Analysis of dispersion and propagation of fine and ultra fine particle aerosols from a busy road

Keywords Combustion aerosols, urban aerosols, outdoor aerosols, background aerosols, particles, ultra-fine particles, particle formation, aerosol evolution, busy road, aerosol dispersion

Queensland University of Technology School of Physical and Chemical Sciences

Analysis of Dispersion and Propagation of Fine and Ultra Fine Particle Aerosols from a Busy

Keywords

Combustion aerosols, urban aerosols, outdoor aerosols, background aerosols, particles, ultra-fine particles, particle formation, aerosol evolution, busy road, aerosol dispersion, air quality, transport emissions, emission factors, canonical correlations analysis, multi-variate analysis, degradation processes, turbulent diffusion, atmospheric monitoring, hydrodynamics, statistical mechanics,

nano-probability, particle deposition

Statement of original

authorship

The work contained in this Thesis has not been previously submitted for a degree or diploma at any other higher education institution To the best of my knowledge and belief, the Thesis contains no material previously published or written by another persons except where due reference is made

Galina Gramotnev

Acknowledgements

I note my appreciation of financial support for this research from the Queensland University of Technology (QUT), Faculty of Science, School of Physical and Chemical Sciences, and QUT Office of Research

I would like to express my sincere gratitude and appreciation to Dr Richard J Brown for very helpful discussions, support, useful directions, and introduction to the theory of turbulent atmospheric processes I also thank Mr Pierre Madl and Ms Maricella Yip for their substantial help and consultations with respect to monitoring equipment, and all my friends from the International Laboratory for Air Quality and Health for their support during my PhD studies

Special thanks go to my husband, Dr Dmitri K Gramotnev, for the comprehensive support during my studies and suggested ideas

Abstract

Nano-particle aerosols are one of the major types of air pollutants in the urban indoor and outdoor environments Therefore, determination of mechanisms of formation, dispersion, evolution, and transformation of combustion aerosols near the major source of this type of air pollution – busy roads and road networks – is one of the most essential and urgent goals This Thesis addresses this particular direction of research by filling in gaps in the existing physical understanding of aerosol behaviour and evolution

The applicability of the Gaussian plume model to combustion aerosols near busy roads is discussed and used for the numerical analysis of aerosol dispersion New methods of determination of emission factors from the average fleet on a road and from different types of vehicles are developed Strong and fast evolution processes

in combustion aerosols near busy roads are discovered experimentally, interpreted, modelled, and statistically analysed

A new major mechanism of aerosol evolution based on the intensive thermal fragmentation of nano-particles is proposed, discussed and modelled A comprehensive interpretation of mutual transformations of particle modes, a strong maximum of the total number concentration at an optimal distance from the road, increase of the proportion of small nano-particles far from the road is suggested Modelling of the new mechanism is developed on the basis of the theory of turbulent diffusion, kinetic equations, and theory of stochastic evaporation/degradation

processes

Several new powerful statistical methods of analysis are developed for comprehensive data analysis in the presence of strong turbulent mixing and stochastic fluctuations of environmental factors and parameters These methods are based upon the moving average approach, multi-variate and canonical correlation analyses As a result, an important new physical insight into the relationships/interactions between particle modes, atmospheric parameters and traffic conditions is presented In particular, a new definition of particle modes as groups of particles with similar diameters, characterised by strong mutual correlations, is introduced Likely sources of different particle modes near a busy road are identified and investigated Strong anti-correlations between some of the particle modes are discovered and interpreted using the derived fragmentation theorem

The results obtained in this thesis will be important for accurate prediction of aerosol pollution levels in the outdoor and indoor environments, for the reliable determination of human exposure and impact of transport emissions on the environment on local and possibly global scales This work will also be important for the development of reliable and scientifically-based national and international standards for nano-particle emissions

LIST OF AUTHOR PUBLICATIONS

1 Refereed journal papers [A1] Gramotnev, G., Brown, R., Ristovski, Z, Hitchins, J., Morawska, L 2003

Determination of emission factors for vehicles on a busy road Atmospheric Environment, 37, pp 465-474 (Number 13 out of 25 most downloaded papers in 2004).

[A2] Gramotnev, G., Ristovski Z., Brown, R., Madl, P 2004 New methods of

determination of emission factors for two groups of vehicles on a busy road,

Atmospheric Environment, vol.38, pp.2607-2610

[A3] Gramotnev, G., Ristovski, Z 2004 Experimental investigation of ultra fine

particle size distribution near a busy road, Atmospheric Environment, vol.38,

pp.1767-1776

[A4] Gramotnev, D.K., Gramotnev, G 2005 A new mechanism of aerosol

evolution near a busy road: fragmentation of nano-particles, Journal of Aerosol Science , vol.36, pp.323-340 (Number 9 out of 25 most downloaded papers in 2005)

[A5] Gramotnev, D.K., Gramotnev, G 2005 Modelling of aerosol dispersion from

a busy road in the presence of nanoparticle fragmentation, Journal of Applied Meteorology, vol.44, pp.888–899

[A6] Gramotnev, G., Gramotnev, D.K Multi-channel statistical analysis of

combustion aerosols Part I: Canonical correlations and sources of particle modes

Atmospheric Environment (accepted 9 January 2007)

[A7] Gramotnev, D.K., Gramotnev, G Multi-channel statistical analysis of

combustion aerosols Part II: Anti-correlations of particle modes and fragmentation

theorem Atmospheric Environment (accepted 9 January 2007)

[A8] Gramotnev, D.K., Gramotnev, G Kinetics of stochastic degradation /

evaporation processes in polymer-like systems with multiple bonds, J Appl Phys

(submitted)

[A9] Gramotnev, D K., Mason, D R., Gramotnev, G., Rasmussen A J Thermal

tweezers for surface manipulation with nano-scale resolution Appl Phys Lett

(accepted 2 January 2007)

[A10] Gramotnev, G., Madl, P., Gramotnev, D K., Urban background aerosols:

Anti-correlations of particle modes and fragmentation mechanism Geophysical Research Letters (submitted)

2 Full-length refereed conference papers [A11] Gramotnev, G., Brown, R., Ristovski, Z., Hitchins, J., Morawska, L 2002

Estimation of fine particles emission factors for vehicles on a road using Caline4

program Proceedings of 4 th Queensland Environmental Conference, Brisbane, Australia, 30 & 31 May 2002, pp 43-48

