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This study undertook a physico-chemical characterisation of particle emissions from a compression ignition engine equipped with 3 biodiesel feedstocks (i.e. soy, tallow and canola) at 4 different blend percentages (20%, 40%, 60% and 80%) to gain insights into their particle-related health effects. Particle physical properties were addressed by measuring particle number size distributions both with and without heating from a thermodenuder (TD). The chemical properties of particulates were investigated by measuring particle and vapour phase Polycyclic Aromatic Hydrocarbons (PAHs) and also Reactive Oxygen Species (ROS) concentrations. The particle number size distributions showed strong dependency on feedstock and blend percentage, and the median particle diameter decreased as the blend percentage was increased. Particle and vapour phase PAHs were generally reduced with biodiesel, with the results not depending heavily on blend percentage. The ROS concentrations increased monotonically with biodiesel blend percentage, but did not exhibit strong feedstock variability. Furthermore, the ROS concentrations correlated quite well with the organic volume percentage of particles - a quantity which increased with increasing blend percentage. At higher blend percentages, the particle surface area was significantly reduced, but the particles were internally mixed with a greater organic volume percentage (containing ROS) which has implications for using surface area as a regulatory metric for diesel particulate matter (DPM) emissions.
Alternative fuels, such as biodiesel, are currently being investigated not only to address global warming (1) but also to ameliorate DPM emissions (2). Whilst a considerable database exists describing the impact of biodiesel feedstocks on regulated emissions (i.e. PM, NOx, CO and HC's) (3, 4), limited information is available addressing the impact of different biodiesel feedstocks on particle emissions. Regulated emissions from compression ignition engines typically exhibit strong dependencies on both feedstock and blend percentage (4), so it is quite likely that particle emissions will display similar dependencies. At present, a detailed database is not in existence characterising the unregulated physico-chemical characteristics of DPM such as: particle number emission factors, particle size distributions, surface area as well as PAHs and ROS with different biodiesel feedstocks and blend percentages. Consequently, a primary objective of this study was to explore the physico-chemical properties of particle emissions from 3 biodiesel feedstocks tested at 4 different blend percentages to shed light on their potential health impacts. A combination of physical and chemical factors influences the health effects of DPM (5), where it is noted with biodiesel combustion that the particles have a much higher organic fraction (6).
The organic fraction of DPM will include many compounds that are deleterious to human health such as PAHs and ROS (7). Previous research has demonstrated a correlation between the semi-volatile organic component (i.e. they partition between the gas and particle phase) of particles and their oxidative potential for DPM (8), and also for wood smoke particles (9). Furthermore, a correlation has been demonstrated between the oxidative potential of particles and also PAH emission factors (10, 11). Typically, the chemical properties of particulate emissions, such as PAHs and ROS are detected using off-line techniques from analytical chemistry. The development of a near real-time technique enabling the detection of semi-volatile organic compounds would be quite useful, given their importance in assessing the health effects of DPM. As PAHs and ROS are both classed as semi-volatile organic compounds, it is therefore possible that heating DPM with a TD will provide near real-time qualitative information on these components. As a result, a secondary objective of this work was to assess whether on-line measurements of the organic volume percentage () of DPM can provide information on genotoxic compounds on the surface of the particle that are usually addressed using off-line techniques from analytical chemistry. To achieve this objective, the relationship between the organic volume percentage of particles and ROS concentrations is explored.
Historically, the regulation of DPM emissions has been achieved using a mass-based emissions standard, however, a particle number standard has recently been introduced in the European Union at the Euro VI stage (12). Whilst there have been studies suggesting that particle number emissions characterise respiratory (13) and cardio-vascular (14) morbidity from DPM more adequately than particle mass, toxicological studies have shown a strong inflammatory response from inert ultrafine particles in a size-dependent manner (15, 16). Consequently, the toxicological literature suggests that particle surface area could be a relevant metric for assessing DPM health effects. Given that DPM is quite often composed of a solid elemental carbon core with internally mixed semi-volatile organics (17), a surface area based metric would provide information on the ability of toxic organic compounds to adsorb or condense on the surface of the particle As a result, a third objective of this work was to critically examine whether regulation of the DPM surface area has merit, undertaken within the framework of particle emissions from a non-road diesel engine operated with various biodiesel feedstocks and blend percentages.
