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Measurement and methods of assessing the impact of prevalent particulate matter sources on air quality in southern California

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Measurement and methods of assessing the impact of prevalent particulate matter sources on air quality in southern California

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MEASUREMENT AND METHODS OF ASSESSING THE IMPACT OF
PREVALENT PARTICULATE MATTER SOURCES ON AIR QUALITY IN
SOUTHERN CALIFORNIA

by

Harich Chandra Phuleria

____________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ENVIRONMENTAL ENGINEERING)

May 2007

Copyright 2007 Harish Chandra Phuleria

ii

Dedication

To my father who showed me the path of patience, perseverance, and righteousness
iii
Acknowledgements

During the development of my graduate studies in the University of Southern California
several persons and institutions collaborated directly and indirectly with my research.
Without their support and timely help it would not be possible for me to complete this
work. That is the reason I want to dedicate these few words to recognize their support
and contribution.

First and foremost, I would like to express my deepest gratitude to my advisor
Constantinos Sioutas, who has become much more than a thesis advisor. His invaluable
guidance and consistent support has enabled me to develop as an individual as well as a
professional. Whether it were the thoughtful discussion concerning with the research or
non-academic philosophical perspectives whiling sipping coffee outside, he has always
been very enthusiastic, encouraging and open to share the ideas.

I also wish to thank the other members of my guidance committee, Dr Philip M. Fine, Dr.
Dennis Phares, Dr. Massoud Pirbazari, and Dr. Nino Kuenzli for providing thoughtful
suggestions on my research work as well as dissertation. I feel really fortunate to have
had the opportunity to work with Dr Fine, who helped me time and again with his
valuable suggestions and to-the-point advice in various research projects I have
undertaken.

My sincere thanks to Bhabesh Chakrabarti and Michael Geller for their support in
familiarizing me with the trivial but important details of the research work we undertook
iv
together. I would also like to thank my colleagues Satya, Babu and Ning for their support
and timely help when I needed it badly. I also wish to extend my thanks to Arantza and
Meg for helping me during those all-day long organics extractions and hair-scratching
GC-MS analysis. Heartiest thanks to Ajay, Subhash, Suresh, Biqin, Prasanna and Suraj
for their moral support and stimulating conversations that made my stay so enjoyable and
fun while on those gray days. Thank you all for listening to me.

I owe sincere thanks to all of the people associated with the Southern California Particle
Center and Supersite for they continually provided support in both field and laboratory
experiments and analyses. I would also like to acknowledge the United States
Environmental Protection Agency and California Air Resources Board as the primary
sponsors of all of this research.

In the end, I would like to mention my parents and my extended family, and my friends
who have been an integral part of my being; whether it is my personal outlook or
professional growth. They have not only been just observer but solid pillars of strength
for whatsoever results I have produced. They are the reasons for my beliefs and
convictions. Eeja, pitaji and mamiji, thank you from the bottom of my heart for your
unconditional love and support.

Harish Chandra Phuleria
Los Angeles, CA

Table of Contents
Dedication ii
Acknowledgement iii
List of Tables vii
List of Figures viii
Abbreviations xi
Abstract xiv
1 Chapter 1: Introduction 1
1.1 Background 1
1.1.1 Particulate matter characteristics 1
1.1.2 Sources of airborne particles 3
1.2 Airborne PM and health effects 4
1.3 Rationale of the current research 8
1.4 Thesis overview 9
1.5 Chapter 1: References 13
2 Chapter 2: Seasonal and spatial trends in particle number concentration
and size distribution at the Children’s Health Study sites in Southern
California 15
2.1 Chapter 2: Abstract 15
2.2 Chapter 2: Introduction 16
2.3 Chapter 2: Methods 19
2.3.1 Sampling sites 20
2.3.2 Instrumentation 24
2.4 Chapter 2: Results and discussion 26
2.4.1 Seasonal and spatial trends 26
2.4.2 Diurnal trends 37
2.4.3 Correlations between PM numbers, surface area and mass 45
2.4.4 Long Beach October 2002 strike analysis 47
2.5 Chapter 2: Summary and conclusions 53
2.6 Chapter 2: References 57
3 Chapter 3: Air quality impact of October 2003 fire in Southern
California 62
3.1 Chapter 3: Abstract 62
3.2 Chapter 3: Introduction 63
3.3 Chapter 3: Methods 66
3.4 Chapter 3: Results and discussion 71
3.5 Chapter 3: Summary and conclusions 86
3.6 Chapter 3: References 88

4 Chapter 4: Size segregated measurement of organic aerosols emissions
from on road vehicles in the Caldecott tunnel, CA 92
4.1 Chapter 4: Abstract 92
4.2 Chapter 4: Introduction 93
4.3 Chapter 4: Methods 98
4.3.1 Tunnel sampling 98
4.3.2 Traffic characterization 99
4.3.3 Pollutant measurement and sample collection 100
4.3.4 Organic speciation analysis 101
4.3.5 Emission factors 102
4.4 Chapter 4: Results and discussion 104
4.4.1 Tunnel concentrations 104
4.4.2 Size resolved emission factors 114
4.4.3 Comparison with other studies 122
4.5 Chapter 4: References 130
5 Chapter 5: Roadside measurements of size-segregated particulate
organic compounds near gasoline and diesel-dominated freeways
in Los Angeles, CA 136
5.1 Chapter 5: Abstract 136
5.2 Chapter 5: Introduction 137
5.3 Chapter 5: Methods 141
5.3.1 Sampling locations 141
5.3.2 Traffic characterization 142
5.3.3 Pollutant measurement and sample collection 143
5.3.4 Organic speciation analysis 144
5.4 Chapter 5: Results and discussion 145
5.4.1 Mean measured organic species concentrations 145
5.4.2 Comparison of CA-110 and I-710 measurements 152
5.4.3 Chemical profiles of organic markers 155
5.5 Chapter 5: Summary and conclusions 166
5.6 Chapter 5: References 168
6 Chapter 6: Conclusions and future research directions 173
6.1 Summary 173
6.2 Conclusion 174
6.3 Future research directions 176
6.4 Chapter 6: References 182
Bibliography 183

vii
List of Tables

Table 1.1 Health effects of PM 7
Table 2.1 Sampling periods during which SMPS-CPC configuration
was employed at various sampling sites
25
Table 2.2 Summary statistics showing average total particle surface
area (SA) and number median diameter (NMD)
27
Table 2.3 Pearson correlation coefficient (R) between total particle
number concentration and total particle surface area
concentration
46
Table 2.4 Correlation coefficient (R) between total particle number
concentration and PM
10
47
Table 3.1 Average concentrations of pollutants with the standard
deviation at the five CHS sites before, during and after the
fire
74
Table 4.1 Traffic volume in the Caldecott tunnel 99
Table 4.2 Mean mass concentrations (in ng/m
3
) of the measured
species in a) Bore 2, and b) Bore 1
105
Table 4.3 Pearson correlation coefficient between mass concentrations
of various measured species in ultrafine and accumulation
modes.
110
Table 4.4 Emission factors (in µg/kg fuel burned) attributable to LDVs
and HDVs in ultrafine and accumulation mode.
116
Table 4.5 Comparison of particle phase PM
2.5
emission factors (in
µg/kg fuel burned) attributable to a) LDVs and b) HDVs and
mixed tunnel fleets.
123
Table 5.1 Mean mass concentration (in ng/m3) of the organic tracers
measured near a) CA-110 freeway and b) I-710 freeway
146
Table 5.2 Mean concentrations of the meteorological and bulk-
chemical parameters measured near CA-110 and I-710
Freeway
148

viii
List of Figures

Figure1.1 Typical particle size distribution by mass and number
showing different size modes
2
Figure 2.1 Locations of sampling sites in Southern California 21
Figure 2.2 Monthly average particle number concentrations and
ambient temperatures at a) Long Beach, b) Riverside, c)
Mira Loma, d) Upland, e) Lancaster, f) Alpine, and g) Lake
Arrowhead
29
Figure 2.3 Average number size distributions in winter and
summer/spring periods at a) USC, b) Long Beach, c)
Riverside, d) Mira Loma, e) Upland, f) Lancaster, g) Alpine,
and h) Lake Arrowhead
33
Figure 2.4 Diurnal trends of size-segregated particle number, O
3
and
NO
x
at USC during a) Dec 2002-Jan 2003 and b) Sep 2003
38
Figure 2.5 Diurnal trends of size-segregated particle number, O
3
and
NO
x
at Long Beach during a) Nov 2002 and b) Aug-Sep
2003
39
Figure 2.6 Diurnal trends of size-segregated particle number, O
3
and
NO
x
at Riverside during a) Nov 2002 and b) Mar-Apr 2002
40
Figure 2.7 Diurnal trends of size-segregated particle number, O
3
and
NO
x
at Mira Loma during a) Jan-Feb 2002 and b) Jun 2002
41
Figure 2.8 Diurnal trends of size-segregated particle number, O
3
and
NO
x
at Alpine during a) Dec 2003-Jan 2004 and b) Apr-May
2003
43
Figure 2.9 Diurnal trends of size-segregated particle number, O
3
and
NO
x
at Lake Arrowhead during Jul-Aug 2002
44
Figure 2.10 Daily traffic data for Freeways 710 and 410 before, during
and after harbor strike at Long Beach in Sep-Oct 2002 a)
total truck counts, and b) total vehicle counts
48
Figure 2.11 24-hour averaged a) PN and PM
10
, b) CO, NO
x
, and O
3
, and
c) temperature and RH - before, during and after the port
strike at Long Beach in Sep-Oct 2002
50
Figure 2.12 Average particle number size distribution before, during and
after the port strike at Long Beach in Sep-Oct 2002
52
ix

Figure 3.1 Map showing the fire area and the sampling sites in the Los
Angeles basin.
67
Figure 3.2 24-hour averaged PM and gaseous pollutant concentrations
during the study at a) Glendora, b) Long Beach, c) Mira
Loma, d) UC Riverside and e) Upland.
72
Figure 3.3 Satellite images from NASA earth observatory showing a)
Southern California during the peak of the fire episode on
October 28
th
, 2003, with the smoke plumes blowing west,
and b) the same area after the wind reversal, on the afternoon
of October 29
th
, 2003, with a visible marine layer and the
smoke plumes blowing towards the east.
73
Figure 3.4 Hourly a) PM and b) gaseous pollutant concentrations at
Upland
79
Figure 3.5 Two-hour averaged fine (FP) and ultrafine (UFP) particle
mass concentrations at USC.
80
Figure 3.6 Particle size distributions at Upland a) at 10AM: before,
10/24/03, and after the fires; and b) at 12PM: before,
10/29/03, and after the fires.
82
Figure 3.7 Particle size distributions on different days at 11PM in
Westwood Village: a) Outdoor; and b) Indoor.
83
Figure 3.8 Indoor/Outdoor particle size distributions at 6AM in
Westwood Village on a) 10/27/03; and b) 11/04/03
85
Figure 4.1 Correlation between mass concentrations of EC and Sum
PAHs with MW 226 and 228 in ultrafine mode
112
Figure 4.2 Correlation between mass concentrations of Sum Hop-Ster
and UCM in a) ultrafine mode; b) accumulation mode
113
Figure 4.3 HDV/LDV emission factor ratios for the measured organics
species
119
Figure 4.4 Correlation between ultrafine and accumulation mode PAHs,
Hopanes and Steranes for a) HDVs; b) LDVs
120
Figure 5.1 Sampling locations near freeways a) CA-110; b) I-710 141

x

Figure 5.2 Correlation of organic species between freeway and
background sites near CA-110 in a) ultrafine

size mode for
hopanes and steranes; b) accumulation size mode for
hopanes and steranes; c) ultrafine

size mode for PAHs; and
d) accumulation size mode for PAHs.
149
Figure 5.3 Correlation of organic species between freeway and
background sites near I-710 in a) ultrafine

size mode for
hopanes and steranes; b) accumulation size mode for
hopanes and steranes; c) ultrafine

size mode for PAHs; and
d) accumulation size mode for PAHs.
151
Figure 5.4 Comparison of measured - a) hopanes and steranes
(normalized to ∆CO
2
); and b) PAHs and EC (normalized to
∆CO
2
) - between CA-110 and I-710 in PM
2.5
size mode.
154
Figure 5.5 Chemical profile of hopanes and steranes (normalized to TC)
in PM
2.5
size mode in a) CA-110 study and Caldecott (LDV)
study; and b) I-710 study and Reconstructed Caldecott study.
157
Figure 5.6 Correlation of hopanes and steranes (normalized to Sum of
hopanes and steranes) in PM
2.5
size mode between a) CA-
110 study and Caldecott (LDV) study; and b) b) I-710 study
and Reconstructed Caldecott study.
158
Figure 5.7 Chemical profile of PAHs (normalized to TC) in PM
2.5
size
mode in a) CA-110 study and Caldecott (LDV) study; and b)
I-710 study and Reconstructed Caldecott study.
160
Figure 5.8 Correlation of measured organic species (normalized to TC
in PM
2.5
) between ultrafine and accumulation size modes in
a) CA-110 study; and b) Caldecott (LDV) study.
161
Figure 5.9 Correlation of measured organic species (normalized to TC
in PM
2.5
) between ultrafine and accumulation size modes in
a) I-710 study; and b) Reconstructed Caldecott study. Error
bars represent SE.
164

xi
Abbreviations

BAM Beta Attenuation monitor
BaP Benzo(a)perylene
BgP Benzo(ghi)perylene
CA California
CARB California Air Resources Board
CHS Children’s Health Study
CMB Chemical Mass Balance
CO Carbon monoxide
Cor Coronene
CPC Condensation Particle Counter
CRCAES Citrus Research Center and Agricultural Experiment Station
EC Elemental Carbon
EF Emission factor
ESP Electrostatic precipitator
GCMS Gas chromatography-Mass spectrometry
HDV Heavy Duty Vehicles
LAB Los Angeles Basin
LDV Light Duty Vehicles
MOUDI Micro Orifice Uniform Deposit Impactor
NAAQS National Ambient Air Quality Standards
NMD Number median diameter
xii
NO Nitrous oxide
NO
2
Nitric oxide
NO
x
Total nitrogen oxides species
O
3
Ozone
OC Organic Carbon
PAH Polycyclic Aromatic Hydrocarbons
PM Particulate matter
PM
10 Particulate matter with aerodynamic diameters less than 10 µm
PM
2.5 Particulate matter with aerodynamic diameters less than 2.5 µm
PN Particle Number
RFG Reformulated gasoline
SA Surface area
SCAQMD South Coast Air Quality Monitoring District
SCPCS Southern California Particle Center and Supersite
SD Standard deviation
SE Standard error
SMPS Scanning Mobility Particle Sizer
SO
2
Sulfur dioxide
TC Total Carbon
TEOM Tapered Element Oscillation Microbalance
TIC Total ion current
UCM Unresolved complex mixture

xiii
UF Ultrafine
USC University of Southern California
USEPA United States Environmental Protection Agency
VOC Volatile organic compounds

xiv
Abstract

Recent focus of studies on health effects of ambient particulate matter (PM) have
suggested particle chemical composition in addition to particle size, shape and number
concentration responsible for the observed health outcomes. However, chemical
composition and size distribution of the atmospheric particles can be strongly affected by
the differences in ambient temperature, relative humidity, photochemical activity and
source contributions. This thesis is intended to demonstrate the importance of
characterizing predominant PM sources from an exposure perspective and develop
methods of assessing their impact on air quality in Southern California. A study of
particle number concentration and size distribution showed seasonal and spatial
variability in Southern California. While contribution of local vehicular emissions was
most evident in winter, effects of long-range transport of particles and photochemical
particle formation were enhanced during warmer periods. Ship emissions are found to be
dominant source of lower accumulation and ultrafine particles near ports. During the
wildfires in October 2003 in Southern California, PM
10
(particulate matter with
aerodynamic diameter 10 µm and less) levels were found highly elevated, while ozone
concentrations dropped during the fire episode and these fire-borne particles were found
to effectively penetrate indoors. To characterize the emission profiles from on-road diesel
and gasoline vehicle-fleets, size-segregated PM samples were collected inside the
Caldecott tunnel in Orinda, CA and analyzed for vehicular organic tracers such as
hopanes and steranes, and polycyclic aromatic hydrocarbons (PAHs). In a separate study,
detailed information on the chemical characteristics of organic PM originating from a
xv
pure gasoline and a diesel dominated mixed-traffic freeway is obtained. While hopanes
and steranes, and high molecular weight PAHs levels are found comparable near these
freeways, elemental carbon and lighter molecular weight PAHs are found much elevated
near diesel dominated mixed-fleet freeway. Remarkably good agreement is observed
between the roadside measurements and the emission factors calculated from the tunnel
measurements especially for hopanes and steranes. Our results indicate that the fleet
composition as well as atmospheric dilution has strong impact on the ambient
concentrations of these organic tracers.
1
Chapter 1: Introduction

1.1 BACKGROUND
1.1.1 Particulate matter characteristics
Ambient particulate matter (PM) is a general term given to the atmospheric aerosols,
where aerosol is a suspension of solid and/or liquid droplets in the atmosphere (Hinds,
1999). National Ambient Air Quality Standards (NAAQS) set primary and secondary
standards for ambient PM to protect human health and to protect against adverse effects
on plants, animals, visibility and public’s enjoyment of life and property.

