10: NHRR at 2000 rpm, 25% of full load ............................................................ 91
Figure 4-11: NHRR at 2000 rpm full load ......................................................................... 92
Figure 4-12: ISNOx of fossil diesel and biodiesels at 2000 rpm ........................................ 93
Figure 4-13: ISNOx /ISFC trade off, at 2000 rpm ............................................................. 93
Figure 4-14: Total particle number concentrations of fossil diesel and biodiesel at 1500
rpm ....................................................................................................................................... 95
Figure 4-15: Total particle number concentrations of fossil diesel and biodiesels at 2000
rpm ....................................................................................................................................... 95
Figure 4-16: Particle size concentrations of fossil diesel and biodiesel at 1500 rpm 25% of
full load ................................................................................................................................ 96
Figure 4-17: Particle size concentrations of fossil diesel and biodiesel at 1500 rpm full
load ...................................................................................................................................... 96
Figure 4-18: Particle size concentrations of fossil diesel and biodiesel at 2000 rpm 25% of
full load ................................................................................................................................ 97
Figure 4-19: Particle size concentrations of fossil diesel and biodiesel at 2000 rpm full
load ...................................................................................................................................... 97
Figure 4-20: Indicated specific ROS of fossil diesel and biodiesels at 1500 rpm, 25% of
full load ................................................................................................................................ 98
Figure 4-21: Indicated specific ROS of fossil diesel and biodiesels at 1500 rpm, full load
............................................................................................................................................. 98
Figure 5-1: Schematic diagram of experimental set up .................................................... 111
Figure 5-2: Brake specific PM emission at (a) 1500 rpm 100% load and (b) 2000 rpm
100% load. The PM emissions shown are calculated based on DustTrak measurement. . 112
Figure 5-3: Brake specific PN emissions at (a) 1500 rpm 100% load and (b) 2000 rpm
100% .................................................................................................................................. 114
Figure 5-4: Variations in particle median size among used fuels at (a)1500 rpm 100% load
and (b) 2000 rpm 100% load , while (c) shows particle median size variation with total
number concentration ........................................................................................................ 116
Figure 5-5: Brake specific NOx emission at (a) 1500 rpm 100% load and (b) 2000 rpm
100% load .......................................................................................................................... 117
Figure 5-6: Variation in specific PM and PN emissions with used fuel surface tension,
viscosity and oxygen content ((a), (b), (c) for PM and (d), (e), (f) for PN), Ordinate of (a),
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(b), (c) and (d), (e), (f) are same where abscissa of (a), (d) and (b), (e) and (c), (f) are same.
............................................................................................................................................ 118
Figure 5-7: Comparison of engine performance (power, BSFC) and particle emissions
(PM, PN) among biodiesels and their blends where petro-diesel was used as a reference
fuel...................................................................................................................................... 119
Figure 6-1: OV for all blends and modes tested. Rows present data measured at the same
load, while columns data are measured at the same blend percentage. Different fuels are
sorted along the x-axis according to their carbon chain length going from the shortest
(C810) to the longest one (C1875). .................................................................................... 142
Figure 6-2: BC Emission factors for all blends and modes tested. The emission factor for
B0 at each mode is shown as a dashed line. Rows present data measured at the same load,
while columns data are measured at the same blend percentage. Different fuels are sorted
along the x-axis according to their carbon chain length going from the shortest (C810) to
the longest one (C1875). .................................................................................................... 143
Figure 6-3: OP of PM for all four fuels, for all the blends and loads ............................... 144
Figure 6-4: Dependence of the BC emissions factor on fuel oxygen content for the four
modes tested ....................................................................................................................... 146
Figure 6-5: Dependence of the (a) Overall volatile content and b) ROS concentration on
fuel oxygen content for the four modes tested. R2 shown in the graphs are McFadden's
pseudo R2 ........................................................................................................................... 147
Figure 6-6: Dependence of the ROS concentration on the overall volatile content (OV) for
the 4 modes tested. R2 shown in the graphs are McFadden's pseudo R
2 ........................... 149
Figure 7-1: Schematic of experimental set up .................................................................. 164
Figure 7-2: Brake-specific TPM emissions of diesel and biodiesel blends ...................... 167
Figure 7-3: Brake-specific accumulation mode PM emissions of diesel and biodiesel
blends ................................................................................................................................. 168
Figure 7-4: Brake-specific particle number emission (accumulation mode) for diesel and
biodiesel blends .................................................................................................................. 170
Figure 7-5: Particle size distribution of diesel and biodiesel blends at 100% (a) and 50%
(b) loads .............................................................................................................................. 172
Figure 7-6: Relationship between accumulation mode PM and PN emissions with fuel
oxygen content at 100% load for diesel and biodiesel blends ........................................... 173
Figure 7-7: Effect of biodiesel blends on nucleation mode particle (a) and TGA analysis of
the diesel and biodiesel blends used (b) ............................................................................. 175
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Figure 7-8: Oxidative potential of particles produced from diesel and microalgal biodiesel
blend combustion ............................................................................................................... 176
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Chapter 1: Introduction
1.1 The investigated research problem
On the verge of science and technological progress, almost every aspect of modern human
life relies on technology. Technological invention has brought ease and comfort into our
lives by providing mechanised devices, none of which are functional without a power
source. Among many others, the diesel engine, also known as a compression ignition (CI)
engine, is a major source of power. It is widely used in land and sea transports, and
provides electrical powers. It also used in construction, farming and countless other
industrial applications. Diesel engines are in the pace of increasing in popularity due to
their higher thermal efficiency, improved vehicle performance and better durability, when
compared to their conventional gasoline counterparts [1]. There are also reports of CO2
emission benefits by switching from gasoline to diesel passenger cars [2-4]. Diesel vehicle
sales make up more than half of the new automobile market in European countries [5],
which are expected to rise significantly in the future [6, 7]. Despite the many advantages
associated with diesel engines, perhaps the most objectionable aspect of their use is the
issue of particulate emissions, often referred to as diesel particulate matter (DPM).
Particulate matter emitted from diesel engines is considered to be the most harmful
substance among all of its exhaust emissions [8-17]. Considering its harmful effect on
human health, the International Agency for Research on Cancer (IARC) recently included
this substance as a carcinogen under group-I[18]. In addition, DPM also affects the
temperature and climate by altering the radiative properties of the atmosphere [19-25].
Therefore, in order to mitigate DPM emissions, diesel engines are facing more stringent
emission standards than their counterpart, gasoline engines [26]. In order to meet these
stringent emission regulation, we have already observed significant changes either in
engine technology or in the exhaust after treatment systems of modern diesel engines [27].
The replacement of mechanical injection systems by a common rail system significantly
reduced PM emissions [28], while the introduction of a three way catalyst not only reduced
PM, but also hydrocarbon (HC) and NOx as well. The recent addition of a diesel oxidation
catalyst (DOC), particle oxidation catalyst (POC) and more importantly, the diesel
particulate filter (DPF), have greatly assisted in reducing PM emissions to within the
standard limit [29-33]. Despite all of these developments, PM emissions from diesel
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engines still remain a considerable hazard, for both human health and the environment, and
pose a huge challenge to the scientific community and automotive engineers.
Apart from improvements in engine technology and after treatment devices, fuel
modification is also considered as a potential means of reducing PM emissions [34-36].
Removal of the sulfur component from fuel and the introduction of ultra-low sulfur fuel
significantly reduced PM emissions [37, 38] and there are even reports of improvements in
urban PM following the introduction of ultra-low sulfur fuel [39, 40]. Biofuel is another
alternative which can significantly reduce PM emissions, in conjunction with other exhaust
emissions as well [41, 42]. There are numerous studies which confirm that the blending of
a small amount of biofuel with diesel could reduce PM emissions [43, 44]. In addition,
biofuels also have carbon footprint emission benefits, as they are a renewable resource.
Among the different kinds of biofuel, biodiesel is considered to be the most suitable for
diesel engines.
Biodiesel is a mono alkyl ester of fatty acid, usually produced by trans-esterification, either
from animal fats or vegetable oils. The chemical composition and physical properties of
these fatty acid esters depend primarily on the fatty acid profile of the fats and oils used
[45-47], which can even vary within the same feedstock, depending upon their diet and
growing conditions, respectively [45, 48, 49]. Similarly, microalgae, which is considered a
3rd
generation biodiesel has numerous species, and the fatty acid profile of these species
could also be entirely different [48]. Studies suggest that the fatty acid profile of these
species can be controlled by either genetic modification of the species or by manipulating
their growing conditions [50, 51]. This means that it is possible to obtain the desired fatty
acid profile from any species once we know the best fatty acid profile for biodiesel, in
terms of optimising engine performance with the lowest possible emissions. Therefore, it is
important to understand the influence of biodiesel physical properties and chemical
composition on particle emissions, in order to maximise the benefits associated with the
use of biodiesel fuel.
Apart from the most commonly used FAME biodiesel, Hydrotreated vegetable oil (HVO),
otherwise known as renewable diesel also originates from renewable vegetable oils, animal
fats and even microalgae. HVOs are produced by hydrogenation of fatty acids, where the
oxygen molecules in fatty acids are replaced by hydrogen. HVOs are mainly mixture of
paraffinic hydrocarbon, and free of sulphur and aromatics which offers some PM
P a g e | 3
emissions benefits, but not to the extent of biodiesel since there is no oxygen in them.
Although both HVOs and biodiesels are originated from the same fatty acids, HVOs are
not the point of interest for this thesis, as they are different from biodiesels in terms of their
chemical compositions, physical properties and production process.
1.2 Study aim The main aim of this study was to explore the influence of the physical properties and
chemical composition of biodiesel on engine exhaust particle emissions. This aim was
achieved by undertaking several experimental measurement campaign using biodiesel fuels
with different physical properties and chemical compositions. The biodiesel fuels tested
included conventional biodiesel feedstocks, biodiesel with a controlled chemical
composition and microalgae biodiesel. In addition, bioethanol and synthetic diesel
(Fischer-Tropsch) were also tested to further investigate the effect of fuel chemical
composition on particle emissions. Changes in particle number and size due to fuel
characteristics were characterised by using a Scanning Mobility Particle Sizer (SMPS).
Information about the volatility of the particles was obtained by using a Tandem
Differential Mobility Analyser (TDMA), which provided important clues about the
presence of toxic organic compounds adsorbed or condensed onto the particle surface.
1.3 Specific study objectives
The primary aim of this study was achieved by several experimental measurements.
Particle emission behaviour of different alternative fuels ranging from ethanol,
conventional biodiesels, and biodiesels with controlled chemical composition, microalgae
biodiesel and synthetic diesel were explored from these experimental measurements. In
addition, chemical composition and important physical properties of the tested fuels were
also measured to understand their role on exhaust particle emissions. Additionally, other
gaseous emissions and engine performance parameters were also accounted simultaneously
during these measurements. The major experimental campaigns conducted in this work
were designed to:
1. Understand the impact of ethanol fumigation, synthetic diesel and conventional
biodiesel feedstocks on engine exhaust particle emissions.
2. Explore the effect of biodiesels with controlled chemical composition and physical
properties on engine exhaust particle emissions.
P a g e | 4
3. Understand the effect of biodiesel chemical composition and physical properties on
the volatility and oxidative potential of emitted particles.
4. Explore the impact of microalgae biodiesel on engine exhaust particle emissions in
comparison with conventional biodiesels.
Reliable particle emission measurements require a proper dilution system. Therefore, a
combination of a partial flow dilution tunnel and a Dekati diluter were employed to dilute
engine exhaust prior to emission measurements. To comply with existing emission
standards, we also developed a methodology which would enable us to report the dilution
corrected PM and other pollutants, in terms of specific emissions.
1.4 Evidence of research progress linking the research papers The relationships among the research papers presented in this thesis are shown in the flow
chart below (Figure 1-1). It indicates how the research papers are coherent and linked with
each other. Further discussions relating to how the outputs of these research papers form a
unified research program are provided below:
Figure 1-1: Flow chart representing the publications arising from the research work and
contributing to thesis
P a g e | 5
The first manuscript in this thesis (Chapter 3) was published in the journal Procedia
Engineering [52]. It investigated the influence of biofuel (i.e. ethanol and biodiesel), as
well as synthetic diesel on engine exhaust particle emissions. This study found that particle
emissions decreased with the use of ethanol fumigation, biodiesels and synthetic diesel,
compared to conventional gasoline fuels. Particle emissions from ethanol fumigation were
also dependent on engine operating conditions, while particle emissions from biodiesels
were highly dependent on their feedstocks, however the reason for this variation was not
yet understood. It was speculated that these variations were either due to the chemical
composition or physical properties of the biodiesels, which prompted the second round of
experimental measurements (using biodiesel fuels with controlled chemical composition
and physical properties) and resulted in the second, third and fourth manuscripts (Chapters
4-6).
These three manuscripts are interlinked and were based on the same experimental
measurement campaign. The second manuscript reported on emission measurements for
100% biodiesel (B100) and was published in the Society of Automotive Engineers (SAE)
International Journal of Fuels and Lubricating Oils [53]. It found that particle emissions
from biodiesel fuels varied significantly according to their fatty acid methyl ester (FAME)
carbon chain length and degree of unsaturation. Particle emissions increased with biodiesel
carbon chain length and degree of unsaturation, where very high unsaturation with the
same carbon chain length was found to slightly reduce particle emissions. It also reported a
distinguishable difference in specific particle size distribution (PSD). Again, biodiesel
continuing shorter carbon chain lengths produced PSDs with a smaller median size, which
increased together with increases in carbon chain length. In addition with particle
emissions, reasonable variations were also observed in specific NOx emissions. However it
was not known which physical properties (i.e. viscosity, density, surface tension etc) and
chemical elements (i.e. carbon, hydrogen or oxygen content etc.) were responsible for
these variations. Therefore, important physical properties and chemical composition of the
tested biodiesels (and their blends) were either experimentally measured or calculated from
their FAME profile (and reported in the third manuscript), in order to explore the
underlying reasons behind the observed variation in particle emission among different
biodiesels.
The third manuscript was published in the journal Fuel [54]. It explored the impact of the
physical properties and chemical composition of biodiesel fuels on engine exhaust particle
P a g e | 6
emissions. It covered those properties of pure biodiesel, as well as their tested blends (i.e.
B20 and B50). This paper revealed that the oxygen content of the tested fuel or fuel mix
was the main driving force behind the reported particle emission reduction, regardless of
any variation in other physical properties (i.e. density viscosity and surface tension). Fuel
oxygen content also affected the particle median size, which was found to decrease with
increasing fuel oxygen. It was also found that particle emissions increased with an increase
in fuel viscosity and surface tension, but only within specific blends. In other words,
particle emissions from two different blends with the same viscosity and surface tension
were found to be significantly different. However, it should be noted that the test engine
for this campaign was equipped with common rail injection system, which may have
served to minimise the effect of small variations in fuel viscosity and surface tension, due
to the high fuel injection pressure.
The fourth manuscript investigated the effect of biodiesel FAME composition on the
volatility and oxidative potential (OP) of the emitted particles. It was observed that the
volatility and OP of particles depended upon the FAME carbon chain length of the
biodiesel fuel. Particles emitted from short chain saturated biodiesel combustion were
found to have a higher fraction of volatile organic substances adsorbed onto them, leading
to higher reactive oxygen species (ROS) emissions. The particles overall volatility (OV)
and ROS correlated exponentially with the oxygen content of the fuel or fuel mix, which
could either be due to a reduction in soot onto which the volatiles condense, or an actual
increase in the amount of volatile materials. However, the exponential relationship
observed between OV and ROS indicated a combination of both effects. These results
further highlighted the importance of FAME composition on the toxicity of particles
emitted from biodiesel combustion. In addition, they also challenge the effectiveness of
current mass and number based emission standards in addressing the health effects of
diesel particulate matter (DPM), and reinforces the importance of more inclusive emission
standards.
The last manuscript was based on measurements using microalgae biodiesel, alongside
cottonseed and waste cook biodiesel. Microalgae biodiesel (widely known as 3rd
generation
biodiesel) is considered the most promising of the biodiesel feedstocks and has the
potential to meet future biodiesel demand. In addition, the chemical composition of
microalgae biodiesel was unique in the sense that it was mainly composed of FAME
molecules having 22 carbons and 6 double bonds. This paper further revealed an increase
P a g e | 7
in particle emissions from the combustion of biodiesel fuels with longer carbon chain
lengths (i.e. particle emissions from biodiesel composed of > C22 can exceed reference
diesel values at higher blends). In addition, the effectiveness of biodiesel oxygen content in
reducing particle emissions (as discussed earlier) was not observed for microalgae
biodiesel. It was also found that biodiesel fuels with a higher boiling point had more
potential to produce nucleation mode particles.
Overall, this PhD thesis studied diesel engine exhaust particle emissions, possible
mitigation strategies (in terms of the use of alternative fuels) and the technologies available
to reduce these emissions. In particular, the impact of chemical composition and physical
properties of biodiesel fuels on diesel engine exhaust particle emissions were investigated.
1.5 References [1] J.B. Heywood, Internal combustion engine fundamentals, (1988).
[2] R. Minjares, K. Blumberg, F. Posada Sanchez, Alignment of policies to maximize the
climate benefits of diesel vehicles through control of particulate matter and black carbon
emissions, Energy Policy, 54 (2013) 54-61.
[3] M.A. Tovar, An integral evaluation of dieselisation policies for households’ cars,
Energy Policy, 39 (2011) 5228-5242.
[4] S.J. Jeong, K.S. Kim, J.-W. Park, CO2 emissions change from the sales authorization
of diesel passenger cars: Korean case study, Energy Policy, 37 (2009) 2630-2638.
[5] L. Schipper, L. Fulton, Disappointed by Diesel?, Transportation Research Record:
Journal of the Transportation Research Board, 2139 (2009) 1-10.
[6] M. Melikoglu, Demand forecast for road transportation fuels including gasoline, diesel,
LPG, bioethanol and biodiesel for Turkey between 2013 and 2023, Renewable Energy, 64
(2014) 164-171.
[7] B. Roy, Diesel Engine and Car Market in India: Where Does Fiat Stand?, (2014).
[8] J.M. Brito, L. Belotti, A.C. Toledo, L. Antonangelo, F.S. Silva, D.S. Alvim, P.A.
Andre, P.H.N. Saldiva, D.H.R.F. Rivero, Acute cardiovascular and inflammatory toxicity
induced by inhalation of diesel and biodiesel exhaust particles, Toxicological Sciences,
116 (2010) 67-78.
[9] D.T. Silverman, C.M. Samanic, J.H. Lubin, A.E. Blair, P.A. Stewart, R. Vermeulen,
J.B. Coble, N. Rothman, P.L. Schleiff, W.D. Travis, R.G. Ziegler, S. Wacholder, M.D.
Attfield, The diesel exhaust in miners study: A nested case-control study of lung cancer
and diesel exhaust, Journal of the National Cancer Institute, 104 (2012) 855-868.
[10] M.D. Attfield, P.L. Schleiff, J.H. Lubin, A. Blair, P.A. Stewart, R. Vermeulen, J.B.
Coble, D.T. Silverman, The diesel exhaust in miners study: A cohort mortality study with
emphasis on lung cancer, Journal of the National Cancer Institute, 104 (2012) 869-883.
P a g e | 8
[11] F.R. Cassee, M.E. Héroux, M.E. Gerlofs-Nijland, F.J. Kelly, Particulate matter
beyond mass: Recent health evidence on the role of fractions, chemical constituents and
sources of emission, Inhalation Toxicology, 25 (2013) 802-812.
[12] N.K. Fukagawa, M. Li, M.E. Poynter, B.C. Palmer, E. Parker, J. Kasumba, B.A.
Holmen, Soy Biodiesel and Petrodiesel Emissions Differ in Size, Chemical Composition
and Stimulation of Inflammatory Responses in Cells and Animals, Environ Sci Technol,
(2013).
[13] J.N. Gangwar, T. Gupta, A.K. Agarwal, Composition and comparative toxicity of
particulate matter emitted from a diesel and biodiesel fuelled CRDI engine, Atmospheric
Environment, 46 (2012) 472-481.
[14] M.E. Gerlofs-Nijland, A.I. Totlandsdal, T. Tzamkiozis, D.L.A.C. Leseman, Z.
Samaras, M. Låg, P. Schwarze, L. Ntziachristos, F.R. Cassee, Cell toxicity and oxidative
potential of engine exhaust particles: Impact of using particulate filter or biodiesel fuel
blend, Environmental Science and Technology, 47 (2013) 5931-5938.
[15] A.L.N. Guarieiro, J.V.D.S. Santos, A. Eiguren-Fernandez, E.A. Torres, G.O. Da
Rocha, J.B. De Andrade, Redox activity and PAH content in size-classified nanoparticles
emitted by a diesel engine fuelled with biodiesel and diesel blends, Fuel, 116 (2014) 490-
497.
[16] H.D. Hosgood Iii, R.S. Chapman, H. Wei, X. He, L. Tian, L.Z. Liu, H. Lai, L.S.
Engel, W. Chen, N. Rothman, Q. Lan, Coal mining is associated with lung cancer risk in
Xuanwei, China, American Journal of Industrial Medicine, 55 (2012) 5-10.
[17] Z.D. Ristovski, B. Miljevic, N.C. Surawski, L. Morawska, K.M. Fong, F. Goh, I.A.
Yang, Respiratory health effects of diesel particulate matter, Respirology, 17 (2012) 201-
212.
[18] I.A.f.R.o.C. (ARC), Diesel engine exhaust carcenogenic, (2012) press release N 213.
[19] M.Z. Jacobson, Strong radiative heating due to the mixing state of black carbon in
atmospheric aerosols, Nature, 409 (2001) 695-697.
[20] T. Cheng, X. Gu, Y. Wu, H. Chen, Effects of atmospheric water on the optical
properties of soot aerosols with different mixing states, Journal of Quantitative
Spectroscopy and Radiative Transfer, 147 (2014) 196-206.
[21] Y. Wu, T. Cheng, X. Gu, L. Zheng, H. Chen, H. Xu, The single scattering properties
of soot aggregates with concentric core–shell spherical monomers, Journal of Quantitative
Spectroscopy and Radiative Transfer, 135 (2014) 9-19.
[22] T. Kühn, A.I. Partanen, A. Laakso, Z. Lu, T. Bergman, S. Mikkonen, H. Kokkola, H.
Korhonen, P. Räisänen, D.G. Streets, S. Romakkaniemi, A. Laaksonen, Climate impacts of
changing aerosol emissions since 1996, Geophysical Research Letters, 41 (2014) 4711-
4718.
[23] G. Lin, J.E. Penner, M.G. Flanner, S. Sillman, L. Xu, C. Zhou, Radiative forcing of
organic aerosol in the atmosphere and on snow: Effects of SOA and brown carbon, Journal
of Geophysical Research: Atmospheres, 119 (2014) 7453-7476.
P a g e | 9
[24] X. López-Yglesias, P.E. Schrader, H.A. Michelsen, Soot maturity and absorption
cross sections, Journal of Aerosol Science, 75 (2014) 43-64.
[25] R. Wang, S. Tao, H. Shen, Y. Huang, H. Chen, Y. Balkanski, O. Boucher, P. Ciais, G.
Shen, W. Li, Y. Zhang, Y. Chen, N. Lin, S. Su, B. Li, J. Liu, W. Liu, Trend in global black
carbon emissions from 1960 to 2007, Environmental Science and Technology, 48 (2014)
6780-6787.
[26] B. Giechaskiel, M. Maricq, L. Ntziachristos, C. Dardiotis, X. Wang, H. Axmann, A.
Bergmann, W. Schindler, Review of motor vehicle particulate emissions sampling and
measurement: From smoke and filter mass to particle number, Journal of Aerosol Science,
67 (2014) 48-86.
[27] M.K. Khair, Engine Design for PM Control, DieselNet Technology Guide, 05a
(2002).
[28] N.C. Surawski, B. Miljevic, G.A. Ayoko, B.A. Roberts, S. Elbagir, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine employing two injection technologies and
three fuels, Environ Sci Technol, 45 (2011) 5498-5505.
[29] W.A. Majewski, Diesel Oxidation Catalyst, DieselNet Technology Guide (2010).
[30] W.A. Majewski, Diesel Particulate Filters, DieselNet Technology Guide, (2011).
[31] A.D. Bugarski, G.H. Schnakenberg, J.A. Hummer, E. Cauda, S.J. Janisko, L.D. Patts,
Effects of Diesel Exhaust Aftertreatment Devices on Concentrations and Size Distribution
of Aerosols in Underground Mine Air, Environ Sci Technol, 43 (2009) 6737-6743.
[32] J.D. Herner, S. Hu, W.H. Robertson, T. Huai, J.F. Collins, H. Dwyer, A. Ayala, Effect
of advanced aftertreatment for PM and NOx control on heavy-duty diesel truck emissions,
Environmental Science and Technology, 43 (2009) 5928-5933.
[33] D. Yao, D.M. Lou, P.Q. Tan, Z.Y. Hu, Q. Feng, Impact of after-treatment devices on
biodiesel engine particle number emission characteristics, in: Advanced Materials
Research, 2014, pp. 263-268.
[34] G. Li, Dimethyl ether(DME): A new alternative fuel for diesel vehicle, in, 2011, pp.
1014-1018.
[35] M. Lapuerta, M. Villajos, J.R. Agudelo, A.L. Boehman, Key properties and blending
strategies of hydrotreated vegetable oil as biofuel for diesel engines, Fuel Processing
Technology, 92 (2011) 2406-2411.
[36] S.S. Gill, A. Tsolakis, K.D. Dearn, J. Rodríguez-Fernández, Combustion
characteristics and emissions of Fischer-Tropsch diesel fuels in IC engines, Progress in
Energy and Combustion Science, 37 (2011) 503-523.
[37] Z.D. Ristovski, E.R. Jayaratne, M. Lim, G.A. Ayoko, L. Morawska, Influence of
diesel fuel sulfur on nanoparticle emissions from city buses, Environmental Science and
Technology, 40 (2006) 1314-1320.
P a g e | 10
[38] J. Zhang, K. He, Y. Ge, X. Shi, Influence of fuel sulfur on the characterization of
PM10 from a diesel engine, Fuel, 88 (2009) 504-510.
[39] A.M. Jones, R.M. Harrison, G. Fuller, B. Barratt, A large Reduction in Airborne
Particle Number Concentrations at the time of the introduction of “Sulphur Free” Diesel
and the London Low Emission Zone, Atmospheric Environment, (2012).
[40] P. Wåhlin, F. Palmgren, R. Van Dingenen, F. Raes, Pronounced decrease of ambient
particle number emissions from diesel traffic in Denmark after reduction of the sulphur
content in diesel fuel, Atmospheric Environment, 35 (2001) 3549-3552.
[41] N.C. Surawski, Z.D. Ristovski, R.J. Brown, R. Situ, Gaseous and particle emissions
from an ethanol fumigated compression ignition engine, Energy Conversion and
Management, 54 (2012) 145-151.
[42] J. Xue, T.E. Grift, A.C. Hansen, Effect of biodiesel on engine performances and
emissions, Renewable and Sustainable Energy Reviews, 15 (2011) 1098-1116.
[43] V. Makarevičiene, S. Lebedevas, P. Rapalis, M. Gumbyte, V. Skorupskaite, J.
Žaglinskis, Performance and emission characteristics of diesel fuel containing microalgae
oil methyl esters, Fuel, 120 (2014) 233-239.
[44] L.-H. Young, J. Yi, M.-T. Cheng, J.-H. Lu, H.-H. Yang, Y.I. Tsai, L.-C. Wang, C.-B.
Chen, J.-S. Lai, Effects of biodiesel, engine load and diesel particulate filter on nonvolatile
particle number size distributions in heavy-duty diesel engineexhaust, Journal of
Hazardous Materials, (2011).
[45] A.M. Ashraful, H.H. Masjuki, M.A. Kalam, I.M. Rizwanul Fattah, S. Imtenan, S.A.
Shahir, H.M. Mobarak, Production and comparison of fuel properties, engine performance,
and emission characteristics of biodiesel from various non-edible vegetable oils: A review,
Energy Conversion and Management, 80 (2014) 202-228.
[46] J. Pullen, K. Saeed, Factors affecting biodiesel engine performance and exhaust
emissions - Part I: Review, Energy, 72 (2014) 1-16.
[47] J. Pullen, K. Saeed, Factors affecting biodiesel engine performance and exhaust
emissions – Part II: Experimental study, Energy, 72 (2014) 17-34.
[48] S.K. Hoekman, A. Broch, C. Robbins, E. Ceniceros, M. Natarajan, Review of
biodiesel composition, properties, and specifications, Renewable and Sustainable Energy
Reviews, 16 (2012) 143-169.
[49] J. Pullen, K. Saeed, Experimental study of the factors affecting the oxidation stability
of biodiesel FAME fuels, Fuel Processing Technology, 125 (2014) 223-235.
[50] H.M. Amaro, A.C. Guedes, F.X. Malcata, Advances and perspectives in using
microalgae to produce biodiesel, Applied Energy, 88 (2011) 3402-3410.
[51] M. Daroch, C. Shao, Y. Liu, S. Geng, J.J. Cheng, Induction of lipids and resultant
FAME profiles of microalgae from coastal waters of Pearl River Delta, Bioresource
Technology, 146 (2013) 192-199.
P a g e | 11
[52] M.M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski, Influence of Different
Alternative Fuels on Particle Emission from a Turbocharged Common-Rail Diesel Engine,
Procedia Engineering, 56 (2013) 381-386.
[53] P.X. Pham, T.A. Bodisco, S. Stevanovic, M.D. Rahman, H. Wang, Z.D. Ristovski,
R.J. Brown, A.R. Masri, Engine Performance Characteristics for Biodiesels of Different
Degrees of Saturation and Carbon Chain Lengths, SAE Int. J. Fuels Lubr., 6 (2013) 188-
198.
[54] M. Rahman, A. Pourkhesalian, M. Jahirul, S. Stevanovic, P. Pham, H. Wang, A.
Masri, R. Brown, Z. Ristovski, Particle emissions from biodiesels with different physical
properties and chemical composition, Fuel, (2014).
P a g e | 13
Chapter 2: Literature review
2.1 Introduction This chapter presents a review of diesel engine exhaust emissions, with special emphasis
on DPM and its impact on human health and climate change. It also updates information
about DPM mitigation strategies, current legislation and the technological means currently
available to control it. This chapter also demonstrates the future of alternative fuels (i.e. the
use of biofuel in a CI engine) and their impact on exhaust emissions, in terms of: (i) the
wide variations which exist in potential biodiesel feedstocks; (ii) how chemical
composition and physical properties vary among those feedstocks; and (iii) their role in in-
cylinder combustion and particle emission. In addition, this chapter also provides an update
on the progress of microalgae biodiesel development and its potential role in reducing
diesel engine exhaust particle emissions.
2.2 Emissions from diesel engines Diesel emissions refer those pollutants which come from diesel engines as a by-product
due to the combustion of fuel inside the engine cylinder[1]. These pollutants raise global
concern as they are toxic to human health and harmful to the environment[2, 3]. In general,
diesel emissions are categorised into two groups. One group is called ‘regulated
emissions’, which includes pollutants regulated by legislation (i.e. mass of particulate
matter (PM), nitrogen oxides (NOx), hydrocarbons (HC) and carbon monoxide (CO)[4].
Particle number (PN) is also included as a regulatory metric for Euro-5 and Euro-6
emission standards[5]. There is no legislative control on the second group, which is called
‘unregulated emissions’ and includes sulfur dioxide, Aldehydes, poly aromatic
hydrocarbons (PAH), the soluble organic fraction (SOF) of DPM, N2O, NO2, metal oxides
and dioxins[4, 6].
Among the regulated emissions, CO and HC emissions from diesel engines are much lower
than from spark ignition (SI) engines, but the opposite is true for PM and NOx
emissions[4]. So, reductions in NOx and PM remain a considerable challenge for engine
researchers. PM is mainly formed due to the presence of locally developed fuel rich zones
in the engine cylinder[7], which hinders the complete combustion of fuel, and poorer
combustion leads to higher PM emission. On the other hand, NOx is produced as a result
of the higher cylinder temperature[1]. Usually, the better the combustion of fuel, the higher
P a g e | 14
the temperature in the engine cylinder, which is more favourable for NOx formation.
Therefore, measures which target the reduction of one pollutant (i.e. NOx) can lead to an
increase in the other pollutant (i.e. PM), while the opposite is true as well.
Particulate matter emitted by diesel engines consists of black carbon/elemental carbon
(soot), hydrocarbons, sulfates and metallic ashes[8, 9]. As shown in Figure 2-1 the primary
carbon particles are agglomerated together to form a particle with a large chain like
structure. Heavy hydrocarbons and sulfuric acid (H2SO4), which are usually in a gaseous
form at high temperatures and partition into volatile particles on cooling, either condense
on the existing carbon particles or stay as separate particles. Metallic ashes derived from
lubricating oil, fuel additives and engine wear products are very small particles that are
either embedded into the existing soot particles or act as nuclei for the nucleation of HC
and H2SO4 particles.
Figure 2-1: An engineer’s depiction of DPM ([8])
Diesel particulate matter usually exhibits a bimodal size distribution [8, 10, 11]. A typical
size distribution of DPM is shown in Figure 2-2, weighted by particle mass, particle
number and particle surface area[11]. It shows that most of the DPM mass and surface area
is in the accumulation mode, whereas particle number is in the nucleation mode[8, 11].
The accumulation mode comprises solid carbonaceous and metallic ash, along with
condensed material and adsorbed hydrocarbons. In the combustion chamber, primary soot
particles (10-30 nm spherules) form in locally fuel-rich regions of the combustion chamber
via pyrolysis of fuel and lubricating oil molecules[9]. The size and concentration of these
P a g e | 15
primary particles depend upon several factors, including the combustion process, fuel type
and engine operating conditions[10]. During combustion the primary soot particles collide
randomly with each other and form fractal-like aggregates. Since diesel engines run with
excess air during the exhaust stroke, around 90-99% of these soot particles burn up.
Simultaneous soot growth by coagulation, and shrinking by oxidation and fragmentation,
causes diesel accumulation mode particles to show lognormal characteristics with a mean
diameter of 40-100 nm or more[10].
Nucleation mode particles, often referred to as nanoparticles, are very small in diameter,
between approximately 5 and 40 nm. Recent studies redefine the nuclei mode particle size
range to be even smaller, from 0.003 to 0.03 µm [12]. Typically, the nucleation mode is
composed of condensed volatiles and comprises very little solid material [13]. Nucleation
mode particle size distributions are not very reproducible, and exhibit very complex
behaviour [11, 14-16]. During the dilution process, semi-volatile species and sulfates
partition into the particle phase, depending on the local pressure, temperature, humidity
and species concentrations, which are dependent on the primary Dilution Ratio (DR)[15,
17-19]. This partitioning of volatiles and semi-volatiles could occur in the form of
adsorption and condensation into existing soot agglomerates or as nucleation of new
particles. Since nucleation and condensation/adsorption are two competing process, the
possibility of separate nucleation mode formation, as well as its magnitude, are determined
by several factors, including engine characteristics, after-treatment devices, type of fuel
and lubricant used, amount of soot present and pre-conditioning and history of the test
etc.[11, 15, 18, 20]. Despite ongoing investigations, neither the composition nor the
formation mechanisms of nucleation mode particles are fully understood, and they still
present a significant scientific challenge. Therefore, the European Union (EU) particle
measurement program (PMP) excludes nucleation mode particles from their particle
number based emission standards and decided to regulate only the number of solid
particles above 23 nm, which correspond to accumulation mode particles[5, 21].
In addition to the nucleation and accumulation modes, a third mode, called the coarse
mode, also exists in the DPM size distribution[10]. Particles in this mode are not generated
by combustion but are formed by wear, deposition and re-entrainment of material from the
engine cylinder, exhaust manifold and also the particulate sampling system, such as a
dilution tunnel. Due to the rapid change in flow and temperature, these particles are
P a g e | 16
resuspended from where they were deposited and enter into the sampling system. These
particles are negligible in terms of number concentration; however they can contribute
significantly to particle mass measurements.
Figure 2-2: Diesel particulate matter (DPM) size distributions [11, 22]
The particle number size distribution of DPM is also dependent on several other factors,
including engine type, operating conditions, fuel type and dilution conditions etc.[15, 17,
19, 20] Engines with a common rail injection system produce reasonably smaller particles
than mechanical injection engines[23]. Particle median size also increases with an increase
in engine load, whereas it decreases slightly with engine speed. The effect of fuel on
particle number and size is also well documented in the literature [18, 20]. There are many
studies which have reported a significant decrease in ultrafine particle emissions with the
reduction of fuel sulfur content [20, 24-26]. Sulfur in the fuel oxidises to form SO3, and
reacts with water to produce H2SO4, one of the gas phase species which can nucleate to
form nucleation mode particles [20, 26]. Alternative fuels also affect the particle number
size distribution of DPM. There are numerous reports [27-34] of a reduction in particle size
with oxygenated fuel (i.e. biodiesel), although it can increase nanoparticle emissions in
certain cases [35]. Fuel containing less or no aromatics also has the potential to reduce
particle emissions [36]. Dilution conditions play a key role in determining total particle
P a g e | 17
number emissions and a slight change in dilution conditions could trigger nucleation and
bias particle number measurements by several orders of magnitude[11, 15, 17-19].
2.3 Importance of DPM emissions in respect to climate change The particulate matter emitted by diesel engines affects the Earth's temperature and climate
by altering the radiative properties of the atmosphere. A large positive component of this
radiative forcing from aerosols is due to black carbon (soot) [3] emissions which originated
from the burning of fossil fuels. The radiative forcing potential of soot particles also
depends on its mixing state with other aerosols and freshly generated soot particles absorb
less solar radiation than aged soot particles. Soot particles acquire a large mass fraction of
sulfuric acid and moisture during atmospheric aging, resulting in the core particle (soot)
being surrounded by a layer of H2SO4 and moisture. This external layer acts like a lens and
concentrates more solar radiation onto the internal (soot) core of the particle, which
increases absorption by nearly two fold [37]. A recent study [38] suggested that the
morphology of these particles can change the way light absorbing carbon aerosols age,
which could significantly alter their optical properties. A larger shell/core diameter ratio of
particles, with volume-equivalent shell-core spheres, can enhance their absorption
properties and also boost their scattering properties. The enhancement of single scattering
albedo due to morphological effects can reach a factor of 3.75 at 0.67 μm [38]. Another
recent study also reported enhanced hygroscopic growth and subsequent absorption in the
presence of an organic layer on soot [39], which gives black carbon the second highest
potential as a greenhouse warming agent after carbon dioxide [3].
The atmospheric effects of soot aerosols also include visibility impairment and the
alteration of cloud formation. Carbonaceous particles act as cloud condensation nuclei
(CCN) which affect cloud optical properties and influence precipitation. Spracklen et al.
reported that carbonaceous combustion aerosols are responsible for more than half (52-64
%) of global CCN [40]. This phenomenon is also highly sensitive to the way in which soot
is internally mixed with other atmospheric aerosol constituents. Soot particles undergo
large hygroscopic growth, both in size and mass, in sub-saturated conditions (<90%
relative humidity) and could efficiently act as cloud-condensation nuclei. The hygroscopic
growth which results from soot particles being coated in sulfuric acid enhances the optical
properties of soot aerosols and it could increase scattering by 10-fold at 80% relative
humidity compared to fresh particles [37]. A recent study suggests that the warming effect
P a g e | 18
due to BC was largely mitigated by the cooling effect of sulfate during the period from
1996 to 2010 [41].
The OC fraction of PM, otherwise known as brown carbon, also has a very important
impact on global radiative forcing [42-45]. A large fraction of secondary organic aerosols
(SOA), produced by biogenic process and the atmospheric aging of VOCs, is also
composed of brown carbon. The combustion of fossil fuels is a major source of VOC
emissions and considering the importance of SOAs in global climate forcing, the
International Panel on Climate Change (IPCC) has included SOAs in their report [46]. A
very recent modelling study [47] has shown that the atmospheric absorption of brown
carbon ranges from +0.22 to +0.57 W/m2, which represents ~27-70% of the absorption
potential of black carbon (BC). In addition, the radiative forcing of OA deposited in snow,
land and sea ice ranges from +0.0011 to +0.0031 W/m2, accounting for as much as 24% of
the forcing caused by BC in ice and snow and simulated in the model. Feng et al. [48] also
reported changes in global mean direct radiative forcing from cooling (−0.08 W m−2
) to
warming (+0.025 W m−2
) when they considered the strong absorption by brown carbon in
their model. Since the combustion of biodiesel increases the OC fraction of DPM, it would
be interesting to include this when looking at the impact of biodiesel emissions on climate
change.
Another study [49] suggests an almost two-fold increase in BC emissions over the past
several decades, mainly due to the increasing use of fossil fuels in diesel engines in the
power plant, transportation and agricultural sectors. Although particles emitted from diesel
engines contribute to the global climate by both direct heating and indirect cooling, the
heating effect is dominant. Therefore, it is important, in terms of climate change
mitigation, to reduce diesel engine particulate matter emissions. Its short atmospheric
lifespan makes black carbon abatement one of the most attractive means for producing a
significant short-term impact on global warming [3]. Moreover, studying the climate
forcing potential of particles produced by biodiesel combustion can enhance scientific
understanding of the underlying mechanisms behind climate change.
2.4 Health effects of DPM In addition to climate change, chronic exposure to diesel exhaust particles (DEP) may lead
to adverse health effects. These may include the exacerbation of pulmonary diseases, such
as asthma and bronchitis, as well as lung cancer [50, 51]. Alveolar macrophages (AM),
P a g e | 19
which are responsible for defending susceptible cells, release pro-inflammatory cytokines
in the presence of DPM constituents, such as poly aromatic hydrocarbons (PAHs), and
may suffer alterations in their gene expressions as a result [52]. A few studies [51, 53, 54]
have also demonstrated the negative impacts of DPM on reproductive systems, liver
function [53] and brain activity [54]. In vitro studies show that particles generate reactive
oxygen species (ROS), deplete endogenous antioxidants, alter mitochondrial function and
produce oxidative damage in lipids and DNA. Particle chemical composition and surface
area reactivity play important roles in their oxidative potential [51, 55] and bio-monitoring
studies in humans have revealed associations between oxidative damage to DNA and
exposure to smoke particulates and air pollution [51, 55]. Recent epidemiological studies
on underground miners reported an increased risk of lung cancer mortality associated with
DPM exposure [56, 57]. By considering the health risks associated with DPM, the
International Agency for Research on Cancer (IARC), which is part of the World Health
Organisation (WHO), classified diesel engine exhaust as carcinogenic to humans.
Despite commendable progress in particle-related toxicological research over the last few
decades, the exact mechanisms by which PM inflicts health injuries are not entirely known
and still constitute a subject of great interest and very active research for the scientific
community. Giechaskiel et al. [58] proposed that the health effects of DPM are related to
both the physical properties and chemical composition of particles. Physical properties of
DPM that influence respiratory health include particle mass, number and size distribution,
surface area and mixing status of particles [51]. For example, deposition of particles in
different parts of the lung depends on their size, such that the smaller the particles the
higher the deposition efficiency and only small particles can penetrate deep into the lung.
