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

P a g e | 2

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.

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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

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Holmen, Soy Biodiesel and Petrodiesel Emissions Differ in Size, Chemical Composition

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potential of engine exhaust particles: Impact of using particulate filter or biodiesel fuel

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Rocha, J.B. De Andrade, Redox activity and PAH content in size-classified nanoparticles

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Xuanwei, China, American Journal of Industrial Medicine, 55 (2012) 5-10.

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[19] M.Z. Jacobson, Strong radiative heating due to the mixing state of black carbon in

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[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.

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[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

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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

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[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.

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[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.

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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

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[52] M.M. Rahman, S. Stevanovic, R.J. Brown, Z. Ristovski, Influence of Different

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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

P a g e | 40

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.

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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: z.ristovski@qut.edu.au

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.

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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

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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

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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

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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.

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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

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[4] M. Kousoulidou, L. Ntziachristos, G. Fontaras, G. Martini, P. Dilara, Z. Samaras,

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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.

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