PM is a mixture of many different components with local and regional variation, e.g.
anthropogenic or naturally emitted; primary or secondary particles; by source – e.g.
combustion products and traffic or by physico-chemical properties such as solubility or
acidity. For practical reasons under aspects of emission measurements, PM is
characterized by particle size i.e. particle aerodynamic diameter (Figure 1.1).

PM
10
is generally defined as all particles equal to and less than 10 µm in aerodynamic
diameter. From the health point of view, particles larger than PM
10
have a deposition of
nearly 100% in the nose and deposition rate decreases with decreasing size below 10 µm
(Hinds, 1999). Particles below aerodynamic diameter of 10 µm are therefore more
important from the health perspective as they are inhalable. PM
2.5
is defined as the
particles with an aerodynamic diameter of 2.5 µm or less and they are called fine PM.
The particles in the range between PM
2.5
and PM
10
-

are termed as coarse mode particles.
Particles with a diameter of less than about 0.1 µm are considered as the ultrafine particle
fraction (UFP) whereas the range between 0.1 to 2.5 µm is referred to as the
accumulation mode (Ibald-Mulli et al., 2002).

FIGURE 1.1: Typical particle size distribution by mass and number showing different
size modes

PM can exist in liquid or in the solid phase and their size can vary from nanometer ranges
(fresh emissions from vehicular exhausts) to as high as 100 µm (coarse particles). Based
on experimental measurement of ambient PM size distribution, PM has been grouped into
three size modes explained above. The number distribution is dominated by the presence
of numerous smaller particles with aerodynamic diameters less than 0.1 µm, which are
PM in the ultrafine mode, whereas the surface area is dominated by particles in the 0.1 to
0.5 µm size range. Two distinct modes are observed in the mass distribution, where the
first fraction is the submicron mode typically referred to as accumulation mode, while the
second mode is the coarse mode.
2
3
1.1.2 Sources of airborne particles
Two distinct processes can form PM in the atmosphere; it can be directly emitted into the
atmosphere or formed indirectly by chemical reactions. The former process is called
primary emissions, while the latter is called secondary formation. The relative importance
of the primary and secondary PM is defined by the geographical location, precursor gas
emissions, atmospheric chemistry and meteorology.

The smallest PM size range is the nuclei mode particles or the ultrafine particles and are
formed either through direct emissions from the engine combustions or through gas-to-
particle conversion mechanisms in the interface of tailpipe and atmosphere. Ultrafine
particles are associated both with natural and anthropogenic sources (Kavouras et al.,
1998; Whitby and Svendrup, 1980; Kotzick and Niessner, 1999). Ultrafine particle sizes
change continuously due to phase transformation processes and this phenomenon
becomes more enhanced as particle size decreases (Seinfeld et al., 1998). Diffusion is the
predominant mechanism of their deposition and removal from atmosphere because of
their small size. These particles also grow by condensation or coagulate with larger
particles and as a result of which, the lifetime of ultrafine particles are very short

The next size range, the accumulation mode (0.1 to 2.5 µm), is the product of
combustion, atmospheric reactions and the result of coagulation of smaller particles in the
nuclei mode with these larger particles. Besides, condensation of gases onto pre-existing
particles also contributes to the accumulation mode. The coagulation rate for particles in
the nuclei size fraction with the larger particles in the accumulation range is generally
4
faster than the self-coagulation of the small particles, which is a result of high mobility of
smaller particles combined with larger target area of the larger particles for coagulation.
The accumulation mode represents a major fraction of aerosol by mass but only a small
fraction of the number distribution. These particles are too small to settle down
gravitationally and hence have a higher atmospheric residence time than the larger
particles (coarse). The longer lifetime combined with visibility reduction resulting from
scattering of solar radiation, cloud formation and health effects make them important for
investigative studies.

The coarse mode consists of particles in 2.5 to 100 µm. They are usually generated by
mechanical processes resulting from the disintegration of bulk solid material, or from
natural sources like sea spray, natural erosion, volcanic explosion, and dust resuspension
from wind. Coarse particles are relatively large and have a short lifetime of a few hours
in the atmosphere, due to gravitational settling. Inhalable coarse particles are defined as
those having diameter smaller than 10 µm. Chemically the composition of the coarse
particles indicates their source and is dominated by inorganics and metals nevertheless
significant amounts of organic compounds are also found to be associated with the dust
particles (Boon et al, 1998).

1.2 AIRBORNE PM AND HEALTH EFFECTS
The health effects of particulate matter have been subjected to intense study in recent
years. Exposure to airborne particulate matter has been associated with increase in
mortality and hospital admission due to respiratory and cardiovascular diseases. These
5
effects have been found in short-term studies, which relate to day-to-day variations in PM
concentrations and health and long term studies, whish have followed cohort of exposed
individuals over time. While epidemiological studies indicate an association between
adverse health effects and ambient fine particle concentrations in susceptible individuals,
toxicological studies aim to identify mechanisms, which are causal for the gradual
transition from the physiological status towards patho-physiological disease. Impressive
progress has been made in recent years when objectives changed from classical tests like
lung function, etc. to endpoints comprising of particle induced oxidative stress, cell
signaling and activation, release of mediators initiating inflammatory processes not only
in the respiratory tract but also in the cardio-vascular system. Particularly, the large
surface area of ultrafine particles provides a unique interface for catalytic reactions of
surface-located agents with biological targets like proteins, cells, etc.

Studies of health effects of particulate matter become complicated by the variable
chemical composition and size, thus toxicity, of ambient particles. The potential for
particulate matter to induce adverse health effects is related to particle size. Particles of
10 µm or less in aerodynamic diameter can be inhaled deep into the lungs where they can
induce tissue damage and various adverse health effects. Particles larger than 10 µm in
diameter are generally filtered out in the nasal passages, and do not enter the lungs to any
great extent.

Numerous epidemiological and toxicological studies have found associations between
measured PM mass and adverse health outcomes (Dockery et al., 1993; Linn et al., 2000;
6
Lippmann et al., 2003; Peters et al., 2001; Pope et al., 2002; Pope et al., 1995; Ritz et al.,
2002; Samet et al., 2000). However, prevailing scientific opinion asserts that, when
considering plausible biological mechanisms of injury, PM mass is probably only a
surrogate measure of other physical or chemical properties of PM that are the actual
cause of the observed health outcomes (NRC, 2004). Several studies have since
attempted to link health effects or toxicity measurements with particle characteristics
such as particle size, number concentration and chemical composition. For instance,
ultrafine particles (with diameters less than about 100 nm) have been demonstrated to be
more toxic and biologically active than larger particles (Li et al., 2003; Oberdorster,
2001). Other studies have found associations with PM chemical constituents and a
summary is provided in table 1.1 (Biswas and Wu, 2005). In general, results from these
studies have been inconsistent due to: a) the different health outcomes considered, b) the
likelihood that health effects are induced by a combination of several physical or
chemical properties of PM, c) the degree of association may be quite low despite being
statistically significant, and d) the possibility of fortuitous associations given the limited
sample sizes and the hundreds of measured particulate organic and elemental chemical
species that may be the cause of the observed health effects.

It is worth mentioning that though greater association between ultrafine particles and
associated health outcomes has been stressed recently, nevertheless it does not exclude
the effect of the other two size fractions. In absence of definite mechanistic pathways
associated with particle related health effects, associating a particular size fraction shall
7
be difficult to underline and would remain the area of focused research in the coming
future.

TABLE 1.1: Health effects of PM (more pronouncedly ultrafine PM)
Category Summary of Findings
Respiratory deposition • Ultrafine particle s effectively deposit in all
respiratory regions by diffusion
• Dependence on age gender and activity
• Different mathematical models developed
• More effective for charged particles

Translocation • Translocation occurs locally to interstitial sites in the
respiratory tract and systematically to
extrapulmonary organs
• Also through olfactory pathway

Dermal uptake • Insignificant penetration of TiO2 nanoparticles
through the skin layer

Toxicity- TiO
2 • Ultrafine more reactive than larger per same mass
basis
• No special reactivity for TiO2 nanoparticles
compared to larger particles
• Different biological mechanism in different species
• Ultrafine particles in sunscreen not penetrating the
skin layer

Toxicity - Carbon • Toxicity varies significantly depending on the type
and structure of carbon
• Effects due to iron in SWCNT that generate free
radicals

Toxicity – Transition metals • Generate free radicals through Fenton reaction
• Acid coated on particles can also cause adverse
health effects

(From Biswas and Wu, 2005)

8
1.3 RATIONALE OF THE CURRENT RESEARCH
The findings from both the toxicological and epidemiological studies further implicate
the absolute need for a better understanding of PM characteristics in terms of their
chemical composition, source emission profiles, variable spatial and temporal exposures
and associated toxicological properties.

PM chemical composition of the atmospheric particles can vary with season, size and
location. Differences in ambient temperature, relative humidity, photochemical activity,
and source contributions are important parameters contributing to the differences in
concentrations and size distributions of a particular chemical species (Neususs et al.,
2002; Christoforou et al., 2000; Cass et al., 2000). Since particle chemistry and particle
size most likely affect the toxicological potential of PM, knowledge of the seasonal and
spatial variability of the size-resolved PM chemical composition is vital in understanding
PM effects on health. This knowledge can also be utilized in the design of health studies
that take advantage of this variability to examine the relative effects of different particle
characteristics.

In addition, although state and federal environmental protection agencies keep regulating
local as well as regional air quality, increasing population and hence increased direct
pressure on resources would result in alleviated concentration of pollutants in ambient
environments. Thus the regulators need to have a more comprehensive understanding of
the seasonal, temporal and spatial variability in pollutant levels to avoid human exposure
to the high concentration of these pollutants. Keeping that in mind, and with the
9
increasing evidence of associations of ultrafine particles and health impacts, a more
complete representation of size-fractionated ultrafine chemical characteristic is attempted
in this thesis across different seasons and locations in the Los Angeles basin.

Vehicles constitute a major source of particulate matter (PM) due to both direct
emissions, via combustion and mechanical wear, and secondary formation, whereby
organic and inorganic vapors undergo gas-to-particle conversions in the atmosphere (Shi
et al., 1999). In Southern California most PM
2.5
originates from vehicular emissions. An
attempt will be made in this thesis to determine the amount of particulate emissions
produced by various vehicles types light-duty and heavy-duty Until now, none of the
studies have examined the size segregated organic tracer emissions from on road vehicles
and apportion the contribution from gasoline and diesel vehicles. In this study we
attempted, for the first time, a thorough investigation of particulate phase organic tracer
emissions from diesel and gasoline vehicles. Such information will add to the existing
body of knowledge and to a better understanding of relative contribution of organic
aerosol emissions from vehicles, which when combined with CMB models and other
source data can provide the background information upon which long-term campaigns for
population exposure assessments will be based.

1.4 THESIS OVERVIEW
The purpose of this research proposal is to demonstrate the importance of characterizing
various airborne PM sources and develop methods of assessing their impact on air quality
in particular in Southern California. Furthermore, the spatial and temporal variability in
10
various fractions of PM will be validated in order to emphasize and support more specific
and rigorous nature of aerosol research.

In order to understand the crucial mechanistic link between airborne PM and PM
constituents and associated health outcomes, characterization and contribution of various
PM sources needs to be assessed. Understanding the definite character of source profiles
under real-world conditions will further facilitate the air pollution mitigation efforts by
state as well as federal environmental protection agencies. This thesis proposal comprised
of five chapters with the first chapter being Introduction and brief account of aerosol
science, establishing the desired motivation and need of such research presented in
subsequent chapters.

Chapter two shall present the spatial and temporal variation of ambient PM and gaseous
copollutants at various source, receptor and pristine sites in Southern California. The aim
of this study is to assess the pollutant levels and PM characteristics such as size
distributions at these sites affected by different sources. Further, this study affirms the
localized nature of air quality in Southern California hence the differential PM exposure
to the residents in these places. Also, studied is the impact of an one month harbor strike
at Long Beach port on air quality and thus the impact of various PM sources targeting
emissions from heavy duty diesel fleets transporting goods to and from the port. One of
the important conclusions of this study was to highlight the impact of idling ship
emissions during the strike and hence the dynamic nature of various activities and potent
sources on Long Beach air quality.
11

In contrast to the very rigorous, planned and systematic study of air quality at different
source and receptor sites in Southern California presented in chapter two, chapter three
presents an “opportunistic” study of the impact of wildfires on air quality in Los Angeles
Basin. We were fortunate to procure PM as well as gaseous copollutants data from few of
the CHS sampling sites when the fire broke in late October 2003. The nature of generated
aerosols from wildfires has been described and fire has been established as one of the
major sources of lower accumulation mode particles. The impact on air quality is
assessed and further ability of these fire-borne particles to penetrate indoors is outlined.

Shifting gears once again, in chapter four, organic aerosol emissions from real-world
traffic on a roadway tunnel is studied. Size segregated emission factors of organic tracers
of diesel and gasoline fleets such as PAHs, hopanes and steranes are calculated
underlining the higher emissions from HDVs compared to LDVs. The real-world
emission profile of steady state vehicle fleets would enable to apportion the contribution
of HDVs and LDVs on freeway emissions and hence their potential as prevalent PM
sources in urban areas.

Continuing further, in chapter five, organic aerosol emissions from real-world traffic is
studied near freeway CA-110 (pure gasoline) as well as I-710 (20% HDV mix). Size
segregated mass concentrations of organic tracers of diesel and gasoline fleets such as
PAHs, hopanes and steranes are measured underlining the higher emissions from HDVs
12
compared to LDVs. The chemical profile of hopanes and steranes, and PAHs are
compared with the one measured inside Caldecott tunnel for LDVs and HDVs. .