[A12] Gramotnev, G., Ristovski, Z., Brown, R., Morawska, L, Jamriska, M.,

Agranovski, V 2003 A new method for obtaining fine particles emission factors

with validation from measurements near a busy road in Brisbane Proceedings of National Environmental Conference, Brisbane, Australia, 18 & 20 June 2003, pp 206-211

3 Conference papers in refereed journals [A13] Gramotnev, G., Ristovski, Z., Brown, R., Morawska, L., Madl, P 2003

New method of determination of emission factors for different types of vehicles on a

busy road Journal of Aerosol Science, EAC 2003, vol.34s, S259-S260

[A14] Gramotnev, G., Ristovski, Z 2003 Nanoparticles near a busy road:

experimental observation of the effect of formation of a new mode of particles

Journal of Aerosol Science, EAC 2003, vol.34s, S255-S256

[A15] Gramotnev, G., Ristovski, Z and Gramotnev, A 2003 Dependence of

concentration of nanoparticles near a busy road on meteorological parameters:

canonical correlation analysis Journal of Aerosol Science, EAC 2003, vol.34s,

S257-S258

[A16] Gramotnev, G., Ristovski, Z., Morawska, L., Thomas, S 2003 Statistical

analysis of correlations between air pollution in the city area and temperature and

humidity Journal of Aerosol Science, EAC 2003, vol.34s, S715-S716

[A17] Gramotnev, G 2004 Determination of the average emission factors for

three different types of vehicles on a busy road Journal of Aerosol Science, EAC

2004, vol.35, S1089-S1090

[A18] Gramotnev, D.K., Gramotnev, G 2004 A new mechanism of aerosol

evolution near a busy road: fragmentation of nanoparticles Journal of Aerosol Science, EAC 2004, vol.35, S221-S222

[A19] Gramotnev, D.K., Gramotnev, G 2004 Modelling of aerosol dispersion

from a busy road in the presence of nano-particle fragmentation Journal of Aerosol Science, EAC 2004, vol.35, S925-S926

4 Other conference publications [A20] Gramotnev, G., Brown, R., Ristovski, Z, Hitchins, J., Morawska, L 2002

Dispersion of fine and ultra fine particles from busy road: the comparison of

experimental and theoretical results, in Chiu-Sen Wang (Ed) Proc of Sixth International Aerosol Conference, Taipei, Taiwan (September 9 – 13, 2002), pp 839-840

[A21] Gramotnev, G., Thomas, S., Morawska, L., Ristovski, Z 2002 Canonical

correlation analysis of fine particle and gaseous pollution in the city area, in

Chiu-Sen Wang (Ed) Proc of Sixth International Aerosol Conference, Taipei, Taiwan

(September 9 – 13, 2002), pp 873-874

[A22] Gramotnev, D.K., Gramotnev, G 2004 Fragmentation of nanoparticles near

a busy road: Justification and modelling Proceedings of 8 th International Conference on Carbonaceous Particles in the Atmosphere, Vienna, Austria, 14-16 September 2004, H3

[A23] Gramotnev, G., Gramotnev, D.K 2004 New statistical method of

determination of particle modes in the presence of strong turbulent mixing

Proceedings of 8 th International Conference on Carbonaceous Particles in the Atmosphere, Vienna, Austria, 14-16 September 2004, H4

[A24] Gramotnev, G., Gramotnev, D K 2005 Theoretical analysis of multiple

thermal fragmentation of aerosol nanoparticles from a line source: Evolution of particle modes Biannual AIP Congress, Canberra, Australia, February, 2005, p.210

[A25] Gramotnev, G., Gramotnev, D K 2005 Numerical and experimental

investigation of thermal fragmentation of aerosol nano-particles from vehicle exhaust Biannual AIP Congress, Canberra, Australia, February, 2005, p.210

[A26] Gramotnev, D K., Gramotnev, G 2005 Combustion nano-particle aerosols:

Mechanisms of evolution and modelling, Aerosol Workshop, 30 March – 1 April

2005, Sydney, Australia (invited talk)

[A27] Gramotnev, D K., Gramotnev, G 2005 Time delays during multiple

thermal fragmentation of nanoparticles: evolution of particle modes European Aerosol Conference (EAC 2005), Ghent, Belgium, p 690

[A28] Gramotnev, G., Madl, P 2005 Multi-channel statistical analysis of

background fine particle aerosols, European Aerosol Conference (EAC 2005),

Ghent, Belgium, p 697

[A29] Gramotnev, D K., Bostrom, T E., Devine, N., Gramotnev, G 2005

Experimental investigation of deposition of aerosol particles near a busy road

European Aerosol Conference (EAC 2005), Ghent, Belgium, p 696

[A30] Mason, D.R., Gramotnev, D.K., Rasmussen, A., Gramotnev, G 2005

Feasibility of thermal tweezers for effective manipulation of nano-particles on

surfaces ACOLS’05, 6 December, Christchurch, New Zealand, ThC6

[A31] Gramotnev, D K., Bostrom, T E., Gramotnev, G., Goodman, S J

“Deposition of Composite Aerosol Particles on Different Surfaces near a Busy Road”, 7th International Aerosol Conference (IAC 2006), 10-15 September 2006, St Paul, Minnesota, USA, p.616-617

[A32] Gramotnev, D K., Gramotnev, G “Multiple thermal fragmentation of

nanoparticles: evolution of particle total number concentration”, 7th International Aerosol Conference (IAC 2006), 10-15 September 2006, St Paul, Minnesota, USA, p.107-108

[A33] Gramotnev, G., Gramotnev, D K “Multi-channel statistical analysis of

combustion aerosols: Canonical correlations and sources of particle modes”, 7thInternational Aerosol Conference (IAC 2006), 10-15 September 2006, St Paul, Minnesota, USA, p.177-178

[A34] Gramotnev, D K., Gramotnev, G “Anti-correlations of particle modes and

fragmentation theorem for combustion aerosols”, 7th International Aerosol Conference (IAC 2006), 10-15 September 2006, St Paul, Minnesota, USA, p.734-

