Exposure Assessment in a Busway Canyon · in an engine under controlled conditions, e.g., of spark...

Exposure Assessment i Introduction

Maricela YIP WONG

Salzburg University Queensland University of Technology

Centre for Medical and Health Physics

Institute of Physics and Biophysics Salzburg, Austria

School of Physical Sciences Brisbane, Australia

Exposure Assessment in a Busway Canyon

Submitted by Maricela Yip Wong

Email:

[email protected]

In fulfilment of the requirements for the degree of Masters of Applied Science

FINAL REPORT November 2003

Exposure Assessment ii Introduction

Maricela YIP WONG

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Exposure Assessment iii Introduction

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Foreword

This report represents the results of a one and half year research project to conduct an exposure assessment in a busway station in Brisbane, Australia. The work is a collaborative project between the Institute of Physics and Biophysics of the University of Salzburg (UoS, Austria) and the Center of Medical Health and Physics at the Queensland University of Technology (QUT, Australia). Abstract

The study of ultrafine particles (particles with diameters less than 100 nm) emitted by diesel buses and the assessment of human exposure was part of our recent research activities. This study was conducted at the Woolloongabba busway station in Brisbane, Australia in the winter months during which temperature inversions occur frequently. Most buses that utilize the station are fuelled by diesel, which causes the exhaust to contain a significant quantity of PM. Such particles may be composed of toxic and carcinogenic substances. The aim of this project was to investigate the exposure of waiting passengers to particles emitted from buses. During the course of this study, passenger census was conducted, based on video surveillance, yielding person-by-person waiting time data. Furthermore, a bus census revealed accurate information about the total number of diesel versus CNG-powered buses. Background (outside of the bus station) and platform measurements of ultrafine particulate number-size distributions were made to determine ambient aerosol concentrations. It was assumed that significant differences between platform and background distributions were due to bus emissions which, combined with passenger waiting times, yielded an estimate of passenger exposure to ultra-fine particles from diesel buses. Keywords: Submicrometer particles, ultrafine particles, carbonaceous particles, soot, diesel

particulate matter, diesel engine exhaust, elemental carbon, exposure assessment, exposure concentration.

German Summary

Die vorliegende Studie befasst sich mit ultrafeine Partikel (Partikel mit Durchmessern weniger als 100 nm) welche von Dieselbussen emittiert werden und die daraus resultierende Exposition am Menschen. Die Untersuchung wurde an der Busway Station Woolloongabba in Brisbane, Australien während der Wintermonaten durchgeführt, in welchen Temperaturinversionen die Regel sind. Da die Busstation überwiegend von Diesel-betriebenen Fahrzeugen angefahren wurde, ergab sich eine bedeutende Menge an Abgaspartikel. Solche Partikel bestehen aus toxischen und krebserzeugenden Substanzen. Das Ziel dieses Projektes war es, die Partikel-Exposition die von den Bussen ausging auf die wartenden Passagiere aufzuspüren. Um die durchschnittliche Wartezeit pro Person berechnen zu können, war es erforderlich eine auf Videobasis bezogene Passagier-Erfassung durchzuführen. Ausserdem brachte eine detaillierte Auszählung der ein- und ausfahrenden Fahrzeuge darüber Aufschluss wie hoch die Gesamtzahl der Diesel- gegenüber CNG-angetriebene Busse war. Neben den Plattform-Messungen wurden auch Hintergrund-Messungen (ausserhalb der Station) gemacht um jenen Aerosolanteil zu ermitteln das durch den restlichen verkehr ausging. Es wurde angenommen, daß bedeutende

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Maricela YIP WONG

Unterschiede zwischen Plattform und Hintergrundverteilungen vor, welche in Kombination mit den Wartezeiten der Passagiere, eine Abschätzung der personenbezogenen Exposition durch diese extrem-feine Partikelfraktion ermöglichten. Statement of Original Authorship

The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher educational 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. Signed: ……………………………… Date: Salzburg in the month of November, 2003

Acknowledgements The author wishes to thank to Dr. Aaron Wiegand from the Photochemical Group who provided basic inspiration, great supervision and computing support for the data analysis and the design of the Microcal-Origin scripts required for final calculation of the exposure assessment. Special thanks to my friend and colleague Pierre Madl, for his technical, graphical, scientific and practical assistance during the course of the experimental and theoretical phase of this project. I would also like to acknowledge Graham Johnson, Dr. Milan Jamriska, and Congrong He for their practical advice. Thanks go to Dr. Lidia Morawska, head of the “International Laboratory for Air Quality and Health” (ILAQH at QUT1), as well as to Dr. Werner Hofmann head for “Dosimetry and Modelling Institute” at the University of Salzburg for making this project possible. Finally, special thanks to the people from the Centre Operations at Woollongabba Busway Station for their kind assistance and cooperation in providing the necessary facilities during the practical part of this research project, to Mr. Graham Weston, Senior Adviser Fleet Management, Brisbane City Council, Brisbane, Australia for providing the necessary bus information from the City of Brisbane and to Mr. Mike King, Technical Officer, Air Services, Environmental and Technical Services, Environmental Protection Agency, for providing the necessary meteorological data.

This work is dedicated to the loving memoryof my father, Santiago Yip Gook, to my mother,

Sai Ping Wong and to all the Sisters of theHolly Eucharistic of the Herrnau Convent at SBG (AUT)

1 http://www.sci.qut.edu.au/physci/cmhp/aerosol/

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Acronyms ADR Australian Design Rules set out design standards for vehicle safety and emissions. BF Bi-fuel vehicle can run on either natural gas or gasoline (see DF). BOC Busway Operation Centre for the South East Busway System (BNE, AUT). C Carbon, a tetravalent chemical occurring in 3 allotropic forms (graphite,

amorphous carbon, and diamond). CARB California Air Resources Board Since its formation in 1967, the ARB has worked

with the public, the business sector, and local governments to protect the public's health, the economy, and the state's ecological resources through the most cost-effective reduction of air pollution.

CBD Central Business District. CEPA California Environmental Protection Agency (USA) CIDI Compression Ignition Direct Injection (more commonly called the diesel engine),

which has the highest thermal efficiency of any internal combustion engine. Challenges to improvements include a lower specific power than the gasoline engine; significant PM and NOX in the exhaust; and the noise, vibration, and smell of the engine.

CMB Chemical Mass Balance involves identification of the sources of materials emitted into the air, quantitative estimation of the emissions rates of the pollutants, understanding of the transport of the substances from the sources to the downwind location and knowledge of the chemical and physical transformation.

CMD Count Median Diameter; the diameter in a log-normal distribution that corresponds to the median diameter.

CNG Compressed Natural Gas CO Carbon Monoxide is a colorless gas or liquid; practically odorless. Burns with a

violet flame. Slightly soluble in water; soluble in alcohol and benzene. Specific gravity 0.96716; boiling point -190oC; solidification point -207oC. Classed as an inorganic compound.

CO2 Carbon Dioxide is one of the gases in our atmosphere, being uniformly distributed over the earth's surface at a concentration of about 0.033% or 330 ppm. Commercially, CO2 finds uses as a refrigerant (dry ice is solid CO2) and in beverage carbonation

COPD Chronic Obstructive Pulmonary Disease is an umbrella term used to describe airflow obstruction that is associated mainly with emphysema and chronic bronchitis.

CPC Condensation Particle Counter, an instrument that measures the number concentration of small particles with a high efficiency.

CPCB Central Pollution Control Board is an Autonomous Body of the Ministry of Environment & Forests, Delhi, India.

CPIEM California Population Indoor Exposure Model is a software tool that combines air pollutant concentration distributions for several microenvironments, including outdoors, and population activity patterns that specify time spent in each microenvironment in a Monte Carlo framework to predict distributions of exposure concentrations for the California population.

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CRT Continuously Regeneration Traps, particulate traps, which resemble mufflers, extract approximately 90% of exhaust soot via a ceramic or metallic filter that’s continuously or periodically purged and regenerated. Regeneration involves incinerating the soot, using the equivalent of an internal toaster or a fuel-fired burner (active), or by relying solely on exhaust heat (passive).

CVD Cardiovascular Disease: Heart disease and stroke—the principal components of cardiovascular disease—are the first and third leading causes of death in the United States, accounting for more than 40% of all deaths.

DE Diesel Engine Exhaust Emissions (commonly known as 'diesel fumes') are a mixture of gases, vapors, liquid aerosols and substances made up of particles.

DEP Diesel Exhaust Particles (see DE). DPM Diesel-PM (see DE) DF Dual Fuel vehicle runs either on diesel only or diesel and natural gas with the

combustion of diesel used to ignite the natural gas. The stop-and-start nature of urban bus cycles limits the substitution of diesel by Natural Gas, and makes dual-fuel unsuitable if the objective is to reduce emissions.

EC Elemental Carbon (or Diesel Particulate, see DE). EC Electrostatic Classifier, an instrument to generate monodisperse particles of

uniform size from a polydisperse source. FP The flash point is the temperature at which the fuel is likely to explode on its own.GHG The principal greenhouse gas concentrations are carbon dioxide (CO2), methane

(CH4), nitrous oxide (N2O), and chlorofluorocarbons CFC-11 (CCl3F) and CFC-12 (CCl2F2).

GVM Gross Vehicle Mass, loader - freight forwarder, tow motor driver, driver of a rigid vehicle (including a motor cycle) not exceeding 4500 kg gross vehicle mass.

H2O Water in any aggregate state; vapor (gaseous), water (liquid), ice (solid). HC Hydrocarbon, a long and short-chained organic molecule made of hydrogen and

carbon atoms only. HDV Heavy-Duty Vehicle is any motor vehicle having a manufacturer’s gross vehicle

weight rating greater than 2721.6 kg, except passenger cars. IAQ Indoor Air Quality from the USEPA, USA. IARC International Agency for Research on Cancer is part of the WHO, its mission is to

coordinate and conduct research on the causes of human cancer, the mechanisms of carcinogenesis, and to develop scientific strategies for cancer control. It is Based in the USA.

ICE Internal Combustion for Alternative Fuels. IRTP Integrated Regional Transport Plan; and represents a fully integrated transport

system in Queensland where cars, buses, trains, ferries, cyclists, and pedestrians work together for the benefit for the people and the environment.

ILAQH International Laboratory for Air Quality and Health at QUT, Brisbane – Australia. LSD Low Sulfur Diesel limits of aromatic HCs (10 % by volume) and on sulfur content

(500 ppmw; parts per million by weight). LDV Light-Duty Vehicle is a passenger car or passenger car derivative capable of

seating 12 passengers or less.

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MON Motor Octane Number of a sample of fuel is determined by burning the gasoline in an engine under controlled conditions, e.g., of spark timing, compression, engine speed, and load, until a standard level of knock occurs. The engine is next operated on a fuel blended from a form of isooctane that is very resistant to knocking and a form of heptane that knocks very easily. When a blend is found that duplicates the knocking intensity of the sample under test, the percentage of isooctane by volume in the blended sample is taken as the octane number of the fuel. Severe, sustained high speed and/or high load driving.

MTBE Methyl Tertiary Butyl Ether, MTBE is found in gasoline (and other petroleum fuels). MTBE is typically added to reformulated gasoline, oxygenated fuel, and premium grades of unleaded gasoline to optimize combustion. However the addition of oxygen to hydrocarbons significantly increases the mutual solubility with water and its strong affinity for it (MTBE solubility in water is 4.3%, water in MTBE 1.4%). While levels found in groundwater in the UK and in the USA do not appear to pose a significant health risk as such, MTBE has a distinctive taste and smell and will taint water groundwater supplies, even at very low concentrations.

N2 Nitrogen gas makes up 78.1% of the Earth’s air, by volume. The atmosphere of Mars, by comparison, is only 2.6% nitrogen. From an exhaustible source in our atmosphere, nitrogen gas can be obtained by liquefaction and fractional distillation. Nitrogen is found in all living systems as part of the makeup of biological compounds.

NIOSH National Institute of Occupational Safety and Health, USA. NMHC Non-Methane-HCs are important factors of the tropospheric O3 formation. NO2 Nitrogen Dioxide, the two most prevalent oxides of nitrogen are nitrogen dioxide

(NO2) and nitric oxide (NO). Both are toxic gases with NO2 being a highly reactive oxidant and corrosive.

NOX Nitrogen oxides is the generic term for a group of highly reactive gases, all of which contain nitrogen and oxygen in varying amounts. Many of the nitrogen oxides are colorless and odorless. However, one common pollutant, nitrogen dioxide (NO2) along with particles in the air can often be seen as a reddish-brown layer over many urban areas. Nitrogen oxides form when fuel is burned at high temperatures, as in a combustion process. The primary sources of NOx are motor vehicles, electric utilities, and other industrial, commercial, and residential sources that burn fuels.

NTP Normal atmospheric Temperature and Pressure; gas volumes are compared at a standard temperature of 293.15K and a pressure of 101.3kPa.

O2 Oxygen gas is a colorless, non-flammable and odorless gas. Sustains combustion of many materials which cannot burn in air.

O3 Ozone gas is formed by the combination of three oxygen atoms. An unstable gas with a strong and irritating odor (which explains its name), ozone is corrosive, a strong oxidant and very toxic. For all of these reasons it is absolutely unsuitable to sustain life. Ozone is generally produced by generating high-power electrical discharges in air or in oxygen. Naturally found in the upper layers of the atmosphere, where it is formed by a photo-chemical reaction, ozone serves as a shield which protects our planet from the sun's ultraviolet radiation.

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OPC Optical Particle Counters see CPC. PAH Polycyclic Aromatic Hydrocarbons: constitute a large class of chemicals with

widespread occurrence in the environment. Benzo[a]pyrene has been extensively studied. It is a powerful mutagenic and carcinogenic agent in various experimental systems and is also suspected to be a significant health risk to humans. Therefore, benzo[a]pyrene is considered as a model PAH.

Pb Lead is a dense, relatively soft, malleable metal with low tensile strength. It is a poor conductor of electricity and heat. Lead has a face-centered cubic crystalline structure. It is below tin in group IVa of the periodic table. Although lead has a lustrous silver-blue appearance when freshly cut, it darkens upon exposure to moist air because of the rapid formation of an oxide film; the film protects the metal from further oxidation or corrosion. All lead compounds are poisonous. Lead resists reaction with cold concentrated sulfuric acid but reacts slowly with hydrochloric acid and readily with nitric acid.

PE Petrol Exhaust complex hydrocarbon mixture in urban air, the alkenes and arenes are of particular interest.

PM Particulate Mass Matter term for particles found in the air, including dust, dirt, soot, smoke, and liquid droplets. Particles can be suspended in the air for long periods of time. Some particles are large or dark enough to be seen as soot or smoke. Others are so small that individually they can only be detected with an electron microscope.

PM10 Includes the suspended mass load of PM less than 10 µm in [g/cm3]. PM2.5 Includes the suspended mass load of PM less than 2.5 µm in [g/cm3]. QUT Queensland University of Technology, Brisbane – Australia. RFG Reformulated Gasoline cleaner gasoline. RFG and conventional gasoline differ

only in the levels at which the ingredients are used, thereby reducing the use of ingredients that contribute to air pollution. Like other gasoline, however, RFG is formulated to burn in a manner that will suit the power requirements of the vehicles in which it is used.

RON Research Octane Number typical mild driving, without consistent heavy loads on the engine.

RVP Reid Vapor Pressure which is designed to reduce evaporative emissions during the summer months when ambient temperatures are their highest. The lower the pressure in gasoline, the less evaporative emissions that generally will occur.

S Sulfur, a nonmetallic and multivalent chemical that occurs in various forms as sulfides and sulfites.

SO2 Sulfur dioxide Sulfur dioxide gas is prepared by burning sulfur in a large flask of oxygen, containing a small amount of base and indicator. As the sulfur dioxide dissolves in the water, the indicator changes color

SOX Sulfur compounds Sulfur forms compounds in oxidation states -2 (sulfide, S2-), +4 (sulfite, SO3

2-), and +6 (sulfate, SO42-). It combines with nearly all elements. An

unusual feature of some sulfur compounds results from the fact that sulfur is second only to carbon in exhibiting catenation--i.e., the bonding of an atom to another identical atom. This allows sulfur atoms to form ring systems and chain structures.

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SEB South East Busway, Brisbane, Australia SEQ South East Queensland, Brisbane, Australia SET South East Transit, Brisbane, Australia SHED Sealed Housing for Evaporative Determination test is performed in an airtight room to

determine the evaporative hydrocarbon emissions a vehicle emits when it isn't running. The diurnal breathing loss and hot soak tests are evaporative tests.

SMD Surface Median Diameter, the geometric mean of the particle surface areas; in a log-normal distribution, it corresponds to the area median diameter of a particle.

SPM Suspended Particle Matter (see PM) SMPS Scanning Mobility Particle Sizer, is a set of instruments that is used to measure

airborne particles in the size range of 3 to 1000 nm; it employs an EC to determine particle size and a CPC to determine particle concentration.

SOF Soluble Organic Fraction; the organic fraction of diesel particulates. SOF includes heavy hydrocarbons derived from the fuel and from the engine lubricating oil. The term “soluble” originates from the analytical method used to measure SOF, which is based on extraction of PM samples using organic solvents2.

SO2 Sulfur Dioxide, a toxic chemical often found in combustion processes and in conjunction with soot particles.

STP Standard atmospheric Temperature and Pressure; gas volumes are compared at a standard temperature of 273.15K (0ºC) and a pressure of 101.3kPa. At STP, one mole of any gas should have a volume of 22.4L.

SW Software, a computer code used to write atomized coding procedures stored in the RAM (random access memory = read- & writeable memory chip) of a data processing unit.

SWEPA Swedish Environmental Protection Agency, Sweden. T Temperature, the degree of hotness / coldness of a body or environment [ºC] or

[K] corresponding to its molecular oscillating activity. TAC Toxic Air Contaminant is a list of 244 substances that have either been identified

by the ARB as Toxic Air Contaminants (TACs) in California or are known or suspected to be emitted in California and have potential adverse health effects.

TSP Total Suspended Particle matter; the total PM emissions including all fractions of diesel particulates, i.e. the carbonaceous, organic (SOF), and sulfate particulates given as a weight related quantity [µg/m3].

UBA German Federal Environmental Agency. ULSD Ultra Low Sulfur Diesel with a sulfur-content less than 30ppm or 0.003% (see also

LSD). USEPA United States Environmental Protection Agency, USA. VKT Vehicle kilometers travelled. Road transportation energy use per distance travelled

is the amount of energy used in road transportation divided by the number of road vehicle-kilometres travelled by motor vehicles. This measure indicates each country's vehicle fleet efficiency. Units are kg oil equivalent per 1000 vehicle-km.

VMD Volume Median Diameter; the geometric mean of the particle volumes; in a log-normal distribution it corresponds to the volume median diameter of a particle.

2 http://www.dieselnet.com/glossary.html

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VOC Volatile Organic Compounds; short-chained hydrocarbon-based emissions released through evaporation or combustion.

WHO World Health Organization is the United Nations specialized agency for health, was established on 7 April 1948. WHO's objective, as set out in its Constitution, is the attainment by all peoples of the highest possible level of health. Health is defined in WHO's Constitution as a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.

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Table of Contents

Foreword.............................................................................................................................. iii Abstract................................................................................................................................ iii German Summary ................................................................................................................ iii Statement of Original Authorship........................................................................................ iv Acknowledgements.............................................................................................................. iv Acronyms.............................................................................................................................. v Table of Contents................................................................................................................. xi

CHAPTER I Goals, Purpose and Structure of Report ....................................... 1 Goals and Objectives ............................................................................................................ 3 Structure of the Report.......................................................................................................... 3

CHAPTER II Literature Review.......................................................................... 5 II. Literature Review......................................................................................................... 7 II.1. Brisbane Meteorological and Topographical Data .................................................. 7 II.2. Transportation in the Metropolitan Area of Brisbane ............................................ 11 II.3. Airborne Quality .................................................................................................... 15 II.4. Street Canyons ....................................................................................................... 17 II.5. Origin and characteristics of Fuel .......................................................................... 20 II.6. Sources of Motor Vehicles Emissions ................................................................... 23 II.7. Motor Vehicle Pollution......................................................................................... 25 II.8. Atmospheric Aerosol Particles............................................................................... 38 II.9. The SMPS System ................................................................................................. 47 II.10. Particle Size Statistics .......................................................................................... 56 II.11. Data Output of the SMPS system......................................................................... 64 II.12. Health Effects of Particles on the Environment and on the Human Organism.... 66 II.13. Exposure Assessment of Vehicle Emissions........................................................ 78 II.14. Population Exposure Assessment Studies - Prerequisites.................................... 80 II.15. Methods for Exposure Assessment for Diesel Exhaust Studies........................... 83 II.16. Multiple Exposure Pathways................................................................................ 84

CHAPTER III Methodology and Experimental Design................................. 87 III.1. Sampling Sites ...................................................................................................... 90 III.2. Data Collection ..................................................................................................... 94 III.3. Instrumentation ..................................................................................................... 97 III.4. Exposure Assessment Model ................................................................................ 99

CHAPTER IV Experimental Results and Discussions .................................. 103 IV. Introduction ........................................................................................................... 105 IV.1. Bus Timetables Data Collection ......................................................................... 105 IV.2. Exposure Assessment Analysis .......................................................................... 108 IV.3. Meteorological Data Analysis ............................................................................ 116 IV.4. Noise Data Analysis ........................................................................................... 117

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CHAPTER V Recommendations and Final Conclusion ................................ 121 V.1. Recommendations................................................................................................ 123 V.2. Final Conclusions ................................................................................................ 126

Appendix ..................................................................................................................... 127 Appendix A Handling & Check Lists...................................................................... 129 Appendix A.01 - Setting up the external Plumbing...................................................... 131 Appendix A.02 - Calibration sheet – calibration performed by GJ on 26-Jul-2001..... 138 Appendix A.03 - Equipment Checklist......................................................................... 139 Appendix A.04 - Butanol, a few words about it: .......................................................... 140 Appendix A.05 - Specifications of the used Equipment............................................... 142 Appendix A.06 - Specifications of EC: ........................................................................ 143 Appendix A.07 - Specifications of CPC 3010:............................................................. 144 Appendix A.08 - Specifications of CPC 3022A........................................................... 145 Appendix A.09 - Equipment Used and Specifications of Equipment involved ........... 146 Appendix A.10 - Setup of PTrak and DustTrak via the Computer............................... 147 Appendix A.12 - Working with the SMPS Script Program Package ........................... 155 Appendix B - Flow Chart for Exposure Assessment.................................................... 159 Appendix C - Measurement Schedules for the Woollongabba Busway Station .......... 165 Appendix D - Summarized Results ............................................................................... 173 Appendix E - Brisbane City Council Bus Data Sheet .................................................. 183 Appendix F - Contact Details, Bibliography & Curriculum Vitae ............................... 193

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List of Figures Fig. II.1-1. Brisbane Topography...........................................................................................................................7 Fig. II.1-2. Annual Climate Pattern of the BMA....................................................................................................8 Fig. II.1-3. T-Inversion...........................................................................................................................................9 Fig. II.2-1. South East Busway Route ..................................................................................................................12 Fig. II.2-2. Opening of the first Busway Tunnel ..................................................................................................12 Fig. II.2-3. Courtesy Ride along the SEB.............................................................................................................13 Fig. II-2-5. One of the technical drawings of the Woolloongabba busway station ..............................................14 Fig. II.4-1. Street Canyon.....................................................................................................................................18 Fig. II.5-1. Pathways of petroleum-based fuels....................................................................................................20 Fig. II.5-2. Distillate Fraction of crude oil. ..........................................................................................................22 Fig. II.6-1. Sources of Emissions from a diesel bus in Brisbane..........................................................................24 Fig. II.7-1. Diesel versus Petrol............................................................................................................................25 Fig. II.7-2. Size distribution of petrol exhaust;.....................................................................................................26 Fig. II.7-3. Source emission by fuel type; ............................................................................................................27 Fig. II.7-4. Size distribution curve of diesel exhaust;...........................................................................................28 Fig. II.7-5. Steps in soot formation. .....................................................................................................................29 Fig. II.7-6. Sketch of diesel particles....................................................................................................................30 Fig. II.7-7. Diesel exhaust composition at various load conditions .....................................................................30 Fig. II.7-8. Particle size distribution in direct inject petrol engine (Gaskow). .....................................................32 Fig. II.8-1. Sizes of Airborne Particles;................................................................................................................38 Fig. II.8-2. Schematic of a typical size distribution..............................................................................................39 Fig. II.8-3. Micrograph of soot particle agglomerate ...........................................................................................40 Fig. II.8-4. Normalized and weighted distribution curve of diesel exhaust; Mass-, .............................................41 Fig. II.9-1. Schematic of the TSI SMPS System operated in under-pressure mode. ............................................47 Fig. II.9-2. Cross-sectional view of an inertial impactor......................................................................................48 Fig. II.9-3. Flow Schematic for the Electrostatic Classifier. ................................................................................50 Fig. II.9-4. Cumulative charge concentration vs. electrical mobility. ..................................................................52 Fig. II.9-5. Structural formula of isobutyl; ...........................................................................................................53 Fig. II.9-6. Flow Schematic for the Condensation Particle Counter.....................................................................54 Fig. II.10-1. Histogram of frequency versus particle size ....................................................................................56 Fig. II.10-2. Frequency/nm versus particle size ...................................................................................................56 Fig. II.10-3. Frequency/nm versus particle size, count distribution .....................................................................57 Fig. II.10-4. Frequency distribution curve............................................................................................................58 Fig. II.10-5. Normal distribution. .........................................................................................................................59 Fig. II.10-6. Frequency distribution plotted against logarithm of particle size. ...................................................60 Fig. II.10-7. Moment averages of count, area, and mass distributions .................................................................62 Fig. II.12-1. The human respiratory system. ........................................................................................................74 Fig. II.12-2. Deposition Efficiency of particles versus diameter..........................................................................75 Fig. II.12-3. Path of the particle into the organism...............................................................................................76 Fig. II.12-4. Schematic presentation of the Airways and its mechanisms of particle deposition .........................76 Fig. III.1-1. Site Location.....................................................................................................................................90 Fig. III.1-2. Woolloongabba Busway Station.......................................................................................................90 Fig. III.1-3. Outbound Platform ...........................................................................................................................90 Fig. III.1-4. Design of the Outbound Platform.....................................................................................................91 Fig. III.1-5. Overall View of the Busway Station. ...............................................................................................92 Fig. III.1-6. EPA Monitoring Station, ..................................................................................................................93 Fig. III.2-1. Video Surveillance............................................................................................................................94 Fig. III.2-2. LED info screens ..............................................................................................................................95 Fig. III.2-3. Induction Loops ................................................................................................................................95 Fig. III.2-4. Background sample SMPS-spectra...................................................................................................96 Fig. III.2-5. Platform sample SMPS-spectra ........................................................................................................96 Fig. III.3-1. Instrumentation used.........................................................................................................................97 Fig. III.3-2. Sound Level Meter, ..........................................................................................................................98

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Fig. III.4-1. Simplified flow Chart for Exposure Assessment ..............................................................................99 Fig. III.4-2. Peak versus average concentration .................................................................................................100 Fig. III.4-3. Passengers pool...............................................................................................................................101 Fig. III.4-4. Calculation of Bus fumes................................................................................................................102 Fig. IV.1-1. Comparison of Buses......................................................................................................................106 Fig. IV.1-2. Histogram of bus frequencies .........................................................................................................107 Fig. IV.1-3. Fuel type of vehicles.......................................................................................................................107 Fig. IV.1-4. Histogram of passenger waiting time .............................................................................................107 Fig. IV.2-1. Diurnal particle concentration ........................................................................................................108 Fig. IV.2-2. Particle number concentration ........................................................................................................109 Fig. IV.2-3. Volumetric particle concentration ..................................................................................................109 Fig. IV.2-4. Volumetric particle exposure..........................................................................................................109 Fig. IV.2-5. Histogram of particle number and volumetric concentration. ........................................................110 Fig. IV.2-6. Number of particles concentration and exposure versus waiting time. ..........................................110 Fig. IV.2-7. Volumetric concentration and exposure versus waiting time of passengers...................................110 Fig. IV.2-8. Exposure due to particle number concentration .............................................................................110 Fig. IV.2-9. Exposure due to volumetric concentration .....................................................................................110 Fig. IV.2-10. Estimated lung deposition ............................................................................................................113 Fig. IV.4-1. Sound Intensity Data ......................................................................................................................118 Fig. IV.4-2. Glass-covered platform design .......................................................................................................119 Fig. A.01-1. SMPS External plumbing ..............................................................................................................131 Fig. A.01-2. Instrumental setup for SMPS applications.....................................................................................132 Fig. A.01-3. CPC Reference Card......................................................................................................................133 Fig. A.01-4. Front Panel of the CPC 3010 .........................................................................................................133 Fig. A.01-5. Instrumentation Setup ....................................................................................................................133 Fig. A.01-6. Front Panel of EC ..........................................................................................................................134 Fig. A.01-7. Bubble counter...............................................................................................................................134 Fig. A.01-8. SMPS SW Instrumental Setup .......................................................................................................135 Fig. A.01-9. SMPS Port Settings........................................................................................................................135 Fig. A.01-10. SMPS View Settings....................................................................................................................135 Fig. A.01-11. SMPS Run Setup .........................................................................................................................135 Fig. A.01-12. SMPS Delay Time Utility ............................................................................................................136 Fig. A.01-13. SMPS Autosave option ................................................................................................................136 Fig. A.01-14. SMPS Saving options ..................................................................................................................136 Fig. A.01-15. SMPS Export Options..................................................................................................................137 Fig. A.10-1. Rear side of DT..............................................................................................................................147 Fig. A.10-2. P-trak with Probe ...........................................................................................................................147 Fig. A.10-3. SW Main Menu..............................................................................................................................148 Fig. A.10-4. Instrument Configuration...............................................................................................................148 Fig. A.10-5. Port Setup.......................................................................................................................................148 Fig. A.10-6. Instrument Clock............................................................................................................................148 Fig. A.10-7. Time Constant................................................................................................................................149 Fig. A.10-8. Logging Intervals ...........................................................................................................................149 Fig. A.10-9. Logging Protocol ...........................................................................................................................149 Fig. A.10-10. Zero-Check ..................................................................................................................................150 Fig. A.10-11. Flow-rate Check...........................................................................................................................150 Fig. A.10-12. Downloading Data Menu.............................................................................................................151 Fig. A.10-13. Data Saving Mode .......................................................................................................................151 Fig. A.11-1. Frontal view of the DataLogger (left) ............................................................................................152 Fig. A.11-2. Meteo veins....................................................................................................................................152 Fig. A.11-3. The automatic weather station .......................................................................................................152 Fig. A.11-4. Opening Hyperterminal (HT) on the PC........................................................................................152 Fig. A.11-5. HT settings.....................................................................................................................................153 Fig. A.11-6. HT settings.....................................................................................................................................153 Fig. A.11-7. Name Session.................................................................................................................................153 Fig. A.11-8. HT Port ..........................................................................................................................................153

Exposure Assessment xv Introduction

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Fig. A.11-9. HT Baude Rate...............................................................................................................................153 Fig. A.11-10. HT status check............................................................................................................................154 Fig. A.11-11. HT Parameter check.....................................................................................................................154 Fig. A.11-12. HT Data download.......................................................................................................................154 Fig. A.11-13. HT Data capturing .......................................................................................................................154 Fig. A.11-14. HT Stop Data Transfer.................................................................................................................154 Fig. A.12-1. The SMPS Origin Menu ................................................................................................................155 Fig. A.12-2. File Selection Window...................................................................................................................155 Fig. A.12-3. The Statistics Spreadsheet..............................................................................................................155 Fig. A.12-4. The SMPS roll-down menu ...........................................................................................................156 Fig. A.12-5. The SMPS spectra option menu (1) ...............................................................................................156 Fig. A.12-6. The SMPS option menu (2) ...........................................................................................................156 Fig. B-1. The Microcal Origin Script Flowchart ................................................................................................159 Fig. C-1. 07th June 2003 Schedule of Particle Measurements. ..........................................................................165 Fig. C-2. 11th June 2003 Schedule of Particle Measurements. ..........................................................................165 Fig. C-3. 12th June 2003 Schedule of Particle Measurements. ..........................................................................166 Fig. C-4. 13th June 2003 Schedule of Particle Measurements. ..........................................................................166 Fig. C-5. 14th June 2003 Schedule of Particle Measurements. ..........................................................................167 Fig. C-6. 17th June 2003 Schedule of Particle Measurements. ..........................................................................167 Fig. C-7. 18th June 2003 Schedule of Particle Measurements. ..........................................................................168 Fig. C-8. Passengers Census...............................................................................................................................169 Fig. D-1. Frequency of Buses.............................................................................................................................173 Fig. D-2. Box Plot ..............................................................................................................................................173 Fig. D-3. Stopping Buses at the Outbound Pl.....................................................................................................173 Fig. D-4. Box Plots for Stopping Buses at the Outbound...................................................................................173 Fig. D-5 Total Number of type of Fuel ..............................................................................................................173 Fig. D-6. Box Plots for Type of Fuel..................................................................................................................173 Fig. D-7. Frequency of Buses – Induction Loops...............................................................................................174 Fig. D-8. Statistical Analysis of Total Buses......................................................................................................174 Fig. D-9. Total Waiting Time of Buses..............................................................................................................174 Fig. D-10. Frequency of Buses – Internet. .........................................................................................................174 Fig. D-11. Statistical Analysis of Total Buses....................................................................................................174 Fig. D-12. Frequency of Buses –Timetables. .....................................................................................................175 Fig. D-13. Statistical Analysis of Total Buses....................................................................................................175 Fig. D-14. Total Number of Bus at the W’Gabba ..............................................................................................175 Fig. D-15. Statistical Analysis of Bus Census....................................................................................................175 Fig. D-16. Total Number of Passengers .............................................................................................................178 Fig. D-17. Statistical Analysis of Total Passengers............................................................................................178 Fig. D-18. Location of outbound passengers on the platform. ...........................................................................178 Fig. D-19. Buses Stopping in the Outbound Platform........................................................................................178 Fig. D-20. Lung Depostion.................................................................................................................................179 Fig. D-21. June 07th, 2002, from 10:00 to 19:00 ................................................................................................180 Fig. D-22. June 11th, 2002, from 10:00 to 19:00 ................................................................................................180 Fig. D-23. June 12th, 2002, from 10:00 to 19:00 ................................................................................................180 Fig. D-25. June 14th, 2002, from 10:00 to 19:00 ................................................................................................180 Fig. D-26. June 17th, 2002, from 10:00 to 19:00 ................................................................................................180 Fig. D-27. June 18th, 2002, from 10:00 to 19:00 ................................................................................................180 Fig. E-1. SG bus .................................................................................................................................................184 Fig. E-2. VLN Bus .............................................................................................................................................185 Fig. E-3. MMN Bus............................................................................................................................................186 Fig. E-4. VBA Bus .............................................................................................................................................187 Fig. E-5. MM Bus ..............................................................................................................................................188 Fig. E-6. VA Bus................................................................................................................................................189 Fig. E-7. VB Bus................................................................................................................................................190 Fig. E-8. MG Bus ...............................................................................................................................................191 Fig. E-9. M Bus..................................................................................................................................................192

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List of Tables Table II.6-1. Substances Emitted from Motor Vehicles. ......................................................................................23 Table II.6-2. Types of losses and emissions.........................................................................................................24 Table II.7-1. Emissions Benefits of Replacing Conventional Diesel with CNG in Buses. ..................................36 Table II.9-1. Charge distribution of aerosol particles according to Boltzmann’s law ..........................................49 Table II.11-1. An Example of a raw SMPS DISTFIT Datafile ............................................................................64 Table II.12-1. Major sources of particles and its Health effects...........................................................................67 Table II.12-2. Substances in Diesel exhaust listed by CEPA as Toxic Air Contaminants ...................................69 Table II.12-3. Cancer potencies of various chemical compounds present in diesel exhaust. ...............................70 Table II.13-1. Properties of Aerosols Particles.....................................................................................................78 Table IV.1-1. Type of Buses in the City of Brisbane (as of year 2002) .............................................................105 Table IV.3-1. Wind Direction & Speed from the EPA Woolloongabba Weather Station..................................116 Table D-1. Comparison of Bus Timetables Collection ......................................................................................176 Table D-2. Bus Census for the Outbound Platform from the Woolloongabba Busway Station.........................176 Table D-3. Bus Census for the Inbound Platform at the Woollongabba Busway Station ..................................176 Table D-4. Passengers Data Collection by Census at the Woolloongabba Busway Station...............................177 Table D-5. Results of Particle Exposure Concentrations from the Woollongabba Busway Station ..................177 Table D-6. Substances in Diesel exhaust listed by CAEPA as Toxic Air Contaminants ...................................178

Exposure Assessment 1 Chapter I

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CHAPTER I

Goals, Purpose and Structure of Report


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Goals and Objectives The aim of this study was to quantify ultrafine particles emitted by diesel buses and the development of a method to conduct rapid exposure concentrations to humans. This study was conducted in winter at Woolloongabba busway station in Brisbane, Australia. The majority of the buses that frequent this station are powered by diesel fuel, which cause black carbon soot, itself a significant source of PM. The objective of this project was to investigate the exposure of waiting passengers to particles emitted from buses. The techniques used in this study were passenger census, which was conducted based on video surveillance, yielding person-by-person waiting time data. Furthermore, the manual and automated census of the buses revealed accurate information about the frequencies and the total number of diesel versus CNG-powered vehicles. Background (street level) and platform measurements of ultrafine particulate number-size distributions were made to determine ambient aerosol concentrations. It was assumed that significant differences between platform and background distributions were due to bus emissions which, combined with passenger waiting times, yielded an estimate of passenger exposure to ultra-fine particles from diesel buses. This enables a long-term prediction of particle concentrations deposited in the passengers lungs. Together this information is suitable to make predictions about the possible health effects on the waiting public at this particular bus stop. Structure of the Report By splitting it up into several chapters, this research report follows a straightforward orientation. Each chapter focuses on key parts of the thesis, which should make the entire document comprehensible to the reader. Chapter I lists in brief the aim and purpose of this research project. It briefly highlights the

reasons why it is essential to conduct an exposure assessment of diesel emissions in that busway station and its possible health effects on passengers.

Chapter II analyzes existing literature that is necessary to understand the complexity of

aerosol science, the setting of the busway station and how these may affect passenger health. Among the covered topics are: Brisbane Meteorological and Topographical Data, Transportation in the Metropolitan Area of Brisbane, Airborne Quality, Street Canyons, Origin and characteristics of fuels, Sources of Motor Vehicle Emissions, Motor Vehicle Pollution, Atmospheric Aerosol Particles, The SMPS System, Particle Size Statistics, Data output at the SMPS System, Health Effects of Particles, Exposure Assessment of vehicle emissions, Population Exposure Assessment Studies and Multiple Exposure Pathways.

Chapter III focuses the scope of the methodology and experimental design of the project, it

included data from particle sampling sites for both background and platform measurements; data collection from passengers census, timetables from four methods, and meteorological data such as wind speed and wind direction are described here. It also illustrates the design of the instrumentation used and calibration procedures applied and the data analysis employed for the exposure concentration.


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Chapter IV interprets the experimental results and discussions of the data collected from particles, passengers census, microclimatic data and the computed exposure concentrations.

Chapter V provides the recommendations and final conclusions in order to minimize particle

exposure. It is based on the results outlined in the previous Chapter IV. Finally, the attached documentation in Appendices A-E contain necessary information to

execute the practical and theoretical part of this research project. Appendix A provides several checklists along with advices for the safe and trouble-free operation of the equipment (i.e. Dust-Track, P-Track, SMPS Monitor Sensors software packages and hardware). Appendix B gives information of the software developed used for this purpose. It lists the flow chart for exposure assessment concentrations routine along with a step by step explanation. Appendix C highlights the timetables used for the particle measurements at the Woolloongabba busway Station. Appendix D displays the results in form of graphs and tables of the data collected from particles, passengers, buses, and wind data. Appendix E contains very useful information about the Brisbane City Council Bus fleet and Appendix F reveals contact details and bibliography of this research study.

The Attached CD-ROM contains the entire report in digital format along with sources,

literature, references, product data sheets, photographs and images, excel spread-sheets and other software support required to run, display and evaluate the various files.

Exposure Assessment 5 Chapter II

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CHAPTER II

Literature Review


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II. Literature Review This Chapter takes a closer look at existing literature. It summarizes information necessary to understand the setting of the busway station and its architectural design. In addition, this chapter also reviews essential aspects in aerosol science and how these may affect passenger health. Among the covered topics are: Brisbane Meteorological and Topographical Data, Transportation in the Metropolitan Area of Brisbane, Airborne Quality, Street Canyons, Origin and characteristics of fuels, Sources of Motor Vehicle Emissions, Motor Vehicle Pollution, Atmospheric Aerosol Particles, The SMPS System, Particle Size Statistics, Data output at the SMPS System, Health Effects of Particles, Exposure Assessment of vehicle emissions, Population Exposure Assessment Studies, Multiple Exposure Pathways. II.1. Brisbane Meteorological and Topographical Data The topographical configuration of Brisbane and their environs are reasonably complex. On the landward side the city of Brisbane is surrounded by a chain of mountains merely 1 km high, that surrounds the city like a horseshoe from the north-west all the way to the south-east. The eastern side instead is facing the Pacific Ocean (see Fig. II-1-1.). This topographical setting determines the expression and manifestations of the lower atmospheric boundary layer of Brisbane’s air shed. Geographically, the Central Business District (CBD) of Brisbane is located 20 km inland from the coast. a. Brisbane Metropolitan Area (BMA)3 The Brisbane Metropolitan area (BMA) covers approximately 22,500 m2; of which about 54% are relatively flat coastal or sub-coastal lowlands. Mostly urban and the associated development is located in these lowlands areas.

Fig. II.1-1. Brisbane Topography

The remainder is characterized by a series of well-vegetated hills and ranges along the coast, and parts of the Great Dividing Range in the west4. The BMA extends to Caboolture in the North, Beenleigh in the South, Ipswich in the West, Redland Shire in the East and includes the northern portion of Beau desert Shire. The major economic activities in the region are light industry, commercial and in the service sectors.

3 SEQ Regional Air Quality Strategy (1998), p.9. 4 SEQ Regional Air Quality Strategy (1998), pp.6-8.


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b. Brisbane Climate and Temperature Inversions Brisbane has a sub-tropical climate, with a summer rainfall peak (see Fig.II-1-2) and high humidity levels year round oscillating around 65%. Winters are drier and there can be drought episodes. Autumn, winter and spring are mild and outdoor activities are carried out all year. The western suburbs experience occasional frosts in winter. Total annual rainfall fluctuates around 1000 mm.5 Due to the topography, temperature inversions6 in Brisbane aggravate pollution problems as they cause pollutants to accumulate in the lower atmosphere instead of facilitating their dispersal. Many of the most serious air pollution episodes in this city (occurrences of extremely adverse health effects) usually happen during these periods7.

Fig. II.1-2. Annual Climate Pattern of the Brisbane

Metropolitan Area (BMA)8.

b1. Temperature Inversions During persistent high-pressure phases during the drier winter months, temperature inversions from aloft can easily occur. Under such meteorological situations, the atmosphere is very stable and the mixing depth is significantly restricted. Warm air overlying cooler air acts as a lid and prevents upward movement, leaving the pollutants trapped in a relatively narrow zone near the ground9. There are two types of inversions: Surface Temperature Inversions: Solar heating can result in high surface temperatures during the late morning and afternoon that increase the environmental lapse rate and render the lower air unstable. During nighttime hours, however, just the opposite situation may occur; temperature inversions, which result in very stable atmospheric conditions, can develop close to the ground. These surface inversions form because the ground is a more effective heat radiator than the air above10. Inversions Aloft: Many extensive and long-lived air pollution episodes are linked to temperature inversions that develop in association with the sinking air that characterizes centers of high air pressure (anticyclones). As the air sinks to lower altitudes, it is compressed and so its temperature rises. Because turbulence is almost always present near the ground, this lowermost portion of the atmosphere is generally prevented from participating in the general

5 GEOGRAPHY - Brisbane's Key Features http://www.vnc.qld.edu.au/enviro/college/env-ch1e.htm 6 Stoker et al., (1972). 7 Stoker et al., (1972), p.83. 8 CBoM (2003), online 9 Lutgens & Tarbuck (1998), p.314-315. 10 Lutgens & Tarbuck (1998), p.314-315.