2.1 Engine and fuel specifications
Particulate emissions testing was performed on a naturally aspirated 4 cylinder Perkins 1104C-44 engine with a Euro II (off-road) emissions certification. The engine investigated is typical of those used in underground mines in Australia. The engine was coupled to a Heenan & Froude water brake dynamometer (DPX 4) to provide a load to the engine.
Ultra-low sulphur diesel (denoted ULSD hereafter, < 10 ppm sulphur) was used as the baseline fuel in this experiment, along with 13 biodiesel blends from 3 different feedstocks, all of which were commercially available in Australia. The 3 biodiesel feedstocks investigated were soy, tallow and canola, with each feedstock being investigated at 4 different blend percentages, namely; 20%, 40%, 60% and 80%. The opportunity arose during testing to undertake particle physical measurements with neat (i.e. 100%) soy biodiesel. The notation "BX" denotes that X% of the total blend (by volume) consists of biodiesel. In total, 14 different fuel settings were investigated in this study, all of which were undertaken at intermediate (i.e. 1400 rpm) speed full load. Particle physical measurements were made with all 14 fuel settings, whereas particle chemical measurements were only made with ULSD, and the 20% and 80% blends with each biodiesel feedstock. Further details on the engine specifications, the daily warm-up and oil changing procedure can be found in Surawski et al. (11).
2.2 Particulate emissions measurement methodology
The methodology used for diluting the exhaust sample follows that of Surawski et al. (18), and consists of a partial flow dilution tunnel followed thereafter by a Dekati ejector diluter. The methodology for measuring particle number size distributions follows that of Surawski et al. (18), however a TSI 3010 CPC was used instead of a TSI 3782 CPC. The methodology for measuring Reactive Oxygen Species (ROS) is identical to that used in Surawski et al. (18). Particle volatility was explored by passing the poly-disperse size distribution through a TSI 3065 TD set to 300 oC. A correction for TD losses was performed using dried sodium chloride (NaCl) particles. The tracheo-bronchial lung deposited surface area was measured with a TSI 3550 Nanoparticle Surface Area Monitor (NSAM). PM10 measurements were obtained with a TSI 8520 DustTrak and were converted to a gravimetric measurement using the tapered element oscillating microbalance to DustTrak correlation for diesel particles obtained by Jamriska et al. (19). The particle mass, surface and number size distributions were all measured after the second stage of dilution.
Measurements of particle phase and vapour phase Polycyclic Aromatic Hydrocarbons (PAHs) were also performed. 2-bromo-naphthalene and the following US EPA priority PAHs in dichloromethane were quantified with a Gas-Chromatography Mass-Spectrometry (GC-MS) system: Naphthalene, Acenaphthylene, Acenaphthene, Fluorene, Phenanthrene, Anthracene, Fluoranthene, Pyrene, Benzo(a) anthracene, Chrysene, Benzo(b)fluoranthene, Benzo(a)pyrene, Indeno(1,2,3-cd)pyrene, Dibenzo(a,h)anthracene, and Benzo(g,h,i)perylene. The methodology for sampling and quantification following guidelines presented in Lim et al. (20), and further information on the extraction procedure and the GC-MS system can be found in (11). Particle phase PAHs were collected on filters and vapour phase PAHs were collected in tubes containing XAD-2 adsorbent prior to their quantification using the GC-MS system. Measurements of particle phase and vapour phase PAHs were performed from the dilution tunnel.
For the ROS measurements, particles were bubbled through impingers (a test impinger, and a HEPA filtered control impinger) containing 20 ml of 4 Î¼M BPEAnit solution, using dimethylsulphoxide (DMSO) as a solvent. More details on the ROS sampling and quantification methodology such as: the impinger collection efficiency, nitroxide probe theory and its application to various combustion sources can be found in Miljevic et al. (9, 21, 22). All the ROS results were normalised to the gravimetric PM10 mass to give ROS concentrations in units of nmol/mg. Sampling was conducted from the dilution tunnel for the ROS measurements to enable sufficiently high concentrations for analysis.