735

[A35] Gramotnev, G., Madl, P., Gramotnev, D K “Anti-symmetric correlations of

particle modes in urban background aerosols”, 7th International Aerosol Conference (IAC 2006), 10-15 September 2006, St Paul, Minnesota, USA, p.1764-1765

[A36] Mason, D R., Gramotnev, D K., Gramotnev, G., Rasmussen, A J

“Thermal tweezers with dynamic evolution of the heat source”, 17th AIP Congress, December 2006, Brisbane, Australia, abstract 461

[A37] Gramotnev, D K., Bostrom, T E., Mason, D R., Gramotnev, G., Burchill,

M J “Deposition and Surface Evolution of Composite Aerosol Particles”, 17th AIP Congress, December 2006, Brisbane, Australia, abstract 796

[A38] Gramotnev, D K., Gramotnev, G “Anti-Symmetric Correlation Pattern for

Particle Modes in Combustion and Background Aerosols: Fragmentation Theorem”,

17th AIP Congress, December 2006, Brisbane, Australia, abstract 795

[A39] Gramotnev, G., Gramotnev, D K “Multi-Channel Statistical Analysis for

the Detailed Investigation of Combustion Aerosols”, 17th AIP Congress, December

2006, Brisbane, Australia, abstract 797

[A40] Gramotnev, D K., Flegg, M B., Gramotnev, G “Stochastic

evaporation/degradation processes in complex structures with multiple bonds”, 17thAIP Congress, December 2006, Brisbane, Australia, abstract 748

LIST OF FIGURES

Fig 3.3 Theory and experiment (linear scale) 60

Fig 3.4 Theory and experiment (logarithmic scale) 61

Fig 5.3 Size distributions with experimental points (20 November 2002) 86

Fig 5.4 Comparison of size distributions (20 November 2002) 87

Fig 5.5 Size distributions with experimental points (23 December 2002) 89

Fig 5.6 Comparison of size distributions (23 December 2002) 91

Fig 5.8 Size distributions with experimental points (24 November 2002) 94

Fig 5.9 Number concentrations (8 January 2003) 95

Fig 6.2 Average wind parameters (25 November 2002) 105 Fig 6.3 Size distributions with experimental points (25 November 2002) 106 Fig 6.4 Moving average correlation coefficients 109

Fig 6.6 Size distributions (20 November 2002) 116 Fig 6.7 Size distributions (23 December 2002) 119

Fig 7.2 Fragmentation rate coefficient 135 Fig 7.3 Total number concentrations (theoretical dependencies) 138 Fig 7.4 Total number concentrations (comparison with experiment) 144 Fig 8.1 Size distributions; moving average approach (25 November 2002) 159 Fig 8.2 Moving average correlation coefficients 161

Fig 8.4 Canonical correlation coefficients 173 Fig 8.5 Canonical weights and loadings for heavy trucks 175 Fig 8.6 Canonical weights and loadings for cars 176 Fig 8.7 Canonical weights and loadings for temperature 187 Fig 8.8 Canonical weights and loadings for solar radiation 188 Fig 9.1 Moving average cross-correlation coefficients 195 Fig 9.2 Anti-symmetric correlation pattern 197 Fig 9.3 Anti-correlations with 13.6 nm mode 201

Fig 9.5 Anti-symmetric correlation pattern (later evolution stage) 203

Fig 10.1 Evolution of the 3-particle from the 1-2 state 215

Fig 10.3 Particle concentrations (no dispersion) 226 Fig 10.4 Particle concentrations (with dispersion) 227

Fig 11.2 Background size distribution (before sunset) 231 Fig 11.3 Comparison of size distributions before and after sunset 232 Fig 11.4 Moving average correlation coefficients for background 233 Fig 11.5 Anti-symmetric correlation pattern for background 236

Contents

2 Background and Theory 10

2.2 Turbulent dispersion of air pollutants 15

2.2a Taylor theorem and asymptotic properties

2.2b Turbulent diffusion from a point continuous sources 20

2.2c Continuous ground level line source 23

2.3 Dispersion of fine particles from a busy road 26

2.5 Statistical approaches: correlation techniques in data analysis 36

3 Determination of average emission factors for vehicles on a busy road 45

3.4.1 Model emission factors 52

3.4.2 Determination of the emission factor 54

3.5 Comparison of numerical and experimental results 58

3.6 An example of application of the model for road design 64

4 New methods of determination of average particle emission factors

for two groups of vehicles on a busy road 68

4.2 Emission factors for two different groups of vehicles 69

4.4 Three types of vehicles on the road 74

4.5 Turbulent corrections to the w-factors 77

5 Experimental investigation of ultra fine particle size distribution

6.3 Maximum of the total number concentration 111

6.4 Failure of the conventional mechanisms of the aerosol evolution 113

6.5 Fragmentation model of aerosol evolution 120

7 Modelling of aerosol dispersion from a busy road

in the presence of nano-particle fragmentation 130

7.4 Existence conditions for the maximum

7.5 Comparison with the experimental results 143

8 Multi-channel statistical analysis of aerosol particle modes

8.2 Experimental data and particle modes 156

8.3 Moving average approach and the canonical correlation analysis 163

9 Correlations between particle modes: fragmentation theorem 191

9.1 Introduction 191 9.2 Moving average approach for particle modes 192 9.3 Numerical results and their discussion 194

10.3 Evolution time and kinetics of degradation 218

11 Multi-channel statistical analysis of background fine particle aerosols 229

CHAPTER 1 INTRODUCTION

Rapid development of high-technology industry, transport, and ever increasing consumption of energy have resulted in increasing changes to our environment, climate, atmosphere, natural resources, etc (Seinfeld and Pandis, 1998) All these changes should prompt a rapid and decisive response, if we want to stop adverse effects of our technological activities on the quality of life, environment, and health Finding such a response is one of the major aims of modern science, including all of its mainstream branches such as environmental sciences, engineering, physics, chemistry, medicine, and applied mathematics