A shift in particle size from 100 nm to 20 nm could drastically increase total lung
deposition around 60%, this increase becomes more significant as they are mainly on the
alveolar and tracheobronchial region[59]. Furthermore, the smaller the particle, the longer
it remains in the atmosphere, so smaller particles have a higher probability of being inhaled
and deposited in the respiratory tract and alveolar regions of the lung. Particle number
governs the ability of particles to grow into larger sized particles by coagulation and
particle surface area determines the ability of the particles to carry toxic substance on their
surface.
P a g e | 20
Recently, Ristovski et al. [51] proposed a model in their review that correlates the physico-
chemical properties of DPM responsible for the respiratory diseases. They found DPM
surface area and organic compounds play a significant role in initiating various cellular and
chemical processes responsible for respiratory disease. The mechanism involves the
development of oxidative stress followed by inflammation which acts as precursor to the
development and acute exacerbation of air ways and lung disease. These findings indicate
that surface area and the organic content of DPM are the most important parameters for
determining a particles associated health risks. Several recent cell exposure studies also
demonstrated that exposure to DPM under dynamic environmental conditions can affect
the level of cellular inflammation and reactive oxygen species differently than in static
environments [60].
On the other hand, particles emitted from biodiesel fuel engines were found to be
somewhat different than particles emitted from normal diesel fuel engines. Despite
significant reductions in PM, the mean particle size from biodiesel combustion is much
smaller than for diesel combustion and it is composed of higher fraction of OC. Therefore,
these particles are expected to be more toxic than diesel emitted particles, which is the
main reason behind current scientific debate about the substitution of diesel for biodiesel.
Increased PAHs and ROS emissions from biodiesels have been reported by several studies
[23, 61-63], while others found a decrease or no significant change [64]. Recently
Guarieiro et al. [65] found a reduction in PAHs for B50 blends, however they increased for
B100 waste cooking oil (WCO), while the redox activity increased consistently with
biodiesel percentage.
In terms of toxicity, limited information is currently available from direct cell exposure
studies, which indicate enhanced pulmonary inflammation and oxidative stress from
biodiesel particles [66, 67]. Fukagawa et al. [28] also reported that the addition of biodiesel
to diesel fuels could reduce PM emissions but not necessarily their adverse health effects.
Enhanced inflammatory responses in human macrophages and lung epithelial cells have
also been reported for particles emitted from waste grease biodiesel blends (B20) [68],
which were assumed to be related to the composition of the particles. In addition, different
toxicities have also been reported for various blends of different biodiesel feedstocks.
Therefore, the higher toxicity of biodiesel particles could be related to the feedstocks, as
well as after-treatment devices, which could change the structure and composition
(especially volatile content) of particulate matter and therefore their toxicity. Hence it is
P a g e | 21
vital to optimise the composition and properties of biodiesel, which could reduce the
toxicity of PM.
2.5 Measurement of DPM emissions
2.5.1 Standardization of measurement technique
The development of a standard method for conducting engine exhaust particle
measurements is vital. Without a standard measurement protocol, particle emission
measurements can vary greatly among different laboratories, mainly due to improper
dilution and conditioning systems. Typically, the variation in PM is around 20-30%, where
as it could be few orders of magnitude for particle number [69]. Therefore, specifying a
standardised measurement method is a prerequisite for developing any kind of legislative
framework for PM emission control. Verification of Emission Reduction Technologies
(VERT) was the pioneering project in this regard, which aimed to define a test protocol for
the particle removal efficiency of DPF. More recently, the EU PMP focused on developing
a standard procedure to non-volatile particle measurement by excluding the volatile and
semi-volatile fraction. In terms of PM, extremely low PM emission limits for light and
heavy duty engines in the EU and US (e.g. 1mg/mile for low emission vehicles (LEV-III)
in California) are testing the capability of current PM weighing techniques. However, it is
not clear whether tightening the measurement conditions or a simplified proxy technique
would be more helpful for implementing PM control strategies.
2.5.2 Requirement of proper dilution system
The measurement of engine exhaust particle emissions is a very sensitive and complex
process, which has undergone many evolutionary changes over last two decades. A
suitable dilution system is a prerequisite for the measurement of any engine exhaust
particle emissions, and has become an integral part of modern engine exhaust particle
measurement. Dilution reduces temperature and particle concentration from the raw
exhaust, and brings them within the measurement range of typical particle measuring
instruments. In addition, proper dilution avoids the condensation of volatile exhaust
constituents and thus, improves measurement accuracy and repeatability. Even sampling
undiluted exhaust from modern engines, with low particulate concentrations, could reduce
the instruments performance and durability. Therefore, the general practice is to dilute the
exhaust and freeze the particle size distribution before measurement.
P a g e | 22
Full flow dilution systems, widely known as constant volume sampling (CVS) tunnels,
were the first dilution system of their kind to be developed. This system dilutes the entire
amount of exhaust with a precisely controlled flow of filtered air, which means that during
transient cycles, the dilution ratio can vary. While it usually operates at a moderate dilution
ratio (<10), the CVS tunnel doesn’t have a means to control dilution temperature or the
heating of dilution air. As a result, CVS tunnels do not provide precise control over the
partitioning of semi-volatiles, which brings uncertainty into their measurements. Long
transfer lines from the exhaust pipe to the CVS tunnel can also introduce measurement
artefacts. In addition, CVS tunnels operate at very high flow rates, especially when
sampling from large engines; which means that they are often bulky and expensive.
Further, their design and operation (i.e. flow rate characteristics, temperature control,
residence time etc) can vary significantly among test facilities, which could be another
potential limitation in terms of the reproducibility of measurements conducted in different
laboratories.
Difficulties associated with the CVS tunnel have mostly been overcome by the use of
partial flow dilution (PFD) systems. This system samples only part of the exhaust from the
exhaust pipe and dilutes it with filtered air, which means it does not have to handle large
volumes of exhaust air. However, it is necessary to measure total exhaust flow in the
exhaust pipe for emissions factor calculations. Reasonably good agreement (i.e. ±20%
difference in PM mass and ±15% for solid particle number) between PFD and CVS
measurements for heavy duty engines is well documented in this review [70].
The porous tube diluter is another dilution system which allows for the heating or cooling
of dilution air, in order to precisely control nucleation phenomena. However, the dilution
ratio, which is actually controlled by mass flow meter, cannot be accurately controlled,
especially during pressure fluctuations in the exhaust pipe. Dilution ratio can be controlled
relatively accurately by mass flow meter. However, measurements for trace gases are
typically needed for dilution ratio calculations. The ejector diluter is another very effective
means to dilute exhaust. It provides a dilution ratio of around 10 by using a single diluter,
with the provision of external heating for the dilution air and the dilution ratio can be
increased by connecting multiple diluters in series. The ejector dilutor is compact and
easily portable, without any moving parts, however injector clogging and subsequent
dilution failure are known to occur, especially when handling highly moist engine exhaust.
P a g e | 23
The rotating disc diluter eliminates almost all of the problems associated with the ejector
diluter and it can produce a precise, constant volume dilution ratio ranging from 10 to 1000
Although the rotating disc diluter does require a bit of maintenance, due to its high number
of moving parts, it is less sensitive to pressure and temperature fluctuations than the ejector
dilutor and it provides a fast response to changes in dilution ratio. Therefore, this diluter is
widely used in PN measuring instruments, including the fast differential mobility
spectrometer (DMS)[71].
2.5.3 Importance of exhaust conditioning prior to measurement
The regulations controlling engine exhaust particle emission levels require the
measurement of both solid and liquid particles in the exhaust, which is usually known as
total particulate matter (TPM). The partitioning of semi-volatiles into gas or particles can
cause significant uncertainty in TPM measurements, since it depends on several factors
(i.e. temperature, vapour pressure of volatile species and the concentration of existing
particles). Uncertainties due to gas particle partitioning are even more prevalent in particle
number measurement. Therefore, EU regulations focus on measuring only the non-volatile
component of PM (which requires the removal of semi-volatiles from the exhaust), in order
to improve measurement reproducibility.
The EU PMP recommends the use of an evaporation tube at 350 0C, after heated 1
st stage
dilution of 10:1 at a temperature of 150 0C, followed by a second stage cold dilution [5,
21].The evaporation tube either removes volatile particles by evaporation or reduces their
size to below 23 nm so that they will not be counted in solid PN measurements [5].
Although homogeneous nucleation of evaporated substances could occur at the end of the
tube, in the presence of very high concentrations (>107) of 30 nm particles, their
concentration is much less in the presence of H2SO4 [72]. The use of a thermodenuder is
another option for removing volatiles from the exhaust, whereby the sample is heated to
more than 250 0C and then passed through an unheated section which contains adsorbing
materials (i.e. activated carbon). This activated carbon section adsorbs most of the
evaporated materials and reduces their vapour pressure. However, there are particle losses
inside the thermodenuder, mainly due to thermophoresis in the adsorbing section, which is
independent of particle size, and the activated carbon requires regular regeneration, given
that its adsorbing capacity decreases with time. The denuder section also causes size
dependent diffusion losses, which are much higher for smaller particles [73]. Moreover,
P a g e | 24
another study [74] suggests that the pyrolysis/charging of semi-volatiles in the evaporation
tube or thermodenuder could produce solid nucleation mode particles. The catalytic
stripper is another effective means of removing an even higher concentration of volatiles
and semi-volatiles from the exhaust [75]. It consists of an oxidation catalyst, which
oxidises volatiles at an elevated temperature (around 300 0C), followed by a sulfur trap to
remove sulfate. Kaleq et al. [76] found that the catalytic stripper was satisfactory when
replacing the evaporation tube in a PMP compliant system. Another recent study [77]
suggests even better performance by the catalytic stripper than the evaporation tube for sub
23 nm particles.
The dilution systems which remove volatiles from the exhaust might minimize inter
laboratory measurement uncertainties by suppressing nucleation. However, they don’t
really mimic the real world conditions how the exhausts are finally emitted in the
atmosphere. Therefore, there is a strong debate about whether or not to use dilution
systems which suppress nucleation. In support of this argument, few researchers suggest to
use a dilution system which mimics a real world scenario [16, 17]. Giechaskiel et al[16]
reported that nucleation mode particles are real for vehicle emissions rather than
measurements artefact both in their on road vehicle and laboratory tests. They also found
that the nucleation mode formation is sensitive to exhaust gas temperature, and its
occurrence in increased temperature is repeatable and stable for long sampling times.
Ronko et al[17] also found consistent nucleation both in laboratory and on road test,
although the magnitudes of the nucleation peak were dependent on meteorological
condition and engine operating condition. Many studies link nucleation mode particles
with fuel sulphur content [18, 20, 78]. However in a separate study, Ronko et al[79] found
nucleation mode particles with non-volatile core and formed before the dilution process.
They found it independent of residence time, and insensitive to fuel sulphur content,
dilution air temperature and humidity. Therefore nucleation mode particles are no longer
considered to be measurement artefacts. Rather, they are real engine emissions but robust
and repeatable measurement of nucleation particles from engine exhaust still pose a
considerable challenge.
2.5.4 PM, BC or EC measurement
In general, the soot fraction of PM is known as BC when measured using the optical
method, or EC when measured thermally. In terms of measuring particle mass, the filter
based gravimetric method is the most widely accepted, whereby PM is collected from
P a g e | 25
diluted exhaust at <52 0C on a poly tetra fluoroethylen (PTFE) filter or quartz filter coated
with PTFE, for a certain period of time. Particles in the nucleation, accumulation and
coarse mode are deposited on the filter and the emission factor is calculated by weighing
the filter before and after collection using a microbalance. However, filter based
measurements can bring significant uncertainty as a result of vapour absorption by the
filter medium, evaporative loss of PM after sampling and reactions between the collected
samples and filter substrate etc. Therefore, great care is needed during sampling, filter
conditioning and weighing (i.e. maintaining a constant sampling temperature (±47 0C),
controlling the temperature and humidity control of the filter conditioning and weighing
room etc). Moreover, the fitting of after-treatment devices, such as DPFs, are also
challenging the detection limit of filter based method. For vehicle equipped with DPF can
cause uncertainty via measurement artefacts which can contribute up to 90% of the
measured PM [69]. Therefore, an alternative technique with much higher sensitivity to
lower levels of PM is urgently needed.
Many of the real time instruments available for measuring the PM in engine exhaust are
also capable of meeting current PM measurement regulations. For example, a range of
instruments use the optical method, where measurements are based on the interaction
between particles and the incident light commonly known as a photometer. In particular,
particles either absorb or scatter the incident light, the sum of which determines the level of
extinction. There are instruments which work on principle of absorption, scattering and
extinction.
Commercial instruments based on the light scattering principle include the DustTrak.
Scattering instruments are more responsive to large particles, and therefore, are strongly
reliant on particle size distribution. In addition, these instruments are not sensitive enough
to detect nucleation mode particles [80, 81].
Instruments based on light extinction (i.e. opacity meter) are one of the oldest methods of
smoke measurement in diesel engines. The Opacity meter measures the fraction of incident
light transmitted through a given volume of exhaust. It then calculates extinction based on
the difference between the incident and transmitted light, since a fraction of the incident
light is scattered or absorbed by the aerosol. Opacity meters have a time resolution of 0.1 s
and a detection limit equivalent to 300 µg/m3 of PM. However, the relationship between
opacity and PM mass is not linear, since it also depends on particle composition and
morphology. Opacity meters are still the simplest and most useful tool for conducting
P a g e | 26
engine-out soot measurements; however their high detection limit decreases their
effectiveness for low emitting modern engines equipped with after treatment devices (i.e.
DPFs).
Instruments based on light absorption are also available for PM measurement, which
actually measure the BC fraction of PM. Spot meters are the most popular instruments
used in engine exhaust PM measurement, whereby the filter smoke number (FSN) is
obtained by filtering exhaust through a paper filter and recording the ratio between the
light reflected by this exposed spot and an unexposed spot of the filter paper. The detection
limit is around 25µg/m3, and therefore, a longer sampling time of up to 10 minutes may be
required for post DPF PM measurement. The Aethalometer is also used to measure BC,
where changes in light absorption of PM collected on a quartz filter are measured as a
function of time at multiple wavelengths. In addition to BC, it can give a rough indication
of the organic content of the aerosol, however, the relatively low time resolution of this
instrument makes it too slow for transient engine emission testing [69]. The photoacoustic
soot sensor (PASS) is a relatively new instrument for conducting BC measurements, based
on the principle that light absorption by an aerosol causes the aerosol to heat up.
Conduction of this heat generates a pressure wave which is detected by a microphone. The
Centrifugal Particle Mass Analyser (CPMA), Aerosol Particle Mass analyser (APM) and
Dekati Mass Monitor (DMM) are also relatively new additions to the suite of instruments
available for measuring PM mass.
2.5.5 Particle number and size distribution measurement
There are number of commercial grade instruments capable of real time particle number
and size distribution measurement. The Optical Particle Counter (OPC) eliminates the
uncertainties of using a photometer to some extent, but its detection limit is over 100 nm.
Condensation particle counters (CPCs) operate using a different technique, where particles
are detected after their growth in a saturated vapour of respective working fluids such as
butanole or water. The detection limit is determined by the properties of the working fluid
and the temperature of the saturator and condenser. The PMP requires the use of a CPC,
which must have 50% counting efficiency above the 23 nm cut off point. Most of the
instruments measures PSD combine the use of a charger (corona charger or neutraliser),
size classifier (impactors or mobility classifiers) and particle counter (CPC or
electrometers) to measure particle size distribution from the diluted exhaust aerosol.
P a g e | 27
The Scanning Mobility Particle Sizer (SMPS) is probably the most widely used instrument
for PSD measurement and it works on the basis of the mobility of a charged particle in an
electric field [82, 83]. The SMPS system consists of a neutraliser, followed by a
differential mobility analyser (DMA) which is coupled to a condensation particle counter
(CPC). A poly-disperse aerosol enters the DMA column via a radioactive bipolar diffusion
charger (85
Kr), which provides a known charge distribution (approximately Boltzmann) to
the particles. The voltage applied between the electrically grounded outer cylinder and the
inner electrode causes positively charged particles to drift across the sheath flow towards
the central electrode, depending upon their electrical mobility. Particles of a given
electrical mobility exit through the sample slit at the bottom of the DMA as mono-disperse
aerosol consisting of particles of essentially the same size, while all other particles exit the
DMA with the exhaust flow. The size of particles exiting through the slit depends on the
voltage in the centre electrode, the charge of the particles, the flow within the DMA and
the aerodynamic size of the particles [83]. The mono-disperse aerosol coming out of the
DMA is then counted by a CPC, in which the particles grow to a detectable size, via the
condensation of liquid (water/ butanol) on its surface, and are then counted according to
the light scattered by the particles [83]. The size distribution of the aerosol is measured by
simultaneously counting the particles while the voltage in the centre electrode is ramped up
exponentially [83]. SMPS measurement is very accurate, however the long sampling time
required for PSD measurements is major barrier for its use in transient emission
measurement.
Recent additions to the suite of instruments used for PSD measurement include the
Differential Mobility Spectrometer (DMS), Fast Mobility Particle Sizer (FMPS) and
Engine Exhaust Particle Sizer (EEPS). These instruments consist of a particle charger, a
classification column and a series of detectors. Particles are initially introduced into a
corona, which emits a unipolar charge distribution, before it passes along the periphery of a
central rod in a cylindrical electrostatic classifier. The outer wall of the classifier consists
of series of electrically isolated rings connected to electrometers. Charged particles are
deposited on the rings depending upon their electrical mobility and produce currents,
which are then converted into particle number size distribution measurements. Due to the
high time resolution of these instruments (0.1to 1 Hz), they are highly useful in transient
emission testing for PN, PSD and even PM mass [71]. Although corona charging can cause
a significant number of particles to have multiple charges, and can bring much higher
P a g e | 28
uncertainty in the measurements than the SMPS, one recent study suggested that these
variations were within the acceptable limit of SMPS measurements [84]. The Electrical
Low Pressure Impactor (ELPI) is another instrument developed earlier than DMS and
EEPS, and capable of measuring size resolved concentration based on a particles
aerodynamic mobility.
2.5.6 Measurement of DPM composition
To date, the regulatory metrics for PM do not include chemical composition; however this
is still often measured for research purposes, with the intention of providing more
information about formation mechanisms and potential damage to human health or the
environment. There are studies suggesting that the effects of PM on human health are more
intrinsically related to its chemical composition rather than just particle mass or number.
The chemical composition of PM is determined in two ways, either via solvent extraction
or thermal evaporation. In solvent extraction, an organic solvent is added to the PM
sample, which is then evaporated to leave behind only the SOF of the PM, whose
composition is then determined by gas chromatography mass spectrometry (GC/MS). In
terms of thermal analysis, particles collected on quartz filter are heated in an inert
atmosphere at a temperature around 600 0C. Any volatile organic materials are oxidised
and detected as CO2 or converted into CH4 and measured by a flame ionisation detector
(FID). Carbon measured in this way is known as organic carbon (OC). The OC measured
using this method is only a fraction of the total organic matter (OM). After that, the
remaining sample is further heated to around 900 oC with supplied oxygen, and the carbon
measured following this step is called EC. During the thermal measurement process, some
OC could be counted as EC due to the paralysis of OC in the inert atmosphere at high
temperatures. Similarly, some EC may be counted as OC due to presence of catalytic or
oxidising species adsorbed in the PM. Therefore, the EC and OC values measured using
this method are not exact and a more robust method needs to be developed prior to
including these chemical elements in regulatory metrics.
A Volatilisation Tandem Differential Mobility Analyser (VTDMA) could also provide
reliable information about the OC and EC content of particles [85, 86]. This technique has
been successfully used by several researchers to characterise DPM [86, 87]. In the
VTDMA system, particles pre-selected by the DMA are passed through a thermodenuder
and heated to above 300 𝐶𝑜 . The thermodenuder removes the volatile content of the
particles by vaporisation and the heated aerosol is then examined for changes in particle
P a g e | 29
size using another DMA and a CPC. The resulting reduction in particles size indicates the
thickness of the organic layer on the particles, from which the volume of OC can be
determined. The difference between total particle volume and organic content volume
equates to EC.
Aerosol mass spectrometry (AMS) is the most advanced method for DPM composition
measurement. In AMS, either a single particle or a stream of particles are heated; vaporised
and ionised, and the resulting ions are detected for mass analysis. The single particle
approach is capable of providing complete information about individual particles, which
allows for the identification of internally and externally mixed aerosols, but lacks in
quantifying their composition. The second approach uses electron impaction ionisation and
is therefore capable of identifying and quantifying each chemical species by using standard
mass spectral database.
Measurement of the reactive oxygen species (ROS) content in PM also provides proxy
information about its OC content and potential health effects. The oxidative potential (OP)
of PM is expressed through the mass concentration of reactive oxygen species (ROS) as
nmol of ROS per mg of PM. In this case, a BPEA (bis(phenylethynyl) anthracene-
nitroxide) molecular probe technique was used for the measurement of OP (potency of PM
to induce oxidative stress). Profluorescent nitroxides (PFN) are very powerful optical
sensors, which can be used as detectors of free radicals and redox active substances. The
probe itself is poorly fluorescent, however upon radical trapping, or redox activity, a strong
fluorescence is observed. Samples for ROS measurement are collected by bubbling an
aerosol through an impinger containing 20 mL of 4 µm BPEA solution (using an AR grade
dimethyl-sulfoxide as a solvent), followed by fluorescence measurements with a
spectrophotometer (Ocean Optics). The amount of BPEA reacting with the combustion
aerosol is then calculated based on a standard curve, obtained by plotting known
concentrations of the methanesulfonamide adduct of BPEA (fluorescent) against the
fluorescence intensity at 485 nm (Lit). For each setting and particulate source, two samples
are taken. Detailed information on the procedure for measuring OP can be found in
Stevanovic et al[88].
2.6 Measures and techniques for DPM mitigation Considering the harmful effects of DPM outlined previously, legislation to control DPM is
getting more stringent every year. Figure 2-3 is an indication how DPM emission standards
P a g e | 30
have changed in Europe and the US over the last two decades. In addition to DPM mass,
standards based on solid particle number were also introduced by the EU in 2011. If this
trend continues, diesel engines will be subject to even more stringent standards in the near
future. There are already studies claiming that emission standards based on particle surface
area will be more appropriate rather than number or mass based standards, in respect to
health effects [58].
Figure 2-3: Change in DPM emission standards in the EU and US over last two
decades[89]
In order to meet the increasingly stringent emissions standards for DPM, diesel engines
have undergone significant evolutionary changes to the design of their fuel delivery
system, air intake system, after treatment system etc. [7]. Among them, the introduction of
a common rail injection system to replace the pump line injector resulted in a significant
change in PM emission behaviour [23, 90], whereby the increase in fuel injection pressure
reduced CO, HC and PM emissions, while at the same time increasing NOx emission [91].
Once optimised, it is thought that multiple injection can minimise both PM and NOx [91].
Similarly, the addition of a turbo-charger in the air intake system not only boosted engine
power output, but also affected PM emissions. However, the most significant reduction in
PM emissions resulted from the introduction of after treatment devices, including the use
of a diesel oxidation catalyst (DOC), particle oxidation catalyst (POC), diesel particulate
filter (DPF) or catalytic diesel particulate filter (CDPF) [92-96]. DOCs oxidise HC and
P a g e | 31
CO, as well as the volatile components of PM, whereas POC only oxidises the volatile
components of PM. In terms of DPF, although the soot particles from diesel engines are
usually much smaller than the filter pore size, the soot particles are loaded onto DPF wall
by filtration, deposition, Brownian diffusion and interception mechanisms. Particles
deposited on the DPF surface also form a thin layer of soot cake, which boosts its filtration
efficiency. There are studies reporting that a combination of these after treatment devices is
even more effective for reducing DPM emissions [96, 97]. Herner et al. [95] found a 95%
reduction in PM with the use of a DPF, irrespective of driving conditions. Liu et al. [96]
found a 90% reduction in PM and 97% reduction in solid particle number by continuously
regenerating a diesel particulate filter (CRDPF). POC can also decrease particle emissions.
Although the efficiency depends upon application, more than 50% reduction of soot
particles by POC have been reported[98] whereas the removal of some of the organic
compound of total PM is well established [96, 99, 100]. POC performs better in reducing
smaller particles, but has very little effect on large particles[96]. DOCs can also oxidise
nitric oxide (NO) in the exhaust to form nitrogen dioxide (NO2), which then assists in the
low temperature oxidation of the PM accumulated in the DPF [101, 102]. On the contrary,
DOCs can also turn sulfur dioxide (SO2) into sulfur trioxide (SO3) at high exhaust gas
temperatures, resulting in higher PM emissions in the form of H2SO4. Systems designed to
remove nitric oxide (NOx) (i.e. Selective Catalytic Reduction (SCR)) can also increase
particle number emissions due to the secondary formation of nanoparticles for both non-
DPF and DPF-equipped heavy-duty diesel engines.
Although DPFs are very effective at capturing solid particles, volatile and semi-volatile
substances escape through them and form new particles in the air. These newly form
particles are much smaller in size and mainly composed of organic matters, and are
considered more toxic than soot particles [103, 104]. Biswas et al. [105] reported
remarkable reductions in PM mass emissions from vehicles retrofitted with an advanced
after treatment system compared to the baseline vehicle. However, they observed enhanced
nucleation mode particles from some of the vehicles, especially during cruise cycles. A
majority of these particles were evaporated by heating them to 150–230 °C, suggesting that
the nucleation mode particles were predominantly internally mixed and consisted of semi-
volatile compounds. They also showed 100 fold increases in particle active surface area
from the retrofitted vehicles compared to the base line vehicle. In an underground mining
engine study, Bugarski et al. [106] also found a substantial increase in nucleation mode
P a g e | 32
particles with the use of a DPF, despite a10 to 20 fold decrease in total PM. Recently, Zhao
et al. [107] reported a ~4% increase in total VOCs when using a DPF with a Fe based fuel
born catalyst, although they found a reduction in carbonyl compounds and PAH emissions
of around 16% and 65%, respectively. There are also reports of higher metal emissions
from vehicles equipped with DPFs [108].
In addition, the continuous deposition of PM in DPFs can increase engine back pressure
and result in power loss, it can even stall the engine in some cases. Therefore, timely
regeneration of the DPF is necessary to avoid these phenomena. DPF regeneration requires
additional energy, regardless of the type of regeneration and it also adds complexity to the
engine management system. In order to raise the exhaust temperature for DPF
regeneration, diesel engines must meet the Euro 5 emission standard, which stipulates the
use a fuel injection strategy consisting of post-injections during the late expansion stroke.
This generates enough exothermic energy across the DPF and raises the DPF temperature
up to 700 0C. However, the additional fuel costs due to engine power loss and DPF
regeneration are making this after treatment system undesirable for some users. There are
also reports of a tremendous increase of nanoparticle emissions during active regeneration
of DPF [109, 110], where secondary fuel injection burns off the accumulated soot from
DPF. Luo et al. [111] reported that the DPF regeneration process causes decomposition
and oxidation of particles in the accumulation mode (sized between 60.4nm and 107.5nm
particles) into fine nucleation mode particles of 29.4nm to 60.4nm in size, which
eventually increases total particle number concentration. Other reports [99] also show
several increases in nucleation mode particle emissions during DPF regeneration of several
orders of magnitude. Moreover, metals and metal oxides originating from either the
lubrication oil or engine wear and tear are also deposited on the DPF. During regeneration,
these substances don’t burn off completely; rather they stay in the DPF channel as metallic
ash and can cause filter clogging [112, 113].
Apart from after treatment devices and engine design modification, alternative fuels or fuel
modification are also considered as a potential means to control DPM emissions. There is
overwhelming evidence of reductions in DPM from the use of biofuel (i.e. ethanol and
biodiesel) [62, 114-118]. In this case, fuel oxygen content is considered as the driving
force for PM reduction [119]. Other oxygenated alternative fuels can also reduce PM
emissions significantly [120, 121]. In addition, aromatic compounds in diesel fuel are
considered as a precursor for soot formation, therefore, any alternative fuel without
P a g e | 33
aromatics (i.e. Fischer-Tropsh) can also reduce PM emissions [35]. Now, combination of
these alternative fuels with after treatment devices can bring down PM emissions even
more.
On the other hand, these alternative fuels can add further dimensions in terms of the use of
after treatment devices. For example, biodiesel significantly reduces soot emissions, so its
use could eventually reduce how frequently DPF regeneration is required. In addition, the
higher reactivity of biodiesel soot could make DPF regeneration easier, faster and less
costly. However, if the biodiesel produces more volatile substances, this could eventually
increase post DPF nanoparticle emissions. However, a very recent study [122] reports that,
despite an increase in total SOF and DTT-redox activity per mass for biodiesel particles,
DPFs can trap a significant amount of those potentially more toxic organic compounds and
oxidise them. This results in potentially less toxic organic emissions from DPF equipped
vehicles compared to non-DPF equipped vehicles. In addition, particles produced from
biodiesel combustion are smaller in size than those produced from diesel combustion,
which could reduce the capturing efficiency of the DPF. Therefore, it may be necessary to
consider some new measures when designing and implementing DPF technology to control
PM emissions. However, in the case of alternative fuel (i.e. biodiesel), a complete
performance evaluation of these after treatment devices is yet to be done and this is an area
currently appealing for full investigation.
2.7 Why Biodiesel Global energy demand is increasing dramatically with the increasing population, as well as
improving living standards. Currently, most of this energy demand (89%) is met by fossil
fuels (i.e. crude oil, coal and natural gas), with nuclear and renewable fuels making a small
contribution (11%) [123]. Total primary fuel consumption in 2012 was observed to be 70%
higher than in 1987 [123]. As shown in Figure 2-4, current global consumption is
equivalent to more than 11 billion tonnes of oil every year, with crude oil reserves
diminishing at a rate of 4 billion tonnes per year. If these rates continues, current crude oil
reserves will be depleted by 2052 and gas by 2060 [124]. Coal reserves will serve our
energy needs for a few more decades, but this form of energy is not suitable for every
application, especially in the transportation sector. A similar prediction can also be found
in an earlier model [125].
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Figure 2-4: The total reserves of coal, natural gas and crude oil and their predicted
diminishing trends[124]
Globally, the transportation sector is the second largest energy consuming sector which
uses fuel in the form of liquid or gas, second only to the industrial sector. It has
experienced steady growth over the last 30 years, due mainly to the increasing number of
cars in operation around the world, and with global transportation sector energy use
expected to rise by an average of 1.8% per year from 2005 to 2035, it is also the fastest
growing energy demand sector in the world. This growing demand for liquid fossil fuel, in
conjunction with its limited reserves, is driving the world to look for alternative energy
sources which can accommodate future demands. Biofuel is considered as a potential
source to meet this demand. According to the International Energy Agency (IEA) 2011
roadmap, 37% of total energy used in the global transportation sector will be replaced by
biofuel in 2050.
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Figure 2-5: IEA prediction of biofuel demand in 2050
Figure 2-5 displays the projected trend of biofuel demand between the periods of 2010 to
2050. Biofuel is considered to be a renewable, biodegradable, environmentally friendly and
energy efficient fuel, which is believed to have the potential to fulfil energy security needs
without sacrificing the engines operational performance. Among the different kinds of
biofuel, biodiesel and bio-ethanol are the most prominent. Biodiesel is a mixture of alkyl
esters of long-chain fatty acids, usually methyl or ethyl esters, obtained by the trans-
esterification of triglycerides, obtained mostly from vegetable oils or animal fats. Interest
in biodiesel is increasing, due to the rising concern in relation to greenhouse gas emissions.
Biodiesel combustion is neutral in terms of CO2 emission, which means that the
combustion of biodiesel liberates the same amount of CO2 as the plant absorbed during
photosynthesis. The use of pure biodiesel instead of diesel prevents 2.8 kg of CO2
emissions being released into the atmosphere per kg of biodiesel. The EU reduced 8% of
their total greenhouse gas emissions between 1992 and 2012 as a result of their biodiesel
use [126]. Biodegradability of biodiesel is twice as fast as diesel, which also prevents soil
and ground water pollution. Biodiesel can be used in conventional diesel engines, either as
a blend with diesel or as 100% biodiesel (B100), without any engine modification. The
lubricity of biodiesel is also higher than diesel, which causes less wear and tear on piston
rings, cylinder walls and the precision parts of pumps for fuel injection, which may extend
engine life.
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Interest in biodiesel is also increasing due to the diversity of its production sources.
Biodiesel can be produced from variety of sources, including vegetable oil, animal fats,
domestic waste, industrial waste and microalgae [127-129]. Biodiesel production from
edible vegetable oils and animal fats is marked as unsustainable by many studies, because
of its growing conflict with human and animal foods [130]. However, this conflict can be
largely minimised by choosing 2nd
generation biodiesel feedstocks, which are non-edible
[123, 131-133]. In addition, 3rd
generation biodiesel feedstocks (i.e. microalgae) are
considered as the most sustainable biodiesel feedstock because of their high production
rate on limited, non-arable land [132, 134]. There is also evidence of environmental
benefits from biodiesel production using industrial and domestic waste [135]. Hernandez et
al. [135] recovered 94% of residual oil as biodiesel from Alperujo (residue of olive oil
industry), which is highly toxic [136] and requires treatment prior to disposal. [137].
Recent literature even indicates that biodiesel can be produced from insects [138].
Apart from the issue of energy security, the use of biodiesel is expanding around the world
due to its low emissions. According to a review study [139], well-to-wheel CO2 benefits of
biodiesel are in the range of 50–80%, compared to fossil diesel, depending on the
feedstock and the production process used. Numerous studies have reported reductions in
CO, HC and PM from using biodiesel [117, 140], which is primarily associated with its
oxygen content, higher cetane value and the absence of aromatic hydrocarbons and sulfur.
Although biodiesel generally reduces PM emissions, studies have found a decrease in
particle size but an increase in particle number (PN) [62, 141, 142], which is significant
when determining the health impact of DPM, with toxicological studies suggesting an
increase in health effect from smaller particles. Some studies [143] suggest biodiesel
decreases PAH emissions, which is beneficial as most PAHs are carcinogenic, however
there are also reports of an increase in the soluble organic fraction (SOF) of DPM when
using biodiesel [62].
Global biodiesel production has grown steadily over the last two decades and has become
one of the fastest growing chemical industries. The average annual growth rate of biodiesel
production was about 17% globally and 35% in EU between 2007 and 2012 [126],
reaching 22.5 billion litres globally in 2012. The EU is third largest producer of biodiesel
after the USA and Brazil, but it consumes highest amount of biodiesel due to government
directives which ensure that 2% of the energy used in transportation comes from biofuels.
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Figure 2-6 illustrates the increasing trend in biodiesel demand in EU nations from 2002 to
2010. This trend is expected to continue, with many new policies and regulations favouring
the use of biodiesel. According to a new EU renewable energy directive
(RED2009/28/EC), member states should meet 20% of their total energy demand and 10%
of transport sector energy from renewable sources [144]. Global trends are likely to follow
the EU, as many other countries are also starting to promote policies favourable to the
industrialisation of biodiesel (i.e. India, Indonesia, Malaysia and other Asian countries)
[126].
Figure 2-6: Existing/required production capacity of biodiesel in the EU [126]
2.8 Emission behavior of biodiesel fuelled engines
There is a large body of literature summarised in two review articles [117, 139] which
reveals the significant reduction of most regulated emissions from diesel engines, like CO,
THC and PM, due to use of either pure biodiesel or diesel-biodiesel blends. This reduction
is primarily caused by higher oxygen content, high cetane number and a lower carbon to
hydrogen ratio in biodiesel compared to diesel. The advancement of injection and
combustion timing when using biodiesel are also responsible for low CO and HC
emissions.
According to the majority of recent studies [139, 145-151], NOx emissions slightly
increase due to use of biodiesel. In a literature review by the United States Environmental
Protection Agency (US EPA) [152], laboratory experiments using different heavy-duty
engines (without an exhaust gas recirculation system (EGR) or after treatment system)
were analysed and the resulting NOx emissions were used to derive an equation for
calculating NOx emissions based on the biodiesel content of fuel. The equation
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demonstrated an almost linear increase in NOx emissions with an increase in biodiesel
content and it was found to be statistically significant, with a confidence level of 95%.
Various arguments exist in the literature to explain this phenomenon, with most
researchers proposing that the combustion process is accelerated as a consequence of the
increased injection rate, which results from the physical properties of biodiesel (viscosity,
density, compressibility, sound velocity) [145, 153]. Recently, an argument which has
received more attention is the increased flame temperature [154] and heat dissipation by
radiation, as a consequence of the lower amount of in cylinder soot formation [145, 153].
Moreover, the higher oxygen content of biodiesel, its high cetane number and the larger
content of unsaturated compounds in biodiesel also favours higher NOx emissions [145,
154]. There are also some studies [155-157] reporting a decrease in NOx emissions when
using biodiesel and some reports showing no change in NOx emission [145]. Recent
studies [63, 158] suggest that biodiesel with a higher cetane number could lead to a
reduction in NOx emissions.
There is an overwhelming evidence that the use of biodiesel, instead of diesel, causes a
reduction in PM mass emissions [117, 123, 139, 154, 158-161]. Generally, PM emissions
decrease remarkably with the increase of biodiesel content in blends. Various reasons have
been proposed in the literature to explain these reductions in PM emissions, with the
oxygen content of biodiesel playing a major role in this regard, enabling the more complete
combustion of biodiesel. Even in fuel rich regions of the combustion chamber, fuel bound
oxygen promotes oxidation of the already formed soot. The different structure of soot
particles by biodiesel and diesel fuels, might also favour the oxidation of soot from
biodiesel [30, 162, 163] and facilitate low PM emissions. Soot produced from biodiesel has
a similar (diesel like) inner-core outer-shell structure, but is more reactive than diesel soot
[162-164], and the mechanism of its high reactivity is not clearly understood. Some
researchers propose that biodiesel soot has more disordered and amorphous structures
[165], while others show the opposite [166, 167]. Soot primary particles produced by
biodiesel combustion are significantly smaller in size [167], and therefore, have higher
specific active surface than those of diesel soot. Therefore, their higher curvature of carbon
fringes increases the probability of collapsing into shorter fringes, enabling layer stripping,
exfoliation and further oxidation. In addition, the presence of more functional oxygen
groups on biodiesel soot also enhance its oxidation [8]. The absence of aromatics, low
sulfur compounds and a higher cetane number for biodiesel also favour low PM emissions.
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Although PM emissions are well below than diesel, study reveals higher fraction of
metallic ash content in biodiesel PM[168, 169]. This might be due to the metal content in
the biodiesel, derived from specific production process[169], otherwise solvent properties
or reactivity of the biodiesel either cleaning the existing deposits on the fuel system or
dissolving fuel system metal into biodiesel, which ending up in PM[168]. Excessive ash
fraction in PM could affect the performance of aftertreatment devices such as DPF by
gradual ash deposition and build up.
The total number and size distribution of diesel engine exhaust particles is greatly affected
by the use of biodiesel. There are reports of both increasing and decreasing total particle
number concentrations with an increase in biodiesel content in diesel-biodiesel blends [62,
142, 146, 170]. Most of the literature reported that biodiesel emits a higher number of
smaller particles in the exhaust than mineral diesel [171-173]. Surawski et al. [62] reported
an increase in specific particle number emissions for canola and tallow biodiesel, while
they decreased for soy biodiesel. Chuepeng at el[142] found thatB30 (30% blend of
rapeseed oil methyl ester (RME) and ultra-low sulfur diesel (ULSD)) increased the total
particle number concentrations, with a peak in the nucleation mode. Puzun et al. [170] used
a common rail diesel engine and found that the proportional increase of RME biodiesel
increases the number of particles in the nucleation mode, while accumulation mode
particles with a size > 50nm decreased and the peak area shifted to the smaller particle
sizes. Recently, Tan et al. [172] also reported increases in nucleation, as well as total
particle number emissions from biodiesel during transient engine testing, although
accumulation mode particle number emissions decreased.
There are also reports of decreased total particle number concentration due to biodiesel.
Gill et al. [174] used B50 and B100(RME) in a single cylinder direct injection engine and
reported that B100(RME) reduced the total particle number by a factor of two below that
of mineral diesel, with a slight reduction in the count median diameter (CMD). Jung et al.
[175] observed an approximately 38% decrease in the number of particles emitted from a
four-cylinder turbocharged diesel engine (fitted with diesel particulate filters) when using
neat SME biodiesel. Young et al. [176] found a slight decrease in non-volatile particle
number with an increase in waste cook oil methyl ester (WCOME) from B2 to B10 and
B20, but he observes very little or no variation in CMD. Very recently, Lou et al. [160]
found reductions in both nucleation and total particle number emissions, while both
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increases and decreases in total particle number have been observed in a few other studies
as well [33, 159, 172, 177].
Most of the studies reported an increase in total particle number and indicated that the
presence of nucleation mode particles could be the underlying reason for this increase.
Nucleation mode particles could be measurement artefacts caused by the dilution system
rather than actual emissions from the engine. However, recently Lu et al. [178] reported a
reduction in both total particle number, as well as non-volatile particle number emissions
from the use of biodiesel. Based on the above discussion, there is no consensus as to why
these contradictory results, in terms of particle number emissions, were observed by
different authors. This could be due to differences in dilution conditions among different
studies, where the conditions in some studies might have favoured nucleation, while others
suppressed it. On the other hand, this could also be linked to the chemical composition and
relevant physical properties of the biodiesels used in the various tests.
Biodiesel also affects the PAH and oxygenated compound reactive oxygen species (ROS)
emissions from CI engines. Aromatic compounds and their derivatives are toxic,
mutagenic and carcinogenic. They also contribute to the formation of tropospheric ozone.
The intensity of these effects significantly depends on their mixing states and structure.
Most researches showed that the aromatic and polyaromatic compound emissions from
biodiesel are reduced when compared to diesel and that they depend on engine operating
conditions (load, cycle mode, etc.) [117, 131, 139], whereas others reported the opposite
effect [179, 180]. The oxygenated compounds of engine exhaust emissions are also
precursors for ozone formation with other oxidative species in the troposphere, by
photochemical reaction. It is widely believed that biodiesel could increase emissions of
these oxygenated compounds as a result of the oxygen content in the biodiesel molecule
[62, 63, 104]. However, from the literature reviewed, it is not clear whether or not all
biodiesel really increases these emissions, especially for biodiesel produced from different
feedstocks, since many other studies found some decreases or insignificant differences
[181]. Further studies on non-regulated emissions from biodiesel from different feedstocks
should be carried out, in order to determine conclusive trends for the reactive oxygen
species.
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2.9 Properties of biodiesel influential to combustion and emission There are several physical properties of biodiesel which have a significant influence on the
combustion and emission of compression ignition engines. Among them cetane number
(CN), kinematic viscosity, heating value, boiling point, flash point, cloud point, pour point
and oxidative stability are prominent.