For the chemical measurements (i.e. PAHs and ROS) five replicates were used for ULSD and B80 soy, whereas for the other fuel settings (B20 and B80 tallow and canola and B20 soy) three replicates were obtained. A diagram of the experiment set-up can be found in the supplementary information from Surawski et al. (11).
2.3 Data analysis
Particles from biodiesel combustion usually exhibit a higher semi-volatile organic fraction (6). As a result, heating biodiesel combustion particles with a TD should lead to a greater reduction in particle size compared with heating diesel particles. To quantify the volume reduction of particles upon heating with a TD, the organic volume percentage of particles ( (%)) (see Figure 6) was calculated from integrated particle volume size distributions obtained with a Scanning Mobility Particle Sizer (SMPS) via:
where: is the particle volume for unheated particles, is the particle volume for particles passed through a TD set to 300 o C. Note that equation  assumes spherical particles.
Raw results reporting the physico-chemistry of DPM for all 14 fuel settings can be found in Table S1. A result of interest from Table S1 is the good correlation between the SMPS and NSAM derived DPM surface area. The organic layer thickness on particles (Î´) was also computed (see Table S2) using both the SMPS-TD approach and also using surface area data from the TSI NSAM. Furthermore, it can be observed that a good correlation between these two techniques for calculating Î´ is demonstrated. Based on these results, the SMPS derived surface area and organic layer thicknesses agree quite well with the NSAM results, which justifies the choice of using the SMPS to derive the organic volume percentage of particles.
3.0 Results and discussion
3.1 PM10 emission factors
Figure 1 displays the brake-specific PM10 emission factors for all 14 fuel settings investigated in this study. This figure shows that PM10 emission factors decrease in a monotonic fashion with respect to biodiesel blend percentage, and that the PM10 emissions are also strongly dependent on biodiesel feedstock. For the soy feedstock, PM10 reductions range from 43% with B20 to 92% with B100, reductions in PM10 range from 58% for B20 to 88% for B80 for the tallow feedstock, whereas for the canola feedstock, the reductions range from 65% with B20 to 88% for B80. The observation of very large reductions in particulate matter emissions with biodiesel is a very commonly reported result in the biodiesel literature (3, 4), with the results from this study confirming this general trend.
Figure 1: Brake specific PM10 emission factors (g/kWh) for the 14 fuel settings investigated in this study.
3.2 Particle number emission factors
Figure 2 shows brake-specific particle number emission factors (#/kWh) for all 14 fuel settings. The results show a strong dependency on both biodiesel feedstock and blend percentage. For the soy feedstock, particle number reductions range from 4% (B40) to 53% (B100), whilst for B20 a 12% particle number increase occurs. Particle number increases range from 71% (B20) to 44% (B80) for the canola feedstock. For the tallow feedstock, particle number increases range from 7% (B20) to 25% (B40), whilst a particle number reduction of 14% occurs for B80.
FIGURE_1_FINAL_EST.tifFigure 2: Brake-specific particle number emissions (#/kWh) for the 14 fuel settings investigated in this study.
A puzzling result to emerge from this study was the non-monotonic trends in particle number emissions with respect to blend percentage. For all 3 feedstocks, a 20% blend increased particle number emissions, and for subsequent increases in blend percentage, the particle number emissions decreased. An exception to this trend was the tallow feedstock, which produced increased particle number emission for both 20% and 40% blends followed by subsequent decreases in particle number emissions with further increases in blend percentage. Non-monotonic particle number emissions (relative to ULSD) with increasing blend percentages were observed by Di et al. (23), where the particle number increases were reduced as the diethylene glycol dimethyl ether blend (an oxygenated alternative fuel) percentage was increased. Di et al. (23) suggested that particle oxidation kinetics were responsible for this result, with oxidation being suppressed at low blend percentages (giving particle number increases) and oxidation being promoted at high blend percentages (giving particle number reductions). This is a finding that should be investigated further with other biofuels. Given the absence of combustion-related diagnostic data, it is quite difficult to provide a detailed mechanistic description of this result at this stage.