Transport emissions are one of the major sources of atmospheric and environmental pollution with the global effect on climate, environment, and quality of

life (Whelan, J 1998, Schauer, et al, 1996, Shi, et al, 1999, Shi, et al, 2001) Choking

atmospheres in major world cities and reducing air quality in residential areas of large metropolitan centres require urgent measures on reduction, control, and effective prediction of air pollution levels from busy roads and road networks One of the major types of pollutants from modern transport and road networks is combustion aerosols comprising fine and ultra-fine particles with diameters from several nanometres to

several hundreds of nanometres (Schauer, et al, 1996, Shi, et al, 1999) It is long known

that such aerosols may have an effect on climate, mainly through cloud formation and rainfall patterns (Seinfeld and Pandis, 1998, Jacobson, 1999) In addition, during the last decade, researchers have established links between fine and ultra-fine particle aerosols

and noticeable health risks for humans in city areas (Pope, et al, 1995, Van Vliet, et al,

1997)

During the last several years, numerous studies have observed health effects of particulate air pollutants Compared to early studies that focused on severe air pollution

episodes (Beaver, H., 1953), recent research is more relevant to understanding health

effects of pollution at levels common to contemporary cities in the developed world Observed health effects include increased respiratory symptoms, decreased lung function, increased hospitalizations and other health care visits for respiratory and cardiovascular disease, increased respiratory morbidity as measured by absenteeism from work and school, or other restrictions in activity, and increased cardiopulmonary disease mortality These health effects have been observed at levels common to many U.S cities including levels below current U.S National Ambient Air Quality Standards

for particulate air pollution (Pope, et al, 1995)

It has also been found that those children who have been living within 100 m of

a freeway had significantly more coughs, wheezes, runny noses, and doctor-diagnosed

asthmas (Van Vliet, et al, 1997) In addition, the same study identified a significant

association between truck traffic density and black smoke concentration on the one hand and chronic respiratory symptoms on the other

Until recently, the main concern has been related to emission of relatively large particles with diameters > 1 µm (Friedlander, 1977) Therefore the current emission standards establish the limits on emission of overall particulate mass, rather than concentration of particles However, recent investigations have made it apparent that

fine and ultra-fine aerosol particles (within the ranges < 1 µm and < 0.1 µm, respectively) emitted from combustion sources may present a significant health risk for

humans (Wichmann, and Peters, 2000, Zhiqiang, et al, 2000, Ziesenis, et al, 1998, Borja-Aburto, et al, 1998), especially for people with specific health problems (e.g., heart, vascular, respiratory, etc problems (Borja-Aburto et al, 1998) Moreover, it is

now clear that adverse health effects related to ultra-fine (< 100 nm) particles with large number concentration but small overall mass appear to be significantly stronger than the effects from larger (fine) particles with diameters between ~ 100 nm and ~ 1 µm (Stone,

2000, Brown, et al 2000) For example, proinflammatory response is greater for fine particles, and is directly proportional to the surface area of the particles (Brown, et

ultra-al, 2001) Therefore, one of the possible explanations of increased health effects of ultra-fine aerosol particles is related to the fact that decreasing particle diameters and increasing their number concentrations results in a strong increase of particle surface

area per unit volume (Peters, 1997, Brown, et al, 2001, Nemmar, et al, 2002) This is

the surface area of the particles that probably drives inflammation in the short term, resulting in significantly larger effect from ultra-fine particles having very large number

concentrations and surface area (Nemmar, et al, 2002) During a study of the

penetration of pollutant particles into the blood stream, it was found that ultra-fine

aerosol particles penetrate into the blood just in ~ 1 minute (Nemmar, et al, 2002) The

concentration in the blood reaches a maximum within ~ 10 – 20 minutes, and remains at

this maximal level for up to ~ 60 minutes (Nemmar, et al, 2002) One of the reasons for

these enhanced and fast effects is probably related to the fact that fine and ultra-fine

particles tend to penetrate much deeper into the respiratory tract (Siegmann, et al,

1999) However, the complete understanding of the observed health problems and risks related to fine and ultra-fine particle aerosols still needs further studies including research into physical mechanisms of particle transformation and evolution, in order to understand which types of particles tend to play a predominant role in human exposure

As mentioned above, the current particulate emission standards restrict the overall particulate mass emissions These standards are thus focusing only on PM10 and

PM2.5 (i.e., the overall particulate mass concentration for particle diameters < 10 µm and

< 2.5 µm, respectively) They are obviously of little use for the development of regulations and policies when it comes to the strong adverse effects of fine and ultra-fine particles, because the contribution of such particles to the overall aerosol mass is negligible Therefore, new standards for fine and ultra-fine particle aerosols are

required, based on number concentrations rather than overall particulate mass This will also require detailed and comprehensive understanding of the major mechanisms of formation and evolution of combustion aerosols, transformation of particle modes, determination of their possible sources, possible places of enhanced health risks, mechanisms of removal and self-removal of particles from the atmosphere, etc At the same time, our current knowledge about fine and ultra-fine aerosol particles, their possible sources and mechanisms of transformation is fairly limited and some times inconsistent with experimental observations (for more detail see Chapter 2)

It is also clear that the development of adequate standards for fine and ultra-fine particle aerosols may only help to determine and identify the existing and potential problems with air pollution and transport and industry emissions Solution of these problems will be another very complex task that will require new approaches for effective reduction and control of air pollution levels (including particulate pollutants) and improvement of the air quality in major metropolitan centres And this is again not possible without the detailed understanding of processes of aerosol formation, interaction, evolution, and eventual removal and/or self-removal from the atmosphere

As a result, significant efforts of a number of aerosol scientists have recently been focused on the advancement of our fundamental knowledge of behaviour of combustion aerosols and their prediction in the urban environment In particular, detailed understanding of dispersion of nanoparticle aerosols is one of the most important goals for achieving reliable and accurate forecast of aerosol pollution levels and the resultant human exposure One of the major physical mechanisms of dispersion

of air pollutants (including nanoparticle aerosols) in the atmosphere is turbulent diffusion (Seinfeld & Pandis, 1998, Jacobson, 1999) If only this mechanism is taken into account, dispersion of aerosols and gasses can be described by the Gaussian plume model (Csanady, 1980, Pasquill and Smith, 1983, Zannetti, 1990) Several successful

software packages for different types of sources including point sources (industry)

(Bowers & Anderson, 1981), area sources (bushfires) (Hanna, et al, 1984), line sources

(busy roads) (Benson, 1992) have been developed for non-reactive pollutants However, modelling of dispersion of reactive gasses and rapidly evolving aerosols is a much more

complex problem (Bilger, 1978, Fraigneau, et al, 1995)