Cetane number is a dimensionless parameter related to the auto ignition quality of fuel in a
diesel engine. Generally, the higher the cetane number, the better the ignition quality of
fuel and vice versa. Cetane number affects the diesel combustion process by reducing the
ignition delay, which leads to a reduction in premixed combustion and reduces the sudden
spike in in-cylinder temperature that is eventually responsible for increasing the
predisposition for thermal nitric oxide (NO). As thermal NO is considered to be one of the
major pathways for in-cylinder NO formation, an increase in cetane number will invariably
reduce NO concentration. Jo-Han et al. [182] found NO can be reduced by 2.5 ppm vol%
per unit increase of cetane number, within the range of 51-67. Despite the cetane number
of biodiesel derived from most feedstocks being greater than that of petro-diesel, it has
been widely reported in the literature that use of biodiesel in diesel engines increases NOx
emissions. In the case of NOx emissions, other properties of biodiesel which enhance in
cylinder NOx formation are likely to override the suppressing effect of cetane number. The
increased cetane number of biodiesel also reduces CO, unburned hydrocarbon (UHC)
emissions and engine knocking, as it facilitates smooth combustion of the fuel in the
engine cylinder [182].
Viscosity is the major reason why fats and oil are trans-esterified into biodiesel. Despite
this, biodiesel viscosity is more than that of petro-diesel. The combustion quality of fuel
depends greatly upon the mixing status of fuel to air inside the engine cylinder. Viscosity
plays a major role in this mixing process. Low viscosity fuel quickly evaporates and mixes
with the air inside the engine cylinder, which reduces the fuel rich zone responsible for
incomplete combustion of fuel, due to unavailability of sufficient amount of oxygen for
complete combustion. High viscosity fuel also leads to poor atomisation, larger droplet
size, narrower injection spray angle and greater in-cylinder penetration of the fuel spray,
which all lead to generally poorer combustion. Given that the key reason for most
regulated and unregulated diesel exhaust emissions is incomplete combustion, it is quite
likely that low viscosity fuel is more favourable than higher viscosity fuel in terms of
P a g e | 42
emission reduction. The viscosity of fuel is largely affected by temperature, so problems
associated with high viscosity are mostly noticeable at low ambient temperatures and
during cold start conditions.
Boiling point, volatility and surface tension are other important properties which have a
noticeable influence on fuel atomisation, vaporisation and mixing, and therefore,
combustion and particle emissions. Fuel with low volatility and a higher boiling point and
surface tension has poor atomisation and mixing tendencies, and therefore, they are more
prone to increased particle emissions. The volatility, boiling point and surface tension of
biodiesel could be reasonably different, depending upon its fatty acid composition [183]
and this could eventually lead to differences in particle emissions among different
biodiesel feedstocks.
Low temperature performance is another important property of biodiesel that affects both
the performance and emission of diesel engines. The poor cold flow properties of biodiesel
cause wax formation, which is responsible for filter clogging and subsequent engine
starvation due to reduced fuel flow. Wax crystallisation is initiated by the close packing of
molecules and therefore, factors which disrupt the close packing of highly ordered
molecules (i.e. branching in the fatty acid or alcohol, introduction of double bond etc), will
eventually increase low temperature performance. Heating value is a measure of the energy
content in the fuel. Usually biodiesel heating value is around 10% less than that of petro-
diesel due its high oxygen content, as the oxygen doesn’t liberate any heat during
combustion. Due to the its heating value, engine fuel consumption of biodiesel increases in
order to maintain the power output. Oxidation stability is one of the most important fuel
properties for evaluating the in use performance of biodiesel, however the oxidation
stability of biodiesel is greatly affected by the age of the biodiesel and the condition under
which it is stored. Boiling point is important, given that a low boiling point fuel quickly
evaporates and mixes with air. Cloud point and pour point are also important factors in the
use of biodiesel at low temperatures.
2.10 Effect of fatty acid profile on biodiesel properties Biodiesels are mono alkyl esters of fatty acids, usually produced by trans-esterification
from vegetable oil and animal fats. The properties of biodiesel related to combustion and
emission depend greatly upon the fatty acid properties. Different fatty acids have different
physical properties due to their elemental content, chemical structure and degree of
P a g e | 43
unsaturation. It is worth mentioning here the degree of unsaturation of an alkyl ester
molecule is an indicator of the number of double bonds present in its fatty acid chain, with
a higher number of double bonds representing a higher degree of unsaturation. The
different vegetable oils and animal fats generally used to produce biodiesel are actually a
mixture of numerous fatty acids in varying proportion. Thus, the biodiesel derived from
different feedstocks can have significantly different physical properties due do their
different proportion of various fatty acid esters and these variations can significantly affect
the combustion and emissions from diesel engines.
Figure 2-7 demonstrates the influence of fatty acid methyl ester (FAME) chemical
structure on the major physical properties of biodiesel. The viscosity of individual FAME
increases with the increase in fatty acid carbon chain length. The effect of the carbon
number of the alcohol used to produce biodiesel on viscosity is less certain, although there
are some reports of the increased viscosity of biodiesel upon changing the alcohol from
methanol to ethanol to propanol and so on. Viscosity also strongly correlates with the
degree of unsaturation in FAME. Generally, the higher the degree of unsaturation, the
lower the viscosity of FAME and vice versa. Moreover, the double bond configuration of
the FAME also influences the viscosity of biodiesel. Usually trans-configuration is
responsible for higher viscosity than the cis-configuration, while the effect of double bond
location in the fatty acid chain on viscosity is negligible. Furthermore, the viscosity of
biodiesel also correlates strongly with density, whereby the higher density of FAME leads
to lower viscosity.
The volatility, boiling point and surface tension of biodiesels also vary with changes in the
fatty acid composition of biodiesel. Boiling point and surface tension increase with an
increase in FAME carbon chain length [184, 185], whereas volatility decreases. Chettri et
al. [186] also found differences in surface tension among canola, jatropha and soap nut
biodiesel, due to variations in their FAME composition. No conclusive trend has been
found between degree of unsaturation and volatility, boiling point, and surface tension.
P a g e | 44
Figure 2-7: Variation of biodiesel properties with FAME chemical structure (Notes:
Length of arrow indicates relative magnitude of effect, thickness of arrow indicates
consistency of effect, symbol “–” indicates highly uncertain effect, blank box indicates that
no relevant information was found.) [156]
The cetane number (CN) of biodiesel also varies with the feedstock used. For example,
biodiesel derived from palm and tallow has a higher cetane number than soy, because
biodiesel from palm and tallow contains a higher percentage of saturated fatty acid esters.
There are also reports in the literature of decreasing CN with increasing degree of
unsaturation in the fatty acid [156]. CN also increases with an increase in the chain length
of the fatty acid and the alcohol used for trans-esterification. Lapuerta et al. proposed a
predictive equation for calculating FAME CN that is largely based on the number of
double bonds and the number of carbon atoms present in FAME [187].
The cold flow properties of biodiesel deteriorate with the presence of long chain saturated
fatty acids. In general, the longer the carbon chain, the higher the melting point and poorer
the low temperature performance. Although the relation between carbon chain length and
low temperature performance is quite strong for pure FAME compounds, the effects
appear to be more indirect when considering actual biodiesels, which are complex mixtures
of FAME. Differences in double bond orientation also affect cold flow properties, with the
trend of improved low temperature performance by the cis-configuration over trans-
configuration. Recently, another low temperature operability problem (i.e. formation of
insoluble particles (saturated mono-glycerides and sterol glucosides)) was reported due to
the storage of FAME at cool temperatures. These materials have high melting points and
very low solubilities, allowing them to form solid residues. These insoluble residues form
from the trace levels of impurities in the FAME, not directly from the major FAME
component itself. Accumulation of these solid residues into the fuel filter causes filter
P a g e | 45
clogging and although gaseous emissions are not affected by these residues, it has a
significant effect on particulate emissions, as these forms of solid residual substances do
not undergo complete combustion. It was also found that smoke opacity level increased by
0.42% for every 0.01%m/m increase in the carbon residue content of biodiesel [182]
Heating value of biodiesel increases with the increase of carbon number in the FAME for
constant degree of saturation as the mass fraction of oxygen decreases. Heating value is
also influenced by the degree of unsaturation in the FAME. Unsaturated FAME has a
lower mass energy content than saturated FAME.
Oxidation stability is also affected by fatty acid composition and structure, with
unsaturation affecting the oxidation stability of biodiesel the most. Oxidative degradation
begins with the extraction of a hydrogen atom from a carbon adjacent to the double bond,
and then a rapid reaction takes place between with molecular oxygen, to form
hydroperoxides. Subsequent reactions involving isomerisation, followed by radical chain
propagation, produce numerous secondary oxidation products (i.e. alcohols, aldehydes and
carboxylic acids) [156]. Oxidation of FAME is also affected by the number and position of
the double bonds. FAME molecules containing carbon adjacent to two double bonds are
more susceptible to oxidative reactions. That is why the relative rate of oxidation of
purified FAME containing oleic (18:1) acid, linoleic (18:2) acid and linolenic (18:3) acid is
1:41:98 [188]. The orientation of carbon-carbon (C-C) double bonds also affects oxidation
stability, with the trans-configuration being more stable than the cis-configuration,
however most natural oils are dominated by the cis-configuration.
Biodiesel doesn’t contain aromatics, which act as a precursor for soot formation, or sulfur,
which produces H2SO4 particulates and usually reduces PM emissions. Another advantage
is that the presence of oxygen in the biodiesel molecular structure reduces the C-C bond
which suppresses the formation of precursor species and subsequent PM emissions. As the
biodiesel derived from any vegetable oil or animal fat is a complex mixture of different
kinds of fatty acid esters, each of which has different physical and chemical properties, it
may be beneficial to find the most suitable fatty acid ester, which has superior combustion
properties, in order to improve combustion and reduce emissions. When found, the target
plant can be genetically modified for biodiesel production so that it will provide highest
amount of the fatty acid in question.
P a g e | 46
2.11 The impact of biodiesel feedstocks on combustion and emissions There are hundreds of different feedstocks investigated in the literature as a potential
source of fatty acids for biodiesel production. Among them, the most dominant are
soybean in the US, rapeseed in Europe, jatropha in India, palm in Southeast Asia, and
tallow and waste cooking oil in other parts of the world. In addition, there are other
feedstocks which also have the potential for commercial biodiesel production (i.e. coconut,
corn, safflower, camelina, and canola). Algal lipid derived from microalgae, which is then
trans-esterified into biodiesel, is also considered an important potential and emerging
source of biodiesel, due to its high yield per hectare of land use [189-192].
Figure 2-8 shows the fatty acid profile of some common biodiesel feedstocks. The fatty
acid profile of triglycerides derived from different vegetable oils or animal fats is always
different to some extent. It may also be different even for the same species, depending
upon the growing conditions of the plants or animals. Variations in fatty acid profile cause
variations in the chemical structure of the biodiesel produced from it. As the chemical
structure of biodiesel affects its physical properties, it is quite likely that it will affect
combustion and emissions as well. McCormick et al. used seven biodiesel feedstocks and
found variations in NOx emission [193], while Redel-Macias et al. found reduced NOx and
noise emissions for palm biodiesel over olive oil biodiesel [194]. Schroder et al. used five
feedstocks (rapseed, linseed, soy, palm and coconut) and found variations in NO, PM and
HC emissions, while in another study, PM and NOx emissions were the lowest and highest
for coconut and linseed oils, respectively [195].
Figure 2-8: Fatty acid composition of some common biodiesel feedstocks [196]
P a g e | 47
Lapuerta et al. [154] used a four-cylinder, 2.2 litre, turbocharged, direct injection diesel
engine operated on a new European driving cycle to identify the effect of degree of
unsaturation of biodiesel on emissions. They found a 20% decrease in particulate mass
emissions and a 10% increase in NOx emissions with a higher degree of unsaturation.
Unsaturated biodiesel reduced the particle mean diameter and caused the late start of
combustion, but a higher in cylinder heat release rate. Sarathy et al. [197] oxidised
saturated (i.e. methyl butanoate) and an unsaturated (i.e. methyl crotonate) C4 in an
opposed flow diffusion flame and a jet stirred reactor, in order to study the effect of
chemical structure of FAME on the composition of combustion products. They reported
that methyl crotonate combustion produces much higher levels of C2H2, 1-C3H4, 1-C 4H8,
and 1,3-C4H6, as well as higher levels of benzene than methyl butanoate, which can all act
as soot precursors. This finding indicates that saturated FAME has a lower tendency for
soot formation than unsaturated FAME.
Recently, Benjumea et al. [151] conducted an experimental study to find the influence of
fuel chemical structure, especially degree of unsaturation ,on the performance and
emissions of diesel engines. The fuel matrix was designed so that the effect of the degree
of unsaturation of the tested biodiesel fuels was isolated from the other properties, such as
the chain length, oxygen content, density, viscosity and volatility, which can also affect
combustion and emissions. Although they found no significant effect on engine
performance, they did observe a noticeable effect on combustion and emissions. Higher
degree of unsaturation leads to longer ignition delay and consequently, a slower start to
diffusion combustion, both of which are likely to increase PM emissions. Very recently,
Salamanaca et al. [198] found 60% more PM emissions from linseed biodiesel than palm
biodiesel. Linseed biodiesel has more unsaturated compounds than palm biodiesel, which
favours soot precursor formation. They also found an increase in the volatile matter content
of particulate matter with an increase in degree of unsaturation and the PM produced from
linseed biodiesel had an aliphatic content twice than of palm biodiesel.
On the other hand, there are also reports of decreasing particle emissions from biodiesel
with a higher degree of unsaturation. Sukjit et al. [199] didn’t observe any noticeable
change in soot emissions from saturated and unsaturated biodiesel with the same carbon
content. However, they observed a noticeable reduction in soot emissions from unsaturated
compared to saturated biodiesel when running at 20% EGR. They also found a reduction in
P a g e | 48
particle SOF from unsaturated compared to saturated biodiesel. Pinzi et al. [200] also
reported no significant change in PM emissions between saturated and mono-unsaturated
biodiesel when running without EGR. However, unsaturated biodiesel provided a stronger
reduction in PM than saturated biodiesel at 30% EGR. They also reported a reduction in
the volatile organic fraction (VOF) of PM with an increase in degree of unsaturation in
biodiesel FAME.
Particle number emissions are also affected by biodiesel feedstock, as the physical
properties and chemical structure of biodiesel originating from different feedstocks vary
substantially. Surawski et al.[201] investigated 3 feedstocks (i.e. soy, tallow and canola)
with different blend percentages in a direct injection diesel engine and found a significant
difference in both the particle number and size distribution among the feedstocks. For the
soy feedstock, particle number reductions ranged from 4% (B40) to 53% (B100), whilst for
B20 a 12% particle number increase was observed. Particle number increases ranged from
71% (B20) to 44% (B80) for the canola feedstock and this increase was even found in the
accumulation mode, which is quiet uncommon. For the tallow feedstock, particle number
increases ranged from 7% (B20) to 25% (B40), whilst a particle number reduction of 14%
was observed for B80. Particle CMD was reduced for all three feedstocks and the higher
the biodiesel content the higher the level of reduction that was observed. Fontaras et al.
[202] used biodiesels from five different feedstock (rapeseed, soy, sunflower, palm, and
used fried oils) blended with diesel at 10% vol. ratio (B10) in a Euro 3 common-rail
passenger car. They reported a consistent reduction in non-volatile particle number for all
five biodiesel feedstocks, but a significant variation in particle number when counting
volatile and semi-volatile particles. The highest reduction was observed for sunflower oil
methyl ester (SUME), followed by palm oil methyl ester (PME), and even the particle
number concentrations for these two feedstocks were well below that of the baseline fuel
when counting total particle number. RME reduced non-volatile particle number by W20,
but produced the highest number of volatile or semi-volatile particles, even under low
power cycles.
Although diesel engine exhausts particle number and size distribution is complex
parameter as it is very sensitive to the level of dilution, heating prior to sampling, engine
type, engine operating conditions etc, but in accordance with the literature there is no doubt
about the strong influence of biodiesel coming from different feedstock on it. So it is quite
P a g e | 49
clear that inconsistent and limited information is available in the literature on particle
number emissions in case of using biodiesel from different feed stocks
2.12 Progress of microalgae as biodiesel feedstock Microalgae are considered to be a third generation biofuel feedstock, due to its higher yield
and relatively low land requirement for production. Practically, algae can grow in any
place where there is enough sunshine, even on land that is infertile or inefficient for the
cultivation of other biodiesel feedstocks and food producing crops [191]. Some algae can
even grow in saline water, as well as in sea water. Microalgae are also the most efficient
photosynthetic organism known to man, consuming both CO2 and NO2 during their
growth, which means its cultivation for biodiesel production can also have benefits in
terms of climate change mitigation [199, 203, 204], as well as for wastewater treatment
and environmental pollution control [205, 206]. Among photosynthesising organisms,
microalgae are the fastest growing and can complete an entire growing cycle within a few
days [189]. According to some estimates, oil production from microalgae ranges from
20,000 to 80,000 l per acre, per year, depending on the species and production method
used, which is 7–31 times higher than that of the highest oil producing terrestrial oil crop
(palm) [189]. The required land space is also 10–340 times smaller than that of their
terrestrial counterparts. Therefore, some estimations suggests that the oil production from
microalgae can be up to 200 times higher than the most efficiently produced vegetable oils
[192].
Currently, about 200,000-800,000 algae species exist, many of which accumulate a
reasonable amount of lipids. The lipid content in microalgae biodiesel varies significantly
among these species, from less than 10% to more than 60% by mass, depending on the
type of species, as well as their growing conditions [153, 207]. In order to make
microalgae biodiesel commercially viable, it is important to find a species with a high lipid
content that can be cultivated in the lowest possible time with the least nutrition. Therefore,
most microalgae biodiesel research is focused on species selection, characterisation,
growing condition optimisation and so on.
Although there are extensive studies in relation to microalgae production, oil extraction
and oil characterisation, limited information is available in the literature about the
composition and properties of biodiesel derived from these potential species. In their
review, Hoekman et al. [156] compiled the fatty acid profiles of 12 microalgae species and
P a g e | 50
reported wider variations in the fatty profile of microalgae feedstock than any other
conventional feedstocks (i.e. vegetable oil or animal fat). Although, most of the microalgae
feedstocks were composed of a significant amount of C16 and C18 fatty acids [208], they
could also contain higher fractions of lighter fatty acids (C8 to C14) [190], Unlike other
conventional feedstocks, microalgae could also contain a considerable amount of heavier
fatty acids (i.e. C20 and C22) [156, 190]. In addition, microalgae feedstock also contain a
higher fraction of poly unsaturated compounds [209, 210] than conventional biodiesel
feedstocks. Heavier fatty acid content, with substantial levels of poly unsaturation, can
significantly affect important biodiesel properties (i.e. viscosity, density, boiling point and
cetane number). This change in biodiesel properties could also alter the engine
performance and emissions. Since these heavier and poly unsaturated fatty acids are more
abundant in microalgae feedstocks, it is important to understand their effect on biodiesel
properties, storage and overall performance as a whole, before its commercialisation.
Despite intensive research on microalgae biodiesel viability as a commercial biodiesel,
very few studies have focused on engine performance and emissions when using
microalgae biodiesel. Very recently, a few studies [211-214] tested only the lower blends
of microalgae biodiesel (up to B30). Rinaldini et al. [213] reported satisfactory engine
performance with around a 30% reduction in soot and 20% increase in NOx from an
indirect mechanical injection engine, when they used a B20 microalgae blend. Patel et al.
[212] investigated 10%, 15% and 20% blends of Chlorella Vulgaris microalgae biodiesel in
a naturally aspirated, single cylinder, variable compression ration engine. They reported
increased efficiency, smoke opacity and NOx emissions, with a reduction in HC and CO
emissions from microalgae blends. Makareviciene et al. [211] used a B30 microalgae blend
and RME biodiesel in a multi cylinder engine and found a 2.5-3% increase in efficiency,
10-75% reduction in smokiness and 5-25% reduction in HC emissions when using the B30
microalgae blend. Tuccar et al. [214] also found improved emissions from adding butanol
to diesel microalgae biodiesel blends. However, none of these studies conducted detailed
combustion analysis or particle emission measurements. In addition, the test engines used
in this study was naturally aspirated with mechanical fuel injection and without any after
treatment system. Moreover, they didn’t present the fatty acid composition of the
microalgae biodiesels used in their studies, which means that the effect of microalgae
biodiesel fatty acid composition on engine performance and emissions cannot be deduced
P a g e | 51
from their studies. Further to this, microalgae biodiesel performance at higher blends also
needs to be investigated in conjunction with modern engine technologies.
In summary, there are many varieties of microalgae species available and the fatty acid
profile of biodiesel produced from those species can be significantly different depending
on their growing conditions. Studies suggest that the performance and emission profile of
biodiesel can be significantly different based on variations in their fatty acid profile [173,
215]. The fatty acid composition of microalgae can be controlled either by genetic
modification of the species or by manipulating the growing conditions, however before
doing this, it is necessary to identify the target fatty acid compositions that will provide
optimum output with the lowest possible emissions.
2.13 References [1] J.B. Heywood, Internal combustion engine fundamentals, (1988).
[2] I.A.f.R.o.C. (ARC), Diesel engine exhaust carcenogenic, (2012) press release N 213.
[3] M.Z. Jacobson, Strong radiative heating due to the mixing state of black carbon in
atmospheric aerosols, Nature, 409 (2001) 695-697.
[4] W.A. Majewski, what are diesel emissions, DieselNet Technology Guide Copyright ©
Ecopoint Inc, (2012) https://www.dieselnet.com/tech/emi_intro.php
[5] B. Giechaskiel, A. Mamakos, J. Andersson, P. Dilara, G. Martini, W. Schindler, A.
Bergmann, Measurement of automotive nonvolatile particle number emissions within the
European legislative framework: A review, Aerosol Science and Technology, 46 (2012)
719-749.
[6] C.A. Sharp, S.A. Howell, J. Jobe, The effect of biodiesel fuels on transient emissions
from modern diesel engines, part II unregulated emissions and chemical characterization,
in, SAE Technical Paper, 2000.
[7] M.K. Khair, Engine Design for PM Control, DieselNet Technology Guide, 05a (2002).
[8] M. Matti Maricq, Chemical characterization of particulate emissions from diesel
engines: A review, Journal of Aerosol Science, 38 (2007) 1079-1118.
[9] W.A. Majewski, Diesel particulate matter, DieselNet Technology Guide Copyright ©
Ecopoint Inc. Revision 2013.08b, (2013) https://www.dieselnet.com/tech/dpm.php.
[10] W.A. Majewski, Diesel exhaust particle size, Dieselnet Technology Guide, Ecopoint
Inc, http://www.dieselnet.com/tech/dpm_size.html (2002).
[11] D. Kittelson, W. Watts, J. Johnson, Diesel Aerosol Sampling Methodology–CRC E-
43, Final report, Coordinating Research Council, (2002).
[12] D. Kittelson, W. Watts, J. Johnson, Diesel aerosol sampling
P a g e | 52
methodology - CRC E-43. Final Report., in, University of Minnesota, Department of
Mechanical Engineering. Report for the Coordinating Research Council, 2002, pp. 1-181.
[13] W.A. Majewski, Diesel exhaust particle size, in, Dieselnet Technology Guide,
Ecopoint Inc, http://www.dieselnet.com/tech/dpm_size.html, 2002.
[14] M.M. Maricq, R.E. Chase, N. Xu, P.M. Laing, The effects of the catalytic converter
and fuel sulfur level on motor vehicle particulate matter emissions: Light duty diesel
vehicles, Environmental Science & Technology, 36 (2002) 283-289.
[15] I.A. Khalek, D.B. Kittelson, F. Brear, Nanoparticle growth during dilution and cooling
of diesel exhaust: Experimental investigation and theoretical assessment, in, SAE technical
paper, 2000.
[16] B. Giechaskiel, L. Ntziachristos, Z. Samaras, V. Scheer, R. Casati, R. Vogt,
Formation potential of vehicle exhaust nucleation mode particles on-road and in the
laboratory, Atmospheric Environment, 39 (2005) 3191-3198.
[17] T. Rönkkö, A. Virtanen, K. Vaaraslahti, J. Keskinen, L. Pirjola, M. Lappi, Effect of
dilution conditions and driving parameters on nucleation mode particles in diesel exhaust:
Laboratory and on-road study, Atmospheric Environment, 40 (2006) 2893-2901.
[18] Z.G. Liu, V.N. Vasys, T.A. Swor, D.B. Kittelson, Significance of Fuel Sulfur Content
and Dilution Conditions on Particle Emissions from a Heavily-Used Diesel Engine During
Transient Operation, in, SAE International, 2007.
[19] I. Abdul-Khalek, D. Kittelson, F. Brear, The influence of dilution conditions on diesel
exhaust particle size distribution measurements, in, SAE Technical paper, 1999.
[20] Z.D. Ristovski, E.R. Jayaratne, M. Lim, G.A. Ayoko, L. Morawska, Influence of
diesel fuel sulfur on nanoparticle emissions from city buses, Environmental Science and
Technology, 40 (2006) 1314-1320.
[21] B. Giechaskiel, R. Chirico, P.F. DeCarlo, M. Clairotte, T. Adam, G. Martini, M.F.
Heringa, R. Richter, A.S.H. Prevot, U. Baltensperger, C. Astorga, Evaluation of the
particle measurement programme (PMP) protocol to remove the vehicles' exhaust aerosol
volatile phase, Science of the Total Environment, 408 (2010) 5106-5116.
[22] C.L. Myung, S. Park, Exhaust nanoparticle emissions from internal combustion
engines: A review, International Journal of Automotive Technology, 13 (2012) 9-22.
[23] N.C. Surawski, B. Miljevic, G.A. Ayoko, B.A. Roberts, S. Elbagir, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine employing two injection technologies and
three fuels, Environ Sci Technol, 45 (2011) 5498-5505.
[24] A.M. Jones, R.M. Harrison, G. Fuller, B. Barratt, A large Reduction in Airborne
Particle Number Concentrations at the time of the introduction of “Sulphur Free” Diesel
and the London Low Emission Zone, Atmospheric Environment, (2012).
[25] P.-Q. Tan, Z.-Y. Hu, D.-M. Lou, Regulated and unregulated emissions from a light-
duty diesel engine with different sulfur content fuels, Fuel, 88 (2009) 1086-1091.
P a g e | 53
[26] D. Kittelson, W. Watts, J. Johnson, C. Thorne, C. Higham, M. Payne, S. Goodier, C.
Warrens, H. Preston, U. Zink, Effect of fuel and lube oil sulfur on the performance of a
diesel exhaust gas continuously regenerating trap, Environ Sci Technol, 42 (2008) 9276-
9282.
[27] S. Liu, L. Shen, Y. Bi, J. Lei, Effects of altitude and fuel oxygen content on the
performance of a high pressure common rail diesel engine, Fuel, (2013).
[28] N.K. Fukagawa, M. Li, M.E. Poynter, B.C. Palmer, E. Parker, J. Kasumba, B.A.
Holmen, Soy Biodiesel and Petrodiesel Emissions Differ in Size, Chemical Composition
and Stimulation of Inflammatory Responses in Cells and Animals, Environ Sci Technol,
(2013).
[29] X. Wang, C.S. Cheung, Y. Di, Z. Huang, Diesel engine gaseous and particle
emissions fueled with diesel–oxygenate blends, Fuel, 94 (2012) 317-323.
[30] G. Austin, J. Naber, J. Johnson, C. Hutton, Effects of biodiesel blends on particulate
matter oxidation in a catalyzed particulate filter during active regeneration, SAE
International Journal of Fuels and Lubricants, 3 (2010) 194-218.
[31] L.C. Ka, A. Polidori, L. Ntziachristos, T. Tzamkiozis, Z. Samaras, F.R. Cassee, M.
Gerlofs, C. Sioutas, Chemical characteristics and oxidative potential of particulate matter
emissions from gasoline, diesel, and biodiesel cars, Environmental Science and
Technology, 43 (2009) 6334-6340.
[32] W. Liu, X. Qiao, J. Wang, Z. Wang, Z. Huang, Effects of combustion mode on
exhaust particle size distribution produced by an engine fueled by dimethyl ether (DME),
Energy and Fuels, 22 (2008) 3838-3843.
[33] Y.-C. Lin, C.-F. Lee, T. Fang, Characterization of particle size distribution from diesel
engines fueled with palm-biodiesel blends and paraffinic fuel blends, Atmospheric
Environment, 42 (2008) 1133-1143.
[34] X. Li, Z. Huang, J. Wang, J. Wu, Characteristics of ultrafine particles emitted from a
dimethyl ether (DME) engine, Chinese Science Bulletin, 53 (2008) 304-312.
[35] M.M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski, Influence of Different
Alternative Fuels on Particle Emission from a Turbocharged Common-Rail Diesel Engine,
Procedia Engineering, 56 (2013) 381-386.
[36] S.S. Gill, A. Tsolakis, K.D. Dearn, J. Rodríguez-Fernández, Combustion
characteristics and emissions of Fischer-Tropsch diesel fuels in IC engines, Progress in
Energy and Combustion Science, 37 (2011) 503-523.
[37] R. Zhang, A.F. Khalizov, J. Pagels, D. Zhang, H. Xue, P.H. McMurry, Variability in
morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric
processing, Proceedings of the National Academy of Sciences of the United States of
America, 105 (2008) 10291-10296.
[38] T. Cheng, Y. Wu, H. Chen, Effects of morphology on the radiative properties of
internally mixed light absorbing carbon aerosols with different aging status, Optics
Express, 22 (2014) 15904-15917.
P a g e | 54
[39] S.E. Bauer, S. Menon, D. Koch, T.C. Bond, K. Tsigaridis, A global modeling study on
carbonaceous aerosol microphysical characteristics and radiative effects, Atmospheric
Chemistry and Physics, 10 (2010) 7439-7456.
[40] D.V. Spracklen, K.S. Carslaw, U. Pöschl, A. Rap, P.M. Forster, Global cloud
condensation nuclei influenced by carbonaceous combustion aerosol, Atmospheric
Chemistry and Physics, 11 (2011) 9067-9087.
[41] T. Kühn, A.I. Partanen, A. Laakso, Z. Lu, T. Bergman, S. Mikkonen, H. Kokkola, H.
Korhonen, P. Räisänen, D.G. Streets, S. Romakkaniemi, A. Laaksonen, Climate impacts of
changing aerosol emissions since 1996, Geophysical Research Letters, 41 (2014) 4711-
4718.
[42] A.M. Fiore, V. Naik, D.V. Spracklen, A. Steiner, N. Unger, M. Prather, D. Bergmann,
P.J. Cameron-Smith, I. Cionni, W.J. Collins, S. Dalsoren, V. Eyring, G.A. Folberth, P.
Ginoux, L.W. Horowitz, B. Josse, J.F. Lamarque, I.A. MacKenzie, T. Nagashima, F.M.
O'Connor, M. Righi, S.T. Rumbold, D.T. Shindell, R.B. Skeie, K. Sudo, S. Szopa, T.
Takemura, G. Zeng, Global air quality and climate, Chemical Society Reviews, 41 (2012)
6663-6683.
[43] P. Jiménez-Guerrero, J.P. Montávez, J.J. Gómez-Navarro, S. Jerez, R. Lorente-Plazas,
Impacts of climate change on ground level gas-phase pollutants and aerosols in the Iberian
Peninsula for the late XXI century, Atmospheric Environment, 55 (2012) 483-495.
[44] M. Kanakidou, J. Seinfeld, S. Pandis, I. Barnes, F. Dentener, M. Facchini, R.V.
Dingenen, B. Ervens, A. Nenes, C. Nielsen, Organic aerosol and global climate modelling:
a review, Atmospheric Chemistry and Physics, 5 (2005) 1053-1123.
[45] J.H. Seinfeld, S.N. Pandis, Atmospheric chemistry and physics: from air pollution to
climate change, John Wiley & Sons, 2012.
[46] T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels,
Y. Xia, V. Bex, P.M. Midgley, Climate Change 2013. The Physical Science Basis.
Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change-Abstract for decision-makers, in, Groupe d'experts
intergouvernemental sur l'evolution du climat/Intergovernmental Panel on Climate
Change-IPCC, C/O World Meteorological Organization, 7bis Avenue de la Paix, CP 2300
CH-1211 Geneva 2 (Switzerland), 2013.
[47] G. Lin, J.E. Penner, M.G. Flanner, S. Sillman, L. Xu, C. Zhou, Radiative forcing of
organic aerosol in the atmosphere and on snow: Effects of SOA and brown carbon, Journal
of Geophysical Research: Atmospheres, 119 (2014) 7453-7476.
[48] Y. Feng, V. Ramanathan, V. Kotamarthi, Brown carbon: a significant atmospheric
absorber of solar radiation?, Atmospheric Chemistry and Physics, 13 (2013) 8607-8621.
[49] R. Wang, S. Tao, H. Shen, Y. Huang, H. Chen, Y. Balkanski, O. Boucher, P. Ciais, G.
Shen, W. Li, Y. Zhang, Y. Chen, N. Lin, S. Su, B. Li, J. Liu, W. Liu, Trend in global black
carbon emissions from 1960 to 2007, Environmental Science and Technology, 48 (2014)
6780-6787.
P a g e | 55
[50] E. Garshick, F. Laden, J.E. Hart, B. Rosner, T.J. Smith, D.W. Dockery, F.E. Speizer,
Lung Cancer in Railroad Workers Exposed to Diesel Exhaust, Environmental Health
Perspectives, 112 (2004) 1539-1543.
[51] Z.D. Ristovski, B. Miljevic, N.C. Surawski, L. Morawska, K.M. Fong, F. Goh, I.A.
Yang, Respiratory health effects of diesel particulate matter, Respirology, 17 (2012) 201-
212.
[52] E. Koike, S. Hirano, N. Shimojo, T. Kobayashi, cDNAmicroarray analysis of gene
expression in rat alveolar macrophages in response to organic extract of diesel exhaust
particles, Toxicological Science, 67 (2002) 241–246.
[53] J.K. Folkmann, L. Risom, C.S. Hansen, S. Loft, P. Moller, Oxidatively damaged DNA
and inflammation in the liver of dyslipidemic ApoE-/- mice exposed to diesel exhaust
particles, Toxicology, 1 (2007) 134-144.
[54] B. Cruts, L. van Etten, H. Tornqvist, A. Blomberg, T. Sandstrom, N.L. Mills, P.J.A.
Borm, Exposure to diesel exhaust induces changes in EEG in human volunteers, Particle
and Fibre Toxicology, 5 (2008) 4.
[55] P. Moller, N.R. Jacobsen, J.K. Folkmann, P.H. Danielsen, L. Mikkelsen, J.G.
Hemmingsen, L.K. Vesterdal, L. Forchhammer, H. Wallin, S. Loft, Role of oxidative
damage in toxicity of particulates, Free Radic Res, 44 (2010) 1-46.
[56] M.D. Attfield, P.L. Schleiff, J.H. Lubin, A. Blair, P.A. Stewart, R. Vermeulen, J.B.
Coble, D.T. Silverman, The diesel exhaust in miners study: A cohort mortality study with
emphasis on lung cancer, Journal of the National Cancer Institute, 104 (2012) 869-883.
[57] D.T. Silverman, C.M. Samanic, J.H. Lubin, A.E. Blair, P.A. Stewart, R. Vermeulen,
J.B. Coble, N. Rothman, P.L. Schleiff, W.D. Travis, R.G. Ziegler, S. Wacholder, M.D.
Attfield, The diesel exhaust in miners study: A nested case-control study of lung cancer
and diesel exhaust, Journal of the National Cancer Institute, 104 (2012) 855-868.
[58] B. Giechaskiel, B. Alfoldy, Y. Drossinos, A metric for health effects studies of diesel
exhaust particles, Journal of Aerosol Science, 40 (2009) 639-651.
[59] T.C. Carvalho, J.I. Peters, R.O. Williams Iii, Influence of particle size on regional
lung deposition – What evidence is there?, International Journal of Pharmaceutics, 406
(2011) 1-10.
[60] H. Patel, S. Eo, S. Kwon, Effects of diesel particulate matters on inflammatory
responses in static and dynamic culture of human alveolar epithelial cells, Toxicology
Letters, 200 (2011) 124-131.
[61] J.N. Gangwar, T. Gupta, A.K. Agarwal, Composition and comparative toxicity of
particulate matter emitted from a diesel and biodiesel fuelled CRDI engine, Atmospheric
Environment, 46 (2012) 472-481.
[62] N.C. Surawski, B. Miljevic, G.A. Ayoko, S. Elbagir, S. Stevanovic, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine: The influence of biodiesel feedstock,
Environmental Science and Technology, 45 (2011) 10337-10343.
P a g e | 56
[63] P.X. Pham, T.A. Bodisco, S. Stevanovic, M.D. Rahman, H. Wang, Z.D. Ristovski,
R.J. Brown, A.R. Masri, Engine Performance Characteristics for Biodiesels of Different
Degrees of Saturation and Carbon Chain Lengths, SAE Int. J. Fuels Lubr., 6 (2013) 188-
198.
[64] P.I. Jalava, P. Aakko-Saksa, T. Murtonen, M.S. Happo, A. Markkanen, P. Yli-Pirilä,
P. Hakulinen, R. Hillamo, J. Mäki-Paakkanen, R.O. Salonen, J. Jokiniemi, M.R. Hirvonen,
Toxicological properties of emission particles from heavy duty engines powered by
conventional and bio-based diesel fuels and compressed natural gas, Particle and Fibre
Toxicology, 9 (2012).
[65] A.L.N. Guarieiro, J.V.D.S. Santos, A. Eiguren-Fernandez, E.A. Torres, G.O. Da
Rocha, J.B. De Andrade, Redox activity and PAH content in size-classified nanoparticles
emitted by a diesel engine fuelled with biodiesel and diesel blends, Fuel, 116 (2014) 490-
497.
[66] N. Yanamala, M.K. Hatfield, M.T. Farcas, D. Schwegler-Berry, J.A. Hummer, M.R.
Shurin, M.E. Birch, D.W. Gutkin, E. Kisin, V.E. Kagan, A.D. Bugarski, A.A. Shvedova,
Biodiesel versus diesel exposure: Enhanced pulmonary inflammation, oxidative stress, and
differential morphological changes in the mouse lung, Toxicology and Applied
Pharmacology, 272 (2013) 373-383.
[67] M.E. Gerlofs-Nijland, A.I. Totlandsdal, T. Tzamkiozis, D.L.A.C. Leseman, Z.
Samaras, M. Låg, P. Schwarze, L. Ntziachristos, F.R. Cassee, Cell toxicity and oxidative
potential of engine exhaust particles: Impact of using particulate filter or biodiesel fuel
blend, Environmental Science and Technology, 47 (2013) 5931-5938.
[68] N. Traviss, M. Li, M. Lombard, B.A. Thelen, B.C. Palmer, M.E. Poynter, B.T.
Mossman, B.A. Holmén, N.K. Fukagawa, Petrodiesel and waste grease biodiesel (B20)
emission particles at a rural recycling center: Characterization and effects on lung
epithelial cells and macrophages, Air Quality, Atmosphere and Health, 7 (2014) 59-70.
[69] B. Giechaskiel, M. Maricq, L. Ntziachristos, C. Dardiotis, X. Wang, H. Axmann, A.
Bergmann, W. Schindler, Review of motor vehicle particulate emissions sampling and
measurement: From smoke and filter mass to particle number, Journal of Aerosol Science,
67 (2014) 48-86.
[70] B. Giechaskiel, W. Schindler, H. Jörgl, V. Vescoli, A. Bergmann, W. Silvis, Accuracy
of particle number measurements from partial flow dilution systems, in, SAE Technical
Paper, 2011.
[71] J.P.R. Symonds, K.S.J. Reavell, J.S. Olfert, B.W. Campbell, S.J. Swift, Diesel soot
mass calculation in real-time with a differential mobility spectrometer, Journal of Aerosol
Science, 38 (2007) 52-68.
[72] B. Giechaskiel, Y. Drossinos, Theoretical investigation of volatile removal efficiency
of particle number measurement systems, in, SAE Technical Paper, 2010.
[73] S. Stevanovic, B. Miljevic, P. Madl, S. Clifford, Z. Ristovski, Characterisation of a
commercially available thermodenuder and diffusion dryer for ultrafine particle losses,
P a g e | 57
Investigation of the Chemical and Physical Basis of Oxidative Stress Generated by
Particulate Matter Using the Profluorescent Probe Technique, (2013) 126.
[74] J. Swanson, D. Kittelson, Evaluation of thermal denuder and catalytic stripper
methods for solid particle measurements, Journal of Aerosol Science, 41 (2010) 1113-
1122.
[75] Z. Zheng, K.C. Johnson, Z. Liu, T.D. Durbin, S. Hu, T. Huai, D.B. Kittelson, H.S.
Jung, Investigation of solid particle number measurement: Existence and nature of sub-
23nm particles under PMP methodology, Journal of Aerosol Science, 42 (2011) 883-897.
[76] I.A. Khalek, T. Bougher, Development of a solid exhaust particle number
measurement system using a catalytic stripper technology, in, SAE Technical Paper, 2011.
[77] L. Ntziachristos, S. Amanatidis, Z. Samaras, B. Giechaskiel, A. Bergmann, Use of a
catalytic stripper as an alternative to the original PMP measurement protocol, in, SAE
Technical Paper, 2013.
[78] R. Casati, V. Scheer, R. Vogt, T. Benter, Measurement of nucleation and soot mode
particle emission from a diesel passenger car in real world and laboratory in situ dilution,
Atmospheric Environment, 41 (2007) 2125-2135.
[79] T. RÖnkkÖ, A. Virtanen, J. Kannosto, J. Keskinen, M. Lappi, L. Pirjola, Nucleation
mode particles with a nonvolatile core in the exhaust of a heavy duty diesel vehicle,
Environ Sci Technol, 41 (2007) 6384-6389.
[80] M.M. Maricq, Monitoring Motor Vehicle PM Emissions: An Evaluation of Three
Portable Low-Cost Aerosol Instruments, Aerosol Science and Technology, 47 (2013) 564-
573.
[81] D.C. Quiros, S. Yoon, H.A. Dwyer, J.F. Collins, Y. Zhu, T. Huai, Measuring
particulate matter emissions during parked active diesel particulate filter regeneration of
heavy-duty diesel trucks, Journal of Aerosol Science, 73 (2014) 48-62.
[82] M.M. Maricq, R. E. Chase, D. H. Podsiadlik, R. Vogt, Vehicle exhaust particle size
distributions: a comparison of tailpipe and dilution tunnel measurements. , Society of
automotive engineers SAE Paper No. 1999-01-1461., (1999).
[83] Heinz Burtscher, W.A. Majewski, PM Measurement: In-Situ Methods, Dieselnet
Technology Guide, Ecopoint Inc, 01 (2012).
[84] H. Kaminski, T.A.J. Kuhlbusch, S. Rath, U. Götz, M. Sprenger, D. Wels, J. Polloczek,
V. Bachmann, N. Dziurowitz, H.J. Kiesling, A. Schwiegelshohn, C. Monz, D. Dahmann,
C. Asbach, Comparability of mobility particle sizers and diffusion chargers, Journal of
Aerosol Science, 57 (2013) 156-178.
[85] G.R. Johnson, Z. Ristovski, a.L. Morawska, Method for measuring the hygroscopic
behaviour of lower volatility fractions in an internally mixed aerosol, Journal of aerosol
science (2004) 443-455.
[86] K. Park, D. Dutcher, M. Emery, J. Pagels, H. Sakurai, J. Scheckman, S. Qian, M.R.