Finally chapter six ties together the work presented herein and future research directions
are presented. The cohesiveness, of the topics outlined above and the ideas proposed
there for future research, would become apparent as the characterization and application
of emission profiles of vehicular and other sources would be applied on freeway
emissions using CMB models.
13
1.5 Chapter 1: REFERENCES
Biswas P. and Wu Y. (2005) Nanoparticles and the environment. Journal of the Air &
Waste Management Association, (6), 708-746.
Cass, G.R., L.A. Hughes, P. Bhave, M.J. Kleeman, J.O. Allen, and L.G. Salmon, (2000).
The chemical composition of atmospheric ultrafine particles, Philosophical Transactions
of the Royal Society of London Series a-Mathematical Physical and Engineering
Sciences, 358 (1775), 2581-2592.
Christoforou, C.S., L.G. Salmon, M.P. Hannigan, P.A. Solomon, and G.R. Cass, (2000),
Trends in fine particle concentration and chemical composition in Southern California,
Journal of the Air & Waste Management Association, 50 (1), 43-53.
Dockery, D.W, Pope, C.A, Xu, X.P. Spengler, J.D., Ware Jh, Fay, M.E. Ferris, B.G.,
Speizer, F.E. (1993). An Association Between Air-Pollution and Mortality in 6 United-
States Cities. New England Journal of Medicine.329 (24): 1753-1759.
Dockery, D.W. and Pope, C.A. (1994). Acute Respiratory Effects of Particulate Air-
Pollution. Annual Review of Public Health.15: 107-132.
Donaldson, K. and MacNee, W. (1998). The Mechanism of Lung Injury Caused by PM
10

in Issues in Environmental Science and Technology, Ed. R.E. Hester and R.M.Harrison,
the Royal Society of Chemistry. 10:21-32.
EPA (2003). National Ambient Air Quality Standards (NAAQS).
http://www.epa.gov/air/criteria.html
Ferin, J., Oberdorster, G., Soderholm, S.C. Gelein, R. (1991). Pulmonary Tissue Access
of Ultrafine Particles. Journal of Aerosol Medicine-Deposition Clearance and Effects in
the Lung.4(1):57-68.
Hinds, W.C. (1999). Properties, Behavior, and Measurement of Airborne Particles.
Aerosol Technology, 2
nd
Ed. John Wiley & Sons Inc., New York.
Ibald-Mulli A, Wichmann HE, Kreyling W, Peters A. (2002). Epidemiological Evidence
on Health Effects of Ultrafine Particles. Journal of Aerosol Medicine. 15 (2): 189-201.
Kavouras, I., Mihalopoulos, N., and Stephanou, E. (1998). Formulation of Atmospheric
Particles from Organic Acids Produced by Forests. Nature. 395:683-686.
Kotzick, R. and Niessner, R. (1997). The Effects of Aging Processes on Critical
Supersaturation Ratios of Ultrafine Carbon Aerosols. Atmos. Environ. 33:2669-2677.
Kunzli, N.; McConnell, R.; Bates, D.; Bastain, T.; Hricko, A.; Lurmann, F.; Avol, E.;
Gilliland, F.; Peters, J. (2003). Breathless in Los Angeles: The exhausting search for
clean air; American Journal of Public Health, 93, 1494-1499.
Li, N., C. Sioutas, A. Cho, D. Schmitz, C. Misra, J. Sempf, M.Y. Wang, T. Oberley, J.
Froines, and A. Nel. (2003). Ultrafine particulate pollutants induce oxidative stress and
mitochondrial damage, Environmental Health Perspectives, 111 (4), 455-460.
14
Misra, C.; Kim, S.; Shen, S.; Sioutas, C. (2002). A High Flow Rate, Very Low Pressure
Drop Impactor for Inertial Separation of Ultrafine from Accumulation Mode Particles.
Journal of Aerosol Science. 33:735-752.
Neususs, C., H. Wex, W. Birmili, A. Wiedensohler, C. Koziar, B. Busch, E.
Bruggemann, T. Gnauk, M. Ebert, and D.S. Covert, (2002). Characterization and
parameterization of atmospheric particle number-, mass-, and chemical-size distributions
in central Europe during LACE 98 and MINT, Journal of Geophysical Research-
Atmospheres, 107 (D21).
Oberdorster, G., Ferin, J., Gelein, R., Soderholm, S.C., Finkelstein, J. (1992). Role of the
Alveolar Macrophage in Lung Injury - Studies with Ultrafine Particles. Environmental
Health Perspectives. 97:193-199.
Peters, A., Dockery, D.W., Heinrich, J., Wichmann, E, (1997). Short term effects of
particulate air pollution on respiratory morbidity in asthmatic children. European
Respiratory Journal, 10, 872-879.
Peters, A., Frohlich, M., Doring, A. (2001). Particulate Air Pollution is Associated with
an Acute Phase Response in Men-Results from the MONICA-Augsburg Study. Eur.
Heart J. 22 (14):1198-1204
Peters, A., Varrier, R.L., Schwartz, J., Gold, D.R., Mittleman, M., Baliff, J., Oh, J.A.,
Allen, G., Monahan, K. and Dockery, D.W. (2000). Air Pollution and Incidence of
Cardiac Arrythmia. Epidemiol. 11:11-17.
Peters, A., Wichmann, H.E. (1997). Respiratory Effects are Associated with the Number
of Ultrafine Particles. American Journal of Respiratory and Critical Care Medicine.155
(4): 1376-1383.
Pope, C.A., Dockery, D.W. and Schwartz, J. (1995). Review of Epidemiological
Evidence of Health Effects of Particulate Air Pollution. Inhalation Toxicology. 7:1-18.
Pope, C.A., Verrier, R.L., Lovett, E.G., Larson, A.C., Raizenne, M.E., Kanner, R.E.,
Schwartz, J., Villegas, M., Gold, D.R. and Dockery, D.W. (1999). Heart Rate Variability
Associated with Particulate Air Pollution. Amer. Heart. J. 138:890-899.
Schwartz J.and Dockery, D.W. (1992).Increased Mortality in Philadelphia Associated
with Daily Air-Pollution Concentrations. American Review of Respiratory Disease.145
(3): 600-604.
Seinfeld, C. and Pandis, S. (1998). Atmospheric Chemistry and Physics. John Wiley &
Sons Inc., New York.
Whitby, K.T. and Svendrup, G.M. (1980). California Aerosols: Their Physical and
Chemical Characteristics. Env. Health Perspectives.10: 477.
15
Chapter 2: Seasonal and Spatial Trends in Particle Number
Concentrations and Size Distributions at the Children’s Health Study
Sites in Southern California*

*Singh M.; Phuleria H.C; Bowers K. and Sioutas C. Seasonal and spatial trends in particle
number concentrations and size distributions at the Children’s Health Study sites in Southern
California, Journal of Exposure Analysis and Environmental Epidemiology, 16: 3-18, 2006.

2.1 Chapter 2: ABSTRACT
Continuous measurements of particle number, particle mass (PM
10
) and gaseous co-
pollutants (NO
x
, CO and O
3
) were obtained at eight sites (urban, suburban and remote) in
Southern California during years 2002 and 2003 in support of University of Southern
California Children’s Health Study. We report the spatial and temporal variation of
particle numbers and size distributions within these sites. Higher average total particle
number concentrations are found in winter (November to February), compared to summer
(July to September) and spring (March to June) in all urban sites. Contribution of local
vehicular emissions is most evident in cooler months, whereas effects of long-range
transport of particles are enhanced during warmer periods. The particle size profile is
most represented by a combination of the spatial effects, e.g. sources, atmospheric
processes and meteorological conditions prevalent at each location. Afternoon periods in
the warmer months are characterized by elevated number concentrations that either
coincide or follow a peak in ozone concentrations, suggesting the formation of new
particles by photochemistry. Results show no meaningful correlation between particle
number and mass, indicating that mass based standards may not be effective in
controlling ultrafine particles. The study of the impact of the Union worker’s strike at
port of Long Beach in October 2002 revealed statistically significant increase in particle
16
number concentrations in the 60-200 nm range (p<0.001), which are indicative of
contributions of emissions from the idling ships at the port.

2.2 Chapter 2: INTRODUCTION
A number of observational studies have demonstrated acute and chronic effects of
ambient particles on human health (Dockery and Pope 1994; Zanobetti et al. 2000; Pope,
2000). To this date, however, there appears to be heterogeneity in particulate matter (PM)
concentrations and PM-associated health effects between locations within an urban
setting, which raises considerable uncertainties as to whether PM mass, number, size,
bulk or surface chemistry are the appropriate metrics associated with PM toxicity. For
example, recent studies have shown that atmospheric ultrafine particles (with physical
diameter < 100 nm) have the potential for eliciting adverse health effect (Oberdörster and
Utell, 2002; Li et al., 2003; Li et al., 2004; Xia et al., 2004). Recent epidemiological
studies by Peters et al. (1997), have demonstrated a higher association between health
effects and exposures to ultrafine particles compared to accumulation mode or coarse
particles.

In the complex environment of an urban atmosphere, there is great variability in the
number and type of sources of particles as well as in the diurnal and seasonal patterns of
their emission strengths, all of which affect human exposure. As one of many sources
contributing to urban air pollution in general, the combustion of fossil fuel in motor
vehicles is one of the major primary emission sources of ultrafine particles in urban
atmospheres, especially in the developed nations. (Shi et al., 1999; Cyrys et al., 2003).
17
Recent studies have shown a dramatic decrease of ultrafine number concentrations with
increasing distance from busy freeways in Los Angeles, thereby demonstrating that
vehicular pollution is a major source of ultrafine particles and that high number
concentrations can be a localized phenomenon, on scales of 100-300 meters (Zhu et al.,
2002a,b). In addition to their direct emission in the atmosphere, particles may be formed
as a result of photochemical reactions from gaseous precursors, including particulate
sulfate formed from precursor sulfur dioxide, and secondary organic aerosols, formed
from oxidation of aromatic hydrocarbons (Derwent et al., 2000). The secondary aerosol
formation is largely governed by meteorological factors (Mäkelä et al., 1997, Kim et al.,
2002). The high degree of temporal variability of the meteorological parameters such as
degree of solar radiation, atmospheric mixing depth, humidity, and temperature - all
contribute to the temporal variation in particulate number concentrations at a location.
Understanding how the number concentrations of particles change as a function of
particle size, time of the day, location and season may help characterize the sources of
these emissions as well as refine human exposure parameters used in epidemiological
studies that attempt to link particulate levels and health effects they induce.

Due to recent health concerns, particle size distributions and number concentrations in
several cities have been measured. Some recent continental sampling campaigns that
measured size distributions include the Pittsburgh Air Quality Study (Stanier et al.,
2004), the Atlanta PM Supersite program (Woo et al., 2001), and sampling campaigns in
Los Angeles (Kim et al., 2002; Fine et al., 2004), Northern Europe (Ruuskanen et al.,
2001), Tennessee (Cheng and Tanner, 2002), Brisbane, Australia (Morawska et al.,
18
2002), the UK (Harrison et al., 2000), Estonia and Finland (Kikas et al., 1996) and
Central Europe (Birmili et al., 2001). Most of these studies were conducted in urban areas
in which the vast majority of ultrafine PM originate from primary sources (Morawska et
al., 1998; Harrison et al., 2000; Woo et al., 2001), thus their diurnal profiles match those
of local vehicular sources. The majority of these studies were also intensive in nature,
conducted for a period ranging from a few weeks to a few months.

Shi et al. (2001) measured temporally resolved number concentrations to examine periods
of nucleation events. Lawless et al. (2001) used the near continuous data obtained from a
Scanning Mobility Particle Sizer and an optical particle counter to distinguish between
primary and secondary contributions to PM
2.5
in Fresno, CA. These studies were
intensive in nature, focusing on one specific location and for a limited time period. The
spatial aerosol characteristics at different locations of a city have also been examined.
Kim et al. (2002) identified periods of photochemistry and long-range advection as
sources of ultrafine PM at two sites in Los Angeles Basin in addition to local vehicular
emissions. Fine et al. (2004) inferred sources of ultrafine particles at two different
locations in the eastern portion of Los Angeles Basin. Buzorius et al. (1999) measured
aerosol characteristics at a series of sites in Helsinki, Finland in order to investigate the
transport of aerosol traveling from source sites to receptor sites. Ruuskanen et al. (2001)
conducted monitoring in three different European cities using continuous monitors to
describe differences among the sites as well as diurnal variations of particle mass and
number concentrations. Little information has been reported on the seasonal patterns of
size distributions due to the lack of long-term monitoring. Stanier et al. (2004) measured
19
aerosol size distributions at one location in Pittsburgh for an entire year, providing one of
the first data sets in Northern United States from which seasonal patterns can be
described.

The work presented in this paper is intended to provide more comprehensive information
about spatial, seasonal as well as diurnal variations of atmospheric particle numbers and
size distributions (14-700 nm) within Southern California. This paper utilizes the data set
generated in support of the University of Southern California (USC) Children’s Health
Study (CHS). The CHS, which began in 1993, is one of the largest investigations of the
long-term consequences of air pollution on the respiratory health of children. The main
goal of CHS is to identify chronic effects of ambient pollutants in Southern California by
performing cross-sectional and longitudinal studies in school children in several
communities with varying exposures to particulate matter, ozone, and acid vapors. In this
paper we present ambient particle number characteristics measured at eight sites
classified as urban (source and receptor) and remote (suburban/ mountainous) sites in
Southern California during the years 2002 and 2003. The particle number concentration
data is supported by gaseous co-pollutants data to help differentiate (mostly) ultrafine
particle sources and formation mechanisms at each site as well as their prevalence over
different times of day and different seasons.

2.3 Chapter 2: METHODS
Concentrations of carbon monoxide (CO), ozone (O
3
), total nitrogen oxide species (NO
x
),
mass of particulate matter with aerodynamic diameters less than 10 µm (PM
10
) and total
20
particle numbers (PN) were continuously measured in several locations in Southern
California as a part of the University of Southern California Children’s Health Study,
supported by the South Coast Air Quality Management District (SCAQMD) and the
California Air Resources Board (CARB). Size resolved sub-micrometer particle numbers
(14-700 nm) were measured under an additional contract from the CARB and SCAQMD.
Continuous data were collected concurrently throughout the calendar years 2002 and
2003. Eight sites were examined in this study, six within the Los Angeles Basin (LAB):
Long Beach, Mira Loma, Upland, Riverside, Lake Arrowhead and USC; and two sites at
other areas of Southern California: Alpine and Lancaster (as shown in Figure 2.1).
Selection of the sampling sites, discussed in greater detail by Künzli et al. (2003), was
made on the basis of their location within the LAB and the presumed contrasting air
quality (hence exposure) regimes in terms of PM and gaseous co-pollutants, which have
differentially affected children’s health.

2.3.1 Sampling Sites
Los Angeles is a unique air basin because it epitomizes a distinct air quality problem in
terms of particle composition, source mix, and meteorology. Unlike other metropolitan
areas, the unique morphology and climate of Los Angeles create major differences in PM
characteristics and composition within the basin. During the past twenty years, growth in
both the population and the density of emission sources has been greatest in the central
and eastern portions of the LAB. Nevertheless, primary emissions of VOC, NOx and PM
are still dominated by the western, or coastal, portion of the region, which contains the
greatest concentration of both mobile and stationary emission sources. Overall the

21

FIGURE 2.1: Locations of sampling sites in Southern California

highest ambient concentrations occur in the coastal areas for primary pollutants such as
CO and NO
2
, and in downwind inland valleys for secondary pollutants such as ozone and
fine particulate matter. Thus, it remains appropriate to view the western/coastal portion
of the LAB as a source region and the inland valleys of the central and eastern basin as
receptor areas.

The winter period in the LAB is characterized by surface temperature inversions in the
coastal region and generally weak on-shore flow. Hence the highest ambient levels of
primary pollutants such as CO and NO
2
are generally observed in the coastal region
during the winter months. In contrast, the “summer/fall” period is characterized by
strong temperature inversions aloft, and by strong onshore flow and interior-mountain up-
22
slope flow, which together produce rapid transport of primary pollutants from the coastal
region to the interior valleys. Combined with high actinic radiation in the summer, these
conditions produce elevated concentrations of a wide spectrum of secondary air
pollutants, including ozone and fine PM, in the central and eastern areas of the Basin in
the summer. In addition, offshore flow under Santa Ana conditions can trap large
quantities of pollution over the coastal regions of the LAB. Particles thus undergo
transformations as they move along a wind trajectory from “source” to “receptor” sites in
the basin. As a result, significant differences occur in the chemical composition and size
distribution of PM in the LAB because of a wide range of sources, meteorological
conditions, atmospheric chemistry, and temporal factors.

Located near a busy surface street, the Long Beach station is about 0.5 km northeast of
freeway I-405 and approximately 1.5 km east of freeway I-710. The Long Beach station
is mostly downwind of these two freeways as well as the Long Beach port which is
situated approximately 7 km south of the sampling station. The Upland site is located in a
residential area inside a community trailer park about 100 m from San Bernardino road,
and is within 2 km (mostly downwind i.e., north-east) of the freeway 210. The Mira
Loma site (about 80 km east of downtown Los Angeles) is located in a building on the
Jurupa Valley High School campus, directly southeast of the intersection of freeways 60
and 15. It is surrounded by several major warehouse facilities with frequent heavy-duty
diesel truck traffic (Sardar et al., 2004; Na et al., 2004) and near several major cattle
feeding operations. The sampling location at Riverside is within the Citrus Research
Center and Agricultural Experiment Station (CRCAES), a part of the University of
23
California, Riverside. It is about 20 km southeast of the Mira Loma site and is situated
upwind of surrounding freeways and major roads (Phuleria et al., 2004). The desert site
of Lancaster is located in the office of Mojave desert AQMD and is approximately 2 km
away from the nearest freeway 14. The Lake Arrowhead monitoring station is located in
the rim of World High School near highway 18, at an elevation of about 1700 m. It is a
purely serene mountainous site with very few local emission sources, but heavily
impacted by transported, aged air pollutants. The sampling site at USC is located near
downtown Los Angeles, just 100 m downwind of freeway 110. The Alpine station is a
remote suburban to rural site located approximately 50 km east of downtown San Diego
(approximately 200 km southeast of downtown Los Angeles).

Fresh emissions from vehicular and industrial sources primarily make Long Beach a
“source” site, which is at a relatively western location in the LAB and has an urban
surrounding. USC, also, has an urban surrounding and is considered a “source” site. It
represents an urban mix of industrial, vehicular and construction sources. Riverside,
Upland and Mira Loma and Lake Arrowhead are designated “receptor” sites where the
aerosol is composed of advected, aged and photochemically processed air mass from the
central Los Angeles Area. The time for air masses to transport from source to receptor
sites can vary from a few hours to more than a day (Sardar et al, 2004). It should be
noted here that the designation of these sites as “receptors” by no means precludes the
impact of local traffic sources, as it will be discussed later in this paper.