Previously, it was fairly commonly assumed that fine and ultra fine particle

aerosols do not undergo significant and rapid transformations (Shi et al, 1999) In this

case, particle size distributions should be more or less constant within a significant period of time, and the Gaussian plume approximation should be applicable for the approximate description of aerosol dispersion from different sources In this case the above-mentioned software packages should be applicable (after the appropriate re-scaling) for the prediction of aerosol dispersion in the atmosphere Therefore, the main interest of aerosol scientists has been focused on the study of decay of the total number

concentration of particles with distance from a source, e.g., a busy road (Shi, et al,

1999, Hitchins, et al, 2000, Zhu, et al, 2002a,b) In particular, exponential decay laws

were used for the description of the total number concentration of fine particles as a

function of distance from the road (Zhu, et al, 2002a,b)

However, several recent experimental observations have suggested that the Gaussian plume approximation is not always applicable, especially for smaller particles within the range < 30 nm Noticeable deviations of the size distributions of fine and ultra fine particles near a busy road from those predicted by the Gaussian plume model

have been observed by Zhu, et al (2002a,b) This suggests that there are significant

processes of evolution of particles during their transport away from the road – see also (Ketzel and Berkowicz, 2004) Such evolution processes may include particle formation

by means of homogeneous and heterogeneous nucleation (Alam, et al, 2003, Kulmala,

et al , 2000, Kerminen, et al, 2002, Lehtinen and Kulmala, 2003, Pirjola, 1999),

coagulation (Jacobson, 1999, Kostoglou & Konstandopoulos, 2001, Piskunov & Golubev, 2002), deposition (Jacobson, 1999, Meszaros, 1999), condensation and

evaporation (Zhang et al, 2005, Uvarova, 2003)

Nevertheless, there are still noticeable discrepancies between the theoretical predictions based on the mentioned mechanisms of aerosol evolution and the

experimental observations and monitoring data near busy roads For example, Zhu, et al

(2002a,b) have observed a shift of one of the particle modes (maximums of the particle size distribution) towards smaller particle diameters when the distance from the road is increased This observation is in obvious contradiction with the suggested coagulation

mechanism, of evolution of the particle size distribution (Zhu, et al, 2002a,b)

Contradictory suggestions regarding the nature of combustion nanoparticles have been presented in the literature Some of the researchers assume that particles with diameters

< 30 nm are mostly volatile (Sakurai, et al, 2003), whereas others suggest that they are predominantly solid – graphite, carbon, or metallic ash (Pohjola, et al, 2003, Abdul- Khalek, et al, 1998, Bagley, et al, 1996) Very few experiments on direct particle

observation and determination of their properties and structure under field conditions have been undertaken so far, while laboratory analysis may give significantly different results from the real-world situations with stochastically varying atmospheric conditions and natural variability of the source (different types of vehicles, their maintenance, etc.) Problems with such field experiments are well known They are related to significant fluctuations/dispersion of monitoring data associated with strong natural stochastic processes, such as atmospheric turbulence, variability of temperature, humidity, solar radiation, traffic conditions, etc Therefore, deriving sensible conclusions about the nature of different types of aerosol particles and their evolution in the presence of strong turbulent mixing requires the development of new extensive and complex methods of statistical analysis

As a result, a number of important questions about the nature of particle modes

in combustion aerosols and their evolution/transformation and physical and chemical structure in the real-world environment have so far been left unanswered Some of these

questions can be listed as follows (1) What is the predominant nature of the exhaust nanoparticles? Are they mainly solid or volatile? (2) What are the dominant sources (if any) of different particle modes? (3) How can we determine emission factors from

different types of vehicles on an actual road (these factors are essential for accurate

prediction of aerosol pollution levels)? (5) How do particle modes evolve with time and distance from the source at different atmospheric, physical, and climate conditions? (6)

Are the known mechanisms sufficient for the complete description of aerosol evolution,

or we are missing something?

Detailed investigation of these and other questions is essential for accurate forecast of aerosol pollution in the urban environment, establishment of working emission standards and, ultimately, reduction or elimination of the impact of these emissions on our environment, air quality and health

Therefore, the general aim of this thesis is to gain better understanding of

behaviour of nanoparticle aerosols by means of detailed experimental, statistical and theoretical investigation of evolution mechanisms, dispersion, and deposition of combustion airborne nanoparticles in the real-world environment, and develop new predictive models and statistical methods of data analysis in the presence of natural variability of the source and environmental conditions

The specific aims of the project can be listed as follows

1 Adaptation of the currently available models for the analysis of dispersion of reactive air pollutants from a busy road (CALINE4 model) for the reliable forecast

non-of aerosol pollution levels

2 Development of new methods for the experimental determination of the average emission factors per one vehicle on the road in the real-world environment, based on the monitoring data for the total number concentration at just one point near a busy road

3 Development of new methods for the determination of average emission factors from different types of vehicles on a road on the basis of monitoring data on different days of observation

4 Detailed experimental investigation of combustion aerosols near busy roads Investigation of particle modes and their evolution as the aerosol is transported away from the road

5 Development of new statistical methods of identification of particle modes and analysis of mechanisms of their rapid evolution near a busy road, based on the moving average approach in combination with the simple correlation and canonical correlation analyses

6 Development of a new major mechanism of aerosol evolution based on intensive thermal fragmentation of nanoparticles Comprehensive interpretation of a complex pattern of aerosol evolution near a busy road

7 Statistical determination of possible sources of nanoparticle modes in combustion aerosols near a busy road Determination and interpretation of mutual correlations between different particle modes

8 Statistical analysis of urban background aerosols, including mode analysis and their correlations

9 Development of a new model of aerosol dispersion near a busy road on the basis of the theory of particle fragmentation Determination of the applicability conditions for the proposed model and derivation of conditions for a maximum of the total number concentration at an optimal distance from the road

10 Development of a theory of stochastic evaporation/degradation processes in composite aggregate structures Determination of substantial time delays during fragmentation of composite nanoparticles Investigation of formation of particle modes during aerosol evolution

CHAPTER 2

BACKGROUND AND THEORY

2.1 Ambient aerosols and their origins

The term ‘aerosol’ refers to an assembly (suspension) of liquid or solid particles

in a gaseous medium Such suspensions are usually the result of either natural processes (e.g., marine aerosols, dust storms, etc.), or human activities mainly related to combustion processes, use of fossil fuels, transport, airplanes, etc (Seinfeld and Pandis,