Variability in regulated emissions from compression ignition engines (i.e. PM, NOx, CO and HC's) employing various biodiesel feedstocks is a topic that has been addressed fairly comprehensively in the diesel emissions literature (4, 24, 25). The variability of particle number emissions with different biodiesel feedstocks, however, is a topic that has only been addressed recently (26). Fontaras et al. (26) found that particle number emissions could be higher for biodiesel (by up to a factor of 3) due to the occurrence of nucleation with soy blends, however, reductions in particle number were achieved with other biodiesel feedstocks (such as palm and used frying oil methyl esters). The observation of variability in particle number emissions with different biodiesel feedstocks has implications for conducting future biodiesel studies as this suggests that measurements should be conducted on an individual basis, rather than assuming generalisable trends with different feedstocks.
3.3 Particle number size distributions
Particle number size distributions for all 14 fuel settings are shown in Figure 3; with all size distributions showing uni-modality with a peak only in the accumulation mode. It can be observed also from the size distributions that biodiesel is particularly effective at reducing particle concentrations at larger mobility diameters (> 200 nm), at the expense of increasing the concentration of smaller particles even without the onset of nucleation. The size distribution results presented here are quite different to those that are commonly reported, since increases in the accumulation mode concentrations are observed (especially for the canola feedstock) without the occurrence of nucleation.
Figure 3: Particle number size distributions for all fourteen fuel settings (top panel: soy feedstock, middle panel: tallow feedstock, bottom panel: canola feedstock). TD denotes tests where diesel aerosol was passed through a TD set to 300 oC.
A significant reduction in the count median diameter (CMD) of particles occurs as the biodiesel blend percentage is increased, which is a result that is commonly reported (but is certainly not a universal trend) in the biodiesel literature (3). Factors that could contribute to a reduced CMD with biodiesel include: the relative ease with which the biodiesel particle surface can be oxidised (27) and also structural compaction of the particles (28). Structural compaction of particles (characterised by particles having a higher fractal dimension) would reduce the drag force on particles in a differential mobility analyser which would reduce a particles transit time hence providing a reduction in the particles electrical mobility diameter.
The particle number size distributions whereby diesel aerosol was passed through a TD (shown in Figure 3 for the B80 blends) can also offer information on the mixing state of particles. Heating the particles with a TD led to a reduction in the median size of particles without introducing a secondary "volatile" peak in the particle number size distribution. Therefore, the semi-volatile organic component of particles for all biodiesel feedstocks are present as an internal mixture, a fact which will be used later in the results section of this manuscript.
3.4 PAH emission factors and ROS concentrations
Figure 4 displays the particle phase and vapour phase PAH emission factors. It can be observed that both particle and vapour phase PAHs are reduced for all 6 biodiesel fuel settings (relative to the ULSD results), except for the B80 soy particle phase result. Particle phase PAH reductions range from a 3.5% increase for B80 soy to a decrease of about 60% for B80 canola. Vapour phase PAH reductions range from 33% for B80 soy to 84% for B20 tallow. Overall, very strong feedstock dependency can be observed for the PAH emissions factors, which is a result consistent with the findings of Karavalakis et al. (29) who found vastly different PAH emission profiles when the biodiesel feedstock was changed.
Figure 4: Brake-specific particle phase (top panel) and vapour phase (bottom panel) PAH emissions for the 7 fuel settings where chemical analysis was performed. Error bars denote ± one standard error of the mean.
In terms of the PAH reductions with biodiesel, the USEPA (4) states that the emissions of toxics (such as PAHs) should decrease with biodiesel. This is due to the correlation between emissions of toxics and emissions of hydrocarbons - which are generally reduced with biodiesel (3). Despite the reduction in particle and vapour phase PAHs with biodiesel, a concerning result is the phase distribution of the PAHs. The percentage of PAHs that are in the particle phase range from 44-75%, a result somewhat higher than that reported by He et al. (30) who reported particle phase PAH percentages (i.e. of the total PAH emissions) ranging from 19 to 31% for a range of soy biodiesel blends.