24
2.3.2 Instrumentation
The concentrations of CO were measured near-continuously by means of a Thermo
Environmental Inc. Model 48C trace level CO monitor. A continuous
Chemiluminescence Analyzer (Monitor Labs Model 8840) was used for the measurement
of concentrations of NO
x
, while O
3
concentrations were monitored using a UV
photometer (Dasibi Model 1003 AH). Total particle number concentrations (greater than
about 10 nm in diameter) were measured continuously by a Condensation Particle
Counter (CPC, Model 3022/A, TSI Incorporated, St. Paul, MN) set at a flow rate of 1.5 L
min
-1
. In addition to the continuous data described above, efforts were made to monitor
the number-based particle size distributions in each site for 1-3 months duration during a
warmer and a cooler period. Accordingly, three Scanning Mobility Particle Sizers
(SMPS, Model 3936, TSI Incorporated, St. Paul, MN) were deployed by rotation at each
site during selected time periods, as shown in Table 1, to measure the size distribution of
sub-micrometer aerosols (14-700 nm) using an electrical mobility detection technique. In
this configuration, the CPC flow rate was maintained at 0.3 L min
-1
(with the sheath flow
of the SMPS set at 3 L min
-1
), and size- segregated particle number concentrations were
recorded. The CPC total count data were excluded for the months when the CPC was in
the SMPS configuration (Table 2.1; Figure 2.2). Continuous particle number and gaseous
co-pollutant concentrations were averaged over 1-hour and 24-hour intervals for the
subsequent analysis.

25
TABLE 2.1: Sampling periods during which SMPS-CPC configuration was employed at
various sampling sites

Site no Site name Sampling periods
1 Long Beach Nov '02; Aug-Sep '03
2 Mira Loma Jan-Feb '02; Jun '02
3 UC Riverside Nov '02; Mar-Apr '02
4 USC Dec '02 - Jan '03; Sep '03
5 Upland Aug to Oct '03; Nov, '03 - Jan '04
6 Alpine Apr-May '03; Dec '03 - Jan '04
7 Lancaster Jun-Jul '03
8 Lake Arrowhead Jul-Aug '02

Hourly PM
10
mass concentrations in each site were measured by low temperature
Differential Tapered Element Oscillating Microbalance monitors (low temperature
TEOM 1400A, R&P Inc., Albany, NY). Jacques et al. (2004) have described the design
and performance evaluation of this monitor in greater detail. Briefly, the system consists
of a size-selective PM
10
inlet, followed by a Nafion
®

dryer that reduces the relative
humidity of the sample aerosol to 50% or less. Downstream from the Nafion dryer and
ahead of the TEOM sensor is an electrostatic precipitator (ESP) to alternately remove
particles from the sample stream or allow the particle laden sample stream to continue to
the sensor. The ESP is alternately switched on and off, for equal time periods of about 10
minutes. Unlike a standard TEOM monitor, which collects samples and reports mass
concentration continuously, the differential TEOM monitor only collects mass on the
TEOM sample filter during half of the measurement time of the monitor, the period
where the ESP is turned off (typically 5 minutes). During the other half of the operation,
the ESP is energized and only the affects of the sampled gases and any evaporation of
previously collected sample are measured by the TEOM sample filter. The change in the
26
collection filter mass obtained while collecting particle-free ambient air provides an
internal reference, for the mass change sensed while collecting ambient particulate. Thus,
the Differential TEOM directly measures ambient PM mass concentrations while
accounting for collection artifacts, including loss of semivolatile aerosols, adsorption of
organic vapors and temperature changes. The study by Jacques et al. (2004) showed that
the time averaged TEOM PM
10
mass concentrations agreed within ± 10% with those of
collocated Federal Reference Methods (FRM).
The Quality Control and Quality Assurance procedures used in the study to assure
accurate and unbiased measurements were performed in accordance with Southern
California Particle Center and Supersite (SCPCS) Quality Assurance Project Plan
(QAPP). The SCPCS QAPP incorporates all of the elements required by the U.S. EPA
for air monitoring programs.

2.4 Chapter 2: RESULTS AND DISCUSSION
The section describing our results is divided into the following parts: Seasonal and spatial
trends; Diurnal trends; Relation between PM mass, PM surface area and PM numbers;
and Long Beach October 2002 strike analysis. The latter part is an “opportunistic” study
focusing on the impact of the union workers strike at the port of Long Beach on air
quality.

2.4.1 Seasonal and Spatial Trends
Descriptive statistics (surface area and number median diameter) of the measured particle
size distributions are included in Table 2.2. The number median diameter is defined as
27
the particle diameter that divides the number based frequency distribution of aerosol in
half; fifty percent of the total aerosol number has particles with a larger diameter, and
fifty percent of the total aerosol number has particles with a smaller diameter. Figure 2.2
shows monthly averaged total particle number concentrations measured using the CPC
along with the monthly averaged minimum and maximum ambient temperatures, in the
eight sites sampled during the calendar year 2003. The error bars indicate the standard

TABLE 2.2: Summary statistics showing average total particle surface area (SA) and
number median diameter (NMD)

Particle SA
( µm
2
/cm
3
) NMD (nm) Site Name Season Period
Grand avg. SD Grand avg. SD
Long Beach Winter Nov '02 609.5 320.1 79.9 18.8
Long Beach Summer Aug-Sep '03 330.0 166.0 59.8 18.3
Mira Loma Winter Jan-Feb '02 674.5 418.5 65.2 19.4
Mira Loma Spring Jun '02 542.9 231.9 81.6 20.5
Riverside Winter Nov '02 290.3 255.2 47.7 16.6
Riverside Spring Mar-Apr '02 334.0 273.0 62.6 21.3
USC Summer Sep '03 437.4 331.7 45.9 11.2
USC Winter Dec '02 - Jan '03 329.4 210.4 45.4 14.2
Upland Summer
1
Aug-Sep-Oct '03 371.7 161.9 61.5 14.1
Upland Winter Nov-Dec '03 - Jan '04 473.7 300.6 56.6 13.6
Alpine Spring Apr-May '03 122.4 101.7 42.9 14.4
Alpine Winter Dec '03 - Jan '04 135.1 116.5 79.3 18.5
Lancaster Spring Jun-Jul '03 164.9 136.0 81.9 18.0
Lake Arrowhead Summer Jul-Aug '02 154.9 117.4 77.9 16.7
--------------------
1
The data corresponding to the October Fire in Southern California is excluded

and the sampling size. A key observation in Figure 2.2 is the higher average particle
number concentrations in winter (November to February), compared to summer (July to
September) and spring (March to June) in all of the urban sites, e.g., USC, Long Beach,
28
Riverside, Upland, Mira Loma and Lancaster. The total particle number concentrations at
these sites were quite similar and ranged from 25,000-30,000 particles/cm
3
in winter
months to 12,000-15,000 particles/cm
3
in summer/spring months. High number
concentrations at the urban sites during winter are likely due to lower temperatures
favoring particle formation by condensable organics freshly emitted from vehicles
(Baltensperger et al., 2002; Ziemann et al., 2001; Shi and Harrison, 1999).

Figure 2.2 a Figure 2.2 b
Long Beach
0
5000
10000
15000
20000
25000
30000
JanFebMarAprMayJun Jul AugSepOctNovDec
Month
PN
(Particles/cm3)
0
20
40
60
80
100
Temperature(0F)
M onthly PN T av,max T av,min Riverside
0
5000
10000
15000
20000
25000
30000
35000
Jan Feb Mar AprMay Jun Jul AugSep Oct Nov Dec
Month
PN
(Particles/cm3)
0
20
40
60
80
100
Temperature (0 F)
M onthly P N T av,max T av,min

Figure 2.2 c Figure 2.2 d

Mira Loma
0
5000
10000
15000
20000
25000
30000
35000
40000
Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec
Month
PN
(Particles/cm3)
0
20
40
60
80
100
Temperature (0 F)
M onthly PN T av,max T av,min
Upland
0
5000
10000
15000
20000
25000
30000
35000
JanFebMarAprMay Jun Jul AugSepOctNovDec
Month
PN
(Particles/cm3)
0
20
40
60
80
100
Temperature(0 F)
M onthly PN T av,max T av,min

FIGURE 2.2: Monthly average particle number concentrations and ambient temperatures at a) Long Beach, b) Riverside,
c) Mira Loma, d) Upland, e) Lancaster, f) Alpine, and g) Lake Arrowhead

29
Lancaster
0
5000
1 0000
1 5000
20000
25000
30000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
PN
(Particles/cm
3)
0
20
40
60
80
1 00
Temperature (
o
F)
Monthly PN T av,max T av,min
Alpine
0
2000
4000
6000
8000
10000
12000
14000
16000
Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec
Month
PN
(Particles/cm
3
)
0
20
40
60
80
100
Temperature
(
o
F)
Monthly PN T av,max T av,min

Lake Arrowhead
0
10 0 0
2000
3000
4000
5000
6000
7000
8000
9000
Jan Feb M ar Apr M ay Jun Jul Aug Sep Oct Nov Dec
Month
PN
(Particles/cm
3
)
0
20
40
60
80
10 0
Temperature (
o
F)
M onthly P N T av,max T av,min

30
FIGURE 2.2: Continued…

Figure 2.2 e Figure 2.2 f

Figure 2.2 g

31

The lowest levels of particle number concentrations were observed at Lake Arrowhead,
which is a remote mountainous site. The averaged particle concentrations at this site
ranged from 6,000-8,000 particles/cm
3
in summer months and 3,000-5,000 particles/cm
3

in winter months (Figure 2.2g). The Lake Arrowhead sampling site is located at an
elevation of 1700 m. The inversion layer is generally below the station location during
morning and evening periods. As the day progresses, the warmer temperature cause the
inversion layer to rise and subsequently the inversion layer passes the station elevation
and the station is under the inversion layer. During summer months, as a consequence of
the elevated mixing height the site is under the inversion layer for longer periods leading
to higher number concentrations. Additionally, low atmospheric pressure and higher mid-
day wind speed during summer favor long-range transport of the aerosol from the much
more polluted upwind areas. Biogenic VOC emissions in the Lake Arrowhead region also
possibly impact particle numbers in the summer months.

Particle counts at Alpine, although higher than those observed at Lake Arrowhead, are
much lower than those observed in the urban sites, discussed earlier. This site is impacted
by very few local traffic emissions and is largely a receptor site of the San Diego
metropolitan area. On most summer days, an afternoon peak of particles of possibly
secondary origin occurs several hours after the change of wind direction from easterly to
westerly. Monthly averaged particle counts range between 9,000 - 13,000 particles/cm
3
.
A detailed discussion about the seasonal variations in particulate characteristics at Alpine
is presented in a later section.

32
Figure 2.3 depicts the particle size distributions measured by the SMPS during different
seasons at our sampling sites. Average number size distributions at USC in summer as
well as winter are very similar and corroborate the hypothesis that this site is heavily
influenced by fresh vehicular emissions. Particles in the 20 - 50 nm range, which could
be attributed to traffic, are the most abundant at this site. Also, number concentrations of
this size range increase during the winter period. USC has similar number median
diameter during both seasons, an indication of the consistency of the sources (i.e., the
traffic emissions from the nearby freeway I-110) affecting PM characteristics in that
location.

Similar to USC, at Long Beach which is also a site highly impacted by vehicular
emissions, the average particle number concentrations are higher in winter than summer
for the particles > 40 nm. However, summer months witness an increase in particles <40
nm diameters. The size distribution in summer supports the hypothesis that this site may
be influenced markedly by photochemically generated particles. Given that this site is
situated close to the ocean, with the only major upwind sources being the port and the
nearby freeway 710, both of which are quite proximal (i.e., within 7 km or less) to the
site, the contribution of long range transport to particle numbers can be ruled out. The
number median diameter of the aerosol is also lower in summer than in winter. Larger
number median diameter in winter (79.9 nm) compared to summer (59.8 nm) may be due
to high relative humidity in the winter months, which would contribute to growth of
particles by condensation of water vapor in the air. It should be noted that the proximity

Figure 2.3 a Figure 2.3 b
USC
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
10 100 1 000
P article mobility diameter, Dp (nm)
dN/dlog(Dp)
(Particles/cm
3
)
Sep '03
Dec'02-Jan'03

Long Beach
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
10 1 00 1 000
P article mo bility diameter (nm)
dN/dlog(Dp)
(Particles/cm
3
)
Aug and Sep '03
Nov '02

Figure 2.3 c Figure 2.3 d

Riverside
0
2000
4000
6000
8000
10000
12000
14000
10 100 1 000
P article mobility diameter (nm)
dN/dlog(Dp)
(Particles/cm
3
)
March and Apr'02
Nov '02

Mira Loma
0
5000
10000
15000
20000
25000
30000
10 100 1 000
P article mobility diameter (nm)
dN/dlog(Dp)
(Particles/cm
3
)
Jun '02
Jan and Feb '02

FIGURE 2.3: Average number size distributions in winter and summer/spring periods at a) USC, b) Long Beach,
c) Riverside, d) Mira Loma, e) Upland, f) Lancaster, g) Alpine, and h) Lake Arrowhead

33
Upland
0
5000
10000
15000
20000
25000
10 100 1 000
P article mo bility diameter, Dp (nm)
dN/dlog(Dp)
(Particles/cm
3
)
Aug -Oct '03
Nov '03 to Jan'04
Lancaster
0
500
1000
1500
2000
2500
3000
3500
4000
4500
10 100 1 000
P article mo bility diameter, D p (nm)
dN/dlog(Dp)
(Particles/cm
3
)
Jun and July '03

Alpine
0
1000
2000
3000
4000
5000
6000
7000
10 1 00 1 000
P article mobility diameter, D p(nm)
dN/dlogDp
(Particles/cm
3
)
Apr-M ay '03
Dec'03 -Jan'04
Lake Arrowhead
0
500
1000
1500
2000
2500
3000
3500
4000
4500
10 100 1 000
P article mobility diameter, D p (nm)
dN/dlog(Dp)
(Particles/cm
3
)
Jul and A ug '02
34
FIGURE 2.3: Continued…

Figure 2.3 e Figure 2.3 f

Figure 2.3 g Figure 2.3 h

35
of that site to the ocean results in unusually higher relative humidity levels compared to
the rest of the urban sites, with prolonged periods of nighttime and morning fog. The
smaller summertime number median diameter could be due to the increased
photochemical production of smaller particles, as observed by Kim et al. (2002) and
Wehner and Wiedensohler (2003). During the first week of October, union workers at
the port of Long Beach went on strike. A detailed analysis of the effect of this strike on
particulate characteristics of Long Beach is discussed in later section of this paper.

Riverside, Mira Loma and Upland are receptor sites downwind of the high concentration
of sources in the western part of LAB. In addition to the effect of few local emission
sources, particle number concentration at these receptor areas is also influenced by aged,
advected aerosol from the west, especially in summer season. The Upland station was
directly impacted by Southern California wildfires during late October 2003 because of
its location some 3.5 km downwind of one of the 13 fires during that period. The impact
of this fire on aerosol characteristics is discussed in detail by Phuleria et al. (2004) and
thus we do not present the analysis here. However, for our seasonal characteristics
analysis, we have excluded the data from that period.

At Riverside, the particle number concentrations are higher in winter compared to spring
for particles <100nm. It is interesting to observe that the particles >100 nm are slightly
higher in the spring period. The increase in the peak median size in springtime may be
due to the contribution of advected, thus aged aerosols, which are generally larger in

36
diameter (Zhang and Wexler, 2002), from the western polluted regions of the Los
Angeles Basin.

The size distribution of aerosols also shows some seasonal variation at Mira Loma. In
addition to a decrease in particle number concentrations, the number size distribution
shifted towards larger sizes in summer compared to winter. Decrease in particle counts of
all size ranges in summer reflects the effect of more dilution with elevated mixing height
in warmer months. As in Riverside, the number median diameter of the aerosol in Mira
Loma is larger in warmer season (Table 2.2). This may be the result of the increased wind
speeds and onshore flow in the warmer months, leading to increased advection of
pollutant air parcels from the western LAB. This advected aerosol is generally larger in
diameter as noted earlier and would lead to larger number size distribution of the
summer/spring aerosols.

At the suburban remote site Alpine, in contrast to all the receptor sites discussed above,
the particle numbers <100 nm are markedly higher in spring than in winter (Figure 2.3g).
The number median diameter also shifts from 79 nm in winter to 43 nm in spring (Table
2.2). This may be due to increased summertime advection and photochemical particle
formation. The influence of summer advection and photochemical particle formation is
supported by wind data, which indicates a change in wind direction from easterly
(offshore) to westerly (onshore). The westerly winds would bring the aging air-mass
from the San Diego metropolitan area to the station. The afternoon peak of aged and

37
photochemically-derived particles occurs several hours after the wind direction change,
allowing time for the air mass to reach the station from San Diego.