Stolzenburg, X. Wang, J. Yang, P.H. McMurry, Tandem Measurements of Aerosol
P a g e | 58
Properties—A Review of Mobility Techniques with Extensions, Aerosol Science and
Technology, 42 (2008) 801-816.
[87] N.K. Meyer, Z.D. Ristovski, Ternary nucleation as a mechanism for the production of
diesel nanoparticles: experimental analysis of the volatile and hygroscopic properties of
diesel exhaust using the volatilization and humifidication tandem differential mobility
analyzer, Environ Sci Technol, (2007) 7309-7314.
[88] S. Stevanovic, B. Miljevic, G.K. Eaglesham, S.E. Bottle, Z.D. Ristovski, K.E.
Fairfull‐Smith, The use of a nitroxide probe in DMSO to capture free radicals in particulate
pollution, European Journal of Organic Chemistry, 2012 (2012) 5908-5912.
[89] U. EPA, Emission standards refrence guide, http://www.epa.gov/oms/standards/.
[90] S.H. Park, J. Cha, H.J. Kim, C.S. Lee, Effect of early injection strategy on spray
atomization and emission reduction characteristics in bioethanol blended diesel fueled
engine, Energy, (2012).
[91] B. Mohan, W. Yang, S.k. Chou, Fuel injection strategies for performance
improvement and emissions reduction in compression ignition engines—A review,
Renewable and Sustainable Energy Reviews, 28 (2013) 664-676.
[92] W.A. Majewski, Diesel Oxidation Catalyst, DieselNet Technology Guide (2010).
[93] W.A. Majewski, Diesel Particulate Filters, DieselNet Technology Guide, (2011).
[94] J.D. Herner, S.H. Hu, W.H. Robertson, T. Huai, M.C.O. Chang, P. Rieger, A. Ayala,
Effect of Advanced Aftertreatment for PM and NO(x) Reduction on Heavy-Duty Diesel
Engine Ultrafine Particle Emissions, Environ Sci Technol, 45 (2011) 2413-2419.
[95] J.D. Herner, S. Hu, W.H. Robertson, T. Huai, J.F. Collins, H. Dwyer, A. Ayala, Effect
of advanced aftertreatment for PM and NOx control on heavy-duty diesel truck emissions,
Environmental Science and Technology, 43 (2009) 5928-5933.
[96] Z. Liu, Y. Ge, J. Tan, C. He, A.N. Shah, Y. Ding, L. Yu, W. Zhao, Impacts of
continuously regenerating trap and particle oxidation catalyst on the NO2 and particulate
matter emissions emitted from diesel engine, Journal of Environmental Sciences, 24 (2012)
624-631.
[97] D. Yao, D.M. Lou, P.Q. Tan, Z.Y. Hu, Q. Feng, Impact of after-treatment devices on
biodiesel engine particle number emission characteristics, in: Advanced Materials
Research, 2014, pp. 263-268.
[98] A. Vakkilainen, R. Lylykangas, Particle oxidation catalyst (POC) for diesel vehicles,
in, SAE Technical Paper, 2004.
[99] C.L. Myung, A. Ko, S. Park, Review on characterization of nano-particle emissions
and PM morphology from internal combustion engines: Part 1, International Journal of
Automotive Technology, 15 (2014) 203-218.
[100] W.A. Majewski, Particle Oxidation Catalysts, DieselNet Technology Guide
Copyright © Ecopoint Inc, (2014).
P a g e | 59
[101] A. Setiabudi, B.A.A.L. van Setten, M. Makkee, J.A. Moulijn, The influence of NOx
on soot oxidation rate: molten salt versus platinum, Applied Catalysis B: Environmental,
35 (2002) 159-166.
[102] M. Shrivastava, A. Nguyen, Z. Zheng, H.W. Wu, H.S. Jung, Kinetics of Soot
Oxidation by NO2, Environmental Science and Technology, 44 (2010) 4796-4801.
[103] S. Biswas, V. Verma, J.J. Schauer, F.R. Cassee, A.K. Cho, C. Sioutas, Oxidative
potential of semi-volatile and non volatile particulate matter (PM) from heavy-duty
vehicles retrofitted with emission control technologies, Environ Sci Technol, 43 (2009)
3905-3912.
[104] N.C. Surawski, B. Miljevic, B.A. Roberts, R.L. Modini, R. Situ, R.J. Brown, S.E.
Bottle, Z.D. Ristovski, Particle emissions, volatility, and toxicity from an ethanol
fumigated compression ignition engine, Environmental Science and Technology, 44 (2010)
229-235.
[105] S. Biswas, S. Hu, V. Verma, J.D. Herner, W.H. Robertson, A. Ayala, C. Sioutas,
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 (2008) 5622-5634.
[106] A.D. Bugarski, G.H. Schnakenberg, J.A. Hummer, E. Cauda, S.J. Janisko, L.D.
Patts, Effects of Diesel Exhaust Aftertreatment Devices on Concentrations and Size
Distribution of Aerosols in Underground Mine Air, Environ Sci Technol, 43 (2009) 6737-
6743.
[107] H. Zhao, Y. Ge, T. Zhang, J. Zhang, J. Tan, H. Zhang, Unregulated emissions from
diesel engine with particulate filter using Fe-based fuel borne catalyst, Journal of
Environmental Sciences, (2014).
[108] S. Hu, J.D. Herner, M. Shafer, W. Robertson, J.J. Schauer, H. Dwyer, J. Collins, T.
Huai, A. Ayala, Metals emitted from heavy-duty diesel vehicles equipped with advanced
PM and NOX emission controls, Atmospheric Environment, 43 (2009) 2950-2959.
[109] C. Beatrice, S. Di Iorio, C. Guido, Detailed characterization of particulate emissions
of an automotive catalyzed DPF using actual regeneration strategies, Experimental
Thermal and Fluid Science, (2012).
[110] A. Mamakos, G. Martini, U. Manfredi, Assessment of the legislated particle number
measurement procedure for a Euro 5 and a Euro 6 compliant diesel passenger cars under
regulated and unregulated conditions, Journal of Aerosol Science, 55 (2013) 31-47.
[111] D.M. Lou, Z.W. Zhu, P.Q. Tan, Z.Y. Hu, Characterization of the particle emissions
from diesel transit bus equipping diesel particulate filter with fuel burner, in: Advanced
Materials Research, 2014, pp. 1386-1393.
[112] A. Liati, P. Dimopoulos Eggenschwiler, E. Müller Gubler, D. Schreiber, M. Aguirre,
Investigation of diesel ash particulate matter: A scanning electron microscope and
transmission electron microscope study, Atmospheric Environment, 49 (2012) 391-402.
P a g e | 60
[113] B.B. Hansen, A.D. Jensen, P.A. Jensen, Performance of diesel particulate filter
catalysts in the presence of biodiesel ash species, Fuel, 106 (2013) 234-240.
[114] K.K. Shandilya, A. Kumar, Physical characterization of fine particulate matter inside
the public transit buses fueled by biodiesel in Toledo, Ohio, Journal of Hazardous
Materials, 190 (2011) 508-514.
[115] X. Shi, K. He, J. Zhang, Y. Ma, Y. Ge, J. Tan, A comparative study of particle size
distribution from two oxygenated fuels and diesel fuel, Frontiers of Environmental Science
and Engineering in China, 4 (2010) 30-34.
[116] E.G. Varuvel, N. Mrad, M. Tazerout, F. Aloui, Experimental analysis of biofuel as
an alternative fuel for diesel engines, Applied Energy, 94 (2012) 224-231.
[117] J. Xue, T.E. Grift, A.C. Hansen, Effect of biodiesel on engine performances and
emissions, Renewable and Sustainable Energy Reviews, 15 (2011) 1098-1116.
[118] R. Zhu, C.S. Cheung, Z. Huang, Particulate Emission Characteristics of a
Compression Ignition Engine Fueled with Diesel–DMC Blends, Aerosol Science and
Technology, 45 (2011) 137-147.
[119] M. Rahman, A. Pourkhesalian, M. Jahirul, S. Stevanovic, P. Pham, H. Wang, A.
Masri, R. Brown, Z. Ristovski, Particle emissions from biodiesels with different physical
properties and chemical composition, Fuel, (2014).
[120] G. Li, Dimethyl ether(DME): A new alternative fuel for diesel vehicle, in, 2011, pp.
1014-1018.
[121] L. Xinling, H. Zhen, Emission reduction potential of using gas-to-liquid and
dimethyl ether fuels on a turbocharged diesel engine, Science of the Total Environment,
407 (2009) 2234-2244.
[122] T. Grigoratos, G. Fontaras, M. Kalogirou, C. Samara, Z. Samaras, K. Rose, Effect of
rapeseed methylester blending on diesel passenger car emissions - Part 2: Unregulated
emissions and oxidation activity, Fuel, 128 (2014) 260-267.
[123] A.M. Ashraful, H.H. Masjuki, M.A. Kalam, I.M. Rizwanul Fattah, S. Imtenan, S.A.
Shahir, H.M. Mobarak, Production and comparison of fuel properties, engine performance,
and emission characteristics of biodiesel from various non-edible vegetable oils: A review,
Energy Conversion and Management, 80 (2014) 202-228.
[124] The World Fact Book, (2013).
[125] S. Shafiee, E. Topal, When will fossil fuel reserves be diminished?, Energy Policy,
37 (2009) 181-189.
[126] D. Tolmac, S. Prulovic, M. Lambic, L. Radovanovic, J. Tolmac, Global trends on
production and utilization of biodiesel, Energy Sources, Part B: Economics, Planning and
Policy, 9 (2014) 130-139.
[127] I.B. Banković-Ilić, O.S. Stamenković, V.B. Veljković, Biodiesel production from
non-edible plant oils, Renewable and Sustainable Energy Reviews, 16 (2012) 3621-3647.
P a g e | 61
[128] B.Z. Bello, E. Nwokoagbara, M. Wang, Comparative Techno-economic Analysis of
Biodiesel Production from Microalgae via Transesterification Methods, in, 2012, pp. 133-
136.
[129] P. Nair, B. Singh, S.N. Upadhyay, Y.C. Sharma, Synthesis of biodiesel from low
FFA waste frying oil using calcium oxide derived from Mereterix mereterix as a
heterogeneous catalyst, Journal of Cleaner Production, 29-30 (2012) 82-90.
[130] M.M. Gui, K.T. Lee, S. Bhatia, Feasibility of edible oil vs. non-edible oil vs. waste
edible oil as biodiesel feedstock, Energy, 33 (2008) 1646-1653.
[131] I.B. Banković-Ilić, I.J. Stojković, O.S. Stamenković, V.B. Veljkovic, Y.-T. Hung,
Waste animal fats as feedstocks for biodiesel production, Renewable and Sustainable
Energy Reviews, 32 (2014) 238-254.
[132] B. Singh, A. Guldhe, I. Rawat, F. Bux, Towards a sustainable approach for
development of biodiesel from plant and microalgae, Renewable and Sustainable Energy
Reviews, 29 (2014) 216-245.
[133] A.B. Chhetri, M.S. Tango, S.M. Budge, K.C. Watts, M.R. Islam, Non-edible plant
oils as new sources for biodiesel production, International journal of molecular sciences, 9
(2008) 169-180.
[134] A. Ahmad, N. Yasin, C. Derek, J. Lim, Microalgae as a sustainable energy source for
biodiesel production: a review, Renewable and Sustainable Energy Reviews, 15 (2011)
584-593.
[135] D. Hernández, L. Astudillo, M. Gutiérrez, C. Tenreiro, C. Retamal, C. Rojas,
Biodiesel production from an industrial residue: Alperujo, Industrial Crops and Products,
52 (2014) 495-498.
[136] M.T. Pozzi, A.J. Filippín, C. Matías, A. Hammann, Possibility of use of the olive oil
mill wastewater in fertilization of agricultural lands, Informacion Tecnologica, 21 (2010)
117-123.
[137] R. Vera, C. Aguilar, R. Lira, P. Toro, L. Barrales, I. Peña, F. Squella, P. Pérez, J.
Quenaya, H. Yutronic, I. Briones, Feeding dry olive cake modifies subcutaneous fat
composition in lambs, noting cake resistance to degradation and peroxidation, Chilean
Journal of Agricultural Research, 69 (2009) 548-559.
[138] F. Manzano-Agugliaro, M.J. Sanchez-Muros, F.G. Barroso, A. Martínez-Sánchez, S.
Rojo, C. Pérez-Bañón, Insects for biodiesel production, Renewable and Sustainable Energy
Reviews, 16 (2012) 3744-3753.
[139] M. Lapuerta, O. Armas, J. Rodríguez-Fernández, Effect of biodiesel fuels on diesel
engine emissions, Progress in Energy and Combustion Science, 34 (2008) 198-223.
[140] L.G. Lackey, S.E. Paulson, Influence of feedstock: Air pollution and climate-related
emissions from a diesel generator operating on soybean, canola, and yellow grease
biodiesel, Energy and Fuels, 26 (2012) 686-700.
P a g e | 62
[141] T. Tzamkiozis, L. Ntziachristos, A. Mamakos, G. Fontaras, Z. Samaras,
Aerodynamic and Mobility Size Distribution Measurements to Reveal Biodiesel Effects on
Diesel Exhaust Aerosol, Aerosol Science and Technology, 45 (2011) 587-595.
[142] S. Chuepeng, H. Xu, A. Tsolakis, M. Wyszynski, P. Price, Particulate Matter size
distribution in the exhaust gas of a modern diesel Engine fuelled with a biodiesel blend,
Biomass and Bioenergy, (2011).
[143] R. Ballesteros, J.J. Hernández, L.L. Lyons, An experimental study of the influence of
biofuel origin on particle-associated PAH emissions, Atmospheric Environment, 44 (2010)
930-938.
[144] T. Howes, The EU’s New Renewable Energy Directive (2009/28/EC), The New
Climate Policies of the European Union: Internal Legislation and Climate Diplomacy,
(2010) 117.
[145] K. Varatharajan, M. Cheralathan, Influence of fuel properties and composition on
NOx emissions from biodiesel powered diesel engines: A review, Renewable and
Sustainable Energy Reviews, 16 (2012) 3702-3710.
[146] C.C. Barrios, A. Domínguez-Sáez, C. Martín, P. Álvarez, Effects of animal fat based
biodiesel on a TDI diesel engine performance, combustion characteristics and particle
number and size distribution emissions, Fuel, 117 (2014) 618-623.
[147] E.G. Giakoumis, C.D. Rakopoulos, A.M. Dimaratos, D.C. Rakopoulos, Exhaust
emissions of diesel engines operating under transient conditions with biodiesel fuel blends,
Progress in Energy and Combustion Science, 38 (2012) 691-715.
[148] G. Karavalakis, E. Bakeas, S. Stournas, Influence of oxidized biodiesel blends on
regulated and unregulated emissions from a diesel passenger car, Environmental Science
and Technology, 44 (2010) 5306-5312.
[149] M. Lapuerta, O. Armas, J.J. Hernández, A. Tsolakis, Potential for reducing
emissions in a diesel engine by fuelling with conventional biodiesel and Fischer-Tropsch
diesel, Fuel, 89 (2010) 3106-3113.
[150] E. Bakeas, G. Karavalakis, S. Stournas, Biodiesel emissions profile in modern diesel
vehicles. Part 1: Effect of biodiesel origin on the criteria emissions, Science of the Total
Environment, 409 (2011) 1670-1676.
[151] P. Benjumea, J.R. Agudelo, A.F. Agudelo, Effect of the degree of unsaturation of
biodiesel fuels on engine performance, combustion characteristics, and emissions, Energy
and Fuels, 25 (2011) 77-85.
[152] EPA, A comprehensive analysis of biodiesel impacts on exhaust emissions, 420
(2002).
[153] S.K. Hoekman, C. Robbins, Review of the effects of biodiesel on NOx emissions,
Fuel Processing Technology, 96 (2012) 237-249.
P a g e | 63
[154] M. Lapuerta, O. Armas, J. Rodríguez-Fernández, Effect of the Degree of
Unsaturation of Biodiesel Fuels on NOx and Particulate Emissions, SAE Int. J. Fuels
Lubr., 1 (2008) 1150-1158.
[155] J. Szybist, J. Simmons, M. Druckenmiller, K. Al-Qurashi, A. Boehman, A. Scaroni,
Potential methods for NOx reduction from biodiesel, in, SAE Technical Paper, 2003.
[156] S.K. Hoekman, A. Broch, C. Robbins, E. Ceniceros, M. Natarajan, Review of
biodiesel composition, properties, and specifications, Renewable and Sustainable Energy
Reviews, 16 (2012) 143-169.
[157] R.L. McCormick, J.R. Alvarez, M.S. Graboski, K.S. Tyson, K. Vertin, Fuel additive
and blending approaches to reducing NOx emissions from biodiesel, in, SAE Technical
Paper, 2002.
[158] J. Pullen, K. Saeed, Factors affecting biodiesel engine performance and exhaust
emissions - Part I: Review, Energy, 72 (2014) 1-16.
[159] G. Fontaras, M. Kalogirou, T. Grigoratos, P. Pistikopoulos, Z. Samaras, K. Rose,
Effect of rapeseed methylester blending on diesel passenger car emissions - Part 1:
Regulated pollutants, NO/NOx ratio and particulate emissions, Fuel, 121 (2014) 260-270.
[160] D.M. Lou, T.Y. Shen, Y. Zhou, Z.Y. Hu, P.Q. Tan, Q. Qiang, Characteristics of
particulate matter about emissions in a heavy-dutydiesel engine with biodiesel blends, in:
Advanced Materials Research, 2014, pp. 802-807.
[161] J. Pullen, K. Saeed, Factors affecting biodiesel engine performance and exhaust
emissions – Part II: Experimental study, Energy, 72 (2014) 17-34.
[162] D. Buono, A. Senatore, M.V. Prati, Particulate filter behaviour of a Diesel engine
fueled with biodiesel, Applied Thermal Engineering, (2011).
[163] N. Lamharess, C.N. Millet, L. Starck, E. Jeudy, J. Lavy, P. Da Costa, Catalysed
diesel particulate filter: Study of the reactivity of soot arising from biodiesel combustion,
Catalysis Today, (2011).
[164] P. Barbier, B. Fasolo, R. Faucon, J. Vandenplas, A Study of the Effects of 30%
Biodiesel Fuel on Soot Loading and Regeneration of a Catalytic DPF, SAE Technical
Paper, (2007).
[165] J. Song, M. Alam, A.L. Boehman, K. Miller, Characterization of diesel and biodiesel
soot, in: ACS Division of Fuel Chemistry, Philadelphia, PA, 2004, pp. 767-769.
[166] J. Song, M. Alam, A.L. Boehman, U. Kim, Examination of the oxidation behavior of
biodiesel soot, Combustion and Flame, 146 (2006) 589-604.
[167] W. Merchan-Merchan, S.G. Sanmiguel, S. McCollam, Analysis of soot particles
derived from biodiesels and diesel fuel air-flames, Fuel, (2012).
[168] A.G. Sappok, V.W. Wong, Impact of Biodiesel on Ash Emissions and Lubricant
Properties Affecting Fuel Economy and Engine Wear: Comparison with Conventional
Diesel Fuel, SAE Int. J. Fuels Lubr., 1 (2008) 731-747.
P a g e | 64
[169] S. Cordiner, V. Mulone, M. Nobile, V. Rocco, Diesel Engine Biofuelling: Effects of
Ash on the Behavior of the Diesel Particulate Filter, in, SAE International, 2013.
[170] A. Puzun, S. Wanchen, L. Guoliang, T. Manzhi, L. Chunjie, C. Shibao,
Characteristics of Particle Size Distributions About Emissions in A Common-rail Diesel
Engine with Biodiesel Blends, Procedia Environmental Sciences, 11, Part C (2011) 1371-
1378.
[171] A.K. Agarwal, T. Gupta, A. Kothari, Particulate emissions from biodiesel vs diesel
fuelled compression ignition engine, Renewable and Sustainable Energy Reviews, 15
(2011) 3278-3300.
[172] P.Q. Tan, S.S. Ruan, Z.Y. Hu, D.M. Lou, H. Li, Particle number emissions from a
light-duty diesel engine with biodiesel fuels under transient-state operating conditions,
Applied Energy, 113 (2014) 22-31.
[173] B.C. Fisher, A.J. Marchese, J. Volckens, T. Lee, J.L. Collett, Measurement of
Gaseous and Particulate Emissionsfrom Algae-Based Fatty Acid Methyl Esters, SAE Int. J.
Fuels Lubr., 03 (2010) 292.
[174] A.T. S.S. Gilla, , , J.M. Herrerosb, A.P.E. York, Diesel emissions improvements
through the use of biodiesel or oxygenated blending components, Fuel, (2011).
[175] H. Jung, D.B. Kittelson, M.R. Zachariah, Characteristics of SME biodiesel-fueled
diesel particle emissions and the kinetics of oxidation, Environmental Science and
Technology, 40 (2006) 4949-4956.
[176] L.-H. Young, J. Yi, M.-T. Cheng, J.-H. Lu, H.-H. Yang, Y.I. Tsai, L.-C. Wang, C.-B.
Chen, J.-S. Lai, Effects of biodiesel, engine load and diesel particulate filter on nonvolatile
particle number size distributions in heavy-duty diesel engineexhaust, Journal of
Hazardous Materials, (2011).
[177] A.N. Shah, G. Yun-shan, F.H. Shah, H.U. Mughal, Z.U. Rahman, A. Naveed, Effect
of biodiesel on particulate numbers and composition emitted from turbocharged diesel
engine, International Journal of Environmental Science and Technology, 11 (2014) 385-
394.
[178] T. Lu, C.S. Cheung, Z. Huang, Influence of waste cooking oil biodiesel on the
particulate emissions and particle volatility of a DI diesel engine, Aerosol and Air Quality
Research, 13 (2013) 243-254.
[179] G. Karavalakis, V. Boutsika, S. Stournas, E. Bakeas, Biodiesel emissions profile in
modern diesel vehicles. Part 2: Effect of biodiesel origin on carbonyl, PAH, nitro-PAH and
oxy-PAH emissions, Science of the Total Environment, 409 (2011) 738-747.
[180] E. Bakeas, G. Karavalakis, G. Fontaras, S. Stournas, An experimental study on the
impact of biodiesel origin on the regulated and PAH emissions from a Euro 4 light-duty
vehicle, Fuel, doi: 10.1016/j.fuel.2011.1005.1018.
[181] J.G. Hemmingsen, P. Møller, J.K. Nøjgaard, M. Roursgaard, S. Loft, Oxidative
stress, genotoxicity, and vascular cell adhesion molecule expression in cells exposed to
P a g e | 65
particulate matter from combustion of conventional diesel and methyl ester biodiesel
blends, Environmental Science and Technology, 45 (2011) 8545-8551.
[182] J.-H. Ng, H.K. Ng, S. Gan, Development of emissions predictor equations for a light-
duty diesel engine using biodiesel fuel properties, Fuel, (2012).
[183] J.W. Goodrom, Volatility and boiling points of biodiesel from vegetable oils and
tallow, Biomass and Bioenergy, 22 (2002) 205-211.
[184] C.E. Ejim, B.A. Fleck, A. Amirfazli, Analytical study for atomization of biodiesels
and their blends in a typical injector: Surface tension and viscosity effects, Fuel, 86 (2007)
1534-1544.
[185] A. Schönborn, N. Ladommatos, J. Williams, R. Allan, J. Rogerson, The influence of
molecular structure of fatty acid monoalkyl esters on diesel combustion, Combustion and
Flame, 156 (2009) 1396–1412.
[186] A.B. Chhetri, K.C. Watts, Surface tensions of petro-diesel, canola, jatropha and
soapnut biodiesel fuels at elevated temperatures and pressures, Fuel, 104 (2013) 704-710.
[187] M. Lapuerta, J. Rodríguez-Fernández, O. Armas, Correlation for the estimation of
the density of fatty acid esters fuels and its implications. A proposed Biodiesel Cetane
Index, Chemistry and Physics of Lipids, 163 (2010) 720-727.
[188] B. Moser, Comparative Oxidative Stability of Fatty Acid Alkyl Esters by
Accelerated Methods, Journal of the American Oil Chemists' Society, 86 (2009) 699-706.
[189] A. Demirbas, M. Fatih Demirbas, Importance of algae oil as a source of biodiesel,
Energy Conversion and Management, 52 (2011) 163-170.
[190] Q. Hu, M. Sommerfeld, E. Jarvis, M. Ghirardi, M. Posewitz, M. Seibert, A. Darzins,
Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and
advances, The Plant Journal, 54 (2008) 621-639.
[191] A.H. Demirbas, Inexpensive oil and fats feedstocks for production of biodiesel,
Energy Education Science and Technology Part A: Energy Science and Research, 23
(2009) 1-13.
[192] Y. Chisti, Biodiesel from microalgae, Biotechnology Advances, 25 (2007) 294-306.
[193] R.L. McCormick, M.S. Graboski, T.L. Alleman, A.M. Herring, K.S. Tyson, Impact
of biodiesel source material and chemical structure on emissions of criteria pollutants from
a heavy-duty engine, Environmental Science and Technology, 35 (2001) 1742-1747.
[194] M.D. Redel-Macías, S. Pinzi, M.F. Ruz, A.J. Cubero-Atienza, M.P. Dorado,
Biodiesel from saturated and monounsaturated fatty acid methyl esters and their influence
over noise and air pollution, Fuel, 97 (2012) 751-756.
[195] O. Schröder, A. Munack, J. Schaak, C. Pabst, L. Schmidt, J. Bünger, J. Krahl,
Emissions from diesel engines using fatty acid methyl esters from different vegetable oils
as blends and pure fuel, Journal of Physics: Conference Series, 364 (2012).
P a g e | 66
[196] M.J. Ramos, C.M. Fernández, A. Casas, L. Rodríguez, Á. Pérez, Influence of fatty
acid composition of raw materials on biodiesel properties, Bioresource Technology, 100
(2009) 261-268.
[197] S.M. Sarathy, S. Gaïl, S.A. Syed, M.J. Thomson, P. Dagaut, A comparison of
saturated and unsaturated C4 fatty acid methyl esters in an opposed flow diffusion flame
and a jet stirred reactor, in, 2007, pp. 1015-1022.
[198] M. Salamanca, F. Mondragón, J.R. Agudelo, P. Benjumea, A. Santamaría, Variations
in the chemical composition and morphology of soot induced by the unsaturation degree of
biodiesel and a biodiesel blend, Combustion and Flame, 159 (2012) 1100-1108.
[199] L. Fan, Y. Zhang, L. Cheng, L. Zhang, D. Tang, H. Chen, Optimization of carbon
dioxide fixation by chlorella vulgaris cultivated in a membrane-photobioreactor, Chemical
Engineering and Technology, 30 (2007) 1094-1099.
[200] S. Pinzi, P. Rounce, J.M. Herreros, A. Tsolakis, M. Pilar Dorado, The effect of
biodiesel fatty acid composition on combustion and diesel engine exhaust emissions, Fuel,
104 (2013) 170-182.
[201] N. Surawskia, B. Miljevica, G.A. Ayokoc, S. Elbagirc, S. Stevanovica, K.E. Fairfull-
Smithd, S.E. Bottled, Z.D. Ristovskia, A physico-chemical characterisation of particulate
emissions from a compression ignition engine: the influence of biodiesel feedstock. ,
Submitted to Environmental Sci. & Tech., (2011).
[202] G. Fontaras, M. Kousoulidou, G. Karavalakis, T. Tzamkiozis, P. Pistikopoulos, L.
Ntziachristos, E. Bakeas, S. Stournas, Z. Samaras, Effects of low concentration biodiesel
blend application on modern passenger cars. Part 1: feedstock impact on regulated
pollutants, fuel consumption and particle emissions, Environ Pollut, 158 (2010) 1451-
1460.
[203] S.Y. Chiu, M.T. Tsai, C.Y. Kao, S.C. Ong, C.S. Lin, The air-lift photobioreactors
with flow patterning for high-density cultures of microalgae and carbon dioxide removal,
Engineering in Life Sciences, 9 (2009) 254-260.
[204] A. Rinanti, E. Kardena, D.I. Astuti, K. Dewi, Improvement of carbon dioxide
removal through artificial light intensity and temperature by constructed green microalgae
consortium in a vertical bubble column photobioreactor, Malaysian Journal of
Microbiology, 10 (2014) 29-37.
[205] N. Abdel-Raouf, A.A. Al-Homaidan, I.B.M. Ibraheem, Microalgae and wastewater
treatment, Saudi Journal of Biological Sciences, 19 (2012) 257-275.
[206] S.R. Subashchandrabose, B. Ramakrishnan, M. Megharaj, K. Venkateswarlu, R.
Naidu, Mixotrophic cyanobacteria and microalgae as distinctive biological agents for
organic pollutant degradation, Environment International, 51 (2013) 59-72.
[207] J.P. Maity, J. Bundschuh, C.-Y. Chen, P. Bhattacharya, Microalgae for third
generation biofuel production, mitigation of greenhouse gas emissions and wastewater
treatment: Present and future perspectives – A mini review, Energy, (2014).
P a g e | 67
[208] H. Pereira, L. Barreira, L. Custódio, S. Alrokayan, F. Mouffouk, J. Varela, K.M.
Abu-Salah, R. Ben-Hamadou, Isolation and fatty acid profile of selected microalgae strains
from the Red Sea for biofuel production, Energies, 6 (2013) 2773-2783.
[209] M. Veillette, A. Giroir-Fendler, N. Faucheux, M. Heitz, High-purity biodiesel
production from microalgae and added-value lipid extraction: a new process, Applied
Microbiology and Biotechnology, (2014).
[210] G. Stansell, V. Gray, S. Sym, Microalgal fatty acid composition: implications for
biodiesel quality, J Appl Phycol, 24 (2012) 791-801.
[211] V. Makarevičiene, S. Lebedevas, P. Rapalis, M. Gumbyte, V. Skorupskaite, J.
Žaglinskis, Performance and emission characteristics of diesel fuel containing microalgae
oil methyl esters, Fuel, 120 (2014) 233-239.
[212] J.S. Patel, N. Kumar, A. Deep, A. Sharma, D. Gupta, Evaluation of Emission
Characteristics of Blend of Algae Oil Methyl Ester with Diesel in a Medium Capacity
Diesel Engine, SAE Technical Paper, 2014-01-1378 (2014).
[213] C.A. Rinaldini, E. Mattarelli, M. Magri, M. Beraldi, Experimental Investigation on
Biodiesel from Microalgae as Fuel for Diesel Engines, SAE Technical Paper, 2014-01-
1386 (2014).
[214] G. Tüccar, T. Özgür, K. Aydın, Effect of diesel–microalgae biodiesel–butanol blends
on performance and emissions of diesel engine, Fuel, 132 (2014) 47-52.
[215] A. Schönborn, N. Ladommatos, R. Allan, J. Williams, J. Rogerson, Effect of the
Molecular Structure of Individual Fatty Acid Alcohol Esters (Biodiesel) on the Formation
of Nox and Particulate Matter in the Diesel Combustion Process, SAE Int. J. Fuels Lubr., 1
(2008) 849-872.
P a g e | 68
Chapter 3: Influence of different alternative fuels on particle
emissions from a turbo charged commonrail diesel engine
Publication: Published in Procedia Engineering, 56 (2013), Pages 381-386.
Contributor Statement of contribution
Md Mostafizur
Rahman Contributed design of the experimental program, conducted the
emissions measurements, performed data analysis, and wrote the
manuscript.
Signature
Date
Svetlana Stevanovic Assisted to the overall study design, data analysis, and reviewed
the manuscript.
Richard J. Brown Assisted to the overall study design, reviewed the manuscript.
Zoran D. Ristovski Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all Co-authors confirming their
certifying authorship.
Name Signature Date
Professor Zoran Ristovski
P a g e | 69
Title: Influence of different alternative fuels on particle emissions from a
turbo charged commonrail diesel engine M. M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski
International Laboratory of Air Quality and Health (ILAQH),
Biofuel Engine Research Facilities (BERF),
Queensland University of Technology (QUT), Brisbane, Queensland, Australia 4001
Abstract:
Influence of alternative fuels on diesel engine exhaust particle emission was investigated
using an ultra-low sulfur diesel fuel as a baseline fuel where two biodiesels (canola &
tallow), Fischer–Tropsch and bio ethanol were used as alternative fuel. Both the biodiesels
coming from canola and tallow feedstocks, as well as F-T were used as 100% to run the
engine where up to 40% energy substitution by ethanol was achieved without any sacrifice
of engine power output. It was found that up to 30% ethanol substitution reduced both
particulate mass (PM) and particle number (PN) emission consistently for all load settings
at 2000 rpm, highest 59% reduction in PM and 70% reduction in PN observed at 100%
load. As previously suggested the possible mechanism for the observed reduction is the
oxidation of particulate matter by OH radicals which are in excess with ethanol fumigation.
For 40% ethanol substitution some inconsistency was observed for PM emission at
different loads but consistent reduction was found for PN. Condensation of
unburned/partially burned hydrocarbons that later condense on existing soot might be
responsible for this, as the maximum increase of PM was observed at quarter load where
low in cylinder temperature favour to nucleation of unburned hydrocarbons. PM emission
was also reduced in case of using 100% FT, and 100% biodiesel and the highest 90%
reduction in PM was observed for biodiesel at 100% load with almost no difference
between the two biodiesels itself. On the other side a considerable difference was observed
between canola and tallow biodiesel in case of PN emission. Canola biodiesel increased
PN, due to the presence of the nucleation mode, for almost an order of magnitude for all
load and speed settings where no such increase was observed for tallow biodiesel.
3.1 Introduction
Compression Ignition (CI) engine is in the pace of increasing popularity due to its higher
thermal efficiency. It powers much of our land and sea transport, provides electrical power,
and is used in farming, construction and industrial activities. Despite its significant
P a g e | 70
advantages over Spark Ignition (SI) engines the tail pipe emissions from CI engines,
especially particulate matter (PM) and NOx, are still a matter of great concern. Particulate
matter emitted by diesel engine affects the Earth's temperature and climate by altering the
radiative properties of the atmosphere [1]. All though Particles emitted from diesel engine
contribute to the global climate both by direct heating and indirect cooling, the heating
effect is dominant. So it is really important for climate change mitigation to reduce the
diesel engine particulate matter emissions. Even, short atmospheric lifespan makes black
carbon abatement one of the most attractive means to make a significant near-term impact
on global warming.
In addition to climate change, chronic exposure of diesel exhaust particles (DEP) may lead
to exacerbation of pulmonary diseases such as asthma and bronchitis as well as lung
cancer. A few studies [2-4] have also described negative impacts of DEPs on reproductive
systems i.e. liver functions [3] and brain activity [4]. A recent epidemiological study on
underground miners reported an increased risk of lung cancer mortality associated with
DPM exposure[5]. By considering the health risk associated with DPM, International
Agency for Research on Cancer (IARC), which is part of the World Health Organisation
(WHO), classified diesel engine exhaust as carcinogenic to humans. This adverse health
effect of DPM is related to both the physical properties and chemical composition of
particles. Physical properties of DPM that influence respiratory health include particle
mass, number and size distribution, surface area and mixing status of particles [2]. As for
example deposition of particles in different parts of the lung depends on their size, the
smaller the particles the higher the deposition efficiency. Even small particle can penetrate
deep into the lung. Furthermore, Smaller the particle, greater the chance of staying longer
time in the atmosphere, so smaller particles have a higher probability that they will be
inhaled and deposited in the respiratory tract and in the alveolar region. Due to the
superiority of particle number over particle mass in determining its health and climate
effect, European Union (EU) has already introduced particle number based emission
standards for euro-v and euro-vi engines.
Use of after treatment technology is one way of reducing diesel exhaust emissions [6]
where alternative fuels can be another potential way [7]. Among different types of
alternative fuels biodiesel and synthetic diesel fuel, such as Fischer–Tropsch (F–T), are
considered to be the most promising options for CI engines as they can be used in CI
P a g e | 71
engines without engine modification [8]. Ethanol, as an alternative fuel, also has the
potential to be used in CI engines [9], but its very low cetane number and poor solubility in
diesel, especially at low temperature, is key barrier on its way of implementation.
Fumigation of ethanol into the intake manifold of the engine where vaporised ethanol
mixes with incoming air can resolve the issue of poor solubility when blending ethanol
with diesel. In this study, a six cylinder 6 litre turbocharged heavy duty Cummins diesel
engine was used to investigate the effect of afore mentioned alternative fuels on diesel
emission with a special emphasis on particle emission.
3.2 Experimental Methods PM and NOx emission measurement was performed from the exhaust of a 6 cylinder,
turbocharged-after cooled, common rail diesel engine. Specification of the engine has
shown in Table 3-1. Engine was coupled to an ECU controlled hydraulic dynamometer for
adjusting the engine load and speed. An ultra-low sulfur diesel fuel was used as a baseline
fuel to run the engine where two biodiesel (canola & tallow), a synthetic diesel F-T
(Fischer–Tropsch) and bio ethanol were used as alternative fuel. Both the biodiesels
coming from canola and tallow feedstocks, as well as F-T were used as 100% basis to run
the engine where up to 40% energy substitution by ethanol was achieved without any
sacrifice of engine power output. Energy substitution by ethanol was accomplished by
fumigating the ethanol into engine intake air.
Model Cummins ISBe220 31
Cylinders 6 in-line
Capacity (L) 5.9
Bore × Stroke (mm) 102 × 120
Maximum power (kW/rpm)
162/2500
Maximum torque (Nm/rpm)
820/1500
Compression ratio 17.3
Aspiration Turbocharged & after cooled
Fuel Injection Common rail
Emissions certification Euro III
Table 3-1: Engine specification
Figure 3-1: Schematic diagram of experimental set up
P a g e | 72
Figure 3-1 displays the experimental setup used to sample exhaust from diesel engine
exhaust pipe. An ejector diluter made by Dekati was used to dilute the raw exhaust from
the engine exhaust pipe, where raw exhaust is mixed with particle free compressed air. The
purpose of the dilution is to bring down the temperature as well as the concentration of
gases and PM within the measuring range of the instrument. A HEPA filter was used to
provide particle free compressed air for the diluter. Diluted exhaust was then sent to
different gaseous and particle measuring instrument for measurement. A CAI 600 series
CO2 analyser was used to measure the CO2, and CO concentration directly from the raw
exhaust. A second CO2 meter (SABLE, CA-10) was used to record the CO2 from the
diluted exhaust. Dilution ratio was calculated from two CO2 measurements by using the
following formula.
𝐷𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑖𝑜(𝐷𝑅) =𝐶𝑂2 (𝑅𝑎𝑤) − 𝐶𝑂2(𝐵𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑, 𝐶𝐴𝐼)
𝐶𝑂2 (𝐷𝑖𝑙𝑢𝑡𝑒𝑑) − 𝐶𝑂2(𝐵𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑, 𝑆𝐴𝐵𝐿𝐸)
A CAI 600 series CLD NOx analyser was used to measure the NOx and NO2 from diluted
exhaust. PM2.5 emissions were measured by a TSI DustTrak (Model 8530). DustTrak
readings were converted into a gravimetric measurement by using the tapered element
oscillating microbalance to DustTrak correlation for diesel particles published by Jamriska
et al. [10]. Particle number and size distribution was measured by scanning mobility
particle sizer (SMPS) consists of TSI 3080 classifier and TSI 3025 butanol base
condensation particle counter (CPC).
3.3 Results and Discussions
3.3.1 PM 2.5 emission from different alternative fuels
Figure 3-2 shows the brake specific PM2.5 emission at engine speed 2000 rpm. For 25%
and 100% load, PM reduced consistently with the increase of ethanol percentage and
maximum 59% reduction was observed for 30% ethanol substitution at 100% Load. For
40% ethanol substitution PM increased slightly at 100% load which was well below the
PM of neat diesel but around 44% increase was observed at 25% load resulted in highest
PM emission among all fuels and engine load settings. On the other hand, no such
reduction in PM was observed due to ethanol substitution for 50% and & 75% load. The
most significant reduction in PM2.5 was achieved when using biodiesel and this trend was
consistent regardless of the load and speed settings of the engine. Highest 93% reduction
P a g e | 73
was achieved for 100% canola biodiesel while it was 91% for tallow biodiesel. A
considerable reduction in PM is also observed for synthetic diesel which was higher than
both of the biodiesels but lower than neat diesel and all ethanol substitutions.
D100 E10 E20 E30 E40 C100 T100 FT 1000.000
0.014
0.028
0.042
0.056
0.070
0.084
0.098
PM
(g/k
W-h
r)
Fuel type
25% load
D100 E10 E20 E30 E40 C100 T100 FT 1000.000
0.014
0.028
0.042
0.056
0.070
0.084
0.098
PM
(g/k
W-h
r)
Fuet type
50% load
Specific PM2.5 Emissions for different alternative fuels
D100 E10 E20 E30 E40 C100 T100 FT 1000.000
0.014
0.028
0.042
0.056
0.070
0.084
0.098
PM
(g/k
W-h
r)
Fuel type
75% load
D100 E10 E20 E30 E40 C100 T100 FT 1000.000
0.014
0.028
0.042
0.056
0.070
0.084
0.098
PM
(g/k
W-h
r)
Fuel type
100% load
Figure 3-2: Brake specific PM2.5 emissions for different alternative fuels at 2000 rpm
engine speed
3.3.2 Particle number and size distribution form different alternative fuels
The reduction in PM due to alternative fuel is further revealed in particle number and size
distribution as shown Figure 3-3 to Figure 3-5. With the increase of ethanol substitution,
brake specific particle number concentration decreased consistently at 25%, 50%, 75% and
100% load, and highest reduction happened for 40% ethanol substitution at 100% load. For
25% load, total particle number concentration at 10% ethanol substitution was higher than
neat diesel and 30 nm increase of particle median diameter was found for 40 % ethanol
substitution. This increase of particle median diameter indicates why highest PM2.5
emission was observed for 40% ethanol substitution at 25% load. Tallow biodiesel
decreased the total particle number concentration with the reduction of 15 nm median
diameter. For canola biodiesel, accumulation mode particles reduced but the presence of 20
nm nucleation mode particles are constantly observed during all load and speed settings as
shown in Figure 3.4 separately. The presence of nucleation mode particle in case of canola
biodiesel increased specific particle number emission almost by an order than neat diesel.
Particle size distribution for synthetic diesel fuel was found almost similar to that of fossil
P a g e | 74
diesel with slight reduction in total number concentration at 100%, 75% and 50% load,
while a small increase in nanoparticle emission was observed at 25% load.