Another feature that may be observed from the PAH vapour phase results is how the emissions are independent of, or do not vary significantly with, biodiesel blend percentage for a particular feedstock. This experimental result was also observed by Ballesteros et al. (31), who noted that PAH reductions with rapeseed and waste cooking oil methyl esters did not exhibit a linear reduction with biodiesel blend percentage.
ROS concentrations for the 6 fuel settings where a fluorescence signal was obtained are shown in Figure 5. From Figure 5, it can be observed that the ROS concentrations increase with biodiesel blend percentage, although there is not strong feedstock dependency, unlike some of the particle physical measurements presented thus far (eg particle number emission factors). Relative to neat diesel, ROS concentrations are reduced by 21% for B20 tallow and are increased by 16% for B20 canola. For the B80 tests, the tallow feedstock increased ROS concentrations by a factor of just over 9, for the soy feedstock an almost 10-fold increase was observed, whilst the B80 canola test increased ROS concentrations by a factor of approximately 7.
Figure 5: ROS concentrations (nmol/mg) for the 6 fuel settings where a fluorescence signal was obtained.
3.5 Particle volatility and ROS correlation
ROS are generally classed as "semi-volatile" organic compounds that evaporate when exposed to thermal treatment with a TD (9). Therefore, it is possible that qualitative information on ROS concentrations can be gained by investigating the volatility of particles. Equation  demonstrated how the organic volume percentage of particles could be calculated from the integrated raw (i.e. non TD) and TD particle number volume distributions. Figure 6 represents an attempt to establish a correlation between the organic volume percentage (i.e. volatility) of particles and their associated ROS concentrations. It can be observed from this graph that as the biodiesel blend percentage is increased; particles are internally mixed with more ROS (heated particle number size distributions in Figure 3 demonstrates internal mixing of particles) and also have a higher organic volume percentage. Despite the presence of considerable scatter in the relationship, the Pearson correlation co-efficient is quite strong (~ 0.91). Consideration of the volatility of particles with a TD is, therefore, able to provide potentially useful information on ROS concentrations.
Figure 6: A correlation between ROS concentrations and the organic volume percentage of particles.
3.6 Particle surface area and organic volume percentage of particles
Toxicological studies, such as (16), have pointed to the particle surface area as a potential metric for assessing the health effects of diesel particulate matter (DPM). The surface area of a particle provides a measure of the ability of toxic compounds (such as PAHs or ROS) to adsorb or condense upon it. Therefore, a particles surface area can be viewed as a "transport vector" for many compounds deleterious to human health. Figure 7 shows a relationship between the TD (i.e. heated) surface area of emitted particles and also their organic volume percentage. The TD surface area is employed in Figure 7 as this provides a good estimate of the total surface area that is available for adsorption or condensation. The results in Figure 7 are plotted with respect to biodiesel blend percentage with results from all 3 feedstocks averaged. With increasing biodiesel blend percentage the TD surface area of emitted particles is reduced, with reductions ranging from 10% with B20 to 62% for B80. Alternatively, as the biodiesel blend percentage is increased the particles are composed of a greater organic volume percentage. Even though a reduction in the organic volume percentage of particles is observed for the B20 and B40 blends, an approximately 2.5 fold increase (relative to ULSD) is observed for the B80 blends. As was demonstrated in Figure 6, particles which contain a greater organic volume percentage display a concomitant increase in their ROS concentrations and hence the ability of these particles to induce oxidative stress. This is a particularly important result, as for alternative fuels to be a viable alternative to ULSD they must be able to deliver not only a reduction in the surface area of particles emitted (without a reduction in particle size) but also a reduction of semi-volatile organics internally mixed within the particle surface.
The results presented in Figures 6 and 7 naturally have implications for the regulation of DPM using a surface area based metric. Regulating purely the particle surface area would not be able to detect results such as those presented in Figure 7, as the surface chemistry of particles is not explicitly considered. Therefore, not only the raw surface area of particles but also the surface chemistry of particles is important for assessing the health impacts of DPM. These results suggest that the development of instrumentation (and standards) that enable the internal mixing status of particles to be determined (within a surface area framework) are potentially required.