Particle size distribution data is available for only summer months at Lancaster and Lake
Arrowhead. Both sites display generally much lower number concentrations than the
urban sites, as one would expect. The relatively large aerosol number median diameter of
82 and 78 nm at Lancaster and Lake Arrowhead, respectively, corroborate the absence of
any major local sources, which would emit fresh hence smaller in size emitted PM.

2.4.2 Diurnal Trends
This section describes our observations of diurnal trends in particle numbers and gaseous
copollutants, which, combined with the size distribution and number concentrations data,
may provide insights into sources and possible formation mechanisms of particulate
matter in each of these sites. Figures 2.4 through 2.9 display the diurnal variations of
particle number (PN) and gaseous pollutants (O
3
and NO
x
) concentrations averaged by
time of day over the period that SMPS sampled at each of the sites. In these figures,
particle sizes have been segregated into three ultrafine size ranges: 15-30nm, 30-60 nm
and 60-100 nm.

The diurnal trends of PN in different size ranges and gaseous pollutants at USC and Long
Beach during the winter sampling periods are shown in Figures 2.4a and 2.5a,
respectively. As mentioned before, USC and Long Beach are close to vehicular sources

38
Figure 2.4a
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 2 4 6 81012 14 1618 20 22
Hour
PN (Particles/cm
3
)
0
50
100
150
200
250
300
O
3
and NO
x
(ppb)
< 30 nm 30 - 60 nm 60-1 00nm O3 X 1 0 NOx
USC Dec2002-Jan2003

Figure 2.4b
0
1000
2000
3000
4000
5000
6000
7000
0 2 4 6 8 10 12 14 16 1 8 20 22
Hour
PN (Particles/cm
3
)
0
10
20
30
40
50
60
70
O
3
and NO
x
(ppb)
< 30 nm 30 - 60 nm 60-1 00nm NOx O3
USC Sep 2003

FIGURE 2.4: Diurnal trends of size-segregated particle number, O
3
and NO
x
at USC
during a) Dec 2002-Jan 2003 and b) Sep 2003

and traffic is expected to be primary source of these particles at these sites. The number
concentrations have also been observed to be higher during winter months. The diurnal
pattern of NO
x
is very similar to diurnal patterns of particle number concentrations. The
morning and evening peaks of these pollutants correspond to morning and evening

39
commutes, which suggests that local traffic is the major contributor to ultrafine PM at
both these sites during winter.

Figure 2.5 a
Long Beach Nov 2002
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0 2 4 6 81012 14 16 18 20 22
Hour
PN (Particles/cm
3
)
0
50
100
150
200
250
O
3
and NO
x
(ppb)
< 30nm 30-60nm 60-100nm O3 NOx

Figure 2.5 b
Long Beach Aug-Sep 2003
0
1000
2000
3000
4000
5000
6000
02468 10 12 14 16 18 20 22
Hour
PN (Particles/cm
3
)
0
5
10
15
20
25
30
35
40
45
50
O
3
and NO
x
(ppb)
< 30nm 30-60nm 60-100nm O3 NOx

FIGURE 2.5: Diurnal trends of size-segregated particle number, O
3
and NO
x
at Long
Beach during a) Nov 2002 and b) Aug-Sep 2003

During summer months, secondary aerosol formation is favored and new ultrafine
particles may form as a result of the condensation of low-volatility products of

40
Figure 2.6 a
Riverside Nov 2002
0
1000
2000
3000
4000
5000
6000
0 2 4 6 8 1012 1416 18 20 22
Hour
PN (Particles/cm
3
)
0
20
40
60
80
100
120
140
O
3
and NO
x
(ppb)
< 30 nm 30-60nm 60-1 00nm O3 NOx

Figure 2.6 b
Riverside March-April 2002
0
500
1000
1500
2000
2500
3000
3500
0 2 4 6 8 101214 1618 20 22
Hour
PN (Particles/cm
3
)
0
10
20
30
40
50
60
70
0
3
and NO
x
(ppb)
< 30 nm 30-60nm 60-100nm O3 NOx

FIGURE 2.6: Diurnal trends of size-segregated particle number, O
3
and NO
x
at
Riverside during a) Nov 2002 and b) Mar-Apr 2002

photochemical reactions (largely organic compounds) onto stable, nanometer-size
particles (O'Dowd et al 1999; Kim et al., 2002; Sardar et al., 2004). Secondary aerosol
formation is the most likely explanation for the diurnal trends in PN during the summer

41
period at USC and Long Beach (Figures 2.4b and 2.5b, respectively) in which the peak
particle concentrations during the afternoon period either coincide or slightly lag behind
the peak in O
3
concentrations.

Figure 2.7 a
Mira Loma Jan-Feb 2002
0
2000
4000
6000
8000
10000
12000
0 2 4 6 8 1012 141618 20 22
Hour
PN (Particles/cm
3
)
0
50
100
150
200
250
O
3
and NO
x
(ppb)
< 30nm 30-60nm 60-1 00nm O3 NOx

Figure 2.7 b
Mira Loma Jun 2002
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 2 4 6 8 1 0 12 14 16 1 8 20 22
Hour
PN (Particles/cm
3
)
0
10
20
30
40
50
60
70
80
O
3
and NO
x
(ppb)
< 30nm 30-60nm 60-100nm O3 NOx

FIGURE 2.7: Diurnal trends of size-segregated particle number, O
3
and NO
x
at Mira
Loma during a) Jan-Feb 2002 and b) Jun 2002

42
Figures 2.6a and 2.7a show the diurnal trends of particle numbers as well as gaseous
copollutants during winter period at Riverside and Mira Loma, respectively. Similar to
the winter diurnal trends of the source sites, we notice a peak in number concentrations in
the morning and another smaller peak in the evening across all particle size ranges. The
diurnal pattern of NO
x
is very similar to diurnal profile of number concentrations at both
these sites, indicating once again a traffic origin for these particles during winter. Since
particle number counts are high and wind speeds are generally low in the morning, the
traffic sources are local and specific to the sampling locations. The higher number
concentrations in morning, relative to evening rush hour levels, may be a result of the low
mixing height during morning hours.

As the day progresses the temperature increases, causing the inversion height to rise. The
lower number median diameter of aerosol during winter may also be explained by
contribution from fresh emissions in winter. The diurnal patterns of particle number
concentrations show an additional peak during the afternoon in spring and summer
months at Riverside and Mira Loma, respectively (Figures 2.6b, 2.7b), similar to those
observed during the summer in Long Beach and USC. This peak is either concurrent or
slightly lagging the O
3
peak, as in the previous sites. We attribute this increase to
secondary aerosol production by photochemical reactions, as discussed earlier, with the
lag between the PN and O
3
peaks possibly being due to the time that is required for the
newly formed particles to grow to a size that can be detected by the SMPS (i.e., >15 nm).

43
Figure 2.8 a
Alpine Dec 2003-Jan 2004
0
200
400
600
800
1000
1200
1400
1600
0 2 4 6 8 101214 1618 20 22
Hour
PN (Particles/cm
3
)
0
5
10
15
20
25
30
35
40
45
O
3
and NO
x
(ppb)
< 30 nm 30-60 nm 60-1 00 nm Ozone NOx

Figure 2.8 b
Alpine Jun 2002
0
500
1000
1500
2000
2500
3000
3500
0 2 4 6 8 1012 141618 20 22
Hour
PN (Particles/cm
3
)
0
10
20
30
40
50
60
70
O
3
and NO
x
(ppb)
< 30 nm 30-60nm 60-100 nm Ozone NOx

FIGURE 2.8: Diurnal trends of size-segregated particle number, O
3
and NO
x
at Alpine
during a) Dec 2003-Jan 2004 and b) Apr-May 2003

Similar diurnal patterns of particle counts for winter and summer to the LAB sites are
observed at Alpine, depicted in Figure 2.8. During winter, higher numbers are observed
in the morning, when the mixing height of the atmosphere is low. As the day progresses,

44
the temperature increases and mixing height rises, correspondingly the particle number
concentrations drop due to dilution and dispersion, and they increase again in evening
and night when the mixing height depresses. The diurnal trends of particle concentrations
also track well those of NO
x
. During the warmer period (April and May 2002), the
diurnal profile of particulates displays a different trend. There is a surge in particle
numbers in the afternoon, especially for particles below 60 nm, following a very similar
pattern to the diurnal profile of O
3
, which implies photochemical formation of these
particles and air mass advection, as seen at the urban sites discussed earlier.

Lake Arrowhead
0
500
1000
1500
2000
2500
1 3 5 7 9 111315 17 1921 23
Hour
PN
(Particles/cm
3
)
0
20
40
60
80
100
120
O
3
and NO
x
(ppb)
< 30 nm 30-60nm 60-100 nm O3 NOx

FIGURE 2.9: Diurnal trends of size-segregated particle number, O
3
and NO
x
at Lake
Arrowhead during Jul-Aug 2002

The diurnal profile of number concentrations and gaseous pollutant concentrations
averaged by time of the day over two months (July-August 2002) of SMPS sampling in
Lake Arrowhead is shown in Figure 2.9. The diurnal patterns of O
3
and NO
x
are very
similar to the diurnal patterns of particle number concentrations. All pollutant

45
concentrations increase during later part of the day. As discussed earlier, Lake
Arrowhead is located at an elevation of approximately 1700 m with negligible local
pollution sources. During early morning and night, the inversion layer is generally below
the station location. As the day progresses, the warmer temperature cause the inversion
layer to rise. Eventually, the station is under the inversion layer. In addition to the
contribution of photochemical activity to the total particle numbers, the rise in particle
numbers during that period is also a result of the increased vertical mixing and advection,
which brings to the site aged and more polluted air parcels originating in the western
parts of LAB. This is also supported by the unusual rise in NO
x
concentrations in the
middle of the day, also seen in Figure 2.9, which cannot be attributed to an increase in
traffic or any other factors.

2.4.3 Correlations between PM Numbers, PM Surface Area and PM mass
Table 2.3 presents the Pearson correlation coefficient (R) between total particle number
concentrations and total surface area concentrations calculated from the SMPS data
assuming spherical particles. A moderate to high correlation (i.e., R=0.55-0.90) was
observed between particle number and surface area concentrations for all sites in both
sampling periods. This correlation was somewhat lower in the warmer period for all the
sites in this study except Riverside and Long Beach. Strom et al. (2003) also found
higher correlations between particle number and surface area in winter compared to
summer. This finding is consistent with the hypothesis that the increased aerosol surface
area acts as a deposition site for gaseous precursors to condense, thereby preventing new

46
TABLE 2.3: Pearson correlation coefficient (R) between total particle number
concentration and total particle surface area concentration
Site name Season Period Pearson correl. coeff.
Long Beach Winter Nov '02 0.76
Long Beach Summer Aug-Sep '03 0.80
Mira Loma Winter Jan-Feb '02 0.76
Mira Loma Spring Jun '02 0.53
Riverside Winter Nov '02 0.69
Riverside Spring Mar-Apr '02 0.69
USC Winter Dec '02 - Jan '03 0.65
USC Summer Sep '03 0.58
Upland Winter Nov-Dec '03 - Jan '04 0.74
Upland Summer Aug-Sep-Oct '03 0.68
Alpine Winter Dec '03 - Jan '04 0.90
Alpine Spring Apr-May '03 0.68
Lancaster Spring Jun-Jul '03 0.57
Lake Arrowhead Summer Jul-Aug '02 0.84

particle formation, as one would expect. The increased surface area may also act as a
sink of ultrafine particles via heterogeneous coagulation.

The correlation of hourly and 24-hour averaged PM
10
and particle number (PN)
concentrations is shown in Table 2.4 for the different CHS sites. In general, the
correlations were found to be weak-to-moderate (i.e., R < 0.5), except of the site in
Alpine, where relatively strong correlations were observed in the springtime between
both the hourly as well as 24-hour averaged concentrations. No particular trend in the
hourly or 24-hour data between different seasons was observed that could be applied to
all sites, as the relationship between the hourly and 24-hour PN and PM
10
varied
differentially from site-to-site and within seasons, as evident in the data shown in Table
2.4.

47
TABLE 2.4: Correlation coefficient (R) between total particle number concentration and
PM
10

Pearson correl. coeff.
Site Season Period
Hourly avg. Daily avg.
Long Beach Winter Nov '02 NA NA
Long Beach Summer Aug-Sep '03 0.28 0.29
Mira Loma Winter Feb '02 0.38 0.31
Mira Loma Spring Jun '02 0.29 0.43
UC Riverside Winter Nov '02 -0.13 0.29
UC Riverside Spring Mar-Apr '02 0.46 0.53
USC Winter Dec '02 - Jan '03 0.14 0.49
USC Summer Sep '03 0.26 0.35
Upland Winter Nov-Dec '03 - Jan '04 0.47 0.20
Upland Summer Aug-Sep-Oct '03 0.19 -0.03
Alpine Winter Dec '03 - Jan '04 0.16 -0.02
Alpine Spring May '03 0.51 0.71
Lancaster Spring Jun-Jul '03 0.48 0.59
Lake Arrowhead Summer Jul-Aug '02 0.36 0.26
2.4.4 Long Beach October 2002 strike analysis
During the period of September 30 to October 9, 2002, union workers at the port of Long
Beach, CA went on strike. The port which is located upwind to the sampling site is
considered a major contributor to PM at Long Beach as a result of emissions from ships
(Isakson et al., 2003), but perhaps more so because of the heavy-duty truck traffic
associated with the port (Chow et al., 1994). It was interesting to determine whether
significant changes in particle and co-pollutant characteristics were observed due to this
strike. In order to understand the effects of this strike, we present the PM as well as co
pollutant characteristics from pre-, during and post-strike periods in this section.
Unfortunately, we do not have the SMPS data from September 25 to October 1, 2002,

48
due to calibration and maintenance performed on the instruments at that time, therefore
PM characteristics for the pre-strike period are studied from September 16-24, 2002 and
for the strike period from October 2-9, 2002.

Figure 2.10 a
Long Beach
0
5000
1 0000
1 5000
20000
25000
30000
35000
9/169/199/229/25 9/28 1 0/1 1 0/4 1 0/7 1 0/101 0/131 0/161 0/1 9
Date
Total trucks (counts/day)
Freway 1 1 0 Freeway 71 0

Figure 2.10 b
Long Beach
0
50000
1 00000
1 50000
200000
250000
9/1 6 9/1 9 9/22 9/25 9/28 1 0/1 1 0/4 1 0/7 1 0/1 0 1 0/1 3 1 0/1 6 1 0/1 9
Date
Total vehicles
(counts/day)
Freeway 1 1 0 Freeway 71 0

FIGURE 2.10: Daily traffic data for Freeways 710 and 410 before, during and after
harbor strike at Long Beach a) total truck counts, and b) total vehicle counts

49
During the strike period, the following three major changes occurred that might have
influenced air pollution in that area. First, there was a significant decrease in diesel truck
traffic both on the nearby freeways 710 and 110 as well as local surface streets (Figure
2.10a). Second, about 200 ships were idling off the coast, immediately upwind of the
Long Beach throughout the strike period (CNN, 2002). Third, there were significant
changes in weather conditions during that period. While in September the weather in
Long Beach was warm with the exception of the morning hours, it changed in early
October (coincidentally with the strike) to cooler with mostly overcast days (Figure
2.11c). These weather conditions continued after the strike period. This change may be
expected to increase particle concentration by enhancing formation by condensation, but
to also particle size condensational growth of the formed particles.

Figure 2.11a shows the 24-hour averaged concentrations of particle number and PM
10

during the strike and non-strike period. It should be noted here that since the CPC was
used in conjunction with the SMPS, the total particle numbers shown in Figure 2.11a
reflect the sum of the particle counts in each size bin of the SMPS and not those
measured by the CPC alone. As other studies have indicated, this may underestimate
quite substantially the total particle concentrations (Liu and Deshler, 2003).