Figure 3-3: Brake specific particle number
emissions for different alternative fuels at 2000
rpm engine speed
100
4.0x107
8.0x107
1.2x108
1.6x108
2.0x108
2.4x108
dN
/dlo
gd
p(c
m-3)
Particle diameter(nm)
100% load
75% load
50% load
25% load
Particle size distribution for Canola biodiesel
Figure 3-4: Particle size
distribution for canola biodiesel
100
6.40x106
1.28x107
1.92x107
2.56x107
3.20x107
3.84x107
4.48x107
Diesel
E10
E20
E30
E40
Tallow
FT
dN
/d lo
gdp(c
m-3
)
Particle diamter (nm)
100% load
100
6.40x106
1.28x107
1.92x107
2.56x107
3.20x107
3.84x107
4.48x107
Diesel
E10D90
E20D80
E30D70
E40D60
Tallow
FT
dN
/d lo
gdp
(cm
-3)
Particle diamter (nm)
75% load
100
4.20x106
8.40x106
1.26x107
1.68x107
2.10x107
2.52x107
2.94x107
Diesel
E10
E20
E30
E40
FT
dN
/d lo
gdp
(cm
-3)
Particle diamter (nm)
50% load
5.60x106
1.12x107
1.68x107
2.24x107
2.80x107
3.36x107
3.92x107
Diesel
E10
E20
E30
E40
FT
dN
/d lo
gdp
(cm
-3)
Particle diamter (nm)
25% load
Particle size distribution for different alternative fuels
Figure 3-5: Particle size distribution for different alternative fuels at 2000 rpm engine
speed
The presence of fuel bound oxygen in the ethanol was the driving force behind the
reduction of both PM and PN due to ethanol substitution. As previously suggested [11] the
0
2E+14
4E+14
6E+14
8E+14
Fullload
3/4Load
1/2load
1/4load
Par
ticl
e N
um
ber
(#/k
W-h
r)
Brake specific particle number emission for different fuel
DieselE10D90E20D80E30D70E40D60Canola
P a g e | 75
possible mechanism for the observed reduction is the oxidation of particulate matter by OH
radicals which are in excess with ethanol fumigation. Higher ratio of hydrogen to carbon,
higher volatility and absence of aromatics and sulfur in ethanol also favoured suppression
of in cylinder PM formation. For 40% ethanol substitution some inconsistency was
observed for PM emission at different loads but consistent reduction was found for PN.
Condensation of unburned/partially burned hydrocarbons that later condense on existing
soot might be responsible for this, as the maximum increase of PM was observed at quarter
load where low in cylinder temperature is favourable to nucleation of unburned
hydrocarbons. For biodiesels, the massive reduction of PM is also due to its oxygen
content and higher cetane value. Difference in specific PN emission between two
biodiesels might be due to its chemical composition. Canola biodiesel composed of 30%
more double unsaturated compound than tallow biodiesel which might favour formation of
nucleation mode particles. In addition viscosity of and density of canola biodiesel also
found higher than tallow biodiesel which may also favour smaller particle emission. For
FT, the absence of aromatics and sulfur supposed to be responsible for low PM emission as
they act as precursor for PM.
3.3.3 Effect of different alternative fuels on specific NOx emission
Figure 3-6 shows the brake specific NOx emission for different alternative fuels at 2000
rpm. For ethanol fumigation, NOx emission decreased from the reference diesel fuel with
the increase of energy substitution by ethanol for each engine load. Highest 25% NOx
reduction was observed for 40% energy substitution (E40) by ethanol at 100% load where
it was 14%, 12% and 9% for E30, E20 and E10 respectively. Same trend was found in
NOx reduction at other engine load as well. Low heating value of ethanol which causes
low in cylinder temperature is mainly responsible for reduced NOx emission for ethanol
fumigation On the other hand brake specific NOx emission increased for both biodiesels
and synthetic diesel. Between two biodiesels tallow biodiesel produced less NOx than
canola biodiesel. NOx emission increased 25%, 11%, 47% and 32% at 25%, 50%, 75%
and 100% load respectively for canola biodiesel while it was 4%, 6%, 5% and 11% for
tallow biodiesel at the same engine load. Higher degree of unsaturation of canola biodiesel
is found to be responsible for higher NOx emission by canola biodiesel than tallow
biodiesel, similar result is also reported by [12] Finally, NOx emission from synthetic
diesel was also found higher than neat diesel but lower than biodiesel.
P a g e | 76
D100 E10 E20 E30 E40 C100 T100 FT1000.00
0.95
1.90
2.85
3.80
4.75
5.70
6.65N
Ox (
g/k
W-h
r)
Fuel type
25% load
D100 E10 E20 E30 E40 C100 T100 FT1000.00
0.95
1.90
2.85
3.80
4.75
5.70
6.65
NO
x (
g/k
W-h
r)
Fuet type
50% load
Specific NOx emission for different alternative fuels
D100 E10 E20 E30 E40 C100 T100 FT1000.00
0.95
1.90
2.85
3.80
4.75
5.70
6.65
NO
x (
g/k
W-h
r)
Fuel type
75% load
D100 E10 E20 E30 E40 C100 T100 FT1000.00
0.95
1.90
2.85
3.80
4.75
5.70
6.65
NO
x (
g/k
W-h
r)
Fuel type
100% load
Figure 3-6: Brake specific NOx emissions for different alternative fuels at 2000 rpm
engine speed
3.4 Conclusions
In general energy substitution by ethanol fumigation reduced both PM and PN
emission compared to petro-diesel and maximum 59% reduction in PM and 70%
reduction in PN observed at full load . Up to 30% energy substitution by ethanol
reduced PM and PN consistently regardless of the engine operating condition
where some inconsistency was observed for 40% ethanol substitution due to
nucleation at some engine operating speeds and loads. Ethanol substitution also
reduced NOx emission.
Biodiesel reduced PM most among all used alternative fuels and the highest 93%
reduction was observed at full load. But canola biodiesel increased PN almost an
order than diesel due to the presence of nucleation mode particles where tallow
biodiesel reduced PN significantly with 15 nm reduction in particle median
diameter.
PM emission for FT was found lower than neat diesel and all ethanol fumigation
but higher than biodiesel, where no considerable difference was observed for PN.
Both biodiesel and FT increased specific NOx emission. Between two biodiesels
P a g e | 77
NOx emission from canola was higher than tallow due to the presence of more
unsaturated compound in canola biodiesel which may cause prolonged premixed
combustion favourable for thermal NOx production.
3.5 Acknowledgement Authors wish to thank all the technicians in the Biofuel Engine Research Facilities (BERF)
in Queensland University of Technology (QUT) for their valuable contribution during
experimental measurement.
3.6 References [1] M.Z. Jacobson, Strong radiative heating due to the mixing state of black carbon in
atmospheric aerosols, Nature, 409 (2001) 695-697.
[2] Z.D. Ristovski, B. Miljevic, N.C. Surawski, L. Morawska, K.M. Fong, F. Goh, I.A.
Yang, Respiratory health effects of diesel particulate matter, Respirology, 17 (2012) 201-
212.
[3] J.K. Folkmann, L. Risom, C.S. Hansen, S. Loft, P. Moller, Oxidatively damaged DNA
and inflammation in the liver of dyslipidemic ApoE-/- mice exposed to diesel exhaust
particles, Toxicology, 1 (2007) 134-144.
[4] B. Cruts, L. van Etten, H. Tornqvist, A. Blomberg, T. Sandstrom, N.L. Mills, P.J.A.
Borm, Exposure to diesel exhaust induces changes in EEG in human volunteers, Particle
and Fibre Toxicology, 5 (2008) 4.
[5] M.D. Attfield, P.L. Schleiff, J.H. Lubin, A. Blair, P.A. Stewart, R. Vermeulen, J.B.
Coble, D.T. Silverman, The diesel exhaust in miners study: A cohort mortality study with
emphasis on lung cancer, Journal of the National Cancer Institute, 104 (2012) 869-883.
[6] J.D. Herner, S.H. Hu, W.H. Robertson, T. Huai, M.C.O. Chang, P. Rieger, A. Ayala,
Effect of Advanced Aftertreatment for PM and NO(x) Reduction on Heavy-Duty Diesel
Engine Ultrafine Particle Emissions, Environ Sci Technol, 45 (2011) 2413-2419.
[7] M. Lapuerta, O. Armas, J.J. Hernández, A. Tsolakis, Potential for reducing emissions
in a diesel engine by fuelling with conventional biodiesel and Fischer-Tropsch diesel, Fuel,
89 (2010) 3106-3113.
[8] J. Xue, T.E. Grift, A.C. Hansen, Effect of biodiesel on engine performances and
emissions, Renewable and Sustainable Energy Reviews, 15 (2011) 1098-1116.
[9] N.C. Surawski, Z.D. Ristovski, R.J. Brown, R. Situ, Gaseous and particle emissions
from an ethanol fumigated compression ignition engine, Energy Conversion and
Management, 54 (2012) 145-151.
[10] M. Jamriska, L. Morawska, S. Thomas, C. He, Diesel bus emissions measured in a
tunnel study, Environ. Sci. Technol., 38 (2004) 6701–6709.
[11] N.C. Surawski, B. Miljevic, B.A. Roberts, R.L. Modini, R. Situ, R.J. Brown, S.E.
P a g e | 78
Bottle, Z.D. Ristovski, Particle emissions, volatility, and toxicity from an ethanol
fumigated compression ignition engine, Environmental Science and Technology, 44 (2010)
229-235.
[12] K. Varatharajan, M. Cheralathan, Influence of fuel properties and composition on
NOx emissions from biodiesel powered diesel engines: A review, Renewable and
Sustainable Energy Reviews, 16 (2012) 3702-3710.
P a g e | 79
Chapter 4: Engine performance characteristics of biodiesels having
different degree of saturation and carbon chain length
Publication: Published in SAE Int. J. Fuels Lubr, 6 (2013,)Pages 188-198
Contributor Statement of contribution
Md Mostafizur
Rahman Contributed in design of the experimental program and conducted
the emissions measurements, assisted in data analysis, and write up
the manuscript.
Signature
Date
P. X. Pham
Contributed in design of the experimental program, data analysis
and write up the manuscript.
T. A. Bodisco Contributed data analysis and assisted in write up the manuscript.
S. Stevanovic
Contributed in emission measurement, data analysis and assisted in
write up the manuscript.
H. Wang
Contributed in emission measurement and reviewed the
manuscript.
R. J. Brown
Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Z. D. Ristovski
Supervised the overall study design, set up and experiments,
reviewed the manuscript.
A. R. Masri
Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all Co-authors confirming their
certifying authorship.
Name Signature Date
Professor Zoran Ristovski
P a g e | 80
P a g e | 103
Chapter 5: Particle emissions from biodiesels with different
physical properties and chemical composition
Publication: Published in the journal Fuel, (2014), pages 201-208
Contributor Statement of contribution
Md Mostafizur
Rahman Contributed in design of the experimental program and conducted
the emissions measurements, analysed data and wrote the
manuscript.
Signature
Date
A. M. Pourkhesalian Contributed in design of the experimental program, assisted in data
analysis and write up the manuscript.
M. I. Jahirul Contributed in measurement, data analysis and assisted in write up
the manuscript.
S. Stevanovic Contributed in emission measurement, and reviewed the
manuscript.
H. Wang Contributed in emission measurement and reviewed the
manuscript.
B. R. Masri Supervised the overall study design, set up and experiments,
reviewed the manuscript.
R. J. Brown Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Z. D. Ristovski Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all Co-authors confirming their
certifying authorship.
Name Signature Date
Professor Zoran Ristovski
P a g e | 104
Title: Particle emissions from biodiesels with different physical
properties and chemical composition
M. M. Rahman a, A. M. Pourkhesalian
a, M. I. Jahirul
a, S. Stevanovic
a, P. X. Pham
b, H.
Wang a, A.R. Masri
b, R. J. Brown
a and
*Z. D. Ristovski
a
a ILAQH and BERF, Queensland University of Technology, Brisbane, QLD4001, Australia
b Clean Combustion Research Group, The University of Sydney, NSW2006, Australia
*E-mail: [email protected]
Abstract:
Biodiesels produced from different feedstocks usually have wide variations in their fatty
acid methyl ester (FAME) so that their physical properties and chemical composition are
also different. The aim of this study is to investigate the effect of the physical properties
and chemical composition of biodiesels on engine exhaust particle emissions. Alongside
with neat diesel, four biodiesels with variations in carbon chain length and degree of
unsaturation have been used at three blending ratio (B100, B50, B20) in a common rail
engine. It is found that particle emission increased with the increase of carbon chain length.
However, for similar carbon chain length, particle emissions from biodiesel having
relatively high average unsaturation are found to be slightly less than that of low average
unsaturation. Particle size is also found to be dependent on fuel type. The fuel or fuel mix
responsible for higher PM and PN emissions is also found responsible for larger particle
median size. Particle emissions reduced consistently with fuel oxygen content regardless of
the proportion of biodiesel in the blends, whereas it increased with fuel viscosity and
surface tension only for higher diesel-biodiesel blend percentages (B100, B50). However,
since fuel oxygen content increases with the decreasing carbon chain length, it is not clear
which of these factors drives the lower particle emission. Overall, it is evident from the
results presented here that chemical composition of biodiesel is more important than its
physical properties in controlling exhaust particle emissions.
Keywords: Biodiesel; particle emissions; fuel physical properties; fuel chemical
composition.
P a g e | 105
5.1 Introduction
Compression Ignition (CI) engines are increasing in popularity due to their higher thermal
efficiency. They power a wide range of land and sea transport as well as provide electrical
power, used in farming, construction and industrial applications. Tail pipe emissions of
diesel engines, especially particulate matter (PM) are still a matter of concern due to its
harmful effects both on human health and the environment [1, 2]. Exposure to diesel
particulate matter (DPM) can cause pulmonary diseases such as asthma, bronchitis and
lung cancer [1] and because of these adverse effects, the International Agency for Research
on Cancer (IARC) included DPM as carcinogenic to human health.
The harmful effects caused by DPM are related to both the physical properties and
chemical composition of the particles. The physical properties that influence respiratory
health include particle mass, surface area, mixing status of particles, number and size
distribution[3]. The particles deposit in different parts of the lung depending on their size.
The smaller the particles the higher the deposition efficiency [4] and the greater the chance
of them penetrating deep into the lung. The smaller particles stay suspended in the
atmosphere for longer thus have a higher probability of being inhaled and consequently
deposited deep in the alveolar region of the lung. Particle number governs the ability of
particles to grow larger in size by coagulation while particle surface area determines the
ability of the particles to carry toxic substances. Recent studies reveal that DPM surface
area and organic compounds play a significant role in initiating various cellular and
chemical processes responsible for respiratory disease [3, 5]. In addition to this, a large
fraction of DPM is black carbon, which is considered the second most potential greenhouse
warming agent after carbon dioxide [2]. After treatment devices (ATD) like diesel
particulate filters (DPF) and diesel oxidation catalysts (DOC) aid in reducing DPM [6].
Alternative fuels are another potential emission reducing source [7]. Of these fuels,
biodiesel is considered one of the more promising for diesel engines [8, 9] as it produces
less PM and other gaseous emissions [9-11]. Biodiesel in diesel engines also has the
potential to neutralise carbon emissions as it comes from a renewable source of energy.
Biodiesel is a mixture of fatty acid esters with physicochemical properties that mostly
depend on the structure of the ester molecule. They can be produced from a variety of
feedstock sources such as vegetable oil, animal fat, municipal and industrial waste and
some even from insects [12-15]. The vast majority of feedstocks actually used now days
P a g e | 106
are derived from vegetable oils and animal fats. An extensive range of fatty acid profiles
exist among these feedstocks [16]. With fatty acid profile being even different within the
same feedstocks. If plant sources are used, these variations can be controlled by
manipulating stock growing conditions. Physical properties and chemical composition of
biodiesel varies among different feedstocks, which can have a noticeable influence on
engine performance and emissions [17]. McCormick et al.[18] reported constant PM
emissions from different biodiesel feedstocks when the density was less than 0.89 g/cm3 or
cetane number was greater than about 45, but increase of NOx emissions with the increase
of biodiesel density and iodine number. In contradiction to these findings, a difference in
particle emissions from biodiesel from different feedstocks has also been reported [19, 20].
Lapuerta et al.[10] reported a 10% increase of NOx and 20% decrease of particle emissions
by unsaturated biodiesel. Benjumea et al.[21] found that the degree of unsaturation in
biodiesel doesn’t significantly affect the engine performance but increases smoke opacity
and THC emissions. Karavalakis et al. [22] reported noticeable influence of biodiesel
origin on particle emissions, especially particle associated PAH and carbonyl emissions.
Very recently Salamanaca et al.[23] reported increased PM and HC emissions from
biodiesel that contains more unsaturated compounds that favour soot precursor formation.
There is no distinction however in the literature, which indicates whether chemical
composition of biodiesel, physical properties or a combination of these is responsible for
this variation in engine performance and emissions. This study therefore, aims to
investigate the effect of biodiesel physical properties and chemical composition on engine
exhaust particle emissions. It is an extension of the previous study [24] where results from
the same experiments were presented for the engine performance characteristics and
emission of pollutants including some preliminary results for the particle emission,
particularly for pure biodiesel. It should be noted that the results for B100 are reproduced
here for comparison purposes. Furthermore, the paper elaborates on these findings and
presents new analysis in terms of the physical properties chemical composition of the fuels
and their blends.
5.2 Materials and methods
5.2.1 Engine and fuel specification
This experimental study was performed in a heavy duty 6 litre, six cylinders, turbocharged
after cooled, common rail diesel engine typically used in medium size trucks. Test engine
is the same as used in Pham et al. [24]. Table 5-1 shows specification of the test engine.
P a g e | 107
The engine was coupled to a water brake dynamometer, and both of them are connected to
an electronic control unit (ECU). Engine was operated at 1500 rpm (maximum torque
speed) and at 2000 rpm (intermediate speed), and four different loads including 25%, 50%,
75%, and 100% for each engine speed. Maximum load at any particular engine speed
depends upon the type of fuel used. Therefore, for each fuel maximum load was measured
at first when engine was in full throttle for a particular speed. This measured load is then
considered as 100% load for that speed and other load conditions were determined based
upon measured 100% load. Although PM emissions can be quiet different for transient
testing, we had to conduct steady state tests as most of our measurement techniques
required steady emission over longer sampling time.
Table 5-1: Test engine specification
Model Cummins ISBe220 31
Cylinders 6 in-line
Capacity (L) 5.9
Bore × Stroke (mm) 102 × 120
Maximum power (kW/rpm) 162/2500
Maximum torque (Nm/rpm) 820/1500
Compression ratio 17.3
Aspiration Turbocharged & after cooled
Fuel Injection Common rail
After treatment devices None
Emissions certification Euro III
An ultra-low sulfur diesel (sulfur content 2.5mg/kg) and four biodiesels with different
physical properties and chemical composition were used to run the engine. All four
biodiesels originated from palm oil, and then were fractionated to separate its fatty acid
ester components with specific composition. Since all of them have originated from the
same feedstock, the given code names (C810, C1214 etc) are to identify them and to
indicate their carbon chain length. For example C810 means biodiesel that is mainly
composed of FAME’s with 8-10 carbon atoms. All four biodiesels were used at three
blending ratios i.e. 100% biodiesel (B100), blends of 50% diesel and 50% biodiesel (B50),
and blends of 80% diesel and 20% biodiesel (B20). Table 5-2 shows the fatty acid profile
of used biodiesels as found using gas chromatography mass spectrometry (GCMS)
P a g e | 108
analysis. Biodiesel samples were analysed using Perkin Elmer clarus 580GC-MS equipped
with Elite 5MS 30m x 0.25mm x 0.25um column with a flow rate of 1mL/min. Before
analysing, each biodiesel was diluted with n-hexane (1:100 v/v). Initial temperature was
120 0C for 0.5 minutes, then raised to 310
0C for 2 minutes at 10
0C/min and kept at 310
0C
for 2 minutes. The mass selective detector was optimised using calibrating standards with
reference masses at m/z (40-350). Among four biodiesels, C810 is fully saturated and
composed of 52% and 46% caprylic acid and capric acid ester respectively. C1214 is also
dominated by saturated compounds but has comparatively longer carbon chain length fatty
acid ester i.e. 48% lauric, 19% % myristic, 10% palmitic and 18% oleic acid ester. On the
other hand both C1618 and C1822 are dominated by long chain unsaturated fatty acid
esters. C1618 is composed of 21% palmitic, 9% stearic, 58% cis-oleic and 10% linoleic
acid ester where C1822 has 10% more oleic and linoleic acid ester. C1822 also has a small
amount (4%) of trans- elaidic acid ester.
Table 5-2: Fatty acid profile of used biodiesels
Biodiesels C810 C1214 C1618 C1822
Common Name Lipid Numbers
Caprylic acid C8:0 52.16 0 0 0
Capric acid C10:0 46.38 0.17 0 0
Lauric acid C12:0 1.38 47.8 0.1 0
Myristic acid C14:0 0 18.89 0.06 0.03
Pentadecylic acid C15:0 0 0 0.03 0.02
Palmitic acid C16:0 0 10.19 21 4.45
Palmitoleic acid C16:1 0 0 0 0.12
Margaric acid C17:0 0 0 0.06 0
Stearic acid C18:0 0 2.55 9.47 2.53
Oleic acid C18:1cis 0 18.53 58.72 68.13
Elaidic acid C18:1trans 0 0 0 3.96
Linoleic acid C18:2 0 1.76 9.98 18.69
Arachidic acid C20:0 0 0.08 0.3 0.49
Gadoleic acid C20:1 0 0 0.24 1.03
P a g e | 109
Behenic acid C22:0 0 0.03 0.03 0.17
Glycerol -- 0.08 0 0 0
Some important properties of all used fuels related to combustion and emissions are shown
in Table 5-3. Among the used biodiesels physical properties varied with the variation in
chemical composition i.e. carbon chain length and degree of saturation/unsaturation.
Viscosity, heating value and iodine value increased with the increase of carbon chain
length and degree of unsaturation, where saponification value decreased. Density of all
four biodiesels was found higher than diesel, and no trend observed among biodiesels
either with the carbon chain length or degree of unsaturation. Surface tension also
increased with the carbon chain length in biodiesels, although no significant change was
observed with the degree of unsaturation. For example, there is almost no difference in
surface tension between C1618 and C1822, although C1822 contains much higher
percentage of unsaturated compounds compared to C1618. Surface tension and cetane
value of diesel were found to be lower than all four used biodiesels where calorific value
was higher. Viscosity of diesel was higher than C810 but lower than the rest of the three
biodiesels.
Table 5-3: Important physicochemical properties of tested fuels
Fuel type C810 C1214 C1618 C1822 Diesel
Relevant properties
Average formula C9.5H19.7O2 C14.8H28.3O2 C18.3H35.3O2 C18.7H35.3O2 CxHy *
Average unsaturation
(AU) 0 0.22 0.7892 1.11 --
Oxygen content (wt%) 18.72 13.25 10.74 10.83 0 *
Stoichiometric air fuel
ratio 11.12 12.05 12.50 12.48 14.5
Relative density(kg/l) 0.877 0.871 0.873 0.879 0.8482
Viscosity (mm2/sec) 1.95 4.37 4.95 5.29 3.148
Surface tension (mN/m) 26.184 28.41 29.9 29.966 26
Cetane Number 62.96 65.57 61.06 53.65 48.5
Iodine number(g I2/100g) 1 max 8 65 105
Saponication value(mg 330 233 195 185
P a g e | 110
KOH/g)
Acid value(mg KOH/g) 0.9 0.4 0.8 0.4 <0.05
Boiling point (0C) -- >150 165.6 >150 >180
*
Gross Calorific
value(MJ/kg) 35.335 38.409 37.585 39.825 44.365
Sulfur content(mg/kg) 0 0 0 0 2.5
values with * superscripts have taken from literature[25, 26]
5.2.2 Exhaust sampling and measurement system
As shown in Figure 5-1, the Dekati ejector diluter was used to partly sample raw exhaust
from the engine exhaust pipe and then dilute it with particle free compressed air. A second
Dekati diluter was connected in series with the first one to further increase the dilution
ratio in order to further decrease concentration. A HEPA filter was used to provide particle
free compressed air for the diluters. The purpose of the dilution was to bring down the
temperature as well as the concentration of gases and PM within the measuring range of
the instruments. Diluted exhaust was then sent to different gaseous and particle measuring
instruments. A CAI 600 series CO2 analyser was used to measure the CO2 concentration
directly from the raw exhaust. A second CO2 meter (SABLE, CA-10) connected via a
three way valve between the two diluters was used to record the CO2 concentration from
the diluted exhaust. Background corrected CO2 was used as tracer gas to calculate the
dilution ratio for each stage. After first stage dilution, CAI 600 series CLD NOx analyser
was used to measure the NOx. PM2.5 emissions were measured by a TSI DustTrak (Model
8530). DustTrak readings were converted into a gravimetric measurement by using the
tapered element oscillating microbalance to DustTrak correlation for diesel particles
published by Jamriska et al.[27]. It is worth noting this conversion can introduce
significant uncertainties if the optical properties of particles significantly changes. The
particle number size distribution for C810, C1214, C1618 and their blends was measured
by a scanning mobility particle sizer (SMPS). This SMPS consisted of a TSI 3080
electrostatic classifier (EC) and a TSI 3025 butanol based condensation particle counter
(CPC). Due to technical problems a new SMPS had to be used for the reference diesel and
C1822. This SMPS system consisted of a 3085 classifier with a nano-DMA(differential
mobility analyser). As the measurement range of the two SMPS’s used was different, we
have used a fitting procedure (see section 3.2) to recalculate the total PN and make the
measurements comparable. A TSI 3089 nanometer aerosol sampler (NAS) was used in
P a g e | 111
conjunction with a Tandem Differential Mobility Analyser (TDMA) to collect preselected
particles on Transmission Electron Microscopic (TEM) grids for morphological analysis.
The EC in the TDMA preselected the size of the particles, which deposited on the TEM
grid in the NAS. An Aethalometer (Magee Scientific) was also connected after second
stage dilution for black carbon (BC) measurement. Results from TEM analysis and BC
data will be published in a separate paper.
Figure 5-1: Schematic diagram of experimental set up
5.3 Results and discussion
5.3.1 Specific PM emissions
All four biodiesels that were used, disregarding the variations in physical properties and
chemical composition, reduced PM emissions in comparison to petro-diesel. Figure 5-2
shows brake specific PM emissions at engine operating speeds of 1500 rpm and 2000 rpm,
respectively. It was found that as the biodiesel percentage in the diesel-biodiesel blends
increased, PM emissions decreased consistently. The maximum reduction in PM was
observed for 100% biodiesel blends, an observation common in the literature [9, 28, 29].
Noticeable variations in PM emissions were also observed among the four biodiesels and
their blends. In the case of using 100% biodiesel, a massive 98% reduction in PM was
observed for biodiesel C810, where C1214, C1618 and C1822 reduced PM 83%, 70% and
76% respectively. Similar trends in PM emissions were also found for B50 and B20 blends
although there was a difference of PM reduction proportion in these blends. For the B50
blend, PM reduction among biodiesels C810, C1214, C1618 and C1822 was 88%, 75%,
P a g e | 112
70% and 76% respectively. B20 was slightly lower, measuring 66%, 57%, 42% and 48%
respectively. PM emissions from other tested engine loads are shown in the appendix
(Figure S1). Similar trends in PM emissions were also observed for these loads at 2000
rpm engine speed, although at 1500 rpm 50% and 25% load, PM emissions from B20
(C1618) were found to be slightly higher than that from the diesel. These variations in PM
emissions among biodiesels could be due to either their chemical composition or their
physical properties. Among the biodiesels, PM emissions increased consistently with
biodiesel carbon chain length with the exception of C1822. This biodiesel carbon chain
length was similar to C1618 but its degree of unsaturation was higher and its PM emissions
were less. Pinzi et al. [30] also reported reduction of PM emission with the increase of
degree of unsaturation but same carbon chain length in FAME. Opposing observations
were also reported in the literature, which suggests that unsaturated compounds have a
tendency to act as soot precursor [21, 23]. In addition, important physical properties of
C1822 in regards to particle emissions i.e. viscosity and surface tension were also higher.
This slight reduction in PM emissions from C1822 might be attributed to its high iodine or
low cetane value. Fuels with low cetane value undergo prolonged premixed combustion
phases that are responsible for less soot formation. In addition, NOx emissions from C1822
were highest among the fuels that were favourable for soot oxidation. This could also be
responsible for comparatively low PM emissions from C1822.
Figure 5-2: Brake specific PM emission at (a) 1500 rpm 100% load and (b) 2000 rpm
100% load. The PM emissions shown are calculated based on DustTrak measurement.
P a g e | 113
5.3.2 Specific PN emissions
Variations that were observed in PN emissions were similar to the PM emissions for the
same fuels used. There were however, slight differences in the amount that PN emissions
changed compared to PM emissions. All PN emissions were calculated for the size range
from 10.2-514 nm. As the measurements for neat diesel and C1822 were done using the
nano-DMA, in the size range from 4.6nm-156nm, a fitting procedure was used to
recalculate the PN concentration to the same size range as used in the other measurements
[31]. As shown in Figure 5-3, PN emissions from B100 were found to be lower than diesel
for all biodiesels. Among the biodiesels used, C810 reduced PN most and C1618 reduced
PN the least compared to neat diesel, at 90% and 20% respectively. Reductions from
C1214 and C1822 were measured at 60% and 35% respectively. For B50, in the case of
C810, C1214 and C1822, the PN emissions remained lower than diesel although C1618
increased approximately 10%. Similar to B100, the lowest PN emissions were observed
with C810 for B50 with C1214 and C1822 following the trend for B100. PN emissions
from C1214 increased 15% with a large standard error at 2000 rpm, while at 1500 rpm it
remained almost same to C1822. Apart from B100 and B50, PN emissions from B20 were
found to be slightly less than diesel and almost the same among the biodiesels with the
exception of around 15% increase from diesel at 1500 rpm. Brake specific PN emissions
from other engine loads at both rpm are shown in appendix (Figure S2). PN emissions
from all other loads and engine speeds showed a similar trend with the exception of 1500
rpm 50% load where PN emission from B20 appeared to have a different trend as
compared to the rest of the results.
P a g e | 114
Figure 5-3: Brake specific PN emissions at (a) 1500 rpm 100% load and (b) 2000 rpm
100%
5.3.3 Particle number size distribution
Particle size distribution (PSD) was always found to be unimodal with a single peak in the
accumulation mode despite the variations in the fuel and the condition of engine operation
(Appendix: Figure S3 to S5). Variations in PSD among the biodiesels were more prevalent
for B100, followed by B50. Comparatively, PSD from B20 was found to be similar to
petro-diesel regardless of the variations of biodiesel. Another important feature is that
biodiesel reduced a higher proportion of large particles (mobility diameter >100 nm)
compared to nanoparticles (mobility diameter <50 nm). Nanoparticle emissions from
biodiesel however, did not exceed that of diesels, which have been reported in few studies
[19, 32]. The presence of a nucleation mode peak in PSD can be responsible for increased
nanoparticle emission. Schönborn et al. [33] found that biodiesel with a higher boiling
point has more tendency to form nucleation mode particles, in an engine with a high
pressure injection system (160 bar). In an engine with a mechanical injection system,
Fisher et al. [34] reported that nucleation depends both on engine operating condition and
biodiesel feedstocks. They concluded that a small variation in dilution and sampling
system might be the main driving force that will trigger nucleation. Biodiesels used in our
study have a noticeable variation in their boiling point. In addition we have used an
unheated first stage dilution which is more prone to producing nucleation mode particles.
In spite of that, we did not observe nucleation in any of our tests. Abundance of volatiles
0.0
2.0x1013
4.0x1013
6.0x1013
8.0x1013
1.0x1014
(b)
Bra
ke s
peci
fic P
N(#
/kW
-hr)
(a)
C810 C1214 C1618 C18220.0
2.0x1013
4.0x1013
6.0x1013
8.0x1013
1.0x1014
1.2x1014 Diesel
B20
B50
B100
Biodiesel Type
P a g e | 115
and semi-volatiles in the exhaust which can partition into particles upon cooling down are
the primary contributor to the nucleation mode peak. Impurities in biodiesel, such as
glycerol, don’t undergo complete combustion due to their high viscosity, poor atomisation
and mixing property. They can form partially oxidised volatiles and semi-volatiles that can
also be a contributor to the formation of the nucleation mode peak. Biodiesels used in this
study were free from glycerol and other impurities, which might facilitate the absence of
nucleation mode peak in PSD. In consensus, the amount of volatile and semi-volatile
materials produced by the biodiesels might not have been enough to trigger nucleation with
the used dilution system.
5.3.4 Particle median size
Particle size also varied among used fuels in a similar way to PM and PN. In case of diesel,
the median size of the particles in the SMPS size distribution was 61 nm and 56 nm at
1500 and 2000 rpm respectively. For 100% biodiesel, particle median size was always
found to be smaller than neat diesel and diesel-biodiesel blends (Figure 5-4, a and b).
Among four used biodiesels, C810 produced the smallest particle median size i.e. 40 nm
and 43 nm at 1500 and 2000 rpm respectively, followed by C1822 which was 53 nm and
44 nm. C1214 and C1618 gave almost the same particle size as neat diesel with slight
difference between two engines operating speeds. Particle size from B50 was found to be
larger than B100 but smaller than B20 blends. Interestingly, particle emitted from all B20
blends were found to be larger than diesel with the largest particle median size observed
for C1618 B20 blend. Similar trends in particle size were also observed at other engine
loads which are shown in appendix (Figures S6 and S7). To gain some insight of the
variation in particle sizes among different biodiesels and its blends, particle median size
from all measurement was plotted against the particle number concentrations. As can be
seen on Figure 5-4c a moderate positive correlation, with a Pearson correlation coefficient
of 0.61 was found between particle median size and total number concentration. This
indicates that total PN number concentration through coagulation could be one of the key
parameters influencing the overall particle size. Higher the particle number emissions,
larger the particle size can be. The other factors may be the biodiesel viscosity, surface
tension and especially oxygen contents which ensure the presence of more oxygen
functional groups on the surface of particles responsible for enhanced particle oxidation
and subsequent size reduction [35, 36].
P a g e | 116
Figure 5-4: Variations in particle median size among used fuels at (a)1500 rpm 100% load
and (b) 2000 rpm 100% load , while (c) shows particle median size variation with total
number concentration
5.3.5 Specific NOx emissions
NOx emissions were also found dependent on biodiesel carbon chain and degree of
unsaturation (Figure 5-5). Biodiesels with higher degree of saturation and shorter carbon
chain length emitted less NOx than biodiesels with relatively longer carbon chain and
higher degree of unsaturation. Even NOx emissions from C810 and C1214 found less than
diesel especially at higher blends. An interesting trend in NOx emissions also observed
among blends of used biodiesels. The usual trend in most of the literature is to increase
NOx emissions with the increase of biodiesel percent in the blend [7, 37]. Similar trend we
observed here for long chained biodiesels with higher degree of unsaturation i.e. C1618
and C1822. However for saturated and short chained biodiesels i.e. C810 the opposite
trend was also observed. NOx formation mostly depends on two phenomena, duration of
premixed combustion phase and in cylinder temperature. Biodiesels with higher degree of
unsaturation have low cetane number, and undergoes to prolonged premixed combustion
favourable for thermal NOx formation. So the higher NOx emissions from C1618 and
C1875 are expected and due to their higher unsaturation as well as their higher heating
C810 C1214 C1618 C182235
40
45
50
55
60
65
70
C810 C1214 C1618 C182240
45
50
55
60
65
Part
icle
media
n s
ize(n
m)
(a)
(b)
Biodiesel type
B20
B50
B100
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
35
40
45
50
55
60
65
70
75
Median(nm)
Linear Fit
Part
icle
me
dia
n s
ize(n
m)
Engine out particle concentration(#/cm3)
c
P a g e | 117
value. On the other hand higher degree of saturation, higher cetane number and lower
heating value of C810 and C1214 may cause shorter premixed combustion and lower in
cylinder temperature responsible for less NOx emissions. Therefore the mystery of higher
and lower [38] NOx emissions among different biodiesel studies in the literature may be
underlying in biodiesels chemical composition, and the concept of NOx emissions increase
in case of biodiesels is not always true. Rather, whether biodiesel will increase or reduce
NOx emissions depends upon their chemical composition.
Figure 5-5: Brake specific NOx emission at (a) 1500 rpm 100% load and (b) 2000 rpm
100% load
5.3.6 Influence of fuel physical properties and chemical composition on particle
emissions
To understand what is the relative influence of fuel physical properties as well as chemical
composition on particle emissions, PM and PN emissions for all used fuels were plotted
against fuel viscosity, surface tension and oxygen contents. Variations in PM and PN
emissions with fuel viscosity, surface tension and oxygen contents at 100% load are shown
in Figure 5-6, where the other loads are shown in appendix (Figures S8 to S10). As shown
in Figure 5-6, particle emissions increased with the increase of fuel viscosity and surface
tension but only within a specific blend. For higher blend percentages (B50 and B100)
there was almost a linear relationship between surface tension, viscosity and particle
emissions. On the other hand, for the same viscosity and surface tension, particle emissions
were also found to be significantly different among fuel/fuel mixes. It is evident from the
literature, both viscosity and surface tensions have noticeable influence on fuel atomisation
process [39], which is a key parameter relative to in-cylinder soot formation. Lower the
0.0
1.5
3.0
4.5
6.0
(b)
(a)
C810 C1214 C1618 C18220.0
1.6
3.2
4.8
6.4 Diesel
B20
B50
B100
Bra
ke s
peci
fic N
Ox(
g/kW
-hr)
Biodiesel type
P a g e | 118
viscosity and surface tension of fuel, more easily they evaporate, atomise and mix into in-
cylinder air, and more complete their combustion are [40, 41]. In this case, the used engine
was employed with a common rail injection system where fuel injection pressure was high
(around 200 bars). Such a high injection pressure might minimise the effects of small
variation in fuel viscosity and surface tension on fuel atomisation and subsequent particle
emissions. On the other hand, a more consistent negative relationship with R value more
than 0.9 was observed between fuel oxygen content and particle emissions. This
relationship did not depend on the blend percentage and went well with both PM and PN.
Similar reduction in particle emissions with fuel oxygen content has also been reported in
the literature [9, 42, 43]. Therefore, this is a clear indication that the fuel chemical
composition, particularly the oxygen content, could be more important than its physical
properties in terms of engine exhaust particle emissions.
Figure 5-6: Variation in specific PM and PN emissions with used fuel surface tension,
viscosity and oxygen content ((a), (b), (c) for PM and (d), (e), (f) for PN), Ordinate of (a),
(b), (c) and (d), (e), (f) are same where abscissa of (a), (d) and (b), (e) and (c), (f) are same.
0.0000
0.0044
0.0088
0.0132
0.0176
0.0220
0.0264
0.0308
0.0352
0.0396
Bra
ke
specific
PM
(g/k
W-h
r)
B100
B50
B20
25 26 27 28 29 30 31
0.00E+000
1.10E+013
2.20E+013
3.30E+013
4.40E+013
5.50E+013
6.60E+013
7.70E+013
8.80E+013
9.90E+013
Bra
ke
sp
ecific
PN
(#/k
W-h
r)
Fuel surface tension(mN/m)
1 2 3 4 5 6
Fuel viscosity(mm2/s)
2 4 6 8 10 12 14 16 18 20
B100
B50
B20
Fuel oxygen content(wt%)
c
f
b a
d e
P a g e | 119
5.3.7 Comparison of engine performance and particle emissions among used biodiesels
A comparison between engine performance parameters and particle emissions is shown in
Figure 5-7. In this figure, the vertical axis represents the percentage change of engine
power, brake specific fuel consumption and specific PM/PN emissions while the horizontal
axis indicates biodiesel proportion in the blends. Neat diesel was used as a reference fuel to
calculate the percentage changes. There is a significant difference among all four
biodiesels used. For example, C810 provides the highest reduction in particle mass and
number but the penalty for that is also highest, around 25% reduction in engine brake
power and an additional 25% increase of specific fuel consumption due to that reduced
engine power. This fuel and power penalty is lowest for C1618 but particle mass and
number reduction is also the lowest in this fuel blend. Therefore it is necessary to make a
trade-off between particle emission reduction, fuel and power penalty, ensuring maximum
benefit; not just for emission levels but for engine power and fuel economy as well.
Considering the aforementioned factors, C1822 seems to have advantage over the rest of
the fuels, as it maintains the lowest power and fuel penalty regardless of the blending ratio
to diesel and provide a reasonable reduction in engine exhaust particle emissions. The
evidence suggests that biodiesels with a longer carbon chain length and higher degree of
unsaturation might be a solution to reduce particle emissions to a certain extent with less
fuel and engine power penalty.
Figure 5-7: Comparison of engine performance (power, BSFC) and particle emissions
(PM, PN) among biodiesels and their blends where petro-diesel was used as a reference
fuel
Power BSFC PM PN
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
Incr
ease
d(%
)R
educ
ed(%
)
B100
Power BSFC PM PN
B50
Power BSFC PM PN
B20
C810
C1214
C1618
C1822
P a g e | 120
5.4 Conclusions
In conclusion, biodiesel fuels with shorter carbon chain lengths and higher degrees of
saturation have more potential to decrease engine exhaust particle emissions. With the
increase of carbon chain length and degree of unsaturation, particle emissions also
increase. Particle size also depends on type of fuel used. Fuel or fuel mix responsible for
higher PM and PN emissions was also found to have a larger particle median size. This
indicates that coagulation plays a role in overall engine exhaust particle size. Particle
emissions increase linearly with fuel viscosity and surface tension but only for higher
diesel-biodiesel blend percentages (B100, B50). On the other side PM emission reduces
consistently with fuel oxygen content regardless of the proportion of biodiesel in the
blends. High fuel injection pressure used by common rail injection systems might
minimise the effects of small variation in fuel viscosity and surface tension on particle
emissions. As the fuel oxygen content increases with the decrease of FAME carbon chain
length it is not clear whether it is the FAME carbon chain length or the oxygen content that
is the driving force which decreases particle emissions. The results support the view that
chemical composition (i.e. carbon chain length, degree of unsaturation, oxygen content) of
biodiesel is more important than its physical properties (i.e. viscosity, surface tension) in
regards to reducing engine exhaust particle emissions.
5.5 Acknowledgement
Authors sincerely acknowledge Mr. Scott Abbett and Mr. Noel Hartnett technicians in the
Biofuel Engine Research Facilities (BERF) at Queensland University of Technology
(QUT) and PhD student Mohammad Aminul Islam for their valuable support during
experimental work. This work was supported by the Australian Research Council under
grants LP110200158, DP1097125 DP130104904 and DP120100126.
5.6 References [1] J.M. Brito, L. Belotti, A.C. Toledo, L. Antonangelo, F.S. Silva, D.S. Alvim, P.A.
Andre, P.H.N. Saldiva, D.H.R.F. Rivero, Acute cardiovascular and inflammatory toxicity
induced by inhalation of diesel and biodiesel exhaust particles, Toxicological Sciences,
116 (2010) 67-78.
[2] M.Z. Jacobson, Strong radiative heating due to the mixing state of black carbon in
atmospheric aerosols, Nature, 409 (2001) 695-697.
[3] Z.D. Ristovski, B. Miljevic, N.C. Surawski, L. Morawska, K.M. Fong, F. Goh, I.A.
Yang, Respiratory health effects of diesel particulate matter, Respirology, 17 (2012) 201-
212.
P a g e | 121
[4] D.M. Broday, R. Rosenzweig, Deposition of fractal-like soot aggregates in the human
respiratory tract, Journal of Aerosol Science, 42 (2011) 372-386.
[5] B. Giechaskiel, B. Alfoldy, Y. Drossinos, A metric for health effects studies of diesel
exhaust particles, Journal of Aerosol Science, 40 (2009) 639-651.