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subsidence. Thus, an inversion develops aloft between the lower turbulent zone and the subsiding warmer layers above11. Dispersal of air pollutants only occurs when no temperature-inversion is present in the lower troposphere. Normally the temperature of air above the earth decreases with altitude. Air closest to the earth's surface is warmed by the earth, rises, expands and becomes less dense than the cooler air above it. The warm, less dense air then rises through the cooler air, it expands and thereby looses heat (adiabatic process) slowing down its rise. Air currents are created this way and pollutants are dispersed. With a diurnal pattern of sea- and landward breezes oscillating to and from the continent, and the persistent temperature-inversion from aloft, pollution loaded air masses are trapped in the BMA basin rather then being swept away (see Fig. II.1.3). With limited horizontal movement, pollutant dispersion becomes dependent on the vertical movement of air.

Fig. II.1-3. Temperature-Inversion

Such inversions favor pollution entrapment within the greater BMA12.

Such situations as this may remain unchanged for days until weather conditions change and the inversion layer breaks up. An added pollution problem occurs with inversion layers in the form of increased photochemical activity. The inversion layer is usually warm, dry, and cloudless, and so transmit a maximum amount of sunlight, which interacts photochemically with the trapped pollutants to form extreme amounts of smog. Thus high levels of smog are usually associated with air pollution episodes involving temperature inversions13. The potential for air pollution particularly photochemical smog is quite high during the winter months – this brown haze is readily visible during this period. As will be mentioned later, some modification in the methodical evaluation was done, as the presence on the inversion layer and the lifting of it during the early morning hours did interfere with the measurements. Therefore, data evaluation was restricted to the time frame between 10:00 to 19:00 hours (see Chapter III.4 – section b). Although SEQ has a history of relatively small industrial based activities and a comparatively low population of about 1.5 million inhabitants, statistical predictions paint a different picture. Pollution entrapment could become a more frequent problem in the future as the region’s population and economic activities continue to grow rapidly14. This growth, coupled with other regionally significant sources of air pollution emissions such as bushfires and agricultural burning, means that air quality in this region could further decline in the not so distant future15.

11 Lutgens & Tarbuck (1998), p.314-315. 12 Botkin et al. (1997) 13 Stoker et al., (1972), p.84. 14 SEQ Regional Air Quality Strategy (1998), pp.6-8. 15 SEQ Regional Air Quality Strategy (1998), pp.6-8.


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c. Air Pollution Formation in Brisbane Currently the air quality indicators in Brisbane are still within legal limits. Research, however, predicts that total emissions could increase significantly. On warm, sunny days with little wind, polluted air remains trapped in the air shed (see T-inversion layer, Fig. II.1.3). If these weather conditions last for several days, pollution levels build up. When pollution from cars and other sources mixes in the atmosphere and is chemically changed by sunlight, photochemical smog and ozone are formed. Ozone as a by product of photochemical reactions is formed at ground level, it is known to have negative effects on people’s health. Indeed, the greatest source of air pollution in Brisbane originate from motor vehicle sources. If current trends in regional population growth and transport use continue, a 70% increase over 1992 levels is expected by 2011; i.e. based on the number of daily car trips made in the Brisbane region. Emissions from cars are responsible for 45% of all air pollution, 80% of all smog-forming gases and 34% of greenhouse gases in Brisbane and the surrounding region16. As road transportation on a global scale is almost entirely dependent on fossil fuels, large amounts of greenhouse gases are released from LDV. CO2 is the substance emitted in the largest quantity. The transportation sector worldwide is responsible for about 25-35% of anthropogenic CO2 emissions, most produced by the combustion of petroleum-based fossil fuels. For each kilogram of gasoline used in a car, about 3 kg of CO2 are released into the atmosphere; therefore, per 100 km, this is about 20 kg of CO2 being released. The US transportation sector accounts for about 5% of the CO2 emitted by human activities worldwide. No other energy use sector in the US or in another country accounts for a significantly larger portion of these emissions17. d. Air Quality in South-East Queensland (SEQ)18 Air quality in SEQ has been recognized as a significant issue since the last 25 years. Continuous monitoring of ambient air quality in this region began in 1978 in Fortitude Valley, an inner Brisbane suburb. Since then, a network of air monitoring stations located in the greater Brisbane area has been developed by the Queensland-EPA. In recent years, several specific air quality studies, including an air emissions inventory and a wind field modelling study, have been undertaken to develop a better understanding of the regional air shed. These studies were triggered partly by the SEQ2001 regional planning project (a co-operative planning exercise by the Queensland Government, the Commonwealth Government and local governments). Their aim was to develop a coordinated approach to manage the region’s dynamic growth.

16 SEQ Regional Air Quality Strategy (1998), p.10. 17 MacLean & Lave (2003), p.12. 18 SEQ Regional Air Quality Strategy (1998), p.10.


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II.2. Transportation in the Metropolitan Area of Brisbane19 Transport by road dominates. Although passenger train-services provide a significant portions of the city’s transportation needs, a well-developed bus network is operational. Bicycle use for commuting is low and not well-established. The river-based ferry system has recently undergone a major upgrade. Road traffic is in danger of reducing the quality of life in parts of 'Greater Brisbane' through delays, noise, exhaust gases and particles and hazards for cyclists and pedestrians. Human health problems are being linked to air pollution from vehicles in some quarters of this metropolitan area. Previous inventories have indicated that transport activity generates around 57.5% of the region’s air pollution, and is the major source of CO and NOX and are a key precursor to photochemical smog. Studies undertaken for the Integrated Regional Transport Plan (IRTP) show that if current trends in regional population growth and transport usage continue, there will be significant increases between 1992 and 2011 in the number of trips (up to 70%), length of trips (from 12.5 km to more than 15 km), and amount of freight carried (up to 80-120%). Together with the trend to reduce vehicle occupancy (down from 1.3 to 1.2 persons per vehicle), these factors are expected to produce an almost 100% increase in motorized travel resulting in an estimated 93 million vehicle kilometers travelled each day. Such dramatic growth in transport activity has the potential to severely reduce air quality, even allowing for advances in vehicle emissions technology. The IRTP has developed targets and associated actions to change these trends by, for example, increasing vehicle occupancy and increasing the proportions of trips made by public transport and no-motorized modes. Even if these targets are achieved, the number of trips made by private vehicles are still estimated to grow by 51% between 1992 and 2011 with associated increases in impacts on air quality, unless appropriate management actions are undertaken. a. Public Transportation Brisbane Transport is the main provider of bus and ferry services in the Brisbane Region. As a commercial business unit, Brisbane Transport has a large fleet of over 600 buses and 16 ferry- boats20. Around 41 million customers travelled on Brisbane Transport's bus services during the past year. The CityCat ferry service was introduced in December 1996. Recent innovations include the introduction of smaller ‘satellite bus depots’ and a redesign of the bus network to improve service delivery and efficiency. Brisbane Transport manages maintenance and operations for: Brisbane City Council buses, Ferries, City Cats, Hail and Ride buses, City Sights Tours. b. The Busway System and its History In June 1995 a network of five busway corridors was conceived, linking the rail network to improve the public transport connectivity across the city. The ideals of the busway projects was to improve the operation of the bus fleet, reducing maintenance and running costs and maximizing the effectiveness of the region’s sunk investment in buses. In March 1996, the Queensland Government announced its intention to widen the South East Freeway to eight lanes between the Brisbane CBD and the Gold Coast. The South East Queensland (SEQ)

19 SEQ Regional Air Quality Strategy (1998), pp.22-29. 20 BCC (2003), online


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Branch of Queensland Transport proposed that the additional lanes should be provided as busway lanes and transit lanes to maximize the people carrying capacity of the new infrastructure. In August 1996 the Queensland Government approved the South East Transit (SET) Project, which was to be constructed: a two-lane, two-way busway from the City Business District, alongside the main traffic corridor of the South East Freeway to Eight Mile Planes; and Two transit lanes between Mt Gravatt and Logan (Fig. II-2-1.).

Fig. II.2-1. South East Busway Route21

The South-Eastern Busway (SEB) is one of a series of busway networks developed in SEQ. This busway, similar to Queensland Road commuter trains, is a road system consisting of streets, bridges and tunnels for the exclusive use of public transportation. The first section of SEB between the Central Business District (CBD) and Woolloongabba opened in September 2000 (Fig. II-2-2.). The second section between Woolloongabba and Eight Miles Plains opened on 30th of April 2001. The SEB was built by the Queensland Government and it includes: 10 stations, 20 platforms(10 inbound and 10 outbound), 2,000 metres of elevated roadway, 1630 m of tunnel, 130,000 m2 of concrete, and 140 security cameras linked to closed circuit television monitors at the Busway Operation Centre.

Fig. II.2-2. Opening of the first Busway Tunnel (SEB archive)

In order to have a feeling about the extension of the SEB, staff members of the Centre of Operations gave us a courtesy ride along the busway from the CBD to the final station at Eight Mile Plains (see Fig. II.2-3.). All the stations of the SEB are visually and functionally similar by giving them a recognizable look irrespective of location: glass, concrete walls and a continues roof along the platforms; however, not all of these stations were semi covered or semi-submerged as the Woolloongabba station.

21 SEB (2001); Transit Info Folder for the public (modified)


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The construction design of this busway was of great concern. As mentioned previously, they offer very little ventilation and do not allow bus fumes to mix well with the air or to escape easily. They rather stay stationary within the platform, facilitating inhalation by the waiting passengers. The stations are unattended and open 24 hours per day. The combination of glass and steel assures visibility. Besides a push-bottom emergency phone system other security items include a 24 hours surveillance system using 2 zoom able and routable colour cameras. Along with timetables, real-time screens on each platform to display the expected arrival time at the station.

Fig. II.2-3. Courtesy Ride along the SEB

c. Woolloongabba Busway Station22 The passenger area for both in- and outbound services are connected by a glass-covered bridge that is fitted with elevators. This pontoon is levelled out to match street level height, i.e. 7 m above platform level (Fig. II-2-4.). The eastern end of the station opens up to street level merging with standard bus lane system of the heavily trafficated Mains Street, while the western end connects the station to the main branch of the SEB.

Fig. II-2-4. Views of the Woollongabba Busway Station.

Left: W’Gabba busway station, inbound & outbound platform. Right: Outbound platform of W’Gabba. Fig. II-2-5. shows the architectural design of the station. The platform area itself is covering only 1/3 of the area used, while the reminder is used as parking area for peak-hour and extra services; extra space is provided for a turn around areas, and restroom facilities for the drivers.

22 SEB (2002).


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Fig. II.2-5. One of the technical drawings of the Woolloongabba busway station


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II.3. Airborne Quality Ambient air is a mixture of gases and small particles of both liquids and solids. Some substances come from natural sources while others arise from human activities. Some gases, such as O2, N2 and CO2 are essential to the lifecycles of plants and animals - including humans. Air pollution occurs when the air contains substances in quantities that could harm the comfort or health of humans, animals and plants – and in chronic cases even monuments. These are called airborne pollutants and they can be either gaseous or particulate in nature23. Several pollutants, while not having an adverse impact in small quantities, can accumulate in a region to the extent that they cause health effects. These are generally termed regional air pollutants. They include volatile organic compounds (VOCs) e.g. petrol or solvent fumes and a wide range of other carbon based gases, mostly with a detectable smell, NOX, dust particles (especially fine dust and inhaleable particles), CO (largely from motor vehicles) and SOX. Other pollutants may have significant effects on health but are not routinely monitored. They may result from very small sources, for example, benzene from fuel use, or they may be associated with a particular type of activity. These substances are often termed hazardous air pollutants (HAPs) or air toxics and include a wide range of substances such as heavy metals, some pesticides, and many synthetic chemicals. Although air pollution is traditionally attributed to human activities, it can also arise from natural sources such as bushfires, dust storms, and biological activity. Pollutants emitted directly from sources such as power stations, industry, motor vehicles and domestic activities are known as primary pollutants. Substances formed when pollutants already in the air go through physical and chemical reactions are known as secondary pollutants. Historical Data on Urban Air Quality and Traffic Emissions24 Urban air pollution was first considered as a local problem mainly from domestic and industrial emissions, which are now controllable to a great extent. In spite of significant improvements in fuel and engine technology, present day urban environments are mostly dominated by traffic emissions. Most of these emissions are directly emitted by vehicles or indirectly produced from primary pollutants through photochemical reactions. Many authors have shown that the main traffic-related pollutants are CO, NOX, HC-products (benzene, toluene, ethane, ethylene pentane, etc.), and particles of condensed carbonaceous material, which are emitted especially by diesel and many poorly maintained vehicles. All of them contribute to the increase of atmospheric pollutants, which are responsible for severe respiratory effects on human health. Photochemical smog is the result of electromagnetic interaction (solar radiation) with primary pollutant and is commonly termed secondary pollutants. Aesthetically, fine mists composed of HCs, SO2, and NOX cause obviously dirty air. Chemical oxidizing agents have a pungent odour. In fact, most people can detect NO2 levels as low as 0.42 ppm. Although direct acute health effects of photochemical smog are difficult to establish, it is a well-known fact that these substances represent additional stressors for an organism’s state of health. Eye irritation is frequent. Excess mortality is less easily demonstrated, because there are cyclical variations in various pollutant levels, and other factors (e.g., season, high temperatures, rapid temperature

23 SEQ Regional Air Quality Strategy (1998), pp.10-18. 24 Vardoulakis (2003), pp.155-156.


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changes, even daily and weekly pollution fluctuations affect pollution concentrations) have a stronger impact on death rates than persistent pollution levels. At current air pollution levels, photochemical smog may increase the risk of asthmatic attacks in a small number of susceptible patients, reduce pulmonary function after long-term exposure, cause mucous membrane irritation and cough, and interfere with athletic performance. Special attention is given to the PM with aerodynamic diameter below 10 µm (PM10) and particularly to the finer fraction with aerodynamic diameter below 2.5 µm (PM2.5). In urban environments and especially in those areas where population and traffic density are relatively high, human exposure to hazardous substances is expected to increase over the years. This is often the case near busy traffic axis in city centres, where urban topography and microclimate contribute to the creation of poor air dispersion giving rise to contamination hotspots. Major Historical Urban Air-Pollution Events: High levels of CO, SOX, and PM with few secondary pollutants occur more frequently in industrialized countries. The high density of adjacent communities increase background concentration of pollutants. A few historical events underline these combined effects:25

• Meuse Valley, Belgium (1936): High SO2 emissions from coal-burning plants combined with light winds to produce several thousand cases of pulmonary irritation and 65 deaths (primarily cardiac failure in elderly patients).

• Donora, Pennsylvania (1948): High concentrations of particulate matter and SO2 emissions from industrial smoke associated with poor environmental mixing of pollutants caused a severe pollution episode. Twenty excess deaths were recorded, and almost half of the city residents developed conjunctival and upper respiratory irritation along with gastrointestinal symptoms. These patients later had an increased prevalence of respiratory disease and increased mortality rates.

• London (1952): High particulate matter and SO2 concentrations in the absence of air movement produced over 4000 excess deaths.

• Los Angeles (1955): During a week-long heat wave, more than 247 deaths per day occurred as a result of the atmospheric and geographic conditions; high mountain ranges reduced the flow of free air through this area. A strong temperature inversion layer reduced the mixing height of pollutants and therefore increased pollutant concentration. High temperatures and emission of HCs from automobile exhausts produced large amounts of photochemical smog. Minimal wind conditions resulted in stagnant air, which intensified smog. Stable, warm weather increases concentrations of contaminants, which combine with sunlight to produce photochemical smog.

Before digging deeper into the issue of species involved in airborne pollutants, it is worthwhile to reflect about the fate of such pollutants with respect to the design of the Woolloongabba Busway Station and its particular setting.

25 Ellenberg (1997), CD-Rom


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II.4. Street Canyons Studies have been shown that relatively high wind speeds, a flat topography and low density of land use do not allow accumulation of pollutants during most of the days as the atmosphere is normally cleaned up during the night26. However, in recent years, modern cities are becoming narrow, densely populated and compartmentalized and flanked by buildings or by concrete blocks in which air and noise pollution constitutes an extended problem. Thus, it is of no surprise that air dispersion in street canyons are different than in open flat regions27. Many authors have referred the term street canyon as a relatively narrow street with buildings lined up continuously along both sides. However, the same term has been used previously to refer to larger streets, the so-called avenue canyons. In the real world, a wider definition has been applied, including urban streets that are not necessarily flanked by buildings continuously on both sides28. Within these urban canopies, wind vortices, low-pressure areas, channelling effects, and wind attenuation may be created under certain meteorological conditions and give rise to air pollution hotspots29. For example, many authors have also reported that high concentrations levels have been often been observed on the leeward side of the regular canyons under perpendicular wind conditions. This explains why facades flanking the canyon usually are much darker (dirtier) on one side of the road than on the other side (refer to Fig.II.4.1.)30. In addition, photochemical activity especially during sunny days may induce high street level concentrations of secondary pollutants. Due to the increased concentrations of traffic emissions and reduced natural ventilation levels of pollutants are usually higher in these narrow urban street canyons31. Fortunately, many efforts have been made in the recent years to improve the research on methods, techniques and understanding of dispersion and transformation of urban air quality The aim of this exposure assessment study is to take into consideration the basic understanding and research of street canyons done in order to apply some aspects to the semi-open design of the W’Gabba busway station. As will be shown later, this design also contributes to an excessive noise pollution level. Consequently, buses are the main sources of traffic emissions accumulate when the local meteorological conditions and geometric configurations worsen and natural ventilation is severely limited. Under these conditions it is more likely to successfully establish a population exposure assessment32.

26 Bogo et al., (2001), p.1717. 27 Sharma & Khare (2001), p.179. 28 Vardoulakis et al., (2003), p.157. 29 Vardoulakis, et al., (2002), p.1036. 30 Spadaro & Rabl (2001), p.4774. 31 Vardoulakis et al., (2003), p.174. 32 Vardoulakis et al., (2002), p.1026.


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a. Physical Dynamics within Street Canyons The street canyon configuration has several impacts on several processes (like thermal modulation, pollution dispersion and pollution concentration). Thermal Modulation: Semi-open, compact environments appear to exhibit the potential for thermal modulation. They may provide solar shading to pedestrians in the daytime hours; on the other hand, they may become relative heat traps due to multiple solar reflection and reduced albedo, which diminishes night sky radiation, and substantially restrict ventilation. This thermally induced flow is combined with mechanically induced flow formed in the canyon when there is no solar heating, producing interesting flow patterns, depending on street aspect ratio and the degree of heating. Due to the geometry of street canyons, they might also have artificial heat storage properties, anthropogenic heat, air pollution and reduced evaporation-transpiration – in the nights. The walls can protect from cold winds and rain, which is the primary factor responsible for a relative reduction in energy loss from the body to its surroundings during the winter months. During the summer, pedestrians can absorb less energy from the environment than in the open33. Pollution Dispersion: Since automotive emissions take place at ground level, street canyons can have a significant influence on the dispersion of pollutants near the source. Canyons affect the dispersion in two ways: they enhance vertical mixing above the canyons, and they tend to trap pollutants within their geographical or physical boundaries, thereby delaying pollutant transport to the freely moving air above the canyons (see top part of Fig. II.4-1)34. During a calm and sunny day, microclimatic effects, mostly in the form of black body absorption of the road causes the pollutant loaded bottom air stratum to rise above having a dillutive effect as long as the canyon bottom is directly hit by sunlight. Otherwise, these effects of canyons on the impact estimates are obvious: higher local concentrations result in higher impacts and damage costs (see bottom part of Fig.II.4-1). The canyon effect is relevant only for primary pollutants (e.g. CO and mixtures of NOX). When the street bottom or building wall is heated by solar radiation, thermally induced flow is formed in the canyon and may modify the stratification of pollutant concentrations within the canyon.

Fig. II.4-1. Street Canyon.

Pollutant dispersion in a regular street canyon under windy conditions (top) and calm & cloudy conditions (bottom)35

33 Pearlmutter et al, (1999), p.4148. 34 Spadaro & Rabl (2001), p.4770. 35 modified after Vardoulakis et al., (2003), p.158,


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Pollution Concentration and Flow: Flow of air in a street canyon is affected by both the meteorological conditions and the geometry of the street canyon. For urban traffic emissions, when combined local and regional dispersion analysis with curbside dispersion (microscale effects) to account for the trapping of pollutants in street canyons. Fluent Software Package Simulations done in the past couple of years indicate that concentrations inside the source canyon can be several times higher than those observed outside. Previous measurements done by several authors showed that the wind speeds and directions, and building configurations have significant influence on the distribution of pollutant concentrations. The atmospheric stability affects street-canyon flow. The street-canyon vortex tends to be weak when the atmosphere is stable and strong when unstable36. The clockwise vortex circulation generated in the street canyon led to higher pollutant concentration levels on the leeward side than on the windward side of the buildings and decreased exponentially in the vertical direction of the leeward side of the upstream (Fig. II.4-1.) building37. However, in order to maintain just vortices, the ambient wind speed should exceed at certain critical value and it depends on ambient wind direction. This explains the importance of the local wind to the concentration of pollutants in street canyons, with emphasis on wind direction, which places an important role in determining vortex number and flow field in the street canyon. Typically, concentrations of pollutants are highest at the base of the leeward wall38. Due to the smaller rotational velocity of the lower vortex circulation, it is difficult to remove the pollutant discharged from the line source out of the street canyon. Hence, it leads to direct impact on human health, particularly on drivers, pedestrians, and people working in the canyon39. b. Future Guidelines in Street Canyon Geometry Many large and populated cities in the past and today have been constructed with no or little knowledge of pollution dispersion and its relationships to street canyon geometry. However, recent research studies placed importance to canyon flow to assess the air quality in urban areas and they can promote a better standard of canyon air and several rules can be made for the purpose of urban planning considerations. It has been found that the released pollutants from a street canyon become more diluted in the following cases: the lower the height of the street canyon, the higher the wind speed; the higher the height of the leeward building (compared to the windward), the better the ventilation effect. Wider canyon promotes better pollutant diffusion. This brief overview covers only part of the vast amount of literature available on this subject; it has only the scope to inform the reader about the complex interactions that one has to consider for such a project. Further comments on this issue are not persuaded as it would unnecessarily expand the scope of this project.

36 Kim & Baik (2003), p.310. 37 Chan et al., (2002), pp.862. 38 Huang et al., (2000), p.697, Chan et al., (2001), pp.5681, 5690. 39 Chan et al., (2002), p.870.


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II.5 Origin and characteristics of Fuel The refining industry has been undergoing a transition due to changes in product demand and the quality of the gasoline and diesel blend components required. These changes result from more stringent emissions standards, fuel economy regulations, and fuel regulations, as well as petroleum price increases. If refineries could purchase any petroleum they wanted and produce any product mix, then a relatively simple refinery with a more or less coarsely refined product would do. However, in line with strict legislative product requirements, and the necessity of accommodating varying types of crude oils, complex refineries are the norm40.

Fig. II.5-1. Pathways of petroleum-based fuels41

The major steps in the gasoline (and diesel) fuel cycles are shown in Fig.II.5-1. These are straightforward and can be summarised in five steps: petroleum recover, crude oil transportation and storage, crude oil refining, productions of oxygenates (if required), transportation, storage and distribution of petroleum42. Due to the nature of fossil fuels, gasoline is a blend of heterogeneous HC-molecules along with some contaminants, including S, N, O and certain metals. The four major constituents groups of gasoline are olefins, aromatics, paraffins, and napthenes. These groups vary in their octane level and reactivity. To meet product specifications, gasoline blending is often required. The important characteristics of gasoline basestocks are density, vapor pressure, distillations range, octane, and chemical composition43. To be attractive, a engine gasoline must have desirable volatility, anti-knock resistance (related to octane rating), good fuel economy, minimal deposition on engine component surfaces, and complete combustion and low pollutant emissions. The most difficult requirements to meet from a technical and economic perspective are volatility and octane rating; i.e. Research Octane Number (RON) and Motor Octane Number (MON). If gasoline volatility is too high or too low, it can deteriorate vehicle performance for cold starting and warming-up time at both hot and cold temperatures, idle stability, acceleration performance and operation at cruising speeds. The octane rating of the fuel is influenced by decreases with increasing chain length, with

40 MacLean & Lave (2003), p.24. 41 modified after McLean & Lave (2003), p.26. 42 MacLean & Lave (2003), p.24. 43 MacLean & Lave (2003), p.24.


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decreasing number of side chains for the same number of carbon atoms, and with decreasing number of ring structures (cycloalkanes and aromatics). Typical RONs of gasoline vary from 90 to 100, while the MONs vary from 81 to 90. In the US, the three grades of gasoline (Regular, Midgrade, and Premium) have Anti-Knock Index (AKI) ratings of 87, 89, and 91, respectively. The AKI is calculated by averaging the RON and MON of the fuel. The flashpoint of gasoline is about –40°C; while the fuel is reasonably safe to store, it is less safe than diesel fuel, which has a much higher flashpoint of around 55°C. Although a natural product, gasoline has a major disadvantage: its biodegradability is very slow; spilled fuel may penetrate the ground and pollute soil and water44. Petroleum distillates with low surface tension and viscosity are serious aspiration hazards. The most viscous by-products are higher-molecular-weight hydrocarbon mixtures, which are usually nontoxic (e.g., lubricating oil, paraffin wax, asphalt, tar, petrolatum)45.

• Compressed Natural gas consists mainly of methane; i.e. CH4. • Gasoline (petrol) is a mixture of C4 to C12 aliphatic hydrocarbons obtained by

"cracking" heavy fractions in the boiling range 40 to 225°C. Appreciable amounts of aromatic hydrocarbons (e.g., xylene) are found in commercial fuels from California and Texas, especially those with high octane ratings. Tetraethyl lead and alcohols are added to boost the octane rating.

• Kerosene consists chiefly of C10 to C16 aliphatic hydrocarbons obtained in the boiling range 175 to 325°C. Small amounts of unsaturated (e.g., xylene) and saturated (naphthalene) aromatics appear in these products.

• Diesel Oil, Fuel Oil: Slightly less volatile (boiling point >250°C) but more viscous than kerosene, these products are complex mixtures of C9 to C20 or even higher hydrocarbons.

a. Fossil Fuel Production, Benefits and Woes In order to understand the health effects of diesel exhaust, it is a good idea to go into diesel production and to the benefits of its uses. First of all Diesel oil (e.g. No.2) comes from the middle distillate fraction a crude oil distillation column (see Fig. II.5-2.). It is a more complex and variable (density, volatility, and general composition) fuel than gasoline. Diesel fuel’s two major benefits over gasoline are its higher energy density (its volumetric heat value is 10% greater and therefore the fuel consumption at the same efficiency would be 10% lower) and its suitability for use in the automotive power plant with the current highest thermal efficiency; i.e. compression ignition direct injection (CIDI) vs. Internal Combustion for Alternative fuels (ICE). These two aspects result in fuel economy benefits compared to other alternative fuels in ICE46. Important characteristics of a diesel fuel are ignition quality (cetane), density, heat of combustion, volatility, cleanliness, and non-corrosiveness. The cetane number indicates how readily the fuel self-ignites. If a fuel has too low cetane number for the engine, the fuel may not ignite or may ignite poorly, especially on cold days

44 MacLean & Lave (2003), p.24. 45 Alternative Fuels Comparison Chart, (2003) - online 46 MacLean & Lave (2003), p.27.


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when starting a cold engine47. The flashpoint of diesel fuel is at least 55°C, and therefore it is safer to store than gasoline. The high sulfur content of diesel fuel is of concern due to fouling of emission control systems48, as well as acids formed in the atmosphere after emissions, leading to deposition as small suspended particles. Functioning of diesel oxidation catalysts is severely compromised unless the fuel is essentially free of sulfur. Removing sulfur from diesel fuel is more difficult (requiring more energy and cost) than removing it from gasoline to the fuel’s molecular structure49. Diesel fuels, like RFG (Reformulated Gasoline), can be reformulated to lower vehicle emissions. This is primarily accomplished through lowering sulfur content. In the US, sulfur content was reduced to 0.05% (500 ppm) by mass in 1993, and Europe adopted this level in 199650. Low sulfur diesel fuels have lower lubricity, lower electrical conductivity and reduced stability, but these can be corrected with additives51. Further information about fuel characteristics can be downloaded from the Fuel property database at the following URL: http://www.ott.doe.gov/fuelprops/

Fig. II.5-2. Land based extraction and Distillate Fractions of crude oil.

The next section, II.6. discusses the sources of motor vehicles emission in cities.

47 MacLean & Lave (2003), p.27. 48 Diesel fuel has been historically high in sulfur, but currently, 1000ppm sulfur is allowed in federal gasoline and only

500ppm sulfur in on-road diesel. 49 MacLean & Lave (2003), p.27. 50 MacLean & Lave (2003), p.27. 51 http://www.powerservice.com/, http://www.chevron.com/prodserv/NewOronite/products/pr_diesel_ad.htm


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II.6. Sources of Motor Vehicles Emissions Vehicle emissions from an individual car are generally low, relative to the smokestack image many people associate with air pollution. But in numerous cities across the world, the personal automobile is the single greatest polluter, as emissions from millions of vehicles on the road add up. Driving a private car is therefore a citizen's most "polluting" daily activity52. Petrol and diesel fuels are mixtures of HCs, compounds that contain hydrogen and carbon atoms. In a perfect engine, oxygen in the air would convert all the hydrogen in the fuel to H2O and all the carbon in the fuel to CO2. In Petrol or CNG engines the N2 in the air remains mostly unaffected. In reality, the combustion process cannot be perfect, and automotive engines emit several types of pollutants53. The energy to propel the vehicle comes from burning fossil fuel in an engine. Pollution from vehicles arises from the by-products of the combustion process (emitted via the exhaust system) and from evaporation of the fuel itself (see Fig.II.6-1.)54. Various types of pollutants are produced in the combustion process. A range of volatile organic compounds (VOCs) is produced because the fuel is not completely burnt (oxidized) during combustion. NOX result from the oxidation of nitrogen at high temperature and pressure in the combustion chamber. CO occurs when carbon in the fuel is partially oxidized rather than fully oxidized to CO2. SO2 and Pb are derived from the S and Pb in fuels. PM is produced from the incomplete combustion of fuels, additives in fuels and lubricants, worn material that accumulate in the engine lubricant (see Table II.6-1.), and those coarse particles brakes, tires, and dust emissions from roads.

Table II.6-1. Substances Emitted from Motor Vehicles55.

Acetaldehyde Cobalt and compounds PM ≤10 µm (PM10) Acetone Copper and compounds Polycyclic aromatic hydrocarbons (PAH) Benzene Ethylbenzene Styrene 1,3-Butadiene Formaldehyde SO2 Cadmium and compounds n-Hexane Toluene Carbon monoxide Pb and compounds Total volatile organic compounds (VOCs) Cyclohexane Manganese and compounds Xylenes Chromium (III) compounds Nickel and compounds Zinc and compounds Chromium (VI) compounds Oxides of nitrogen Fuel additives and worn materials also contain trace amounts of various metals and their compounds, which may be released as exhaust emissions. Evaporative emissions come mainly from petrol (diesel fuel has a much lower vapor pressure) and consist of VOCs and small amounts of Pb. These emissions may occur in several ways and are listed in Table II.6-2.

52 EPA (1994), p.1 53 EPA (1994), p.2 54 EPA (1994), p.2 55 NPI (2000), p.4


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Table II.6-2. Types of losses and emissions56

Diurnal Losses: As the ambient air temperature rises during the day, the temperature of fuel in the vehicle’s fuel system increases and increased vapor is produced.

Running Losses: Heat from the engine and exhaust system can vaporize gasoline when the car is running.

Hot Soak Losses: Because the engine and exhaust system remain hot for a period of time after the engine is turned off, gasoline evaporation continues when a car is parked.

Resting Losses: Vapor may be lost from the fuel system or the evaporative emission control system as a result of permeation through rubber components and other leaks. Evaporative emissions also occur from vehicle refueling at service stations or from fuel tanker loading and unloading

Thus, the principal factors affecting vehicle emissions can be summarized as follows57:

• Vehicle type. • Type and composition of the fuel used by a vehicle. • Age of a vehicle. • Types of roads on which a vehicle travels. • Driving patterns of vehicle (urban city cycle or inter-urban services). • Aggressive driving is known to result in increased emissions.

Fig. II.6-1. Sources of Emissions from a diesel bus in Brisbane.

56 EPA (1994), p.3 57 NPI (2000), p.7


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II.7. Motor Vehicle Pollution The assessment of vehicular contribution to urban pollution levels and its possible health effects have been extensively studied. Exhaust particles differ from country to country according to the percentage of the motor vehicle fleet (petrol vs. diesel operated) and by the type of fuel used (chemical composition). Analysis in the UK alone have shown that a fleet park of 8% diesel and 92% gasoline vehicles generate particle masses that are attributed to 42% to diesel and 58% to gasoline exhaust58. Analysis in Australia revealed that the 10% of cars and trucks that run on diesel make around 80% of the fine particle pollution. They are responsible for most of the air quality problems in cities, which shorten the lives of about 2000 Australians every year59. The most common engine types (among others) currently used are gasoline or diesel powered motor vehicles. The gasoline-powered motor is an internal-combustion engine that operates by combining air and volatile liquid fuel into a cylinder and compressing it with air. Shortly past the maximum compression an electric spark ignites the fuel-air mixture. The diesel engine likewise operates by combining air and heavy fuel into a cylinder and compressing it with air. Rather than using an electrically generated spark to ignite the fuel-air mixture, this type of engine utilizes a process called compact-ignition by spontaneous combustion; i.e. the fuel self-ignites once a certain pressure threshold is reached. When related to petrol exhaust, diesel engines burn lower volatile fuel oil instead of the highly volatile gasoline making the engine not only heavier, but generating exhaust products that are also oilier, and have an increased particles size range (see Fig.II.7-1.).

Fig. II.7-1. Diesel versus Petrol.

One diesel bus can pollute as much as 100 cars combined60.

In mega cities, exhaust from diesel trucks and buses are a significant source of particles. PM originating from such engines carry toxic substances, including heavy metals and chemicals, and result in the formation of acid aerosols. Together, these particles cause irritation to the respiratory tract in humans (see Chapter II.12). a1. Control of Petrol Particles Exhaust emissions from petrol engines vehicles of more recent design (e.g. all passenger cars manufactured after 1986) are primarily controlled by catalytic converters. These catalysts convert HC and CO to CO2, H2O, and (in the case of three-way converters) reduce NOX to N2 and O2. However, as Pb is able to “poison” or deactivate the catalyst, the more stringent exhaust emission standards introduced in 1986 necessitated the use of unleaded petrol. Sulfur

58 Clarke et al., (1996), p.417. 59 Horstman et al., (2003), transcript. 60 Farleigh et al., (2000), p.2.


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also adversely affects catalyst performance and durability, and regulation of the sulfur content of petrol has led to lower emissions of SO2. Increasingly sophisticated emission control technologies have been employed progressively to meet emission standards, commencing with tighter control of air/fuel mixtures and exhaust gas recirculation on the mid-1970s. Current vehicle models are equipped with computer-operated engine management systems, oxygen sensors and three-way catalytic converters, enabling catalysts to operate at optimal conversion efficiency during different modes of engine operation. Vehicle emissions are being further reduced by increasing catalyst durability, improving the control of evaporative emissions, and using computerized diagnostic systems that identify malfunctioning emission controls61. General improvements in vehicle technology and fuel efficiency are also resulting in overall emission reductions. Evaporative emissions have been controlled primarily through design features in the fuel system. Reducing the volatility of petrol (especially in summer) has also enabled reductions in evaporative emissions. a2. Characteristics of Petrol Particles Car exhaust particles are primarily aerosol-aggregates of small spherical particles several nanometers in size and are covered with semi-volatile adsorbed organic products. Due to their chemical production and their chemical content (carbon), these particles are supposed to be high adsorbents62. Petrol agglomerates appear to have a more linear “chainlike” structure; these aggregates are formed with “primary” spherical particles with a diameter estimated between 31-35 nm, primary petrol particles are shorter than diesel particles. It is assumed that this size difference is possibly due to a water layer (or hydrocarbon contamination) adsorbed onto the particles in ambient conditions. These primary particles are made of smaller grains from 14-16 nm. The surface roughness may have implications on specific area of this kind of particles. The interstitial spaces on the surface may also have an impact on the way they interact with the atmospheric compounds63.

Fig. II.7-2. Size distribution of petrol exhaust;

This frequency distribution was obtained from a 4 stroke spark ignition engine running under idling load

conditions64.

a3. Composition of Petrol Exhaust (PE) The size distribution of petrol exhaust particles tends to be asymmetrical with mean diameters ranging from 40 to 80 nm65. Figure II.7-2. shows the number weighted size distributions of exhaust particles originating from a gasoline engine66.

61 MacLean & Lave, (2003), p.34. 62 VEECO, (2001), p2. 63 VEECO, (2001), p3. 64 Madl, (2003), p. 105.


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The composition of petrol exhaust is dependent on the chemical composition of the gasoline and engine operating conditions (i.e., temperature, rpms, and O2 intake). Fuel additives such as MTBE also contribute to the chemical composition of petrol exhaust. PE is made up of nearly 90% carbon67 with most of it as CO2 (see Fig.II.7-3.). Gasoline powered vehicles in average emit twice as much CO but only 1/6th in particle mass68 as well as 1/8th of NOX than heavy duty diesel fueled vehicles69. At this stage it should be mentioned that by using shorter chained HC-fuel (petrol) over longer chained fuel (diesel) the obviously reduced particle mass emission in petrol is accompanied by a substantial increase in the particle number emissions of sub-micrometer to nano-particles. Modern engines are capable of emitting even much lower particle mass concentrations, but these are achieved by an even more dramatic increase in particle number emissions70. As gasoline is a lot more volatile than diesel, the major hydrocarbon exhaust components (VOC) exceed those of diesel exhaust are 1,3-butadiene (C4H6), benzene (C6H6), and formaldehyde (CH2O).

Fig. II.7-3. Source emission by fuel type71; Emission estimates according to the EPA

MOBILE5 model, excluding H2O and CO2.

b1. Characteristics of Diesel Particles Diesel particles appear as agglomerates of primary particles (20-30 nm), these agglomerates are formed in the combustion chamber. They have a typical particle size of around 50-20 nm, they can, in extreme cases, grow up to 10 µm, particularly by accumulation on the surfaces (exhaust pipe, muffler, particle filter)72. Diesel-motor primary particles consist of 0.35 nm of radial symmetry with laminated layers of carbon as nets separators. The individual crystal packet has surfaces of around 2 nm and a thickness of 1.5 nm. By incomplete burning of diesel fuel, the result is soot and diesel particles. And by chemical compound, for example, CaO (gypsum) from the Calcium of the lubricating oil and the sulfur

65 Harris et al. (2001), p.750. 66 Madl, (2003), p. 105. 67 NAS, (2000), p.23, Summarized from pictrograms (figs.1-3). 68 IANGV Emission Report (2000), p.88. 69 Harrison, (1993), p.18, p.22. 70 Graskow et al, (1998), p.155. 71 Modified after NAS, (2000), p.23. 72 AKPF, (2000), p5.


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also from the same lubricating oil or from the fuel. Consequently, particles can be brought into the motor process (as resuspended road dust) and/or in modified form in the exhaust aerosol. Particle formation and particle dismantling can be due to the process of the accelerated catalytic effects. Catalysts are usually made from precious metals or oxides overcoated with metals or additives on their substrates that are brought into the fuel; therefore, they are able to form additive and/or oxide particles73. b2. Composition of Diesel Exhaust Particles (DE) The size distribution of diesel engine particles is nearly log-normal with mean diameters ranging from 60 to 120 nm74. While the composition of diesel-PM may be similar to that of petrol exhaust, the particles are considerably larger. Figure II.7-4. shows the number weighted size distributions of exhaust particles originating from a diesel engine75. DE is a complex chemical cocktail of about 450 different compounds76. At least 40 are toxic contaminants like arsenic, benzene, cadmium, dioxins, toluene, formaldehyde and even some major chemical carcinogens like 3-nitrobenzanthrone (C16H9NO3) and 1,8-dinitropyrene (C16H8N2O4)77 are found in either as gas or particle form.

Fig. II.7-4. Size distribution curve of diesel exhaust; This frequency distribution was obtained from a diesel engine running under idling load conditions78.

b3. Structure of Diesel Soot The hydrophobic matrix of aromates stuck together to form a loose mesh. Agglomeration is a continuous process, thus the growing cluster adds layer upon layer until a sphere with about 0.35 nm is obtained (Fig. II.7-5.) illustrates the formation of diesel spheres. This “crystalline” structure of soot corresponds, therefore, to that of a graphite-like structure79. As this process already takes place in the combustion chamber, the primary particles keep growing to a diameter of 20-30 nm (with a considerable part of even 10-80 nm80). The density of these primary particles is about 1.5 g/cm3, the density of the agglomerates is about 0.02 - 0.06 g/cm3, in the deposited soot cake can add up to 0.4 g/cm3.

73 AKPF, (2000), p2. 74 Harris et al. (2001), p.750 75 Madl, (2003, p. 105. 76 Horstman et al. (2003), transcript. 77 Pearce, (1997), online. 78 Madl, (2003), p. 105. 79 AKPF (2000), p.6. 80 HEI (2002), p.23


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While still in the exhaust system of the engine, these particles start to form aggregates that are visible as black smoke, and are so typical for diesel soot. It consists of fine particles (diameter <2.5 µm), including a subgroup with a large number of ultrafine particles (<100 nm). The characteristic sponge-like structure and large surface area (50 to 200 m2 / gram of soot) of these agglomerates make it an excellent carrier for organic compounds of low volatility. As shown in Fig. II.7-6, these compounds reside on the particle surface (as a liquid), or are included inside the particle or both81. It is important to note that diesel exhaust makes a larger contribution to elemental carbon than does gasoline exhaust, and these two sources account for nearly all of the elemental carbon. In the PM2.5 range, petrol exhaust has significantly more organic carbon than diesel exhaust82.

Fig. II.7-5. Steps in soot formation.

This simplified sketch displays the various steps involved in the formation of a diesel aerosol83

The composition of the soot depends strongly on their developing process (low load condition, fuel rich combustion, etc.) as well as on the sampling conditions84. The densities of the agglomerates range from 0.02 - 0.06 g/cm3, but can reach 0.4 g/cm3 in the deposited soot cake. The physical characteristics of soot are inert, odourless, and insoluble in water and in organic solutions. It is, however, a high adsorbent for hydrocarbons, aldehydes, oxygen-containing odour compounds and sulfur containing molecules. The SO4

= fraction of diesel exhaust particles is composed primarily of the sulfuric acid (H2SO4) formed from the sulfur contained in the fuel and or the lubricating oil. It is generated when SO2 is oxidized to sulfur trioxide (SO3

-). Upon exposure with water vapor (likewise a by-product of the combustion process) it converts to H2SO3 and eventually to H2SO4 85. Fig. II.7-6. illustrates how these H2SO4 particles adhere to the carbonaceous structure. Formation of H2SO4 is widely load-dependent; only a small amount is produced in low load conditions, while more of it occurs at high load operation conditions (Fig. II.7-7.). As sulfur content in diesel has a detrimental effect on diesel fume purification, it is a major player in the negative health effects on humans of diesel exhaust.

81 Krieger et al. (1998), p.A-10. 82 MAG (2001), p.E-75. 83 AKPF (2000), p.4. 84 AKPF (2000), p6. 85 Atkins, (1997), p.534.


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Fig. II.7-6. Sketch of diesel particles; Schematic representation showing agglomeration of diesel exhaust and adsorption of the vapor-phase.

Unfortunately, the sulfur content in the exhaust stream makes catalysts useless, as it interferes with the precious metals or oxides of the catalyst is coated with86. To really deal with the particle problem, the sulfur in the fuel has to be gotten rid off at the refinery. Reducing sulfur content to 5 ppm or lower brings dramatic improvements, by eliminating the sulfur nanoparticles. Only then can the exhaust particle stream react with the metal-coated substrate of the catalysts to further oxide them while the solid fraction could be trapped in particle filters87.