Figure 7: A graph showing the relationship between the TD surface area of heated particles, and the organic volume percentage of particles. The adjusted R2=0.98 for the TD particle surface linear fit. The adjusted R2=0.99 for the organic volume percentage 2nd order polynomial fit.
The authors wish to acknowledge support and funding provided by SkillPro Services Pty Ltd and the Australian Coal Association Research Program for funding project C18014. Special thanks go to Mr Julian Greenwood and Mr Dale Howard, from SkillPro Services, for their technical expertise throughout testing, and also for operating the dynamometer and providing the gaseous emissions and diagnostic test data.
Supporting Information Available
Two tables constitute the supplementary material for this manuscript.
(1) Agarwal, A. K., Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines. Progress in Energy and Combustion Science 2007, 33, (3), 233-271.
(2) McCormick, R. L., The impact of biodiesel on pollutant emissions and public health. Inhalation Toxicology 2007, 19, (12), 1033-1039.
(3) Lapuerta, M.; Armas, O.; Rodriguez-Fernandez, J., Effect of biodiesel fuels on diesel engine emissions. Progress in Energy and Combustion Science 2008, 34, (2), 198-223.
(4) United States Environmental Protection Agency A comprehensive analysis of biodiesel impacts on exhaust emissions. Draft technical report.; 2002; pp 1-126.
(5) Giechaskiel, B.; Alfoldy, B.; Drossinos, Y., A metric for health effects studies of diesel exhaust particles. Journal of Aerosol Science 2009, 40, 639-651.
(6) Knothe, G.; Sharp, C. A.; Ryan, T. W., Exhaust emissions of biodiesel, petrodiesel, neat methyl esters, and alkanes in a new technology engine. Energy & Fuels 2006, 20, (1), 403-408.
(7) Sklorz, M.; Briede, J. J.; Schnelle-Kreis, J.; Liu, Y.; Cyrys, J.; de Kok, T. M.; Zimmermann, R., Concentration of oxygenated polycyclic aromatic hydrocarbons and oxygen free radical formation from urban particulate matter. Journal of Toxicology and Environmental Health-Part a-Current Issues 2007, 70, (21), 1866-1869.
(8) Biswas, S.; Verma, V.; Schauer, J. J.; Cassee, F. R.; Cho, A. K.; Sioutas, C., Oxidative Potential of Semi-Volatile and Non Volatile Particulate Matter (PM) from Heavy-Duty Vehicles Retrofitted with Emission Control Technologies. Environmental Science & Technology 2009, 43, (10), 3905-3912.
(9) Miljevic, B.; Heringa, M. F.; Keller, A.; Meyer, N. K.; Good, J.; Lauber, A.; Decarlo, P. F.; Fairfull-Smith, K. E.; Nussbaumer, T.; Burtscher, H.; Prevot, A. S. H.; Baltensperger, U.; Bottle, S. E.; Ristovski, Z. D., Oxidative Potential of Logwood and Pellet Burning Particles Assessed by a Novel Profluorescent Nitroxide Probe. Environmental Science & Technology 2010, 44, (17), 6601-6607.
(10) Cheung, K. L.; Ntziachristos, L.; Tzamkiozis, T.; Schauer, J. J.; Samaras, Z.; Moore, K. F.; Sioutas, C., Emissions of Particulate Trace Elements, Metals and Organic Species from Gasoline, Diesel, and Biodiesel Passenger Vehicles and Their Relation to Oxidative Potential. Aerosol Science and Technology 2010, 44, (7), 500-513.
(11) Surawski, N. C.; Miljevic, B.; Ayoko, G. A.; Roberts, B. A.; Elbagir, S.; Fairfull-Smith, K. E.; Bottle, S. E.; Ristovski, Z. D., A physico-chemical characterisation of particulate emissions from a compression ignition engine employing two injection technologies and three fuels. Submitted to Environmental Science & Technology 2011.
(12) Dieselnet, Emissions standards, European Union, heavy-duty truck and bus engines, http://www.dieselnet.com/standards/eu/hd.php. In Ecopoint Inc: 2009.