[6] J.D. Herner, S.H. Hu, W.H. Robertson, T. Huai, M.C.O. Chang, P. Rieger, A. Ayala,
Effect of Advanced Aftertreatment for PM and NO(x) Reduction on Heavy-Duty Diesel
Engine Ultrafine Particle Emissions, Environ Sci Technol, 45 (2011) 2413-2419.
[7] E. Bakeas, G. Karavalakis, S. Stournas, Biodiesel emissions profile in modern diesel
vehicles. Part 1: Effect of biodiesel origin on the criteria emissions, Science of the Total
Environment, 409 (2011) 1670-1676.
[8] E.G. Varuvel, N. Mrad, M. Tazerout, F. Aloui, Experimental analysis of biofuel as an
alternative fuel for diesel engines, Applied Energy, 94 (2012) 224-231.
[9] J. Xue, T.E. Grift, A.C. Hansen, Effect of biodiesel on engine performances and
emissions, Renewable and Sustainable Energy Reviews, 15 (2011) 1098-1116.
[10] M. Lapuerta, O. Armas, J. Rodríguez-Fernández, Effect of the Degree of Unsaturation
of Biodiesel Fuels on NOx and Particulate Emissions, SAE Int. J. Fuels Lubr., 1 (2008)
1150-1158.
[11] N.C. Surawski, B. Miljevic, G.A. Ayoko, B.A. Roberts, S. Elbagir, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine employing two injection technologies and
three fuels, Environ Sci Technol, 45 (2011) 5498-5505.
[12] B.L. Salvi, N.L. Panwar, Biodiesel resources and production technologies – A review,
Renewable and Sustainable Energy Reviews, 16 (2012) 3680-3689.
[13] Y.C. Sharma, B. Singh, S.N. Upadhyay, Advancements in development and
characterization of biodiesel: A review, Fuel, 87 (2008) 2355-2373.
[14] M. Morshed, K. Ferdous, M.R. Khan, M.S.I. Mazumder, M.A. Islam, M.T. Uddin,
Rubber seed oil as a potential source for biodiesel production in Bangladesh, Fuel, 90
(2011) 2981-2986.
[15] E. Alptekin, M. Canakci, H. Sanli, Evaluation of leather industry wastes as a
feedstock for biodiesel production, Fuel, 95 (2012) 214-220.
[16] Moser, B. R., Impact of fatty ester composition on low temperature properties of
biodiesel–petroleum diesel blends, Fuel, 115 (2014) 500-506.
[17] S.K. Hoekman, A. Broch, C. Robbins, E. Ceniceros, M. Natarajan, Review of
biodiesel composition, properties, and specifications, Renewable and Sustainable Energy
Reviews, 16 (2012) 143-169.
[18] R.L. McCormick, M.S. Graboski, T.L. Alleman, A.M. Herring, K.S. Tyson, Impact of
biodiesel source material and chemical structure on emissions of criteria pollutants from a
heavy-duty engine, Environmental Science and Technology, 35 (2001) 1742-1747.
P a g e | 122
[19] N.C. Surawski, B. Miljevic, G.A. Ayoko, S. Elbagir, S. Stevanovic, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine: The influence of biodiesel feedstock,
Environmental Science and Technology, 45 (2011) 10337-10343.
[20] R. Allan, J. Williams, J. Rogerson, Effect of the Molecular Structure of Individual
Fatty Acid Alcohol Esters (Biodiesel) on the Formation of Nox and Particulate Matter in
the Diesel Combustion Process, SAE Int. J. Fuels Lubr., 1 (2008) 849-872.
[21] P. Benjumea, J.R. Agudelo, A.F. Agudelo, Effect of the degree of unsaturation of
biodiesel fuels on engine performance, combustion characteristics, and emissions, Energy
and Fuels, 25 (2011) 77-85.
[22] G. Karavalakis, V. Boutsika, S. Stournas, E. Bakeas, Biodiesel emissions profile in
modern diesel vehicles. Part 2: Effect of biodiesel origin on carbonyl, PAH, nitro-PAH and
oxy-PAH emissions, Science of the Total Environment, 409 (2011) 738-747.
[23] M. Salamanca, F. Mondragón, J.R. Agudelo, P. Benjumea, A. Santamaría, Variations
in the chemical composition and morphology of soot induced by the unsaturation degree of
biodiesel and a biodiesel blend, Combustion and Flame, 159 (2012) 1100-1108.
[24] P.X. Pham, T.A. Bodisco, S. Stevanovic, M.D. Rahman, H. Wang, Z.D. Ristovski,
R.J. Brown, A.R. Masri, Engine Performance Characteristics for Biodiesels of Different
Degrees of Saturation and Carbon Chain Lengths, SAE Int. J. Fuels Lubr., 6 (2013) 188-
198.
[25] S. Liu, L. Shen, Y. Bi, J. Lei, Effects of altitude and fuel oxygen content on the
performance of a high pressure common rail diesel engine, Fuel, (2013).
[26] W.A. Majewski, H. Jääskeläinen, What is Diesel Fuel, DieselNet Technology Guide
Ecopoint Inc
06a (2013).
[27] M. Jamriska, L. Morawska, S. Thomas, C. He, Diesel bus emissions measured in a
tunnel study, Environ. Sci. Technol., 38 (2004) 6701–6709.
[28] M. Lapuerta, O. Armas, J. Rodríguez-Fernández, Effect of biodiesel fuels on diesel
engine emissions, Progress in Energy and Combustion Science, 34 (2008) 198-223.
[29] N.C. Surawski, B. Miljevic, G.A. Ayoko, S. Elbagir, S. Stevanovic, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine: the influence of biodiesel feedstock,
Environmental Science & Technology, 45 (2011) 10337-10343.
[30] S. Pinzi, P. Rounce, J.M. Herreros, A. Tsolakis, M. Pilar Dorado, The effect of
biodiesel fatty acid composition on combustion and diesel engine exhaust emissions, Fuel,
104 (2013) 170-182.
[31] J. Heintzenberg, Properties of the Log-Normal Particle Size Distribution, Aerosol
Science and Technology, 21 (1994) 46-48.
P a g e | 123
[32] X. Shi, K. He, J. Zhang, Y. Ma, Y. Ge, J. Tan, A comparative study of particle size
distribution from two oxygenated fuels and diesel fuel, Frontiers of Environmental Science
and Engineering in China, 4 (2010) 30-34.
[33] A. Schönborn, N. Ladommatos, J. Williams, R. Allan, J. Rogerson, The influence of
molecular structure of fatty acid monoalkyl esters on diesel combustion, Combustion and
Flame, 156 (2009) 1396–1412.
[34] B.C. Fisher, A.J. Marchese, J. Volckens, T. Lee, J.L. Collett, Measurement of
Gaseous and Particulate Emissionsfrom Algae-Based Fatty Acid Methyl Esters, SAE Int. J.
Fuels Lubr., 03 (2010) 292.
[35] J. Wang, F. Wu, J. Xiao, S. Shuai, Oxygenated blend design and its effects on
reducing diesel particulate emissions, Fuel, 88 (2009) 2037-2045.
[36] R. Zhu, C.S. Cheung, Z. Huang, Particulate Emission Characteristics of a
Compression Ignition Engine Fueled with Diesel–DMC Blends, Aerosol Science and
Technology, 45 (2011) 137-147.
[37] S.K. Hoekman, C. Robbins, Review of the effects of biodiesel on NOx emissions,
Fuel Processing Technology, 96 (2012) 237-249.
[38] M.D. Redel-Macías, S. Pinzi, M.F. Ruz, A.J. Cubero-Atienza, M.P. Dorado, Biodiesel
from saturated and monounsaturated fatty acid methyl esters and their influence over noise
and air pollution, Fuel, 97 (2012) 751-756.
[39] C.E. Ejim, B.A. Fleck, A. Amirfazli, Analytical study for atomization of biodiesels
and their blends in a typical injector: Surface tension and viscosity effects, Fuel, 86 (2007)
1534-1544.
[40] S.-w. Lee, D. Tanaka, J. Kusaka, Y. Daisho, Effects of diesel fuel characteristics on
spray and combustion in a diesel engine, JSAE Review, 23 (2002) 407-414.
[41] P.-C. Chen, W.-C. Wang, W.L. Roberts, T. Fang, Spray and atomization of diesel fuel
and its alternatives from a single-hole injector using a common rail fuel injection system,
Fuel, 103 (2013) 850-861.
[42] A.T. S.S. Gilla, , , J.M. Herrerosb, A.P.E. York, Diesel emissions improvements
through the use of biodiesel or oxygenated blending components, Fuel, (2011).
[43] M.M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski, Influence of Different
Alternative Fuels on Particle Emission from a Turbocharged Common-Rail Diesel Engine,
Procedia Engineering, 56 (2013) 381-386.
P a g e | 124
5.7 Appendix
Figure S1: Brake specific PM emissions at 75%, 50% and 25% loads respectively while
the engine operated at 1500 and 2000 rpm.
P a g e | 125
Figure S2: Brake specific PN emissions at 75%, 50% and 25% loads respectively while
the engine speed was 1500 and 2000 rpm
P a g e | 126
Figure S3: Particle number size distribution for B100, B50, and B20 at 1500 rpm 100%
load (a, b, c respectively) and 2000 rpm 100% load (d, e, and f respectively)
1010 20 30 40 50 60 70 8090100 200 300
2.40x106
4.80x106
7.20x106
9.60x106
1.20x107
1.44x107
1.68x107
1.92x107
2.16x107
dN
/d lo
gd
p(c
m-3)
1010 20 30 40 50 60 70 8090100 200 300200 300
Diesel
C810
C1214
C1618
C1822
1010 20 30 40 50 60 70 8090100 200 300
2.40x106
4.80x106
7.20x106
9.60x106
1.20x107
1.44x107
1.68x107
1.92x107
2.16x107
(b)
10 20 30 40 50 60 70 8090 200 300
(e)
dN
/d lo
gd
p(c
m-3)
1010 20 30 40 50 60 70 8090100 200 300
2.40x106
4.80x106
7.20x106
9.60x106
1.20x107
1.44x107
1.68x107
1.92x107
2.16x107
(c)
Particle electrical mobility diameter(nm)Particle electrical mobility diameter(nm)
1010 20 30 40 50 60 708090100 200 300
(f)
(d)
dN
/d lo
gd
p(c
m-3)
(a)
P a g e | 127
Figure S4: Particle number size distribution for B100, B50, and B20 at 1500 rpm 50%
load (a, b, c respectively) and 2000 rpm 50% load (d, e, and f respectively)
10 20 40 60 80 100 2000.0
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
3.0x107
dN
/d lo
g d
p(#
/cm
3)
10 20 40 60 80 100 2000.0
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
3.0x107
(f)
(e)(b)
(d)
Diesel
C810
C1214
C1618
C1822
(a)
10 20 40 60 80 100 2000.0
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
3.0x107
dN
/d lo
g d
p(#
/cm
3)
10 20 40 60 80 100 2000.0
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
3.0x107
10 20 40 60 80 100 2000.0
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
3.0x107
(c)
dN
/d lo
g d
p(#
/cm
3)
Particle electrical mobility diameter(nm)
10 20 40 60 80 100 2000.0
5.0x106
1.0x107
1.5x107
2.0x107
2.5x107
3.0x107
Particle electrical mobility diameter(nm)
P a g e | 128
Figure S5: Particle number size distribution for B100, B50, and B20 at 1500 rpm 25%
load (a, b, c respectively) and 2000 rpm 25% load (d, e, and f respectively)
10 20 40 60 80 100 2000.00
7.60x106
1.52x107
2.28x107
3.04x107
3.80x107
dN
/d log d
p(#
/cm
3)
10 20 40 60 80 100 2000.00
7.60x106
1.52x107
2.28x107
3.04x107
3.80x107
(f)
(e)(b)
(d)
Diesel
C810
C1214
C1618
C1822
(a)
10 20 40 60 80 100 2000.00
7.60x106
1.52x107
2.28x107
3.04x107
3.80x107
dN
/d log d
p(#
/cm
3)
10 20 40 60 80 100 2000.00
7.60x106
1.52x107
2.28x107
3.04x107
3.80x107
10 20 40 60 80 100 2000.00
7.60x106
1.52x107
2.28x107
3.04x107
3.80x107
(c)
dN
/d log d
p(#
/cm
3)
Particle electrical mobility diameter(nm)
10 20 40 60 80 100 2000.00
7.60x106
1.52x107
2.28x107
3.04x107
3.80x107
Particle electrical mobility diameter(nm)
P a g e | 129
Figure S6: Variations in particle median size among fuels at 1500 rpm 75%, 50% and 25%
load
P a g e | 130
Figure S7: Variations in particle median size among fuels at 2000 rpm 75%, 50% and 25%
load
P a g e | 131
Figure S8: Variation in specific PM emissions with fuel oxygen content at 75%, 50% and
25% load
P a g e | 132
Figure S9: Variation in specific PM emissions with fuel surface tension at 75%, 50% and
25% load
P a g e | 133
Figure S10: Variation in specific PM emissions with fuel viscosity at 75%, 50% and 25%
load
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Chapter 6: The influence of fuel molecular structure on the volatility
and oxidative potential of biodiesel particulate matter
Publication: Published in journal of Environmental Science & Technology doi:
10.1021/es503160m
Contributor Statement of contribution
Md Mostafizur
Rahman Contributed in design of the experimental program and conducted
the emissions measurements and data analysis, assisted in
manuscript preparation.
Signature
Date
A. M. Pourkhesalian
Contributed in design of the experimental program and conducted
the emissions measurements, analysed data and wrote the
manuscript.
S. Stevanovic Contributed in experimental design, measurement, data analysis
manuscript preparation.
F. Salimi Contributed in data analysis and manuscript preparation.
H. Wang Contributed in emission measurement and reviewed the
manuscript.
C. R. Masri Supervised the overall study design, set up and experiments,
reviewed the manuscript.
R. J. Brown Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Z. D. Ristovski Supervised the overall study design, set up and experiments, data
analysis, manuscript preparation and also reviewed the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all Co-authors confirming their
certifying authorship.
Name Signature Date
Professor Zoran Ristovski
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Title: The influence of fuel molecular structure on the volatility and
oxidative potential of biodiesel particulate matter
A.M. Pourkhesalian, S. Stevanovic, F. Salimi, M.M. Rahman, H. Wang, X.P. Pham, S.E.
Bottle, A. R. Masri, R. J. Brown, Z.D. Ristovski
Keywords: Diesel exhaust nanoparticles, ROS, Volatility, conventional diesel fuel, methyl
ester biodiesel, carbon chain length, level of saturation
Abstract:
We have studied the effect of chemical composition of biodiesel fuel on the physical
(volatility) and chemical (reactive oxygenated species concentration) properties of
nanoparticles emitted from a modern common-rail diesel engine. Particle emissions from
the combustion of four biodiesels with controlled chemical compositions and different
varying unsaturation degrees and carbon-chain lengths, together with a commercial diesel,
were tested and compared in terms of volatility of particles and the amount of reactive
oxygenated species carried by particles. Different blends of biodiesel and petro-diesel were
tested at several engine loads and speeds. We have observed that more saturated fuels with
shorter carbon chain lengths, result in lower particle mass but produce particles that are
more volatile and also have higher levels of Reactive Oxygen Species (ROS). This
highlights the importance of taking into account metrics that are relevant from the health
effects point of view when assessing emissions from new fuel types.
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6.1 Introduction Alternative and renewable sources of fuels draw a substantial amount of attention
motivated by the constant movement towards more stringent regulation against diesel
emissions, the rising price of crude oil, and the vulnerability of fossil fuel resources.
Biodiesel is currently one of the most promising alternatives to fossil fuels, and depending
on its chemical and physical properties, may be considered as a suitable choice for
blending with diesel fuel to account for increasing fuel demands [1-3]. Biodiesel is a
processed fuel which is derived from biological sources by trans-esterifying vegetable oils,
animal fats or algae [3]. In terms of chemical composition, biodiesel fuels are mono-alkyl
esters of fatty acids [3]. Most of the biodiesel fuels are composed of Fatty Acid Methyl
Esters (FAMEs) [3]. Interestingly, Rudolf Diesel, the inventor of compression ignition
engine, used peanut oil the first time he ran his engine [1]. The reasons that make
biodiesels a suitable and practical fuel choice are numerous. They can be derived from a
number of feedstocks such as rapeseed, soybean, palm, waste cooking oil, tallow, jatropha,
algae etc [1-3]. Biodiesels are renewable and degradable [2, 4]. Lack of polycyclic
aromatic hydrocarbons reduces HC and CO concentrations in the exhaust [5]. In addition,
the oxygen content of biodiesel plays an important role in reducing black carbon (BC)
formation when local oxygen concentration decreases due to a diffusion combustion [5-7].
The higher cetane number of biodiesel leads to a more complete combustion [8]. On the
other hand, an increase in combustion temperature causes higher levels of NOx
concentration in the exhaust gas [9].
Recently, particulate matter (PM) produced by biodiesel combustion has become a popular
research topic [10-15]. This is understandable considering that emissions from diesel
engines have been recently declared as carcinogenic [16]. It is commonly seen from
literatures that biodiesel causes less PM mass concentration in the exhaust gas [10, 17, 18].
However, some studies have reported an increase in the particle number concentration [11,
19, 20] as well as particle number per unit of particle mass [21]. An additional concern was
the observation that, non-petroleum diesel fuels emit excessive amounts of volatile
compounds upon combustion, which potentially results in more toxic emissions [14, 22,
23]. It is also reported that combustion of biodiesel generates smaller particles that can
penetrate deeper in lungs causing inflammation at the sites of deposition [12-14, 22].
The quality of combustion is a key factor that affects all the outputs of an engine.
Viscosity, surface tension, density, cetane number, low-temperature properties (cloud
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point, pour point, etc. ) [2, 8], heating value and lubricity [8] are amongst the physical
parameters of the fuel affecting the combustion process. On the other hand, chemical
properties such as carbon chain length, number of double bonds and the amount of oxygen
borne by the fuel may also affect the aforementioned physical properties and in turn, the
combustion process. There have been a growing number of publications that study the
correlation between fuel composition and engine emission, especially diesel particulate
matter (DPM) [9, 17, 24]. The relationship between biodiesel chemical properties and
resulting emissions were also investigated [2, 5, 8, 9, 24, 25]. It has been found that blends
with higher oxygen content produce less PM but more volatile particles and increased
levels of NOx [5, 9, 25]; while biodiesel fuels with longer carbon chain length led to more
PM [8, 24].
DPM is well known for its adverse effects on humans, animals and the environment [26-
28]. Recently, based on the current body of knowledge, the International Agency for
Research on Cancer (IARC) has labelled diesel exhaust as carcinogenic within class 1 [16].
While the mechanism by which DPM causes the adverse health effects are not known, a
great deal of the harmful effects relate to its ability to cause oxidative stress [29-35].
Therefore, measurement of oxidative potential (OP), expressed through ROS
concentration, can be used as a good estimate for its reactivity and toxicity. Based on the
data provided in the literature so far, it remains unclear which chemical species contribute
to the measured redox potential and the overall toxicity. Several studies in this area showed
that [23, 29, 36] the organic fraction, more precisely semi-volatile component of PM,
correlated well with the measured OP. Stevanovic et al. [29] further showed that the
oxygenated fraction of the semi-volatile component of DPM was most responsible for the
OP of DPM.
The primary objective of this work is to critically examine the influence of the carbon
chain length and saturation levels of biodiesel fuel molecules on the overall volatility (OV)
and OP of DPM. All the work has been undertaken by investigating particle emissions
from a common-rail engine using four palm oil biodiesels and different blend percentages.
All tested biodiesels were FAMEs with controlled fatty acid composition. This enabled us
to assign the influence of a particular parameter more easily. This paper is an extension of
a previous study [37] where the engine performance characteristics and emission of
pollutants were investigated and presented, including some preliminary findings for the
ROS emission, particularly for pure biodiesel. It should be noted that the results for B100
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are reproduced here for comparison purposes. Furthermore, the paper elaborates on these
findings and new analysis in terms of physico-chemical properties of the fuels and their
blends.
6.2 Experimental setup and methodology A schematic of the experimental setup can be found in Figure S1 in the supporting
information (SI). The test bed included a diesel engine coupled to a dynamometer and the
accompanying instruments for PM and gas measurement. The specifications of the engine
and dynamometer are shown in SI Table S1. Raw diesel exhaust was sampled from the
exhaust line and passed through a dilution tunnel and a Dekati ejector diluter both of which
were fed with HEPA-filtered air. A TSI Dustrak (model 8530) measured the mass of
particles. Gas analysers, which consisted of a NDIR (Non-dispersive infra-red) CAI 600
series CO2 and CO analyser and a CAI 600 series CLD (Chemiluminescence detector)
NOx analyser, measured CO, CO2 and NOx concentrations before the dilution system.
Also, to determine the dilution ratio, a SABLE CA-10 CO2 analyser was used to measure
CO2 concentrations after each dilution step. Real-time measurement of black carbon
concentration were performed by an Aethalometer (AE33, 7-wavelength). A Scanning
Mobility Particle Sizer (SMPS TSI 3080, with a 3025 CPC) measured the size distribution
of diesel exhaust. A Volatility Tandem Differential Mobility Analyser (VTDMA)
consisting of an electrostatic classifier, a thermo-denuder and an SMPS (in-house designed
column with a 3010 CPC) measured the volatile content of particles. The VTDMA gave
the change in particulate diameter for six pre-selected sizes: 30, 60, 90, 120, 150, 200 and
220 nm after they have passed through the thermo-denuder with the temperature set at 300
°C. The flow rate through the thermo-denuder was kept constant at 1 lpm, which lead to a
residence time of around 2 s. As the concentration of particles entering the thermo-denuder
is rather small, there was no need to equip the thermodenuder with a charcoal section.
Absorption of the evaporated semi-volatiles on the walls of the thermo-denuder was
sufficient to reduce the vapour pressure and prevent them recondensing on to the existing
non-volatile particles [38].
The volumetric volatile fraction (𝑉𝑉𝐹) for each size was calculated from the difference in
diameter of particles before and after the thermo-denuder [39]. This difference was
considered as the thickness of the volatile layer that coats the soot core of the particles
[39].
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This study approximates the fractal-like diesel aerosols by particles with the corresponding
electrical mobility diameter. Although the mentioned assumption is not accurate for larger
agglomerates of diesel particles, the assumption would not distract the results as this study
aims at comparing various fuels and not at quantification of the amount of volatile material
emitted by combustion of biodiesel.
The total amount of volatile matter is calculated for every sampling point, and the
parameter used for its quantification is the Overall Volatility (OV). This was previously
introduced in the literature by Giechaskiel (2009), where it was used for the calculation of
non-volatile fraction of PM. More details of the concept and the procedure used for
calculation of OV can be found in SI.
The BPEA molecular probe (bis(phenylethynyl) anthracene-nitroxide) was applied in-situ
to assess the OP of different fuel stocks. Samples were collected by bubbling aerosol
through an impinger which contains 20 mL of 4 μM BPEA solution (using an AR grade
dimethylsulfoxide as a solvent). More details on the ROS sampling methodology, theory
behind its application and proof of concept in the case of various combustion sources can
be found in Miljevic et al. and Stevanovic et al. [40-44].
6.3 Fuel Selection The present study investigates four biodiesels of controlled composition, differing in levels
of unsaturation, carbon chain length and oxygen content. Commercial petro-diesel was
used for blending. Some of the fuel properties were measured experimentally in a previous
study [45], and others were estimated based on the chemical composition of methyl-esters
which are shown in SI, Table S2. We labelled the tested FAMEs as C810, C1214, C1618
and C1875, based on the number of carbon atoms in the most abundant fatty acid in that
particular biodiesel stock. The Iodine values of C810 and C1214 are very small, implying
that they are almost fully saturated; but the large difference in saponification values shows
that the carbon chain length of their molecules is quite different and, so is the oxygen
content. Conversely, C1618 and C1875 have different iodine numbers but pretty close
saponification values; which indicates that the carbon chain length of their molecules is
close but with different levels of unsaturation. We chose these controlled fuels to be able to
differentiate between the effect of unsaturation and carbon chain length.
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The engine operated on blends of 100%, 50%, 20% and 0% of biodiesel and petro-diesel
namely B100, B50 and B20 and B0, respectively. We conducted tests at idle, quarter (1500
and 2000 rpm) and full load (1500 rpm).
6.4 Results and discussion
6.4.1 Volatility measurements
To estimate the thermodenuder temperature at which the volatile content of DPM is
removed, measurements of the temperature dependent volatility were conducted for 30 and
150 nm particles using B0 at idle (SI Figure S2). The temperature was ramped from room
to 280C in steps of 20C. By 180C the 30nm particles were almost completely
evaporated and the larger 150nm particles reached a stable size. Therefore we can be
confident that at the set temperature of 300C all of the volatile material has been
evaporated from the diesel particles and there will be no effect of the size and residence
time of particles [46].
The VVF, calculated using SI eq S1, for all the measured particle sizes and all the fuels
and blends are shown in SI Figure S3. Out of the 4 loads studied, the highest volatility was
observed for particles produced during idling. This is valid for all the measured blends and
fuels. In addition to the idling conditions, higher volatility was only observed at full load,
while particles produced at quarter load and at both speeds did not show significant
volatility. For almost all of the fuels and blends tested and for most of the loads, higher
volatility was observed for smaller particles. This is the most obvious for idling conditions
where 30nm particles were mainly volatile with a VF of over 75%. Similar dependence on
the volatility as a function of particle size has been observed previously [47, 48].
To get a better insight into the influence of the fuel type and blend on the VVF, from the
data presented in SI Figure S3, we calculated the OV for each of the 3 fuel blends (B20,
B50 and B100) and for all 4 tested modes being presented in Figure 6-1. Unfortunately, the
volatility for C1214 at B100 was not measured.
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Figure 6-1: OV for all blends and modes tested. Rows present data measured at the same
load, while columns data are measured at the same blend percentage. Different fuels are
sorted along the x-axis according to their carbon chain length going from the shortest
(C810) to the longest one (C1875).
What is obvious from all of the graphs is that for all of the higher blends (B50 and B100)
the measured volatility is larger than for B0. Furthermore an increase in the volatility with
the increase of the biodiesel blend percentage for the same fuel type can also be observed.
While previously [23] have observed an increase in the volatility with increase in the blend
percentage, the results did not show a stock dependency. In these measurements we do see
a dependence on the feedstock, with the fuel with the shortest carbon chain length (C810)
producing the most volatile particles for all the tests except one, B20 at quarter load and
2000 rpm. Although C810 did not produce particles with the largest OV the observed
values were all within the measurement error. Also, in the case of the higher blend
percentages B50 and B100, a decrease in the OV is followed by an increase in the carbon
chain length. This refers to all the loads tested but is most significant idle and a full load
where a larger OV was measured. This is in contrast with a previous observation [49]
where an increase in the volatile organic fraction (VOF) was observed for an increase in
the carbon chain length. It is worth mentioning that Pinzi et al. [49] have used a mechanical
direct-injection engines, as compared to the common rail engine used in our case, for which
the fuel physical properties have a significant influence on the spray pattern and therefore
emissions.
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Some previous measurements conducted for petro-diesel point out that the volatile part of
DPM is mainly due to the lubricating oil [50-52], and some recent measurements in the
vicinity of busy roads also show that the main contributor to the primary organic aerosol
(POA) is lubricating oil from both gasoline and diesel powered vehicles [53]. If that was
the case, in our observations, than the increase in the OV could be interpreted as due to a
decrease in the total non-volatile mass (volume) as the lubricating oil contribution would
be the same for all of the different fuels and blends tested.
However, measurements on biodiesel fuels show a large fraction of fuel derived organics
contributing to the volatile part of DPM [54]. This contribution can be as large as 90% for
B50 blends[55].
Figure 6-2: BC Emission factors for all blends and modes tested. The emission factor for
B0 at each mode is shown as a dashed line. Rows present data measured at the same load,
while columns data are measured at the same blend percentage. Different fuels are sorted
along the x-axis according to their carbon chain length going from the shortest (C810) to
the longest one (C1875).
6.4.2 BC Emissions
The change in the OV could be either due to the change in the emission of primary organic
aerosol (POA) or could be due to the change in the emission of the non-volatile soot
component of DPM with the change of the carbon chain length. A good measure of the
non-volatile mass emitted from a diesel engine is the BC concentration. Figure 6-2 shows
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the BC emissions for each of the 3 fuel blends (B20, B50 and B100) and for all 4 tested
modes.
We can see that all blends and all fuels reduce the BC emissions as compared to B0. Higher
blend percentages of biodiesel (B50 and B100) show a clearer trend where the fuel with the
shortest carbon chain length has the smallest BC emissions. An increase in BC emission is
observed with the increase in carbon chain length up to C1618, afterwards a decrease is
observed for C1875. While there is not a large difference in the carbon chain length of the
FAMEs that make C1618 and C1875, as both fuels contain large fraction of oleic acid of
53 and 63.5% respectively, the later fuel contains additional 28.1% of FAMEs that are fully
unsaturated (linoleic and linolenic acids). A similar increase in soot emission with the
increase in carbon chain length and a decrease with the level of saturation was also
observed by Pinzi et al. [49], although the later was not as obvious.
Figure 6-3: OP of PM for all four fuels, for all the blends and loads
6.4.3 ROS measurements
OP measurement, expressed through ROS concentration of PM can be used as a good estimate
for its reactivity and toxicity. An in-house developed profluorescent molecular probe BPEAnit
was applied in an entirely novel, rapid and non-cell based way to assess particulate OP. Based on
the data provided in the literature so far [44], there are some uncertainties related to the nature of
chemical species responsible for the measured redox potential and overall toxicity. However, the
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majority of research in this field reported that organic fraction, more precisely semi-volatile
organic content, is in a good correlation with ROS concentration [23, 36, 56].
Figure 6-3 illustrates the OP of particles for the same loads and blends as for the volatility
measurements that were shown in Figure 6-1(note logarithmic scale is used here). In idle
mode, the ROS concentration is much higher than in the other cases. This may be a result
of the additional combustion of lubricating oil, which would subsequently increase the
overall organic content. Results do not show blend dependency in this mode, although it is
clearly visible that combustion of C810 fuel generates the largest amount of ROS for all 3
blends and combustion of the fuel with the longest carbon chain length C1875 smallest
amount of ROS. This could be a consequence of the way the OP is presented as the
concentration of ROS per mass of emitted particles. As the fuels with the highest oxygen
content have the lowest mass emission even for the same (or similar) ROS concentration
they will exhibit the highest OP when calculated per unit mas of PM.
For the other 3 loads and for most of the blends, C810 produced higher ROS emissions.
This is most apparent for B100, but it can also be observed even in 20 percent blends.
While there is an increase in ROS emissions for 50 and 100 percent blends for all the
tested fuels, C810 shows the largest increase of almost two orders of magnitude. This is a
reasonable result as the C810 fuel molecules are of the shortest chain, fully saturated and
with the largest oxygen content. These three parameters have synergetic effect in terms of
prediction of resulting OP as have been seen previously as well [37].
Another important parameter that will influence resulting OP is the load of the engine.
Generally, engine operating conditions significantly affect physicochemical properties of
emitted PM and their subsequent toxicity that were demonstrated to change with varying
engine loads and speeds. Also, OP tends to decrease with increasing engine load. This
result can be explained by a more complete combustion that results from higher
temperatures and lower air to fuel ratio at higher loads. In addition, at full load, the
amount of lubricating oil that is available for combustion is minimal.
Regarding the role of oxygen content as seen previously [57], out of all the fuel physical
and chemical properties, it has the most significant influence on particle mass and number
emissions. To further investigate the influence of the fuel oxygen content on particle
volatility and ROS concentration, we have presented the dependence of the BC emission
(Figure 6-4 ), ROS emissions (Figure 6-5b) and OV (Figure 6-5a) as a function of fuel
P a g e | 146
oxygen content. This is done for each of the four modes tested. Oxygen content is
calculated for all the fuels and blends assuming that oxygen content of base-line diesel
fuel is considered to be zero. As all the fuels are methyl esters with varying carbon chain
lengths, oxygen percentage increases with the decreasing carbon chain length in
respective molecules. Consequently, C810 is the fuel with the highest oxygen content,
followed by C1214, C1618 and finally C1875, as the fuel with the longest carbon chain
length as well as the most unsaturated fuel.
The relationships between the variables were analysed using the linear regression or
generalised linear model with a log link function. The model assumptions were validated
using the residuals vs fitted values and QQ plots. The resulting fitted functions, and their
95% confidence intervals, indicate the relationship between the variables. Modelling and
visualisations were performed using the ggplot2 package in R [58].
Figure 6-4: Dependence of the BC emissions factor on fuel oxygen content for the four
modes tested
Figure 6-4 illustrates the decrease in the emitted BC mass with the increase of oxygen
content in the fuels. The effect of oxygen content on BC formation and emissions was
explained in the work of Gill et al. [59] . It was suggested that oxygen present in the fuel
molecule could promote the diffusion phase combustion, where soot is mainly produced,
even though the diffusion phase is extended. Increased diffusion phase temperature will
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promote soot oxidation. Therefore the increase in oxygen content will result in a decrease
in the soot or BC emissions. It is important to note that there was a linear relationship
between the oxygen content and black carbon. Apart from idle, all other three modes
showed very similar trends; so on the average, BC is reduced by 270 (mg/kg fuel) per
every 10 percent of increase in oxygen content in the fuel.
Figure 6-5: Dependence of the (a) Overall volatile content and b) ROS concentration on
fuel oxygen content for the four modes tested. R2 shown in the graphs are McFadden's
pseudo R2
As can be seen from Figure 6-5b, increasing the oxygen content leads to an increase in the ROS
concentration, and therefore the OP of particles, for all of the modes tested. There is also an
increase in the overall volatile content, or OV, as shown on Figure 6-5a. It is interesting to
observe that both the OP and OV do not seem to have a linear dependence on the oxygen
content, as with BC, but exhibit a more complex behaviour with an exponential dependency. The
data also shows much more scattering for OP than the BC data and is strongly biased for the two
1500rpm modes (full and quarter) with the measurement of B100 C810 that had the highest
oxygen content. Although not explicitly discussing the change in the volatile content with
oxygen content, Pinzi et al. [49] saw an increase in the volatile content with an increase in the
carbon chain length of the fuel. As fuels with longer carbon chain length have lower oxygen
content they essentially observe an opposite effect. As mentioned previously, their trend could be
due to the mechanical direct injection diesel engine that they have used whose emissions are
P a g e | 148
more sensitive to the physical properties of the fuels as compared to a high-pressure common rail
engine used in our case. In any case, as most of the new engines used today are common rail our
example seems to be more relevant. The increase in the OV can be due to either the decrease in
the available non-volatile soot onto which the volatiles condense or an actual increase in the
amount of volatile material. If the trend displayed in Figure 6-5a was linear, then one of the
above effects would have been dominant. As the trend displayed on Figure 6-5b is non- linear,
in fact exponential, then one can assume that there is a combination of both effects: reduction in
the mass of soot and increase in the fuel derived volatile (organic) material.
Similarly there are two processes that could lead to the increase in the OP. The first is the
increase in the amount of ROS that is carried by the particles that could also be seen as the
increase in the OV. The second is the decrease in the total mass of DPM as the OP is calculated
per unit mass of particles. If the increase in the OP were due only to the decrease in the total
mass than the dependence on oxygen content would be a reciprocal function and not an
exponential. It is clear that the oxygen content plays a major role in the OP of DPM and most
likely this is a combination of the decrease of the total DPM and an increase of the volatile
organic fraction but also could be due to a change in the chemical composition of the organic
fraction.
Figure 6-6 shows the dependence of the OP expressed through the ROS concentration as a
function of the OV for all of the 4 modes tested. If the chemical composition of the volatile
organic material that contributes to the OV was similar for the same mode than one would expect
a linear dependence between the ROS concentration and OV. While this has been observed for
the same wood combustion conditions [40] and in some cases even for the organic carbon in
diesel exhaust [36] in general, various fuels and loads produce a VVF with significantly different
OP [29].
As can be seen from Figure 6-6, it seems that idle mode and quarter load 2000 mode can also be
modelled with linear fit. So, linear fit was also applied to those panels (i.e. idle1200 and
quarter2000) to further explore the nature of this correlation. Calculated R2 values for linear fit
were 0.45 and 0.27 for idle1200 and quarter2000 respectively. This indicates the existence of an
unspecific pattern (neither exponential nor linear). The explanation for this may be that for idle
1200 most of the volatile species contribute to the measured OP. However these compounds are
not equally reactive but still very volatile. In the case of quarter load at 2000 rpm, as the
consequence of an incomplete combustion, a number of unburnt hydrocarbons are expected to be
P a g e | 149
present in the exhaust. These hydrocarbons will contribute to the OV values and do not
necessarily need to carry measurable oxidative potential. This phenomenon would lead to an
unspecific pattern (neither exponential nor linear).
Figure 6-6: Dependence of the ROS concentration on the overall volatile content (OV) for
the 4 modes tested. R2 shown in the graphs are McFadden's pseudo R
2
The conclusion that follows the investigation of the fuel type on the OV and related OP is that
generally, the OP is ultimately coupled to the structure of fuel molecules, especially its oxygen
content, chain length and level of unsaturation. It also highlights the fact that this relationship is
dependent on the chemical composition of volatile matter. Even though complete combustion
can yield volatile paraffins, their OP is very low compared to oxygenated organic aerosols.
It appears that chemical composition of volatile matter, which contributes to high levels of ROS,
is different in partial and full load and therefore a more detailed chemical analysis of the organic
content of DPM is required to shed more light on the toxicity of DPM. The findings of this
research show that the more saturated fuels caused more potentially toxic substances in the
exhaust; and even though the more oxygenated fuels decreased PM, they also led to higher levels
of ROS in the particles. It was established that the majority of the redox activity is due to the
amount of organics in the PM. This highlights the fact that “less PM” does not necessarily mean
less harmful effect for the environment or less toxicity for humans and that new metrics (taking
into account the potentially toxic parts of DPM) should be considered.
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6.5 Acknowledgments The authors would like to acknowledge the support from the Australian Research Council under
Grants LP110200158, DP1097125, DP130104904 and DP120100126. Laboratory assistance
from Mr Noel Hartnett and Mr Scott Abbett were also appreciated.
6.6 References [1] F. Ma, M.A. Hanna, Biodiesel production: a review, Bioresource Technology, 70
(1999) 1-15.
[2] Y. Li, G. Tian, H. Xu, Application of Biodiesel in Automotive Diesel Engines, (2013).
[3] A. Demirbas, Biodiesel: a realistic fuel alternative for diesel engines, Springer, 2008.
[4] X. Zhang, C. Peterson, D. Reece, R. Haws, G. Moller, Biodegradability of biodiesel in
the aquatic environment, Transactions of the ASAE, 41 (1998) 1423-1430.
[5] J. Wang, F. Wu, J. Xiao, S. Shuai, Oxygenated blend design and its effects on reducing
diesel particulate emissions, Fuel, 88 (2009) 2037-2045.
[6] M.S. Graboski, R.L. McCormick, Combustion of fat and vegetable oil derived fuels in
diesel engines, Progress in Energy and Combustion Science, 24 (1998) 125-164.
[7] J. Song, K. Lee, Fuel Property Impacts on Diesel Particulate Morphology,
Nanostructures, and NOx Emissions, SAE Technical Paper, (2007) 01-0129.
[8] G. Knothe, Dependence of biodiesel fuel properties on the structure of fatty acid alkyl
esters, Fuel Processing Technology, 86 (2005) 1059-1070.
[9] G. Knothe, C.A. Sharp, T.W. Ryan III, Exhaust emissions of biodiesel, petrodiesel,
neat methyl esters, and alkanes in a new technology engine, Energy & Fuels, 20 (2006)
403-408.
[10] C.H. Cheng, C.S. Cheung, T.L. Chan, S.C. Lee, C.D. Yao, Experimental investigation
on the performance, gaseous and particulate emissions of a methanol fumigated diesel
engine, Science of the Total Environment, 389 (2008) 115-124.
[11] C.H. Cheng, C.S. Cheung, T.L. Chan, S.C. Lee, C.D. Yao, K.S. Tsang, Comparison of
emissions of a direct injection diesel engine operating on biodiesel with emulsified and
fumigated methanol, Fuel, 87 (2008) 1870-1879.
[12] K.L. Cheung, A. Polidori, L. Ntziachristos, T. Tzamkiozis, Z. Samaras, F.R. Cassee,
M. Gerlofs, C. Sioutas, Chemical Characteristics and Oxidative Potential of Particulate
Matter Emissions from Gasoline, Diesel, and Biodiesel Cars, Environmental Science &
Technology, 43 (2009) 6334-6340.
[13] N.C. Surawski, B. Miljevic, G.A. Ayoko, B.A. Roberts, S. Elbagir, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine employing two injection technologies and
three fuels, Environ Sci Technol, 45 (2011) 5498-5505.
P a g e | 151
[14] S. Sidhu, J. Graham, R. Striebich, Semi-volatile and particulate emissions from the
combustion of alternative diesel fuels, Chemosphere, 42 (2001) 681-690.
[15] P. Kumar, A. Robins, S. Vardoulakis, R. Britter, A review of the characteristics of
nanoparticles in the urban atmosphere and the prospects for developing regulatory controls,
Atmospheric Environment, 44 (2010) 5035-5052.
[16] L. Benbrahim-Tallaa, R. Baan, Y. Grosse, B. Lauby-Secretan, F. El Ghissassi, V.
Bouvard, N. Guha, D. Loomis, K. Straif, International agency for research on cancer
monograph working group. Carcinogenicity of diesel-engine and gasoline-engine exhausts
and some nitroarenes, Lancet Oncol, 13 (2012) 663-664.
[17] M. Tinsdale, P. Price, R. Chen, The impact of biodiesel on particle number, size and
mass emissions from a Euro4 diesel vehicle, (2010).
[18] S. Kalligeros, F. Zannikos, S. Stournas, E. Lois, G. Anastopoulos, C. Teas, F.
Sakellaropoulos, An investigation of using biodiesel/marine diesel blends on the
performance of a stationary diesel engine, Biomass and Bioenergy, 24 (2003) 141-149.
[19] G. Fontaras, G. Karavalakis, M. Kousoulidou, T. Tzamkiozis, L. Ntziachristos, E.
Bakeas, S. Stournas, Z. Samaras, Effects of biodiesel on passenger car fuel consumption,
regulated and non-regulated pollutant emissions over legislated and real-world driving
cycles, Fuel, 88 (2009) 1608-1617.
[20] H. Lee, C.L. Myung, S. Park, Time-resolved particle emission and size distribution
characteristics during dynamic engine operation conditions with ethanol-blended fuels,
Fuel, 88 (2009) 1680-1686.
[21] P.X. Pham, T.A. Bodisco, Z.D. Ristovski, R.J. Brown, A.R. Masri, The influence of
fatty acid methyl ester profiles on inter-cycle variability in a heavy duty compression
ignition engine, Fuel, 116 (2014) 140-150.
[22] Y.Y. Liu, T.C. Lin, Y.J. Wang, W.L. Ho, Biological toxicities of emissions from an
unmodified engine fueled with diesel and biodiesel blend, Journal of Environmental
Science and Health Part a-Toxic/Hazardous Substances & Environmental Engineering, 43
(2008) 1735-1743.