Fig. II.7-7. Diesel exhaust composition at various load

conditions 88

86 Horstman et al. (2003), transcript. 87 AKPF (2000), p.2. 88 Krieger et al. (1998), p.A-11.


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By weight, some 88% is made up of carbon, while the remaining 22% belong to trace elements contained in the type of fuel and those other atmospheric constituents like O2, N2, S, H2O vapor and other trace elements.

b4. Formation of Burned Particles in the Diesel Engine Older engine technology operating at lower injection pressures, larger droplets and greater portion of wall depositions, generate less ultrafine particle emission in favor of larger soot flakes. These soot flakes are visible smoke clouds. The Diesel soot from these old engines is characterized by the excess air from the coking processes (heating in the absence of air). This effect is undesirable as inefficient fuel usage unnecessarily pollutes the environment. On the other hand, modern diesel engine design avoids this homogeneously distributing the droplets, and thereby improving combustion significantly89. Diesel soot particles are not formed from the coking drop process (resulting from the heterogeneous air/fuel mixture), but originate from the molecular basis of new development. Currently, two hypotheses of the soot particle formation are accepted:

b4.1. The Polycyclic Hypothesis The formation of condensation nuclei begins with the thermal or the oxidative pyrolysis, i.e. the partial oxidation of aliphatic and aromatic compounds. The absence of oxygen favours hydrolysis in which unsaturated hydrocarbons are formed, for example, acetylene (C2H2). The reaction further leads to the formation of radicals and the accumulation of unsaturated HCs. This in turn triggers the formation of cyclic higher-molecular compounds resulting in one or more aromatic rings; i.e. polycyclic HCs. Splitting off hydrogen atoms, ultimately leads to the continued accumulation of acetylene similar to the graphite-like soot particles. These nuclei have a size of 2 – 10 nm, and are usually spherical and quite closed90. b4.2. The Elemental Carbon hypothesis The higher combustion temperatures dissociate a HC-cloud from the fuel rich mix. Hydrogen (H) diffuses 10 times more rapidly to an oxygen-containing environment than carbon does. The HCs dehydrate rapidly and carbon atoms remain; over which the valences quadruple into clusters so that primary particles form, which grow finally in the shortest time (milliseconds) to a typical particle size of about 10 nm. Consequently, hexagonal and pentagonal structures grow alternatively. Therefore, according to the model of spherical layers, similarly to the Fullereren form (molecular clusters consisting of C60, C70, C76, C84, …)91, they are clearly less stable than graphite structures (graphite will burn by approx. 3000°C) 92.

89 AKPF (2000), p3. 90 AKPF (2000), p3. 91 MPI-FKF, (1996); online 92 AKPF (2000), p3.


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Previous trials in methane flames confirmed the soot formation hypothesis, whereby it becomes clear that parallel to the formation of the high-carbon-containing soot primary particles, become the very source of nucleation of the expanding and cooling exhaust stream. Concerning the particle formation, both the petrol engine and the modern diesel engine do not differ significantly. Particles from burned HCs are always characterized by PAH coating on a solid core consisting of elementary carbon93 (compare Fig. II.7-8.).

Fig. II.7-8. Particle size distribution in direct inject petrol engine (Gaskow).

Conventional petrol engines in the partial load range (typical operating cycles) reveal very small particle emissions. This pattern changes only slightly with increasing load and by greasing of the mixture. With direct inject petrol engine (see Fig.II.7-8.), the situation is similar to the diesel engine; therefore, high concentrations of fine particles were shown on the entire diagram94. Even the LPG-operated spark ignition engine forms particles, which are very similar to the so-called diesel particles in certain operation ranges. The lack of oxygen might also increase the probability of particle formation in the petrol engine. The temperature remains high in the conventional Lambda-1-petrol engine and in the partial load range till the shoving off the gas in the emission exhaust. Under these conditions, particles still can burn off into the gaseous phase. Large differences exist, however, concerning the afterburning of the particles in the expansion phase and in the exhaust strand. Within the Diesel motor, the temperature sinks so rapidly that carbon particles do not react with oxygen anymore – as a result soot is exiting the tailpipe system. b5. Diesel Vehicles Around the World95 Diesel-powered vehicles now comprise between 8 to 15 % of the car fleets in much of Western Europe, and more than a fourth in France. On the global energy basis, fleets of diesel consume 10-15 % less energy per kilometre than the corresponding fleets of gasoline automobiles. Moreover, diesel vehicles tend to have larger engines than the gasoline powered cars, with the overall effect that both yield roughly equivalent performance. Drivers who use their cars more tend to switch to diesel to save money on fuel, but also because significantly lower fuel prices are an incentive for car users to switch to diesel or simply to drive more. The generation of wealth will clearly put increasing pressure on natural ecosystems and generate huge amounts of pollution. In the post-World War II period that the world began to see what was then an unprecedented economic boom in Europe, Japan and North America. By the 1950s itself, cities from Tokyo and London to Los Angeles were choking under pollution. The western society responded to his problem with increasing investment in pollution control. It is estimated that in the early 1970s, Japan spent over 25 % of all industrial investment in

93 AKPF (2000), p4. 94 AKPF (2000), p5. 95 CSE (2000), pp.5-10.


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pollution control measures. According to the Central Pollution Control Board (CPCB), diesel is responsible for 100 % of the PMs, 95 % of NOX and 96 % of SOX emissions produced by vehicles. It is known that particles in the accumulation mode (compare with Fig. II.8-4, with an average size range of 250 nm) easily penetrate the lungs, even more so the smaller particles within the nucleation mode and their condensates (particle sizes in the 20 nm range). Experts soon realized that it is not the total quantity of particles that matters so much for public health as the size of the particles in vehicular exhaust. The smaller the particles, the worse they are. This small fraction penetrates deep into the lungs and stay there for prolonged times. Experiments in the UK have shown that 90 % of the particles in diesel exhaust are extremely tiny of a size of one micron or less. In other words, diesel exhaust not only has a very high quantity of particles but it also made of very tiny particles. A US study conducted in 1991 found that engines generate 15 to 35 times more particles than an engine survey conducted in 1988. Consequently, the improved engine design resulted in a higher environmental particle load. The second set of damaging studies have come from epidemiologists who have found that out of all the air pollutants, particles kill the most people – some 460,000 every year. The toxicological studies have also been showing that diesel particles are highly carcinogenic. In 1997, a Japanese scientist even reported the discovery of the most potent carcinogen found as of today in diesel exhaust96. With all this disturbing information in mind, the California Air Resources Board (CARB) formally declared in August 1998 that diesel particles are Toxic Air Contaminants (TAC) (see Appendix D in Table D6), which means automobile companies will now have to drastically reduce particles in diesel exhaust, and this is not going to be an easy task. b6. Trends in Diesel Powered Vehicles Studies are throwing up disturbing evidence that even using clean diesel fuel may not help to solve the problem of toxic particulate emissions. It appears that the total quantity of particles goes down only slightly while the size of particles gets reduced significantly making them even more dangerous. The greater the number of particles in the emissions, the smaller will be their average size and more deeply will they be able to penetrate the respiratory tract97. The level of sulfur in the diesel has a direct and linear relationship with particulate emissions in diesel exhaust. Therefore, it is very important to eliminate or reduce sulfur to the maximum possible level. Variables which determine the levels of various harmful constituent in diesel fumes are sulfur content, the fraction of aromatic HCs in the fuel, fuel additives and the volatility of the diesel fuel. Compared to the best diesel available in the world that is, Swedish diesel98 which has 0.001 % sulfur and 5 times more than the 0.05 % sulfur diesel currently used in Europe. Very soon, Europe as a whole will be moving towards even lower sulfur content in diesel. According to the Health Effects Institute (HEI) based in Cambridge, USA, despite a substantial reduction in the weight of the total PM, the total number of particles from a 1991-model diesel engine was 15 to 35 times greater than the number of particles from a 1988 diesel engine when both engines were operated without emission control devices. Thus, newer diesel engines may be emitting a smaller mass quantity of particles but not fewer particles. California standards

96 Enya et al., (1997) 97 CSE (2000), pp.39-45. 98 CSE (2000), pp.36-37.


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state that manufacturers will have to certify that these emissions will be met for at least 80,000 - 240,000 km running of the vehicle, depending on the type of vehicle. Furthermore, catalytic converters reduce CO2 and HCs in diesel vehicles but not PM and NOX. To reduce PM, another device called particulate traps is needed but it is still an experimental stage. But at the same time, governments are beginning to counter this industry response to the global warming problem by tightening diesel emission standards concerned by the health effects of diesel particles. As a result, there are wide differences in the trend in sales in diesel cars from country to country even within Europe. It is important to note that until recently PM in Westerner Countries was not considered a serious problem. The European Union does not even have an ambient air quality standard for particulates and is considering one only now. Moreover, particulate standards were invariably built on total quantity of PM but now there is growing concern about the size of particles and the recognition that there is no safe level of particles. Meanwhile, there is new evidence to show that diesel cars may not even help prevent global warming. Diesel cars are more damaging to environment and health than gasoline cars, according to a study by the Swedish Environmental Protection Agency (SWEPA). As a result, European counties are beginning to rethink diesel technology99. A recent Dutch study, for instance, which, has tried to evaluate the optimum fuel mix for the country’s road traffic in the year 2010, from an environmental point of view, has concluded that the proportion of diesel should drop from 57 % in 1997 to 43-46% in 2010 with the proportion of petrol going up from 36% to 43-45% and that of gaseous fuels like LPG and CNG should go up from 7% to 9-14%. The study recommends that the percentage of passenger cars run on diesel should drop by more than half and even that of light commercial vehicles by over a third. The Study recommends city buses and coaches, distribution trucks and refuse collection vehicles should move towards the use of CNG and LPG. As a result, Swedish sales of new diesel cars have almost disappeared. In summary, it is best to reduce the use of diesel to the minimum extent possible because of increasing evidence of the acute cancer-causing potential of diesel-related pollutants and their other health effects. b7. Advantages of Diesel over Gasoline As mentioned previously, diesel fuel is heavier and oilier than gasoline. Because diesel fuel requires less refining, it is generally cheaper than gasoline. Diesel fuel also has a higher energy density than gasoline, which means that a liter of diesel fuel provides more power than a liter of gasoline. In fact, diesel engines typically offer 45% to 60% better fuel economy than gasoline engines100. In a gasoline engine a spark ignites the fuel, whereas in a diesel engine the fuel is ignited by the compression of air in the engine's cylinders. A diesel engine's ability to constantly change the mixture of air and fuel allows it to generate additional power when needed and to haul heavy loads more easily101.

99 CSE (2000), pp.43-45. 100 VEECO (2001), p3. 101 VEECO (2001), p3.


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b8. Advanced Particulate traps or CRTs What holds promise for diesel vehicles is the application of state of the art of continuously regeneration particulate traps (CRT) in combination with other catalytic converters for reducing fine particles and lean de-NOX catalysts for reducing NOX and ULSD with less than 30 ppm sulfur or a dramatic reductions in the level of below 0.005 % and preferably even below 0.001 % or even better total sulfur free is essential102. But this is combination is still experimental and not yet commercially viable. Particle filters get loaded to the point when a reaction process is activated that burns off the trapped matter. Although a genuine approach, it is not enough to control the soluble organic fraction of the PM. Therefore, CRTs have to be fitted along with oxidation catalysts, which can oxidize the toxic organic components of the exhaust. Since this kind of application require active regeneration technology, these systems are very expensive. Neither the industry nor their experts explain adequately that simple soot or particulate filters are grossly inefficient when used along with high sulfur fuel and that advanced filters like the CRTs do not even work on high sulfur fuel. Studies done in Hong Kong show that fitting diesel vehicles with “low-cost” particulate traps has had very little impact. Fitting low cost particulate traps to 66,400 diesel vehicles weighing lower than four tones and run on 500 ppm (0.05%) sulfured diesel, has cut particulate emissions by only 7.5%. Even though these exhaust emission control devices have a self-cleansing system, fitting catalysts into 83,000 diesel vehicles weighing more than four tones has lowered particulate emissions by only 13.2%. According to the CARB catalyst-based diesel particulate filters can be used with diesel fuel of varying sulfur content, the greatest reductions come from using very low sulfur fuels. Used with very low sulfur diesel fuel (less than 15 ppm), catalyst-based diesel particulate filters have been reported to reduce diesel PM emissions by over 85%. Emission test results from USEPA show that heavy-duty diesel engines fitted with CRT and run on low-sulfur diesel (3 ppm), the PM dips by 96%. Test results from the UK also show that lower the level of sulfur in diesel, greater is the efficiency of CRT to reduce PM emissions. c1. CNG Particles Natural Gas powered vehicles are “much cleaner” than vehicles fuelled by conventional diesel103. With respect to the emissions of fine PM, they emit dramatically less PM than diesel buses; it revealed a 58% NOX reduction, and a 97% reduction in PM (very low PM emissions rates, but number counts may be very high). Natural gas buses, can be equipped with basic pollution control equipment, like oxidation catalysts; such devices achieve even lower particulate emissions than found in earlier generations of this technology - and significantly lower than the diesel results104. Vehicular particle emissions in the lower size class are a concern (small particles are especially harmful), as these are not only numerous but occur near ground level where people live and work105. Although for the greater part, the mean particle size emitted was found to be similar to those from gasoline vehicles106, natural gas contains no

102 CSE (2000), p.7. 103 WB (2001), p1. 104 http://www.betterworldgroup.com/, p.1. 105 WB (2001), p1. 106 Alternative Fuels Data Center, (2003); http://www.afdc.doe.gov/pdfs/mithfact.pdf, p.1


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benzene or aromatic HCs, resulting in fewer harmful emissions of such nature from natural gas-powered engines. The ignition temperature of CNG is 650°C, much higher than gasoline’s 417°C, so using CNG as fuel is a lot more efficient than usage of gasoline107. Advantages and Disadvantages of NGVs108

Advantages

• Very low particulate emissions. • Low emissions of airborne toxins. • Negligible emissions of SOX. • More quiet in operation. • Diversification of energy sources.

Disadvantages

• Much more expensive distribution storage. • Higher vehicle cost. • Shorter driving range. • Heavier fuel tank. • Potential performance and operational

problems compared to liquid fuels.

With respect to emissions, it is worth noting that advanced technology gasoline vehicles with 3-way catalysts are so clean than the fuel itself (that is, whether liquid or gas) plays a relatively minor role, especially for the regulated emissions. Under these circumstances, converting an advanced gasoline vehicle to gaseous fuel could even increase emissions. But CNG has a marked advantage over conventional diesel engines. Example data taken from the US comparing compressed CNG with diesel, shown in Table II.7-1, illustrates this point.

Table II.7-1. Emissions Benefits of Replacing Conventional Diesel with CNG in Buses.

Exhaust type CO NOX PM Diesel 2.4 g/km 21 g/km 0.38 g/km CNG 0.4 g/km 8.9 g/km 0.012 g/m Reduction [%] 84 58 97 c2. Comparison of Pollution Potential of Vehicle Fuels109 The pollution potential of conventional fuels depends on the ratio of carbon to hydrogen atoms. Petrol and diesel belong to long-chain HCs with a larger number of carbon atoms forming the chain with hydrogen atoms. On the other hand, fuels like CNG, LPG and propane belong to the group of short-chain HCs having a lesser number of carbon atoms. Hence, the latter are less polluting. This factor, together with the combined effect of fuel characteristics, fuel additives and exhaust treatment systems in automobiles as well as secondary pollutants generated through atmospheric reactions, is the reason for air pollution and its health effect caused by automobile emissions.

107 Yang & Kraft-Oliver ( 1997), p.401. 108 WB (2001), p2. 109 CSE (2000), pp.11-15.


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c3. Benefits of Moving to CNG 110 : • Problem of toxic particle emissions will be virtually eliminated. • Total HC emissions will be high but most of it is methane. • The non-ethane HC components that are cancer-causing and come mostly from diesel and

petrol vehicles constitute a small fraction of the total HC emissions from CNG vehicles. • The NOX emissions though high compared to other emissions from CNG vehicles will still

be much lower compared to diesel vehicles. • SO2 emissions that also lead to formation of deadly sulfate particles will be virtually

eliminated. • CO levels will be considerably lower. c4. Summary of Control Issues for The Three Most used Fuels Today111 Gasoline: There have been significant improvements in the last decade for these vehicles, including vehicles certified up to certain countries’ standards. These vehicles require very “clean”, low sulfur fuels. The USEPA designated toxics emitted as conventional automobile exhaust and evaporative emissions are benzene, 1,3-butadiene, formaldehyde, acetaldehyde and polycyclic organic matter. Diesel: The primary constraint to the future use of diesel fueled vehicles are particle exhaust emissions and NOX. There have been recent advances in emissions control from diesels that have been assisted by the upcoming regulations for clean fuel, sulfur of 15 ppm, but major issues still require resolution. One important issue is that there will be a loss in fuel efficiency due to the energy required to power the emission control systems necessary to meet PM and NOX emissions standards. There are two issues associated with PM from diesels that are of concern, the amount of the emissions from the vehicles (diesel engines inherently produce more PM than do gasoline engines), and the toxicity of adsorbed substances on exhaust particles (e.g. 3-nitrobenzathrone and 1,8-dinitropyrene). CNG: It is a “cleaner burning” fuel than gasoline or diesel. CNG vehicles have lower CO and NMHC emissions, although total HCs may be higher due to unburned methane. The emissions of HCs that cause air pollution problems are lower with CNG vehicles. A gas-fueled engine does not require cold-start enrichment and therefore, emissions from “cold” engine operation are lower than with liquid fuels. Gas systems are designed to be “airtight” and so should have almost no evaporative emissions. The low sulfur content of CNG leads automatically to lower catalyst poisoning.

110 CSE (2000), pp.11-15. 111 MacLean & Lave (2003), pp.34-37.


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II.8. Atmospheric Aerosol Particles When the majority of people think about air pollution, they commonly attribute those pollutants to gaseous substances. These classical pollutants are commonly known as CO, NOX, NOY, SO2, VOC, etc. During and after the Great Smog of London in 1952 (see Chapter II.3; p.17), communities realized that reduced visibility due to suspended matter does affect people's health. Since then, it is essential to increase the spectrum of airborne pollutants to include also those substances that are not in the gaseous state (see Fig. II.8-1.). These small solid particles and liquid droplets, collectively called particles (aerosol, PM or suspended PM) present in the air in great numbers significantly contribute to the problems associated with pollutants in the lower troposphere.

a. What are Aerosols? Aerosols are a suspension of fine solid particles or liquid droplets in a gas. They are usually stable for at least a few seconds and in some cases may last for years. Aerosol particle sizes range from about molecular diameters (< 10 nm) to particles in the visible range (more than 100 nm).

Fig. II.8.-1. Sizes of Airborne Particles;

superimposed are the settling velocities in still air at 0°C and 101.3kPa for particles having a density of 1g/cm3 as a function of particle diameter112.

112 modified and redrawn from Colbeck (1998), p.3 / fig.1.1.

Hinds (1999), p.9/fig.1.6; Stoker et al. (1972), p.66 / fig.6-1 & p.67 / fig.6-2.


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b. Sources of Aerosols According to their sources, aerosols can be classified as primary and secondary. Primary aerosols are mainly of natural origin and have only slightly changed during the past century. Secondary aerosols, on the other hand, are predominantly of anthropogenic origin. With the onset of the industrial revolution (some 150 years ago), emission of this type of aerosol has undergone a dramatic change. Once aerosols are released into the atmosphere physical and chemical interactions change their size, number, and chemical composition.

Particles tend to fall into three size classifications referred to as modes. Figure II.8-2. displays these modes of particle size distribution, their origin and their principal mechanism of generation: • Nucleation mode: recently emitted

particles undergo condensation as long as these are warmer than ambient air or are freshly formed within the atmosphere by gas particle conversion.

• Accumulation mode: coagulation and condensation of smaller nuclei generate a pool of larger aerosols.

• Coarse mode: mechanically generated aerosols like soil dust, sea spray and many industrially generated fumes.

In similar fashion, illustrates figure II.8-4. the relationships between idealized diesel particles of number-, surface-, and mass weighted size distributions. It also illustrates the alveolar deposition for particles with different diameters.

Fig. II.8-2. Schematic of a typical size distribution. It shows the mass weighted formation mechanisms for atmospheric particles113

Where most of the mass is due to larger particles in the accumulation mode, most of the number is due to particles in the nucleation mode. All of the three weighted spectra in Fig. II.8-4. follow a lognormal, trimodal size distribution with the concentration in any size range being proportional to the area under the corresponding curve in that range. The size distribution varies depending upon the type of the engine, fuel characteristics, and operation conditions. There is some suggestion that as mass of emissions goes down, a greater fraction of ultrafine particles is emitted114.

113 modified after Colbeck (1998), p.6 / fig.1.3; Morawska (1999), lecture script,; AKPF (2000), p17. 114 HEI (2002), p.22; Horstmann (2003), transcript.


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c. Physical Properties of Aerosols A number of physical properties apply to aerosols in general. Among those are particle size, shape, interaction with light and particle sorption. c1. Particle Size The most important particle property is size. Figure II.8-1. shows the range of sizes possessed by airborne particles (note the area in the lower right which shows the range to the visible eye). In the size ranges given above, a particle's lifetime in the suspended state depends not only on its density but also on its chemical reactivity. Figure II.8-1. also shows the relationship between settling velocity and size of particles assumed to have the same density. One can see that a 100 nm particle settles at a velocity of 800 nm/s, and one with a diameter of 1 mm settles at a velocity of 3.9 m/s115. A 10000-fold increase in diameter results in a 4 million-fold increase in settling rate. Even permanently suspended particles have settling rates, but they remain suspended because of air movements. As this project predominantly deals with aerosols originating from combustion processes in the size range from 10 to 800 nm, settling velocity is not really an issue. As such aerosols tend to remain suspended in the air, they will, consequently, be available for both (photo-) chemical and physical interaction with other aerosols. Being respirable, these aerosols can deeply penetrate and deposit in the mammalian lungs to unfold their toxic potentials right at the most vulnerable part of living organisms -- the gas-exchanging structures -- the alveoli. In the section II.10. deals in depth with particle statistics and how few parameters that can be used to describe entire size spectra, while on section II.4, there is a closer look at the Health Effects of exhaust fumes. c2. Particle Shape and Light Interaction Manufactured and naturally produced particles found in ambient air also diverge in shape, density and chemical composition. Spherical (droplets) and cylindrical particles (fibres), and more complex shapes which include crystalline particles or even clusters of carbon black that exist as an extended framework of small spheroids, do change the way particles behave. To characterize an aerosol, an equivalent diameter could be helpful (this refers to a measurable index of the parameter); i.e. the diameter of a sphere having the same value of a specific physical property as the irregularly shaped particle116. But there are some limitations using optical means to detect particles. Compared with a single particle, such an aggregate will result in changed Brownian motion, gravity, inertia, light scattering, and other

Fig. II.8-3. Micrograph of soot particle agglomerate117

115 Stoker & Seager (1972), p.68. 116 Willeke (1993), p.12. 117 Mayer (2003), p.2.


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patterns. In order to detour these difficulties, sensing equipment utilizes monochromatic light; depending on the shape and size of the particulate, its dimensions will scatter light differently. Those with diameters below 100 nm are sufficiently small compared to the wavelength of light, that they refract light in a manner similar to molecules. Aerosols with diameters much greater than 1 µm are so much larger than the wavelength of visible light that they obey the same laws as macroscopic objects and intercept or scatter light roughly in proportion to their cross-sectional areas. Although these optical properties are important in the detection of aerosols, there are methods available to compensate these deficiencies. c3. Particle Sorption Another property of particles is their ability to act as sites for sorption. This property is a function of surface area and is illustrated in Fig. II.8-4. The process of sorption takes place when an individual molecule impacts on the surface of a particle and does not rebound, but sticks or sorbs onto it. This process can occur in three ways and are important in the health effects of particles118:

• Adsorption: electrostatic forces cause the impacting molecule to be physically attracted and held to the particulate surface.

• Chemisorption: sorption, which involves a chemical reaction between the impacting molecule and the particulate surface.

• Absorption: hygroscopic, hydrophilic, or hydrophobic properties cause the impacting molecule to dissolve into the particle.

Fig. II.8-4. Normalized and weighted distribution curve of diesel exhaust; Mass-,

surface-, and number weighted particle-size distributions shown with alveolar deposition fraction119

118 Stoker & Seager (1972), pp.66-68. 119 modified after Kittelson (2000), p.1; HEI (2002), p.22.


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c4. Particle Diameter The diameter is often used as a measure of particle size. The most common size descriptor is the aerodynamic diameter. It is a function of particle density and is defined as the diameter of a spherical particle (assigned a unit density of 1 g/cm3) with the same settling velocity as the particle being measured120. Particle sizes are usually measured indirectly. Rather than measuring each particle separately, they are weighed as a collective per size class; i.e. the equipment used determines the total number in a given size class (or bin). Doing so reveals the geometric particle diameter per channel. The SMPS program sizes particles based on their electrical mobility121, which closely matches particles’ geometric diameter at a standard density of 1 g/cm3. The section II.10 deals with Particle Size Statistics and includes a detailed description about particle diameters, mathematical definitions, and particle size spectra. c5. Acoustic Coagulation Particles in motion respond to an acoustic field; these vibratory patterns include oscillation, circulation or net drift in some direction, in response to the gas motion. Such pressure waves increase particle coagulation, or in other cases, tend to enhance droplet evaporation or condensation122. Acoustic coagulation is therefore a phenomenon frequently encountered on bus platforms with idling and accelerating vehicles. The frequency spectrum of an accelerating bus changes from 10 Hz (idling mode) to 50 Hz (acceleration mode) which corresponds to 600 to 3000 rpm, with observed sound pressures fluctuating between 80 to 100 dBA (see Chapter IV.4; p.117). Thus, the general trend shown in Fig. II.8-4. will be an accelerated shift in particle distribution towards the accumulation mode.

120 Hofmann (2001), p.5. 121 TSI-SMPS 3934 Manual (2000), p.5-59. 122 Willecke (1993) p.39; Hinds (1999), p.275.


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d. Particles in Motion As defined in the introduction, an aerosol consist of a gas or a gas mixture (commonly air) and the particles suspended in it. As exhaust gases from combustion processes are found in the sub-micrometer range (<1 µm), they neither follow the continuum region of the kinetic gas theory (particles submerged in a continues gaseous fluid) nor the transition zone123 – see Fig. II.8-1. Thus, the motion of gas will largely dictate the behavior of the suspended particles. This section should briefly introduce the concepts behind these parameters governing particle behavior124. d1. Reynolds Number (Re) The flow pattern, whether it is laminar or turbulent, is governed by the ratio of the inertial force of the gas to the friction force of the gas moving over the surface. This ratio is known as Reynolds number, a dimensionless quantity. Actual values for Re depend on how the gas is bounded. Laminar flow in a circular duct has Re values smaller than 2000, while turbulent flow occurs for Re-values >4000. Since the number characterizes the flow, it depends on only gas density and not particle density. For NTP conditions (293°K, 101kPa), Re is expressed as:

Pdv6.5Re ⋅⋅= [-] v, velocity of the gas

dp, particle dimension [m/s]

10-800⋅E-9 [m] II.8-1. As will be shown later, the instrumentation used operates with an aerosol flow of 0.3 L/min. Taking into account the inner diameter of the sampling hoses and those of the instrument piping (both around 5 mm), it converts to a gas speed of 8 m/s and a Re of <5⋅E-6 as a rough estimate. d2. Stokes’s Number (Stk)125 The characteristic dimension depends on the application, e.g. in fibrous filtration it is the diameter of the fiber and in impaction flows it is the radius of the impactor nozzle. For a given percentage of particle removal, the Stokes number values is, therefore application-specific. For example, the Stokes number of an impactor with one or several identical circular nozzles is:

D9CvdStk C

⋅⋅⋅⋅⋅

=ρ

ρ 2

50 [-]

ρ, particle density 1g/cm3 d, particle diameter v, velocity of the jet (0.3L/min) η, gas viscosity at 20°C CC, Cunningham Slip Corr. Factor D, nozzle diameter

1000 [kg/m3] 10-800⋅E-9 [m]

24.7 [m/s] 18.13⋅E-6 [Pa⋅s]

1-10 [-] 0.508⋅E-3 [m]

II.8-2

d3. Knudsen Number (Kn) In the sub-micrometer range, the “bombardment” of gas molecules from all directions affect a particles location in space. As mentioned earlier, the particles motion is no longer determined by gas kinetics. The mean free path of a particle, defined as the distance between collisions of

123 Willecke (1993) p.23. 124 detailed formulation can be found in Hinds (1999), p.27. 125 Willecke (1993) p.35.


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gas molecules, is depended on temperature and pressure. Thus, the Knudsen number (again a dimensionless quantity), relates to the gas molecular mean free path to the physical dimension of the particle. Operating at NTP conditions, a simplified expression for Kn can be made:

Pd2Kn λ⋅

= [-] λ, mean free path of gas molecules dp, particle diameter

66.5⋅E-9 [m] 10-800⋅E-9 [m] II.8-3.

For motor vehicle particles, the Knudsen factor oscillates between 0.1 and 7. As a reference, a Kn of <<1 indicates continuum flow, whereas a Kn>>1 indicates free molecular flow. Therefore, motor vehicle emissions seem to follow the transition or slip-flow regime. In order to verify this assumption, the Cunningham Slip Correction Factor (Cc) can help to support this claim. d4. Cunningham Slip Correction Factor (CC) As motor vehicle exhaust particle are about or slightly larger (factor of 10) than the mean free path of gas molecules (66.5 nm), smaller particles will almost “slip by” the gas molecules, while the larger ones will frequently collide with them. The Cunningham Slip Correction Factor is an empirical value and slightly corrects for those ones that slip through. Particles <100 nm fall in that regime and a corresponding CC must be used. At NTP conditions it can be simplified as:

( )KneΚn1CC ⋅γ−⋅+⋅+= βα [-] α, for oil droplets126 β, for oil droplets γ, for oil droplets

1.2310 [-] 0.4695 [-] 1.1783 [-]

II.8-4.

In the continuum regime (>1 µm), CC takes the value of one, while in the slip regime, it will have larger values; i.e. CC = 1.02 for 10 µm particles, CC = 1.15 for 1µm particles, and rapidly increases with smaller sized particles; i.e. CC = 2.9 for 100 nm particles, CC = 9.3 for 25 nm particles127. Therefore, CC for vehicle emissions will fluctuate between 1 and 20⋅Kn. In practical terms this means that a 1 µm particle will settle 15% faster than predicted (or almost 10 times faster for a 25 nm particle)128. d5. Particle Diffusion (D)

Diffusion of gas molecules (also known as Brownian motion), always results in net movement from regions of higher to regions of lower concentrations. As we are only interested in the diffusion of particles suspended in a gaseous medium, it is necessary to focus on particles rather than on the gaseous diffusion itself. Particle diffusion is dependent on the size and shape of the aerosol, resulting in slower diffusion of larger particles than in smaller ones. For particles in gas, the diffusion coefficient can be computed as:

126 Colbeck (1998), p.16. 127 Willeke (1993), p.27. 128 Hinds (1999), p.49-50.


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P

C

d3CTk

D⋅⋅⋅

⋅⋅=

ηπ [m2/s]

k, Boltzmann constant T, temperature CC, Cunningham Slip Corr. factor π, circle constant η, gas viscosity (at 20°C)129 dp, particle diameter

138.1⋅E-21 [J/K] 293.3 [K]

1-10 [-] 3.142 [-]

18.13⋅E-6[Pa⋅s] 10-800⋅E-9 [m]

II.8-5.

Again, for NTP conditions, diffusion of exhaust particles range from 296 nm2/s for small to about 237 µm2/s for larger ones. Particle diffusion occurs because of the relatively high velocity of small particles. Sometimes it is useful to estimate how far (on average) these particles move in a given time frame. The root-mean square (rms) distance provides this kind of information:

tD2xRMS ⋅⋅= [m] D, diffusion t, time

[m2/s] 1 [s] II.8-6.

For the given particle spectrum of combustion fumes (10-800 nm), the xrms values for 1 second are found to range from 21.8 mm for the lower sized and 769 µm for the larger sized particles. d6. Terminal Settling Velocity (vts) Gravity exerts a pulling force upon the particles. The gravitational pull depends on the difference between the particle density (proportional to the particle’s mass) and that of the surrounding medium. The effect on buoyancy in air is neglectable as the particle’s density is significantly larger than that of air. As the particles begin to move downwards, the surrounding gas molecules exert a dragging force, which resist the particulate’s acceleration till to the point they balance each other out. This terminal velocity can be calculated as:

P

Cpts d3

CTv

⋅⋅⋅

⋅⋅=

ηπρ

[m/s]

ρp, particle density (1g/m3) T, temperature g, gravity constant Cc, Cunningham Slip Corr. factor π, circle constant η, gas viscosity dp, particle diameter

1000 [kg/m3] 293.3 [K]

9.806 [m/s2] 1-10 [-]

3.142 [-] 18.13⋅E-6[Pa⋅s] 10-800⋅E-9 [m]

II.8-7.

According to Hinds130, the terminal settling velocity at NTP conditions stretches from 70 nm/s for the small sized particles to 30 µm/s for larger sized particles. Especially larger ones (accumulated particles around 800 nm - which fall into the transition size range - refer to Fig. II.8-1 - settle as time passes. In an hour, such particles can cover almost 0.1 m. Given a standard density of 1 g/cm3 for carbonaceous combustion products, such as diesel soot, these tend to deposit on the ground, or accumulate as a "soot-lake" in low lying, poorly ventilated basins. Under calm weather conditions, especially in winter, it can be often observed that parking areas frequently visited by diesel buses, so to speak “stand” in a sea of exhaust products. On a larger scale this is commonly seen in urban air pollution and temperature inversion.

129 Hinds (1999), p.25; viscosity calculated with the Sutherland equation (accurate over the range of 100-1800K). 130 Hinds (1999), p.51 / table 3.1


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d7. Thermophoresis (vth)

It is the particle motion in a temperature gradient, i.e. from a hotter to a colder region. Particles in a thermal gradient are “bombarded” more intensely by gas molecules on the hotter than on the cooler side. In the absence of electrostatics, heated surfaces tend to remain clean, while cooler ones tend to “collect” particles. Thermophoresis is relatively independent of particle size; the only influence on vth is imposed by the thermal conductivity of the gas. Since vth is dealt with at particle sizes smaller than the mean free path (dp<100 nm), at standard pressure conditions, it can be calculated as (for dp < λ):

TTv

gth ⋅

∇⋅⋅−=

ρη0.55 [m/s] for dp<λ

η, gas viscosity (at 20°C) ∇T, temperature gradient ρg, gas density (at 20°C)131 T, temperature

18.13⋅E-6 [Pa⋅s] [K/m]

1.20 [kg/m3] 273.3+20 [K]

II.8-8.

Therefore, the thermophoretic velocity at room temperature and particles <100 nm is -28.33⋅E-9 ∇T⋅m/s (the negative sign is required because the force is in the direction of decreasing temperature). Thermophoresis changes drastically for aerosol with higher densities as this gradient becomes established within the particle. This is especially true for particles exceeding 100 nm in diameter. Thus, the equation above has to be extended with a molecular accommodation coefficient (H) that takes into account the thermal conductivity of the aerosol, along with the Cunningham slip correction factor (for dp > λ):

TTHC

vg

Cth ⋅⋅

∇⋅⋅⋅⋅−=

ρ2η3 [m/s] for dp>λ

η, gas viscosity (at 20°C) Cc, Cunning. Slip Corr. factor H, thermal conductivity (soot)132 ∇T, temperature gradient ρg, gas density (at 20°C)133 T, temperature

18.13⋅E-6 [Pa⋅s] 1-10 [-]

4.2 [W/(m⋅K)] [K/m]

1.20 [kg/m3] 273.3+20 [K]

II.8-9.

Accordingly, the thermophoretic velocity at room temperature and particles in-between the size range of 0.1 to 1 µm oscillates from 324.5⋅E-9 to 3.25⋅E-6⋅∇T m/s.

131 Hinds (1999), p.29. 132 Reist (1993), p.171; conductivity based on carbon: 0.01 cal/(cm⋅s⋅K) = 4.184W/(m⋅K) 133 Hinds (1999), p.29


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II.9. The SMPS System The Scanning Mobility Particle Sizer system (SMPS TSI model 3934), as shown in Fig. II.9-1, measures the size distribution of sub-micrometer (5 nm to 1 µm) aerosols using an electrical mobility separation technique (for details about the handling of the instrument refer to the Appendix – A1). The particles are classified with an Electrostatic Classifier (EC TSI model 3071) and their concentrations is measured with a Condensation Particle Counter (CPC TSI model 3010, 3022, or 3025). The SMPS is an automated system that is operated via an attached computer. By modifying the parameter settings of the SMPS software, it is possible to control the individual instruments according to the experimental requirements.

Fig. II.9-1. Schematic of the TSI SMPS System operated in under-pressure mode. To exclude particles sizes exceeding 1 µm from entering the system, an impactor is attached to the aerosol inlet of the classifier. It makes sure that these particles and those carrying extra charges (more than one) are separated on the impactor surface.


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a. Inertial Impaction

To exclude all particles exceeding the sub-micrometer range, an impactor is mounted onto the aerosol inlet of the electrostatic classifier. All inertial impactors operate on the same principle. As shown in Fig. II.9-2, an aerosol is passed through a nozzle. According to Bernoulli’s principle134, the constriction in the orifice results in a faster moving aerosol jet. The exiting jet is directed against the flat impaction plate. This plate deflects the flow to form an abrupt 90° bend in the streamlines. The orifice of the nozzle has such a diameter that particles with sufficient inertia (i.e. larger than 1 µm) are unable to follow the streamlines and impact on the slightly greased plate (usually silicon grease is used for that purpose). Smaller particles avoid hitting the plate and remain airborne.

Fig. II.9-2. Cross-sectional view of an inertial impactor135

The parameter that governs collection efficiency is the dimensionless quantity called Stokes number (Stk). It is defined as the ratio of particle stopping distance at the average nozzle exit velocity to the jet radius. The cut-point diameter is a function of the impactor flowrate and nozzle diameter136:

D9Cvd

Stk C2

50 ⋅⋅⋅⋅⋅

=ρ

ρ [-]

ρ, particle density 1g/cm3 d, particle diameter v, velocity of the jet (0.3L/min) η, gas viscosity at 20°C CC, Cunningham Slip Corr. Factor D, nozzle diameter

1000 [kg/m3] 10-800⋅E-9 [m]

24.7 [m/s] 18.13⋅E-6 [Pa⋅s]

1-10 [-] 0.508⋅E-3 [m]

II.9-1.

The size in question is called the “cut-point diameter” and results to be 920 nm when using the 0.508 mm orifice, Stk50 for 0.925µm =0.505. The Stk50 is the location of the ideal cut-point number, in which all particles greater than that certain (aerodynamic137) diameter are collected and all particles below that size pass through138. Another reason why it is beneficial to exclude particles larger than 1 µm, is embedded in the principle of the classification process. Employing electrical mobility as the sole size filtering mechanism, particles undergo a charging process, which tend to assign particles with larger diameters multiple charges (see Table II.9-1). The following section takes a closer look into detection mechanisms.

134 Tipler (1999), p.385. 135 Hinds (1999), p.122. 136 Reist (1993), p.102. 137 Willecke (1993), p. 13:

particleparticleaero dd ρ⋅= 138 Hinds (1999), p.126


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b. Electrostatic Classifier (EC) Electrostatic size classification is a powerful technique for either producing small quantities of calibrant particles from about 10 to 800 nm diameter from a polydisperse aerosol or to classify particles in the same size range139. Extensive theoretical study and calibration of the EC was done by Liu and Pui (1974). Particles are sized according to their electrical mobility. The instrument consists of two major components: an aerosol charger and a differential mobility analyzer. As the instrumentation classifies an aerosol by means of an applied electrical field, it is important that the aerosol entering the device has the same net charge; i.e. either one positive or one negative charge. The dry polydisperse aerosol entering the EC, is passed over an 85Kr bipolar discharger, (or neutralizer), that exposes the aerosol particles to high concentrations of bipolar ions which alters the charge distribution to Boltzmann’s equilibrium. In that process, the particles and ions undergo frequent collisions due to the random thermal motion of the ions. The particles – within milliseconds -- reach a state of equilibrium, in which the particles carry a bipolar charge distribution; whereas in the free atmosphere, cosmic radiation would take as much as 30 mins to achieve that140. As the single charge condition is an idealized assumption, one has to keep in mind that particles can gain more than one charge. The Boltzmann charge distribution assumes that the distribution is symmetrical around zero; that is, the fraction of particles with n positive charges equals the fraction with n-negative charges. Thus, when particles are exposed to a gaseous medium containing bipolar ions, the particles and ions will undergo frequent collisions due to random thermal (Brownian) motion. Thus, in time, an equilibrium state is attained in which the particles carry a bipolar charge distribution141, as shown by the Table II.9-1.

Table II.9-1. Charge distribution of aerosol particles according to Boltzmann’s law142

dp Charges on aerosol particles [%]

np -4 -3 -2 -1 0 +1 +2 +3 +4 0.01 0.34 99.32 0.34 0.02 5.23 89.53 5.23 0.04 0.23 16.22 67.10 16.22 0.23 0.06 0.01 1.25 21.30 54.88 21.30 1.25 0.01 0.08 0.08 2.780 23.37 47.53 23.37 2.780 0.08 0.10 0.26 4.39 24.09 42.52 24.09 4.39 0.26 0.20 0.32 2.33 9.66 22.63 30.06 22.63 9.66 2.33 0.32 0.40 2.19 5.92 12.05 18.44 21.26 18.44 12.05 5.92 2.19 0.60 3.82 7.41 11.89 15.79 17.36 15.79 11.89 7.41 3.82 0.80 4.83 7.94 11.32 14.00 15.03 14.00 11.32 7.94 4.83 1.00 5.42 8.06 10.71 12.70 13.45 12.70 10.71 8.06 5.42

139 Reist (1998), p.51. 140 Willeke (1993), p.415. 141For details regarding the ratio of particles carrying 1, 2, 3 (np) elementary charge units to uncharged

particles, refer to the instruction manual of the 3071A Electrostatic Classifier from TSI, p.8-2. 142 TSI-EC 3071A manual, p.8-4.


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From the entries in Table II.9-1 it is also obvious that most of the particles with diameters <0.1 µm carry no more than one unit of charge. The computer proceeds in this way through successively smaller size channels, subtracting out the contribution of these multiple-charged particles. As the Boltzmann distribution is integrated into the SMPS software, the program considers up to six electrical charges per particle, making the detection of particles in-between the size range of 10 to 800 nm quite accurate. The Table II.9-1 also points at the limitations when dealing with particles larger than 1 µm as they not only contribute to a decreased instrument accuracy (>5%), but also in lower number of particles carrying a single charge, thus resulting in reduced detection efficiency per size class (bin). c. Particle Sizing Once the particles have been “neutralized” to Boltzmann equilibrium (mentioned above), particle size can be determined by measuring the distribution of electrical mobility. Separation of particles occurs along the centreline between two oppositely charged parallel cylinders. In order to break up the dynamic process a step-by-step illustration helps to make it more comprehensible. As illustrated in Fig. II.9-3 this is achieved by grounding the outer cylinder and applying a negative voltage to the centre rod. For a given voltage, charged particles with mobilities greater than a certain amount will migrate to the oppositely charged cylinders as the aerosol gas stream pushes them through. Uncharged particles pass through unaffected. The laminar flowing aerosol is fed into the classifier. A sheath of particle free air surrounds the central rod. At the beginning of a scan, the inner cylinder is maintained at the same potential as the outer cylinder; i.e. grounded. Once the scanning process is started, the centre rod voltage gradually decreases in voltage. As this process is controlled by the microcomputer, recordings of the concentration measurement are correlated with the aerosol size distribution due to the response function of the EC.

Fig. II.9-3. Flow Schematic for the Electrostatic Classifier.


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P

CP d3

CenZ

⋅⋅⋅⋅⋅

=ηπ

[cm2⋅V-1⋅s-1]

n, number of charged particles e, elementary charge CC, Cunningham slip correction fact. η, gas viscosity at 20°C dP, particle diameter

[-] 1.6⋅E-19 [C]

1-10 [-] 18.13⋅E-6 [Pa⋅s] 10-800⋅E-9 [m]

II.9-2.

From Table II.9-1 it is known that the aerosol produced is monodisperse. The size distribution with the count median diameter (CMD) is based on the electrical mobility diameter and is determined by the following parameter of the classifier:

)r/rln()QQ(3CVLenE4

CMDOIES

C10

em ⋅−⋅⋅⋅⋅⋅⋅⋅⋅

=η

[m]

n, number of charged particles e, elementary charge L, length of classifier column V, applied average voltage CC, Cunningham slip correction fact. η, gas viscosity at 20°C Qs, flow rate of sheath air (3L/min) Qe, flow rate of excess air (2.7L/min) ro, outer diameter of classifier ri, inner diameter of classifier

[-] 4.8⋅E-10 [C]

0.436 [m] 0-10⋅E3 [V]

1-10 [-] 18.13⋅E-6[Pa⋅s]

5⋅E-3 [m3/s] 4.5⋅E-3 [m3/s] 19.58⋅E-3 [m]

9.37⋅E-3 [m]

II.9-3.