(13) Peters, A.; Wichmann, H. E.; Tuch, T.; Heinrich, J.; Heyder, J., Respiratory effects are associated with the number of ultrafine particles. American Journal of Respiratory and Critical Care Medicine 1997, 155, (4), 1376-1383.
(14) Pekkanen, J.; Peters, A.; Hoek, G.; Tiittanen, P.; Brunekreef, B.; de Hartog, J.; Heinrich, J.; Ibald-Mulli, A.; Kreyling, W. G.; Lanki, T.; Timonen, K. L.; Vanninen, E., Particulate air pollution and risk of ST-segment depression during repeated submaximal exercise tests among subjects with coronary heart disease - The exposure and risk assessment for fine and ultrafine particles in ambient air (ULTRA) study. Circulation 2002, 106, (8), 933-938.
(15) Brown, D. M.; Wilson, M. R.; MacNee, W.; Stone, V.; Donaldson, K., Size-dependent proinflammatory effects of ultrafine polystyrene particles: A role for surface area and oxidative stress in the enhanced activity of ultrafines. Toxicology and Applied Pharmacology 2001, 175, (3), 191-199.
(16) Oberdorster, G., Pulmonary effects of inhaled ultrafine particles. International Archives of Occupational and Environmental Health 2001, 74, (1), 1-8.
(17) Maricq, M. M., Chemical characterization of particulate emissions from diesel engines: A review. Journal of Aerosol Science 2007, 38, (11), 1079-1118.
(18) Surawski, N. C.; Miljevic, B.; Roberts, B. A.; Modini, R. L.; Situ, R.; Brown, R. J.; Bottle, S. E.; Ristovski, Z. D., Particle Emissions, Volatility, and Toxicity from an Ethanol Fumigated Compression Ignition Engine. Environmental Science & Technology 2010, 44, (1), 229-235.
(19) Jamriska, M.; Morawska, L.; Thomas, S.; He, C., Diesel bus emissions measured in a tunnel study. Environmental Science & Technology 2004, 38, (24), 6701-6709.
(20) Lim, M. C. H.; Ayoko, G. A.; Morawska, L.; Ristovski, Z. D.; Jayaratne, E. R., Influence of fuel composition on polycyclic aromatic hydrocarbon emissions from a fleet of in-service passenger cars. Atmospheric Environment 2007, 41, (1), 150-160.
(21) Miljevic, B.; Fairfull-Smith, K. E.; Bottle, S. E.; Ristovski, Z. D., The application of profluorescent nitroxides to detect reactive oxygen species derived from combustion-generated particulate matter: Cigarette smoke - A case study. Atmospheric Environment 2010, 44, (18), 2224-2230.
(22) Miljevic, B.; Modini, R. L.; Bottle, S. E.; Ristovski, Z. D., On the efficiency of impingers with fritted nozzle tip for collection of ultrafine particles. Atmospheric Environment 2009, 43, (6), 1372-1376.
(23) Di, Y.; Cheung, C. S.; Huang, Z. H., Experimental investigation of particulate emissions from a diesel engine fueled with ultralow-sulfur diesel fuel blended with diglyme. Atmospheric Environment 2010, 44, (1), 55-63.
(24) Durbin, T. D.; Collins, J. R.; Norbeck, J. M.; Smith, M. R., Effects of biodiesel, biodiesel blends, and a synthetic diesel on emissions from light heavy-duty diesel vehicles. Environmental Science & Technology 2000, 34, (3), 349-355.
(25) Wang, W. G.; Lyons, D. W.; Clark, N. N.; Gautam, M.; Norton, P. M., Emissions from nine heavy trucks fueled by diesel and biodiesel blend without engine modification. Environmental Science & Technology 2000, 34, (6), 933-939.
(26) Fontaras, G.; Kousoulidou, M.; Karavalakis, G.; Tzamkiozis, T.; Pistikopoulos, P.; Ntziachristos, L.; Bakeas, E.; Stournas, S.; Samaras, Z., Effects of low concentration biodiesel blend application on modern passenger cars. Part 1: Feedstock impact on regulated pollutants, fuel consumption and particle emissions. Environmental Pollution 2010, 158, (5), 1451-1460.