[23] N.C. Surawski, B. Miljevic, G.A. Ayoko, S. Elbagir, S. Stevanovic, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine: the influence of biodiesel feedstock,
Environmental Science & Technology, 45 (2011) 10337-10343.
[24] S. Pinzi, P. Rounce, J.M. Herreros, A. Tsolakis, M. Pilar Dorado, The effect of
biodiesel fatty acid composition on combustion and diesel engine exhaust emissions, Fuel,
(2012).
[25] R. Zhu, C.S. Cheung, Z. Huang, Particulate Emission Characteristics of a
Compression Ignition Engine Fueled with Diesel-DMC Blends, Aerosol Science and
Technology, 45 (2011) 137-147.
P a g e | 152
[26] S. Kim, S. Shen, C. Sioutas, Y.F. Zhu, W.C. Hinds, Size distribution and diurnal and
seasonal trends of ultrafine particles in source and receptor sites of the Los Angeles basin,
Journal of the Air & Waste Management Association, 52 (2002) 297-307.
[27] R.O. McClellan, HEALTH-EFFECTS OF EXPOSURE TO DIESEL EXHAUST
PARTICLES, Annual Review of Pharmacology and Toxicology, 27 (1987) 279-300.
[28] B. Giechaskiel, B. Alfoldy, Y. Drossinos, A metric for health effects studies of diesel
exhaust particles, Journal of Aerosol Science, 40 (2009) 639-651.
[29] S. Stevanovic, B. Miljevic, N. Surawski, K.E. Fairfull-Smith, S.E. Bottle, R. Brown,
Z.D. Ristovski, The influence of oxygenated organic aerosols (OOA) on the oxidative
potential of diesel and biodiesel particulate matter, Environmental Science & Technology,
47 (2013) 7655-7662.
[30] Z.D. Ristovski, B. Miljevic, N.C. Surawski, L. Morawska, K.M. Fong, F. Goh, I.A.
Yang, Respiratory health effects of diesel particulate matter, Respirology, 17 (2012) 201-
212.
[31] Q. Li, A. Wyatt, R.M. Kamens, Oxidant generation and toxicity enhancement of aged-
diesel exhaust, Atmospheric Environment, 43 (2009) 1037-1042.
[32] M. Shinyashiki, A. Eiguren-Fernandez, D.A. Schmitz, E. Di Stefano, N. Li, W.P.
Linak, S.H. Cho, J.R. Froines, A.K. Cho, Electrophilic and redox properties of diesel
exhaust particles, Environmental Research, 109 (2009) 239-244.
[33] P.I. Jalava, M. Tapanainen, K. Kuuspalo, A. Markkanen, P. Hakulinen, M.S. Happo,
A.S. Pennanen, M. Ihalainen, P. Yli-Pirila, U. Makkonen, K. Teinila, J. Maki-Paakkanen,
R.O. Salonen, J. Jokiniemi, M.R. Hirvonen, Toxicological effects of emission particles
from fossil- and biodiesel-fueled diesel engine with and without DOC/POC catalytic
converter, Inhalation Toxicology, 22 (2010) 48-58.
[34] H.M. Kipen, S. Gandhi, D.Q. Rich, P. Ohman-Strickland, R. Laumbach, Z.H. Fan, L.
Chen, D.L. Laskin, J.F. Zhang, K. Madura, Acute Decreases in Proteasome Pathway
Activity after Inhalation of Fresh Diesel Exhaust or Secondary Organic Aerosol,
Environmental Health Perspectives, 119 (2011) 658-663.
[35] I.M. Kooter, M. van Vugt, A.D. Jedynska, P.C. Tromp, M.M.G. Houtzager, R.P.
Verbeek, G. Kadijk, M. Mulderij, C.A.M. Krul, Toxicological characterization of diesel
engine emissions using biodiesel and a closed soot filter, Atmospheric Environment, 45
(2011) 1574-1580.
[36] S. Biswas, V. Verma, J.J. Schauer, F.R. Cassee, A.K. Cho, C. Sioutas, Oxidative
Potential of Semi-Volatile and Non Volatile Particulate Matter (PM) from Heavy-Duty
Vehicles Retrofitted with Emission Control Technologies, Environmental Science &
Technology, 43 (2009) 3905-3912.
[37] P. Pham, Bodisco, T., Stevanovic, S., Rahman, M, Engine Performance
Characteristics for Biodiesels of Different Degrees of Saturation and Carbon Chain
Lengths, SAE Int. J. Fuels Lubr, 6 (2013) 188-198.
P a g e | 153
[38] D.A. Orsini, A. Wiedensohler, F. Stratmann, D.S. Covert, A new volatility tandem
differential mobility analyzer to measure the volatile sulfuric acid aerosol fraction, Journal
of Atmospheric and Oceanic Technology, 16 (1999) 760-772.
[39] A.P. Leskinen, J.K. Jokiniemi, K.E.J. Lehtinen, Characterization of aging wood chip
combustion aerosol in an environmental chamber, Atmospheric Environment, 41 (2007)
3713-3721.
[40] B. Miljevic, M.F. Heringa, A. Keller, N.K. Meyer, J. Good, A. Lauber, P.F. Decarlo,
K.E. Fairfull-Smith, T. Nussbaumer, H. Burtscher, A.S.H. Prevot, U. Baltensperger, S.E.
Bottle, Z.D. Ristovski, Oxidative Potential of Logwood and Pellet Burning Particles
Assessed by a Novel Profluorescent Nitroxide Probe, Environmental Science &
Technology, 44 (2010) 6601-6607.
[41] S. Stevanovic, B. Miljevic, G.K. Eaglesham, S.E. Bottle, Z.D. Ristovski, K.E.
Fairfull-Smith, The Use of a Nitroxide Probe in DMSO to Capture Free Radicals in
Particulate Pollution, European Journal of Organic Chemistry, 2012 (2012) 5908-5912.
[42] S. Stevanovic, B. Miljevic, N.C. Surawski, K.E. Fairfull-Smith, S.E. Bottle, R.
Brown, Z.D. Ristovski, Influence of Oxygenated Organic Aerosols (OOAs) on the
Oxidative Potential of Diesel and Biodiesel Particulate Matter, Environmental Science &
Technology, 47 (2013) 7655-7662.
[43] B. Miljevic, R.L. Modini, S.E. Bottle, Z.D. Ristovski, On the efficiency of impingers
with fritted nozzle tip for collection of ultrafine particles, Atmospheric Environment, 43
(2009) 1372-1376.
[44] S. Stevanovic, Z.D. Ristovski, B. Miljevic, K.E. Fairfull-Smith, S.E. Bottle,
APPLICATION OF PROFLUORESCENT NITROXIDES FOR MEASUREMENTS OF
OXIDATIVE CAPACITY OF COMBUSTION GENERATED PARTICLES, Chemical
Industry & Chemical Engineering Quarterly, 18 (2012) 653-659.
[45] M. Islam, M. Magnusson, R. Brown, G. Ayoko, M. Nabi, K. Heimann, Microalgal
Species Selection for Biodiesel Production Based on Fuel Properties Derived from Fatty
Acid Profiles, Energies, 6 (2013) 5676-5702.
[46] I. Riipinen, J.R. Pierce, N.M. Donahue, S.N. Pandis, Equilibration time scales of
organic aerosol inside thermodenuders: Evaporation kinetics versus thermodynamics,
Atmos Environ, 44 (2010) 597-607.
[47] S.B. Kwon, K.W. Lee, K. Saito, O. Shinozaki, Size-Dependent Volatility of Diesel
Nanoparticles: Chassis Dynamometer Experiments - Environmental Science &
Technology (ACS Publications), … science & technology, (2003).
[48] J. Ristimäki, K. Vaaraslahti, M. Lappi, J. Keskinen, Hydrocarbon Condensation in
Heavy-Duty Diesel Exhaust, Environmental Science & Technology, 41 (2007) 6397-
6402.
[49] S. Pinzi, P. Rounce, J.M. Herreros, A. Tsolakis, M. Pilar Dorado, The effect of
biodiesel fatty acid composition on combustion and diesel engine exhaust emissions, Fuel,
104 (2013) 170-182.
P a g e | 154
[50] D.B. Kittelson, W.F. Watts, J.P. Johnson, On-road and laboratory evaluation of
combustion aerosols—Part1: Summary of diesel engine results, J Aerosol Sci, 37 (2006)
913-930.
[51] H. Sakurai, K. Park, P.H. McMurry, D.D. Zarling, D.B. Kittelson, P.J. Ziemann, Size-
dependent mixing characteristics of volatile and nonvolatile components in diesel exhaust
aerosols, Environmental Science & Technology, 37 (2003) 5487-5495.
[52] H. Sakurai, H.J. Tobias, K. Park, D. Zarling, K.S. Docherty, D.B. Kittelson, P.H.
McMurry, P.J. Ziemann, On-line measurements of diesel nanoparticle composition and
volatility, Atmos Environ, 37 (2003) 1199-1210.
[53] D.R. Worton, G. Isaacman, D.R. Gentner, T.R. Dallmann, A.W.H. Chan, C. Ruehl,
T.W. Kirchstetter, K.R. Wilson, R.A. Harley, A.H. Goldstein, Lubricating Oil Dominates
Primary Organic Aerosol Emissions from Motor Vehicles, Environmental Science &
Technology, 48 (2014) 3698-3706.
[54] I. Sadiktsis, J.H. Koegler, T. Benham, C. Bergvall, R. Westerholm, Particulate
associated polycyclic aromatic hydrocarbon exhaust emissions from a portable power
generator fueled with three different fuels–A comparison between petroleum diesel and
two biodiesels, Fuel, 115 (2014) 573-580.
[55] T. Grigoratos, G. Fontaras, M. Kalogirou, C. Samara, Z. Samaras, K. Rose, Effect of
rapeseed methylester blending on diesel passenger car emissions – Part 2: Unregulated
emissions and oxidation activity, Fuel, 128 (2014) 260-267.
[56] B. Miljevic, M.F. Heringa, A. Keller, N.K. Meyer, J. Good, A. Lauber, P.F. Decarlo,
K.E. Fairfull-Smith, T. Nussbaumer, H. Burtscher, A.S. Prevot, U. Baltensperger, S.E.
Bottle, Z.D. Ristovski, Oxidative potential of logwood and pellet burning particles
assessed by a novel profluorescent nitroxide probe, Environmental Science & Technology,
44 (2010) 6601-6607.
[57] M.M. Rahman, A.M. Pourkhesalian, M.I. Jahirul, S. Stevanovic, P.X. Pham, H.
Wang, A.R. Masri, R.J. Brown, Z.D. Ristovski, Particle emissions from biodiesels with
different physical properties and chemical composition, Fuel, 134 (2014) 201-208.
[58] H. Wickham, ggplot2: elegant graphics for data analysis, Springer, 2009.
[59] S.S. Gill, A. Tsolakis, J.M. Herreros, A.P.E. York, Diesel emissions improvements
through the use of biodiesel or oxygenated blending components, Fuel, 95 (2012) 578-586.
P a g e | 155
6.7 Supporting information (SI)
𝑉𝐹(%) = (𝑉0−𝑉300
𝑉0) × 100 = (1 − (
𝑑300
𝑑0)
3
) × 100 ……….Equation 1
𝑂𝑉(%) =∑(𝑉𝑂,𝐷𝑝𝑖
×𝑁𝐷𝑝𝑖)
∑(𝑉𝐷𝑝𝑖×𝑁𝐷𝑝𝑖
)× 100 ……………..Equation 2
Table S1. Engine specifications
Number of cylinders 6
Injection type High-pressure common-rail
Number of valves 4 / cylinder
Bore x stroke [mm] 102 x 120
Swept volume [litre] 5.9
Max. power [kW] 162 (at 2500 rpm)
Max. torque [Nm] 820 (at 1500 rpm)
Compression ratio 17.3:1
Dynamometer type Hydraulic
Emission standard Euro III
Figure S1: Experimental setup, HEPA: High-Efficiency Particulate Air Filter; EC:
Electrostatic Classifier; TD: Thermo-Denuder; VTDMA: Volatility Tandem Differential
Mobility Analyser; CPC: Condensation Particle Counter; SMPS: Scanning Mobility
Particle Sizer; Aethalometer: BC meter; the impingers are used to collect DPM for ROS
measurements
P a g e | 156
Table S2. Methyl esters and petro-diesel properties
C810 C1214 C1618 C1875
Commercial
Diesel Est. Ctane Number 44.12 61.54 63.28 58.75
Exp. Ctane Number 42 69.8 65.4 59 45
Hexanoic [wt%] C6:0 6 max - - -
Formatic [wt%] C8:0 50-60 - - -
Decanoic [wt%] C10:0 35-50 1.0 max - -
Lauric [wt%] C12:0 1.5 max 45–55 - -
Miristic [wt%] C14:0 - 15–20 - -
Palmitic [wt%] C16:0 - 8 –15 23-32 4.3
Stearic [wt%] C18:0 - 1–5 8 2.2
Oleic [wt%] C18:1 - 11–19 53 63.5
Linoleic [wt%] C18:2 - 4.0 max - 18.9
Linolenic [wt%] C18:3 - - - 9.2
Heneicosanoic [wt%] C20:0 - 0.5 max 1.5 max 0.4
Enoic [wt%] C20:1 - - - 1.1
Average formulae C9.5H19.7O2 C14.8H28.3O2 C18.3H35.3O2 C18.7H35.3O2
O2 [wt%] 19.29 13.47 11.14 10.96
Stoichiometric air fuel
ratio
11.12
11.12
12.05
12.50
12.48
14.5 (for
D#2) Relative density [kg/m
3], at 20
oC 0.877 0.871 0.873 0.879 0.848
Sulfur [mg/kg] - - - - 2.5
Iodine number 1.0 max 8 65 105 -
Saponification value 330 233 195 185 -
Boiling point [oC] 190 >150 165.6 >150 -
Acid value 0.9 0.4 0.8 0.4 0.05
Viscosity [m2/s] at 40
oC 0.997E-6 2.4E-6 - 4.5E-6 -
P a g e | 157
Figure S2. Dependence of the particle volume on the temperature of the thermodenuder
for two particle sizes: circles 30nm and triangles 150nm.
.
25
50
75
100
40 60 80 100 120 140 160 180 200 220 240 260 280
Temperature (C)
No
n−
vo
latile
fra
ction
(%
)
dp
30
150
B0 B20 B50 B100
0
25
50
75
0
25
50
75
0
25
50
75
0
25
50
75
C8
10
C1
214
C1
61
8C
18
75
30 60 90 120 150 200 30 60 90 120 150 200 30 60 90 120 150 200 30 60 90 120 150 200
Dp (nm)
VV
F (
%)
mode
idle1200
quarter1500
full1500
quarter2000
Figure –S3: Volume of the Volatile Fraction (VVF) for all pre-selected particle sizes
and all fuel blends.
P a g e | 159
Chapter 7: The role of microalgal biodiesel composition on diesel
engine exhaust particle emissions and their oxidative potential
Publication: Submitted to the journal of Fuel
Contributor Statement of contribution
Md Mostafizur
Rahman Contributed in experimental design, conducted emissions
measurements, analysed data and prepared the manuscript. Signature
Date
Muhammad Aminul
Islam
Contributed in experimental design, measurements, and reviewed
the manuscript.
S. Stevanovic Contributed in experimental design, measurement, data analysis and
manuscript preparation.
Md. Nurun Nabi Contributed in experimental design, measurement and reviewed the
manuscript.
Kirsten. Heimann Supervised the overall study and reviewed the manuscript
George Thomas Contributed in experiment design, measurement and reviewed the
manuscript.
Bo Feng Supervised the overall study design, set up and experiments,
reviewed the manuscript.
R. J. Brown Supervised the overall study design, set up and experiments,
reviewed the manuscript.
Z. D. Ristovski Supervised the overall study design, set up and experiments, data
analysis, manuscript preparation and also reviewed the manuscript.
Principal Supervisor Confirmation
I have sighted email or other correspondence from all Co-authors confirming their
certifying authorship.
Name Signature Date
Professor Zoran Ristovski
P a g e | 160
Title: The role of Microalgal Biodiesel Composition on Diesel Engine
Exhaust Particle Emissions and Their Oxidative Potential
M. M. Rahman1, M. A. Islam
1, S. Stevanovic
1, M. N, Nabi
1, K. Heimann
2 G. Thomas3, B.
Feng3, R. J. Brown
1, Z. D. Ristovski
1
1International Laboratory of Air Quality and Health (ILAQH) & Biofuel Engine Research
Facilities (BERF),Queensland University of Technology (QUT), Brisbane, QLD,
Australia 4001 2School of Marine and Tropical Biology, James Cook University, Townsville, QLD,
Australia 4811 3Department of Mechanical Engineering, University of Queensland (UQ), QLD,
Australia 4072
Abstract:
Microalgae are considered to be one of the most viable biodiesel feedstocks for the future
due to their potential for providing economical, sustainable and cleaner alternatives to
petroleum diesel. This study investigated the particle emissions from a commercially
cultured microalgae and higher plant biodiesels at different blending ratios. With a high
amount of long carbon chain lengths fatty acid methyl esters ( C20 to C22), the microalgal
biodiesel used had a vastly different average carbon chain length and level of unsaturation
to conventional biodiesel, which significantly influenced particle emissions, leading to
similar total particulate matter (TPM) emissions as petroleum diesel, especially at higher
blending ratios. In contrast, blends of up to 10% showed slight reductions in PM
emissions. However, based on measurements of reactive oxygen species (ROS), the
oxidative potential of particles emitted from the microalgal biodiesel combustion were
lower than that of regular diesel. Biodiesel oxygen content was less effective in
suppressing TPM emissions for biodiesels containing a high amount of polyunsaturated
C20-C22 fatty acid methyl esters and generated significantly increased nucleation mode
particle emissions. The observed increase in nucleation mode particle emission is
postulated to be caused by very low volatility, high boiling point and high density,
viscosity and surface tension of the microalgal biodiesel tested here. Therefore, in order to
preserve PM emission benefits attributed to the use of biodiesel, this study recommends
that the target composition of future microalgal biodiesel should not include FAMEs with
high amounts of polyunsaturated long-chain fatty acids (≥ C20).
P a g e | 161
7.1 Introduction Biodiesel is considered to be a potential alternative fuel for use in compression ignition
(CI) diesel engines. It is compatible with existing engine technology, without any
significant modifications and it also provides emission benefits, including a reduction in
carbon footprint emissions. Biodiesel produced from either renewable vegetable oils or
animal fats is considered as neutral in terms of carbon emissions [1]. In addition, numerous
studies report low CO, HC and particulate matter (PM) emissions from biodiesel [2-4], and
while some show an increase in NOx emissions [5, 6], others report no significant change
[6, 7]. Despite these advantages, the consumption of biodiesel is not widespread. The main
barrier to wide-spread use is a higher price compared to petroleum diesel. In addition, the
use of vegetable oil biodiesels raises food versus fuel conflicts, since most commercial
biodiesel feedstocks are also used as either human or animal food. Therefore, in order to
ensure a sustainable future for biodiesel, it is necessary to find feedstocks that will be able
to address these problems. Microalgae are considered to be one of the most promising
feedstock alternatives, which have potential to provide a viable solution for overcoming
present barriers.
Microalgae are considered to be a third generation biofuel feedstock, due to higher yields
and relatively low land requirement for production. Practically, microalgae can be grown
in any place where there is sufficient sunshine and water of low quality (industrial tailing
dams, secondary treated sewage, saline/brackish), including infertile land not suitable for
the cultivation of other biodiesel feedstocks and food producing crops [8]. Among
photosynthesising organisms, microalgae are the fastest growing and they can complete an
entire production cycle within a few days [9]. According to some estimates, annual oil
production from microalgae ranges from 20,000 to 80,000 L per acre, depending on
species and production method, which is 7–31 times higher than that of the highest oil-
producing terrestrial crop (palm) [9]. The required land footprint is also 10–340 times
smaller than that of their terrestrial counterparts. Therefore, some estimates suggest that oil
production from microalgae can be up to 200 times higher than the most efficiently
produced vegetable oils [10].
Although microalgae production, oil extraction and oil characterisation has been
extensively studied, very few have focused on engine performance and emissions from
microalgae biodiesel. Recently, Makarevičiene et al. [11] and others [12-14] investigated
engine performance and emission characteristics using low blends of microalgae biodiesel
P a g e | 162
(up to B30), but none of the studies conducted detailed particle emission measurements. In
addition, there are many varieties of microalgal species available and the fatty acid profile
of biodiesel produced from those species can be significantly different per se and is
strongly influenced by growth conditions. Some studies suggest that variations in the fatty
acid profiles of biodiesel can affect the performance and emission profiles [15, 16]. The
fatty acid composition of microalgae can be controlled either by selecting species with
ideal fatty acid profiles, genetic modification of a species, typically aimed at improved
growth and/ or fatty acid (lipid) production, or by manipulating growth conditions.
However, before embarking on the use of genetically modified microalgae or adding costs
for controlling growth conditions, it is necessary to determine which fatty acid
compositions will provide optimal output with the lowest possible emissions. In order to
address this knowledge gap, we conducted detailed particle emission measurements for a
number of different blends of a microalgal biodiesel (engine performance analysis is
reported in Islam et al [17]).
Diesel particle emissions have been in the spotlight in recent years since the International
Agency for Research on Cancer (IARC) included particulate matter emitted from diesel
engine exhaust as carcinogens. Our previous study [18] investigated particle emissions
from biodiesel with a FAME carbon number ≤ 18 and a high degree of poly-unsaturation.
This study established particle emissions dependence on carbon chain length and degree of
unsaturation of the biodiesel fatty acids, as well as oxygen content. Biodiesel with FAME
carbon numbers of more than 18 and a high degree of poly-unsaturation have not been
studied to date. Therefore, PM emissions from a microalgal biodiesel with high amounts of
C20 and C22 polyunsaturated fatty acids (PUFAs) were investigated and compared to B20
blends of vegetable biodiesels (cotton seed oil (CSO) and waste cooking oil (WCO)). This
study is a continuation of our previous study [18] with the aim of investigating the
influence of a biofuel with high amounts of very long chain poly-unsaturated FAMEs on
exhaust particle emissions.
7.2 Materials and Methods Experimental measurements were performed on a turbo-charged common rail engine
typically used in passenger cars. Detailed specifications of the engines are given in Table
7-1. A two-stage dilution system, as shown in Figure 7-1, was used for emission
measurements, where two ejector diluters (Dekati DI-1000) were connected in series.
Exhaust was sampled after the exhaust manifold via a 0.5 meter long stainless steel tube. A
P a g e | 163
fraction of the exhaust was then transferred to gas analysers via a copper tube fitted with a
HEPA filter and water trap. The rest of the sampled gas was sent to the diluter for dilution,
followed by particle measurement. A CAI 600 series CO2 analyser and a CAI 600 series
CLD NOx analyser were used for raw CO2 and NOx measurements. A SABLE CA-10
recorded CO2 concentrations from diluted exhaust. Particle number size distribution was
measured with a DMS-500 (Cambustion Ltd) without the heated sample line connected.
PM mass was calculated from DMS 500 data by using a re-inversion tool in the DMS data
analysis suite (version UIv 7.11), as suggested by Jonathan et al. [19]. In this case, a
density factor of 2.2 x 10-15
and a power coefficient of 2.65 and 5.2 x 10-16
and 3 were
applied to accumulation mode particles and to nucleation mode particles, respectively. In
addition, a TSI DustTrak 8530 measured PM. Oxidative potential (OP) of PM (nmol of
ROS per mg of PM) was based on the mass concentration of reactive oxygen species
(ROS). Profluorescent nitroxides (PFN) are very powerful optical sensors which can be
used as detectors of free radicals and redox active substances. The probe itself is poorly
fluorescent; however, upon radical trapping, or redox activity, a strong fluorescence is
observed [20]. Therefore, a BPEA (bis(phenylethynyl) anthracene-nitroxide) molecular
probe was used for the measurement of OP (potency of PM to induce oxidative stress).
Samples for ROS measurements (n=2) were collected by bubbling the aerosol through an
impinger containing 20 mL of 4 µM BPEA solution (containing dimethyl-sulfoxide (AR-
grade, supplier and details) as a solvent), followed by fluorescence measurements with a
spectrophotometer (Ocean Optics). The amount of BPEA reacting with the combustion
aerosol was calculated from a standard curve obtained by plotting known concentrations of
the methanesulfonamide adduct of BPEA (fluorescent) against the fluorescence intensity at
485 nm [21, 22].
Table 7-1: Test engine specifications
Model Peugeot 308 2.0 HDi
Cylinders 4
Compression ratio 18
Capacity 2.0 (L)
Bore × Stroke 85 × 88 (mm)
Maximum power 100 kW @ 4000 rpm
Maximum torque 320 Nm@ 2000 rpm
P a g e | 164
Aspiration (Turbo-charged) Inter-cooled
Fuel injection system Common rail (Multiple fuel injection)
Injection pressure: 1600 bar
Dynamometer Froude Holfmann AG150 eddy current dyno
Emission Certification Euro-IV
Microalgal biodiesel, derived from the dinoflagellate Crypthecodinium cohnii (Martek,
Singapore) was tested for three blending ratios of 10%, 20% and 50% biodiesel to
petroleum diesel (v/v) (supplied by Caltex Australia), designated as A10D90, A20D80 and
A50D50, respectively. A single batch of diesel was used to prepare all blends. In addition
to neat diesel, a 20% blend of waste cooking oil (WCO) and cotton seed oil (CSO)
biodiesel, designated as WCO20D80 and CSO20D80, were used as reference fuels with
shorter carbon chain lengths and different level of saturation. All blends were prepared in
volumetric flasks and then poured into the custom built engine fuel tank. The engine was
operated at a maximum torque speed of 2000 rpm and under four different loads (i.e. 25%,
50%, 75% and 100%).
Figure 7-1: Schematic of experimental set up
Compressed air Dekati dilutor 1
4-cylinder DI
diesel engine
Dynamometer
Sable
DustTrak
Crank angle
encoder Pressure transducer
Control
Compressor
Turbocharger
Air filter
Exh
aust
an
alyz
er
Exhaust gas
CO2
CO
NOx
THC
Computer
Computer
Dekati dilutor 2
DMS 500
ROS
ROS
P a g e | 165
The fatty acid profile of the used microalgae and WCO biodiesel are published in [17] and
are provided with CSO biodiesel for convenience in the supporting information (SI) Table
SI-1. The microalgal biodiesel was dominated by long chain poly-unsaturated fatty acids
resulting in longer average carbon chain length (20.38) and higher average degrees of
unsaturation (3.46) compared to the other two biodiesels tested (CSO and WCO). Also,
this microalgal biodiesel did not contain any mono-unsaturated fatty acids (MUFA), but
had a poly-unsaturated fatty acids (PUFA) content of ~69%. Average carbon chain length
and average unsaturation for the CSO was 18.94 and 1.47 respectively, followed by 18.78
and 1.03 for WCO. WCO biodiesel was composed of a higher fraction (67%) of MUFA,
whereas CSO biodiesel was composed of a higher fraction (51%) of PUFA.
Important physical properties of the pure microalgae and WCO biodiesel can be found in
Islam et al [17], and is provided for convenience in Table SI-2) alongside with CSO
biodiesel for comparison. Elemental compositions and relevant properties of the blends
used in engine testing are shown in Table 7-2. As suggested by Benjumea et al [23], all
these blend properties are calculated from the measured pure fuel properties by using the
Grunberg–Nissan mixing rule [24]. Viscosity, density and NBP increased with the increase
of biodiesel contents in the blends, where HHV and CN decreased. Among the biodiesel
blends, microalgal biodiesel blends had higher viscosity, density and NBP than WCO and
CSO for the same blending ratio. Despite these differences, all of the relevant properties
were found to be within the range of biodiesel standard ASTM 6751-12 or EN 14214,
although the CN of the pure microalgal biodiesel was slightly lower than prescribed in the
ASTM standard.
P a g e | 166
Table 7-2: Elemental compositions and important physical properties of the biodiesel
blends used for engine testing
A05D95 A10D90 A20D80 A50D50 CSO20 WCO20 Diesel
Elemental composition
Carbon (wt%) 85.59339 85.18983 84.39171 82.067 84.17697 84.14855 86.67
Hydrogen (wt%) 13.04125 12.93331 12.71985 12.09809 12.90075 12.95812 13.15
Oxygen (wt%) 0.560893 1.117584 2.21855 5.425364 2.375388 2.365388 0
Relevant physical properties
Viscosity(mm2/s) 2.761 2.882 3.124 3.85 2.946 3.076 2.64
Density (Kg/l) 0.8436 0.8472 0.8544 0.876 0.848 0.846 0.84
HHV (MJ/kg) 45.62365 45.3203 44.7136 42.8935 44.4264 44.6924 45.927
NBP (oC) 145.5 151 162 195 146 148 140
CN 50.3 50.1 49.7 48.5 57.8 52.12 50.5
HHV: Higher heating value, NBP: Normal boiling point, CN: cetane number
7.3 Results and Discussion
7.3.1 Specific particulate matter (PM) emissions
Brake-specific total particulate matter (TPM) emissions from the reference diesel and
different blends of biodiesel are shown in Figure 7-2. TPM emissions were calculated
from DMS data using a re-inversion tool in the DMS data analysis software. The
microalgal biodiesel blend-TPM emissions were load-dependent, where TPM emissions
were lower than petroleum diesel for all blends at 25% load, nearly as high or higher at
50% and 75% load for A20:D80 and A50:D50 blends, but only nearly as high for
A50:D50 blends at 100% load (Figure 7-2). Generally, TPM emissions positively
correlated with an increase in petroleum diesel of the microalgae biodiesel blends (Figure
7-2). In comparison to the microalgal biodiesel blends, TPM emissions from WCO20D80
and CSO20D80 biodiesels were found to be lower than for the A20 blend, with TPM
emissions from CSO20D80 being ≥ 50% lower than WCO20D80, except at 25% engine
load, where the difference was not that pronounced (Figure 7-2).
P a g e | 167
Figure 7-2: Brake-specific TPM emissions of diesel and biodiesel blends
When considering accumulation mode particles only, the trend in PM emissions was
similar to TPM emission patterns, except for the A50D50 blend. As shown in Figure 7-3,
accumulation mode PM emissions from A50D50 were always lower than for A20D80 at
all engine loads. Even at 100% load, accumulation mode PM emissions were consistently
reduced with increase in the microalgal biodiesel percentage, in contrast to the TPM
emissions presented above. This difference clearly indicates the significant contribution of
nucleation mode particles to TPM emissions, especially for the 50% microalgae blend.
DustTrak TPM measurements, as shown in the supporting information (Figure SI-1),
followed exactly the same trend as for the accumulation mode PM calculated from DMS
measurements. Therefore, it is reasonable to assume that the DustTrak is not sensitive
enough to measure PM in the nucleation mode, which has also been reported in other
studies [25, 26].
A few recent studies [12, 13] tested a B20 blend of biodiesel from the green freshwater
microalga Chlorella vulgaris and reported lower smoke opacity and soot emissions than
diesel. However, information on the FAME composition of the feedstock used was not
reported. It can be seen from other studies that FAME compositions of Chlorella vulgaris
Diesel A5A10
A20A50
CSO20
WCO20
0.00
0.05
0.10
0.15
100% load75% load
50% load
Bra
ke-s
pecific
TP
M(g
/kW
h)
25% load
Diesel A5A10
A20A50
CSO20
WCO200.00
0.02
0.04
0.06
0.08
Diesel A5A10
A20A50
CSO20
WCO200.00
0.02
0.04
0.06
Bra
ke-s
pecific
TP
M(g
/kW
h)
Fuel typeDiesel A5
A10A20
A50CSO20
WCO20
0.00
0.01
0.02
0.03
0.04
Fuel type
P a g e | 168
contain fatty acids with a carbon chain length of ≤ 18 [27, 28]. Taking into account the
results presented in this study, which are contrary to the work of Patel et al. [12], and
excluding the variations in the engine operating parameters, the reason for the difference
could be in the FAME profile of the biodiesel used. Due to the lack of detailed FA profile
information provided, this very important phenomenon should be investigated in more
detail. This should be done by testing microalgae biodiesels of different origins using the
same or a similar engine and keeping all other parameters unchanged, so the impact of
biodiesel chemical composition on the overall emission pattern can be determined.
Figure 7-3: Brake-specific accumulation mode PM emissions of diesel and biodiesel
blends
Biodiesel literature [23, 29-31] suggests linear correlation between blend properties and
pure biodiesel properties and their blending ratio. Study on microalgae biodiesel further
support this [32]. Likewise pure microalgal biodiesel, the diesel-microalgal biodiesel
blends tested had a higher density, viscosity, boiling point, surface tension and lower
cetane number than the reference diesel, and the other two biodiesels for the same blending
ratio. In addition, the average carbon chain length and average unsaturation of the
microalgal biodiesel was also higher than for the other two biodiesels, while the oxygen
content was almost the same. Therefore, as per our previous study [18], higher PM
Diesel A5A10
A20A50
CSO20
WCO20
0.00
0.05
0.10
0.15
100% load75% load
50% load
Bra
ke-s
peci
fic
PM
(g/k
Wh)
25% load
Diesel A5A10
A20A50
CSO20
WCO200.00
0.02
0.04
0.06
0.08
Diesel A5A10
A20A50
CSO20
WCO200.00
0.02
0.04
0.06
Bra
ke-s
peci
fic
PM
(g/k
Wh)
Fuel typeDiesel A5
A10A20
A50CSO20
WCO20
0.00
0.01
0.02
0.03
Fuel type
P a g e | 169
emissions were expected from the microalgal biodiesel blends than for the CSO and WCO
blends. Schönborn et al. [16] also tested pure C22:0 FAME in their custom-made engine
system, and found PM emissions to be almost the same as for diesel. In terms of carbon
number, the microalgal biodiesel tested in this study was similar to the one used in
Schönborn et al. [16], except that it contained high amounts of by C22:5 and C22:6. The
effect of biodiesel poly-unsaturation on PM emissions is not clear yet. Although one study
reported an increase in PM emissions with the increase of biodiesel degree of unsaturation
[33], others showed a small decrease or no significant change [18, 34, 35]. On the other
hand, a decrease in PM emissions for lower blends of microalgae biodiesel (B5 and B10)
might be due to their oxygen content, while the change of the other properties (i.e.
viscosity and boiling point) typically responsible for increased PM emissions may have
been insufficient to produce an effect at such low blend ratios.
7.3.2 Particle number (PN) emission
Before discussing specific PN emissions, it is worth mentioning that DMS 500 provides a
separate log normal PSD spectrum for both nucleation and accumulation mode particles.
Nucleation or accumulation mode number concentration is actually an integrated number
of that particular PSD spectrum. The total PSD spectrum is the best fit for the nucleation
and accumulation mode spectrum, and the integrated total number under this best fit
spectrum is considered as the total particle number concentration. In the presence of a
nucleation mode peak, total particle number (TPN) is dominated by nucleation mode
particles. Therefore, a trend in TPN emissions among different biodiesel blends is not
expected here (Figure SI-2). An almost 10-fold increase in TPN emissions was observed
for the A50D50 blend, which was predominantly driven by the presence of nucleation
mode particles. However, as shown in Figure 7-4, a trend similar to that presented for
particle mass, was observed for accumulation mode PN emissions. Accumulation mode PN
from the microalgal blends decreased for 5% and 10% blends, and then increased for 20%
and 50% blends, except at 100% engine load, where it consistently decreased with the
increase in biodiesel content. Unlike the microalgal biodiesel blends, TPN from WCO and
CSO blends was found to be slightly lower than from the reference diesel and this followed
the trend for TPM emissions. This finding indicates that WCO and CSO blends did not
contribute as much to nucleation mode particles compared to the microalgal blends,
although the measurement conditions were the same for the overall duration of tests. This
can be explained by the difference in chemical composition and physical properties of the
P a g e | 170
tested biodiesels, as outlined above. The higher boiling point due to high amounts of
C22:5 and C22:6, the microalgal biodiesel blends tested could result in unburned fuel
escaping from the combustion process and staying in the exhaust as volatiles and semi-
volatiles, along with other partially oxidised substances [36]. These volatiles and semi-
volatiles could also have a higher boiling point and lower saturation vapour pressure,
which means that they are more prone to condense, and form nucleation mode particles,
than low boiling point substances under the same conditions [37]. In addition, the presence
of fewer accumulation mode particles/soot for the microalgal biodiesel blends could also
enhance this process [38, 39].
Figure 7-4: Brake-specific particle number emission (accumulation mode) for diesel and
biodiesel blends
7.3.3 Particle number size distribution
Particle size distributions (PSD) for the different blends of biodiesel are shown in Figure
7-5. Due to the presence of a large nucleation mode for some blends, the whole PSD
spectrum has been shown as an inset, with the main graph clearly presenting the variation
among the different blends. The microalgal biodiesel blends consistently exhibited 20 nm
nucleation mode peaks at 100% load. The peak of the nucleation peak was positively
correlated with the increase in microalgae biodiesel content, being highest in the A50D50
blend (almost 10-fold). WCO and CSO blends did not produce such nucleation mode
Diesel A5A10
A20A50
CSO20
WCO20
0
1x1014
2x1014
3x1014
4x1014
100% load75% load
50% load
Bra
ke-s
peci
fic
PN
(#/k
Wh)
25% load
Diesel A5A10
A20A50
CSO20
WCO200.0
5.0x1013
1.0x1014
1.5x1014
2.0x1014
Diesel A5A10
A20A50
CSO20
WCO200.0
5.0x1013
1.0x1014
1.5x1014
2.0x1014
Bra
ke-s
peci
fic
PN
(#/k
Wh)
Fuel typeDiesel A5
A10A20
A50CSO20
WCO20
0.0
2.0x1013
4.0x1013
6.0x1013
8.0x1013
1.0x1014
Fuel type
P a g e | 171
peaks, although their accumulation mode size was well below that of the reference diesel.
On the other hand, at 50% engine load, a nucleation mode peak was only observed for
A50D50. Other blends of the microalgal biodiesel produced the same accumulation mode
peak as the reference diesel, however the A20D80 peak was higher than the diesel peak.
Schönborn et al. [16] also found similar nucleation peaks in their measurements using pure
C22:0. As summarised above, the higher density, viscosity and surface tension, as well as
low volatility due to the microalgal fatty acid profile could have led to the formation of
excessive partially oxidised semi-volatile substances. Upon cooling, these semi-volatiles
then either nucleate to form new particles or condense on the surface of existing soot
particles. For example, the amount of soot produced from A50D50 did not have a large
enough surface area where the semi-volatiles could condense, therefore those semi-
volatiles were more likely to undergo nucleation. However, in the case of A20D80, the
higher levels of C22:6, which has a low volatility and mixing tendency, may also be
responsible for excessive soot formation, which is likely to have occurred under part load
conditions. Therefore, while A20D80 can be expected to produce some semi-volatiles
under part load conditions, it was not likely to be enough to trigger nucleation, since there
was enough soot surface area on which it could condense.
10 100 1000
0.0
5.0x107
1.0x108
1.5x108
2.0x108
10 100 10000.0
3.0x108
6.0x108
9.0x108
1.2x109
1.5x109
1.8x109
CSO20D80
WCO20D80
A50D50
A5D95
D100
A20D80
dN
/dlo
gD
p(#
/cm
3)
Particle size(nm)
A10D90
D100
A5D95
A10D90
A20D80
A50D50
WCO20D80
CSO20D80
A20D80
dN
/dlo
gD
p(#
/cm
3)
Particle size(nm)
A50D50
(a)
P a g e | 172
Figure 7-5: Particle size distribution of diesel and biodiesel blends at 100% (a) and 50%
(b) loads
7.3.4 Relationship between fuel oxygen content and particle emissions
Fuel-bound oxygen plays an important role in combustion, soot oxidation and subsequent
PM reduction. It either prevents in cylinder soot formation or oxidises already formed soot
particles. In our previous study [18], reductions in PM and PN were observed to be
inversely correlated with biodiesel oxygen content, regardless of variations in other
properties. However, a slightly different trend was observed in terms of the microalgal
biodiesel blends tested here. As shown in Figure 7-6, accumulation mode PM and PN
emissions decreased with increasing oxygen content at 100% engine load, however this
was not the case for TPM. To the contrary, although a number of studies showed consistent
reductions in PM with increased biodiesel oxygen content [18, 40], both TPM and TPN
increased for the 20% and 50% microalgal biodiesel blends in this study, which have a
relatively higher oxygen content. Some studies [41, 42] reported increased nanoparticle
emissions, which might be due to the increase in the nucleation mode particles. Barrios et
al. [43] used oxygenated additives (i.e. Ethyl Tertiary Butyl Ether (ETBE) and Diglyme
(Bis (2-methoxy ethyl ether)) which consistently resulted in reduction in accumulation
mode particles with increasing blending ratios, whereas nucleation mode particles followed
10 100 1000
0.0
2.0x107
4.0x107
6.0x107
8.0x107
10 100 1000
0.0
2.0x108
4.0x108
A50D50
D100
A20D80
dN
/dlo
gD
p(#
/cm
3)
Particle size(nm)
(b)
D100
A5D95
A10D90
A20D80
A50D50
WCO20D80
CSO20D80A20D80
dN
/dlo
gD
p(#
/cm
3)
Particle size(nm)
A50D50
P a g e | 173
the opposite trend. Several other reports also demonstrate reductions in PM in the presence
of oxygenates [18, 44, 45], with some studies suggesting that soot produced from
oxygenated fuels possesses more oxygen functional groups [2]. This would make biodiesel
soot more reactive, which could result in reductions in PM [46]. Others suggest that
oxygen atoms in the ester molecule decompose into two separate reactive oxygen carriers,
which then contribute to reductions in soot-precursors [47]. Therefore, while the positive
effect of fuel-bound oxygen on particle emissions is well established, none of the previous
studies have tested biodiesels with the same FAME content as those tested here. Only one
study, by Schönborn et al. [16], reported diesel-like TPM emissions from biodiesel having
22 carbon atoms in their FAME. Their study also demonstrated that biodiesel oxygen
content did not effectively reduce TPM emissions when the carbon number was >22.
Therefore, the generally accepted opinion that biodiesel oxygen content is the main driving
force behind reduced TPM emissions might not always hold true, especially for biodiesels
having a carbon number >22 in their FAME.
Figure 7-6: Relationship between accumulation mode PM and PN emissions with fuel
oxygen content at 100% load for diesel and biodiesel blends
0.00
0.01
0.02
0.03
0.04
0 1 2 3 4 5 6
0.0
2.0x1013
4.0x1013
6.0x1013
8.0x1013
1.0x1014
D100
A5D95
A10D90
A20D80
A50D50
CSO20D80
WCO20D80
Bra
ke-s
pecific
PM
(g/k
Wh)
Bra
ke-s
pecific
PN
(#/k
Wh)
Fuel oxygen content (wt%)
P a g e | 174
7.3.5 Influence of microalgae biodiesel on nucleation mode particle formation
Particle nucleation in engine exhaust emission measurements is a very complex process. It
largely depends on dilution conditions (i.e. pressure, temperature, humidity etc.) and the
saturation vapour pressure of volatile substances present in the exhaust. A small change in
dilution conditions can either promote or reduce nucleation and it is this uncertainty that
makes nucleation mode particle measurement difficult to reproduce [48-51]. Large
variations are mainly due to the exponential relationship between saturation vapour
pressure and temperature [52]. Therefore, small changes in the cooling gradients will cause
large changes in the saturation vapour pressure. Considering the above-mentioned
circumstances, the European Union (EU) Particle Measurement Program (PMP) excluded
nucleation mode particles from their particle number-based emission standards (i.e.