The particle diameter corresponding to a selected size channel CMDem/CC can be calculated from equation II.9-2. with n = 1. However, the Boltzmann distribution of Table II.9-1, predicts that a significant fraction of particles larger than 100 nm carry more than one charge. One must use a more complex data reduction scheme for multiple-charged particles. Fig. II.9-4. illustrates the fraction of the particle concentration within each mobility channel, which carries 1,2,3,4 and 5 or 6 positive units of charge for a log-normal distribution with geometric mean diameter (GMD) of 200 nm and geometric standard deviation of 2.5. Thus a positively charged particle that just misses the tip of the center-electrode, passes through the small slit below the base via a central collecting tube (Fig. II.9-3); together with other like- charged particles of a given size-range (bin) they emerge from the EC as a nearly “monodisperse” and single charged aerosol143. Monodisperity for each size channel follows a log-normal distribution with a geometric standard deviation σg between 1.04 to 1.10144. By gradually decreasing the centre rod voltage further towards the maximal negative value (-10.6kV), particle electrical mobility increases even for heavier/denser particles, with particle precipitation occurring at increased heights on the collector rod.

143 Hinds (1999), p. 343. 144 Reist (1998), p.53.


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Each stepwise decrease in voltage corresponds to a narrow mobility and thus size range. The lower size limit and minimum concentrations that can be measured are determined by the capabilities of the CPC (condensation particle counter) used. The EC itself is capable of sizing particles from 5 nm to almost 1 µm at concentrations from 1 to 1⋅E8 particles per cm3. The SMPS system is able to scan up to 147 rod voltages from –19 V to -10.6 kV in 146 size channels under computer control in 1 to 8 minutes. These particles are then conducted to a particle sensor, such as the CPC to determine the particle concentration. The remaining particles are removed from the classifier via the excess airflow.

Fig. II.9-4. Cumulative charge concentration vs. electrical mobility. It shows the concentration of +1, to +6 charges distributed over the size channels for a simulated log-normal aerosol distribution. The assumed total number concentration is 10⋅E3 particles/cm3.


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d. Condensation Particle Counter (CPC) The EC is capable of grouping particles into size classes in the range of 10-800 nm. Counting these particles and to ultimately assign this concentration to the appropriate size class (bin) a CPC is required. It is widely used for the detection of the number concentration of sub-micrometer particles. A continuous-flow CPC has a wide dynamic range (<1⋅E-5 to 1⋅E7 particles/cm3). A CPC uses monochromatic light of a single frequency (usually 780 nm laser) by focusing on the passing train of particles to detect them. Since such a wavelength falls short to detect particles in the smaller size range (<700 nm), the sized aerosols from the EC must undergo condensation to increase in size. CPCs saturate an aerosol by alcohol vapor and then to cool it in a supersaturated environment (condenser) to achieve rapid growth of sub-micrometer particles under steady flow conditions. Before going through the working principles of a CPC, briefly some theoretical aspects of the physical principles involved. Gas cooling takes place by conduction and convection, which leads to super-saturation in the cooled aerosol stream. The alcohol condenses onto the particles and the particles grow into droplets large enough to be counted optically. The saturation vapor pressure (ps) for a plane liquid surface is an empirical expression:

37)-4060/(T-16.7e3E1sp ⋅⋅= [Pa] T, absolute temperature [K] II.9-4.

For particles smaller than 10 nm, the Kelvin effect interferes with the detection of all of the particles (it predicts that the smallest particle size, which will serve as a condensation site for a vapor in a supersaturated aerosol, is inversely proportional to the supersaturation ratio)145. Modern CPCs achieve supersaturation between 200 to 400%146. As the curvature of the surface slightly modifies the attractive forces between surface molecules (the smaller the droplet, the easier it is for molecules to leave the droplet surface) an equilibrium is required to prevent evaporation. This is ensured when the partial pressure of the butyl-alcohol (Fig. II.9-5) surrounding the droplet is greater than the saturation pressure (ps). For pure liquids, the ratio required for equilibrium (or Kelvin ratio KR) is given by the Thomson-Gibbs equation:

Fig. II.9-5. Structural formula of isobutyl;

(CH3)2CHCH2OH147

145 for in depth analysis of the Kelvin effect, see Hinds, (1999), p281. 146 Hinds (1999), p.293 147 Structural formula of DEHS: http://chemfinder.cambridgesoft.com/


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d*)TRM/(4eRKsp

p ⋅⋅⋅⋅⋅== ργ [-]

γ, surface tension of butanol148 M, molecular weight of butanol ρ, density of butanol d*, the Kelvin diameter R, universal gas constant T, absolute temperature

2.298 [N/m3] 74.12 [g/mol]

806 [g/L] 2⋅E-6 [m]

8.31[N⋅m/(K⋅mol)] 273+35 [K]

II.9-5.

d1. The Kelvin Diameter (d*) The Kelvin Diameter is the droplet size that will neither grow nor evaporate when the partial pressure of the butanol vapour at the droplet surface matches atmospheric pressure, and according to the TSI manual for the CPCs ranges between 2 – 3 µm149. Accordingly, the Kelvin ratio (KR) for the given Kelvin diameter (d*), is determined as KR = 1.0003⋅ps. In order to maintain equilibrium at 2 µm, the partial pressure of isobutyl alcohol at the droplet surface should be around 3.32 kPa. As shown in Fig. II.9-6, the CPC consists of a saturator, condenser, and particle detector. As the monodisperse, unipolar and positively charged particle train enters the CPC, it is saturated with n-Butyl alcohol vapor as it passes over a heated pool of alcohol. The residence time is such that the aerosol will be saturated with the working fluid at a set temperature of 35°C (308 K). Then, the vapor-saturated aerosol flows into the 10°C (283 K) cold condenser, where it is cooled by thermal diffusion.

Fig. II.9-6. Flow Schematic for the Condensation Particle Counter

148 Surface tension of butyl alcohol: http://www.bandj.com/BJProduct/SolProperties/SurfaceTension.html 149 TSI-SMPS 3934 Instruction Manual [Revison E,( 2000)]; Appendix p.B-16


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The optical system in Fig. II.9-6 is commonly known as an optical particle counter (OPC) in which the aerosol enters a nozzle (measuring 10 µm by 2 mm) and flows through a focused laser beam as a thin stream surrounded by sheath air. Ideally, only one particle at a time is illuminated and scatters light to the photo detector, which converts it to an electrical signal. The electronic pulse height (area under the curve) is used to interpret the pulse and direct a count to the proper size channel where the total counts in each size range (bin) are accumulated. Together with the EC, the SMPS-software combines information of centre-rod voltage and particle concentration to calculate and plot the size distribution. Below 1000 particles/cm3, the TSI models count individual particles making it sensitive to extremely low concentrations (the lowest detectable concentration is limited only by the amount of time one wishes to wait for the CPC to count a statistically significant number of particles). Thus a zero-test with HEPA filtered air is essential and calibration of the SMPS flow rates is a must to obtain correct readings. Even though laser operated OPCs are known to have counting efficiencies close to 100% (ratio of indicated versus true count), the response of an OPC depends on the size and refractive index of the particles. When the refractive index is known, suitable calibration will permit an accurate measurement of the size distribution. For aerosol with unknown refractive indices, the error in size estimation can be significant and can range from -50 to 140%. When the refractive index is not known, this huge spread limits the usefulness of OPCs150. Since each particle grows to a droplet, the number concentration of droplets and nuclei remain the same. Droplets exiting the condenser tube can be counted individually for low concentrations by monitoring the scattered light pulses with an optical particle counter. At high concentrations, two or more particles are occasionally in the sensing volume at the same time. The pulses they generate and overlap are detected and counted as one larger particle. This effect is known as the coincidence error, and causes a spurious signal that leads to an underestimation of the particle number concentration (and theoretically, an overestimation of the particle size). The ratio of observed count (NO) to true count (NT) is given as:

τ⋅⋅= QN- TeTNON

[Pa] NT, true particle number Q, aerosol flow through nozzle τ, beam traversing time for a particle

[-] [L/min]

[s] II.9-6

Although, for most of the range, especially in clean air, this effect is insignificant, reducing the flow rate, the dimension of the beam, and the processing time can minimize coincidence error. The coincidence correction is particularly important at higher concentrations. Model 3022/A and 30245/A CPCs have built-in coincidence correction. The Model 3010 CPC, however, uses the SMPS software to compute the actual particle concentration151. For very high concentrations exceeding the specifications, a flow splitter or aerosol diluter must be used.

150 Hinds (1999), p.374 151 TSI-SMPS 3934 Instruction Manual [Revison E, (2000)]; Appendix p.B-17


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II.10. Particle Size Statistics All the statistical aspects discussed in this section are implemented in both the SMPS software152 and the Origin Microcal SMPS scripts153. Particle size distributions are usually presented as frequency, histograms, or cumulative distributions, thus, the briefing in this section provides a closer look of how the data are stored, converted, and displayed. Data processing occurring within the instrument, file formats are also covered herein. For simplicity, the effect of particle shape is neglected by considering only spherical particles. Most aerosols are polydisperse when formed, meaning that they contain particles of different diameters or sizes. Since raindrops grow by condensation or by a series of collisions with other drops, they are expected to be polydisperse, and thus range over two or more orders of magnitude. In fact monodisperse aerosols, those that consist of a single size, are very rare in nature, and when they do appear, generally they do not last very long. Polydisperse aerosols can be described in a number of ways using mathematical or visual methods. As for the instrumentation involved, the detected counts per size range (channel or bin) are processed electronically and analyzed into various types formats that are discussed in a sequential order. a. Histograms Although summary statistics, such as mean and standard deviation of particle diameter, can be used, it is often required to present more detailed information of the data, perhaps by an approximate description of the particle sizes; using a histogram or a cumulative size distribution can achieve this. A histogram is one of the simplest ways to display a particle size distribution. The number of particles in various size classes can be plotted as bar or line charts. This was done using a NaCl aerosol with a concentration of 0.5 g/L. The transformation from raw data to a useful plot is demonstrated in Fig. II.10-1. It is a particle frequency distribution that shows the total number counts per size class.

Fig. II.10-1. Histogram of frequency versus particle size

Fig. II.10-2. Frequency/nm versus particle size

152 TSI-SMPS v3.2, TSI Inc. 153 Wiegand (1997), p.17.


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This histograms tends to show a distorted representation of the size distribution as the height of any window is dependent on the width of the interval. To prevent this distortion, the histogram is normalized for each interval by dividing the number of particles in each size class by the width of that interval (Fig. II.10-2). Doing so usually modifies the ordinate (compare also Fig. II.10-3. showing the height of each bar as frequency/nm. The height of each rectangle then equals the number of particle per unit size interval. This transformation allows a comparison among heights of intervals with different widths. Furthermore, the area of each rectangle is proportional to the number (frequency) of particles in that size range. In order to display a standardized chart, which transforms the ordinate into a relative value (usually given in %), it is necessary to divide each size class by the total particle number observed in a scan. Doing so modifies the chart in a way that the area under the curve is 1 or 100%; and is mathematically expressed as:

1)dh(f iii =⋅= ∑∑ ∆ hi, height per unit size di, width per unit size

[#/nm] [nm] II.10-1.

The result of this operation is illustrated in Fig. II.10-3. The ordinate reflects each size class as a fraction. This change allows direct comparison of histograms obtained from different samples. Finally, by increasing the number of size rectangles (the SMPS uses a standard channel width of 1 nm) to infinite, thus narrowing its width approaching zero, it is possible to generate a continuous (smooth) curve through their tops. This is best described mathematically as a definite integral:

Fig. II.10-3. Frequency/nm versus particle size, count

distribution

1d)d(f)x(f800

10PP

800

10

=⋅= ∫∫ d hi, height per unit size di, width per unit size

[#/nm] [nm] II.10-2.

The function obtained in this way displays a pattern in which data points at the smaller end of the spectrum are closely spaced, while those at the larger end of it are further spaced. The result is shown in Fig. II.10-3. It displays a particle size distribution curve that is the graphical representation of the frequency function. It is also known as a probability density function154. Such a modified chart has the advantage of showing at a glance what the particle size distribution of an aerosol looks like and is perhaps the best way of visually representing complex size distribution data. The same figure also shows various diameters that characterize this arithmetic plot. Whether the distribution is plotted as number, area, mass or volume distribution, the form of the size distribution can look very different. The reason for this is rooted in the fact that particles at the smaller end of the size spectrum can

154 Hinds (1999), p.78. For a step to step description in the build up of a histogram, see Hinds p.75.


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be very abundant in number, but because area is proportional to the square and mass (or volume) to the cube of the diameter, such particles may contribute only to a small extent to the total area, mass, or volume respectively. Commonly used quantities for defining the location of a distribution are the mean, median, mode, geometric mean, etc. In order to better comprehend these diametric entries, the following section provides a short overview.

Fig. II.10-4. Frequency distribution curve

It displays the location of various average diameters of a NaCl aerosol; note that the diameters of: mode < median < mean

b. Mean Diameter and Standard Deviation The arithmetic mean diameter, usually simply termed the “mean” diameter, is the arithmetic average particle diameter of the distribution. The value of the arithmetic mean is sensitive to the quantities of PM at the extreme lower and upper ends of the distribution. The simplest way of treating a group of different particle diameters is to add all the diameters and divide by the total number of particles. It is thus easy to calculate from the count data an average. This arithmetic average yields the "mean particle diameter" and is mathematically expressed as:155

∑∑ ⋅

=i

ii

n

dnd ni, number of particles

di, midpoint diameter [-]

[nm] II.10-3.

Many phenomena in nature appear to occur on a more or less random basis, but exhibit certain characteristics, which can be used to predict future trends. One such model applied is the "bell-

155 Reist (1993), p.26.


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shaped" normal or Gaussian distribution. The primary virtue of such a distribution is that it is predictable and it can be described with two simple characteristic parameters. A “mean” value and its standard deviation. The standard deviation is the square root of the mean of the squared differences between individual values and their mean:

1n

)dd(n

i

2ii

−

−⋅=

∑∑σ ni, number of particles

di, midpoint diameter [-]

[nm] II.10-4.

Means and standard deviation can be calculated for any set of data. For data which are normally distributed (linear scale), however, the mean value lies at the midpoint of the data, (hence it is also the median), and accounts for around 68% of the distribution falling between the range of plus or minus one standard deviation (or 95% of the particles are found between σ = ±2, and 99% within σ = ±3). As will be shown later on, such a distribution in generally is not the case.

Fig. II.10-5. Normal distribution.

It displays the location of the mean and the associated standard deviations

For most aerosols a plot of frequency versus size results in a skewed graph, similar to that one in Fig. II.10-4. c. Mode Diameter Often, in a given size distribution, it is worth knowing the size class with the highest count. The mode represents the value that occurs most frequently in a distribution. This diameter is usually associated as the count mode diameter (dmode); i.e. is the most frequent size range, or the diameter associated with the maximal particle count per channel. It is mathematically determined by applying the first derivative on the frequency curve.

iemod d'fdx)x('fd == ni, number of particles di, midpoint diameter

[-] [nm] II.10-5.

d. Median Diameter The median particle size is the particle diameter that divides the frequency distribution in half; 50% of the aerosol count has larger particle diameter, and 50% a smaller diameter. e. Geometric Mean Diameter The geometric mean (dg) is defined as the Σnth root of the product of all particle diameters. For the count distribution, the dg is customarily replaced by the CMD.

NN321g d....dddd ⋅⋅⋅⋅= ni, number of particles

di, midpoint diameter [-] [nm] II.10-6.


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f. The Log-normal Distribution

As most distributions exhibit a skewed (large shoulder with a long tail) function, the normal distribution is rarely used to describe most aerosol particle size distributions. In addition, the size class (width of channel or bin) increases exponentially as the diameter increases. For many man-made sources, the observed PM distribution approximates a log-normal distribution. The same is valid for the 0.5 g/L NaCl solution used to plot these figures. Furthermore, with the spectrum stretching over several magnitudes, it is most convenient to use a logarithmic abscissa (natural logarithm, lnx). When the particle diameters are plotted on a logarithmic scale against the frequency of occurrence, a bell-shaped curve is generated. As shown in Fig. II.10-6 the particle size windows are altered to produce equidistant ranges. This bell-shaped histogram is called a log-normal curve. Doing so enables a Gaussian function to be fitted through such a distribution. The Gaussian is thus an idealized particle size distributions. The particles are said to be log-normally distributed and the distribution is called a log-normal distribution. Only then, can the Gaussian distribution (among others) be selected to empirically fit the wide range and skewed shape of most aerosol size distributions156. By analogy with a normal distribution, the mean and standard deviation are known as the geometric mean diameter (GMD) and σg is known as the geometric standard deviation (GSD). Thus, the log-normal distribution requires only the geometric mean and the geometric standard deviation to characterize the spectrum.

Fig. II.10-6. Frequency distribution plotted against logarithm of particle size. it displays a symmetric (i.e. bell-shaped) distribution with the population mean µ and its standard deviation σ.

With a log-normal distribution, one geometric standard deviation represents a range of particles within which lie 68% of all sizes. As mentioned before, there is no fundamental theoretical reason why particle size data should approximate the log-normal distribution, but it has been observed that it applies to most single-source aerosols. It should be noted though, that a mixture of several log-normal single-sourced aerosol distributions will no longer be log-normal. Having introduced the log-normal distribution, it is now far more easier to explain the remaining diametric averages listed in Fig. II.10-4. As these diameters originate from log-normally distributed patterns (besides surface area and volume which increase by the square and cube respectively), their position in a linear scale exceeds those other standard averages. g. Count Median Diameter and Geometric Standard Deviation

The count median diameter (CMD) is defined as the diameter for which one-half the total number of particles is smaller and one-half are larger (in a cumulative chart it is the inflection point). It is also the diameter that divides the frequency distribution curve into equal areas. Listing all diameters in order from the smallest to the largest and then finding the particle

156 Hinds (1999), p.90.


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diameter that splits the list into two equal halves can determine the median particle diameter.

∑∑ ⋅== iii n/)dln(ne)gdln(CMD ni, number of particles di, midpoint diameter

[-] [nm] II.10-7.

Similar to the average standard deviation, the geometric standard deviation (σg or GSD) is the Σ of all diameters divided by the total number of particles:

∑

⋅∑==

1-n

2)lnd-d(lnn)gln(GSD

i

giiσ hi, height per unit size di, width per unit size

[#/nm] [nm] II.10-8.

Average Surface Diameter The average surface diameter (ds) of a hypothetical particle having average surface:

∑∑ ⋅

=i

2ii

S n

dnd

ni, number of particles di, midpoint diameter

[-] [nm] II.10-9.

Average Volumetric Diameter The average volumetric diameter (dv) of a hypothetical particle having average volume/mass:

∑∑ ⋅

=i

3ii

V n

dnd


[-] [nm] II.10-10.

Surface Median Diameter The surface median diameter (SMD) is the geometric mean of the particle surface areas for log-normal distributions:

2din/)dln(2din iiie)smddln(SMD ⋅⋅⋅ ∑∑== ni, number of particles di, midpoint diameter

[-] [nm] II.10-11.

Surface Mean Diameter The surface mean diameter (dsm) is the average diameter based on unit surface area of a particle:

2ii

3ii

smddn

dnd

⋅

⋅=

∑∑


[-] [nm] II.10-12.

Volume and Mass Median Diameter The volume median or mass median diameter (VMD or MMD) is the geometric mean of the particle volumes or mass for log-normal distributions:


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3din/)dln(3din iiie)vmddln(VMD ⋅⋅⋅ ∑∑==


[-] [nm] II.10-13.

Volume and Mass Mean Diameter The volume mean or mass mean diameter (dvm or dmm) is the average diameter based on the unit volume of a particle:

3ii

4ii

vmdn

dnd

⋅

⋅=

∑∑ ni, number of particles

di, midpoint diameter [-] [nm] II.10-14.

For symmetrical log-normal distributions such as the Gaussian, the mean, median, and mode have the same value, which is the diameter of the axis of symmetry (for a monodisperse aerosol, ⎯d = dg; otherwise, ⎯d < dg). For asymmetrical or skewed distributions, these values differ157. Conclusively, the two parameters that totally describe the log-normal distribution are the geometric mean (which equals the count median diameter CMD), and the geometric standard deviation; the remaining median diameters are the surface median diameter (SMD), and volume median diameter (VMD) which equals the mass median diameter (MMD), for log-normally distributed data sets, these can be calculated on the basis of CMD using the Hatch-Choate relationship158. h. Moment Averages As mentioned earlier, aerosol size is usually measured indirectly. Frequently, it is required to calculate quantities proportional to particle size, like surface area (∝ to d2) or volume (∝ to d3 that also corresponds to mass when ρ =1g/cm3). Rather than measuring each particle separately, they are weighed as a collective per size class. Doing so reveals the geometric particle diameter per channel (see Fig. II.10-7.). These moment averages represent mean values calculated for different powers (d2 or d3) converted back to units of diameters.

Fig. II.10-7. Moment averages of count, area, and mass distributions

Thus, the higher the moment, the larger the moment average: CMD < SMD < VMD All moment distributions of any log-normal distribution will be again log-normally distributed

157 Hinds (1999), p.81. 158 for details see Hinds (1999), p.97.


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(have the same shape). Thus, the geometric standard deviation (σg) for a log-normal distribution is independent of the power of the diameter being measured; σg will be the same whether particles are counted or weighted for any moment distribution159. These median diameters are useful depending how they are applied; for example, volume or mass distribution are much more common, since it is the volume or mass of a substance delivered by a particle that determines its adverse effect in the human respiratory tract160. In addition, studying chemical reaction rates or decay rates, the surface median diameter may be more important than just particle number. Taking the surface, volume or mass median diameter and multiplying it with the total particle count of the same sample, gives the total surface area, volume or mass of a mono- or polydisperse aerosol161.

159 Willeke (1993), p.155. 160 Finlay (2001), p.5. 161 Reist (1993), p.25.


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II.11. Data Output of the SMPS system The microcomputer in charge of the control parameters (i.e. centre rod voltage of the EC, particle number concentration of the CPC) calculates the aerosol size distribution from the response function of the EC, and displays the data in a variety of graphical and tabular forms. The operator can choose from a variety of video display modes, including number, cumulative number, surface, and volume concentration as a function of particle diameter; or number concentration as a function of electrical mobility. Exporting the data format is usually done in the form of a simple text file. In order to take advantage of the already existing statistical package based on Microcal’s Origin software162, the SMPS instrumentation's data output consists of several file options of which only the DISTFIT file format for later data analysis is used. Upon exporting files of that type, the SMPS software includes several other parameters that are shown in Table II.11-1.

Table II.11-1. An Example of a raw SMPS DISTFIT

Datafile

[email protected] 101 1.000000 293 1.000000 1 1 1 18.434230,37.884620 19.109530,18.043204 19.809568,27.927234 20.535250,25.898478 21.287517,23.185624 22.067341,9.273566 22.875732,22.525787 ....Lines Deleted.... 649.381632,76.384710 673.170382,45.712926 723.394163

.... file version and name

....number of Y data values

....particle density (always 1g/cm3)

.... temperature (always 293°K, or 20°C)

....data type (I implies interval)

....pressure (always 1atm or 101kPa)

....moment type (1 implies geometric diameter)

.... weighting type (1 implies number concentration) ....lower particle size boundary [nm] .... upper particle size boundary [nm] for last particle size bin

Distfit exported files are designated with an "@" symbol as the first character of the file (SMPS exported files are almost identical except for the "@"). As Distfit file information contain the size class width and the number of counts measured within it, this format is intended to be used in other programs such as Origin, Sigma-plot or Excel spreadsheets where surface area, volume and mass concentrations are calculated separately. The first eight lines of a Distfit file contain instrumentation parameters, whereas the file information thereafter is composed of pairs of numbers. Each size channel (bin) is outlined by the first number of each line (low-end size range of the measurement channel) while the first number of the following line represents the high-end limit of it. The second number on the line is the number of counts measured by the instrument in that particular channel. The very last one of the file contains the high-end size range of the last channel (bin).

162 Wiegand (1997), p.5.


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a. The Origin Microcal SMPS Script The most useful information required from any spectrum includes total count, area and volume concentrations, as well as spectrum characteristics such as median and deviation. This section briefly describes the use of script programs, which have been written163 for the rapid analysis of SMPS and APS data. As shown in Table II.11-1, the surface area concentration and volume concentration in each bin can be calculated from these pairs of numbers. Subsequent statistics include total counts, area and volume concentrations, as well as spectral characteristics such as count median diameter, surface median diameter and volume median diameter. The script programs calculate these statistics automatically. The script programs here described are all menu-driven and are generally self-explanatory (for details about the handling of the software pack refer to the Appendix A01 -The SMPS Script Handling). Upon importing the DISFIT files into the Origin program, the SMPS scripts automatically calculates the surface area, and volume concentration in each bin. Subsequent statistics include total counts, area and volume concentrations, as well as spectral characteristics such as count median, surface median and volume median diameter. The equations used to calculate these statistical values have been already provided in previous sections of this chapter (see equations II.10-7, II.10-11, and II.10-13. respectively). Some authors mentioned that small sized aerosol is temporarily trapped in the SMPS system, but passes through the detector once the SMPS is scanning large-diameter aerosol. Although this does not affect total particle counts, it does have a significant effect on total surface area concentration and total volume concentration. Therefore, the SMPS script package supports an option in which an arbitrary 1% of the large-diameter count data can be removed from the spectrum. However, as investigations into this phenomenon are continuing, it is recommended to inspect plots of the data to determine whether to use the 99% spectra or the original spectra164. For samples that involve several scans per sample, the scripts offer the possibility to calculate averages. Likewise, the average count, area and volume spectra are calculated as arithmetic means of the counts along with the standard deviations in each equivalent bin. The script programs here described are all menu-driven and are generally self-explanatory.

163 Script files written by Dr. A. Wiegand (1997). 164 Wiegand (1997), p.9.


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II.12. Effects of Particles on the Environment and on the Human Organism. Today local exhaust emissions165 are of concern directly in the area they are released; they have the most impact on urban areas where they are concentrated. Six criteria and related pollutants are of concerned and regulated in the USA: CO, NOX, VOC, SO2, PM, and Pb . These emissions are harmful to health, ecosystems, materials, and damage even buildings. Exhaust and evaporative emissions from automobiles are the primary sources of CO, NOX, and VOC (or HC), which react in the atmosphere to form ozone, especially in summer. Regulated air pollutants that comprise a substantial portion of exhaust emissions are VOC or HC, NOX, CO and from diesel vehicles, PM. As fine particles are inhaled and trapped in the lungs (aerodynamic diameter less than 1 µm) many studies have associated these aerosols with a wide range of adverse respiratory symptoms; i.e. decreases in lung function, increases in breathing problems, hospitalisation and even premature death. Although exhaust emissions are the primary concern for air pollutants, these same pollutants and others are also released during other stages of the life cycle, such as during the production of materials necessary for the automobile itself166. The particles released in urban air are also responsible for serious health effects, i.e. long-term effects like cancer and cardiovascular decease as well as acute effects like allergy or irritation of eyes, nose and throat. Especially, the very small particles – ultrafine particles – are assumed to be important for the adverse health effects of particles167. As outlined in the previous sections of this chapter, particles in the air (aerosols) come from a number of sources, including motor vehicles, industrial processes and wood burning. Secondary formation of particles (formation from gaseous emissions) can also contribute significantly to particle levels. Some atmospheric particles are from natural sources: wind-blown, pollen, sea salt, and material from volcanic eruptions. Long- and short-term exposure to such particles has been linked with increased deaths from heart and lung disease. With the reaction of the photochemical smog, a number of other harmful secondary pollutants such as peroxyacetyl nitrate and aldehydes, which are severe irritants, particularly to the eyes, (see Table II.12-1)168. This section will discuss the health effects and toxins of motor vehicle fuels to humans and the environment and the mode of lung penetration of these toxins into the human lungs. Particles come from all kinds of combustion sources; it is the toxicity of the particulate emissions that help to prioritize the control of emissions. Numerous studies are now available that establish that CNG is a cleaner fuel compared to diesel and scientific studies have established that PM from diesel exhaust is extremely toxic. The USEPA has focused most of air toxins efforts to date on carcinogens, which are compounds that are mutagenic. Non-cancer health effects such as reproductive and neurological problems are also of concern and have been thoroughly investigated by the USEPA169.

165 Mclean & Lave (2003), p.9. 166 Mclean & Lave (2003), p.10. 167 Wåhlin, & Palmgren (1999), p.1. 168 CSIRO (2003), p.1. 169 USEPA (2003), online


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Table II.12-1. Major sources of particles and its Health effects170 Pollutant Sources Health Effects

CO Motor vehicles, burning of fossil fuels.

Blood absorbs carbon monoxide more readily than oxygen, reducing the amount of oxygen being carried through the body. Carbon monoxide can produce tiredness and headaches. People with heart problems are particularly at risk.

SO2 Coal and oil burning power stations, mineral ore processing and chemical manufacture.

Attacks to the throat and lungs. People with breathing problems can suffer severe illness.

NO2 Fuel combustion Affects the throat and lungs

VOC Motor vehicles, fuel combustion, solvent use.

Some VOCs cause eye and skin irritation, headaches or nausea, while some are classed as carcinogens.

O3 Formed from NOX and HCs in sunny conditions. These chemicals are released by motor vehicles and industry.

Ozone attacks the tissue of the throat and lungs and irritates the eyes.

Pb Exhaust gases from motor vehicles that use leaded petrol, smelters.

Particles containing Pb can enter the lungs. Pb can then be absorbed into the blood stream. Over a period Pb can affect the nervous system and the body’s ability to produce blood.

Particles Motor vehicles, burning of plant materials, bushfires.

May cause breathing difficulties and worsen respiratory diseases. Some particles contain cancer-producing materials.

How dangerous are air toxins? It is hard to say. Some air toxins have been proven to cause even cancer in humans. However, most of these toxins are identified through laboratory experiments in which animals receive very high doses of the compound being studied. People almost never breathe in aerosols in such high doses. And still, lower exposure doses may still pose risks. One fact is clear: vehicles are such an integral part of our society that virtually everyone is exposed to their emissions171.

170 CSIRO (2003), p.1. 171 USEPA (2003, online.


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a. Carcinogenic Effects of Vehicle Emissions Conventional diesel is 100 times more, Euro II diesel 30 times more, Euro III diesel 20 times, Euro IV 10 times more carcinogen than CNG. Emissions from the cleanest diesel vehicles, equipped with particulate filters and running on best quality diesel fuel are still four times more carcinogenic than CNG172. Several organizations like the NIOSH (USA), the International Agency for Research on Cancer (IARC), the WHO, the EU, as well as the USEPA have indicted diesel exhaust for its toxicity and carcinogenicity173. In 1998, the California Air Resources Boards (CARB) declared diesel particles to be toxic air contaminants (TAC, see also Table II.12-2). A number of other research organizations and regulatory agencies too have branded diesel fumes as a likely carcinogen and CNG is still the safer option than diesel. A study conducted by the German Federal Environmental Agency (UBA) showed that Euro IV diesel vehicles using ULSD and fitted with CRT would be over four times more carcinogenic than CNG vehicles. It is to be noted that Euro IV technologies is still under development and will be introduced in Europe in 2005. The decision to declare diesel particulates as carcinogen and not diesel exhaust was a political decision and reached after heated discussion with the automobile industry because declaring all of diesel exhaust as carcinogenic would have meant that diesel vehicles would simply have to go off the road. Now if diesel engine manufactures can control diesel particles in diesel exhaust then they can still sell diesel engines, though even doing this is going to be technically very difficult. The Scientific Review Panel of the CARB points out that a chronic exposure to 1 µg/m3 of diesel exhaust will lead to 300- additional cases of lung cancer per million people. Lung cancer remains even today a largely incurable cancer. Diesel exhaust has a higher potency for cancer than even benzene, a potent carcinogen produced in petrol exhaust which is implicated in blood cancers. Diesel soot particles have the potential to be carcinogenic due to bioassays and experiments with cell cultures and epidemiological findings. In 1987 the WHO on the basis of the evaluation of the IARC classified this evaluation and confirmed examinations of further findings from the1999 by the IARC. In Germany, diesel soot particles are classified under the MAK class III A 2, are subject thus to the minimization requirement. The UBA evaluated the conclusions of the research project from the Frauenhofer Institute that diesel exhaust gas, from the technical conditions of 1999, as 18 times more carcinogenic potential than the exhaust gas of petrol engines (Under the Unit Risk of 1·10-6mg/m3). Under the projections of 2005, it is still high the carcinogenic potential even with introduction of particle filters. According to the criteria of the industrial medicine, particles are regarded as particularly health endangering if they are difficult to dissolve in the lung and in the organism as this applies to soot particles174.

172 CSE (2001), pp.16-17. 173 CSE (2001), pp.16-17. 174 AKPF (2000), p.9.


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b. Health Effects of Emissions on Cities A classic pollution study has vindicated its conclusions that city-dwellers in Europe and the US are dying young because of microscopic particles in the air175. Most of the concern about particle pollution began in 1993 with the publication of the Harvard “Six Cities” study, which identified particle mass with a diameter of less than 10 µm (PM10) as a threat to public health. The research also focused on particle mass with diameters less than 2.5 µm (PM2.5). These fine particles are thought to kill by lodging even deeper into the lungs. The researchers found that the long-term death rate from lung cancer rose by 8% for every 10 µg increase in the average concentration of PM2.5 per m3. Typical PM2.5 in the US are 20 µg in Los Angeles and 16 µg in New York. British levels are average 32 µg. The implications are bleakest for developing countries. In heavily polluted cities such as Beijing or Delhi, particulate levels average over 300 micrograms and most of this is probably PM2.5

176. This research concluded that the death rates increased in almost direct proportion to the level of particulate pollution. People living in the most polluted cities has a 26% risk of dying young compared with residents of the cleanest city. A larger study by the American Cancer Society in 1995 tested these findings by following 555,000 adults over seven years. Once again, there appeared to be a strong link between death rates and particulate pollution. The HEI study suggests that tiny particles with a diameter of less than 2.5 micrometers, or PM2.5, are more dangerous than PM10. Most of the PM2.5 fraction is caused by-products of combustion, which may contain more carcinogens.

Table II.12-2. Substances in Diesel exhaust listed by CEPA as Toxic Air Contaminants177

Acetaldehyde Creosol isomers Phosphorus Acrolein Cyanide compounds Aniline Dibutylphthalate Benzene Dioxins and dibenzofurans

Polycyclic organic matter, including PAHs and their derivatives.

Beryllium compounds Ethyl benzene Propionaldehyde Bis [2-ethylhexyl] phthalate Formaldehyde Selenium Compounds 1,3-butadiene Inorganic Pb Styrene Cadmium Manganese compounds Toluene Chlorine Methanol Xylene isomers and mixtures Chlorobenzene Naphthalene O-xylenes Chromium compounds Nickel M-xylenes Cobalt compounds Phenol P-xylenes

175 NS (2000), online. 176 NS (2002a), online. 177 CSE (1999), p.28


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Table II.12-3. Cancer potencies of various chemical compounds present in diesel exhaust178

Compound Unit Risk (µg/m3) Expected Incidence of Cancer (per million population)

Range (µg/m3) a Range of expected Cancer Incidence (per million population)

Chromium VI 1.5·10-1 150,000 1.2·10-2 to 1.5·10-1 12,000 - 150,000 Cadmium 4.2·10-3 4,200 2,0·10-3 to 1.2·10-2 200 - 12,000 Inorganic Arsenic 3.3·10-3 3,300 6.3·10-4 to 1.3·10-2 630 – 13,000 Benzo[a]pyrene 1.1·10-3 1,100 1.1·10-3 to 3.3·10-3 1,100 – 3,300 Diesel Exhaust 3·10-4 300 1.3·10-4 to 2.4·10-3 130 - 2400 Nickel 2.6·10-4 260 2.1·10-4 to 3.7·10-3 210 – 3,700 1,3-Butadiene 1.7·10-4 170 4.4·10-6 to 3.6·10-4 1.4 - 360 Ethylene Oxide 8.8·10-5 88 6.1·10-5 to 8.8·10-5 61 - 88 Vinyl Chloride 7.8·10-5 78 9.8·10-6 to 7.8·10-5 9.8 - 78 Ethylene Dibromide 7.1·10-5 71 1.3·10-5 to 7.1·10-5 13 - 71 Carbon Tetrachloride 4.2·10-5 42 1.0·10-5 to 4.2·10-5 10 - 42 Benzene 2.9·10-5 29 7.5·10-6 to 5.3·10-5 7.5 - 53 Ethylene Dichloride 2.2·10-5 22 1.3·10-5 to 2.2·10-5 13 - 22 Inorganic Pb 1.2·10-5 12 1.2·10-5 to 6.5·10-5 12 - 65 Perchloroethylene 5.9·10-6 5.9 3.0·10-7 to 1.1·10-5 0.3 - 11 Formaldehyde 6.0·10-6 6.0 2.5·10-7 to 3.3·10-5 0.25 - 33 Chloroform 5.3·10-6 5.3 6.0·10-7 to 2.0·10-5 0.97 - 27 Trichloroethylene 2.0·10-6 2.0 8.0·10-7 to 1.0·10-5 0.8 - 10 Methylene Chloride 1.0·10-6 1.0 3.0·10-7 to 3.0·10-6 0.3 - 3 Asbestos 1.9·10-4

(per 100 fibre/m3)

190 (per 100 fibre/m3)

Lung: 11 – 110·10-1 (per 100 fibre/m3) Mesotheliona: 38 – 190·10-1

(per 100 fibre/m3)

Lung: 11 – 110 (per 100 fibre/m3) Mesotheliona: 38 – 190

(per 100 fibre/m3) a). Unit risk is defined as an increase in the risk of developing cancer by a life-time exposure of a population to a chemical at an ambient concentration of µg/m3.

178 CSE (1999), p.30; adapted from Anon (1998), Findings of the Scientific Review Panel on the Report on

Diesel Exhaust as adopted at the Panel’s April 22, 1998, mimeo


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c. Chemicals in Air Pollution The effect of air pollution depends on pollutant concentration, duration of exposure, and presence of underlying cardiopulmonary disease. Both acute symptoms and aggravation of chronic disease may result from exposure, but the complex variables involved in large epidemiologic studies make the detection of injury and the establishment of limits on exposure (threshold limit values) difficult. Susceptibility to urban air pollution occurs in premature infants, the newborn, the elderly, those with chronic cardiac and pulmonary disease, some hypersensitive individuals, and heavy cigarette smokers. Suggested toxic effects of pollutants include impaired expiratory flow rate, airway inflammation, increased cough and sputum production, reduced resistance to pulmonary infections, decreased exercise tolerance in cardiac patients, unfavourable ECG changes, impaired oxygen transport, transient eye irritation, and excess death rates. Exercise performance is definitely reduced179. Sulfur Oxides (SOX): Both sulfates and SO2 together with PM and photochemical pollutants

aggravate chronic pulmonary disease and increase the risk of acute and chronic respiratory illness. These compounds impair pulmonary mucociliary clearance, primarily in those patients with pre-existing pulmonary disease, probably as a result of hydrogen ion deposition on the bronchial lining. Exposures of two miners to SO2 concentrations of at least 40 ppm resulted in severe airway obstruction, hypoxemia, markedly reduced exercise tolerance, ventilation perfusion mismatch, and evidence of active inflammation as documented by a positive gallium lung scan. Respirable sulfuric acid (H2SO4) aerosol studies - products of the reaction of SO2 with H2O vapour - have indicated no respiratory irritant effects in normal subjects exposed to H2SO4 concentrations <100 µg/m3 or less. In asthmatic subjects some studies with H2SO4 aerosol have found a decrease in lung function, while others have found no decrease180.

Polycyclic Aromatic Hydrocarbons (PAH): PAHs in diesel exhaust are extremely toxic because they can cause cancer and affect the genetic make-up of human beings. PAHs like 3-nitrobenzathrone (carcinogen) and 1,8-dinitropyrene (mutagen) were found to be among the most toxic substances known181. 3-nitrobenzanthrone, produced the highest score ever reported in an Ames test (6·E6 mutations/nM), and caused “considerable chromosomal aberrations” in the blood cells of mice, suggesting that it is likely to have similar effects on other mammals, including humans. Being a nitrated polycyclic aromatic HC (nitro-PAH), it is produced during reactions between ketones by products of burning fuel-and airborne NOX that take place on the surface of HC particles in diesel exhaust182. Besides that, diesel exhaust contains other 40 substances that the USEPA lists as hazardous air pollutants, 15 of these are considered “probable” or known human carcinogens.

Nitrogen Oxides (NOX): Oxides of nitrogen are formed chiefly during combustion of diesel fuel in motor vehicles. Of the NOX occurring in ambient air, NO2 is the most toxic. It is not only a toxic gas by itself but it is also a precursor to the production of more toxic O3. 183 Haemoglobin has much greater affinity for absorbing NO2 than O2. About 80 to 90% NO2

179 Ellenhorn (1997), CD-Rom 180 CSE (1999), p.19 181 CSE (1999), p.32 182 Pearce F., (1997); NS (2002b), online; Horstman M., Seega B. (2003), online. 183 CSE (1999), pp.25-26.


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inhaled is easily absorbed, which then reduces the O2 carrying capacity of the haemoglobin. Inhalation of high levels of NO2 results in pulmonary injury that is dose-dependent and is characterized morphologically by loss of ciliated cells (type I) in the airways and degeneration of alveolar epithelial cells leaving the basement membrane denuded184. Apart from smog, NOX also contribute to the formation of ground-level O3. Other health effects include coughing, shortness of breath, and decreased lung function. Interstitial edema, epithelial proliferation, and, in high concentrations, fibrosis and emphysema develop after heavy NO2 exposures185.

Hydrocarbons (HC): HCs are the result of incomplete fuel combustion. When combined with NOX in the presence of sunlight, HC's produce ground-level ozone or "smog," which can irritate eyes, damage lungs, and aggravate respiratory problems. Symptoms include coughing, shortness of breath, and decreased lung function. Many HCs are also considered hazardous air pollutants.

Volatile Organic Compounds (VOC's) such as formaldehyde, airborne alcohols, aldehydes, aliphatic, and aromatic and halogenated HCs and ketones found are associated with irritation phenomena. Massive exposure to VOCs may result in reactive airway dysfunction syndrome, characterized by bronchial hyper-responsiveness. Examples of aldehydes include acrolein and formaldehyde. Pulmonary irritation and bronchospasm are the health effects observed during exposure to concentrations present in the atmosphere.

Carbon Monoxide (CO): It is formed by incomplete fuel combustion. CO blocks the oxygen-binding sites of hemoglobin, thus reducing the content of O2 in the bloodstream required for target tissues and vital organs; it is of particular concern to people with heart disease. High concentrations impair O2 transport; this may affect cardiac patients on smoggy days, but generally no effects occur in healthy individuals at existing levels.

Ozone (O3): This chemical causes eye irritation and bronchitis, aggravates chronic obstructive pulmonary disease, and perhaps increases the risk of acute and chronic pulmonary disease, mutagenesis, and fetotoxicity. O3 damages tissue by rapidly oxidizing thiol-containing compounds and unsaturated fatty acids. Symptoms of respiratory irritation and decreased forced expiratory volume occur at exposures above 0.3 ppm, but strenuous exercise causes these effects to develop at lower ozone levels. Exposure to 0.5 ppm of ozone decreases athletic performance by 50%, which is greatest on the second day of exposure and minimal by the fifth day. In animals short-term exposure to ambient concentrations of ozone (below 1 ppm) indicates that ozone inhibits the capacity to kill intrapulmonary organisms and allows purulent bacteria to proliferate, but the applicability of these models to humans remains unclear. Respiratory effects of O3 may be affected by the level of activity in most healthy adults exposed to ozone concentrations from 0.10 to 0.16 ppm. Peroxyacetyl Nitrates, a byproduct in the O3 generation is known to cause lacrimation and pulmonary irritation.