1998, Hinds, 1982, Willeke & Baron, 1993, Fuch, 1989, Kaye, 1981)

Aerosols play a very important role in atmospheric behaviour, climate patterns, global climate changes, air quality in large metropolitan centres, inside our homes and

at the workplace For example, atmospheric aerosols may result in additional reflection

of sunlight from the Earth atmosphere, which may lead to a cooling effect on the global climate Atmospheric aerosols may lead to a substantial increase in the number of precipitation centres, resulting in extensive cloud formation with smaller size of the droplets This may lead to decreased rainfall and increased reflectivity of sunlight (increased brightness of the clouds (Seinfeld & Pandis, 1998, Jacobson, 1999) On the other hand, aerosol particles that strongly absorb solar energy may result in an additional heating effect, leading to intensified global warming (Seinfeld and Pandis,

1998, Jacobson, 1999) Which of these tendencies appear to be predominant in reality is still to be determined

On the more local scale, the most significant effect of aerosols is a decrease of the air quality in our homes, at the workplace, and at other places of our everyday activities and leisure They are one of the most significant air polluting factors in modern cities and large metropolitan centres, produced by a number of different anthropogenic sources resulting from human activities The most significant sources of

aerosol air pollution and reduction of the air quality in the urban environment are transport and industrial emissions (Seinfeld and Pandis, 1998, Willeke & Baron, 1993) These emissions have been shown to have a significant adverse effect on human health and state of our environment

Aerosols are normally classified in terms of size of the particles (Hinds, 1982, Willeke & Baron, 1993), which usually ranges from ~ 1 nm to ~ 100 µm (Kaye, 1981) This classification usually includes three major particle groups: coarse particles (with diameters ≥ 1 µm), fine particles (with diameters between ~ 0.1 µm and ~ 1 µm), and ultra-fine particles or nanoparticles (with diameters < 100 nm) Such a classification is important because size of particles is one of the most important factors in the determination of aerosol properties and behaviour (Mandelbrot, 1983) Moreover, different physical laws and approaches should be used for their description and

characterisation (Cliff, et al, 1978)

Properties of the particles in the atmosphere have been of interest for physicists and meteorologists since the late 1880s, when John Aitken measured for the first time number concentrations of dust and fog particles However, only during the last decade has it become possible to measure concentrations of nano-scale particles in the atmosphere Aerosol number distributions have been widely measured in urban, rural and remote environments to characterise properties of small particles starting from diameters as small as ~ 3 nm (Seinfeld and Pandis, 1998) Unfortunately, the smallest size range (with diameters < 3 nm) is still difficult to access, and there is no clear understanding of concentration and composition of these particles

Coarse particles can be detected and analysed relatively easily, and their contribution to the overall particulate mass of an aerosol is predominant (Friedlander, 1977) They also produce significant visible effects (such as dust storms, smoke from open fires, etc.) Aerosols of such particles may have significant health effects, cause

respiratory problems, result in poor visibility and equipment breakage, and lead to other problems in the metropolitan and rural environments Therefore, until recently, the main efforts have been concentrated on the analysis, monitoring and forecasting of aerosols consisting of coarse particles

At the same time, due to relatively large size of coarse particles, they are effectively stopped by the upper air ways of the human air tract, predominantly in the nose, from where they can be removed very effectively (Siegmann, 1999) This significantly reduces the adverse health effects of course particles on humans (unless during relatively rare significant bushfire events, dust storms, etc.) On the contrary, smaller particles may penetrate deeper into the respiratory tract (Siegmann, 1999) In addition, the highest levels of concentration of trace elements and toxins from anthropogenic sources are usually associated with very small particles mainly in the fine and ultra-fine ranges, i.e., between ~ 1 nm and ~ 2.5 µm (Thomas, et al, 1997)

Commonly, urban aerosols are a mixture of emissions from industrial sources, transport, power plants, natural sources, and particles from the gas-to-particle formation processes Therefore, the aerosol size distribution in urban environment is quite variable Extremely high concentrations of fine particles (up to ~106 cm-3) are found

close to sources, for example, near highways (Zhu et al, 2002ab), but the concentration decreases rapidly with distance from the source (Zhu et al, 2002ab) Nevertheless,

typical particle concentrations in the urban aerosols are substantially (at least an order of magnitude) higher than in the remote areas The particle number distribution in the urban environment is dominated by particles within the range less than ~ 100 nm These particles are of a special interest for aerosol scientists, because they are regarded to be most harmful, their number concentrations are typically high, and a large proportion of

population in the developed countries is exposed to them on the daily basis (Hussein et

al, 2005)

Aerosols in rural areas are mainly of natural origin, but with moderate influence

of anthropogenic sources (Hobbs, et al, 1985) and secondary aerosol formation products

in remote continental areas (Bashurova, 1992) The number distribution is mainly characterized by two modes ~ 0.02 µm and ~ 0.1 µm (Jaenicke, 1993), and the mass distribution mode ~ 7 – 10 µm (Jaenicke, 1993) Similar modes are typical for remote continental aerosols Aerosol number concentrations are typically from ~ 2×103

cm-3 to

~ 104 cm-3 (Bashurova, 1992), with the PM10 concentrations (i.e., the mass concentrations for particles with diameters less than 10 µm) being from ~ 10 µg/m3 to ~

20 µg/m3 (Koutsenogii & Jaenicke, 1994)

An important source of aerosols is particle nucleation and growth in relatively clean air of rural and remote areas The resultant particles are usually in the nanometre range This process is of a growing interest because of its possible effect on climate and health Although, despite more than 100 publications in the literature on this topic, the

nature of the nucleation process is still not entirely clear (Kulmala, et al, 2004)

Therefore, this topic of aerosol science is under intensive current investigation

Particles over the remote oceans are largely of marine origin (Savoie, 1989) The typical concentrations in marine atmospheric aerosols are normally within the range between ~ 100 cm-3 and ~ 300 cm-3 The size of the particles is usually relatively large

an is typically characterized by three distinct modes: D p < 0.1 m, 0.1 < D p <0.6 m, D p