(27) Jung, H.; Kittelson, D.; Zachariah, M., Characteristics of SME biodiesel-fueled diesel particle emissions and the kinetics of oxidation. Environmental Science & Technology 2006, 40, (16), 4949-4955.
(28) Smekens, A.; Godoi, R. H. M.; Vervoort, M.; Van Espen, P.; Potgieter-Vermaak, S. S.; Van Grieken, R., Characterization of individual soot aggregates from different sources using image analysis. Journal of Atmospheric Chemistry 2007, 56, (3), 211-223.
(29) Karavalakis, G.; Bakeas, E.; Stournas, S., Influence of Oxidized Biodiesel Blends on Regulated and Unregulated Emissions from a Diesel Passenger Car. Environmental Science & Technology 2010, 44, (13), 5306-5312.
(30) He, C.; Ge, Y. S.; Tan, J. W.; You, K. W.; Han, X. K.; Wang, J. F., Characteristics of polycyclic aromatic hydrocarbons emissions of diesel engine fueled with biodiesel and diesel. Fuel 2010, 89, (8), 2040-2046.
(31) Ballesteros, R.; Hernandez, J. J.; Lyons, L. L., An experimental study of the influence of biofuel origin on particle-associated PAH emissions. Atmospheric Environment 2010, 44, (7), 930-938.
A physico-chemical characterisation of particulate emissions from a compression ignition engine: the influence of biodiesel feedstock
N.C. Surawskia,b, B. Miljevica,d, G.A. Ayokoc, S. Elbagirc, S. Stevanovica,d, K.E. Fairfull-Smithd, S.E. Bottled, Z.D. Ristovskia*
aInternational Laboratory for Air Quality and Health, Queensland University of Technology, 2 George St, Brisbane QLD 4001, Australia
bSchool of Engineering Systems, Queensland University of Technology, 2 George St, Brisbane QLD 4001, Australia
cDiscipline of Chemistry, Faculty of Science and Technology, Queensland University of Technology, 2 George St, Brisbane QLD 4001, Australia
dARC Centre of Excellence for Free Radical Chemistry and Biotechnology, Queensland University of Technology, 2 George St, 4001 Brisbane, Australia
*Corresponding author: Z.D. Ristovski
Email address: [email protected]
Telephone number: +617 3138 1129
Fax number: +617 3138 9079
Table S1: A tabulation of raw results for this study. Uncertainties are calculated as ± one standard error of the mean. NC denotes that the quantity was not computed (i.e. chemical measurements for B40 and B60). * Indicates that the control fluorescence was greater than the test sample fluorescence, implying that an ROS concentration could not be calculated..
Primary dilution ratio
Secondary dilution ratio
Total dilution ratio
Particle number emission factors (#/kWh)
TD particle surface area (nm2/cm3)
NSAM particle surface area
Particle phase PAHs (mg/kWh)
Vapour phase PAHs (mg/kWh)
ROS concentrations (nmol/mg)
Organic volume percentage (%)
17.3 ± 2.7
14.1 ± 8.7
0.7 ± 0.5
117.4 ± 0.2
7.1 ± 1.3
9.0 ± 2.1
113.0 ± 0.3
103.9 ± 0.04
100.7 ± 0.4
17.9 ± 6.3
9.5 ± 5.3
6.3 ± 1.7
86.2 ± 0.9
112.2 ± 0.6
7.1 ± 1.0
2.3 ± 0.9
0.5 ± 0.2
102.2 ± 0.3
87.0 ± 0.3
14.5 ± 5.3
6.4 ± 1.4
6.1 ± 2.2
100.9 ± 0.9
17.3 ± 6.4
6.3 ± 1.8
0.8 ± 0.03
94.5 ± 1.7
2.0E+15 ± 1.1E+13
Table S2: Organic layer thickness (Î´) for the TSI NSAM approach and the TSI SMPS-TD approach.
Î´ TSI NSAM (nm)
Î´ TSI SMPS (nm)