184 CSE (1999), p.32. 185 Ellenhorn (1997), CD-Rom


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d. Particle Pollutants and the Human Respiratory System Because of its small size, Fine Particle Matter (PM2.5) can be deposited deep into the lungs, where it can cause health problems and is known to alter lung functions in children186. NOX and SO2 are also major sources of fine PM. Recent studies have shown an association between PM and premature mortality from respiratory and cardiovascular disease, and increased incidence of respiratory illness, particularly in children and the elderly. For adults with heart or lung conditions, exposure to fine PM can cause more illness and in some cases premature death. More than 90 % of the particulates found in diesel exhaust are fine particles. In Cleveland, USA, average PM10 levels were only 43 µg/m3, this study estimated that an increase of 100 µg/m3 led to an additional 12% of the population suffering from respiratory illness. Another study carried out in Lyon, France from 1985 to 1990 showed that respiratory and cardiovascular deaths increased by 4% when PM13 levels increased by 50 µg/m3 187. There is growing scientific evidence worldwide that diesel produces extremely toxic pollutants. There is a study done in the UK in which it was found that smaller the size of the particulates, the higher was the share attributed to diesel emissions – with 17% of PM0.1 and lesser size particles originating from diesel vehicles188. In the U.S, existing database on national emissions shows that sources of both primary and secondary particles make significant contributions. On a nationwide basis, the greatest mass of primary and precursor emissions come from combustion, fugitive, and industrial sources. In specific localities, any of these source categories can be dominant189. These and similar studies have raised concern about particles in the fine, ultrafine, and nanoparticle size ranges, but the decision to base the proposed USEPA standard on particles less than 2.5 µm in diameter was somewhat arbitrary. This decision was made with insufficient data on the health impact of even smaller particles and nearly no data on human exposure to these very small particles. In view of the strong adverse health effects shown by nanoparticles in animal studies, future standards might be imposed on ultrafine particles or nanoparticles. Nearly all of the aerosol mass emitted by engines is in the fine particle range and the nearly all the number is in the nanoparticle range. Emission inventories suggest that engines and vehicles are the principal contributors of fine particles to the atmosphere in urban areas190. Risk management researchers are currently emphasizing source characterization research that produces new information on PM2.5 emission factors and chemical profiles to enable better source apportionment by either source- or receptor-oriented modeling. They are also extending collaborations with health researchers to increase toxicity testing of particles for a wide variety of sources. Types of sources include on-the-highway diesel trucks, residential wood combustion, oil- and coal-fired boilers, fugitive dust-generating construction activities, open burning of biomass, and indoor sources of ultrafine PM191.

186 Ellenhorn (1996), CD-Rom 187 CSE (1999), p.32. 188 CSE (1999), p.27. 189 Tucker (2000), p.390. 190 HEI (2002), p.157. 191 Tucker (2000), p.391.


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e. Mechanism of particle penetration in the human lungs The ideal gate for the penetration of air contaminants is the lung. The respiratory system branches out in approximately 20 stages up to the bronchia, and ends into about 500 million alveoli (lung bags) in which the gas exchange takes place. The air speed becomes shorter, and the retention times longer, which means, the deeper the PM stays in the organism, the longer the air pollutants stays for deposition. For the evaluation of the particulate contaminants is thus:

Particles → Lung penetration → Possible deposition is of great importance192. Particle pollutants enter the human body almost exclusively by the way of the respiratory system, and their most important immediate effects involve this system. Particulate size is probably the most important factor to be considered, for it is this factor, which determines the extent of penetration into the respiratory system. In the course of evolution in the breathing way system, efficient defence mechanisms were formed against the natural types of dust. Dust is separated to damp surfaces, the mucous layer is moved by fine cilia constantly in the direction of the throat and a warning system with sensitive chemical sensors ensures that various mechanisms, ranging from coughs to sneezing that the lung remains at all cost free of particulates. Nostril hair filters out larger particles. Those over 5.0 µm diameter are stopped and deposited mainly in the nose and the throat (see Fig. II.12-1). Smaller particles are stopped by mucous membranes that line the respiratory system and provide a surface to which the particles adhere. The sizes and shapes of air passages effectively block some of the other particle fraction between 0.5 - 5.0 µm in diameter193 by depositing them in the bronchioles (see Fig. II1.12-2). Although they penetrate the lungs, they usually do not go beyond the air ducts or bronchi, and are soon removed by ciliary’s action194.

Fig. II.12-1. The human respiratory system.

Top: anatomy of the airways and lungs. Center: close-up view of the branched passageways, or bronchioles, and the clusters of tiny air sacks, or alveoli. Bottom: the diffusion of oxygen and CO2 between the alveoli and the blood passing in the nearby vessel. 195

192 AKPF (2000), p.7. 193 Stoker (1972), p.72. 194 Stoker (1972), p.73. 195 modified after Postlethwait & Hopson (1995), p.628


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Most particles deposited in the bronchioles are removed by the cilia within two hours. Indeed, the bronchi’s and bronchioles cilia wave back and forth and move mucous along in a current that carries trapped the smaller particle fraction out of the respiratory system to the throat, where they are swallowed or ejected as sputum. The alveolar sacs, resulting from much branching of the air ducts, are the sites at which oxygen and CO2 are exchanged between the atmosphere and the blood. Few particles can reach the alveoli, but particles less than 0.5 µm in diameter do in fact reach this area and may settle there. The removal of such particles from these areas is less rapid and less complete than in the upper respiratory region. Some of the particles retained in the alveoli are absorbed into the blood. PM which enters and remains in the lungs can exert a toxic effect in three different ways:196 • The particles may be intrinsically

toxic because of chemical or physical characteristics.

• The particles may be inert themselves but once in the respiratory tray they may interfere with the removal of other more harmful material.

• The particles may carry adsorbed or absorbed irritating gas molecules and thus, enable such molecules to reach and remain in sensitive areas of the lungs. Carbon, in the form of soot, is a common particulate with a very good ability to sorb gas molecules on the surface.

Fig. II.12-2. Deposition Efficiency of particles versus diameter197

The technical types of dust, however, in particular kinds of particles from engine exhaust, exhibit grain sizes that are 100 times smaller than the natural types of dust. Such ultrafine particle discourages the fine defense mechanisms of the lung: these tiny particles penetrate past the protected regimes of the bronchioles to reach the alveolar section and stay there for months or even years198. And still, the human body is not without defensive mechanism for this cilia-less area. Mobile devouring cells (macrophages) digest these particles are (phagocytes) and remove them in a timely manner so that these ultrafine particles do penetrate the gas exchange barrier199. The only factor regulating this final barrier is the concentration of that ultrafine particle load. As outlined in the previous segments of this chapter the particle number dose is the crucial factor regulating the toxic effects. The risks that this particle fraction crosses over into the blood vessel system or the lymphatic systems and thus spreading throughout the entire organism is quite high. After spreading though the entire organism, acute irritating effects can be released, for example, an increase of the blood viscosity or influence of the heart rhythm. On the other hand if these particles do not cross over or are digested by macrophages, they settle themselves into the lung fabrics, where they constantly irritate the lung epithelium200.

196 Stoker, (1972), p.73. 197 Pawlak (2003), submitted for publication 198 AKPF (2000), p.7. 199 AKPF (2000), p.7.


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Epidemiological long-term studies refer to this as cardiovascular damages and above all also as carcinogen potential by fine particle exposition. According to the Swiss “Monetarisierungstudie” that fine particles inhalation could lead to 3,800 premature deaths, 53,000 cases of Bronchitis in children and 791,000 days of inability to work per year201. Other scientific investigations have shown that the carcinogen potential of inert particles are the causality for tumor emergence. In addition mutagenic PAHs are among the culprits as these are deposited on the surface of the particles. Thus, the health risk increases with the reduction in particle size.

Fig. II.12-3. Path of the particle into the organism.

(after Donaldson)

f. Lung Penetration and Lung Deposition of Particles The lung can be seen as a selective filter, into which the particle are stripped off the gas stream in different ways202. In the upper air ways (nose, throat) the air-speed is high enough to cause particles to deposit by impaction (see Fig. II.12). . Figure II.12-4 shows the deposition characteristics of various particle size classes in different parts of the lung. Penetrating deeper, the number of respiratory branches increases resulting in an overall reduction of air speed (Qnose = QΣbronchi). Going even further towards the alveolar region of the lungs, air speed stagnates; here diffusion and mobility diameter of the particles are the decisive deposition factors. The lung has, like any filter, a certain range in which neither impaction nor diffusion predominates and typically occurs at around 300 nm (compare Fig. II.12-2).

Fig. II.12-4. Schematic presentation of the Airways and its

mechanisms of particle deposition (after BUWAL)

200 AKPF (2000), p.8. 201 BUWAL (2003); AKPF (2000), p.8. 202 AKPF (2000), p.9.


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Larger particles are efficiently trapped by the nose and throat; however, finer particles, particularly in the alveolar region, are increasingly deposited with rising breathing frequency and inhalation volume203. As the particles become smaller, there is an increase of separation in the bronchiole range due to the increasing mobility of the particles. Still smaller particles (<1 nm) would not reach the alveoli any more as the curve for 750 mL already shows204. g. Hygroscopy It is of great importance whether the inhaled particles are hygroscopic or not. Hygroscopic particles increase rapidly in size within the damp-close- and narrow range setting of the respiratory system. Thus, hydrophilic particles are sooner separated, while non-hygroscopic (hydrophobic) particles can penetrate all the way down into the alveolar region. Particle compounds, which are not water soluble, must be classified more critically. This applies to gases and fine types of dust. Freshly generated Diesel soot (not yet subject to agglomeration – see accumulation mode in Fig. II.8-2, p.40), is hydrophobic; being still part of the nucleation regime, they have a small diffusion pressure on the mucous area, and they can penetrate the alveoli205. As there are no nerve receptors within the alveoli, they do not signal the brain of inflammatory reactions; i.e. pain. One can easily inhale large quantities of air pollutants without feeling really any effects but once these air contaminants reach the alveoli, then, these foreign pollutants can be transferred to the blood system where they can easily spread and damage tissues and target organs away from the lung system (coronary heart disease, heart attacks, etc. see sections a to d of this Chapter). Since cleaner fuels generally result in lower emissions of toxins, programs to control toxic air pollution have focused around changing fuel composition as well as improvements in vehicle technology and performance. Some of the alternative fuels are inherently cleaner than gasoline and diesel, and therefore their use has the potential to result in reductions of toxics. However and based on current technology, there may be tradeoffs resulting in the increases of certain toxins (e.g. aldehydes from alcohol fuels.)206.

203 AKPF (2000), p.10. 204 AKPF (2000), p.11. 205 AKPF (2000), p.11. 206 MacLean & Lave (2003), p.11.


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II.13. Exposure Assessment of Vehicle Emissions The study of aerosols and the assessment of human exposure to them are of relatively recent activities207. Exposure occurs when an aerosol particle reaches the entrances to a person’s nose or mouth (predominates for respiratory deposition) or the skin (insignificant for dermal adsorption). The best measurements for the assessment of exposure to aerosols typically require that a sampler be placed within a person’s breathing zone to characterize the inhaled air. Such a microenvironment is a relatively small area in which the aerosol distributions are assumed to be homogeneous or of a well-described distribution-that can be fully represented by an individual sample208. A representative sample is then defined as one in which the concentration and size distribution of particles collected by the sampling device are equivalent to the concentration and size distribution of particles present in the atmosphere of concern209. The exposure of an individual to aerosols in both industrial and residential settings is related to the breathing process (e.g. inhalation rate, tidal volume, respiratory physiology, etc.) in combination with the aerosol’s characteristics (concentration, size distribution, morphology, chemistry, etc). The dynamic size of aerosol particles provides a direct relationship to the deposition of such particles in a human being’s respiratory system (see figure of previous section, Fig. II.12-1, p.75). a. Physical and Chemical Properties, Particle Size and Adverse Biological Effects Aerosols are complex chemically, biologically and physically, and their characteristics influence directly both IAQ and exposure measurements. Table II.13-1 highlights the most significant physical, biological, and chemical properties of aerosols.

Table II.13-1. Properties of Aerosols Particles210

Physical Chemical Biological Density Phase distribution Viability Number (count) Stability reactivity Colony-forming potential Mass Organic / Inorganic Reproductive requirements Surface area Solubility (aqueous) Pathogenicity Size (aerodynamic & physical Diameter -------------- Carcinogenicity Size distribution Acidity / Alkalinity Mutagenicity Shape Constituency Toxicity Degree of Agglomeration --------------- -------------- b. Physical Sampling Methods Considerations Physical sampling methods provide information regarding particle size, number and mass concentration. Analyses of specific compounds in the aerosol may be needed to determine a specific pollutant or marker compounds / elements to characterize likely sources of the pollutant211.

207 Willeke & Baron (1993), p.659. 208 Willeke & Baron (1993), p.660. 209 Willeke & Baron (1993), pp.661-662. 210 Willeke & Baron (1993), p.665 211 Willeke & Baron (1993), p.666.


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c. Physical Analysis Methods of Aerosols The type of sampling method selected depends largely on the physical characteristics of the aerosol being studied. Different types of instruments provide different sizing information, based on the physical parameter being measured. Factors such as particle mass, number, surface area distribution, and the influence of each on respiratory deposition influence the type of measurement method selected. An analytical method is selected based on factors such as the need to collect a physical sample for subsequent analysis, the need to restrict sampling to a particular size fraction, or the need to place the sampler appropriately (outdoor, indoor). The time period necessary for sample integration may range from real-time (optical measurements) to longer time intervals (necessary to obtain an adequate sample)212. Optical Particle Counters (OPCs): These instruments are based on light scattering by particles, according to physical size and concentrations. Even thou the same aerosol may be sampled by different techniques, the particle size distribution measured may be very different if the techniques are based on measurements of different aerosol properties – such as optical diameter, inertial mass, or electrical mobility. This difference may be reconciled if suitable calibrations are performed and compensations are made for the measurement methods. Multichannel optical particle count distributions, from which the estimated mass distribution can be computed213 (refer to CPC, Chapter II.8 – section d, p.54, for an in-depth explanation of the instrumentation used).

212 Willeke & Baron (1993), p.666. 213 Willeke & Baron (1993), pp.666-667.


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II.14. Population Exposure Assessment Studies - Prerequisites An exposure survey study may be used to assess the distribution of exposure of a population. Inferences to a larger population (or frame) can be made by using statistical probability sampling methods to draw or select the study population. The survey and sampling instruments, methodologies, and protocols used in defined-focus / objective studies can be adapted for this type of study214. a. Requirements of Exposure Assessment They all include taking representative probability samples, measuring pollutant concentrations, measuring body burden and recording daily personal activities. The objectives of population exposure studies are to produce representative frequency distributions of the exposure of human beings to aerosols, to determine how exposures compare with existing regulatory standards or mitigation guidelines to establish indoor/outdoor/personal relationships for aerosol exposure, and to determine the significant sources of aerosol exposure215. Each micro-environment that is encountered in daily exposures must be defined. The relationships between personal and micro-environmental sampling, spatial and temporal variance in aerosol concentrations, exposure and activity, and the frequency distribution of exposure must be determined for the test population. Once exposure information has been collected, it can be used for risk assessment. b. Risk Assessment Exposure It requires the determination of the number of persons exposed, the sources and transport factors relating to the pollutant from its source to the receptor (person or sampler), the exposure-related significance of sampling data, and the health effects of exposure. Risk assessment also requires an estimate of the population at risk. The risk assessment can then be used to help define the relationship of exposure to outdoor standards216. c. Design Considerations of Aerosol Exposure Assessments The most critical element in the planning of an aerosol exposure study is a thorough analysis of the purposes and objectives. For example:

• Specific contaminants to be measured. • Instrumentation suitable to detect contaminants. • Population being studied. • Types and numbers of samples that will be needed to provide statistically significant

correlations. When studying aerosols, questions regarding whether to include or exclude smokers are especially important. Including smoking participants confounds the relationship between personal and micro-environmental data217.

214 Willeke & Baron (1993), p.675. 215 Willeke & Baron (1993), p.676. 216 Willeke & Baron (1993), p.676. 217 Willeke & Baron (1993), p.680.


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If the objective is to examine the exposure to combustion aerosols, it is important to choose the proper inlets of sampling particulates of a given size; for example, using PM2.5 inlets would gather samples that would be more representative for deep alveolar deposition in the lungs studies than using PM10 or total suspended particulate (TSP). If the purpose of the exposure study is for later risk analysis, it is important to determine the problem of the risks218. The assessment of human exposure to aerosols requires characterizing the levels of aerosol pollutants in particular microenvironments, determining the proportion of time spent and the activities conducted in those microenvironments, and calculating the degree of personal exposure to the aerosol over the time period spent on that microenvironment. To address these problems, a variety of instruments are required, including personal and micro-environment sampling systems. The sampling location(s) must be well defined by the study objectives and by the expected temporal and spatial variations in aerosol concentrations. Air movement patterns may help determine the placement of samplers within microenvironments. Additional factors, such as access to electrical power and the number available sampling instruments, may limit the number of microenvironments studied. The number of samples, types of samples, an analytical free decision, and the number of replicates required to reach the data quality objectives mandated by the study goals must be understood before the sampling instruments are selected. The expected variance in the measurements will help determine the number of samples required for a specific level of confidence. This may require pre-testing and evaluating the study instruments and sites to make reasonable estimates of the expected variation in aerosol concentrations (if historical or background data are unavailable). The sampling instrumentation must be designed or selected with the study in mind. The need for a low level of unobtrusiveness must be addressed for portable, self-contained systems that do not require house current, and for lightweight, quiet, personal sampling systems219. d. Logistics for Exposures Assessments Studies Good field implementation is the result of successful logistics, i.e. planning, training, and field testing. Pre-training of field technicians and participants before the actual study begins is extremely important. In addition to the defined-focus/objective studies previously mentioned, small pilot field studies can be performed specifically to test instruments prior to their use in larger exposure programs. Field testing of equipment, methodologies, and protocols is necessary to address the hardiness of the sampling systems under operating conditions similar to those of the population exposure studies. An implementation plan should be elaborated that includes scheduling, training, contingency planning, and providing backups for equipment and for data collection efforts220.

218 Willeke & Baron (1993), p.681. 219 Willeke & Baron (1993), p.681. 220 Willeke & Baron (1993), p.681.


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e. Modeling for Cost Effective Short-Term Studies Personal exposure monitoring studies to estimate population exposure levels can be expensive and, in some cases, impractical. An alternative approach combines limited-scale measurement studies and existing data with an appropriate exposure model. The most common exposure model is the simple summation model of the measurements of interest (e.g. mass concentration for a specific aerosol size fraction) in each microenvironment, weighted by the time spent in each micro-environment. These relationships are express as follows:221

iimean cfE ⋅= ∑ [min/cm3] E, mean exposure fi, time spent in microenvironment ci concentration in microenvironment

[min/cm3 [min]

[#/cm3] II.14-1

A study participant is required to keep a time budget diary that provides information on the fraction of time spent in each micro-environment. The mean exposure and variance estimates representing specific micro-environments can be catalogued and used subsequently to predict the distributions of exposures for other combinations of activity patterns222.

221 Willeke & Baron (1993), p.682. 222 Willeke & Baron (1993), p.683.


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II.15. Methods for Exposure Assessment for Diesel Exhaust Studies Diesel exhaust is a complex mixture of thousands of gases and particles. Because of its complex mixture and that many of the individual constituents of diesel exhaust may be emitted from other combustion sources, whole diesel exhaust has no been directly monitored or quantified in the atmosphere223. The most common approach to monitoring diesel exhaust has been to select a surrogate measure of exposure that is representative of the exhaust as a whole. Although diesel exhaust markers (substances unique to diesel exhaust that could be used to qualify and quantify its presence in the atmosphere) have not been found, fine particles and elemental carbon have been used as surrogates of exposure to diesel exhaust PM. Consequently, ambient diesel exhaust PM concentrations are often used by researchers to represent the public’s exposure to whole diesel exhaust. Estimation of diesel exhaust concentrations uses a PM-based exposure method for the following reasons:

• Comprehensive emissions inventory and ambient concentrations database for diesel exhaust-derived PM.

• Diesel exhaust PM contains many of the toxic components of the exhaust. • PM has been shown to contribute a significant portion of the exposure to the whole

exhaust. • The PM has been associated with approximately 50 to 90% of the mutagenic potency of

whole diesel exhaust. Toxic gas-phase exhaust constituents like acetaldehyde, benzene, 1,3-butadiene, formaldehyde, 1,3-NitroBenzanthrone and 1,8-DinitroPyrene are not directly included in a PM-based exposure estimation because they are not typically carried on or in the particles. Many studies done in the past were focus to improve and to refine the estimates of exposure to the gas-phase portion of the exhaust until more sophisticated estimation methods can be devised. However, PM-based estimation methods provide a useful tool to develop diesel exhaust exposure estimates. Ambient diesel exhaust PM concentrations range from 0.2 to 23 µg/m3, whereas ambient particle number concentration is typically found to be between 1000 to 2000 counts/cm3. a. Tracer Methodology for Diesel Exhaust Studies Although diesel exhaust may not contain markers that can be used to qualify and quantify its presence in the atmosphere, it does contain substances predominately emitted from diesel-fueled engines (tracers), which can be used to estimate its presence and concentrations. Tracer’s substances are consistently emitted from diesel-fuelled engines, and can be consistently monitored in the air. Diesel exhaust tracers that have been suggested by various researchers include particle-associated diesel fuel additives, elemental carbon, PAH and its ratios, and lubricating oil combustion products. Known quantities of substances not normally found in the atmosphere can also be added to the fuel as artificial tracers. Although this approach would enable establishment of direct relationships between exhaust generated by the vehicles and the pollution inhaled. For obvious reasons this method has not been used in this study.

223 OEHHA (1998), p.9.


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II.16. Multiple Exposure Pathways a. Occupational Exposure Studies Occupational exposure studies have certain advantages over environmental studies conducted in outdoor ambient atmosphere. The diesel exhaust concentrations are generally higher, the exposed workplace population is better defined, duration of exposure more predictable, and sources of diesel exhaust are usually definable. b. Other Routes of Diesel Exhaust Exposure Exposure assessment also involves determining concentrations of the various pollutants in other media by which humans are exposed. Air emissions contaminate not only the air, but deposit onto water, soil, and vegetation. These media represent the additional possible pathways of exposure to ambient diesel exhaust concentrations. In order to estimate long-term exposures resulting from ambient concentrations, the risk assessment must address both inhalation and non-inhalation pathways of exposure. These pathways may contribute to the total exposure to a pollutant: Primary pollutant exposure pathways for humans are inhalation, ingestion of dirt and contaminated food products, water ingestion, and dermal absorption of pollutants or contaminated dirt deposited on the skin. Near-Source Exposure occur near busy roads and intersections where diesel vehicle are operating. We also expect higher than average concentrations of diesel exhaust near oil and gas production areas. These near-source exposures areas could result in an increased potential health risk for exposed individuals224. Secondary pollutant exposure pathways are a result of the assimilation of the pollutant into a food source (not to be confused with secondary pollutants generated via photochemical reactions – see Chapter II.3 - Airborne Quality, p16). In order to assess non-inhalation pathways, substance and site-specific data are needed. Since the specific parameters for diesel exhaust are not known for these pathways225. c. Exposure assessment in Street Canyons From a population exposure point of view, air quality in a street canyon is of major importance, since the highest pollution levels and the larger targets of impact are often concentrated in this kind of streets. The so-called canyon effect (i.e. the reduced natural ventilation in urban streets) results in greater health impacts (e.g. indicated by an increased number of respiratory hospital admissions) and damage costs for the exposed population226 (see Chapter II.4 – Street canyons, p18).

224 OEHHA (1998), p.11. 225 Krieger, (1998), p.63 226 Spadaro & Rabl, (2001), p.4770


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d. Individual / Personal exposure Can be calculated as the product of pollutant concentration and time spend in a specific microenvironment (see EQ.II.14-1), which is defined as a confined space (e.g. bedroom, office, car, parking, pavement, busway station, etc.) where pollutant concentrations are assumed to be uniform will be then the sum of all such products. However, the assumption of spatial uniformity of air pollution might be erroneous for certain microenvironments like street canyons, where strong spatial concentration gradients are often observed. In these cases, exposure calculations should be refined by subdividing microenvironments into sub-microenvironments, taking into account pollution hot spots, and refined human breathing zones (e.g. for residents, pedestrians, cyclists, drivers, etc.). Few examples of these studies can be found in the literature. There have been suggested that a side of the street factor should be introduced if the prevailing wind direction is perpendicular or near-perpendicular to the street axis.


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Exposure Assessment 87 Chapter III

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CHAPTER III

Methodology and Experimental Design


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The aim of this chapter is to describe the methodology used to collect and analyze the data from the Woolloongabba Busway Station in Brisbane, Australia for the purpose of conducting exposure assessment from bus emissions. The methods and the techniques for recording and analyzing exposure concentrations will be explained. The platform data will be compared with the background data to determine the degree of exposure concentration that passengers are exposed to on a daily basis. In this study we used the indirect approach227 for exposure assessment because it predicts exposures using the activity pattern model to combine activity pattern data with micro-environment concentrations data. This approach is lower in cost and imposes both a less respondent burden as well as being less vulnerable to sample selection biases. However, it was vulnerable to systematic measurement error in the predicted exposures because the background particle concentration data might need to be “interpolated” because background and platform data measurements were taken with the same set of equipment but at different moments in time; i.e. relocating equipment from exposure site to background site. The following methodology has been developed which allows the calculation of exposure concentrations of bus emissions. The main steps in the methodology are: 1. Conducting a preliminary survey at the busway station. 2. Selecting a suitable background site and the site of monitoring 3. Collecting bus data by different methods, passengers data by video surveillance, particle

concentration data, and meteorological data. 4. Analyzing the data. 5. Developing a model for exposure calculation with routine scripts. 6. Verifying the model. 7. Making suggestions and recommendations as to ways to minimize bus emissions and

improve ventilation at the busway station.

227 Duan & Mage (1997), p.439.


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III.1. Sampling Sites The Woolloongabba Busway station is located between Spencer and Vulture street. It was first considered for the research area because it is a semi-covered and semi-submerged station - resembling a street canyon (see Fig. III.1-1). The platform location, in which the particle concentration were collected, lies approximately 10 meters below the street level. Furthermore, this station was also chosen because it was easily accessible and nearby the QUT – Gardens Point and the Centre Operations of the entire South East Busway. In addition, there were also available the power source and other facilities. This station has two platforms one outbound in which buses leave the city of Brisbane, the other side, is the inbound, which buses go to the city. Both of them are panelled with glass and concrete and a roof that runs continuously along the platform areas offering shelter and protection for the passengers (see Fig. III.1-4). Adding more comfort to the passengers, the busway has two elevators so that the passengers can move between platforms without crossing the busway. Aluminium benches are provided - due to their large heat capacity (especially during the winter months) can be quite uncomfortable to those passengers condemned to wait more than 5 minutes.

Fig. III.1-1. Site Location

of the Busway Station (left - Site 1) and EPA Weather Station (right - Site 2).

Fig. III.1-2. Woolloongabba Busway Station (view from Stanley street)

Fig. III.1-3. Outbound Platform

(non peak hours).


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However, the architectural construction design of this busway was of great concern because the exhaust emissions from the buses are not easily dispersed. The glass panels, the roofs and the canyon walls of the station offer little or no ventilation. Moreover, the glass panels also amplifies the noise spectrum of the running buses. Refer to Fig. III.1-4 in which X marked where particles were measured, the white letters marked with SLM where the sound measurements took place. In order to tackle the study of exposure assessment and to try to quantify the concentrations of bus fumes, a preliminary survey was conducted on May first, 2002 at this busway station. During this day, the entire W’Gabba station was surveyed in order to become familiar with the station’s architectural design. The preliminary survey was continued in the week covering the 6th till 9th of May in order to assess passenger movements, passenger waiting times, and bus frequencies.

Fig. III.1-4. Design of the Outbound Platform It also shows the sampling location marked by X (for SMPS) and SLM (for Sound Level Measurements).

Accordingly, it was necessary to determine: • The total number of buses running along the platforms during a day (from 07:00 till 19:00). • The peak hours and off hours from buses and passengers. • The location in which most buses stop and pick up passengers. • The total number of passengers coming into the station. • The location in which most passengers wait and take on the buses. • Which platform was mostly frequented by passengers.


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Site 1 - Street Level – Background Particle Sampling This sampling site was a fixed location where (for the duration of 7 days) it was located in front of the Centre of operations of the SEB Centre Operations (see Fig. III.1-2). It was not frequently used by many vehicles, instead, this area was used for parking for the vehicles of the employees of the Print shop, the Busway station Centre Operations and the nearby Dental Clinic. Thus, this site was used in air sampling to obtained the background information. Three sets of samples were taken daily and each set run from 30 minutes each, in this 30 minutes, there were 5 measurements, which lasted 6 minutes each. The samples were taken in equal intervals of time from 4 to 5 hours apart throughout the day for seven days (see Appendix C). Site 1 - Platform # 2-Outbound Particle Sampling As the outbound platform was the significantly more frequented half of the W’Gabba station, measurements along this platform were taken for the duration of 7 days (7th of June till 18th of June - see Fig. III.1-5. and Fig. III.1-4.). About 20 sets of samples were taken throughout the day in equal intervals of time of 30 minutes each and each 30 minute sample, there were 6-5 minute sample. The traffic of buses is a “stop-start” fashion along the busway during peak and off hours (see Appendix C - Schedules).

Fig. III.1-5. Site 1 - Overall View of the Busway Station.


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Site 2 - EPA at Woolloongabba Air Monitoring Station The site is on the corner of the Prince Andrews Hospital (it is located on the edge of the doctors car park) opposite the intersection of Cornwell Street and Ipswich Road. It provides essential data about the meso-climatical conditions prevalent in the Woolloongabba suburb. There is no address as such but the GPS co-ordinates are -27.3555º latitude and 153.0344º longitude (Fig. III.1-1). Data were collected for PM10, CO and meteorological conditions such as wind speed and direction, temperature and humidity (see Fig. III.1-6). Wind data were sampled at a distance of 4 m from the road and 10 m above ground. The Meteorological data were processed in 30 minutes interval of time.228

Fig. III.1-6. Site 2 - EPA Monitoring Station,

(1) High Volume Sampler, (2) Meteorological Station.

228 Data kindly provided by Mr. Mike King, Australian Environmental Protection Agency


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III.2. Data Collection In order to determine the potential exposure assessment of buses to passengers at the busway station, it was necessary to collect a series of data in two stages: Stage 1 - Preliminary survey: Getting familiar with the platform design, infrastructure (power outlets, storage shed, restroom facilities and security access, etc.). Furthermore, this preliminary survey helped to locate the site of particle exposure. During the platform surveys executed between the 6th and the 9th of May 2002, it was quickly found that the inbound platform was unsuitable for this purpose, as most passengers getting off the bus walked away rather than waiting on the platform. Thus, this survey helped to evaluate the general characteristics of this Busway station in passenger movement by performing the following tasks: Passengers arrival and departure time, places that are more likely to wait for their buses, total

waiting time at the station and frequency (see Fig. III.2-1). Passenger count data used in this study were collected for 4 hour periods on 4 subsequent days (e.g. Day 1: from 7:00 to 10:00 mornings; Day 2: from 10:00 to 13:00; Day 3: from 13:00 to 14 and Day 4: from 14:00 till 19:00) for the duration of 4 subsequent days on both the inbound and outbound platforms during the month of May 2002. Combined these time windows cover passenger frequencies for a typical working day for a period of 12 hours. The data were collected with the help of video cameras taken from the station with superimposed date and time data see Fig. III.2-1.

Fig. III.2-1. Video Surveillance.

On-site cameras at the Busway Station were used to perform the passenger census during rush hour. (Left) – Inbound Platform, (Right) – Outbound Platform.


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Bus type, frequency, stopping interval, and other bus-related data (like idling and aggressive driving mode) were recorded manually. Traffic count data used in this study were collected for four hours periods (e.g. Day 1: from 7:00 to 10:00 mornings; Day 2: from 10:00 to 13:00; Day 3: from 13:00 to 14 and Day 4: from 14:00 till 19:00) for the duration of 4 days for both the inbound and outbound platforms for the month of May 2002. These bus data have been matched with the induction loop data obtained from the SEB headquarters. In terms of fuel supply only dedicated (runs entirely on natural gas) CNG buses are next to the dominating fleet of diesel and few service vehicles (petrol) and others not specified used the SEB (Appendix D).

The electronic time schedule for accuracy and functionality: Time tables from different sources

were used to establish bus frequencies: internet, postings at the busway station, induction loops and hand census and actual counting of the buses by hand for the time period of 7:00 am and 19:00 pm. The hand-census was performed as it became evident that not all vehicles using the station were detected with the induction loop system.

Fig. III.2-2. LED info screens Used to inform passengers of the incoming bus.

Fig. III.2-3. Induction Loops

integrated into the driveway of the Busway Station. Site of Platform Measurement: based on the waiting location of the attending public and the

availability of electrical power the measurements loci have been determined. Site of Background Measurement: the parking area just in front of the SEB headquarters and the

QLD-Government Printing Office was chosen as the background reference site was the only area were electrical power was available without increasing the risk of entanglement for bystanders and the public (see Fig.III.1-5).

Stage 2 - Actual data collection for further analysis: The technical and manual skills necessary to safely operate the equipment have been acquired during countless laboratory-hours. Detailed handling lists have been generated and are accessible in the Appendix A01 - Setting up the external Plumbing for the EC and the CPC. The set-up of the mobile sampling equipment with which platform measurements were executed can be seen in Fig. III.3-1.


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Particle Sampling Procedures: The air sampling measurements were performed continuously at Woolloongabba Busway station over 7 days (7th, 11th, 12th, 13th, 14th, 17th and 18th of June 2002) for both the street level background and the outbound platform starting at about 7:00 in the morning and ending at about 19:00 on each day. At the outbound platform, measurements were done on the lead side, as well as the end side of the platform for detection in variations of particle concentration distributions. This was done to investigate the possibility of particle loaded hot-spots along the platform itself. In addition, it was found that peak-hour traffic contributed significantly to a spreading effect in spatial passenger distribution as buses rushing in, stopped along the entire section of the platform. Because of poor passenger presence between 07:00 till 09:00 morning, data evaluation was then limited to the time period starting at 10:00 till 18:00 hours.

Fig. III.2-4. Background sample SMPS-spectra With peaks typically below 500 particles/cm3; Left: windy conditions; Right: stagnating conditions.

Fig. III.2-5. Platform sample SMPS-spectra With sharp peaks originating from exhaust fumes; Left: at windy conditions; Right: during stagnating conditions


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III.3. Instrumentation For the practical execution of the particle inventory at the Woolloongabba Busway Station, the following set of instruments have been used (Fig. III.3-1):

• SMPS, TSI Model 3934 consisting of an EC 3071, a CPC 3010 , and an external vacuum pump.

• Dust-Trak model 8520. • P-Trak model 8525. • The Monitor-Sensors weather

station equipped with the short arm anemometer AN2 sensor (wind speed) and the WD2 vain sensor (wind direction).

• Micro-climatical data have been completed with a handheld Testo 610 for temperature and humidity data.

• Noise pollution was recorded with a handheld Sound Level Meter (Quest SLM 2400).

• Laptop with operational software for each of the peripheral instruments.

Fig. III.3-1. Instrumentation used

Instrumentation Performance and Calibration The particle number were measured in real-time with the SMPS system (Scanning Mobility Particle Sizer, TSI, Inc.) which uses an electrical mobility detection technique (EC, model 3071) that charges and classifies the particles to a known charge distribution. Then these particles are characterized according to their ability to traverse an electrical field and counted with a Condensation Particle Counter (CPC, model 3010). Very important for the operation of the SMPS is that it was necessary to select the sheath air flow rates and inlet orifice diameters in which it selected the particle size range of interest to be examined (see more details on the SMPS system in Chapter II, section II.9, p. 48). The instrumentation covers a particle diameter range from 13.3 nm till 805 nm with scanning times set to be 300 seconds (enables accurate measurements in the low concentration range). The samplings conditions of the SMPS were air flow rate of 0.3 L/min. Calibration of the SMPS system for both particle size and delay time was performed in the laboratory using polystyrene latex (PSL) micro-spheres (PSL spheres from Duke Scientific Corp.: Nanosphere Size Standard NIST traceable PSL diameter of 102 nm +/- 3 nm; Cat. No: 3100A, Lot No. 18833). For cross-calibration, two SMPSs (with differing CPC models, 3010 against 3022) were used to compare particle concentration from ambient air. Differences in detection sensitivities in the


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various CPCs have been compensated during evaluation. Finally, calibration charts have been generated and were found to be in line with previously made calibration results229. Total number particle concentration was measured and the data logged in real-time with the PTrak. These measurements are made in units of particles per cubic centimetre (pt/cm3), enlarged under supersaturated conditions and counted via sensitive photometers (the working principles are exactly the same as with the particle counting device of the SMPS system (see Chapter II, section II.9.d – CPC). Due to the simplicity of this device and prior to measurements, calibration was limited to re-zeroing it (using a miniature HEPA filter and after filling the condensation cartridge with isobutyl alcohol). The Dust Trak converts the light reading into a voltage, which is proportional to the amount of light scattered, which in turn is proportional to the volume concentration of the aerosol particles. The Dust Trak’s internal calibration constant is then multiplied by the voltage reading to obtain the mass concentration of the aerosol. A sheath air system isolates the aerosol in the chamber to keep the optics clean for improved reliability and low maintenance. The calibration was routinely made and cross-validated against the lab-based Dust-Trak 8520 (reference model located at the International Laboratory for Air Quality and Health - ILAQH). Two dimensional wind direction and speed were measured with the Monitor Sensors AN2 short arm anemometer and the WD2 vain sensors. According to the manufacturer, there was no need to calibrate wind speed, although wind direction needed to be aligned and “northed” with the help of a compass. Since the architectural design of the platform acted as a resonating body, it was interesting to know the actual acoustic noise level generated during peak and off-peak hours. Sound measurements have been taken using a handheld Sound Level Meter (Quest, model 2400, Fig. III.3-2). Calibration was performed using the acoustic adapter operating at 1kHz / 110dB docked onto the sensor capsule (CG-12). For measurements, the SLM was set to “A-weighted” recordings (corresponds to the physiological hearing pattern in humans) and set to a “slow” response cycle. Measurements were taken in intervals of a second each. The position of the instrument was chosen in such a way to match the of a seated passenger’s ear at the leading end of the platform; i.e. one meter above ground level and four meters away from the driveway. Background measurements have been taken at street level (same site as for SMPS background data collection) matching the height of a medium sized person’s ear in standing position.

Fig. III.3-2. Sound Level Meter,

Model 2400 and stop watch.

229 measurements performed by Dr. Wiegand (2002), QUT, Brisbane, Australia


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III.4. Exposure Assessment Model a. Exposure Particle Concentration Calculation Personal exposure can be calculated as the product of pollutant concentration and the time spent in a specific microenvironment, which is defined as a confined space where pollutant concentrations are assumed to be uniform. Exposure itself is defined as the average concentration of a toxin multiplied by the time an individual is exposed to that concentration; it is based on the equation EQ. II.14-1 (p.83):

E = t⋅c [particles⋅min/cm3] t, time exposed to particle load c, particle concentration

[min] [pt/cm3]

As the measurements in this particular case deals with sub-micrometer particles, exposure can be either calculated in terms of particles of pollutant present in a unit volume or by referring the total volume of pollutants in the very same unit volume. The concept proposed in the presented study deals with the determination of particle concentration in a unit volume. To speed up the exposure calculation all the relevant data are included into a mathematical routine that is imbedded in an exposure model (Fig. III.4-1.). Rapid and easy evaluation of the pollutant concentration and subsequently exposure situation to passengers on the platform is best done with this approach. It represents a valuable tool in order to assess real values by providing sufficient information about the necessity to develop an exposure model. As this model has been realized and programmed as a script package, the detailed flow chart of the script routine is located in Appendix B - Flow Chart for Exposure Assessment.

• based on waiting time of passenger, choose appropriate SMPS files, calculate the maximum particle concentration using the 95% confidence intervals.

• calculate dMP, dA, dV, and statistics of each SMPS file used, the average distribution & statistics of used SMPS files.

• calculate background data from the street level measurements and determine minimal concentration distribution by using the 95% confidence intervals.

• deduct background data and calculate absolute concentration and multiply with waiting time to obtain exposure.

Fig. III.4-1. Simplified flow Chart for Exposure Assessment

As mentioned previously, the low density of waiting individuals at the inbound platform, made exposure evaluation on that side of the Woolloongabba busway station obsolete (see Fig. III.2-1). In fact, this was the main reason why only the outbound platform with passengers densities outnumbering those of the inbound platform by a factor of 100 was chosen. In addition, it was found that the outbound platform where exposure assessment was calculated, was only relevant after the morning rush hour (simply due to the fact that only few people waited for a bus at this particular time of the day heading out into the suburbs). Even though it would be possible to calculate the exposure for each passenger registered during the census, it is not feasible to assign each waiting passenger the same pollutant concentration as microclimatic data may well be different in all of the three spatial dimensions; e.g. a sitting


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passenger will experience a different exposition than a nervous passenger walking up and down the platform. Likewise, a smoking (although prohibited on the platforms) passenger sitting in the center-end position cannot be assigned the same exposition as another passenger standing at the far outer edge of the platform boundary. Furthermore, personal constituents, like total lung capacity, breathing rates, and other physiological parameters vary widely, making exposure calculation based on an individual level an almost impossible task as many more sets of data relating to these issues are required for that purpose. Thus, for the sake of simplicity, it is more applicable to operate with averages. Working with average values, though does somehow flatten peak exposure values frequently present in peak hours, it is regarded sufficiently accurate enough for the preliminary data analysis.

=>

In this aspect, the basic tool as such involves the simple and frequently used mathematical routine known as averaging. In particular, it generates an average concentration for a given time window that a person spent on the platform. This averaged concentration is then multiplied by the time this passenger was present at the platform. Such measurements ultimately will convert the randomly fluctuating pollutant concentration into a time-average similar to the schematics shown in Fig. III.4-2.

Fig. III.4-2. Peak versus average concentration Averaging of multiple single peak pollutant measurements to obtain an averaged pollutant concentration for a given time window.

Total exposure, which is made of the averaged pollutant concentration (background related concentration and bus related concentration) can be calculated according to equation Eq. III.14-1, bus related exposure; on the other hand, it must be treated differently in that the background pollutant concentration must be subtracted from the concentration levels measured on the platform. Finally, the products of time multiplied by concentration are grouped together to generate a histogram, showing exposure values for each sampled day. Such parameters such as time averages of waiting passengers, concentration averages for such a waiting period, that are ultimately plotted in several diagrams showing exposure conditions over the hours of a day (10:00 to 19:00). b. Modelling of Exposure Concentrations This section describes the structure and design of the MicroCal Origin Scripts used for the calculation of the exposures study in the Woolloongabba busway station230. Exposure calculation is limited to the hours after 10:00 to 19:00 hours. This restriction has been made as the preliminary data analysis (mentioned in the previous section) has shown that there were hardly any passengers waiting at the outbound platform at times between 07:00 to 10:00 hours, and bus frequencies for that given time period in the outbound direction are significantly lower than those for the inbound services of this busway station. Based on the previously mentioned concept, the pool of 1-day passenger waiting time data was applied onto all of the 7 days where SMPS platform-measurements have been executed. As passengers have varying waiting times that do not necessarily match scanning times of the

230 script programs written by Dr. Aaron Wiegand


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SMPS system (preset to 5 mins), the following procedure was applied to assign concentrations scans:

An individual waiting for about 3 mins at the platform is already considered to be exposed to aerosol pollutants on the way down to the waiting area. Therefore, the scan prior to the arrival of the passenger, the follow-up scan after his departure, and the scan while physically at the platform (when waiting time exceeded the 5 min interval) were used to calculate the concentration average for that particular time window. Doing so provides an estimate of the possible exposure that a passenger is subjected to.

Fig. III.4-3. Passengers pool. Number of passengers used per time window

and SMPS platform measurements

Based on the lower number and the lifting of the inversion layer during the early morning hours, some cuts have been made. From the possible 2547 cases to which SMPS platform-data could be assigned to, only 2013 cases were theoretically possible; i.e. deducting the passenger pool from the morning window between 07:00 and 10:00 hours (Figure III.4-3). From the remaining 353 passengers available between 10:00 and 19:00 hours effectively 1992 single cases have been calculated, based on the platform measurements covering 7 working days, this results in 1992 individual cases of exposure calculation. Adding up all size bin (size channels) of those 1992 particle distributions yields the passenger-based exposure. In a final step all those 1992 exposures can be averaged to obtain a single exposure date. In addition, performing these calculations by using both the number and volume concentrations made it possible to determine total particle load and total mass inhaled per unit time spent on the platform. c. General Procedure for a Model to calculate Exposure Particle Concentrations As passenger census data revealed that exposure can only take place with a significantly high number of passengers waiting on the outbound platform, the actual exposure calculation was limited to a daily time window between 10:00 and 19:00 hours. Based on each individual passengers time window spent waiting on the busway platform, the exposure concentration for each individual passenger was determined; i.e. 1992 single cases had to be correlated with the time correlated SMPS particle profiles. In order to assign the worst case bus emissions per passenger waiting on the platform the following procedure was used: Step 1: To do so, it is necessary to determine the time intervals of each individual spent on the platform. The data of passenger No. 67 are used to illustrate this procedure:

• passenger number 67 arrived at 11:11 hours and was waiting 17mins for a particular bus (see Appendix C, Fig. C-8. Passengers Census);


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• each SMPS scan required 5 mins to cover the entire particle diameter spectrum; thus 4 such SMPS profiles, correlating with the arrival time of this passenger, constitute the core data; i.e. 20min;

• assuming that this particular person was confronted with a residual particle load already present at the platform (generated by buses prior to the person’s arrival), an extra SMPS scan was added prior and after the core scans to account for this fact, requiring a total of 6 SMPS scans for this particular case.