> 0.6 m (Fitzgerald, 1991) Particles in marine aerosols are usually of biological and organic nature, iodine compounds, sea salt, or their combination (Fitzgerald, 1991) Due

to their usually low number concentration, these particles present little or no problems for human health Therefore, the main aim of their investigation is usually related to the effect of these aerosols on climate, cloud formation, and possible spread of microscopic species and their products in the atmosphere

Background free tropospheric aerosols have received relatively little attention These aerosols are usually characterized by two different particle modes in the size distribution These are at ~ 0.01 µm and ~ 0.25 µm (Jaenicke, 1993) The middle troposphere typically has more particles in the accumulation (~ 0.25 µm) mode, compared to the lower troposphere This was explained by precipitation, scavenging, and deposition of smaller and larger particles (Leaitch and Isaac, 1991)

The described types of aerosols rarely appear separately from each other Typically, an observed aerosol is a mixture of these types, resulting in a complex pattern of the corresponding particle size distribution within a huge range of particle diameters of ~ 4 orders of magnitude A number of different modes of particles can appear mixing with each other and thus leading to results that may be difficult to interpret and subdivide into separate groups originating from particular sources On top

of this, we have the natural instability of the atmospheric and environmental parameters (e.g., atmospheric turbulence and the corresponding strong stochastic fluctuations of the measured parameters), which makes the aerosol analysis and interpretation especially difficult

Accurate prediction of concentrations and dispersion of aerosols from different polluting sources in the urban environment is one of the major problems of modern environmental science Solution of this problem is complicated by changing size distribution of the aerosol particles, caused by possible evolutionary and formation processes Therefore, clear understanding of these physical and chemical processes in urban aerosols is an essential goal of modern aerosol science (Seinfeld and Pandis,

1998, Jacobson, 1999) This naturally leads us to the next section where we consider the first (and probably the most important) mechanism of aerosol evolution – turbulent dispersion in the atmosphere

2.2 Turbulent dispersion of air pollutants

One of the major physical mechanisms of dispersion of air pollutants in the atmosphere is turbulent diffusion (Csanady, 1980, Pasquill & Smith, 1983, Zannetti, 1990) Turbulence is stochastic (random) motion of the air caused by breakage of the unstable laminar flow of the air/fluid due to its interaction with obstacles and/or uneven heating (Landau & Lifshitz, 1987) Turbulent diffusion occurs when the diffusing substance (aerosol particles) is transported by such random motion, which is similar to random motion of separate molecules in the air, resulting in conventional molecular diffusion (Csanady, 1980) Mathematical and physical consideration of turbulent diffusion is the same as for molecular diffusion – the diffusing particles experience random walk caused by their random transport by means of air parcels moving randomly due to turbulence

If only the mechanism of turbulent diffusion is taken into account, then dispersion of the aerosol can be described in exactly the same way as for non-reactive gasses – by the Gaussian plume model (Csanady, 1980, Pasquill & Smith, 1983, Jacobson, 1999) This model is based upon the solution of the well-known diffusion equation in a moving incompressible fluid

χ

∇

=χ

∇

•+

where χ is the volume density (or concentration) of the diffusing substance, u is the

velocity of the fluid, D is the coefficient of diffusion, and t is the time (see, for example, Stull, 1989, Jacobson, 1999, Pasquill, 1983)

For the simplest case of one-dimensional diffusion in a uniform medium at rest

(u = 0, D = const), the solution to Eq (2.1) can be written as:

Dt x

e Dt

This equation gives the time-dependent concentration of the diffusing

substance/particles at some moment of time t and the coordinate x The constant Q is the surface density (or surface concentration) of the diffusing substance at x = 0 at the moment t = 0 (at this moment of time, all the diffusing substance is concentrated at the plane x = 0)

It is possible to see that σ = 2 Dt is the typical distance within which the

substance (particles) have spread due to diffusion within the time interval t This

distance can be regarded as a convenient scale of the width of the spatial distribution of particles during their diffusion, and is termed as standard deviation for diffusing

particles from the position x = 0 Eq (2.2) represents the Gaussian distribution that also

applies to turbulent diffusion in the atmosphere However, in this case, the diffusion

coefficient D is much larger than that for molecular diffusion, simply because the scale

of random motion (given by the turbulence scale, i.e., typical size of the turbulent eddies (Landau & Lifshitz, 1987, Csanady, 1980)) is much larger than for molecular diffusion where it is given by the mean free path of the molecules Therefore, in the problems with turbulent diffusion in the atmosphere, molecular diffusion does not play any noticeable role and thus can be neglected

To determine σ in the case of turbulent diffusion of air pollutants in the real environment, a statistical theory of diffusion has been developed, based on the history

of random-walk motion executed by the diffusing particles (Csanady, 1980) Turbulent motion appears to be one of the most difficult problems of modern physics, and the complete understanding of this process in the general case has not yet been achieved Historically, the first successful treatment of turbulent diffusion by Taylor (Taylor, 1922) was directly based on the statistical theory of Brownian motion that was developed by Einstein in 1905 (Einstein, 1905)

In a process of random walk of particles (Brownian motion) or air parcels (turbulent diffusion), the displacement of the diffusing particle is a function of time At the same time, the actual value of this displacement at any given time is random and can only be specified in terms of probability distribution (Taylor, 1922) If this probability distribution is time independent, then we have a steady-state stochastic process that has some simple properties and can be analysed relatively easily Turbulent diffusion is steady-state if the temperature, mean velocity of the air, and turbulent intensity are homogeneous over the whole turbulent field, i.e., over the whole space (Taylor, 1922, Csanady, 1980) The concept of turbulent intensity was introduced to characterize the

“strength” of a turbulent state Turbulent intensities are defined as follows We determine the average velocity of the air, i.e the average wind velocity Then, we determine instantaneous values of wind components at some moment of time and the considered point in space The time average squares of the differences between the instantaneous and average wind components u2, v2, w2 are called turbulent

intensities, where u, v and w are these differences between the instantaneous and average wind components along the x-, y-, and z-axes, respectively