The results of Figure 2.11a as well as our statistical analysis did not reveal any
statistically significant impact of the strike on PN as well as PM
10
concentrations

50

Figure 2.11 a
Long Beach
0
2
4
6
8
10
12
14
9/ 1 6 9/ 18 9/ 20 9/ 22 9/ 24 1 0/ 2 10/ 4 10/ 6 1 0/ 8 1 0/ 1 0 1 0/ 12 10/ 1 4 1 0/ 16 1 0/ 1 8
Date
CO (ppb X 0.01)
0
10
20
30
40
50
60
70
80
90
NO
x
and O
3
(ppb)
CO
NOx
O3

Figure 2.11 b
Long Beach
0
10
20
30
40
50
60
70
80
90
100
11 0
9/ 16 9/ 18 9/ 20 9/ 22 9/ 24 10/ 2 10/ 4 10/ 6 10/ 8 10/ 1 0 1 0/ 1 2 10/ 1 4 1 0/ 1 6 10/ 1 8
Date
Temperature (
0
F)
0
10
20
30
40
50
60
70
80
90
100
% RH
T max T min
RHm ax RHm in

FIGURE 2.11: 24-hour averaged a) PN and PM
10
, b) CO, NO
x
, and O
3
, and c)
temperature and RH - before, during and after the port strike at Long
Beach in Sep-Oct 2002

(p > 0.05). The corresponding concentrations of gaseous co pollutants during the
strike/non- strike period are presented in Figure 2.11b. There is a statistically significant
increase in NO
x
and CO concentrations during the strike compared to pre- as well as

51
FIGURE 2.11: Continued…
Figure 2.11 c
0
10
20
30
40
50
60
70
80
90
100
110
9/16 9/18 9/20 9/22 9/24 10/2 10/4 10/6 10/8 10/10 10/12 10/14 10/16 10/18
Date
Temp (
0
F)
0
10
20
30
40
50
60
70
80
90
100
% RH
T max T min
RHmax RHmin

Long Beach

post-strike period (p < 0.001). High amounts of NO
x
and CO emissions from ships have
been observed in previous studies (Corbett and Fishback, 1997; Sinha et al., 2003;
Cooper, 2003; Saxe and Larsen, 2004). These emissions have been reported to be more
pronounced when the ships are at berth and idling (Cooper, 2003). We believe that the
majority of the increase in CO levels must be attributed to emissions from the idling
ships.

Emissions from diesel engines operating in ships contribute significantly to sub-
micrometer range particles and typically have bimodal size distributions, with a dominant
mode in the sub-40 nm and a weaker mode in the range of 70-100 nm (Isakson et al.,
2003). The average size distributions of the particle number concentrations before, during

52
and after the strike are shown in Figure 2.12. Particle concentrations below 60 nm seem
virtually unaffected by the strike. Even if a large number of these particles were emitted
by ships, it is conceivable that a substantial fraction of them did not reach the sampling
station due to coagulation, and-or volatilization processes that may have occurred during
their transport. Particle numbers concentrations in the 60-200 nm range were, however,
significantly elevated during the strike (p <0.001), which may be indicative of the
contributions of emissions from the idling ships. Also, the mode before and after the
strike is smaller compared to the strike period, further supporting the argument for the
larger-sized particles originating from ship emissions compared to those from heavy and
light duty vehicles.
Long Beach
0
5000
10000
15000
10 1 00 1 000
Particle mobility diameter, Dp (nm)
dN/dlog(Dp) (Particles/cc)
Post-strike
Pre-strike
During-strike

FIGURE 2.12: Average particle number size distribution before, during and after the
port strike at Long Beach in Sep-Oct 2002

53
2.5 Chapter 2: SUMMARY AND CONCLUSION
Particle number concentrations and size distributions in complex urban environments can
be seen to be highly variable on temporal scales, from diurnal to seasonal, and spatially,
from local scale influences, such as distances from highways, to regional scale
influences, such as long range transport across air basins. Seasonal difference in solar
intensity, temperature, and relative humidity can also strongly influence the diurnal size
profile.

In this study we see enhanced contribution of local emission sources during cooler
months with stagnant meteorological conditions at all sites. During warmer months,
effects of long-range dispersal of aerosol are observed most clearly at the easterly
receptor sites of Riverside, Mira Loma and Lake Arrowhead. The increased wind speeds
and onshore flow in the warmer months, lead to increased advection of pollutant parcels
from the polluted western areas of the LAB (Fine et al., 2004). Additionally, dry and hot
summer conditions would limit ultrafine particle growth to accumulation mode during
transport (Kim et al., 2002).

In addition to the contribution of vehicular emissions to particle concentrations in Los
Angeles, photochemical formation by secondary reactions in the atmosphere appears to
be a major source of PM during the afternoon periods in the warmer months at all sites.
Current studies by a number of groups have investigated and confirmed the
photochemical formation of ultrafine particles in urban atmosphere. In addition to our

54
observations in Los Angeles, secondary particle formation events have been observed in
urban areas, including Pittsburgh (Stanier et al., 2004), St. Louis (Shi and Qian, 2003),
and Mexico City (Baumgardner et al., 2004). An excellent review of this topic is given by
Kulmala et al. (2004). The actual formation mechanism of nanoparticles in the range of
1-3 nm remains largely unknown and has recently become the subject of intensive
research in the field of atmospheric science. Current hypotheses on the composition of
these fresh nuclei include the binary nucleation of water and sulfuric acid (Kulmala,
2002), ternary nucleation of ammonia-sulfuric acid-water (Weber et al., 1997), and ion-
induced nucleation (Yu and Turco, 2001). There is also general consensus that the
species responsible for further growth of these nanoparticles to the > 10 nm range are
different than the nucleating species (Stanier et al., 2004). Our current understanding of
atmospheric nanoparticle processes suggests that growth of these particles to larger sizes
within the ultrafine PM mode occurs by condensation of low volatility organic species.
These species are products of photochemical oxidation of volatile organic precursors on
these pre-existing nuclei (O’Dowd et al., 1999; Kulmala et al., 2004). In fact, recent
studies by Zhang et al. (2004) showed that nucleation rates of sulfuric acid are greatly
increased in the presence of organic acids (including products of atmospheric
photochemical reactions), by forming unusually stable organic-sulfuric acid complexes,
thereby reducing the nucleation barrier of sulfuric acid.

It is interesting to note in our field measurements that summertime levels of ultrafine
particles at source sites, such as long Beach and USC peaked in midday (i.e., noon to 1

55
pm), whereas ultrafine PM numbers peak slightly later (i.e., between 3-4 pm) in the
inland receptor sites. A time delay in the peak concentrations observed at the receptor
sites is possibly due to the transport time for polluted air masses to reach those sites. The
correlation between particle number concentrations and PM
10
has been widely studied
and weak-to-moderate correlations have been generally observed between the two
(Morawska et al., 1998; Woo et al., 2001; Noble et al., 2003; Fine et al., 2004; Sardar et
al, 2004). Since the fine to ultrafine particle counts are dominated by very small particles
and the PM
10
mass is dominated by fewer, much larger particles, low correlation should
be expected, especially in air masses dominated by fresher particles (either primary
emission particles or freshly formed secondary particles). In our study, we also found
weak-to-moderate correlations between PM
10
and number concentrations with no
particular seasonal trend. These findings are very important from a regulatory perspective
because they imply that controlling ambient PM
10
mass via national air quality standards
may not necessarily reduce human exposure to ultrafine particles that dominate the
particle counts and have recently been shown to have toxic effects (as discussed in the
introductory part of the paper).

In conclusion, the results presented in this paper indicate that location and season
significantly influence particle number and size distributions in locations within Southern
California. Strong diurnal and seasonal patterns in number concentrations are evident as a
direct effect of the sources, formation mechanisms, as well as meteorological conditions
prevalent at each location during different times of the day and year. These results will

56
be used in the CHS as a first order indicator of not only human exposure, but also inhaled
dose to ultrafine PM. They will also be used for the development and validation of
predictive models for population exposure assessment to ultrafine PM in complex urban
environments, such as that of the Los Angels Basin.

57
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61
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62
Chapter 3: Air Quality Impacts of the October 2003 Southern
California Wildfires*

*Phuleria H.C.; Fine P.M.; Zhu Y.and Sioutas C. Air quality impact of October 2003 Southern
California Wildfires, Journal of Geophysical Research-Atmosphere 10 (D7): Art. No. D07S08,
2005.

3.1 Chapter 3: ABSTRACT
In Southern California, dry summers followed by hot and dry westerly wind conditions
contribute to the region’s autumn fire season. In late October of 2003, 13 large Southern
California wildfires burned more than 750,000 acres of land, destroyed over 3,500
structures, and displaced approximately 100,000 people. The fire episode was declared
the deadliest and most devastating in more than a decade, and local media advised
individuals to stay indoors to avoid exposure to excessive levels of PM, CO, VOCs, and
ozone caused by the wildfires. This study examines the actual impact of these wildfires
on air quality in urban Los Angeles using “opportunistic” data from other air pollution
studies being conducted at the time of the fires. Measurements of pollutant gases (CO,
NO
x
, and ozone), particulate matter (PM), particle number concentrations (PN) and
particle size distributions at several sampling locations in the LA basin before, during,
and after the fire episode are presented. In general, the wildfires caused the greatest
increases in PM
10
levels (a factor of 3-4) and lesser increases in CO, NO, and PN (a
factor of up to 2). NO
2
levels remained essentially unchanged and ozone concentrations
dropped during the fire episode. Particle size distributions of air sampled downwind of
the fires showed number modes at diameters between 100 and 200 nm, significantly
larger than that of typical urban air. The particles in this size range were shown to
63
effectively penetrate indoors, raising questions about the effectiveness of staying indoors
to avoid exposure to wildfire emissions.

3.2 Chapter 3: INTRODUCTION
Wildfires can produce substantial increases in the concentration of gaseous pollutants
such as carbon monoxide (CO), nitrogen oxides (NO
x
), ozone (O
3
), and volatile organic
compounds (VOCs) (Cheng et al., 1998; Crutzen and Andreae, 1990) as well as
particulate matter (PM) (Dennis et al., 2002; Lighty et al., 2000). In recent years, there
has been much interest in studying the impact of wildfires in elevating the concentrations
of pollutants in the atmosphere. For instance, high CO concentrations that occurred
episodically in the Southeastern United States during the summer of 1995 have been
attributed to large forest fires in Canada (Wotawa and Trainer, 2000). In addition to
regional and local impacts (Bravo et al., 2002) wildfires contribute significantly to global
emissions of atmospheric trace gases including NO
x
, CO, and CO
2
(Crutzen et al., 1979).
Concerns arising from PM emissions from wildfires include acute health effects, direct
and indirect climate forcing, and regional visibility (Bravo et al., 2002; LeCanut et al.,
1996).

Emission inventories by the United States Environmental Protection Agency (US EPA)
estimate that, for the calendar year 2001, wildfires in the U.S. emitted 7.1 million tons of
CO, 0.98 million tons of VOCs, 0.60 million tons of PM
2.5
, and 0.66 million tons of PM
10

to the atmosphere (National Emissions Inventory – Air Pollutant Emissions Trends,
Current Emission Trends Summaries, August 2003, U.S. Environmental Protection
64
Agency (USEPA), http://www.epa.gov/ttn/chief/trends/index.html). These amounts are
significant, contributing 6%, 5%, 8% and 3% of the total CO, VOC, PM
2.5
, and PM
10

emissions to the atmosphere in the United States in 2001, respectively. These figures
obviously vary from year-to-year with the degree of wildfire activity, and in the severe
fire season of 2000, 18% of the total PM
2.5
emissions in the U.S. were estimated to
originate from wildfires. Other emission inventories in specific areas have calculated
significant NO
x
emissions from wildfires as well (Dennis et al., 2002). Some systematic
studies and source testing have been carried out for prescribed burns and controlled fires
in North America (Einfeld et al., 1991; Radke et al., 1991; Woods et al., 1991). Other
studies on wildfire emissions have taken advantage of existing pollution monitoring
networks and other focused air pollution studies which happen to be sampling when a
wildfire event occurs (Bravo et al., 2002; Brunke et al., 2001; Cheng et al., 1998; Goode
et al., 2000). Such “opportunistic” studies can provide valuable information on wildfire
pollutant emission rates and the impacts on air quality levels.

Dry summers, followed by conditions of hot and dry westerly winds (known as Santa
Ana winds) contribute to Southern California’s fire season in the autumn months. While
the fire season usually starts around the middle of May, the exact date varies from year to
year based on weather patterns and the moisture content, distribution, and amount of wild
vegetation present. The fire season usually ends when cooler weather and precipitation
conditions prevail. This usually occurs towards the end of October, but the fire season is
occasionally extended well into January in some Southern California areas (California
Department of Forestry and Fire Protection, Fire Statistics,
65
http://www.fire.ca.gov/MiscDocuments/FAQs.asp#13). The presence of thick and dry
foliage and bushy chaparral adds to the fire danger in the fire season in Southern
California. In general, pollution levels are observed to be high during fire events (Bravo
et al., 2002). The Los Angeles basin is surrounded by high mountains on three sides,
opening to the Pacific Ocean to the west and southwest. The topography and frequent
temperature inversions lead to the accumulation of airborne pollutants, particularly in the
eastern portion of the basin, due to the prevailing westerly sea breeze (Lu and Turco,
1996).

In late October of 2003, 13 large Southern California wildfires, ranging from Simi Valley
in the North to San Diego 150 miles to the south, burned more than 750,000 acres of
land, destroyed over 3,500 structures, including 2,700 homes, and displaced 100,000
people. Twenty human deaths were attributed to the wildfires. The cost of the damage
has been estimated to be US$ 2 billion. The fires having the greatest effect on the air
quality of the Los Angeles (LA) Basin included the Grand Prix and Old fires in San
Bernardino County and the adjacent Padua fire in Los Angeles County. These fires were
located to the northeast of central Los Angeles, with Santa Ana wind conditions, blowing
towards the southwest, transporting emissions to the western portions of the Basin. The
fuel was predominantly mixed chaparral, California sagebrush, annual grass and canyon
live oak. Pine, perennial grass and other urban vegetation were also burned. The fires
started around 23 October and had significant impacts on the air quality of the LA basin
until 29 October, when the winds reversed direction and resumed their normal on-shore
pattern (National Interagency Coordination Centre, 2003, Statistics and Summary,
66
http://www.nifc.gov/news/2003_statssumm/intro_summary.pdf). This fire episode was
declared the deadliest and most devastating in more than a decade, and there was a
significant level of worldwide press coverage. Local media advised individuals to stay
indoors to avoid exposure to excessive levels of PM, CO, VOCs, and ozone caused by the
wildfires. This motivated the following analysis that examines the actual impact of these
wildfires on air quality and measured pollutant concentrations in urban Los Angeles.
This paper presents measurements of pollutant gases (CO, NO
x
, and ozone) as well as
PM concentrations and characteristics at different sampling locations in the LA basin
before, during, and after the October 2003 fire episode. In addition, the effect of fire on
indoor particle concentrations and size distributions was also investigated. Since the fire
episode could not be predicted, the current study took advantage of several pre-existing
air pollution studies that were being conducted at the time of the wildfires. Given the
“opportunistic” nature of these samples, the measurement techniques were not
necessarily targeted for fire emissions, and not all of the data is complete in all sampling
sites.

3.3 Chapter 3: METHODS
As part of the routine sampling of an ongoing study associated with the University of
Southern California (USC) Children’s Health Study (CHS), supported by the South Coast
Air Quality Management District and the California Air Resources Board, concentrations
of carbon monoxide (CO), ozone (O
3
), nitrogen oxide (NO), nitrogen dioxide (NO
2,
),
particulate matter with aerodynamic diameters less than 10 µm (PM
10
) and particle
number (PN) are continuously measured in several locations in Southern California.
Continuous data were collected concurrently throughout the calendar year 2003, and five
sites within the LA Basin impacted by the wildfires were examined in this study: Long
Beach, Glendora, Mira Loma, Upland and Riverside (see Figure 3.1). The choice of these
sampling sites was based on their location within the Los Angeles Basin, the availability
of the data for the desired period, and the observed impacts of the Grand Prix, Old and
Padua fires. Generally, these urban sites are the most polluted among the monitoring sites
of the CHS.

Pacific Ocean
Los Angeles Basin
Downtown LA
LAX
Long Beach
Upland
Riverside
= Sampling site
10 km
N
Glendora
Mira Loma
USC
UCLA
= County lines
= Approx. Fire area

FIGURE 3.1: Map showing the fire area and the sampling sites in the Los Angeles basin.

Located near a busy surface street, the Long Beach station is about 1 km northeast of a
major freeway. The Glendora station is located in a residential area nestled in the
67
68
foothills of the San Gabriel Mountains. It is at least 1 km away from major roadways and
3 km from the nearest freeway. The Upland site is also located in a residential area about
6 km downwind of the Glendora site, but is located within 1 km of the I-210 freeway.
The Mira Loma site is located in a building on the Jurupa Valley High School campus. It
is directly east of a major freeway interchange, is surrounded by several major warehouse
facilities, and is located about 80 km east of downtown Los Angeles. The sampling
location at Riverside is within the Citrus Research Center and Agricultural Experiment
Station (CRCAES), a part of the University of California, Riverside. It is about 10 km
southeast of the Mira Loma site and is situated upwind of surrounding freeways and
major roads.