Step 2: Exhaust fumes due to buses are calculated based on the difference of background and platform measurements. In order to determine the particle spectrum caused by buses it is necessary to:

• calculate the average concentration distribution for these 6 SMPS scans covering this 30 mins time interval is then calculated. Additionally, the maximum 95% confidence interval of the average distribution is determined;

• likewise, the interpolated background particle concentration (street level) for the same time window was used;

• the particle load due to buses can be calculated by subtracting the averaged background data concentration from the averaged platform data.

Figure III.4-4 summarizes these steps by highlighting the particle size spectra of the background (yellow), the platform (red), and the passenger related exposure concentration (green).

Fig. III.4-4. Calculation of Bus fumes. Top: background data, Center: platform data

(background & bus fumes); Bottom: particle load due to buses.

Exposure Assessment 103 Chapter IV

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CHAPTER IV

Experimental Results and Discussions


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IV. Introduction The conduction of a preliminary survey at the busway station proved to be of crucial importance for the practical design of particle measurements at the Woolloongabba busway station. It paved the way for suitable site location with regards to platform and background data collection along with micro-climatical measurements, bus census, and passenger collection utilizing the on-site video surveillance camera system. During the platform measurements, it was found that influencing factors such as wind speed and direction influencing propagation, dispersion, or entrapment of the exhaust related particle load induced a level of complexity into the already overflooded pool of data, that it was quickly realized that such information would inflate the scope of this project to an extent that would be unmanageable with the evaluative tools utilized so far. Consequently, evaluation of the resulting pool of data in combination with the development of an appropriate exposure model (realized with computational software) proved critical for the management of the huge amount of information gathered. This automated series of calculations (scripts) were created to match passengers’ waiting time data with the particle size data obtained from the SMPS system. Data from the Dust-Track, and P-Track were not taken into consideration because those instruments were used only for 2-3 days. Besides the poor statistical significance for the calculation of exposure concentration, these instruments have shown that verification of the detected particle load is within credible limits. Furthermore, micro-climate data have only been utilized to verify assumptions regarding wind patterns within the busway canyon, and therefore, have been likewise excluded in the exposure model. As an extra feature, the interpretation of sound data collected during the registration of the particle inventory aided in the final interpretation and overall recommendations. IV.1. Bus Timetables Data Collection Information collected from the City Brisbane Council that is in charge of the transportation system showed that diesel buses were introduced since 1982 till the present, these buses are one of the oldest buses fleet from the entire Australia; therefore, they are mostly powered by diesel which makes the most exhaust pollution in this city. Only recently CNG buses were introduced since 2000 till the present time; however, there will be another 40 more buses coming into the city in the near future (see. Table IV.1-1.).

Table IV.1-1. Type of Buses in the City of Brisbane (as of year 2002)231

Into Service Fleet Code Number in Service CNG September 2000 – Present SG 80 (40 will be added in the near future)232 Diesel January 1997 – January 1999 VLN 132 Diesel December 1996 – October 1997 MMN 20 Diesel December 1991 – January 1994 VBA 132 Diesel August 1990 – October 1991 VA 13 Diesel October 1986 – November 1990 VB 166 Diesel February 1982 – December 1990 MG 12 Diesel February 1982 – May 1986 M 180

totaling 655 Diesel buses and 80 CNG Buses in the City of Brisbane.

231 Mr. Graham Weston, Senior Adviser Fleet Management, Brisbane City Council. 232 http://www.scania.com/about/news/press_releases/press_9857.asp


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The frequencies of bus services were collected utilizing four different sources: manual census, internet schedules, busway station timetables, and real-time data of induction loops. Appendix D-Table D1 shows that the data collection by the Manual Census return the highest frequency number. In comparison with the other methods, it was found that this was the most accurate way to document the on-site situation for various reasons (irregularly updated internet schedules, static platform timetables, and incomplete detection by the in-road induction loops). As a result, the manual census returned 1132 buses in comparison with 994 detected buses by the induction loops. Both internet and platform timetables returned 849 buses. Altogether a shortfall of buses in the range of 10-20 % with respect to the manual census. The manual census had an additional advantages as it provided extra information about the type of fuel used by the buses, stopping and pick up location, time of arrival and departure and total waiting time at the busway station besides providing information about the number of service vehicles utilizing the driveway along the busway. (see box plots from Fig. IV.1-1). As with the manual census bus frequencies were registered using the lowest possible time resolution in minutes, the induction loops census proved to be better in time resolution as data were obtained in minutes and seconds. Fortunately, induction loops data have been kindly provided by the Centre of Operations at the Woolloongabba Busway Station. As mentioned before, the discrepancies in frequencies between manual and induction loops data are mainly the result of the inexistence of induction tags on some vehicles or misalignment of some tags which were not recognizable by the detectors. In few cases same buses were registered twice by one and the same detector resulting in double readings at the printout.

Fig. IV.1-1. Comparison of Buses (per hour)

The registered device could be damaged or in need of repairs, the same bus entered the busway from one platform and leaving at the other platform, and other possible reasons. The Internet Bus schedule and the Schedule at the Station showed same total number of buses that are scheduled to arrive and depart the station, but they do not show the extra buses that might run at the busway and are not picking up any passengers, type of fuel used, time of arrival and departure and total waiting time at the station and other pertinent information that the Manual Census and the Induction Loops provided. a. Outbound / Inbound Platforms – Manual Census Looking at the Manual Census collection data, it was noted that the outbound platform returned a total of 463 buses versus only 354 buses counted at the inbound platform (see Fig. IV.1-2). Moreover, the Manual Census also shows that there were more diesel buses registered than CNG or Petrol fuels (see Fig. IV.1-3). The diurnal frequencies of buses is shown figure IV.1-2 and make it quite obvious that diurnal fluctuations are based on the travel patterns of the passengers itself; i.e. lower requirements during the mid-day hours, while higher frequencies


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during peak hours (morning and late afternoon). More frequency charts are listed in Appendix D - Results on Buses.

Fig. IV.1-2. Histogram of bus frequencies in 60 minutes intervals

Fig. IV.1-3. Fuel type of vehicles.

Comparison of hourly bus frequencies based on the fuel utilized

b. Passengers

Passengers’ data from the video tape recordings were collected and kindly provided by the Centre of Operations from the Woolloongabba Busway Station. The time-consuming process of collecting arrival time, departure time of selected passengers could be performed in the warmth and comfort at QUT’s library-owned video terminals. The reason why only selected passengers where chosen is simply rooted in the fact that some passengers were walking out of the camera angle during the waiting period, while at other times crowds of passengers covered those waiting in the background, rendering registration extra difficult. Therefore, passenger data collection as shown in Appendix D, Table D4, reflect a somewhat distorted picture as those few people waiting in the morning and the evening periods could be counted exactly, while those at peak hours (especially in the early afternoon) only a selected number clearly distinguishable could be identified exactly. In combination with the low number of outbound buses during the time window of 07:00 till 10:00, actual data evaluation of the on the platform particle load did not start until 10:00 in the morning. Passenger census also had shown that those 185 passengers per day at the inbound platform were far outnumbered by the 447-plus counted at the outbound platform, restricting particle measurements onto the outbound platform. Passenger census also revealed another fact, that most of the people were waiting at the leading end of the platform during off-peak hours, while spreading out to the center-most section during peak-hours.

Fig. IV.1-4. Histogram of passenger waiting time

at the outbound platform.


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Significantly few passengers were located at the tailing end of the platform. One reason why most passengers were crowded at the leading end of the platform, was found in the impatience of bus drivers when dropping off and picking up passengers at the busway station; i.e. many passengers missed their service, as they were waiting within a section of the platform not rapidly accessible for the brief stop – especially during off-peak hours. This observation was not observable during peak-hours as the more frequently running buses queued up along the platform – see Appendix D, Fig. D1 for increased bus frequencies. The passengers’ waiting time at the outbound platform revealed an average period of 4.7 minutes spent on the platform (see Fig IV.1-4. indicated in red). Only few cases returned waiting periods below 1 minute, while on the other extreme, some unfortunate passengers waited up to 17 minutes for their appropriate service. IV.2. Exposure Assessment Analysis The most common approach to monitoring diesel exhaust has been to select a surrogate measure of exposure that is representative of the exhaust as a whole. Consequently, ambient diesel exhaust PM concentrations are often used by researchers to represent the public’s exposure to whole diesel exhaust233. As outlined in the previous chapter (section III.4 - Exposure Assessment Model), the combination of passengers and SMPS measurements resulted in 1992 individual exposure cases. Incorporated into the software routine realized with the script routines in MicroCal Origin the model returned the following exposure data: Particle number exposure shows an average value of 2.61⋅104 particles per cm3 within those 4.7 minutes present on the platform. For individual cases, passengers who waited longer, expose themselves to a greater extent than people falling below this average time frame. Fig IV.2-1 on the right reveals an increase in particle concentration during the morning rush hours and during the late afternoon rush hour. The depression during the midday hours are mainly due to the low frequencies in buses for these time window (Fig IV.1-2). It should be mentioned though that during 14:00 to 17:00 hour in addition to adult commuters having terminated their working shift, the platform is frequented by school children from the nearby high school heading home. In fact, it is this period of the day when the platform is most frequented by the traveling public, a conclusion also supported by the varying steepness of the superimposed morning and late-afternoon rush hour trend lines.

Fig. IV.2-1. Diurnal particle concentration for passengers waiting at the outbound platform.

233 http://www.oehha.ca.gov/air/toxic_contaminants/html/Diesel%20Exhaust.htm, p.9.


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Fig IV.2-2, displays a different aspect of particle concentration versus waiting time of passengers on that platform. The two peaks of particle number concentrations C1 and C2 (marked in red) are independent from the time that the passengers wait. These two concentrations peaks are probably the results of microclimatic changes during SMPS measurements and confirm the assumptions made earlier about the busway canyon and the trapping effect it has on the generated bus-exhaust particles in certain wind patterns favor the accumulation rather than dilution of those exhaust particles. Figure IV.2-3 on the other hand has a similar representation as seen with figure IV.2-1. The difference between these two is that the latter highlights volumetric (mass) concentration of the particle load generated by the Brisbane City bus fleet. Although it revealed a similar trend as that one observed in the former figure, it must be kept in mind that volumetric concentrations are proportional to the cube of particle diameter and thus are essential for dose-response relationships, or the effect of particles upon the human body. Based on the diurnal particle load present on the platform, it can be said that the dose is heaviest during the late afternoon hours when bus-traffic heading out to the suburbs stretches over a time frame of more than 3 hours compared to the 2 hour morning rush (Fig IV.1-2). This prediction is also supported by the red trend lines superimposed onto the volumetric particle concentration (morning rush hour slope is steeper than late afternoon rush hour slope). Based on the volumetric particle concentration and the passenger waiting time, the resulting exposure can be calculated (Fig IV.2-4.). As expected, the volumetric exposure were most pronounced during the early morning but more so during late afternoon hours of the day, (emphasized as superimposed red circles). The numeric particle concentration is not shown here.

Fig. IV.2-2. Particle number concentration versus passengers’ waiting time.

Fig. IV.2-3. Volumetric particle concentration registered at the outbound platform.

Fig. IV.2-4. Volumetric particle exposure at the outbound platform vs. passengers’ waiting time.


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The maximum numeric and volumetric particle exposure can also be plotted differently. Fig IV.2-5 shows both distributions as a function of cases versus occurrence. It is this representation which makes it most obvious how the cubic relationship in the volumetric predominates the numeric (one-dimensional) exposure on the waiting public. Within the numeric domain, 10, 40, 50, and 60% are bus related, while in the volumetric domain bus related exposure shifts to the 10, 50, 60, and 70% region. Together, the histographic data ranging from 20 to 100 % exhaust fumes originating from buses far outnumber the initial 10 % peak. After that sudden drop of exposure, it was reversed again. This dependency seemed plausible when considering the fact that only a few passengers were lucky enough to catch their service just in time as they arrived at the platform, or coincided with the few occasions with favorable atmospheric conditions. Based on their respective waiting time on the platform, the majority of passengers though were exposed to exhaust fumes to varying degrees. Focusing on the particle concentration versus time spent on the platform, figure IV.2-6 depicts a more or less linear relationship. Although particle concentration became diluted over time with a declining trend over time, particle exposure concentration revealed a rising trend. This conflicting result becomes only obvious when keeping in mind that exposure is a product of concentration by time. Consequently, the longer the passengers waited on the platform, the higher the resulting exposure concentration. Likewise, volumetric concentration and exposure returned a similar result (Fig IV.2-5). As discussed previously, the cubic relationship between particle number and volume (mass) results in a larger exposition in terms of inhaled mass. Usually the dose determines the effect on the human body and is defined as the

Fig. IV.2-5. Histogram of particle number and volumetric concentration.

Fig. IV.2-6. Number of particles concentration and exposure versus waiting time.

Fig. IV.2-7. Volumetric concentration and exposure versus waiting time of passengers

quantity of an active agent taken in or absorbed at any one time, in terms of effect (given in


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mass of substance related to the unit mass of the human body; e.g. ng/kg/BW). Since the dose is directly proportional to the mass and this in turn to the effect of the inhaled exhaust particles, this chart is more significant than the one of the previous figure, Fig. IV.2-6. a. Estimates of an Environmental Exposure The National Institute for Occupational Safety and Health (NIOSH, 1998) estimates that approximately 1.35 million workers are occupationally exposed to DE emissions. Such workers emissions include mine workers, railroad workers, bus and truck drivers, truck and bus maintenance garage workers, loading dock workers, firefighters, heavy equipment operators, and farm workers. Measurable DPM exposure in occupational environments have included respirable particles (<3.5 µm), smoking-corrected respirable particles, combustible inspirable particles, and elemental carbon, among other types234. Referring to the descriptions outlined in chapter III, section 4 (Exposure Assessment Model) which refers to the equation II.14-I, the average exposure values can be estimated:235.

2210−⋅⋅⋅= BRavgVavgtotal QtcE [cm-3]

cVavg, avg. vol. concentration tavg, avg. time on the platform QBR, breathing rate (20L/min)

16.4 ⋅109 [nm3/cm3] 4.7 [min]

20⋅103 [cm3/min] IV.2-1

Therefore the exposure is the product of the average time (4.7 min) spent on the platform multiplied by the breathing rate (e.g. 20L/min for a seated passenger at rest). Multiplying the resulting value with the averaged concentration data obtained from the computed SMPS particle data the resulting particle number exposure and volumetric exposure are computed. In terms of particle number concentration the calculations done with the Microcal Origin script programs returned an averaged number concentration of 13900 particles/cm3. Deducting the background data information, yields the particle load that originated from the buses at the busway station, and amounted to be 6224 particles/cm3; i.e. 36% of the particle load originated from the buses that were driving into the station (Fig. IV.2-8).

Fig. IV.2-8. Exposure due to particle number concentration

The depression observed with passengers 800 to 1200 are the result of a particularly windy day.

234 Health Assessment Document For Diesel Engine Exhaust, (2002),. p.2-107. 235 Health Assessment Document For Diesel Engine Exhaust, (2002), p.2-110.


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In a similar fashion, the volumetric concentration is calculated. Using the averaged volumetric particle load of 16.4⋅109 nm3/cm3 and deducting the background concentration resulted in a 10.3⋅109 nm3/cm3 bus related volumetric load; i.e. 56 % of the volumetric particle load originated from the buses that were driving into the station (Fig. IV.2-9). It is important to note that the volumetric concentration in terms of health effects is much more important than the particle number concentration, since the dose of toxins contained in a particle is dependent on the particle mass / volume. Therefore, the subsequent calculations focus on the volumetric aspect of the inhaled particle load. The algorithm used utilized the following procedures:

Fig. IV.2-9. Exposure due to volumetric concentration The depression observed with passengers 800 to 1200 are the result of a particularly windy day.

i) determination of the inhaled air volume: tAVG (4.7 min) * Rate of Inhalation (20 L/min) results in total inhaled volume of 94 L or 94000 mL (which corresponds to 94000 cm3);

i) multiplied by the average volumetric particle concentration VC-AVG (16.4·109 nm3/cm3): returns the inhaled aerosol-volume VAerosol = 1.54·1015nm3 or 154·10-9cm3;

i) using a standard density value for diesel soot of (ρ = 1 g/cm3) yields the mass associated with the inhaled particle volume mPartivle = 154ng;

i) and relating it to the volumetric contribution on the platform due to buses (Fig. IV.2-9) results in the volumetric exposure originating from the buses running into and out of the busway station of 86.3ng in 4.7mins;

Based on equation IV.2-1, a 4.7 min time interval results in a volumetric particle exposure of 154.16·10-9 cm3. Based on this daily value, the following section extrapolates this data for the working week, month, or even lifetime exposure.


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b. Estimated Lung Deposition The environmental exposure that is equivalent to an occupational lifetime exposure, the fraction of lifetime worker inhalation exposure (calculated as the amount of air breathed at the site multiplied by the typical amount of time spent there) is calculated relative to a 30-year lifetime environmental inhalation exposure. With the cancerogenity of diesel exhaust fumes in mind (based on experiments using the bacterium Salmonella typhimurium 236), the calculation of the lifetime exposure can be considered as a risk factor that should be considered in much more details. In order to make a lifetime exposure predictions, the total exposure (buses and background included) equation IV.2-2 as an extension of the previous formula is used; it takes into account the lung deposition efficiency (η), fraction of bus related contribution (ε) and the time frame beyond the daily exposure:

22/ 10−⋅⋅⋅⋅⋅⋅⋅⋅= LTyrworkdepoBusExhaustBRavgVavgLifetime yrdQtcE ηερ [g⋅yr] IV.2-2

cVavg, avg. volumetric concentration tavg, avg. time on the platform QBR, breathing rate ρexhaust, density of diesel exhaust ε, fraction due to buses ηdepo, lung deposition eff. (<1µm)237 dwork/yr, working days per year yrLT, lifetime working years

16⋅109 [nm3/cm3] 4.7 [min/day]

20⋅103 [cm3/min] 1 [g/cm3]

0.56 [-] 0.542 [-]

260 [day] 30 [yr]

The daily aerosol lung deposition while waiting on the platform is shown in Fig IV.2-10. Based on a breathing rate of 20 L/min it results in a theoretical aerosol mass deposited is in the order of 47 ng for a daily exposure time of 4.7 min.

Fig. IV.2-10. Estimated lung deposition

based on a calculation for daily, weekly, monthly, annual and lifetime exposures.

Based on a working week of 5 days, it results in a exposure of 235 ng. Extrapolating this value to a monthly interval consisting of 20 working days it results in an exposition of 0.9 µg. An annual exposure is calculated to be around 12 µg based on 260 working days. Finally for a 30

236 Enya et al., (1997), p. 2772-2776 237 Pawlak (2003), submitted for publication.


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year long career, a lifetime exposure of about 0.4 mg deposited aerosol mass can be estimated. Due to the variations of inhaled particle diameters (ranging from 13 to 800 nm) and the resulting changes in deposition efficiency within the human lungs an average deposition efficiency of ηdeposition 54.2% was calculated. Micro-environmental exposures of significant concern include in-vehicle exposures such as school buses and passenger cars as well as near highways and in urban canyons. Because DPM from mobile sources is emitted into the breathing zone of humans, this source has a greater potential for human exposure (per kilogram of emissions) compared to combustion particulates emitted from point sources238. Although there is little quantitative information regarding personal exposure to DPM, certain exposure situations are expected to result in higher than average exposures. Those in the more highly exposed categories would generally include people living in urban areas in which diesel delivery trucks, buses ,and garbage trucks frequent the roadways, but also included would be people living near freeways, bus stations, construction sites, train stations, marinas frequented by diesel-powered vessels, and distribution hubs using diesel truck transport. In any situation in which diesel engines operate and a majority of time is spent outdoors, personal exposures to diesel exhaust are expected to exceed average exposures. Because a large but currently undefined portion of DPM is emitted during acceleration, those living and working in the vicinity of sources operating in this transient mode could experience highly elevated levels of DPM239. Studies investigating the chemical and physical changes of DE emissions suggest that there is little or no hygroscopic growth of primary diesel particles. This observations suggests that the small size of DPM particles might be maintained upon inhalation, particularly near the emission source, for example, most of the passengers are waiting at the lead end of the platform where most buses stops, allowing these particles to reach the lower portions of the respiratory track. Increase solubility can increase the removal efficiency of secondary diesel particles compared with their precursor compounds. Secondary aerosols from DE may also exhibit different biological reactivities from the primary particles. For example, there is evidence for nitration of some PAH compounds resulting in the formation of nitroarenes that are often more mutagenic than their precursors240.

238 USEPA (2002), p.2-115. 239 USEPA (2002), p.2-116. 240 USEPA (2002), p.2-124.


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c. Sources of Uncertainty on the Results Analysis of DE Exposure241 The possible potential of human health effects of DE is very persuasive, many uncertainties exist because of the use of assumptions to bridge data and knowledge gaps about human exposures to DE and the general lack of understanding about underlying mechanisms by which DE causes observed toxicities in humans and animals. Some of the possible causes of uncertainties are:

• Available data are not sufficient to provide definitive answers to questions because changes in DE composition over time cannot be confidently quantified, and the relationship between the DE components and the mode(s) of action for DE toxicity is/are unclear.

• The assumptions that health effects observed at high doses may be applicable to low doses, and that toxicologic findings in laboratory animals generally are predictive of human responses. In the absence of a more complete understanding of how DE may cause adverse health effects in humans and laboratory animals, related assumptions (i.e., the presence of a biological threshold for chronic respiratory effects based on cumulative dosage and absence of a threshold for lung cancer stemming from subtle and irreversible effects) are considered reasonable and prudent.

• There is no DE-specific information that provides direct insight to the question of differential humans susceptibility. Given the nature of DE’s non-cancer effects on the respiratory system it would be reasonable, for example, to consider possible vulnerable subgroups to include infants/children, the elderly, or individuals with preexisting health conditions, particularly respiratory conditions.

• An appreciation for differences in exposure is needed only at an order-of-magnitude level for this assessment, one should recognize that individual exposures is a function of both the variable concentrations in the environment and the related breathing and particle retention patterns of the individual. Because of variations in these factors across the population, different subgroups could receive lower or higher exposure to DE. Effects of DE exposure could be additive to or synergistic with concurrent exposures to many other air pollutants. However, the absence of more definitive data demonstrating interactive effects (e.g. potentiation of allergenicity effects, potentiation of DPM toxicity by ambient ozone and NOX) from combined exposures to DE and other pollutants, it is not possible to address this issue. Further research is needed to improve the knowledge and data on DE and potential human health effects, and thereby reduce uncertainties of future assessments of the DE health effects data.

241 USEPA (2002), p.1-6


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IV.3. Meteorological Data Analysis Wind speed has been broadly documented as one of the factors that lead to a reduction in ambient concentrations of pollution emissions. The problem is to quantify to what extent a particular wind speed will change the level of particles. It was noted that wind direction and wind speed were found to be variable on the seven days that the measurements took place and a constant pattern was not easily determined (see Appendix D – Wind Roses and Table IV.3-1). Although an east-west pattern was often obtained, it was concluded that this variable wind patterns influenced the particle load distribution in such a way to prohibit the postulation of simple conclusions. Therefore, the meteorological data presented here have only informative character.

Table IV.3-1. Wind Direction & Speed from the EPA Woolloongabba Weather Station

Date for June 2003

Prevailing Wind Direction

Degrees [°]

Wind Speed [m/s] Wind Speed [%] Calms

07th North East 22.5 3.00-6.00 47 0 1.50-3.00 20 0.50-1.50 10

11th South-West 247.5 1.50-3.00 32 0 0.50-1.50 11

12th West 270 3.00-6.00 32 2 1.50-3.00 16 0.50-1.50 5

13th West 270 6.00-10.0 89 1 3.00-6.00 57 1.50-3.00 10 0.50-1.50 5

14th West-South 270 3.00-6.00 42 1.50-3.00 37 0.50-1.50 5 1

17th West-South 247.5 3.00-6.00 78 0 1.50-3.00 57 0.50-1.50 16

18th South-East 135 3.00-6.00 57 0 1.50-3.00 32


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IV.4. Noise Data Analysis Noise data gathered both on the platform and at the background reference site revealed a distinct picture. The box plots shown in figure IV.4-1, outline platform readings peaking at 94 while the minimum reading was found to be around 60 dB (red elements). Still such a minimal value is considered a very loud signal, especially when considering the background values fluctuating between 50 and 70 dB (green elements). This difference is mainly the result of the architectural design of the Woolloongabba’s busway station. The box plot representation has been chosen here to emphasize the easy visual comparison between the background and the passenger sound exposure. The center half of the data – form the first (Q1) to the third quartile (Q3) – is presented by a rectangle (box ) with the median indicated by a bar. A line extends from the maximum to Q3 and another from Q1 to the minimum. The long line from Q3 to the maximum value is a consequence of the single high noise level (e.g. 85 dBa) and followed by the second largest level. The concept of quartiles reflect the amount of data enclosed by such a partition; i.e. if we take p = 0.5 of the sound intensity data within a sample, the 50th percentile specifies that at least half the observations are equal or smaller and at least half or equal are larger. If p = 0.25, the 25th percentile has a proportion of ¼th of the observations that are the same or smaller with ¾th of that proportion are the same or larger. Here, the convention is adopted: taking an observed value for the sample percentile except when two adjacent values satisfy the definition, in which their average is taken as the percentile. This coincides with the way the median is defined when the sample size is even. When all values in an interval satisfy the definition of a percentile, the particular convention used to locate a point in the interval does not appreciable alter the results in large data sets, except for the determination of extreme percentiles (those before the 5th or after the 95th percentile. The sample quartile here are:

• lower (1st) quartile Q1 which equals the 25th percentile; • center (2nd) quartile Q2 which equals the 50th percentile; • upper (3rd) quartile Q3 which equals the 75th percentile;

Thus the data spread out from the smallest to the largest dBA-reading to cover 100% of the recorded values per set. The middle half of data is the difference of the 3rd from the 1st quartile (Q3-Q1) with the middle (or median) located at the 2nd quartile (Q2). Accordingly, it is easily possible to evaluate the “skewness” of the data sets. A symmetric set of sound intensity readings is a mirror image of the halves cut along Q2 (M). The background data set (ST13b of figure IV.4-1, though not entirely symmetrical) gives a good example, whereas the recorded platform levels (PF14 of the same figure) are definitely skewed toward the 25th percentile (Q1). The horizontal lines terminating the minimal and maximal values of a given sound data set denote the 5th and 95th percentile error bars. The data points below and above those error bars indicate the boundaries of the data sets; i.e. the 0th and 100th percentile data points (isolated breaker); those isolated cross’ enclosed by these boundaries and error bars denote the 1st and 99th percentile data points. The square in the box denotes the mean of the column of the data of each sound spectrum.


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It could be concluded that the dominant material such as glass and concrete contributed greatly to the increase and magnification of the noise level originating from buses at the Woolloongabba busway station.

Fig. IV.4-1. Sound Intensity Data

gathered from the busway station. PF (platform-red) noise intensity levels of the passenger waiting area; ST (street level-green) noise intensity levels of the background.

In addition to the loud acoustic spectrum, particles in motion in this acoustic field (induced to perform oscillation, circulation, or net drift along an acoustic gradient) were believed to experience accelerated particle growth rates – although this assumption requires further research. Such pressure waves increase particle coagulation, or in other cases, tend to enhance droplet evaporation or condensation242. Acoustic coagulation is therefore a phenomenon frequently encountered in (semi) enclosed environments during acceleration mode of departing vehicles with insufficient noise absorption. The frequency spectrum of an accelerating bus changes from 10 Hz (idling mode) to 50 Hz (acceleration mode) which corresponds to 600 to 3000 rpm, with observed sound pressures fluctuating between 80 to 100 dBA. The L-shaped protective roof-like steel construction, designed to protect the passengers from atmospheric elements (against wind, rain and sun) was made of steel and glass. In combination with the concrete outlining the busway canyon further acted as a sound resonating element. Altogether, the materials and the design chosen did efficiently shield traffic noise, but also traped bus generated noise. Glass being a hard material is an excellent sound reflecting surface. Since it is widely used along the SEB, it is probably an insignificant material in open busway stations, but due to the semi-submerged design, it is a very controversial element at this particular busway station (Fig.IV.4-2). It was therefore not surprising that the acoustic factor was another element that contributed negatively to the already problematic situation.

242 Willecke (1993) p.39; Hinds (1999), p.275.


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Remarks: Aggressive acceleration or driving mode is generally considered an adverse factor by generating an extra burden to the already stressed situation. Since measurements were taken at 4 meters away from the driveway, while seated on the bench with the glass panels in the back resulted in increased sound levels than to those passengers waiting in a standing position close to the driveway. As a result of traffic light positioned at either end of the busway station, noise as well as particle exposure are generated by buses driving into the station in groups and not individually.

Fig. IV.4-2. Glass-covered platform design within the semi-submerged busway station.


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Exposure Assessment 121 Chapter V

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CHAPTER V

Recommendations and Final Conclusion


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V.1. Recommendations Common pollutants were traditionally assigned a threshold effect level, and that living organisms could be exposed repeatedly to levels below that threshold. This assumption is often difficult to apply for several reasons243:

• dealing with a multitude of substances at the same time; • individuals have different tolerances and sensitivities to particular substances; • due to the complex chemical composition, a safe threshold level for inhaleable fine

particles may be inapplicable. In acknowledgment of the difficulties in defining threshold levels, agencies such as the WHO are now advocating that air quality management strategies should avoid focusing unduly on arbitrary air quality standards, instead, the focus should be on maintaining air pollution at levels as low as possible244. The WHO guidelines are generally influential in establishing national standards. The following amendment from 1998, the USEPA also established ambient goals for a range of hazardous air pollutants. A greater understanding of health effects and the wider use of the precautionary principle have led to reduced ambient standards for some of these. The priorities of this strategy are to achieve the following:

• reduction emissions of NOX; • reduction in significant localized emission sources of VOCs; • reduction in levels of inhaleable particles; • reduction in levels of visibility-reducing particles; • improved understanding of the sources and the impacts of trace air pollutants (in

particular, materials regarded as hazardous air pollutants) and fine particles in both PM2.5 and PM1 regimes;

• controlling and monitoring SO2 and CO emissions; Accordingly, the following recommendations for future research activities at Woolloongabba Busway Station and beyond should be employed: a. Research Design regarding platform measurements Of real advantage would be the use of two instruments simultaneously. Such a setup enables simultaneous measurement of background and platform concentrations and to detect rapid variations in ambient conditions. During the course of platform measurements it was found that covering two locations by shifting the instrument along the platform is not essential. Measurement should be restricted to one fixed location at the platform where most of the passengers are located. Besides increasing the concentration measurements at this particular location, it avoids unnecessary strain and stress to the sensitive instrumentation during relocation (e.g. optics floated with butanol) and to the operator. It is also recommended to improve passenger census technique to include every single individual.

243 SEQ Regional Air Quality Strategy (1998), pp.10-18. 244 CSE (2001);


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b. Woolloongabba Busway Station As buses contribute such a large fraction in the particle load to which waiting passengers are exposed to, we recommend that buses do not wait at the platform edge for long periods of time with the engine running in idling mode; this is of crucial importance during change of shift in drivers where we observed that the engine kept running for several minutes. The canyon-like setting in which the bus station is embedded, prevents the dilution and dispersion of exhaust gases and particles. Based on our results (see Fig. IV.2-8, p.111 - low particle concentration during windy days) we suggest that the platform architecture be redesigned to improve both natural ventilation (wind) and artificial ventilation (fans or filtered air pumps)245. In addition, plexi-glass and sound absorbing panels should be mounted in-between the driveway and the platform, allowing only 3-4 doorways for dis-/embarking passengers. Such a protective feature would not only limit pollutant load concentration, but would also reduce the acoustic burden for the waiting public. It is suggested to use sound and shock absorbers to reduce the noise generated by the buses. In this regard the front-side mounted panels would make the currently positioned rear panels obsolete. Therefore recommendations can be summarized into three categories:

(i) Short term: improving ventilation by modifying the platform design; e.g. placing transparent panels in-between the buses and the waiting area has two obvious advantages: (i) it significantly reduce noise levels and (i) keeps exhaust fumes more or less confined within the driveway area.

(ii) Medium term: enforcing the use of sulfur-free diesel fuel together with particle traps or introducing already available B20 Bio-Diesel246 and gradually substituting fossil fuel diesel altogether.

(iii) Long term: switching the entire bus-fleet or at least those vehicles that utilize this particular route to CNG or hydrogen technology.

c. Future Experiments It is known that the pollutant concentration of the passenger cabin of vehicles reveal higher concentration levels than outside – especially driving with closed windows and open air intake. Conflicting reports exists as to whether it is better to drive with the windows open or closed in heavy traffic. The air space inside a vehicle can act as a buffer against sudden high levels of pollution, but this would also slow down the beneficial effect of driving from an area with a high level of pollution into an area with a lower level. Therefore, passenger and driver exposition within vehicles which drive along heavily trafficated roads should be included in a follow-up study to evaluate the pollutant concentration inside the vehicles247. For such a purpose, several techniques are available, either using a mobile gas sensor, or collecting samples of vehicular air for subsequent analysis. The mobile gas detector method has the advantage of not only giving the total pollution dose, but also the pollutant concentrations at various landmarks which can be compared with climate, traffic density, queuing time at junctions, and so on. However, it may be necessary to collect gas samples or to use some other

245 Recommendations from Dr. Wiegand (2003), Photochemical Group, QUT, Brisbane, Australia. 246 de Blas (2003), transcript 247 Clifford, et al., (1996), p. 271


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total exposure method to detect volatile organic compounds, and the results may be useful in estimating the average exposure of a typical commuter during the entire trip248. Future investigation should also include particle size distribution measurements during maintenance and servicing intervals of Brisbane City Council diesel buses. Such monitoring tools would enable proper fine tuning of combustion processes to minimize particle formation. d. Technology Reduction of Particle Pollutants Recent studies have shown that traffic congestion around the country is getting worse. One way to solve the problem is to charge those motorists more who utilize the urban road network during rush hours. Such an incentive would help to use public transportation rather than one’s own vehicle. With increasing usage of public means, more buses could be put in service that would compensate overcrowded conditions on buses. Rather than constructing busway systems that burry green land spaces (important in an urban setting) with concrete, bus lanes on existing main roads should be implemented, forcing individual motorist to deal with the left-over and crowded lanes. The more vegetation is available within an urban setting, the more effective pollutant removal from the air; plants also cool the urban environment and limiting the need for air conditioning249. Another policy to follow could be one to stimulate car sharing (car pooling) of charging motorist with an entrance fee when intending to drive into the city center during rush hours. Officials in London observed that charging such motorists brought a 20% reduction in the number of private cars entering the CBD. In general such revenues could be used to improve means of mass transportation. The degrading health effects of fine particle emissions are still not fully perceived by the public. A greater understanding of how particulates damage health is needed before individuals are willing to make effective changes. From a single set of petrol-particles, one could easily find 150 different major chemicals on their surfaces. Diesel exhaust particles reveal a cocktail of up to 450 components. Deepening the understanding about the toxicity of both particles and their composition is vital to accelerate improvements in both the industry building such vehicles as well as the consumers who will be become more responsible in their usage250. Curbing greenhouse gas emissions will improve not only people's health but will also slow global warming guaranteeing survival of biodiversity as we know it. This is of essential importance especially for Australia as this obtains almost all of its energetic requirements from fossil sources. Even CNG is extracted from carbon, utilizing an energy- and CO2-demanding process251. Already now, researchers report in that cutting pollution in New York, Mexico City, Santiago Shanghai, and São Paulo would avoid some 64,000 premature deaths and 65,000 cases of chronic bronchitis by 2020. The researchers looked at the effects of using less-polluting fuels, reducing levels of harmful particulates and reducing traffic by 30 % . Means that can be already achieved using existing technologies252.

248 Clifford et al., (1996), p. 271 249 Planet Ark (2003) 250 NS (2001), online 251 Brandl, Kurier (2003) 252 NS (2002), online


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V.2. Final Conclusions The study of ultrafine particles (particles with diameters less than 100 nm) emitted by diesel buses and the assessment of human exposure in a semi-closed environment such as a busway station proofed to be an important research activity. This study provide good data of exposure concentration of particles to humans and should be considered as a starting project for future projects of this kind. The method used has the advantages of being very simple, comprehensive and cost effective. The concept and evaluation of this study was also very significant because it raised many issues regarding the architectural design of the busway station; here in particular it was found that the particle concentrations and noise pollution data were very high. Both of these issues require further investigation and appropriate quantification in long term studies investigating their impacts on passenger health that must not necessarily be limited to the busway. In another project of this kind it would be interesting to test similar locations with the methodical refinements mentioned previously – here in particular Matter Hill Station and the Queen Street Mall Busway terminal in Brisbane, Australia. The results obtained from this and future studies should impact in the following ways:

• on a local scale, to assist government and city planners in the design and construction of future busway stations in order to minimize stressors such as sound and particle concentration emitted and produced by the buses;

• on a global scale, to assist in the planning of future cities, urban traffic control management, in the planning, implementation, extension, and improvement of the public transportation system, and the enforcement of more research funneled to cleaner air;

• finally, to promote and the used of alternative fuels that are environmentally safe and clean.

Exposure Assessment 127 Appendix A

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Appendix


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Appendix A

Handling & Check Lists


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Appendix A.01 - Setting up the external Plumbing for the EC and the CPC:

External plumbing is required to connect the Impactor and the CPC to the EC. The EC can be operated in two different modes (under- and overpressure). In the under-pressure mode air is drawn through the system by a vacuum pump, whereas in the overpressure mode, the aerosol is forced through the system by an external pressure device. In this application, the EC is operated in the under-pressure mode only. The impactor attached to the aerosol inlet makes sure that particles above the “upper end of the size distribution” or larger particles that can have charges greater than 1 (see appendix - Boltzman distribution) are excluded from the detection range of the classifier. Upon choosing the correct impactor, complete the external plumbing as follows (to avoid leakages, use Nylon or Steel Ferrules, each consisting of a front and back ring, to seal the piping properly): 1. Connect the stainless steel tubing and fitting

configuration to the Monodisperse Aerosol and Excess Air outlets on the classifier.

2. Connect the sheath filter assembly to the Sheath Air inlet of the classifier - tighten the fitting finger tight.

3. Connect the impactor to the Polydisperese Aerosol inlet on the classifier with one modified reducing union - tighten fitting finger tight.

4. Connect the other modified reducing union to the impactor inlet - tighten finger tight

5. Using vinyl tubing, connect the high pressure port on the pressure gauge to the inlet barb on the impactor, and connect the low-pressure port to the outlet barb on the impactor.

6. Connect the brass port connector to the suction connection of the vacuum pump. Tighten.

7. Cut a 76cm length of Polyflo tubing and connect the vacuum pump to the classifier: • Connect one end of the remaining Polyflo tubing

to the brass port attached to the vacuum pump. • Connect the other end of the Polyflo tubing to the

T-shaped valve subassambly. 8. Position the CPC to the left of the classifier. Attach

the reducing union to the inlet tube on the front panel of the CPC. Mount the port connector on the reducing union.

Fig. A.01-1. SMPS External plumbing

9. Cut a 6.4cm length of Tygon tubing. • Connect one end of the tubing to the port connector (attached to the CPC inlet). • Connect the other end to the port connector assembly.

10. Attach the elbow union to the vacuum connector on the back panel of the CPC. • Connect one end of the Polyflo tubing to the elbow union. • Connect the other end of the tubing to the valve subassemly.

11. Using a wrench, tighten all fittings on the SMPS system one full turn. 12. Connect the CPC to the PC port by using the RS232 interface cable. 13. Electrically connect the CPC to the EC bayonet port using the BNC cable (rod voltage control).


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Fig. A.01-2. Instrumental setup for SMPS applications


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Using the EC in Under-pressure Mode (setting the flow rates for Model 3010): In order to obtain reliable data, it is essential to align the equipment. This is best done by following the procedure below (using the 0.0508cm orifice SN 512 and an impactor for particles >1µm and or particles with charges >1): Ticker Task Location

1. As the CPC requires a 3mins warm up phase,

apply power to it. The green status lights (Laser, Temp, and Liquid LEDs) light or flash (the Flow light seems not working). Note: Proceed with the setup-procedure, but do not make any measurements until the Laser, Temp and Flow green indicators light are lit and not-blinking.

2. Manually filling the CPC with butanol: Connect the fill bottle (remember: do not tip the instrument while full - drain the butanol reservoir before moving the instrument - see end of this paper). • Make sure the AUTOFILL function is

switched on (check DIP switches on the rear of the instrument).

Fig. A.01-3. CPC Reference Card

• Connect the fill bottle fitting on the CPC mating fitting, and loosen the bottle cap to provide an air vent.

• Hold the bottle as high above the CPC as you can to allow liquid to flow into the CPC. Press the FILL button on the CPC (for at least 15 secs) while observing attentively the window of the reservoir.

• Once the liquid level appears at the bottom of the reservoir window, stop filling by closing the lid and unplugging the butanol flask from the CPC. Note: The fluid is gravity-fed, so raise very high above the CPC. Do not fill till the marked liquid level.

Fig. A.01-4. Front Panel of the CPC 3010

3. Check the following settings: On the Classifier (EC): • Make sure that the switch of the Meter

Range is in the 10V position, and • Verify that the switch of the Rod Voltage

is at External Program position. • Before turning on the pump, check the

following valves (Open - turn counter-clockwise, to close turn clockwise): Sheat Air valve-fully open. Monodisperse Aerosol valve – fully closed. Excess Air valve – fully open.

On the external plumbing (valve): • External valve subassembly – fully closed.

Fig. A.01-5. Instrumentation Setup


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4. Apply power to the Classifier and the vacuum pump.

Reminder: as the vacuum pump becomes quite hot, make sure no cables are resting on the pump’s housing.

5. Rotate the Meter Selector knob on the Classifier to Excess Air position. Slowly open the External

Air Valve on the subassembly until the desired flowrate is reached; i.e. 3L/min (approx. 2.570V on the V-meter).

6. Rotate the Meter Selector knob on the Classifier to the Monodisperse Aerosol position. Slowly adjust the Monodisperse Aerosol Valve to get the correct reading of 2.009V on the V-meter.

7. Rotate the Meter Selector knob on the Classifier to

Excess Air position. Carefully readjust the External valve on the subassembly until the desired reading of 2.570V is reached.

8. Rotate the Meter Selector knob on the Classifier to

Sheath Air posiiton, and slowly close the Sheath Air Valve to obtain a reading of 2.401V on the V-meter.

Fine-tune the flowrate adjustments: • Rotate the Meter Selector knob on the Classifier to

Excess Air position, and use the external subassembly valve to 2.570V.

• Rotate the meter Selector knob to the Monodisperse Aerosol position and adjust the Monodisperse aerosol valve to the propper reading of 2.009V.

• Rotate the Meter Selector knob on the Sheath Air position. Adjust the Sheath Air Valve to obtain a pressure drop of 6.0cm H20 (using this orifice with the given SN) across the external pressure gauge. If the Sheath Air reading is not within ±20mV i.e. 2.401 ± 0.02V, check (1) the fittings to ensure they are leak-tight, (2) the plumbing configuration, and (3) compare the Sheath Air to the Excess air voltage to

ensure they match the charted values. Fig. A.01-6. Front Panel of EC

After setup, ensure that the desired flowrate is met by verifying this with a bubble flowmeter at the Aerosol Inlet of the EC. • By pushing the manual pump wet the

interior of the bubble chamber. • Connect the Outlet Boss of the Flowmeter

to the polydisperse aerosol inlet of the EC. • Press the manual pump of the flowmeter

once to generate a smooth looking bubble - the reading on the flowmeter should be 5 ± 0.5cm3/s = 300[cm3/min].

• Detach flowmeter and connect the aerosol sampling pipe back to the EC.