If the magnitude of the mean velocity is U, we can introduce relative turbulent

2.2.1 Taylor theorem and asymptotic properties of the diffusing cloud

Consider a point source of particles with the position vector x’ Let a particle be

released from the source at time t = 0 (the particle position vector at t = 0 is x’) Suppose that during the diffusion time t the particle has moved to its new position x Then P(x – x’, t)dx is the probability that the displacement vector x – x’ ends within the volume element dx around the point x at time t If χ is the mean mass concentration of

the particles at the position x of the volume element dx, then

where Q is the total mass of particles released at the point x’ at the moment of time t =

0

If u is the component of the fluid velocity relative to a reference frame moving

with the average velocity of the fluid (i.e U = 0), then in the steady-state case

0)

(t =

const t

with the same relationships holding for v and w

An important characteristic of a steady-state stochastic process is its

autocorrelation function R(τ) that measures the “persistence” of a given value of a random variable (e.g., velocity) within a time interval τ In other words, the autocorrelation function determines the likelihood that if a particle has a particular

velocity at some moment of time t, then this velocity will have similar magnitude and direction at the moment of time t + τ (Csanady, 1980) It can be shown that the autocorrelation function is given by the equation (Csanady, 1980):

2

)()()

(

u

t u t u

=

where the averages are taken in time In a steady-state process, R(τ) is independent of time t At τ = 0 the autocorrelation function is equal to one, and R(t) → 0 when τ → +

for the x-component (and similar for the y- and z-components), we have

∫

= t u t u t dt dt

t x

d

0

2

')'()(2)]

(

[

Taking an average over the whole ensemble of the diffusing particles of the both sides

of this equation, gives (Taylor, 1922, Csanady, 1980):

)(

where x2 is the square of displacement of a diffusing particle averaged over the whole considered ensemble of the particles Therefore, x2 is a function of time On the contrary, the other averages in Eq (2.7) are taken over time (and thus are time-independent)

Eq (2.7) is the most important fundamental result of the random-walk theory of diffusion (Brownian motion or turbulence) In the theory of turbulent diffusion it is known as the Taylor theorem (Taylor, 1922)

For an ensemble of independently diffusing particles that are released at x = 0

and t = 0, the spread of the plume along the x-axis is given by the so-called radius of

inertia σx of the mean concentration distribution (Csanady, 1980):

The length σx defined by Eq (2.8) is a measure of the cloud size after some time of turbulent diffusion (it has the same meaning as σ = 2 Dt introduced below Eq (2.2))

Assuming the χ(x,y,z,t) is the concentration formed by the independently diffusing particles and substituting Eq (2.4) into Eq (2.8), gives:

2 2

2

),,,(x y z t dxdydz x P

)()(2)

Asymptotic properties of σx can be determined from the behaviour of R(τ) at small and

large values of τ For example, when τ → 0 (and R → 1), Eq (2.10) is reduced as

2 2 2

t u

x ≈

with the accuracy of ~ t4 – see (Csanady, 1980)

On the other hand, when τ → ∞, R(τ) → 0 In this case,

)(

2 2 0 1

2

t t t u

t

Thus for small dispersion times (small t), the size of the plume xincreases linearly with

time, whereas for large t, it is proportional to the square root of time

2.2.2 Turbulent diffusion from a point continuous source

Let the point source of particles be at the origin of the frame (x,y,z) and the wind

be parallel to the x-axis We assume that the total mass of the particles released from the source at the moment of time t = 0 is equal to Q Then, assuming that the probability

distribution P(x,y,z,t) is Gaussian, the resultant average particle concentration as a

function of time and coordinates can be written as (Csanady, 1980):

−σ

−

−σ

σσπ

=

2 2

2 2

2 2

/ 3

22

2

)(exp)

2(),,,

(

z y x

z y x

z y Ut

x Q

t z y

where U is the wind speed

If we have a continuous point source of particles, then such a source can be represented as a number of releases of particle plumes at infinitesimally close moments

of time In other words, we have a number of instantaneous point sources located at the same point in space (frame origin) and releasing particles at different (infinitesimally close) moments of time, thus producing a continuous particle release by a continuous source

One of the main assumptions of the mathematical analysis of turbulent diffusion

is that the motion of each of the air parcels (elements) is independent of the other neighbouring parcels (Csanady, 1980) Therefore, in this approximation, turbulent diffusion is a linear phenomenon, and the concentration field from multiple sources (releasing particles at different moments of time) can be represented as a simple superposition of the fields from each of the individual sources Thus, the steady-state concentration from a continuous point source is give as (Csanady, 1980):

z y x z y x

dt z

y Ut

x q

z y

x

σσσ

−σ

−

−

∫π

=

22

2

)'(exp)

2(),,

(

2

2 2

2 2

2 0

2 /

where q is release rate, i.e., the total mass of the particles released per unit of time

At short distances from the source, where the size of the cloud grows linearly with time (see Eq (2.11)):

x≈ u m t, y≈ v m t, z≈ w m t ,

and integration of Eq (2.14) gives (Frenkiel, 1953):

χ

r u

Ux r

u

x U r

u

Ux u

U r

w v

qu z

y

x

m m

m m

m m

m

2

1erfexp

2

12

exp)

2()

,

,

(

2 2

2 2

2 2

2 / 3

where

2 2

2 2 2

2 2 2

z w

u y v

u x

r

m

m m

+

However, for a point source that is not too high above the ground level (within ~

100 m), the application of this formula is non-trivial, since at these heights the wind speed and the turbulence parameters (e.g., diffusivity) are height-dependent (Csanady, 1980) The detailed analysis of this situation is very complex and has not been done in the general case Usually, the wind speed is assumed to be approximately constant with height (Benson, 1992a,b) or has a logarithmic profile (Stull, 1989), which in a number

of situations is associated with significant errors Even if we assume that the turbulence

is homogeneous in the first approximation, we have to take into account the surface of the ground that works as a rigid boundary Usually, the ground is assumed to be a perfect reflector, and it can be introduced by a mirror-image source placed below the ground (Csanady, 1890, Jacobson, 1999, Seinfeld & Pandis, 1998) This approach has been confirmed by the experimental evidence (see, for example, (Csanady, 1968, Clauser, 1956)) Therefore, the mean concentration field due to a continuous elevated

point source of strength q can be written as (Csanady, 1980):

−+

−σ

σπ

=

2 2

2 2

2 2

2

2

)(2

exp2

)(2

exp2

z y

z y

h z y h

z y U

q z