The concentrations of CO were measured near-continuously by means of a Thermo
Environmental Inc. Model 48C trace level CO monitor. Concentrations of NO and NO
2

were measured with a Continuous Chemiluminescence Analyzer (Monitor Labs Model
8840), and O
3
concentrations were monitored using a UV photometer (Dasibi Model
1003 AH). Total particle number concentrations (greater than about 10 nm in diameter)
were measured continuously by a Condensation Particle Counter (CPC, Model 3022/A,
TSI Incorporated, St. Paul, MN) set at a flow rate of 1.5 L min
-1
. At the Upland site, the
CPC was connected to a Scanning Mobility Particle Sizer (SMPS, Model 3936, TSI
Incorporated, St. Paul, MN), to measure the size distribution of submicrometer aerosols
(15 - 750 nm) using an electrical mobility detection technique. In this configuration, the
CPC flow rate was maintained at 0.3 L min
-1
(with the sheath flow of the SMPS set at 3 L
min
-1
), and particle number counts were calculated from the SMPS size distributions.
69
Unfortunately, due to a brief power outage and limited site access resulting from the
nearby fires, SMPS data was lost from the morning of 24 October to noon of the 29

October (the peak of the fire impact). However, the other monitors at this site continued
to function properly in this time window. Continuous particle number and gaseous co-
pollutant concentrations were averaged to form 1-hr and 24-hr average values for the
subsequent analysis.

Hourly PM
10
mass concentrations in each site were measured by a low temperature
Differential Tapered Element Oscillating Microbalance monitor (low temperature TEOM
1400A, R&P Inc., Albany, NY). The design and performance evaluation of this monitor
is described in greater detail by Jaques et al. [2004]. Briefly, the system consists of a
size-selective PM
10
inlet, followed by a Nafion
®
dryer that reduces the relative humidity
of the sample aerosol to 50% or less. Downstream from the Nafion dryer and ahead of the
TEOM sensor is an electrostatic precipitator (ESP) allowing for the removal of particles
from the sample stream. The ESP is alternately switched on and off, for equal time
periods of about 10 minutes. This dual sampling channel design makes it possible to
account for effects such as volatilization of labile species, adsorption of organic vapors
and changes in relative humidity and temperature, all of which affect the TEOM signal.
The study by Jaques et al. [2004] showed that the time averaged TEOM PM
10
mass
concentrations agreed within ± 10% with those of collocated Federal Reference Methods
(FRM).

70
In addition to the data collected at the CHS sites, semi-continuous PM
2.5
(fine) and
ultrafine PM mass concentrations were measured at the Southern California Supersite
located near downtown Los Angeles at the University of Southern California (USC).
Two-hour PM mass concentration data was collected with a Beta Attenuation Monitor
(BAM, Model 1020, Met One instruments, Inc., OR) (Chung et al., 2001). The BAM
consisted of a size-selective inlet (2.5 µm for fine and 0.15 µm for ultrafine) (Chakrabarti
et al., 2004), a filter tape, a beta radiation source, and a beta radiation detector. The
difference in the transmission of beta radiation through the filter tape before and after a
particulate sample has been collected, is measured and used to determine the mass of
collected particulate matter. Continuous operation is achieved by automatic advancement
of the filter tape between sampling periods.

Finally, in a concurrent but unrelated study, particle size distributions were measured
indoors and outdoors of a two-bedroom apartment in the Westwood Village area near the
University of California, Los Angeles. The residence is located about 100 m mostly
downwind (east) of the I-405 freeway, a very busy traffic source. A Scanning Mobility
Particle Sizer (SMPS 3936, TSI Inc., St. Paul., MN) was set up in a bedroom and
sampled alternating indoor and outdoor size distributions on a 24-hr basis. The aerosol
sampling flow rate of the SMPS was set to 1.5 L min
-1
in order to measure particles as
low as 6 nm as well as to minimize the diffusion losses of ultrafine particles during
sampling. The maximum size detectable at these settings was 220 nm, and a scan time of
180 s was used. The sampling lines were kept the same length and as short as possible
(1.5 m) for both indoor and outdoors samples. Measurements were made through a
71
switching manifold that alternately sampled indoor and outdoor air, each for 9-minute
periods, in which three size distributions were taken in sequence. There were no known
major indoor sources of aerosols in the residence for the period from 10 am to 7 pm,
when the residents were at work and from 11 pm to 7 am when the residents were asleep
in the other bedroom. The door of the sampling bedroom was always kept closed to
minimize the influence of any other possible indoor activity. The residence was under
natural ventilation with windows closed at all times during the sampling period. This
study provided a unique opportunity to monitor infiltration of PM of outdoor origin into
the indoor environment, and to estimate indoor exposures to PM from the wildfires.

3.4 Chapter 3: RESULTS AND DISCUSSION
Figures 3.2a-e present the 24-hour average concentrations of CO, NO, NO
2
, O
3
, PM
10
and
particle number (PN) before, during and after the October fire period in Southern
California at the five CHS sampling sites examined in this study. A summary of the
average concentrations of the pollutants before, during and after the fire is given in Table
3.1. As surmised from the news reports and the data, the period of fire influence was
from the 23–29 October. Figure 3.2 clearly shows that the concentrations of all the
pollutants drastically decreased on 30 October and then increased back to more typical
levels by 4 or 5 November. The rapid decline is associated with the wind reversal on the
afternoon of 29 October when an on-shore wind pattern replaced the Santa Ana
conditions, followed by rainfall on 30 and 31 October. Figure 3.3a displays a satellite
photo from NASA Earth Observatory on 28 October 2003 showing the extent of the fires
and the prevailing wind direction during the peak of the fire episode. On 29

October, the
0
10
20
30
40
50
60
70
80
GasConc (ppb)
0
25
50
75
100
125
150
175
200
PN (# cm
-3
) and PM
10
( µg m
-3
)
Glendora
0
10
20
30
40
50
60
70
80
90
100
110
Gas Conc (ppb)
0
50
100
150
200
250
300
PN (# cm
-3
) and PM
10
( g
m
-3
)
Long Beach
0
20
40
60
80
100
120
140
160
180
Gas Conc (ppb)
0
50
100
150
200
250
300
350
400
PN (# cm
-3
) and PM
10
( µg m
-
3
)
Mira Loma
0
10
20
30
40
50
60
70
80
90
Gas Conc (ppb)
0
50
100
150
200
250
300
350
PN (# cm
-3
) and PM
10
( µg m
-3
)
UC Riverside
0
10
20
30
40
50
60
70
80
90
10 0
11 0
1 0/1 5 1 0/1 7 1 0/1 9 1 0/21 1 0/23 1 0/25 1 0/27 1 0/29 1 0/31 1 1 /2 1 1 /4
M onth/Day
0
50
10 0
15 0
200
250
300
350
400
µ
CO (X 5.0 E-2) NO NO2
O3 PM 10 PN (X 1.0 E-2)
Upland

FIGURE 3.2: 24-hour averaged PM and gaseous pollutant concentrations during the study at
a) Glendora, b) Long Beach, c) Mira Loma, d) UC Riverside and e) Upland
72
Figure3.3 a

= Fire hot spots
Figure 3.3 b

= Fire hot spots

FIGURE 3.3: Satellite images from NASA earth observatory showing a) Southern California
during the peak of the fire episode on October 28
th
, 2003, with the smoke plumes
blowing west, and b) the same area after the wind reversal, on the afternoon of
October 29
th
, 2003, with a visible marine layer and the smoke plumes blowing
towards the east.
73
74
winds shifted to an on-shore pattern (Figure 3.3b) blowing fresh fire emissions towards
the east away from the LA Basin. The fires continued to burn for many days after, but
the cooler and wetter weather helped the firefighting effort and the fires were under
control within another week.

TABLE 3.1: Average concentrations of pollutants with the standard deviation at the five
CHS sites before, during and after the fire

Average Concentration (+ SD)

CO (ppm) NO (ppb) NO
2
(ppb) O
3
(ppb) PM
10
( µg m
-3
) PN (particles cm
-3
)
Pre Fire
Glendora 9 + 3 11 + 16 37 + 16 37 + 21 12 + 14 10400 + 5500
Long Beach 6 + 6 23 + 49 47 + 19 29 + 18 33 + 16 19300 + 12400
Mira Loma 6 + 4 45 + 54 29 + 14 25 + 26 61 + 35 16200 + 8200
UC Riverside 8 + 6 40 + 29 33 + 19 29 + 29 47 + 23 16200 + 12100
Upland 10 + 4 24 + 28 44 + 16 21 + 23 39 + 18 9000 + 3700
During Fire
Glendora 11 + 5 25 + 30 39 + 28 44 + 23 27 + 25 12200 + 6200
Long Beach 14 + 9 55 + 68 56 + 24 15 + 16 93 + 92 18000 + 8500
Mira Loma 12 + 8 105 + 85 39 + 26 17 + 18 215 + 171 28500+ 14600
UC Riverside 12 + 7 46 + 36 42 + 22 18 + 21 121 + 112 28800 + 16100
Upland 15 + 7 43 + 34 47 + 24 15 + 16 165 + 138 Data not available
Post Fire
Glendora 5 + 2 5 + 5 17 + 11 31 + 11 18 + 29 11000 + 6300
Long Beach 8 + 6 39 + 49 32 + 11 16 + 12 21 + 10 8600 + 9700
Mira Loma 4 + 3 57 + 45 20 + 11 19 + 15 28 + 16 23900 + 10700
UC Riverside 6 + 4 14 + 25 20 + 10 23 + 15 18 + 10 17400+ 11000
Upland 6 + 4 21 + 25 23 + 12 17 + 13 19 + 10 16700 + 8600

Data in bold indicate statistically significant difference between the pre- and during fire concentrations at p=0.05

The data summary in Table 3.1 indicates that with the exceptions of NO
2
and O
3
, the
concentrations of CO, NO, PM
10
and PN during the fire event were significantly higher
(at the p=0.05 level) than their respective values preceding the fire event. Statistical
comparisons between during and post-fire concentrations was not conducted, because, as
evident from the data in Table 3.1 and Figure 3.2, the unstable and wet weather
75
conditions during the week of 30 October to 5 November resulted in lower than average
air pollutant concentrations. It is of particular note, however, that the most dramatic
increase in the concentrations of any pollutant during the fire events was observed for the
PM
10
concentrations, which, with the exception of one site (Glendora), rose by almost
three to four-fold in all sites during this period. While typical PM
10
concentrations in Los
Angeles are on the order of 50 µg m
-3
or less (Christoforou et al., 2000), levels rose to
near or above 200 µg m
-3
at some sites during the fires. PM
10
levels at Glendora did not
rise to the same degree, possibly due to the site’s location at the base of a canyon in the
San Gabriel Mountains. The Santa Ana winds tend to blow down the mountain canyons,
and there was little or no fire activity in or upwind of this particular canyon. Upland, on
the other hand, was within 2-3 kilometers and directly downwind of extreme wildfire
activity. The other three sites were all further downwind from the wildfires, but all sites
experienced atypical PM
10
levels. It is possible that the higher wind speeds during Santa
Ana conditions increased re-suspended dust emissions that contributed to the elevated
PM
10
levels. This effect, if dominant, should be observed at all sites. However, the fact
that Glendora PM
10
levels remained within the “typical” range indicates that the impact
of fire smoke plumes is the main cause of the elevated PM
10
levels. Previously reported
data during Santa Ana events without fires also demonstrate that such high levels of PM
10

are not typically observed on a 24-hour basis (Geller et al., 2003, in press).

By contrast, the total particle number concentrations, also shown in Figure 3.2, did not
exhibit the same extreme concentration increases during the fires. PN levels increased
significantly only in Mira Loma and perhaps Riverside, and only by an approximate
76
factor of two. Even these higher levels of PN have been observed on occasion under
typical, non-fire influenced, conditions in the LA Basin (Kim et al., 2002) No significant
increase in PN was observed at Long Beach, and Glendora, the latter being minimally
affected by the fires as discussed above. Due to the aforementioned power outage, PN
data was not available at the closest site to the fires, Upland, during the wildfire period.
Emissions testing of foliar fuels demonstrate that high particle number levels are emitted
from these sources. However, given the observed high PM mass levels, and thus the
increased PM surface area in the fire smoke plumes, it is conceivable that emitted smaller
particles are scavenged by coagulation with larger particles in the smoke plume
(Formenti et al., 2003). This process may occur over the few hours that it takes for the
fire particles to reach our sampling sites. Many of the smaller particles, which make up
the majority of particle number concentrations, may no longer exist as individual
particles. Thus, PM mass levels remain high while PN levels are diminished. This
hypothesis may explain why the largest PN increase was seen at Mira Loma and
Riverside, both of which are much closer to the fire areas than the sites further downwind
such as Long Beach.

Similar to particle number, CO concentrations at these sites were only modestly affected
by the fires. With the exception of Glendora, the observed increases were statistically
significant at the p=0.05 level, but the degree of increase was much less than that
observed for PM
10
. Mira Loma, Upland and Long Beach experienced CO around twice
normal levels during the fire. As in the case of PN, CO concentrations in the area of
Glendora appear to be unaffected by the fire events. The relatively low increase in CO
77
due to the fires can be explained by other, more significant sources of CO in Los
Angeles. Emission factors from the USEPA and other studies (Barbosa et al., 1999;
Pereira et al., 1999; Scholes et al., 1996) show that the ratio of CO mass to PM
10
mass in
wildfire emissions lies typically between 8-16. The same ratio for various motor vehicles
under varying driving conditions is much higher, ranging from about 200 to over 2000
(Cadle et al., 2001; Chase et al., 2000). In urban areas dominated by vehicular sources,
wildfires will thus affect ambient levels of CO to a lesser degree than the ambient levels
of PM
10
. A review of historical pollutant data during Santa Ana conditions without fire
activity (9 February 2002 and 6 January 2003) shows that CO levels can diminish due to
fewer CO sources upwind and increased basin ventilation. However, this effect is
inconsistent, and varies greatly with sampling site and from event to event. Thus, no true
“Santa Ana baseline” can be established for comparison purposes. For this reason,
comparisons are limited to the “typical” conditions before the fire episode.

NO concentrations follow similar trends with those for CO and PN (i.e. they increase
significantly in every location during the fire) but this increase is on the order of two-fold
or less, hence smaller than the increase observed for PM
10
. While the increase in NO
concentrations during the fire event seems to be minor at the Riverside location, the
nearby Mira Loma site shows more than double the NO levels relative to levels before
the fire events. It is possible that Mira Loma may have been more directly downwind of
fire areas than Riverside, which would explain this discrepancy. This is supported by the
observed PM
10
levels at these two sites, which also increased more dramatically in Mira
Loma than in Riverside. Relative to NO, PN, and PM
10
, the effect of fires was negligible
78
for NO
2
as the concentrations did not change significantly in any of the five sampling
sites during the fire events. While some NO
2
is emitted directly from combustion
processes, most of the NO
2
in urban air is formed in the atmosphere by the reaction of
NO with ozone. Under normal conditions in Los Angeles, NO, and thus NO
2
levels are
dominated by diesel vehicle emissions (Fujita et al., 2003). However, the NO increases
observed during the fires were not accompanied by corresponding increases in NO
2

concentrations. Although no conclusive explanation can be determined from the current
data, it is possible that the PM in the fire smoke blanketing the LA basin blocked
incoming solar radiation and thus reduced photochemical activity in the atmosphere.
This would result in lower ozone levels and thus, lower observed levels of NO
2
.
Increased concentrations of organic gases (VOCs) emitted by the fires may also play a
role in the complex atmospheric chemistry of NO, NO
2
, and ozone (Cheng et al., 1998).
Interestingly, with the exception of Glendora, which experienced marginally (but not
significant) increased O
3
concentrations during the fire episode; the concentrations of O
3

decreased by about 25-50% at all the other sites during the fire period. As mentioned
above, the fire smoke covering the basin and the corresponding reduction in
photochemical activity may be a possible explanation for this decrease in concentration.

The effect of the wind direction change can also be seen in the hourly concentrations of
the measured pollutants in Upland as shown in Figures 3.4a and b. The high
concentrations of PM
10
at Upland can be clearly seen during the entire fire period, with
the highest hourly concentration measured at 769 µg m
-3
. On October 29
th
, at 12:00 P.M.,
the PM
10
level was 153 µg m
-3
and within one hour it dropped to 65 µg m
-3
. Within four
Figure 3.4 a
0
100
200
300
400
500
600
700
800
10/21 0:00 10/23 0:00 10/25 0:00 10/27 0:00 10/29 0:00 10/31 0:00
Month/Day Time
PM ( µg m
-3
); PN (# cm
-3
)
PM10 PN X 1.0E-2 Vertical dashed lines
correspond to size
distributions in Figures 6(a)
and (b).