Fig. A.01-7. Bubble counter


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At the Computer: Start the TSI scanning software v.3.0 and verify the following software parameters in the main menu bar:

• Click the Hardware Setup scroll down menu to enter the Instrument Setup folder and adjust the following parameters accordingly:

i) Impactor Type: 0.508 cm i) CPC Model: 3010 i) Scantime1): ↑ 300 / ↓60 sec i) Size Range: ◄13.3 / ►805 nm i) DMA Model: 3071 i) DMA flowrate: sheath 3 3 L/min aerosol 0.3 L/min

Fig. A.01-8. SMPS SW Instrumental Setup

• Click the Hardware Setup scroll down menu to enter the Instrument Setup folder and adjust the following parameter accordingly:

i) Com Port Setup COM 1

Fig. A.01-9. SMPS Port Settings

• Click the View scroll down menu to enter the View Setup folder and adjust the following parameter accordingly:

i) Source2): base i) Weighted by2): number i) Display2): Conc. dW/dlg Dp i) Verify Size limits ◄13.3 / ►805 nm i) Channel resolution1): 64

Fig. A.01-10. SMPS View Settings

• Click the Run scroll down menu to enter the Run Setup folder and adjust the following parameter accordingly:

i) Number of Scans: 1 i) Number of Samples3): 5 i) Intersample Delay: 0 sec

Fig. A.01-11. SMPS Run Setup

1) for quicker scans modify scantime to 60/45 secs; 2) modify setting according to manual 3) increase number f samples for statistical data acquisition


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• Click the Utilities scroll down menu to enter the Delay Times folder and adjust the following parameter accordingly:

i) Delay Times td = 8sec i) Charge Correction activate ticker

Fig. A.01-12. SMPS Delay Time Utility

• Click the File scroll down menu and

activate the Save Settings option

Fig. A.01-13. SMPS Autosave option

• Click the File scroll down menu and activate the Auto Save Settings option: i) Define path in directory where logged

files should be stored; e.g. c:/docs/W'Gabba/-date-/SMPS/13th.001

i) make sure that file following file type extension is activated (useful for Originv6.0): Both SMPS and DISTFIT

Fig. A.01-14. SMPS Saving options

Start Scan by clicking to the Run scroll down menu and using the RUN option or abort a running scan by using the CANCEL RUN option.

Important: Once the measurement is finished, switch off the EC, vacuum pump and CPC, make sure you drain the CPC's butanol reservoir before transporting it! Draining the CPC:

• Connect the fill bottle. • Loosen the cap on the drain-bottle and hold the bottle below the instrument.

Tip the CPC toward the connector to drain the last drops. Even after draining by gravity, keep in mind that a lot of fluid will remain soaked in the porous saturator block.


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Retrieving data and exporting them as a *.txt file: Click the File scroll down menu and activate the Open option:

i) Load the first file of the data package to be exported.

Click the File scroll down menu and activate the Export option:

i) Click the Add All icon to shift the data package to the Export File List.

i) Make sure that the export format is weighted by Number and Cumulative in units.

i) Activate the Charge Correction ticker. i) Use the Comma option as the export file

delimiter. i) Verify path in directory where logged files

should be stored; e.g. c:/docs/W'Gabba/-date-/SMPS/13th.001.

i) Name export file; e.g. 19th-wed. i) click the Export icon to start procedure

Fig. A.01-15. SMPS Export Options


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Appendix A.02 – Calibration sheet – calibration performed by GJ on 26-Jul-2001 Classifier 3071 CPC 3010 EC Serial No 485 Impactor Nozzle

∅ [cm] SN Delay time 50% Cut SMPS

Range [nm] Mono Excess Sheath

0.0508 512 8 808 13-835 Flow Rate [L/min] [cm3/min] 0.3 3 3

Output [V] 2.09 2.570 2.401 ∆P [cm H2O] 5.22 6.09 6.00 892 15-960 Flow Rate [L/min] [cm3/min] 0.25 2.5 2.5

Output [V] 1.975 2.514 2.351 ∆P [cm H2O] 3.82 4.63 5.88

0.0457 576 12-670 Flow Rate [L/min] 0.4 4 4

Output [V] 2.065 2.664 2.485 ∆P [cm H2O] 25.41 508 10-560 Flow Rate [L/min] 0.5 5 5

Output [V] 2.109 2.738 2.551 ∆P [cm H2O] 36.60 457 10-485 Flow Rate [L/min] 0.6 6 6

Output [V] 2.144 2.799 2.605 ∆P [cm H2O] 50.28 653 418 9-435 Flow Rate [L/min] 0.7 7 7

Output [V] 2.174 2.855 2.655

∆P [cm H2O] 66.46 Flow Rate [L/min] 0.8 8 8

Output [V] 2.203 2.912 2.706 ∆P [cm H2O] 85.13

*) not measurable with the Gilibrator-2


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Appendix A.03 - Equipment Checklist • Bubble Flow Meter • Computer

Gilibrator-2 (GILAN base & cell module) Toshiba TE 2000 laptop 0.5m rubber hose Power supply w/ main leads 7.5V/0.2A AC charger / adaptor (ARLEC) TSI-SMPS scanning SW (SMPS v3.0) Carry case TSI-DT data analysis SW (TrakPro-v2.12)

Dual port serial interface (PCMCIA) Scanning Mobility Particle Sizer Carry case

Electrostatic classifier (TSI-3071A) Floppy disks Pressure gauge (DWYER) Impactor w/ 0.508mm orifice • Sound Level meter External valve subassembly (TSI - 1035425) Sound Level Meter (Quest Model 2400) Sheath filter (TSI - 1030689) Calibration Generator (CG-12 / 110dB) Stainless fteel unions (TSI - 1601275) Cary case Stainless steel plug (TSI - 1601563) Stainless steel reducing union (TSI - 1601706) • Tools Vinyl sampling tubing (TSI - 3929403) 2 Allen HEX keys (1.5mm & 5mm) Set of nylon seal compression fittings Bags

(small/large ferrule Swagelock) Cable ties (10 x 30cm long) 240V mains cable Chair

Compass Condensation Particle Counter Double sided tapes

Condensation particle counter (TSI - 3010) Duct tape, or Tesa N-butanol bottle (filled) 2 Extension cable (29m / 240V – ye & ora.) Spare N-butanol bottle for draining Earplugs RS232 cable (data connection to PC) Grease BNC cable (rod voltage control of SMPS) Latex gloves (2 pairs) 240V mains cable 2 screwdrivers (5mm ⊕& θ) Vacuum pump (TSI/GE/Gast - 303310) 2 spanner (small) Conductive Polyflo tubing (TSI - 3929436) Stanley knife or scissors

Pencils, pens & marker • DustTrak Aerosol Monitor 2 Pillows

DtustTrak (TSI Model 8520) Posties Flow meter 3 power board (multiple outlets) DQ grade filter for zero calibration Roll towel, wipes, kleenex, Tubing hose (0.5 m) Stopwatch AC adaptor Suspension board (wood) Cable for the computer (25pin to RJ-45) Trolley (foldable) Carry case Wooden slate for writing

Working papers (schedule, timetables, etc) • Monitor Sensor

Data Logger Vane for wind direction sensor Rotating blades for anemometer sensor Mounting frame w/ WD2 & AN2

• McKenzy • P-Trak Mass flow controller GFC 471 (ANALYT)

Ultrafine particle sizer (PT Model 8525) AC adaptor (PS-GFC-240UK-2) AC adaptor AC-mains cable for AC adaptor 6 AA-size batteries Vacuum pump (TSI/GE/Gast - 303310) Alcohol cartridge 25 filter holder tubes for PAH Isopropyl refilling bottle 2 filter holding assemblies (30mm) Zero-check filter 25 200nm waxed teflon filters (30mm) Cable for the computer (25pin to RJ-45) Impactor w/ carry case Cary case


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Appendix A.04 - Butanol, a few words about it253

ISOBUTYL ALCOHOL

*Base chemical name: IsoButyl-Alcohol *Primary name: IsoButyl-Alcohol

*Chemical Formula: C4H10O

Synonyms: N-Butanol, 2-Methyl, Fermentation Butyl Alcohol, 1-Hydroxy-MethylPropane, Isobutanol, IsoPropyl-Carbinol, 2-Methyl-Propanol, 2Methyl-1-Propanol, 2-Methyl-Propan-1-Ol, 2-Methyl-Propyl Alcohol, 1-Propanol, UN 1212 Physical / chemical data: Physical Description according to literature: colorless, oily, refractive liquid Repository: clear, colorless liquid

Molecular Weight

[g/mol] Specific Gravity254

[g/mL H2O] @ 20/4°C Density

[g/mL] @ 15°C Melting Point

[°C] Boiling Point

[°C] @ 4 mm Hg 74.12 0.8018 0.806 -108 107

Solubilities: (n.a. = not available) Water DMSO 95% ethanol Methanol Acetone Toluene Other

solvents Ether

50-100 mg/mL @ 16°C

≥100 mg/mL @ 16°C

≥100 mg/mL @ 16°C

n.a. ≥100 mg/mL @ 16°C

n.a. n.a. miscible

Volatility: Vapor pressure: 10mm Hg @ 21.7°C; 9mm Hg @ 20°C Vapor density255: 2.55 (Air =1) Flammability (Flash Point): This chemical has a flash point of 28°C. It is flammable. Fires involving this material can be controlled with a dry chemical, carbon dioxide, or Halon extinguisher. The autoignition temperature is 426°C. Reactivity: This chemical is incompatible with strong oxidizers Stability: This chemical is stable under normal laboratory conditions. Solutions of this chemical should be stable for 24hours under normal lab conditions.

Other Physical Data: Refractive index: 1.3976 @ 15°C; 1.3960 @ 20°C Mild, non-residual odor

Closed cup flash point: 37.8°C log P oct: 0.65/0.83 Burning rate: 3.5 mm/min

Toxicity: typ. dose mode species amount unit LD 50 (lethal dose w/ 50% killed) Oral Rat 2460 [mg/kg] LC Lo (lowest publ. lethal conc.) Inhalation Rat 8000 [ppm/4hours] LD 50 Intravenous Mouse 609 [mg/kg] LD Lo (lowest publ. lethal dose) Intravenous Cat 18 [mg/kg] LD Lo Oral Rabbit 3750 [mg/kg] LD 50 Skin Rabbit 4240 [mg/kg]

SAX Toxicity Equation: MODERATE via oral, inhalation and dermal routes. An experimental carcinogen and equivocal tumorigenic agent. It causes skin and eye irritation in rabbits. It may be mildly irritating to the skin and mucous membranes. In high concentrations it may be narcotic. Carcinogenicity: Tumorigenic Data (lowest published toxic dose): TDLo: oral-rat 29 gm/kg/71W-I

TDLo: subcutanea-rat 9 gm/kg/78W-I Mutation Data:

Test Lowest Dose Test Lowest Dose Mutations in micro-

organisms (E.coli) 25000 ppm Cytogenetic analysis

(S.cereviciae) 20 mmol/tube

253 NAP 2002; http://ntp-server.niehs.nih.gov/htdocs/CHEM_H&S/NTP_Chem7/Radian78-83-1.html 254 The specific gravity of a substance is a comparison of its density to that of water. 255 Vapor Density is the ratio of density of vapor to the density of air. Substances with vapor density greater

than 1 are heavier than air and tend to accumulate in low or enclosed spaces.


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Teratogenicity (Reproductive Effects Data): Not Available Other Toxicity Data: Skin and Eye Irritation Data:

skin-rabbit 500 mg/24H with moderate irritation effects (MOD) eye-rabbit 2 mg with severe irritation effects (SEV)

Standards and Regulations: DOT-IMO: Flammable or combustible liquid; Label: Flammable liquid Status: "NIOSH Manual of Analytical Methods, 3rd. Ed."; see Method 1401 Reported in EPA TSCA Inventory, 1983 Meets criteria for proposed OSHA Medical Records Rule Handling Procedures: Acute / chronic hazards: This compound emits toxic fumes when heated. It is mildly irritating to the eyes,

skin and mucous membranes. In high concentrations it can be narcotic. Minimum protective clothing: Not available Recommended glove material: The following gloves show the best resistance based on permeation testing.

It is recommended that two different glove types be used for best protection. However, if this chemical makes direct contact with your glove, or if a tear, puncture or hole develops, remove them at once. Suggested Gloves: Nitrile, Neoprene, Viton, Butyl rubber

Recommended respirator: Where the neat test chemical is weighed and diluted, wear a NIOSH- approved half face respirator equipped with an organic vapor/acid gas cartridge (specific for organic vapors, HCl, acid gas and SO2) with a dust/mist filter.

Storage precautions: You should store this chemical under refrigerated temperatures, and keep it away from oxidizing materials. STORE AWAY FROM SOURCES OF IGNITION. Spills & Leakage: If you spill this chemical, FIRST REMOVE ALL SOURCES OF IGNITION. Then, use

absorbent paper to pick up all liquid spill material. Seal the absorbent paper, as well as any of your clothing which may be contaminated, in a vapor- tight plastic bag for eventual disposal. Wash any surfaces you may have contaminated with a soap and water solution. Do not reenter the contaminated area until the Safety Officer (or other responsible person) has verified that the area has been properly cleaned.

Disposal and waste treatment: Not available Emergency Procedures: Skin Contact: IMMEDIATELY flood affected skin with water while removing and isolating all contaminated clothing. Gently wash all affected skin areas thoroughly with soap and water. If symptoms such as redness or irritation develop, IMMEDIATELY call a physician and be prepared to transport the victim to a hospital for treatment. Inhalation: IMMEDIATELY leave the contaminated area; take deep breaths of fresh air. If symptoms (such as wheezing, coughing, shortness of breath, or burning in the mouth, throat, or chest) develop, call a physician and be prepared to transport the victim to a hospital. Provide proper respiratory protection to rescuers entering an unknown atmosphere. Whenever possible, Self-Contained Breathing Apparatus (SCBA) should be used; if not available, use a level of protection greater than or equal to that advised under Respirator Recommendation. Eye Contact: First check the victim for contact lenses and remove if present. Flush victim's eyes with water or normal saline solution for 20 to 30 minutes while simultaneously calling a hospital or poison control center. Do not put any ointments, oils, or medication in the victim's eyes without specific instructions from a physician. IMMEDIATELY transport the victim after flushing eyes to a hospital even if no symptoms (such as redness or irritation) develop. Ingestion: DO NOT INDUCE VOMITING. If the victim is conscious and not convulsing, give 1 or 2 glasses of water to dilute the chemical and IMMEDIATELY call a hospital or poison control center. Be prepared to transport the victim to a hospital if advised by a physician. If the victim is convulsing or unconscious, do not give anything by mouth, ensure that the victim's airway is open and lay the victim on his/her side with the head lower than the body. DO NOT INDUCE VOMITING. IMMEDIATELY transport the victim to a hospital. Symptoms: symptoms of exposure to this compound may include conjunctivitis, eczemoid dermatitis, irritation of the eyes and respiratory tract, headache, dizziness, drowsiness, narcosis (paralysis), polishers' keratitis and dry throat. Fire fighting: FF.1


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Appendix A.05 - Specifications of the used Equipment Specifications of SMPS: The following table gives specifications for the Scanning Mobility Particle Sizer (SMPS system). SMPS Specifications Concentration range Model 3010-S CPC 1 particle to 100⋅E6/cm3 Model 3022/A-S CPCa) 2 particle to 1⋅E9/cm3

Model 3025/A-S CPCa) 20 particle to 100⋅E6/cm3

Particle diameter range Model 3010-S CPC 10 nm to 1µm Model 3022/A-S CPC 7 nm to 1µm Model 3025/A-S CPC 5 nm to 1µm Display Resolution 4, 8, 16, 32 or 64 geometrically equal channels per decade. Flowrate of the EC adjustable

Aerosol Model 3010-S CPC 0,20 to 2.0 L/min Model 3022/A-S CPC 0,20 to 2.0 L/min Model 3025/A-S CPC 0,20 to 2.0 L/min

Sheath 10 times aerosol flow rate (nominal 2-20 L/min) Measurement cycle timeb) total: 60 to 600 seconds, user selectable. Up scan: 30 to 300 seconds. Sampling averaging one sample can average 1 to 999 scans. Power requirements 100 / 115 / 220 / 240 VAC 50-60 Hz, single phase Model 3071 EC 25 W Model 3010-S CPC 40 W Model 3022/A-S CPC 200 W Model 3025/A-S CPC 200 W Aerosol temperature range 10 to 35°C Aerosol pressure range 1.0 ± 0.2 atmospheres (1.013 bar or 101.3⋅E3 ± 20.3⋅E3 Pa);

the EC must not be subjected to pressure above 34.5⋅E3 Pa


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Appendix A.06 - Specifications of EC The following table gives specifications for the Model 3071A Electrostatic Classifier. EC Specifications Mode of operation Electrostatic charging and differential mobility analysis. Particle size range adjustable over 5 nm to 1.0 µm. Particle type solids and nonvolatile liquids Input concentration range 10⋅E9 particles/cm3 maximum at 10 nm (not recommended

for concentrations below 1⋅E-3 particles/cm3) Flowrates

aerosol 0 to 5 L/min, manually adjustable sheath and excess air 0 to 20L/min, manually adjustable air mover 25L/min at 80 cm H2O (not supplied with the instrument

Aerosol temperature range -10 to 50°C (instrument must be within 3°C of aerosol temperature)

Aerosol pressure range 1 ± 0.2 atm (1.013 bar or 101.3⋅E3 ± 20.3⋅E3 Pa) Particle bandwidth

Mobility ± 5% of mean mobility Size ± 5% of mean size for singly charged particles

Charger bipolar, 85Kr, 2mC, halflife 10.4 years (Model 3077 aerosol neutralizer supplied with instrument)

LED display output Aerosol flowrates Sheath air flowrates Excess air flowrates

nominally 1.5 to 4.5 V (calibrated for 0 to 60 L/min)

Analyzer voltage 0 to 11 V (corresponds to 0 to 11kV in the DMA) Analyzer voltage adjustments

Manual adjust analyzer voltage knob Computer controlled use model 390077 interface box or TSI condensation

particle Counter (CPC) for computer controlled adjustment of analyzer voltage through RS 232 port.

Calibration traceable to monodisperse aerosol calibration at 1 atm (1.013 bar or 101.3⋅E3 Pa) at 20°C.

Dimensions 35 cm x 22 cm x 62 cm (LWH) Weight 20 kg Environmental conditions indoor use, altitude up to 2000m, ambient humidity 0-90%

relative humidity - noncondensing. Power requirements 240 VAC / 0.2 A 25 W max. 50-60 Hz at any voltage Fuse requirements 230 / 240 V ~ T 0.25A, SB / 250V


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Appendix A.07 - Specifications of CPC 3010 The following table gives specifications for the Condensation Particle Counter (CPC Model 3010). CPC Specifications Sample flowrate 1.0 L/min ± 10% Outlet (total) flowrate 2.0 L/min Vacuum source 450 mm Hg minimum Particle size range:

minimum detectable particle 50% of 0.01 µm particles maximum detectable particle >3 µm

Particle concentration range 0.1⋅E-3 to 10⋅E3 particles/cm3 with <10% coincidence at 10⋅E3 particles/cm3

Background count <0.1⋅E-3 particle/cm3 Working fluid Reagent-grade n-butyl alcohol Reservoir capacity 350mL (7 day supply at 22°C) Light source 50 mW, 780 nm laser diode Signal-to-noise ratio 20:1 minimum Digital output (note: not compatible with TSI Models 370x or 71xx

multiplexer or processors) square pulse 5 V, 500 ± 100 ns 16 bit analog output 0-10 V full scale (0-11 V under HOST control) Serial Communications I/O Pinouts compatible with standard 9-pin IBM-AT style RS-

232 cables and interfaces Power 100/120/220/240 VAC at 50/60 Hz, 25W max. Dimensions 19 x 22 x 19 cm (LWH) Weight 5.5 kg Ambient temperature range 10 to 35°C Ambient humidity range 0 to 90% relative humidity


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Appendix A.08 - Specifications of CPC 3022A The following table gives specifications for the Condensation Particle Counter (CPC Model 3022A). CPC Specifications Minimum particle size 50% detection at 7 nm 90% detection at 15 nm Flowrate

Sensor 0.3 ± 0.015 L/min Inlet, high flow 1.5 ± 0.15 L/min Inlet, low flow 0.3 ± 0.015 L/min Sheath 10 times aerosol flow rate (nominal 2-20 L/min)

Working fluid n-butyl alcohol saturation temperature 35°C ± 0.3°C condenser temperature 10°C ± 0.3°C optics temperature 36°C ± 2.0°C

Concentration 0 to 9.99⋅E6 particles/cm3 Accuracy ± 10% up to 500⋅E6/cm3 ± 20% from 500⋅E6/cm3 to 9.99⋅E6/cm3

Particle-pulse height of the photo-detector ≅ 1.4V typical Laser power ≅ 3 - 5 mW Detector 788nm (red light laser diode) Environmental conditions indoor use, altitude up to 2000m; ambient temperature

range n-butyl alcohol 10-37°C; ambient humidity 0-90% relative humidity - noncondensing.

False background counts 0.01 particle/cm3 Response time < 13 seconds for 95% to concentrations step changes. Dimensions 24 cm x 20 cm x 38 cm (LWH) Weight 11 kg Power requirements 230 / 240 V at 50 / 60 Hz, 200W, 0.6 A max. 25 W max. 50-60 Hz at any voltage Fuse requirements 230 / 240 V ~ T 1A, SB / 250V


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Appendix A.09 - Equipment Used and Specifications of Equipment involved SMPS • Scanning Mobility Particle Sizer

Pressure Gauge 240V Line Cable

TSI - 3934 SMPS Dwyer Instruments Inc

• external Plumbing: Impactor w/ 0.508mm orifice Ext. valve subassembly Sheath Filter Stainless Steel unions Stainless steel plug Stainless steel reducing union Vinyl Tubing Conductive Polyflo Tubing Ferule Nylon seal (compression fittings)

TSI TSI - 1035425 TSI - 1030689 TSI - 1601275 TSI - 1601563 TSI - 1601706 TSI - 3929403 TSI – 3929436 Swagelock (front & back ferule)

• Condensation Particle Counter N-Butanol Tank filled w/ Butanol RS232 Cable (Data Connection to PC) BNC Cable (Voltage Control of SMPS) 240V Line Cable

TSI - 3022A CPC

• Vacuum Pump TSI/GE/Gast - 303310 • IBM compatible PC w/ Monitor

peripherals and associated cables TSI-scanning software

TSI - SMPS-v2

Flow rate Measurements: • Bubble Flow Meter

AC Charger / Adaptor (7.5V/0.2A)

GILLAN - Gilibrator-2 ARLEC

Dust-Track256

TSI 8520 Dust Track Aerosol Monitor

P-Track • Particle Track

TSI 8525 P.Tract Aerosol Monitor

Monitor Sensors Smart Logger: Up to 250 mSmart sensors. 512K or 1Mbyte memory

Tools: • Spanner • Knife • Screwdriver (5mm cross) • Screwdriver (5mm slitt)

256 http://www.tsi.com/shared/ieg/1980198M.pdf


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Appendix A.10 - Setup of PTrak and DustTrak via the Computer Ticker Task Display

Back Panel of the DustTrak 1. Data port 2. Display / keypad lockout switch 3. External power socket 4. Flow adjustment screw 5. Sample inlet cap and port 6. Cyclone holder clip 7. Exhaust port

Fig. A.10-1. Rear side of DT

Preparing the PTrak • Insert 6 AA-size batteries into the BAT-

holder (batteries face PT); doing so will bridge time windows where not AC-power is available (will only work once the DC-connector is removed from the PT).

• Grab the alcohol fill capsule (transparent cylinder) from the storage compartment of the carry case and open it by twisting counter clockwise; fill with Isopropyl alcohol (till fill line). Gently place the Cartridge back into the fill capsule, turn clockwise until locked and allow wick to soak for a few minutes.

• Once soaked, remove Storage Cap from the PTrak and insert the alcohol soaked cartridge into the cartridge cavity (gently drip off excess alc from cartridge before inserting it). Make sure cartridge is snapped into position properly (cartridge handle shows horizontal orientation) and alcohol fill capsule is sealed using the storage cap.

Fig. A.10-2. P-trak with Probe

NOTE: When PT is not in use, always store the alcohol cartridge inside the alcohol fill capsule. Reminder: While in operation, always keep the PT in a horizontal position – do not tilt the instrument (flooding of the optics may occur).

• Attach t he inlet screen assembly to the instrument (make sure the quick-connect fitting is snapped into place).


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1. Apply power to the DT or PT directly from an AC adapter wall outlet.

2. Connect the DT / PT with the Laptop using the computer cable (25pin to RJ-45) with the COM 3 port via the PCMCIA adaptor (use the DB-9 connector closer to the DVD drive).

3. Boot up the Laptop and launch the TrackPro Software.

4. Press ON/OFF to start the DustTrak or PTrak (rear of instrument – requires a 60sec warm up period).

Fig. A.10-3. SW Main Menu

5. Open Options from the scroll down menu in the Main Menu bar and click to the Software Configuration Option to assign the DustTrak or PTrak protocol to the software, or alternatively activate the Auto configuration option.

Fig. A.10-4. Instrument Configuration

6. Communications port Setup: Also under Options activate the Instrument Setup subfolder and open the Communications folder. Using the PCMCIA-Interface card, select COM-port 3 (port B on double serial PCMCIA card; i.e. the slot facing the DVD drive of the Toshiba TE laptop). Select the Baud rate as shown in the scan. Click the Test-button to verify the communication link. Confirm with OK.

Fig. A.10-5. Port Setup

7. Set the Real-time Clock: Also under Options activate the Instrument Setup subfolder and open first the Parameters followed by the Clock folder. As the software retrieves the actual time settings from the DT / PT, adjust the actual time with the laptop's built in computer time using the >> ikon. Click the Send-button to reprogram the DustTrak / PTrak with the modified time and confirm with OK.

Fig. A.10-6. Instrument Clock


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8. Programming the Logging Modes and the Time constant for the DT / PT. Also under Options activate the Instrument Setup subfolder and open first the Parameters followed by the Logging Interval folder. As two computer controlled logging modes are available, program both channels the following way (see figures A.10-7 & 8).

9. Programming the Log Modes (LOG2 and

LOG3). Also under Options activate the Instrument Setup subfolder and open the Logging Protocols

Fig. A.10-7. Time Constant

Fig. A.10-8. Logging

Intervals

• Start date: defines the logging date in which the DT starts logging.

• Start time: defines the DustTrak's / PTrak’s logging start time at the date specified above. Use LOG2 for morning scans and LOG3 for afternoon logs (see right hand scan).

• Log interval: makes the DT / PT to record in 15 sec intervals.

• Test length: duration of sampling in a particular LOG-mode (5 hrs and 45 mins each).

• Number of tests: number of days the DT / PT operates with the protocols specified in each LOG-mode; i.e. 5 days in a row.

Fig. A.10-9. Logging Protocol

• Time b/w tests: this option will not be used here. • Transmit data entry by clicking the Send button and confirm with OK.

10 a) Changing between Logging Modes on the DT: To start with LOG mode 2, • Press the Sampling Mode button on the DT until the LOG 2 mode shows up on the LCD display. • To enable sampling in that mode press the Sample key;

Note: the DT will automatically turn off, once the programmed sampling cycle is finished. To start with LOG mode 3, • Press the Sampling Mode button on the DT until the LOG 2 mode shows up on the LCD display. • To enable sampling in that mode press the Sample key.

Note: the DT will automatically turn off, once the programmed sampling cycle is finished. Reminder: The TrackPro-SW will not communicate with the DT whenever survey sampling or a programmed sampling mode is active. Make sure that no other SW is allocated to the same COM-port.

10. b) Changing between Logging Modes on the PT: To start with LOG mode 2, • Press the ▼ key on the PT until the LOG 1 mode is highlighted. • Use the ► key to switch to LOG 2 or LOG 3 mode. The TrackPro-SW is able to communicate with the PT also during survey sampling or a programmed sampling mode is active.

11. Clearing the Memory (can only be performed on the DT itself – not via the TrackPro SW):

Attention: this procedure will erase all stored data of the DT’s built-in memory! • Change the DT operation mode into 1 of the 3 LOG modes; press the Sampling Mode button

several times (LOG-1,-2, or -3) –the %-memory message will appear on the LCD screen. Press the Clear Memory key until the countdown reaches zero, then release quickly (releasing the key too soon or too late will prevent memory from being cleared) – the display should read 100% memory.


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Calibration of the DustTrak Perform the following adjustments daily before running the instrument

Ticker Task Display

1. Zero Checking: • Put the DT in survey mode by pressing the

Sampling Mode key until the LOG indicators are no longer visible.

• Put a zero filter (e.g. DQ-grade filter) on the aerosol inlet.

• Press and hold the Time Constant key until 10 is displayed, then release.

• Wait for 30 secs for the display values to settle to zero (i.e. 0 mg/m3). If the displayed value deviates by ±0.001mg/m3, re-zero the DT: Fig. A.10-10. Zero-Check

Re-Zeroing (with the filter placed on the aerosol inlet boss): • Put the instrument in survey mode by pressing the Sampling Mode key again. • Wait 30 secs before pressing the Calibrate key and wait for the countdown to display 0, then

release the key - the message "calibrate zero" will be shown - if not repeat procedure. • Press the Sample key and wait for the 60 secs countdown to be finished. • Press the Calibrate key again to leave the "calibrate zero" mode.

2. Zero Checking the PTrak

• Put a zero filter (e.g. HEPA filter) on the aerosol inlet. After a while, the reading should approach zero. If it does not check the following: i) Inlet screen loose (snaps in when attached be mounted properly). i) Inlet screen is missing (not screwed tightly together). i) Inlet screen assembly is missing internal screen / washer. i) Inlet screen assembly is missing O-ring. i) Alcohol cartridge loose or has a broken O-ring. i) Bad HEPA filter

3. Checking the flow rate on the DT: • Insert the flowmeter (even better use the

Gilibrator-2) into the Aerosol inlet boss. The flowrate should read 1.7 L/min. If the reading does not meet this specification, use a screwdriver to adjust it accordingly.

Fig. A.10-11. Flow-rate Check

4. After use of the PT: remove the alcohol cartridge and from the instrument, cover port with storage caps

and store the cartridge inside the alcohol fill capsule.


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Retrieving data from DT and exporting them as a *.txt file: Export data from TrakPro software: 1. Use the Receive option from the

File function in the menu bar or the Receive icon from the toolbar.

2. Select the desired files by clicking onto the

appropriate files (more than one can be selected by simply clicking onto them).

3. Presses the Receive icon to download the stored

data from the DT's logger, and conform with OK. Fig. A.10-12. Downloading Data Menu

4. Define the target directory by double-clicking the folder-symbol of the Directory, and choose the DT-subfolder in the Docs-directory.

5. Select the *.txt file and confirm with OK.

Fig. A.10-13. Data Saving Mode


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Appendix A.11 - Setting up the Hardware of the Monitor Sensor Weather Station

Ticker Task Display

1. Set up the rack of the Monitor Sensor as shown in the scan on the right.

Note: make sure that all required electrical cables are slipped through the metal framework; i.e. power supply and data cables through the center part of the rack into the Monitor Sensor housing box welded onto the center part of the rack. Likewise, feed the anemometer and wind direction sensor cables from the upper construction arm into the housing box and screw together both segments tightly.

2. lace the wind vane and the anemometer blades onto the respective sensors.

3. lace the logger into the housing box and connect both the power supply and the RS-232 to the DataLogger as well as the sensors to one of the 6 sensor ports.

Fig. A.11-1. Frontal view of the DataLogger (left)

Fig. A.11-2. Meteo veins

Short-armed anemometor blades and wind-direction vane

Fig. A.11-3. The automatic weather station

Task

Windows with Desktop open …. 1. View data by opening Hyperterminal

click Start – Programs – Accessoirs – Communications from the Task bar

2. start Hyperterminal application

Fig. A.11-4. Opening Hyperterminal (HT) on the PC


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Opening the Hyperterminal application the first time requires some extra data to be entered, i.e. area code and Click the

Fig. A.11-5. HT settings

Fig. A.11-6. HT settings

3. name session and choose appropriate icon e.g. “W'Gabba” and the

☂ icon and confirm with OK

Fig. A.11-7. Name Session

4. Define Port connection by choosing “Com 3” and press OK

Fig. A.11-8. HT Port

5. Define properties by setting the baud-rate to 9600, the flow control

to Xon/Xoff, and click the Advanced button to deactivate the FIFO buffer

Fig. A.11-9. HT Baude Rate


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6. Hitting any key will activate the main menu

Fig. A.11-10. HT status check

7. View sensor state – hit key 1 …. all sensors attached to the logger should appear …. Hit any key to switch back to the main menu.

Fig. A.11-11. HT Parameter check

8. Download data from Logger • press 3 to start the download procedure • press Enter to accept all sensors • press Enter to start with today’s date • press Enter to stop with today’s date

9. File transfer:

• click the Start option of the file transfer menu• press 1 to capture stored data from logger (or

press ESC to stop download) Fig. A.11-12. HT Data download

10. Export data from Hyperterminal to MS Excel to open, use menu bar Transfer – Capture Text and export as “*.txt” or “*.csv” file and use “z modem with crash recovery” option and save in the following directory: c:/Docs/W’Gabba/-date-/MS/*.txt

Fig. A.11-13. HT Data capturing

11. Once download is complete use the Stop-

command in the Transfer - Capture Text scroll down menu

Fig. A.11-14. HT Stop Data Transfer

12. End Hyperterminal press ESC to abort terminate current Hyperterminal session hit key 9 to exit the current session and close the application


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Appendix A.12 - Working with the SMPS Script Program Package (adapted from the Microcal Origin Script Programs for SMPS Manual, written by Dr. A. Wiegand, 1997): Ticker Task Display

At the Computer: Start the computer and locate the Distfit files that have been exported from the TSI’s SMPS software pack and open the Microcal Origin software v.6.0.

Open the scroll down menu from the main menu bar and:

• Run the Import SMPS DISTFIT File option from the submenu; this prompts the user to select all SMPS files from the source directory.

• Assign the source directory where these files are located and click OK.

Fig. A.12-1. The SMPS Origin Menu

• Use the directory box to change to the desired directory. To select files for analysis, double-click on the desired files displayed in the box on the left hand side, or click the “Add File” button. The selected files will be displayed in the “Files to Open” box on the right hand side. Highlighting them with a single click and clicking on the “Remove” button can deselect files in this box. When all the files for analysis are selected, click the “OK” button.

Fig. A.12-2. File Selection Window

The following files are generated automatically and imported into a worksheet named “Spectra”. For each bin, the midpoint diameters, the surface area, and volume spectra are calculated. The names of the files are displayed in the first column of the spreadsheet. The names of the subsequent columns are Diam#, MPDiam#, Cnt#, Area# and Vol#. The # represents the file number and corresponds to the row in which the filename appears in column Filename. Column “Diam” contains the diameters at which the bin ranges begin and end. Column “MPDiam” contains the midpoint diameters [nm] of each bin. Columns are the count shown as “Cnt” [particles/cm3], surface area “Area” [nm2/cm3], and volume concentrations “Vol” [nm3/cm3].

A second worksheet named “Statistics” is created. The statistics for each spectrum are calculated and placed in the appropriate column. The headers of each column are listed on the right. The scan below shows the spreadsheet of the statistics.

TotCnt = Total Count Concentration TotArea = Total Surface Area Concentration

TotVol = Total Volume Concentration MeanD = Arithmetic Mean Diameter

CMD = Count Median Diameter (= Geometric Count Mean) GSD = Geometric Standard Deviation

SMD = Surface Median Diameter (=Geometric Surface Mean) VMD = Volume Median Diameter (=Geometric Volume Mean)

Fig. A.12-3. The Statistics Spreadsheet


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At this stage it is recommended to save the file. Calculating the average with several measurements of the same sample.

• Run the Average Spectrum File option from the submenu; this prompts the user to select all similar (from the same sample) SMPS files from the source directory.

Fig. A.12-4. The SMPS roll-down menu

Calculate the average spectrum. • Deactivate the Average 99% Spectrum

option and click OK.

Fig. A.12-5. The SMPS spectra option menu (1)

Enter details of plots to be calculated: • Select the options as shown on the right; • Again, deactivate the 99% spectrum option

and click OK. Note: as the program has been ordered to calculate spectra w/o the 99% option it may display several error messages if the 99% Spectrum is not deselected.

Fig. A.12-6. The SMPS option menu (2)

Exposure Assessment 157 Appendix B

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Appendix B

Flow Chart for Exposure Assessment


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Appendix B - Flow Chart for Exposure Assessment

Fig. B-1. The Microcal Origin Script Flowchart

Legend used on the “do_exposure_calcs.txt“

num = total number concentration vol = total volume concentration CI = confidence interval str = street (background)

pf = platform bus = pf - str (due to buses) pcnt = percent expmins = minutes of exposure for this passenger expminsCI = 0.5 = uncertainty in the time (30 seconds)


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Data Analysis Scripts for Exposure Assessment at the Woollongabba Busway station I. start_new.txt: (c:\origin\maricela\start_new.txt)

The Microcal Origin Script procedure starts by creating a new SMPS project. It basically does nothing else than formatting the required worksheets where the calculated data are stored into. The routine commands are follows:

• turn off save flag and close current project • rename worksheet name to "Spectra" • create required columns in worksheet (filename) • assign the first column text status • set column width

II. get_details.txt: (c:\origin\maricela\get_details.txt)

Based on the source data (passenger waiting time and SMPS file allocation that matches time period of waiting passenger on platform), this subroutine allocates the SMPS files that match the passenger waiting time period. This script routine calculates each of the 1992 cases covering the 7 day SMPS measurements executed on the platform; e.g.:

IF (j=1 ) {day=20020607; passenger =16 ; first=3; last=4 ;} These details are taken into consideration the day of the SMPS measurements (i.e. June 07th, 2002 or 20020607), passenger number of that day, first and last SMPS measurements that coincides with the time of the passenger’s arrival and departure times.

III. do_basic_calcs.txt: (c:\origin\maricela\do_basic_calcs.txt)

This set of subroutines creates a master spreadsheet from a series of SMPS disfit files. It does so by first: 1. import. txt: (c:\origin\maricela\import.txt)

it imports the 1st and the last SMPS distfit files for a particular passenger 2. mpdiam.txt: (c:\origin\smps\import\mpdiam.txt)

This routine converts the given lower and upper bin diameter-boundaries to a geometric mid-point diameter or geometric mean diameter (GMD);

1nn ddGMD ++= dn, lower diametric boundary of bin dn+1, upper diametric boundary of bin

In an additional step, this routine also presets the counters required in order to determine the number of size classes per spectrum; i.e. number of rows: c:\origin\smps\misc\rowcount.txt.

3. fix3010pf.txt: (c:\origin\maricela\fix3010pf.txt) and (c:\maricela\cpc_comparison\fix3010.OGW) This script corrects any deviation of the 3010-CPC platform data pool (number data) by using a correction factor derived from comparative measurements obtained from the 3022-CPC. Note257: these factors are the same for both number and volume distributions, but are only applied to the number distributions here, before the calculation of the area and volume distributions. I could equally have multiplied the volume distributions by the factors after the volumes had been calculated, but that adds more calculations.

257 Dr. Wiegand, (2002) QUT, Brisbane, Australia (c:\maricela\cpc_comparison\fix3010.OGW)


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4. area&vol.txt: (c:\origin\smps\import\area&vol.txt) This routine determines the total surface area (S) and total volume (V) of the aerosol for each size class (bin) of every SMPS distribution. Total Surface area was obtained by multiplying the total count (number concentration) of the bin and the mid point diameter of that bin. In general, the surface area is the sum of all the areas of all the shapes that cover the surface of the object. Total Surface area for the bin is calculated as:

nr4S 2 ⋅⋅⋅= π r, particle radius π, circle constant (pi = 3.141592) n, counts per size class

Total Volume of the bin was obtained by multiplying the total count (number concentration) and the mid point particle radius of that bin. The general formula for spherical unit volumes is as follows:

nr34V 3 ⋅⋅⋅= π

r, particle radius π, circle constant (pi = 3.141592) n, counts per size class

5. stat100.txt: (c:\origin\smps\import\stat100.txt) This routine imports and calculates the statistical values that come along with each set of SMPS data: e.g. count mean diameter (NMD), count median diameter (CMD), geometric standard deviation (GSD), surface median diameter (SMD) and volume median diameter (VMD) of each spectrum258 (see chapter II, equations II.10-7, -8, -11, --13). In addition subroutines determine the total count (TotCnt), total area (TotArea), total volume (TotVol), their standard deviations, 99% percentiles, etc. Statistical results are incorporated into the main data sheet.

6. preptime.txt: (c:\origin\maricela\preptime.txt) This script prepares the worksheets to accept “times” data by initializing certain variables that are not identical with the smps version.

7. get_time.txt: (c:\origin\smps\get_time.txt) As time data are not imported with the SMPS-distfit files, this routine extracts and imports the corresponding time data from the raw SMPS (non-Disfit) files.

8. stattime.txt: c:\origin\smps\times\ stattime.txt While this script inserts the time data into the statistics worksheets.

258 Wiegand, (1997), p.17.


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VI. do_avg_calcs.txt: (c:\origin\maricela\do_avg_calcs.txt) This set of subroutines determines the average values for the distributions and calculates the exposure time averages of each day: 1. avg_spec.txt: (c:\origin\smps\average\avg_spec.txt)

This program will calculate the average spectra (see chapter II, equation II.10-3), standard deviations (see chapter II, equation II.10-4) and 95% confidence intervals for each bin of count, area and volume (see equation below)259.

n296.1IC σ

⋅= σ, standard deviation of count, area or volume n, numbers of particles per size class

2. avg_stat.txt: (c:\origin\smps\average\avg_stat.txt) This script works out the statistics of the average spectrum by averaging the stats of all spectra (averages, sums of squares and standard deviations, 95% confidence interval).

V. do_exposure_calcs.txt: (c:\origin\maricela\ do_exposure_calcs.txt)

The exposure calculations are based on the total concentrations calculated per spectrum and time interval of each passenger while at the platform. To do so it is necessary to: 1. fix3010str.TXT: (c:\origin\maricela\ fix3010str.txt)

based on comparative measurements with the 3022-CPC, any deviation of the 3010-CPC platform data pool are corrected via a form factor; then get the background distributions by appending the appropriate background worksheets (c:\origin\maricela\working.ogw) from midday & evening data (str-data); and fill the working spreadsheet with the background data;

2. neg_to_zero_cnt.txt: (c:\origin\maricela\neg_to_zero_cnt.txt) Set negative number concentrations to zero

3. neg_zero_vol.txt: (c:\origin\maricela\neg_to_zero_vol.txt) Set negative volume concentrations to zero

4. exposure_minutes.txt: c:\origin\maricela\ exposure_minutes.txt Minutes of exposure for each passenger endured at the busway station. • Get the number of minutes this passenger was on the platform. • Calculate absolute exposure and exposure due to buses (numbers and volumes) and

write the calculated data to the script window.

259 Johnson & Kuby, (2000), p.356-357.

Exposure Assessment 163 Appendix C

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Appendix C

Schedule of Particle Measurements from the Woolloongabba Busway Station


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Appendix C – Measurement Schedules for the Woollongabba Busway Station

Fig. C-1. 07th June 2003 Schedule of Particle Measurements.



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Fig. C-8. Passengers Census (see attached CD-ROM).


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Exposure Assessment 171 Appendix D

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Appendix D

Results


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Appendix D – Summarized Results D.1 Bus Timetables - Census

Fig. D-1. Frequency of Buses

Fig. D-2. Box Plot

Fig. D-3. Stopping Buses at the Outbound Pl.

Fig. D-4. Box Plots for Stopping Buses at the Outbound

Fig. D-5. Total Number of type of Fuel

Fig. D-6. Box Plots for Type of Fuel


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Induction Loops

Fig. D-7. Frequency of Buses – Induction Loops

Fig. D-8. Statistical Analysis of Total Buses

at Both Platforms [Inbound - Outbound] – Induction Loops.

Fig. D-9. Total Waiting Time of Buses

at the Busway Station – Induction Loops.

Internet

Fig. D-10. Frequency of Buses – Internet.


at Both Platforms [Inbound - Outbound] – Internet.


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Bus Station Timetables

Fig. D-12. Frequency of Buses –Timetables.


at Both Platforms [Inbound - Outbound] – Timetables. Total Bus Comparison

Fig. D-14. Total Number of Bus at the W’Gabba

Busway Station for the Month of May 2002.

Fig. D-15. Statistical Analysis of Bus Census

at the Woolloongabba Busway Station for the Month of May 2002.