EURO5/6). Keeping in mind the complexities of measurements involving nucleation mode
particles, we kept the dilution system settings constant for the entire measurement period in
this study. Despite this, nucleation in microalgae biodiesel measurements was repeatedly
observed, especially with the higher blends. As shown in Figure 7-7(a), specific nucleation
mode PN increased linearly with increasing microalgal biodiesel content. On the other
hand, WCO20D80 and CSO20D80 were not observed to produce nucleation. This could
indicate that the unusual chemical composition of the microalgal biodiesel blends tested
here played a role in triggering the nucleation. This is likely explained by the above
mentioned significant differences of the microalgal biodiesel and the WCO and CSO
biodiesel blends. Therefore, it is likely that the density, viscosity and boiling point will
increase with increasing blend ratios [53, 54]. Figure 7-7(b) shows the thermos-
gravimetric analysis (TGA) of the diesel and biodiesel blends used in this study which
clearly demonstrates that the microalgal biodiesel blends are less volatile than CSO and
WCO biodiesel blends, showing a significant mass fraction even at temperatures above
350C.
P a g e | 175
Figure 7-7: Effect of biodiesel blends on nucleation mode particle (a) and TGA analysis of
the diesel and biodiesel blends used (b)
A fuel with relatively high density, viscosity, boiling point and low volatility could cause
poor atomisation and improper in-cylinder mixing with air [54, 55], which results in the
presence of unburned hydrocarbon and partially oxidised semi-volatiles in the exhaust. In
addition, the boiling point of these volatiles/semi-volatiles from the unburned fuel is also
expected to be higher, as indicated by lower saturation vapour pressure. This low
saturation vapour pressure could also be responsible for a higher tendency for the gas to
particle partitioning. Therefore, it is reasonable to assume that the microalgal biodiesel
blend properties in this regard are influenced by the blend ratios which should correlate
positively with gas to particle partitioning and increased nucleation mode particle numbers.
This is supported by some of the studies demonstrating a positive correlation between
nucleation mode particle increase and the carbon number of the biodiesel molecules [16,
56]. Fischer et al. [15] observed the presence of nucleation mode particles when using
canola biodiesel containing a higher amount of glycerol, a finding that was further
supported by one of our earlier studies [41]. Relevant properties of glycerol i.e. density,
viscosity and boiling point are also higher than diesel and commercial biodiesel [57],
therefore it could be speculated that biodiesels with substantial amounts of either ≥ 22
carbon number FAME molecules or impurities (i.e. glycerol) could induce nucleation
mode particles in the engine exhaust.
7.3.6 Oxidative potential of particles emitted from microalgae biodiesel blends
The measurement of oxidative potential (OP), based on the ROS concentration of PM, can
be used as a good indicator for reactivity and toxicity [58]. An in-house-developed
(a) (b)
P a g e | 176
profluorescent molecular probe BPEAnit was applied in a unique, rapid and non-cell-based
way to assess particulate OP [20, 59]. Based on data provided in the literature [22] there
are some uncertainties as to which chemical species are responsible for the measured redox
potential and overall toxicity. Generally, there is a consensus that the organic fraction is a
carrier of ROS [60]. Alternatively, ROS can be formed as a consequence of organic species
reactivity within the cell environment [61]. The latter can be also considered as a
secondary organic species.
Figure 7-8: Oxidative potential of particles produced from diesel and microalgal biodiesel
blend combustion
The oxidative potential of the tested microalgal biodiesel blends was smaller compared to
diesel (Figure 7-8). ROS concentrations were measured at two different loads: at idle load
and 50% load. It was expected that idle emissions would result in the emission of higher
concentrations of ROS, as previously observed [62]. This result can be explained by the
possible contribution of the combusted lubricating oil to overall OP. Furthermore,
biodiesel content of the blends did not significantly affect OP, although B20 had the
highest OP of the blends. This result suggests that OP and associated toxicity of the
particles can be lowered by blending with the microalgal biodiesel.
P a g e | 177
7.4 Conclusion This study investigated the particle emission behaviour of microalgal biodiesel blends as a
fuel with a high carbon chain length (20.38) and unsaturation (3.46) compared to
conventional biodiesel feedstocks, such as WCO and CSO blends. Results showed that the
percentage of C22 FAMEs in the biodiesel blends produced similar TPM emissions as
diesel, although at lower blends (up to 10%) a slight reduction was observed. Particle
emissions from the 20% microalgae biodiesel blends were significantly higher than 20%
WCO and CSO biodiesel blends. This study also demonstrated that the increased biodiesel
oxygen content was unable to effectively suppress TPM emissions, if the biodiesel blend
contained high percentages of FAMEs with a carbon number >22 and a high degree of
poly-unsaturation. Such biochemical composition of biodiesel blends could also trigger a
significant increase in nucleation mode particle emissions, but lower OP and particle
associated toxicity compared to diesel, irrespective of blend ratio. In contrast, biodiesel
blends with a FAME carbon chain length of <18 were less prone to produce nucleation
mode particles, unless blends contain a significant amount of impurities (i.e. glycerol). It is
possible that the very low volatility and high boiling point of the microalgal biodiesel
blends tested here in conjunction with other properties (i.e. high density, viscosity and
surface tension) were the driving forces for the formation of nucleation mode peak.
Therefore, in order to preserve the PM emission benefits associated with the use of
biodiesel blends, this study recommends that the target composition of future microalgae
biodiesel blends should not include FAMEs with >22 carbon atoms. This could be ensured
through appropriate species selection, the genetic modification of a target species or by the
manipulation microalgae growth conditions.
7.5 Acknowledgement
The authors sincerely acknowledge the technicians in the Engine Lab at the University of
Queensland (UQ), as well as PhD students in Queensland University of Technology (QUT)
Mr. Md. Jahirul Islam and Ali Mohammad Pourkhesalian for their valuable support during
the experimental work. The authors also acknowledge the North Queensland Algal
Identification/culturing Facility (NQAIF) at JCU, for providing the microalgal biomass and
access to their analytical facilities. The authors also acknowledge EcoTech Biodiesel
Company for providing waste cooking oil methyl ester for this experiment and Caltex
refinery for their valuable fuel property tests.
P a g e | 178
7.6 References [1] B.L. Salvi, N.L. Panwar, Biodiesel resources and production technologies – A review,
Renewable and Sustainable Energy Reviews, 16 (2012) 3680-3689.
[2] M. Salamanca, F. Mondragón, J.R. Agudelo, A. Santamaría, Influence of palm oil
biodiesel on the chemical and morphological characteristics of particulate matter emitted
by a diesel engine, Atmospheric Environment, 62 (2012) 220-227.
[3] M. Lapuerta, O. Armas, J. Rodríguez-Fernández, Effect of the Degree of Unsaturation
of Biodiesel Fuels on NOx and Particulate Emissions, SAE Int. J. Fuels Lubr., 1 (2008)
1150-1158.
[4] M. Kousoulidou, L. Ntziachristos, G. Fontaras, G. Martini, P. Dilara, Z. Samaras,
Impact of biodiesel application at various blending ratios on passenger cars of different
fueling technologies, Fuel, (2012).
[5] S.K. Hoekman, C. Robbins, Review of the effects of biodiesel on NOx emissions, Fuel
Processing Technology, 96 (2012) 237-249.
[6] K. Varatharajan, M. Cheralathan, Influence of fuel properties and composition on NOx
emissions from biodiesel powered diesel engines: A review, Renewable and Sustainable
Energy Reviews, 16 (2012) 3702-3710.
[7] L.G. Lackey, S.E. Paulson, Influence of feedstock: Air pollution and climate-related
emissions from a diesel generator operating on soybean, canola, and yellow grease
biodiesel, Energy and Fuels, 26 (2012) 686-700.
[8] A.H. Demirbas, Inexpensive oil and fats feedstocks for production of biodiesel, Energy
Education Science and Technology Part A: Energy Science and Research, 23 (2009) 1-13.
[9] A. Demirbas, M. Fatih Demirbas, Importance of algae oil as a source of biodiesel,
Energy Conversion and Management, 52 (2011) 163-170.
[10] Y. Chisti, Biodiesel from microalgae, Biotechnology Advances, 25 (2007) 294-306.
[11] V. Makarevičiene, S. Lebedevas, P. Rapalis, M. Gumbyte, V. Skorupskaite, J.
Žaglinskis, Performance and emission characteristics of diesel fuel containing microalgae
oil methyl esters, Fuel, 120 (2014) 233-239.
[12] J.S. Patel, N. Kumar, A. Deep, A. Sharma, D. Gupta, Evaluation of Emission
Characteristics of Blend of Algae Oil Methyl Ester with Diesel in a Medium Capacity
Diesel Engine, SAE Technical Paper, 2014-01-1378 (2014).
[13] C.A. Rinaldini, E. Mattarelli, M. Magri, M. Beraldi, Experimental Investigation on
Biodiesel from Microalgae as Fuel for Diesel Engines, SAE Technical Paper, 2014-01-
1386 (2014).
[14] G. Tüccar, T. Özgür, K. Aydın, Effect of diesel–microalgae biodiesel–butanol blends
on performance and emissions of diesel engine, Fuel, 132 (2014) 47-52.
P a g e | 179
[15] B.C. Fisher, A.J. Marchese, J. Volckens, T. Lee, J.L. Collett, Measurement of
Gaseous and Particulate Emissionsfrom Algae-Based Fatty Acid Methyl Esters, SAE Int. J.
Fuels Lubr., 03 (2010) 292.
[16] A. Schönborn, N. Ladommatos, R. Allan, J. Williams, J. Rogerson, Effect of the
Molecular Structure of Individual Fatty Acid Alcohol Esters (Biodiesel) on the Formation
of Nox and Particulate Matter in the Diesel Combustion Process, SAE Int. J. Fuels Lubr., 1
(2008) 849-872.
[17] M.A. Islam, M.M. Rahman, K. Heimann, M.N. Nabi, Z.D. Ristovski, A. Dowell, G.
Thomas, B. Feng, N. von Alvensleben, R.J. Brown, Combustion analysis of microalgae
methyl ester in a common rail direct injection diesel engine, Fuel, 143 (2015) 351-360.
[18] M. Rahman, A. Pourkhesalian, M. Jahirul, S. Stevanovic, P. Pham, H. Wang, A.
Masri, R. Brown, Z. Ristovski, Particle emissions from biodiesels with different physical
properties and chemical composition, Fuel, 134 (2014) 201-208.
[19] J.P.R. Symonds, K.S.J. Reavell, J.S. Olfert, B.W. Campbell, S.J. Swift, Diesel soot
mass calculation in real-time with a differential mobility spectrometer, Journal of Aerosol
Science, 38 (2007) 52-68.
[20] K.E. Fairfull‐Smith, S.E. Bottle, The synthesis and physical properties of novel
polyaromatic profluorescent isoindoline nitroxide probes, European Journal of Organic
Chemistry, 2008 (2008) 5391-5400.
[21] S. Stevanovic, B. Miljevic, G.K. Eaglesham, S.E. Bottle, Z.D. Ristovski, K.E.
Fairfull‐Smith, The use of a nitroxide probe in DMSO to capture free radicals in particulate
pollution, European Journal of Organic Chemistry, 2012 (2012) 5908-5912.
[22] S. Stevanović, Z. Ristovski, B. Miljević, K.E. Fairfull-Smith, S. Bottle, Application of
profluorescent nitroxides for measurements of oxidative capacity of combustion generated
particles, Chemical Industry and Chemical Engineering Quarterly/CICEQ, 18 (2012) 653-
659.
[23] P. Benjumea, J. Agudelo, A. Agudelo, Basic properties of palm oil biodiesel–diesel
blends, Fuel, 87 (2008) 2069-2075.
[24] C.A.W. Allen, K.C. Watts, R.G. Ackman, M.J. Pegg, Predicting the viscosity of
biodiesel fuels from their fatty acid ester composition, Fuel, 78 (1999) 1319-1326.
[25] D.C. Quiros, S. Yoon, H.A. Dwyer, J.F. Collins, Y. Zhu, T. Huai, Measuring
particulate matter emissions during parked active diesel particulate filter regeneration of
heavy-duty diesel trucks, Journal of Aerosol Science, 73 (2014) 48-62.
[26] M.M. Maricq, Monitoring Motor Vehicle PM Emissions: An Evaluation of Three
Portable Low-Cost Aerosol Instruments, Aerosol Science and Technology, 47 (2013) 564-
573.
[27] S.K. Hoekman, A. Broch, C. Robbins, E. Ceniceros, M. Natarajan, Review of
biodiesel composition, properties, and specifications, Renewable and Sustainable Energy
Reviews, 16 (2012) 143-169.
P a g e | 180
[28] J.-Y. Park, S.-A. Choi, M.-J. Jeong, B. Nam, Y.-K. Oh, J.-S. Lee, Changes in fatty
acid composition of Chlorella vulgaris by hypochlorous acid, Bioresource Technology, 162
(2014) 379-383.
[29] A.S. Silitonga, H.H. Masjuki, T.M.I. Mahlia, H.C. Ong, W.T. Chong, M.H. Boosroh,
Overview properties of biodiesel diesel blends from edible and non-edible feedstock,
Renewable and Sustainable Energy Reviews, 22 (2013) 346-360.
[30] Q. Shang, W. Jiang, H. Lu, B. Liang, Properties of Tung oil biodiesel and its blends
with 0# diesel, Bioresource Technology, 101 (2010) 826-828.
[31] Z. Al-Hamamre, A. Al-Salaymeh, Physical properties of (jojoba
oil + biodiesel), (jojoba oil + diesel) and
(biodiesel + diesel) blends, Fuel, 123 (2014) 175-188.
[32] Y.-H. Chen, B.-Y. Huang, T.-H. Chiang, T.-C. Tang, Fuel properties of microalgae (<
i> Chlorella protothecoides</i>) oil biodiesel and its blends with petroleum diesel, Fuel, 94
(2012) 270-273.
[33] A. Schönborn, N. Ladommatos, J. Williams, R. Allan, J. Rogerson, The influence of
molecular structure of fatty acid monoalkyl esters on diesel combustion, Combustion and
Flame, 156 (2009) 1396–1412.
[34] S. Pinzi, P. Rounce, J.M. Herreros, A. Tsolakis, M. Pilar Dorado, The effect of
biodiesel fatty acid composition on combustion and diesel engine exhaust emissions, Fuel,
104 (2013) 170-182.
[35] E. Sukjit, J.M. Herreros, K.D. Dearn, R. García-Contreras, A. Tsolakis, The effect of
the addition of individual methyl esters on the combustion and emissions of ethanol and
butanol -diesel blends, Energy, 42 (2012) 364-374.
[36] J. Su, H. Zhu, S.V. Bohac, Particulate matter emission comparison from conventional
and premixed low temperature combustion with diesel, biodiesel and biodiesel–ethanol
fuels, Fuel, 113 (2013) 221-227.
[37] M. Inoue, A. Murase, M. Yamamoto, S. Kubo, Analysis of volatile nanoparticles
emitted from diesel engine using TOF-SIMS and metal-assisted SIMS (MetA-SIMS),
Applied surface science, 252 (2006) 7014-7017.
[38] T. RÖnkkÖ, A. Virtanen, J. Kannosto, J. Keskinen, M. Lappi, L. Pirjola, Nucleation
mode particles with a nonvolatile core in the exhaust of a heavy duty diesel vehicle,
Environ Sci Technol, 41 (2007) 6384-6389.
[39] P.Q. Tan, S.S. Ruan, Z.Y. Hu, D.M. Lou, H. Li, Particle number emissions from a
light-duty diesel engine with biodiesel fuels under transient-state operating conditions,
Applied Energy, 113 (2014) 22-31.
[40] J. Xue, T.E. Grift, A.C. Hansen, Effect of biodiesel on engine performances and
emissions, Renewable and Sustainable Energy Reviews, 15 (2011) 1098-1116.
P a g e | 181
[41] M.M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski, Influence of Different
Alternative Fuels on Particle Emission from a Turbocharged Common-Rail Diesel Engine,
Procedia Engineering, 56 (2013) 381-386.
[42] N.C. Surawski, B. Miljevic, G.A. Ayoko, S. Elbagir, S. Stevanovic, K.E. Fairfull-
Smith, S.E. Bottle, Z.D. Ristovski, Physicochemical characterization of particulate
emissions from a compression ignition engine: The influence of biodiesel feedstock,
Environmental Science and Technology, 45 (2011) 10337-10343.
[43] C.C. Barrios, C. Martín, A. Domínguez-Sáez, P. Álvarez, M. Pujadas, J. Casanova,
Effects of the addition of oxygenated fuels as additives on combustion characteristics and
particle number and size distribution emissions of a TDI diesel engine, Fuel, 132 (2014)
93-100.
[44] M.N. Nabi, R.J. Brown, Z. Ristovski, J.E. Hustad, A comparative study of the number
and mass of fine particles emitted with diesel fuel and marine gas oil (MGO), Atmospheric
Environment, 57 (2012) 22-28.
[45] X. Wang, C.S. Cheung, Y. Di, Z. Huang, Diesel engine gaseous and particle
emissions fueled with diesel–oxygenate blends, Fuel, 94 (2012) 317-323.
[46] R. Zhu, C.S. Cheung, Z. Huang, Particulate Emission Characteristics of a
Compression Ignition Engine Fueled with Diesel–DMC Blends, Aerosol Science and
Technology, 45 (2011) 137-147.
[47] K. Kohse-Hoinghaus, P. Osswald, T.A. Cool, T. Kasper, N. Hansen, F. Qi, C.K.
Westbrook, P.R. Westmoreland, Biofuel combustion chemistry: from ethanol to biodiesel,
Angew Chem Int Ed Engl, 49 (2010) 3572-3597.
[48] I.A. Khalek, D.B. Kittelson, F. Brear, Nanoparticle growth during dilution and cooling
of diesel exhaust: Experimental investigation and theoretical assessment, in, SAE technical
paper, 2000.
[49] B. Giechaskiel, L. Ntziachristos, Z. Samaras, V. Scheer, R. Casati, R. Vogt,
Formation potential of vehicle exhaust nucleation mode particles on-road and in the
laboratory, Atmospheric Environment, 39 (2005) 3191-3198.
[50] Z.D. Ristovski, E.R. Jayaratne, M. Lim, G.A. Ayoko, L. Morawska, Influence of
diesel fuel sulfur on nanoparticle emissions from city buses, Environmental Science and
Technology, 40 (2006) 1314-1320.
[51] T. Rönkkö, A. Virtanen, K. Vaaraslahti, J. Keskinen, L. Pirjola, M. Lappi, Effect of
dilution conditions and driving parameters on nucleation mode particles in diesel exhaust:
Laboratory and on-road study, Atmospheric Environment, 40 (2006) 2893-2901.
[52] R.C. Weast, M.J. Astle, W.H. Beyer, CRC handbook of chemistry and physics, CRC
press Boca Raton, FL, 1988.
[53] P. Benjumea, J. Agudelo, A. Agudelo, Basic properties of palm oil biodiesel–diesel
blends, Fuel, 87 (2008) 2069-2075.
P a g e | 182
[54] C.S. Lee, S.W. Park, S.I. Kwon, An experimental study on the atomization and
combustion characteristics of biodiesel-blended fuels, Energy & fuels, 19 (2005) 2201-
2208.
[55] M.A. Ahmed, C.E. Ejim, B.A. Fleck, A. Amirfazli, Effect of Biodiesel Fuel Properties
and Its Blends on Atomization, SAE Technical paper, 2006-01-0893 (2006).
[56] A. Schönborn, N. Ladommatos, J. Williams, R. Allan, J. Rogerson, The influence of
molecular structure of fatty acid monoalkyl esters on diesel combustion, Combustion and
Flame, 156 (2009) 1396-1412.
[57] N. Rahmat, A.Z. Abdullah, A.R. Mohamed, Recent progress on innovative and
potential technologies for glycerol transformation into fuel additives: A critical review,
Renewable and Sustainable Energy Reviews, 14 (2010) 987-1000.
[58] J.G. Ayres, P. Borm, F.R. Cassee, V. Castranova, K. Donaldson, A. Ghio, R.M.
Harrison, R. Hider, F. Kelly, I.M. Kooter, Evaluating the toxicity of airborne particulate
matter and nanoparticles by measuring oxidative stress potential-a workshop report and
consensus statement, Inhalation Toxicology, 20 (2008) 75-99.
[59] B. Miljevic, K.E. Fairfull-Smith, S. Bottle, Z. Ristovski, The application of
profluorescent nitroxides to detect reactive oxygen species derived from combustion-
generated particulate matter: Cigarette smoke–A case study, Atmospheric Environment, 44
(2010) 2224-2230.
[60] N. Li, C. Sioutas, A. Cho, D. Schmitz, C. Misra, J. Sempf, M. Wang, T. Oberley, J.
Froines, A. Nel, Ultrafine particulate pollutants induce oxidative stress and mitochondrial
damage, Environmental Health Perspectives, 111 (2003) 455.
[61] A.M. Knaapen, P.J. Borm, C. Albrecht, R.P. Schins, Inhaled particles and lung cancer.
Part A: Mechanisms, International Journal of Cancer, 109 (2004) 799-809.
[62] A. Pourkhesalian, S. Stevanovic, F. Salimi, M. Rahman, H. Wang, P. Pham, S. Bottle,
A. Masri, R. Brown, Z. Ristovski, Influence of Fuel Molecular Structure on the Volatility
and Oxidative Potential of Biodiesel Particulate Matter., Environmental Science &
Technology, (2014).
P a g e | 183
Supporting Information (SI):
Table SI-1: Fatty acid profile of biodiesels used
FAME Biodiesel feedstocks
Crypthecodinium cohnii (Microalga) Cotton
seed oil Waste cooking oil
C14:0 8.30 0.544
0.00
C16:0 22.20 17.854
11.2
C18:0 0.56 2.532
3.50
C18:1(Tran) 0.00 24.203 2.70
C18:1 (cis) 0.00 1.268
64.4
C18:2(cis-9,12) 0.00 50.246 18.3
C18:3(all cis 6,9,12) 0.41 1.537 0.00
C20:3 0.50 0.00 0.00
C20:4 0.70 0.00 0.00
C20:5 (EPA) 1.70 0.00 0.00
C22:5 17.90 0.00 0.00
C22:6 (DHA) 47.70 0.00 0.00
SFAs 31.06 21.805 14.72
MUFA 0.00 26.316 67.03
PUFA 68.94 51.879 18.25
Average chain length
20.38 18.94 18.78
Average unsaturation 3.46 1.47 1.03
Table SI-2: Properties of biodiesels and reference diesel used
Fuel property Test method Microalgal
biodiesel
Cotton
seed oil
biodiesel
Waste
cooking oil
biodiesel
Biodiesel
Standard
ASTM
6751-12
Biodiesel
Standard
EN 14214
Petro-
Diesel
CN DIN 51773 46.5 87 58.6 47 min 51 53.3
Kinematic viscosity
@40C ( mm2/s)
ASTM D445 5.06 4.17 4.82 1.9-6.0 3.5-5.0 2.64
Density @15C
( kg/L) ASTM D4052 0.912 0.88 0.87 0.86-0.90 0.86-0.90 0.84
HHV ( MJ/kg) - 39.86 39.73 39.9 - -
LHV ( MJ/kg) - 37.42 37.2 - 44.0
Acid value
( mg KOH/g) ASTM D974 0.14 - 0.5 max 0.5max 0.0
Flash point
(close cup) ○C
ASTM D93 95.0 - 93 min - 71.0
Flash point (○C ) ASTM D93 - ≥180 130 120 -
P a g e | 184
Diesel A5A10
A20A50
CSO20
WCO200.00
0.02
0.04
0.06
100% load75% load
50% load
Bra
ke-s
peci
fic
PM
(g/k
Wh)
25% load
Diesel A5A10
A20A50
CSO20
WCO200.00
0.01
0.02
0.03
Diesel A5A10
A20A50
CSO20
WCO200.00
0.01
0.02
0.03
Bra
ke-s
peci
fic
PM
(g/k
Wh)
Fuel typeDiesel A5
A10A20
A50CSO20
WCO20
0.000
0.005
0.010
0.015
0.020
Fuel type
Figure SI-1: Brake specific TPM emissions measured by DustTrak of the diesel and
biodiesl blends
Sulfur content
( mg/kg) ASTM D7039 7.5 - 15max 10max 5.9
cloud point (○C) IP 309 16.1 - report report 4.0
Lubricity @○60
(mm) IP 405 0.136 - - - 0.406
copper corrosion
(3 hrs @50○C)
ASTM D130 1a - 1max 1 max 1a
water sediment
( vol%) ASTM D2709 0 -
0.005%
max
0.005%
max
0.0
FBP (○C) ASTM D86 - - - - 357.7
Oxygen content
( vol%) - 10.47 10.91 10.93 - - -
Hydrogen content
( vol%) - 11.12 11.94 12.21 - - -
Carbon content
( vol%) - 78.41 77.15 76.93 - - -
P a g e | 185
Diesel A5A10
A20A50
CSO20
WCO20
0.0
5.0x1014
1.0x1015
100% load75% load
50% load
Bra
ke-s
peci
fic P
N(#
/kW
h)
25% load
Diesel A5A10
A20A50
CSO20
WCO200.0
5.0x1014
1.0x1015
Diesel A5A10
A20A50
CSO20
WCO200.0
4.0x1014
8.0x1014
Bra
ke-s
peci
fic P
N(#
/kW
h)
Fuel typeDiesel A5
A10A20
A50CSO20
WCO20
0.0
5.0x1014
1.0x1015
Fuel type
Figure SI-2: Brake specific total PN emissions of the diesel and biodiesel blends
Diesel A5A10
A20A50
CSO20
WCO20
0
1x1015
2x1015
3x1015
100% load75% load
50% load
Bra
ke-s
pecific
PN
(#/k
Wh)
25% load
Diesel A5A10
A20A50
CSO20
WCO200.0
2.0x1014
4.0x1014
6.0x1014
8.0x1014
1.0x1015
Diesel A5A10
A20A50
CSO20
WCO200.0
2.0x1014
4.0x1014
6.0x1014
8.0x1014
1.0x1015
Bra
ke-s
pecific
PN
(#/k
Wh)
Fuel type
Diesel A5A10
A20A50
CSO20
WCO20
0.0
6.0x1014
1.2x1015
1.8x1015
2.4x1015
3.0x1015
Fuel type
Figure SI-3: Brake specific nucleation mode PM emissions of the diesel and biodiesel
blends
P a g e | 187
Chapter 8: Conclusion and further research
8.1 Conclusion arising from this thesis
This research has made a significant contribution to our understanding of how the chemical
composition and physical properties of biodiesel can influence particle emissions. The
experimental investigations involved a range of alternative fuels (i.e. bioethanol,
conventional biodiesel, biodiesel with controlled composition and microalgae biodiesel)
and therefore, the knowledge gathered from this study will help to formulate future
biodiesels and their blends, which could offer satisfactory performance with the lowest
possible emissions.
The first manuscript investigated the influence of ethanol fumigation and conventional
biodiesel on diesel engine exhaust particle emissions. It was observed that around a 90%
reduction in PM can be achieved when conventional biodiesels were used in place of
diesel, with a slight difference between canola and tallow feedstocks. However, a
discrepancy in PN of an order of magnitude was observed between canola and tallow
feedstocks, due to the presence of a nucleation mode peak in the case of canola. The
underlying reason for this difference was not fully understood, however it was assumed
that it could be related to the chemical composition and physical properties of the
biodiesel, which motivated the rest of the studies in this thesis. In addition, up to 30%
ethanol substitution reduced both PM and PN emissions by 59% and 70%, respectively, at
full engine load. Despite an overall reduction in air to fuel ratio, the observed reductions in
PM and PN were thought to be due to the enhanced oxidation of particulate matter by OH
radicals, which were in excess following ethanol fumigation. More than 30% ethanol
substitution could alter particle emission reductions, since it consumes enough intake air to
make fuel rich combustion favourable for soot formation. The low auto ignition properties
of ethanol could also favour soot formation, especially at higher fumigation levels (40%),
however additional studies on combustion diagnosis are required for further clarification. It
is also worth noting here that synthetic diesel, which is free from aromatics, was also found
to be a potential alternative fuel source for reducing particle emissions.
The 2nd
manuscript investigated the influence of biodiesel fatty acid profile on engine
performance and emissions. The experimental investigation used four biodiesels, with
different fatty acid carbon chain lengths and degrees of unsaturation. The use of saturated
P a g e | 188
biodiesel, having a short carbon chain length (C8-10) FAME, has not only improved
combustion efficiency, but also reduced PM, PN and NOx emissions. However, particles
emitted from this short chained biodiesel have been found to be more toxic, since ROS
emissions per unit mass of particles increased significantly. Both PM and PN emissions
increased with increases in carbon chain length among biodiesels, whereas a slight
reduction was observed with increases in the degree of unsaturation.
The 3rd
manuscript compared the different blends (20%, 50% and 100%) of biodiesels used
in the 2nd
manuscript with diesel, in terms of their chemical composition and important
physical properties. The trend in PM and PN emissions observed from B100 in the 2nd
manuscript was the same for B50 and B20 as well. A linear relationship was also observed
between particle emissions and oxygen content of fuel/fuel mix, regardless of the blend
percentages or types of biodiesel. However, since fuel oxygen content increases with
decreasing carbon chain length, it was not clear which of these factors drove the lower
particle emissions. Particle emissions also decreased linearly with the viscosity and surface
tension of the fuel/fuel mix, but only within specific blends. Particle size was also affected
by fuel type, and the fuel or fuel mixes which produced higher PM and PN emissions were
found to be responsible for larger particle median size. Overall, it is evident from this
section of the study that the chemical composition of biodiesel may be more important than
its physical properties in controlling exhaust particle emissions.
The 4th
manuscript had looked at the physical and chemical properties of particles emitted
from the same biodiesel feedstocks used in the 2nd
and 3rd
manuscript. Particles emitted
from short chain saturated biodiesel combustion were found to have a higher fraction of
volatile organic substances adsorbed or condensed onto their surface and therefore, they
also produced higher ROS emissions. Both OV and ROS correlated positively with fuel
oxygen content; however the relationships were found to be somewhat exponential rather
than linear. This increase in OV and ROS could be due to either a decrease in the available
non-volatile soot onto which the volatiles condense or an actual increase in the amount of
volatile material. However, the exponential relationship observed between OV and ROS
suggests a combination of both effects, such as a reduction in the mass of soot and an
increase in the fuel derived volatile (organic) material. This results further highlights the
impact of biodiesel FAME composition on the toxicity of particles emitted from their
combustion. In addition, it also challenges current mass and number based emission
P a g e | 189
standards, and fuels the debate about whether less particle emissions always means less
harm to human health and the environment.
The last manuscript investigated microalgae biodiesels with a distinctive FAME
composition, being that of 22 carbon atoms and 6 double bounds. Therefore, the average
carbon chain length and unsaturation was considerably higher for the microalgae biodiesel
than any other conventional biodiesel feedstocks. Experimental investigation revealed that
microalgae biodiesel with such a composition could result in almost the same TPM
emissions as diesel, especially at higher blends (i.e. B50). In this particular case, biodiesel
oxygen content could not effectively suppress PM emissions, rather it could trigger a
significant increase of nucleation mode particle emissions. It is possible that the very low
volatility and high boiling point of microalgae biodiesel is the driving force behind the
increase in nucleation mode particle emissions, in conjunction with other properties (i.e.
high density, viscosity and surface tension. Therefore, in order to preserve the PM
emissions benefits of biodiesel, it might be necessary to regulate the composition of
microalgae biodiesel, which should not include FAME with >22 carbon atoms in their
molecule.
It is clear that alternative fuels, especially biodiesels, are useful for reducing diesel engine
exhaust particle emissions and careful regulation of biodiesel FAME composition can
enhance these particle emissions benefits. Biodiesel with a higher saturation and lower
carbon chain length could maximise reductions in particle emissions, but not necessarily
their toxicological effects. Rather, they have been observed to be more toxic than those of
longer carbon chain biodiesels, in terms of OV and ROS emissions. Other drawbacks of
biodiesel use include a higher specific fuel consumption and lowest specific power output
than conventional diesel fuel. On the other hand, very high FAME carbon chain length
almost eliminates the aforementioned particle emission benefits. Therefore, based on the
desired output, the FAME composition of biodiesel should be regulated so that short chain
saturated FAMEs are used for NOx, PM and PN emission benefits and long chain
unsaturated FAMEs are used for engine power and specific fuel consumption benefits.
A compromise between emission benefits (i.e. PM, PN, ROS, NOx etc) and engine power
output and specific fuel consumption is also possible. In particular, this study recommends
to limit biodiesel FAME composition within 16 to 18 carbon atoms and with a higher
degree of unsaturation. Species selection criterion for new and emerging biodiesel
P a g e | 190
feedstock microalgae should also actively consider FAME composition, in addition to
yield percentages, growth rates, land requirements and so on. This will not only improve
biodiesel quality, but it will also enhance biodiesel performance and emission benefits,
including reductions in particle emissions.
Finally, based on the findings of this thesis, it is recommended that current biodiesel
standards should be brought under scrutiny, in order to maximise the emission benefits
associated with its use. Recent biodiesel standards ASTM D6751-12 and EN 14214 mainly
focus on the physical properties of biodiesel, although EN 14214 has included a percentage
limit for linolenic oil methyl ester (12% wt) and total unsaturated ester 1% wt [1, 2].
However, this work suggests that biodiesel FAME composition is equally as important as
the physical properties of biodiesel, in terms of its impact on performance and emissions.
8.2 Future research direction The higher fuel economy, better performance and durability are making diesel engines a
popular choice, particularly in the transportation sector [3]. With the rapid economic
growth of highly populated countries like China and India, the transportation sector is also
expanding very quickly and vehicles operated by diesel engines are expected to dominate
in this sector [4]. This clearly indicates that more and more people are going to be exposed
to particles emitted from these diesel fleets. Increasing concerns in relation to the health
and environmental effects of these particles have motivated research targeting the
mitigation of these particle emissions. In addition, the use of alternative fuels (i.e.
biodiesel) is also increasing around the world [5]. Although biodiesel reduces some aspects
of diesel emissions [6-8], it also raises additional issues in relation to particle emissions
[8]. Therefore, more inclusive research initiatives, with collaboration between different
branches of science and engineering, are needed in order to find a way out of eliminating
these pollutants from engine exhaust.
The main findings of this thesis are that reductions in biodiesel particle emissions improve
with a decrease in FAME carbon chain length and increased oxygen content. However,
biodiesel carbon chain length and oxygen content are interrelated in such a way that
oxygen content increases with the decrease in FAME carbon chain length and vice versa.
Therefore, it is not completely understood whether oxygen content or FAME carbon chain
length is the dominant reason for reductions in particle emissions. It would be interesting
to design experiments which will evaluate the effect biodiesel carbon chain length, whilst
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keeping the oxygen content constant or vice versa. However, for this type of investigation,
an additional oxygen carrier will need to be required into the biodiesel blend, which can
also bias the results.
The effect of biodiesel unsaturation on particle emissions also needs to be investigated
further. This thesis found a slight decrease in particle emissions with increased
unsaturation in biodiesel with a similar carbon chain length, when using fractionated
FAME. However, for microalgae biodiesel, no such reduction was observed, despite the
presence of much higher unsaturation. This could be due to the number and location of the
double bonds, with the microalgae biodiesel comprising a large fraction of di-unsaturated
and tri-unsaturated compounds, whereas the fractionated FAMEs were mainly composed
of monounsaturated compounds. In addition, several other studies claimed to have found
an increase in particle emissions with biodiesel unsaturation and termed the unsaturated
compounds as a soot precursors [9, 10], whereas another study reported results consistent
with the findings of this thesis[11]. Therefore, more investigation is needed to fully
understand the influence of biodiesel unsaturation on particle emissions. These studies
should formulate FAMEs with varying degrees of unsaturation, while the carbon chain
length remains the same. Information about the nature of unsaturation (i.e. mono, di or tri
unsaturation) and the location of the double would also be useful, in order to understand
their effects.
This study also found that the presence of a nucleation mode peak in PSD from some
biodiesel feedstocks could be related to their composition. Biodiesel composed of FAME
species with a higher boiling point or impurities (i.e. glycerol) could lead to the nucleation
mode peak in PSD. However, since nucleation is a very complex phenomenon and it is
largely dependent on dilution conditions, more studies are required to confirm this. This
thesis recommends using a very precisely controlled dilution system for this kind of study,
which will enable researchers to vary the dilution ratio, temperature, pressure and humidity
of the system accurately.
This thesis also reported an increased volatile content in particles with increased fuel
oxygen content, in the case of fractionated FAME. However, this study did not conduct
particle volatility measurements for microalgae and canola biodiesel, where the presence of
a nucleation mode peak was observed. The presence of a nucleation mode peak is an
indication of a higher percentage of volatile or semi-volatile content in the exhaust.
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Therefore, there is likely to be a higher fraction of volatile material in the soot coming
from microalgae and canola biodiesel combustion as well. Both canola and microalgae
biodiesels are composed of long chain FAME; therefore, they have a relatively lower
oxygen content than that of short chain biodiesels (C810 & C1214). If these hypotheses are
not rejected, then the relationship found between particle volatility and fuel oxygen content
would be questionable, and other factors (i.e. fuel volatility, boiling point etc) would need
to be considered. Therefore, more studies are required to confirm the relationship between
particle volatility and biodiesel oxygen content.
This thesis has shown that biodiesel FAME composition significantly influences particle
emissions, however the mechanisms by which this occurs are not yet fully understood.
Although suppression of in cylinder soot formation is the primary reason for particle
emission reductions from biodiesel use, in cylinder combustion diagnosis using high
resolution imaging technology would increase our understanding of the difference in soot
formation mechanisms when using biodiesel with a distinctive FAME composition. In
addition, the lattice structure of soot plays an important role in its oxidation [12, 13] and it
would also be interesting to investigate the variations in soot lattice structure for biodiesels
with a distinctive FAME composition. A combination of high resolution transmission
electron microscopy, helium ion microscopy, raman spectroscopy, thermo gravimetric
analysis and xrd analysis could reveal valuable information about particle morphology and
inner structure.
The presence of oxygen functional groups, either condensed or adsorbed onto the soot
surface, can also influence soot oxidation. This study found that biodiesels with a shorter
carbon chain length are responsible for higher ROS emissions, which is an indication of
the presence of more oxygen functional groups on the soot surface. However, the
composition of these oxygen functional groups, in terms of individual species, were not
investigated in this study. Information about the variation of these species for different
FAMEs could enhance our understanding of soot formation mechanisms, as well as our
knowledge on the health effects of the particles that they produce.
In addition, the compatibility of after-treatment devices with various biodiesels also needs
to be further investigated. Since particles emitted from biodiesel combustion are smaller in
size and composed a higher fraction of volatile substances [14], it is necessary to ensure
that after-treatment devices are efficient and effective enough to handle these particles, and
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will not cause additional difficulties. Also, after-treatment devices are actually a proxy way
of reducing particle emissions from engine exhaust and future research should be focused
on finding a better way to reduce emissions before they reach the exhaust. One possible
breakthrough could be research into homogeneously charged compression ignition (HCCI)
in diesel engines, or formulating a fuel based on hydrogen rather than HC, which will
replace the current HC fuel responsible for particle emissions.
Finally, a robust and suitable measurement technique is a prerequisite for implementing
any particle emission control strategies effectively. Current mass and number based
metrics exclude volatiles, semi-volatiles and sub 23nm non-volatile PN due to a lack of
reproducible measurement techniques. Therefore, future research should be focused on
improving measurement techniques for semi-volatiles and sub 23nm non-volatile particles.
In addition, more research should be directed towards the improvement of current
instruments and development of new instruments, which will help facilitate vehicle on-
board emission measurements.
8.3 References [1] R. Shah, S. Porter, Evolution of biodiesel testing standards and specifications globally,
Biofuels, 5 (2014) 17-19.
[2] H. Jääskeläinen, Biodiesel Standards & Properties, DieselNet Technology Guide,
Ecopoint Inc (2009).
[3] L. Schipper, L. Fulton, Disappointed by Diesel?, Transportation Research Record:
Journal of the Transportation Research Board, 2139 (2009) 1-10.
[4] B. Roy, Diesel Engine and Car Market in India: Where Does Fiat Stand?, (2014).
[5] D. Tolmac, S. Prulovic, M. Lambic, L. Radovanovic, J. Tolmac, Global trends on
production and utilization of biodiesel, Energy Sources, Part B: Economics, Planning and
Policy, 9 (2014) 130-139.
[6] J. Pullen, K. Saeed, Factors affecting biodiesel engine performance and exhaust
emissions – Part II: Experimental study, Energy, 72 (2014) 17-34.
[7] J. Pullen, K. Saeed, Factors affecting biodiesel engine performance and exhaust
emissions - Part I: Review, Energy, 72 (2014) 1-16.
[8] N. Yanamala, M.K. Hatfield, M.T. Farcas, D. Schwegler-Berry, J.A. Hummer, M.R.
Shurin, M.E. Birch, D.W. Gutkin, E. Kisin, V.E. Kagan, A.D. Bugarski, A.A. Shvedova,
Biodiesel versus diesel exposure: Enhanced pulmonary inflammation, oxidative stress, and
differential morphological changes in the mouse lung, Toxicology and Applied
Pharmacology, 272 (2013) 373-383.
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[9] M. Salamanca, F. Mondragón, J.R. Agudelo, P. Benjumea, A. Santamaría, Variations
in the chemical composition and morphology of soot induced by the unsaturation degree of
biodiesel and a biodiesel blend, Combustion and Flame, 159 (2012) 1100-1108.
[10] G. Karavalakis, V. Boutsika, S. Stournas, E. Bakeas, Biodiesel emissions profile in
modern diesel vehicles. Part 2: Effect of biodiesel origin on carbonyl, PAH, nitro-PAH and
oxy-PAH emissions, Science of the Total Environment, 409 (2011) 738-747.
[11] S. Pinzi, P. Rounce, J.M. Herreros, A. Tsolakis, M. Pilar Dorado, The effect of
biodiesel fatty acid composition on combustion and diesel engine exhaust emissions, Fuel,
104 (2013) 170-182.
[12] M. Lapuerta, F. Oliva, J.R. Agudelo, A.L. Boehman, Effect of fuel on the soot
nanostructure and consequences on loading and regeneration of diesel particulate filters,
Combustion and Flame, (2012).
[13] W. Merchan-Merchan, S.G. Sanmiguel, S. McCollam, Analysis of soot particles
derived from biodiesels and diesel fuel air-flames, Fuel, (2012).
[14] M. Rahman, A. Pourkhesalian, M. Jahirul, S. Stevanovic, P. Pham, H. Wang, A.
Masri, R. Brown, Z. Ristovski, Particle emissions from biodiesels with different physical
properties and chemical composition, Fuel, (2014).