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Table D-1. Comparison of Bus Timetables Collection

Time Census Induction Loops Internet Schedule at the Station [Hours] In Out Total In Out Total In Out Total In Out Total 07:00 59 25 84 63 29 92 52 24 76 55 24 79 08:00 68 39 107 58 46 104 45 35 80 50 34 84 09:00 62 50 112 44 48 92 45 35 80 45 33 78 10:00 46 43 89 43 54 97 36 33 69 35 31 66 11:00 43 43 86 30 39 69 33 33 66 31 31 62 12:00 34 37 71 28 36 64 33 33 66 30 31 61 13:00 45 40 85 35 38 73 33 33 66 31 32 63 14:00 42 51 93 32 39 71 34 36 70 32 35 67 15:00 46 69 115 40 53 93 34 48 82 32 47 79 16:00 49 66 115 51 54 105 34 46 80 29 49 78 17:00 40 67 107 28 55 83 24 45 69 24 52 76 18:00 27 41 68 21 30 51 14 31 45 22 34 56

Table D-2. Bus Census for the Outbound Platform from the Woolloongabba Busway Station

Time Platform Location Type of Fuel [Hours] Lead Middle End Total Diesel CNG Petrol Total 07:00:00 14 6 0 20 22 3 0 25 08:00:00 27 9 0 36 35 4 0 39 09:00:00 20 13 6 39 44 5 1 50 10:00:00 40 24 8 72 34 9 0 43 11:00:00 15 10 3 28 33 6 0 39 12:00:00 20 5 2 27 33 3 1 37 13:00:00 23 7 1 31 33 7 0 40 14:00:00 24 12 3 39 40 9 2 51 15:00:00 24 19 14 57 55 12 2 69 16:00:00 27 11 4 42 55 5 6 66 17:00:00 23 16 2 41 56 9 2 67 18:00:00 21 9 1 31 32 9 0 41

Table D-3. Bus Census for the Inbound Platform at the Woollongabba Busway Station

Time Platform Location Fuel Type [HH:00:00] Lead Middle End Total Diesel CNG Petrol Total

07:00:00 27 5 0 32 52 6 1 59 08:00:00 33 9 4 46 61 7 0 68 09:00:00 31 9 2 42 52 10 0 62 10:00:00 24 5 2 31 36 10 0 46 11:00:00 21 2 0 23 38 4 1 43 12:00:00 16 5 1 22 23 10 1 34 13:00:00 30 7 0 37 32 12 1 45 14:00:00 21 7 0 28 34 7 1 42 15:00:00 23 5 1 29 38 7 1 46 16:00:00 18 6 1 25 47 6 1 54 17:00:00 17 5 1 23 31 9 0 40 18:00:00 14 2 0 16 19 8 0 27

Total 275 67 12 354 463 96 7 566


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Table D-4. Passengers Data Collection by Census at the Woolloongabba Busway Station

Time Inbound Platform Outbound Platform

[Hours] Lead Middle End Total Lead Middle End Total 07:00 16 -- -- 16 25 7 0 32 08:00 15 -- -- 15 27 3 0 30 09:00 4 -- -- 4 28 4 0 32 10:00 10 1 -- 11 20 7 3 30 11:00 14 1 -- 15 12 16 0 28 12:00 9 1 -- 10 20 10 0 30 13:00 9 2 -- 11 31 11 2 44 14:00 8 2 -- 10 20 14 0 34 15:00 19 1 -- 20 63 31 11 63 16:00 30 1 -- 31 54 13 0 53 17:00 25 7 1 32 26 18 0 44 18:00 9 1 -- 10 18 9 0 27 Total 168 17 1 185 344 143 16 447

Table D-5. Results of Particle Exposure Concentrations from the Woollongabba Busway Station

With day 3 Std.dev. [%] No day 3 Std.dev. [%]

Average total number concentration from the platform [particles per cm3]

14,089 13,900

Average number concentration due to bus particles [particles per cm3]

5,825 6,224

Per cent of particles due to buses (by number)

34% 22 37% 20

Average total number exposure from the platform [particles per cm3]

63,877 61,911

Average number exposure due to bus particles [particles per cm3]

36,061 41,168

Per cent of exposure due to buses (by number)

34% 22 37% 20

Average total volume concentration from the platform [nm3 per cm3]

1.64⋅1010 1.64⋅1010

Average volume concentratration due to bus particles [nm3 per cm3]

0.93⋅1010 1.03⋅1010

% of particles due to bus (by volume) 50% 23 56% 18

Average total volume exposure from the platform [nm3 per cm3 ⋅ minutes]

7.6⋅1010 7.68⋅1010

Average volume exposure to bus particles [nm3 per cm3 ⋅ minutes]

4.31⋅1010 5.87⋅1010


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Table D-6. Substances in Diesel exhaust listed by CaEPA as Toxic Air Contaminants260

Acetaldehyde Creosol isomers Phosphorus Acrolein Cyanide compounds Aniline Dibutylphthalate Benzene Dioxins and dibenzofurans Beryllium compounds Ethyl benzene

Polycyclic organic matter, including Polycyclic Aromatic Hydrocarbons (PAHs) and their derivatives.

Bis [2-ethylhexyl] phthalate Formaldehyde Propionaldehyde 1,3-butadiene Inorganic lead Selenium Compounds Cadmium Manganese compounds Styrene Chlorine Methanol Toluene Chlorobenzene Naphthalene Xylene isomers and mixtures Chromium compounds Nickel O-xylenes Cobalt compounds Phenol M-xylenes Acetaldehyde Creosol isomers P-xylenes Phosphorus D.2 Passengers

Fig. D-16. Total Number of Passengers

for both Platforms [Inbound – Outbound] - Census

Fig. D-17. Statistical Analysis of Total Passengers for both Platforms [Inbound - Outbound] - Census

Fig. D-18. Location of outbound passengers on the

platform.

Fig. D-19. Buses Stopping in the Outbound Platform.

260 CaEPA, (1998), p.5


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Fig. D-20. Lung Depostion (see attached CD.ROM).


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D.3 Wind Data

Fig. D-21. June 07th, 2002, from 10:00 to 19:00







Exposure Assessment 181 Appendix E

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Appendix E

Brisbane City Council Bus Data Sheet


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Appendix E – Brisbane City Council Bus Data Sheet Contents

Model Fleet Code Scania CNG SG Volvo B10L VLN MAN Mini MMN

Volvo B10M Austral Denning VBA MAN Midi MM

Volvo Arctic VA Volvo B10M QBB (Comeng) VB

MAN Gas MG MAN M

*The whole bus fleet is comprised by 11% CNG and 90 % Diesel buses for the entire Brisbane City

Note: This is a guide only an should not be used for critical information. For specific details contact SAFM. Information gladly provided by Mr. Graham Weston, Senior Adviser Fleet Management, Brisbane City Council, Brisbane, Australia.


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Scania CNG

Fig. E-1. SG bus

Number off 80 Fleet code SG Fleet Nos. 625-705 Into service Sep 2000 - present Passengers Chassis Scania 94UB Total 62 Seated 44 Engine Standing 18 Model OSC9G Type 6 cylinder turbo CNG Dimensions Position rear Length 12.455 m Capacity 8.97 litres Wheel base 6.225 m Power 190 kW @ 2000 rpm Front overhang 2.860 m Torque 970 Nm @ 1300 rpm Rear overhang 3.395 m Height 3.260 m Gearbox Model ZF 5HP500 Mass Speeds 5 GVM 16 tonne Retarder Integral Front axle tare 3.86 tonne Front axle laden 6.0 tonne Body Rear axle tare 7.60 tonne Manufacturer Volgren Rear axle laden 10.0 tonne Frame Aluminium Doors Gliders Top speed 100 kph Entry Flat floor Wheelchair access front Tyres 11R22.5 Kneeling Yes Seats Styleride Fuel capacity 1120 litres Glazing fixed Air conditioning Air International Destination System Mobitec Numeral system Mobitec


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Volvo B10L

Fig. E-2. VLN Bus

Number off 132 Fleet code VLN Fleet Nos. 543-582 Into service Jan 1997 - 1999 Passengers Chassis Volvo B10L Total 67 Seated 41 Engine Standing 26 Model D10HA Type 6 cylinder turbo diesel Dimensions Position Rear offset mounted Length 12 m Capacity 9.6 litres Wheel base 6.0 m Power ~ 180 kW @ 2000 rpm Front overhang 2.51 m Torque ~ 1050 Nm @ 1300 rpm Rear overhang 3.20 m Height 3.3 m Gearbox Model ZF 5HP500 Mass Speeds 5 GVM 16 tonne Retarder integral Front axle tare 3.86 tonne Front axle laden 6.0 tonne Body Rear axle tare 7.60 tonne Manufacturer Austrian Pacific Rear axle laden 10.0 tonne Frame Tubular steel Doors gliders Top speed 100 kph Entry Flat floor Wheelchair access front Tyres 275/ 70 R22.5 Kneeling Yes Seats Saydair cloth Fuel capacity 220 litres Glazing Fixed Air conditioning Air international Destination System Alcatel Numeral system Alcatel


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MAN Mini

Fig. E-3. MMN Bus

Number off 20 Fleet code MMN Fleet Nos. Into service Dec 1996 – Oct 1997 Passengers Chassis MAN 10.155 HOCL Total 44 Seated 26 Engine Standing 18 Model DO824LFL Type 4 cylinder Diesel Dimensions Position Rear mounted vertical Length 8.17 m Capacity 4.58 litres Wheel base 3.86 m Power 114 kW @ 2400 rpm Front overhang 2.035 m Torque 590 Nm @ 1400 rpm Rear overhang 2.275 m Height 3.18 Gearbox Model Alison AT545R Mass Speeds 4 GVM 10.5 tonne Retarder Integral Front axle tare Tonne Front axle laden Tonne Body Rear axle tare Tonne Manufacturer Ansair Rear axle laden Tonne Frame Tubular steel Doors Glider Top speed 80 kph Entry 2 steps Wheelchair access No Tyres 265/70R 19.5 Kneeling No Seats Saydair Fuel capacity 150 litres Glazing Slider Air conditioning Air International Destination System Dot matrix Numeral system 7 segment


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Volvo B10M – Austrial / Denning

Fig. E-4. VBA Bus

Number off 132 Fleet code VBA Fleet Nos. 219 to 350 Into service Dec 1991 – Jan 1994 500 to 542 Passengers Chassis Volvo B10M Mk3 Total 69 Seated 49 Engine Standing 20 Model THD101GC Type 6 cylinder turbo Diesel Dimensions Position Mid mounted Length 12 m Capacity 9.6 litres Wheel base 6.0 m Power 180 kW @ 2200 rpm Front overhang 2.55 m Torque 590 Nm q 1480 rpm Rear overhang 3.20 m Height 3.2 m Gearbox Model ZF 4H500 Mass Speeds 4 GVM 16 tonne Retarder Integral Front axle tare 4.98 tonne Front axle laden 6.0 tonne Body Rear axle tare 5.22 tonne Manufacturer Austral / Denning Rear axle laden 10.0 tonne Frame Tubular steel Doors Glider Top speed 95 kph Entry 2 steps Wheelchair access No Tyres 11 R 2.5 Kneeling No Seats Saydair cloth Fuel capacity 300 litres Glazing Slider/hopper Air conditioning No Destination System Alcatel


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MAN Midi

Fig. E-5. MM Bus

Number off 7 Fleet code MM Fleet Nos. 351 to 357 Into service Aug 1990 – Oct 1991 Passengers Chassis Total 46 Seated 31 Engine Standing 15 Model DO826TOH Type 6 cylinder Diesel Dimensions Position Rear mounted vertical Length 8.25 m Capacity 6.59 litres Wheel base 4.3 m Power 137 kW @ 2600 rpm Front overhang 1.96 m Torque 645 NM @ 12- 1500 rpm Rear overhang 2.52 m Height 2.97 m Gearbox Model ZF 4HP500 Mass Speeds 4 GVM ~11.48 tonne Retarder integral Front axle tare 2.34 tonne Front axle laden Tonne Body Rear axle tare 4.89 tonne Manufacturer Austral SV Rear axle laden Tonne Frame Tubular steel Doors Glider Top speed 89 kph Entry 2 steps Wheelchair access No Tyres 265/70R 19.5 Kneeling No Seats Saydair Fuel capacity 150 litres Glazing slider Air conditioning Air International Destination System Dot Matrix Numeral System Dot Matrix


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Volvo artic

Fig. E-6. VA Bus

Number off 13 Fleet code VA Fleet Nos. 375 - 380 Into service Jun 1985 – Oct 1991 977 – 982, 993 Passengers Chassis Volvo B10M Total 100 Seated 75 Engine Standing 25 Model THD100EC Type 6 cylinder turbo Diesel Dimensions Position Mid mounted Length 18 m Capacity 9.6 litres Wheel base 12.7 m Power 180 kW @ 2200 rpm Front overhang 2.51 m Torque 900 Nm @ 1400 rpm Rear overhang 2.80 m Height 3.3 m Gearbox Model ZF 5HP500 Mass Speeds 5 GVM 22 tonne Retarder integral Front axle tare ~4.8 tonne Front axle laden ~6.3 tonne Body Drive axle laden ~5.7 tonne Manufacturer Austral SV Drive axle laden ~9.0 tonne Frame Tubular steel Rear axle tare ~4.7 tonne Doors Glider Rear axle laden ~6.0 tonne Entry 3 steps Wheelchair access No Top speed 95 kph Kneeling No Seats Saydair cloth Tyres 11 R22.5 Glazing Sliders Air conditioning ND Fuel capacity 450 litres Destination System Blinds


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Volvo B10M –QBB

Fig. E-7. VB Bus

Number off 166 Fleet code VB Fleet Nos. 400 to 486 Into service Oct 1986 – Nov 1990 140 to 218 Passengers Chassis Volvo B10M Total 70 Seated 45 Engine Standing 25 Model THD101GC Type 6 cylinder turbo Diesel Dimensions Position Mid mounted Length 11.28 m Capacity 9.6 litres Wheel base 5.5 m Power 180 kW @ 2200rpm Front overhang 2.58 m Torque 900 Nm @ 1400 rpm Rear overhang 3.2 m Height 3.3 m Gearbox Model ZF 4HP500 Mass Speeds 4 GVM 16 tonne Retarder Integral Front axle tare 5.12 tonne Front axle laden 6.27 tonne Body Rear axle tare 5.14 tonne Manufacturer Comeng / QBB Rear axle laden 8.53 tonne Frame Tubular steel Doors Gliders Top speed 95 kph Entry 2 steps Wheelchair access No Tyres 10.00 R20 Kneeling No Seats Henderson cloth Fuel capacity 300 litres Glazing Sliders Air conditioning No Destination System Manual


Maricela YIP WONG

MAN gas

Fig. E-8. MG Bus

Number off 12 Fleet code MG Fleet Nos. 926 - 927 Into service Feb 1982 – Dec 1990 948 - 954 Passengers Chassis MAN SL 200 Total 75 Seated 42 Engine Standing 33 Model D2566MUH modified Type 6 cylinder CNG Dimensions Position Rear mounted Length 11.05 m Capacity 11.4 litres Wheel base 5.5 m Power ~135 kW @ 2200 rpm Front overhang 2.40 m Torque ~700 Nm @ 1480 rpm Rear overhang 3.05 m Height 3.3 m Gearbox Model Renk Doromat Mass Speeds 3 GVM 16 tonne Retarder Integral Front axle tare Tonne Front axle laden Tonne Body Rear axle tare Tonne Manufacturer Denning Rear axle laden 10.53 tonne Frame Tubular steel Doors Rufus Top speed 90 kph Entry Sloping platform Wheelchair access No Tyres 10.0 R 20 Kneeling No Seats Saydair Fuel capacity 980 litres Glazing Slider Air conditioning Nil Destination System Manual


Maricela YIP WONG

MAN

Fig. E-9. M Bus

Number off 180 Fleet code M Fleet Nos. 10 to 139 Into service Feb 1982 – May 1986 828 to 920 928 to 947 955 to 971 Passengers Chassis MAN SL200 Total 75 Seated 39 Engine Standing 36 Model D2566MUH Type 6 cylinder Diesel Dimensions Position Rear mounted Length 11.05 m Capacity 11.4 litres Wheel base 5.5 m Power 177 kW @ 2200 rpm Front overhang 2.40 m Torque 824 Nm @ 1500 rpm Rear overhang 3.05 m Height 3.3 m Gearbox Model Renk Doromat Mass Speeds 3 GVM 14 tonne Retarder Integral Front axle tare 3.22 tonne Front axle laden 4.62 tonne Body Rear axle tare 6.45 tonne Manufacturer Denning Rear axle laden 9.86 tonne Frame Tubular steel Doors Kacknife Top speed 90 kph Entry Sloping platform Wheelchair access No Tyres 10.00 R 20 Kneeling No Seats Saydair Fuel capacity 300 litres Glazing Slider Numeral system Brose

Exposure Assessment 193 Appendix F

Maricela YIP WONG

Appendix F

Contact Details, Bibliography & Curriculum Vitae


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Appendix F.1 - Contact Details Busway Operations Centre

Mr. Ray Donato, General Manager. e-mail: [email protected] Fax: +661-07-3435-4422; Phone: +661-07-3435-4400; Busway Operations Centre, Brisbane, Australia, Busway Management Unit, Public Transport Management Branch, Public Transport Division, GoPrint Complex, 371 Vulture Street, Woolloongabba, QLD 4102.

Brisbane City Council

Mr. Graham Weston, Senior Adviser Fleet Management, Brisbane City Council. Phone: +661-07-3403-6907

Queensland EPA

Mr. Mike King, Technical Officer Air Services Environmental and Technical Services, Environmental Protection Agency 80 Meiers Rd Indooroopilly Q 4068 Ph: +661-(07) 3896 9268

Appendix F.2 - Literature Chapter II Section II.1. Brisbane Meteorological and Topographical Data

Section II.2. Transportation in the Metropolitan Area of Brisbane. BCC, (2003), Brisbane City Council - Dedicated to a better Brisbane (online edition)

http://www.brisbane.qld.gov.au/transport_parking/index.shtml Botkin, Daniel B., Keller, Edward A., (1997), Environmental Science: Earth as a Living Planet, 2nd Edition, Rand

McNally, Inc. USA. CBoM, Commonwealth Bureau of Meteorology, (2003); Averages for BRISBANE AERO;

http://www.bom.gov.au/climate/averages/tables/cw_040223.shtml Lutgens, Frederick K., Tarbuck, Edward, J., (1998), The Atmosphere, An introduction to Meteorology, 7th

Edition, Prentice Hall, Upper Saddle River, New Jersey 07458, USA. SEB, (2002); Onlie edition of the Facts sheets of the South East Busway Operation Centre, 10th February 2002.

http://www.transport.qld.gov.au/busways/South%20East%20Busway.htm South-East Queensland Regional Air Quality Strategy, (1998) A Strategy for Improving Air Quality in South-East

Queensland. Stoker, H. Stephen, Spencler L. Seager, (1972), Environmental Chemistry - Air and Water Pollution, 1st ed, Scott,

Foresman and Company, Glenview, Illinois, USA. WWW-References & Images: Commonwealth Bureau of Meteorology - Climate Averages for Australian Locations – Brisbane Area

http://www.bom.gov.au/climate/averages/tables/cw_040223.shtml Brisbane Catholic Education, Public Schools, Brisbane Region

http://www.bne.catholic.edu.au/pub/schools/brisbane_region.htm Geography - Brisbane's Key Features, Villanova College (Brisbane, Qld)

http://www.vnc.qld.edu.au/enviro/college/env-ch1e.htm Brisbane Queensland Government, Environmental Council

http://www.brisbane.qld.gov.au/council_at_work/environment/air/clean_air.shtml


Maricela YIP WONG

Queensland Government Transport http://www.transport.qld.gov.au/busways/South%20East%20Busway.htm

Section II.3. Airborne Air Quality Ellenhorn, Matthew J. (1997) Ellenhorn's Medical Toxicology Diagnosis and Treatment of Human Poisoning:

Medical Toxicology CD-Rom / Corporate Technology Ventures South-East Queensland Regional Air Quality Strategy, (1998) A Strategy for Improving Air Quality in South-East

Queensland. Vardoulakis Sotiris, Fisher, Bernard E.A., Pericleous, Koulis, Gonzales-Flesca, Norbert, (2003), Modelling air

quality in street canyons: a review, -Atmospheric Environment, 37:155-182. Section II.4. Street Canyon Bogo, H., Gòmez D.R., Reich, S.L., Negri, R.M., Romàn E.San, (2001) Traffic pollution in a downtown site of

Buenos Aires City, Atmospheric Environment, 35:1717-1727. Chan, Andy T., So, Ellen S.P., Samad, Subash C., (2001) Strategic guidelines for street canyon geometry to

achieve sustainable street air quality, Atmospheric Environment, 35:5681-5691. Chan T.L., Dong, G., Leung, C.W., Cheung, C.S., Hung, W.T., (2002), Validation of a two-dimensional pollutant

dispersion model in an isolated street canyon, Atmospheric Environment, 36:861-872. Huang, Hong, Akutsu, Yoshiaki, Arai, Mitsuru, Tamara, Masamitsu, (2000), A two-dimensional air quality model

in an urban street canyon: evalutation and sensitivity analysis, Atmospheric Environment, 34:689-698. Kim, Jae-Jin, Baik, Jong-Jin, (2003), Effects of inflow turbulence intensity on flow and pollutant dispersion in an

urban street canyon, 91:309-329. Pearlmutter D., Bitan, A., Berliner, P., (1999), Microclimatic analysis of compact urban canyons in a arid zone,

33:4143-4150. Sharma, Prateek, Khare, Mukesh, (2001), Modelling of vehicular exhausts – a review, Transportation Research,

Part D, 6:179-198. Spadaro, Joseph, Rabl, Ari, (2001), Damage costs due to automotive air pollution and the influence of street

canyons, Atmospheric Environement, 35:4763-4775. Uehara, Kiyoshi, Murakami, Shuzo, Oikawa, Susumu, Wakamatsu, Shinji, (2000), Wind tunnel experiemtns and

how thermal stratification affects flow in and above urban street canyons, 34-1553-1562. Väkevä, M., Hämeri, K., Kulmala, M., Lahdes, R., Ruuskanen, J., Laitinen, T., (1999) Street level versus rooftop

concentration of submicron aerosol particles and gaseous pollutants in an urban street canyon, 33:1385-1397.

Vardoulakis, S., Gonzales-Flesca, N., Fisher, B.E.A., (2002), Assessment of traffic-related air pollution in two street canyons in Paris: implication for exposure studies, Atmospheric Environment, 36:1025-1039.

Vardoulakis Sotiris, Fisher, Bernard E.A., Pericleous, Koulis, Gonzales-Flesca, Norbert, (2003), Modelling air quality in street canyons: a review, -Atmospheric Environment, 37:155-182.

Whener, Birgit, Birmili, Wolfram, Knauk, Thomas, Wiedensohler, Alfred, (2002), Particle number size distributions in a street canyon and their transformation into the urban-air background: measurements and a simple model study, Atmospheric Environment, 36:2215-2223.

Section II.5. Origin and Characteristics of Fuel Alternative Fuels Comparison Chart, (2003); These charts compare alternative fuels and include information on

fuel properties, environmental impacts, maintenance issues, fueling information, safety, and more. http://www.eere.energy.gov/cleancities/afdc/pdfs/afv_info.pdf

Clarke A.G., Chen J.M., Pipitsangchang S., Azadi-Bougar G.A. (1996); Vehicular particulate emissions and roadside air pollution; Elsevier Science, Amsterdam – NL

ConococoPhyllips, Material Safety Data Sheet, GTL Semi-Works Diesel Product. http://seweb2.phillips66.com/hes%5CMSDS.nsf/MSDSID/US775323/$file/30017694.pdf

Coutinho, et al, (2000); Measurements and modeling of wax formation in diesel fuels; Fuel, 79:607-616. Ellenhorn, Matthew J. (1997) Ellenhorn's Medical Toxicology Diagnosis and Treatment of Human Poisoning:

Medical Toxicology CD-Rom / Corporate Technology Ventures


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Farleigh A., Kaplan L. (2000); Dangers of Diesel; Public Interest Research Groups, Boston (MA) – USA. Graskow B.R., Kittelson D.B., Abdul-Khalek I.S., (1998); Characterization of Exhaust Particulate Emissions from

a Spark Ignition Engine; SAE Technical Paper Series, Warrendale PA - USA Harris S.J., Maricq M.M. (2001); Signature size distributions for diesel and gasoline engine exhaust particulate

matter; Journal of Aerosol Science, Pergamon; Oxford – UK. Harrison, R.M., Yin, J., (2000), Particulate matter in the atmosphere: which particle properties are important for

its effects on health, The Science of the Total Environment, 249:85-101. Horstman M. Seega B., (2003); Norman Swan’s Health Report, ABC - Radio National, Sydney (NSW) - AUS

http://www.abc.net.au/rn/talks/8.30/helthrpt/stories/s838424.htm Krieger R.K. (1998); Report to the Air Resources Board on the Proposed Identification of Diesel Exhaust as a

Toxic Air Contaminant, Part A, Public Exposure To Sources and Emissions of Diesel Exhaust in California - USA; http://www.arb.ca.gov/toxics/dieseltac/part_a.pdf

Lexikon Verbrennunsmotor- Partikel, Vorschlag TTM, (2000), p5. IANGV, (2000), International association for natural gas vehicles, Emission Report 2000;

http://www.iangv.org/jaytech/default.php?PageID=79 McLean, H.L., Lave, L.B., (2003), Evaluating automobile fuel/propulsion system technologies, Progress in Energy

and Combustion Science, 29:1-69. Madl, Pierre, (2003), Masters Thesis: Instrumental development and Application of a Thermodenuder, University

of Salzburg, Austria. NAS, (2000); Modeling Mobile Source Emissions; National Academy of Sciences, National Academic Press;

Washington D.C. – USA http://books.nap.edu/books/0309070880/html/23.html#pagetop

Pearce F., (1997); Devil in the Diesel; Fiba Canning Inc.; Ontario CDN Ministry of Economic Development, Manatu, Ohanga; Petrol and Diese: Delivery Quality.

http://www.med.govt.nz/ers/oil_pet/fuelquality/resource/resource.pdf WWW-References & Images Environmental Protection Agency, USA.

http://www.epa.gov/otaq/05-autos.htm Nonovations, VEECO European Newsletter No-4, März 2001

http://www.veeco-europe.com/de/pdf/nano4_de.pdf Why is Gasoline Composition Changing?

http://www.faqs.org/faqs/autos/gasoline-faq/part2/section-1.html Section II.6. Sources of Motor Vehicles Emissions National Pollutant Inventory of Australia – Aggregated Emission Data Manuals (2000)

http://www.npi.gov.au/handbooks/aedmanuals/index.html or http://www.npi.gov.au/handbooks/aedmanuals/motorvehicles1-0.pdf

USEPA, (1994) Environmental Protection Agency; USA http://www.epa.gov/otaq/05-autos.htm or http://www.epa.gov/otaq/consumer/05-autos.pdf

Section II.7. Motor Vehicle Pollution AKPF, Arbeitskreis Partikel-Filter-Systemhersteller (2000); Lexikon Verbrennungsmotor Partikel; Diesel

Particulate Filter Manufacturers Task Force; AUT / CH / FRG, http://www.akpf.org/pubs.html Atkins P., Jones L. (1997); Molecules, Matter, and Change; Chemistry 3rd ed, W.H.Freeman & Co. New York –

USA Clarke A.G., Chen J.M., Pipitsangchang S., Azadi-Bougar G.A. (1996); Vehicular particulate emissions and

roadside air pollution; Elsevier Science, Amsterdam – NL. CSE, (2000); Centre For Science and Technology, Dossier - Diesel Fact Sheets.

http://www.cseindia.org/campaign/apc/pdf/factdies.pdf Enya, T.; Suzuki, H.; Watanabe, T.; Hirayama, T.; Hisamatsu, B. (1997); 3-Nitrobenzanthrone, a powerful

bacterial mutagen and suspected carcinogen found in diesel exhaust and airborne particles; Environmental


Maricela YIP WONG

Science and Technology, 31, 2772-2776. Graskow B.R., Kittelson D.B., Abdul-Khalek I.S., (1998); Characterization of Exhaust Particulate Emissions from

a Spark Ignition Engine; SAE Technical Paper Series, Warrendale PA - USA Harris S.J., Maricq M.M. (2001); Signature size distributions for diesel and gasoline engine exhaust particulate

matter; Journal of Aerosol Science, Pergamon; Oxford – UK. Harrison, R.M., Yin, J., (2000), Particulate matter in the atmosphere: which particle properties are important for

its effects on health, The Science of the Total Environment, 249:85-101. HEI (Health Effects Institute) (2002); Research Directions to Improve Estimates of Human Exposure and Risk

from Diesel Exhaust; Boston MA - USA Horstman M. Seega B., (2003); Norman Swan’s Health Report, ABC - Radio National, Sydney (NSW) – AUS

http://www.abc.net.au/rn/talks/8.30/helthrpt/stories/s838424.htm Farleigh A., Kaplan L. (2000); Dangers of Diesel; Public Interest Research Groups, Boston (MA) – USA. IANGV(international association for natural gas vehicles), (2000); Emission Report; Krieger R.K. (1998); Report to the Air Resources Board on the Proposed Identification of Diesel Exhaust as a

Toxic Air Contaminant, Part A, Public Exposure To, Sources and Emissions of Diesel Exhaust in California - USA; http://www.arb.ca.gov/toxics/dieseltac/part_a.pdf

Madl, Pierre, (2003), Masters Thesis: Instrumental development and Application of a Thermodenuder, University of Salzburg, Austria.

MAG (Maricopa Association of Governments) (2001); THE 1999 Brown cloud project for the maricopa county area; Phoenix (AZ) – USA,

MPI-FKF, (1996); Anderson Group: superconductivity in alkali-doted Fullerens http://www.fkf.mpg.de/andersen/fullerene/jahrb96.html

McLean, H.L., Lave, L.B., (2003), Evaluating automobile fuel/propulsion system technologies, Progress in Energy and Combustion Science, 29:1-69.

NAS (National Academy of Sciences), (2000); Modeling Mobile Source Emissions; National Academic Press; Washington D.C. - USA http://books.nap.edu/books/0309070880/html/23.html#pagetop

Pearce F. (1997); Devil in the Diesel; Fiba Canning Inc.; Ontario CDN. VEECO, Nanovations - European Newsletter (2001), p2.

http://www.veeco-europe.com/de/pdf/nano4_de.pdf WB, (2001), Urban Air Pollution, International Experience with CNG

http://lnweb18.worldbank.org/SAR/sa.nsf/Attachments/Briefing2/$File/Briefing_Note_No_2_revised.pdf Yang, M., Kraft-Oliver, T., (1997), Compressed Natural Gas: Monitoring Towards a Cleaner Beijing, Applied

Energy, 56:395-405. Section II.8. Atmospheric Aerosol Particles Colbeck I., (1998), Physical and Chemical Properties of Aerosols, first edition, Blackie Academic & Professional,

Chapman & Hall, New York - USA. HEI (Health Effects Institute) (2002); Research Directions to Improve Estimates of Human Exposure and Risk

from Diesel Exhaust; Boston MA - USA Hinds C.W., (1999); Aerosol Technology - Properties, behavior, and measurements of airborne particles; 2nd ed.;

John Wiley & Sons; LA - USA. Kittelson, D. et al. (2000); Diesel Aerosol Sampling in the Atmosphere; Center for Diesel Research, University of

Minnesota; SAE International; Warrendale (PA) – USA. Mayer A. (2003); Particulate Filter Systems – Particle Traps, AKPF, Diesel Particulate Filter Manufacturers Task

Force; AUT / CH / FRG Morawska L., (1999), Introduction to Aerosol Physics; lecturing script; QUT, Brisbane – AUS. Reist P.C., (1993); Aerosol Science and Technology, 2nd edition; McGraw-Hill Inc.; USA. Stoker S.H, Seager L.S, (1972); Environmental Chemistry - Air and Water Pollution; Scott, Foresman and Co;

Illinois - USA. TSI, Instruction Manual for the Model 3934 SMPS - Scanning Mobility Particle Sizer (Revison E, 2000); TSI Inc.;

Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1930081f-3934.pdf TSI, Instruction Manual for the Model 3071A Electrostatic Classifier Instruction Manual (Revision E, 2000); TSI

Inc.; Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1933765f-3071A.pdf TSI, Instruction Manual for the Model 3010 Condensation Particle Counter (Revision G, 2000); TSI Inc.;


Maricela YIP WONG

Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1933010f-3010.pdf TSI, Instruction Manual for the Model 3022A Condensation Particle Counter (Revision H, 2000); TSI Inc.;

Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1933763i-3022A.pdf Tipler P.A., (1999); Physics for scientists and engineers, 4th ed.; W.H.Freeman / Worth Publ.; New York - USA Willeke K., Baron P.A., (1993); Aerosol Measurement - Principles, Techniques, and Applications; Van Nostrand

Reinhold; NY - USA. Section II.9. The SMPS System Hinds C.W., (1999); Aerosol Technology - Properties, behavior, and measurements of airborne particles; 2nd ed.;

John Wiley & Sons; LA - USA. Reist P.C., (1993); Aerosol Science and Technology, 2nd edition; McGraw-Hill Inc.; USA. Stoker S.H, Seager L.S, (1972); Environmental Chemistry - Air and Water Pollution; Scott, Foresman and Co;

Illinois - USA. Tipler P.A., (1999); Physics for scientists and engineers, 4th ed.; W.H.Freeman / Worth Publ.; New York – USA TSI, Instruction Manual for the Model 3934 SMPS - Scanning Mobility Particle Sizer (Revison E, 2000); TSI Inc.;

Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1930081f-3934.pdf TSI, Instruction Manual for the Model 3071A Electrostatic Classifier Instruction Manual (Revision E, 2000); TSI

Inc.; Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1933765f-3071A.pdf TSI, Instruction Manual for the Model 3010 Condensation Particle Counter (Revision G, 2000); TSI Inc.;

Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1933010f-3010.pdf TSI, Instruction Manual for the Model 3022A Condensation Particle Counter (Revision H, 2000); TSI Inc.;

Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1933763i-3022A.pdf Wiegand A., (1999); Graphical Display and Analysis of Aerosol Distributions: What Instruments Show vs What is

Actually There (internal report); Environmental Aerosol Laboratory; QUT - AUS Willeke K., Baron P.A., (1993); Aerosol Measurement - Principles, Techniques, and Applications; Van Nostrand

Reinhold; NY - USA. Honeywell, Burdick & Jackson, Product Information

http://www.bandj.com/BJProduct/SolProperties/SurfaceTension.html Section II.10. Particle Size Statistics CSIRO Atmospheric Research, Urban and Regional Air Pollution, Information sheet - online

http://www.dar.csiro.au/information/urbanpollution.html Finlay W.H., (2001); The mechanics of inhaled Pharmaceutical Aerosols - an Introduction; Academic Press;

London - UK. Hinds C.W., (1999); Aerosol Technology - Properties, behavior, and measurements of airborne particles; 2nd ed.;

John Wiley & Sons; LA - USA. Johnson, R., Kuby, P., (2000), Elementary Statistics, 8th Edition, Duxbury Thomson Learning, Pacific Grove, CA,

USA. Reist P.C., (1993); Aerosol Science and Technology, 2nd edition; McGraw-Hill Inc.; USA. Wåhlin P., Palmgren F., (1999); Source apportionment of particles and particulates (PM10) measured by DMA

and TEOM in a Copenhagen street canyon National Environmental Research Institute, Frederiksborgvej 399, DK-4000 Roskilde, Denmark

Wiegand A., (1999); Graphical Display and Analysis of Aerosol Distributions: What Instruments Show vs What is Actually There (internal report); Environmental Aerosol Laboratory; QUT – AUS

Willeke K., Baron P.A., (1993); Aerosol Measurement - Principles, Techniques, and Applications; Van Nostrand Reinhold; NY - USA. http://192.167.230.2/meetings/venice2000/Contributions/PeterWahlin.pdf

TSI, Instruction Manual for the Model 3934 SMPS - Scanning Mobility Particle Sizer (Revison E, 2000); TSI Inc.; Shoreview (MN) - USA http://www.tsi.com/particle/downloads/manuals/1930081f-3934.pdf

Section II.11. Data Output of the SMPS system Wiegand A., (1999); Graphical Display and Analysis of Aerosol Distributions: What Instruments Show vs What is


Maricela YIP WONG

Actually There (internal report); Environmental Aerosol Laboratory; QUT – AUS. Section II.12. Health Effects of Particles on the Environment and on the Human Organism AKPF, Arbeitskreis Partikel-Filter-Systemhersteller (2000); Lexikon Verbrennungsmotor Partikel; Diesel

Particulate Filter Manufacturers Task Force; AUT / CH / FRG, http://www.akpf.org/pubs.html BUWAL, (2003); Monetarisierung verkehrslärmbedingter Gesundheitsschäden; Bundesamt für Umwelt, Wald und

Landschaft; Bern - CH http://www.krenglbach.gruene.at/files/Monetarisierung_verkehrslaermbedingter_Gesundheitsschaeden.pdf

CSE, (2003); Centre for Science and Environment - Engines of the Devil (Agarwal A., editor); http://www.cseindia.org/campaign/apc/pdf/dieselmo.pdf

CSIRO, (2003); Atmospheric Research, Urban and Regional Air Pollution, Information sheet http://www.dar.csiro.au/information/urbanpollution.html

Ellenhorn, Matthew J. (1997) Ellenhorn's Medical Toxicology Diagnosis and Treatment of Human Poisoning: Medical Toxicology CD-Rom / Corporate Technology Ventures

HEI, (2002), A Special Report on the Institute’s Diesel Epidemiology Working Group Horstman M., Seega B., (2003); Norman Swan’s Health Report, ABC - Radio National, Sydney (NSW) – AUS

http://www.abc.net.au/rn/talks/8.30/helthrpt/stories/s838424.htm International Agency for Research on Cancer (1989) Diesel and gasoline engine exhaust. In: IARC Monographs

on the Evaluation of Carcinogenic Risks to Humans. Vol. 46: Diesel and Gasoline Engine Exhaust and Some Nitroarenes. Lyon: IARC, pp.41-185. http://www.inchem.org/documents/iarc/vol46/46-01.html

Krieger R.K. (1998); Report to the Air Resources Board on the Proposed Identification of Diesel Exhaust as a Toxic Air Contaminant, Part A, Public Exposure To Sources and Emissions of Diesel Exhaust in California - USA; http://www.arb.ca.gov/toxics/dieseltac/part_a.pdf

McLean, H.L., Lave, L.B., (2003), Evaluating automobile fuel/propulsion system technologies, Progress in Energy and Combustion Science, 29:1-69.

NS (2000), New Scientist – Hold Your Breath (online) http://www.newscientist.com/hottopics/pollution/pollution.jsp?id=22500400

NS (2002a), New Scientist – Big City Killer (online) http://www.newscientist.com/hottopics/pollution/pollution.jsp?id=23331100

NS (2002b), New Scientist – Pollution triggers Genetic Defects (online) http://www.newscientist.com/hottopics/pollution/pollution.jsp?id=23730400

Pearce F., (1997); Devil in the Diesel (online) http://www.fibacanning.com/articles/devilin.htm

Postlethwait. J.H., Hopson, J.L., (1995), The Nature of Life, 3rd Ed., McGRAW-HILL, INC, New York, USA. Stoker, H. Stephen, Spencler L. Seager, (1972), Environmental Chemistry - Air and Water Pollution, 1st ed., Scott,

Foresman and Company, Glenview, Illinois, USA. Tucker W.G., (2000), An overview of PM2.5 sources and control strategies, Fuel Processing Tech, 65-66:379-392. USEPA, (2003); About Air Toxics, Health and Ecological Effects; US-Environmental Protection Agency;

http://www.epa.gov/air/toxicair/newtoxics.html Vardoulakis S., Fisher, B.E.A., Pericleous, K., Gonzales-Flesca, N. (2003), Modelling air quality in street canyons,

a review, Atmospheric Environment, 37:155-182. Willeke K., Baron P.A., (1993); Aerosol Measurement - Principles, Techniques, and Applications; Van Nostrand

Reinhold; NY - USA. Wåhlin, P., Palmgren, F., (1999), Source apportionment of particles and particulates (PM10) measured by DMA

and TEOM in a Copenhagen street canyon, http://192.167.230.2/meetings/venice2000/Contributions/PeterWahlin.pdf

Section II.13. Exposure Assessment of Vehicle Emissions &

Section II.14. Population Exposure Assessment Studies Willeke K., Baron P.A., (1993); Aerosol Measurement - Principles, Techniques, and Applications; Van Nostrand

Reinhold; NY - USA.


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Section II.15. Methods for Exposure Assessment for Diesel Exhaust Studies & Section II.16. Multiple Exposure Pathways

Krieger R.K. (1998); Report to the Air Resources Board on the Proposed Identification of Diesel Exhaust as a

Toxic Air Contaminant, Part A, Public Exposure To Sources and Emissions of Diesel Exhaust in California - USA; http://www.arb.ca.gov/toxics/dieseltac/part_a.pdf

OEHHA, (1998), Executive Summary for the "Proposed Identification of Diesel Exhaust, as a Toxic Air Contaminant"; Office of Environmental Health Hazard Assessment; CA - USA http://www.oehha.ca.gov/air/toxic_contaminants/html/Diesel%20Exhaust.htm

Spadaro, Joseph, Rabl, Ari, (2001), Damage costs due to automotive air pollution and the influence of street canyons, Atmospheric Environment, 35:4763-4775.

Chapter III Duan & Mage, (1997), DT. Combination of direct and indirect approaches for exposure assessment.

Journal of Exposure Analysis and Environmental Epidemiology Wiegand A., (1999); Graphical Display and Analysis of Aerosol Distributions: What Instruments Show vs What is

Actually There (internal report); Environmental Aerosol Laboratory; QUT – AUS Chapter IV Enya, T.; Suzuki, H.; Watanabe, T.; Hirayama, T.; Hisamatsu, B. (1997); 3-Nitrobenzanthrone, a powerful

bacterial mutagen and suspected carcinogen found in diesel exhaust and airborne particles; Environmental Science and Technology, 31, 2772-2776.

USEPA, (2002), /600/8-90/057F, Health Assessment Document For Diesel Engine Exhaust, National Center for Environmental Assessment Office of Research and Development U.S. Environmental Protection Agency Washington, DC. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060

OEHHA, (1998), Executive Summary for the "Proposed Identification of Diesel Exhaust, as a Toxic Air Contaminant"; Office of Environmental Health Hazard Assessment; CA - USA http://www.oehha.ca.gov/air/toxic_contaminants/html/Diesel%20Exhaust.htm

Pawlak, (2003), submitted for publication Weston, Graham, Senior Adviser Fleet Management, Brisbane City Council. Willeke K., Baron P.A., (1993); Aerosol Measurement - Principles, Techniques, and Applications; Van Nostrand

Reinhold; NY - USA. SCANIA, (1999), Press Release: Scania gas buses for Brisbane, Australia

http://www.scania.com/about/news/press_releases/press_9857.asp Chapter V Brandl, M., (2003), „Gas-Busse: Nicht billig, aber auch nicht umsonst“, Kurier, 3rd of October 2003. CSE, (2001); Centre for Science and Environment - Presentation to the committee on auto fuel policy;

http://www.cseindia.org/campaign/apc/pdf/Apc.pdf De Blas A., (2003); Alexandra de Blas’s Earthbeat, ABC - Radio National, Sydney (NSW) - AUS

http://www.abc.net.au/rn/science/earth/stories/s996503.htm NS, (2001); UK aims for halving of particle pollution (online)

http://www.newscientist.com/hottopics/pollution/pollution.jsp?id=ns99991308 NS, (2002); Fishy fossil reveals first steps on land (online)

http://www.newscientist.com/hottopics/pollution/pollution.jsp?id=ns23052500 Planet Ark, (2003), Study Shows Massive Tree Loss in Cities,

http://www.planetark.org/dailynewsstory.cfm/newsid/22281/story.htm CaEPA, (1998); Proposed Identification Of Diesel Exhaust As A Toxic Air Contaminant; Californian

Environmental Protection Agency - USA http://www.arb.ca.gov/toxics/dieseltac/staffrpt.pdf


Maricela YIP WONG

Appendix F.3 - Curriculum Vitae Maricela Yip Wong

Address: Institute for Physics & Biophysics

Hellbrunnerstr.# 34 A-5020 Salzburg AUT - EU tel: +43.662 . 8044.5719 fax: +43.662 . 8044.150 e-mail: [email protected]

Academic Career

Activity Location Period Task

Masters program Queensland University of Technology, Brisbane / Australia and Universität Salzburg / Austria.

1994 -2003 MSc in Ecology

Teaching qualification

San Francisco State University (due to lack of funds not completed)

1988 - 1990

Elementary school teaching and Liberal Arts

Associate Degree City College of San Francisco / USA 1988 Liberal studies

Diploma

Heald Business College San Francisco / USA

1985 Accounting

Bachelors & Teaching Diploma

Escuela National de Maestros, Mexico City. 1982

BS in social studies & pedagogics in combination with teaching at elementary level

Lecturing Activity

Activity Location Period Task

Tutorial Services University of Salzburg

2001 – & running

Microbiology, Physics & Chemistry

Elementary school Mexico City 1981 - 1982 Primary school teacher

Practical Mexico City

1980 - 1981

Primary school teacher

Languages: Spanish: native language

English: word and script (very good) German: word and script (very good) Italian: word only (sufficient for basic conversation)

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ExposureAssessment in a Busway Canyon Maricela Yip Wong

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