Figure 3.4 b
0
20
40
60
80
100
120
140
160
10/21 0:00 10/23 0:00 10/25 0:00 10/27 0:00 10/29 0:00 10/31 0:00
Month/Day Time
Gas concentration (ppb)
CO X 1.0E-2
NO
NO2
O3

FIGURE 3.4: Hourly a) PM and b) gaseous pollutant concentrations at Upland

79
hours, PM
10
concentrations dropped to below 20 µg m
-3
. This marks the time of the wind
reversal mentioned above. Unfortunately, hourly data of particle number concentrations
in this time frame are not available due to the power outage. Similar to the 24-hr data,
the hourly gaseous pollutant levels did not increase as much as the PM
10
levels during the
period of wildfire influence. However, with the exception of ozone, concentrations of all
the gaseous pollutants dropped precipitously when the wind reversal occurred.

Semi-continuous ultrafine and fine (PM
2.5
) particle mass concentration data support the
argument that the atmospheric concentrations of smaller particles (measured above as
PN), increased to a lesser extent than the larger particles. Figure 3.5 displays the 2-hour
ultrafine and fine PM mass obtained from the BAM measurements at the USC site. The
average ultrafine particle mass concentrations increased from an average value of 5.4 (±

0
20
40
60
80
100
120
10/18 10/19 10/20 10/21 10/22 10/23 10/24 10/25 10/26 10/27 10/28 10/29 10/30
Date
µ
FP UFP

FIGURE 3.5 Two-hour averaged fine (FP) and ultrafine (UFP) particle mass
concentrations at USC.

80
81
2.3) to 6.9 (± 2.7) µg m
-3
. While this increase is statistically significant (p<0.01), it is
still less dramatic than the obvious increase in PM
2.5
during the fire events. The average
concentration of PM
2.5
more than doubled, from 19.1 (± 5.2) to 51.3 (± 26.1) µg m
-3
.

The highest fine particle mass measured during the fire episode at USC was 115 µg m
-3
.
The wind reversal was marked by a steep reduction in fine particle mass midday on 29
October when the fine PM dropped from 105 µg m
-3
in the morning to 25 µg m
-3
by 2:00
P.M.

Figures 3.6a and b show the one-hour averaged particle size distribution at Upland
corresponding to the times marked by vertical lines in Figure 3.4(a). Because of the loss
of SMPS data for almost entire fire period, we have selected times just before (Figure
3.6a) and just after (Figure 3.6b) the power outage. The particle size distribution at a
given hour (10 A.M and 12 P.M.) was averaged for different days before and after the
fire, and compared to the same hour during the influence of the fires. It can be seen that
the size distribution corresponding to the periods of fire influence significantly differs
from those without the fire influence. The mode in the number-based particle size
distribution spans from 100 to 300 nm and is indicative of the wildfire smoke.

Previous emissions testing have shown similarly large number modes in the particle size
distributions from the burning of foliar fuels (Hays et al., 2002). Such large diameter
number modes are not normally seen in urban locations (Kim et al. 2002) where particle

Figure 3.6 a
0
50
100
150
200
250
300
350
400
450
500
10 100 1000
Particle Size (nm)
PN (Particles cm
-3
)
Av pre fire
10/24
Av post fire

Figure 3.6 b
0
50
100
150
200
250
10 100 1000
Particle Size (nm)
PN (Particles cm
-3
)
Av pre fire
10/29
Av post fire

FIGURE 3.6: Particle size distributions at Upland a) at 10AM: before, 10/24/03, and after the
fires; and b) at 12PM: before, 10/29/03, and after the fires.

number concentrations are dominated either by primary vehicular emissions or by
nucleation processes (Woo et al., 2001). Since particle volume is proportional to the cube
of the diameter, a modest increase in particle number concentrations in these larger size
modes is sufficient to account for the larger increases observed for PM mass.

82
Indoor and outdoor particle number size distributions were also available from a
concurrent study near UCLA in the western portion of the Los Angeles Basin. Figures
3.7a and b display ambient and corresponding indoor particle size distributions from 11
pm- midnight for different days during and after the fire events. This period was selected
to minimize the influence of any possible indoor sources (i.e., cooking, cleaning) and
outdoor traffic from the nearby freeway. The effect of the fires on indoor concentration is
Figure 3.7 a
0
10
20
30
40
50
60
70
1 10 100 1000
Particle Size (nm)
PN (Particles cm
-3
)
10/2
6
10/2
8
10/3
0
11/1

Figure 3.7 b
0
10
20
30
40
50
60
70
1 10 100 1000
Particle Size (nm)
PN (Particles cm
-3
)
10/2
6
10/2
8
10/3
0
11/1
(b)

FIGURE 3.7: Particle size distributions on different days at 11PM in Westwood
Village: a) Outdoor; and b) Indoor.
83
84
evident, with an aerosol mode diameter at about 200 nm on 26 and 28 October, and then
a shift to a lower size range (between 50 to 70 nm) on 30 October and 1 November,
respectively. Number concentrations both indoors and outdoors also decrease as we
move away from the fire period. It is of interest to note that on 26 October (i.e., in the
middle of the wildfire period), the indoor and outdoor size distributions are virtually
identical in both number concentration and mode, which suggests that the majority of the
outdoor aerosol infiltrated indoors with a penetration value close to 1. This is not a
surprising result, considering that based on our measurements, the majority of the
particles emitted from the fire are in the 100 – 300 nm range. This is also the range of
maximum indoor penetration of outdoor aerosols and minimum indoor deposition rate
(Allen et al., 2003; Long et al., 2001). As the mode in aerosol size distributions shifts to
smaller sizes, the indoor concentrations are approximately 50 – 75% lower than outdoors,
which is also consistent with the penetration values determined by Long et al., (2001) and
Wallace and Howard-Reed, (2002) for the particles in the 40 – 80 nm range.

To put the above results in perspective, Figures 3.8a and b show the measured indoor and
outdoor particle size distributions during the morning traffic commute period, from 6
A.M. to 7 A.M., while the wildfires were still active (27 October) and after the fire event
(November 4). The outdoor size distribution on 27 October is characterized by one
dominant mode at about 25 nm, which is associated with vehicular emissions (Zhu et al.,
2002a; Zhu et al., 2002b), followed by a second mode at about 200 nm, which reflects the
influence of the wildfires. The indoor size distribution for that date (Figure 3.8a) shows
that the super-100 nm particles are virtually at identical concentrations with their
corresponding outdoor levels, whereas the concentrations of smaller particles indoors are
substantially lower than those outdoors. Similar trends are also shown in Figure 8b, with
the exception that the second mode in the 200 nm range observed during the fire period
no longer exists in either the indoor or outdoor environment. The data plotted in Figures
3.8a and b indicates an average outdoor-to-indoor penetration ratio of about 0.15 to 0.20
for particles in the 20-50 nm range, which, as stated above, originate from nearby traffic
Figure 3.8 a
0
10
20
30
40
50
60
70
80
1 10 100 1000
Particle Size (nm)
PN (Particles cm
-3
)
IN OUT

Figure 3.8 b
0
10
20
30
40
50
60
1 10 100 1000
Particle Size (nm)
PN (Particles cm
-3
)
IN OUT

FIGURE 3.8: Indoor/Outdoor particle size distributions at 6AM in Westwood Village
on a) 10/27/03; and b) 11/04/03

85
86
sources. This value is somewhat lower than the indoor penetration ratios reported by
Long et al., (2001) and Wallace and Howard-Reed, (2002) for that size range, which
normally range between 0.3 – 0.7, depending on home characteristics and air exchange
rates. One possible explanation for the lower values observed in our study may be that,
as shown in recent reports in the literature (Sakurai et al., 2003; Tobias et al., 2001), sub-
50 nm particles from vehicular emissions consist of semi-volatile material, compared to
the mostly non-volatile particles in the 50-100 nm range. Thus, after penetrating indoors,
they may have completely evaporated or shrunk to sizes below about 6 nm, the lower size
detection limit of the SMPS. It is unknown what source, size or composition of ambient
PM is responsible for the observed health effects. But our results show that the prevailing
advice during the fire episode for people to stay indoors may not be effective in reducing
exposure to most of the particles emitted from wildfires.

3.5 Chapter3: SUMMARY AND CONCLUSIONS
Coincidental air pollution sampling campaigns proved valuable in determining the
impacts of the October, 2003 wildfire episode on pollutant levels in the Los Angeles
Basin. The greatest impact was observed on PM
10
concentrations which increased by
factors of three or four depending on location. CO and NO levels increased to a lesser
extent (a factor of approximately two), most likely due to the different relative emission
rates of these pollutants from wildfires compared to typical urban sources such as traffic.
Particle number concentrations and NO
2
were essentially unchanged, except at the sites
nearest the fires where PN levels almost doubled. Ozone levels during the fires were
observed to be lower during the fires at some sites, a possible result of light scattering by
87
the smoke plume reducing photochemical activity levels. Particle number distributions
downwind of the fires displayed number modes with diameters between 100 and 200 nm,
larger than typical urban aerosol and explaining the larger increases in PM
10
and PM
2.5

mass concentrations than that for ultrafine particle mass and particle number. These
particles were also shown to penetrate effectively indoors, calling into question the
prevailing advice to the public to remain inside to avoid exposure to harmful wildfire
emissions.
88
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(2003), Size-dependent mixing characteristics of volatile and nonvolatile components in
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R. Waytulonis, and D.B. Kittelson (2001), Chemical analysis of diesel engine
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aerosol size distributions: Observations of ultrafine particle events, Aerosol Science and
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92

Chapter 4: Size-Resolved Emissions of Organic Tracers from Light- and
Heavy-Duty Vehicles Measured in a California Roadway*

*Phuleria H.C.; Geller M.D.; Fine P.M. and Sioutas C. Size-resolved emissions of organic tracers
from light- and heavy-duty vehicles measured in a California Roadway tunnel, Environmental
Science and Technology, 40(13), 4109-4118, 2006

4.1 Chapter 4: ABSTRACT

Individual organic compounds found in particulate emissions from vehicles have proven
useful in source apportionment of ambient particulate matter. Species of interest include
the hopanes, originating in lube oil, and selected PAH generated via combustion. Most
efforts to date have focused on emissions and apportionment PM
10
or PM
2.5
. However,
examining how these compounds are segregated by particle size in both emissions and
ambient samples will help efforts to apportion size-resolved PM, especially ultrafine
particles which have been shown to be more potent toxicologically. To this end, high
volume size-resolved (coarse, accumulation and ultrafine) PM samples were collected
inside of the Caldecott tunnel in Orinda, CA to determine the relative emission factors for
these compounds in different size ranges. Sampling occurred in two bores, one off-limits
to heavy-duty diesel vehicles, which allows determination of the different emissions
profiles for diesel and gasoline vehicles. Although tunnel measurements do not measure
emissions over a full engine duty cycle, they do provide an average emissions profile
over thousands of vehicles that can be considered characteristic of “freeway” emissions.
Results include size-fractionated emission rates for hopanes, PAHs, elemental carbon,
and other potential organic markers apportioned to diesel and gasoline vehicles. The
results are compared to previously conducted PM
2.5
emissions testing using dynamometer
facilities and other tunnel environments.
93

4.2 Chapter 4: INTRODUCTION
Motivated by the growing concern that human exposure to ultrafine particles poses a
significant health hazard; characterization of particles emitted from gasoline and diesel
engines has been the focus of many studies (Allaban et al., 2002; Geller et al., 2005;
Lighty et al., 2000). Vehicular particulate emissions are of specific interest because of
their ultrafine size and their potentially toxic components, such as polycyclic aromatic
compounds (PAHs) and trace metallic elements. A number of health studies have
demonstrated the adverse health effects of diesel exhaust particles (Mauderly, 1993;
1994; Weingartner et al., 1997).

The recent focus on the smaller vehicle-derived ultrafine particles has been due to their
higher number and surface area relative to larger particles, their ability to penetrate cell
membranes and to their increased toxicity on a per mass basis (Li et al., 2003; Xia et al.,
2004). For example, Li et al. (2003)

have shown that ultrafine particles (defined in this
case as those having diameters less than about 180 nm) induce a higher degree of
oxidative stress and cause more cell damage than an equivalent mass of fine particles
(aerodynamic diameter <2.5 µm). Animal exposure studies have further corroborated
increased adverse health outcomes from ultrafine airborne particles (Johnston et al.,
2000; Kleinman et al., 2003; 2005; Oberdorster, 2001; Oberdorster et al., 2002).

Since ultrafine particle number and mass do not necessarily correlate with mass
concentrations of larger particles (PM
10
or PM
2.5
) (Chakrabarti et al., 2004; Fine et al.,
2004; Sardar et al., 2004), these more common measurements cannot be used for
94

information on ultrafine particle concentrations. Given the results of the recent health
studies that may drive future regulatory strategies, it is essential that the sources of and
the human exposure to vehicular ultrafine particle emissions are well understood (Janhall
et al., 2004; Sioutas et al., 2005).

Several different approaches have been applied for examining vehicular emissions,
including roadside measurements (Kuhn et al., 2005), on-road chase experiments
(Canagaratna et al., 2004; Kittelson, 1998), laboratory dynamometer studies (Gross et al.,
2005; Rogge et al., 1993; Schauer et al., 1999; 2002; Zielinska et al., 2004), and
measurements inside of roadway tunnels (Geller et al., 2005; Allen et al., 2001; Chellam
et al., 2005; Fraser et al., 1998; Kirchstetter et al., 1999; Marr et al., 1999; Miguel et al.,
1998). Dynamometer and chase experiments are useful due to the ability of carefully
control testing conditions. Emissions control technologies can be evaluated, and
emissions differences over different driving conditions and cycles, including cold-start
(Schauer et al., 2003), can be examined. However, the high cost and complexity of such
tests limit the number of vehicles which can be assessed, and thus may not provide a
good representation of the vehicle fleet composition on the road. Moreover, changing the
dilution conditions in dynamometer testing is known to affect the measurements,
especially for ultrafine particles (Mathis et al., 2004). Such studies may not account for
particle aging effects, the mixing of emissions from different vehicles (Weingartner et al.,
1997), and non-tailpipe emissions from tire wear, break wears, and re-suspended road
dust (Allen et al., 2001).

95

An alternative to single vehicle emission measurements are roadway tunnel studies
measuring the emissions from a large population of the on-road vehicle fleet mix under
real-world, although very specific, driving conditions. Although the dilution, temperature,
humidity and sunlight conditions inside a tunnel may differ from conditions outside
tunnels, they more closely approximate real-world conditions than dynamometer tests.
Roadside measurements will sample under actual ambient conditions, but are subject to
wind and other environmental processes, which might vary the emissions and background
conditions (Gross et al., 2005). Since air is directly sampled from inside the tunnel bore,
very high concentrations are encountered and thus, practically all measurements can be
attributed to vehicles (Kean et al., 2003). In tunnels with multiple bores that have traffic
restrictions based on vehicle size, such as the Caldecott tunnel in California (Geller et al.,
2005; Marr et al., 1999; Miguel et al., 1998), apportionment of emissions to vehicle or
fuel type can be achieved (i.e. most heavy-duty vehicles (HDV) consume diesel fuel,
while most passenger cars and other light-duty vehicles (LDV) are powered by gasoline)
(Gross et al., 2005). The main limitation of tunnel measurements is that they measure
emissions over the very specific driving conditions of the particular tunnel, missing cold-
start and some transient effects. However, a highway tunnel can provide detailed
emissions characterization under typical highway driving conditions, resulting in a real-
world average highway source signature. The derived vehicular PM emission factors are
important to assessing effects on human exposure and health (Dockery et al., 1993;
Mazzoleni et al., 2004).

96

Several studies have measured individual organic compounds in atmospheric particulate
matter (PM) samples. Using gas chromatography/mass spectrometry methods, hundreds
of particle-phase individual compounds have been identified and quantified in ambient
air (Fine et al., 2004; Fraser et al., 2003; Schauer et al., 1996; Schauer and Cass, 2000;
Simoneit, 2002; Zheng et al., 2002). Usually, only between 10 and 20% of the total
particulate organic compound mass can be quantified as individual organic species, but
many of these compounds have been used to trace primary particle emissions via source
apportionment techniques (Schauer et al., 1996; Schauer and Cass, 2000; Zheng et al.,
2002). Accurate and up-to-date source profiles are essential for the successful application
of these methods. Specific organic tracers of primary vehicular particle sources include
hopanes and steranes. These species are found in the lubricating oils used by both
gasoline and diesel powered motor vehicles and are thus found in the particulate
emissions from both types of vehicles (Rogge et al., 1993; Schauer et al., 1996; Cass,
1998). High molecular weight PAHs have also been used as additional tracers for motor
vehicle exhaust, although there are other combustion sources of PAH as well including
wood combustion (Rogge et al., 1993; Cass, 1998; Fine et al., 2001). Miguel et al. (1998)
reported that LDVs are a significant source of the higher molecular weight PAHs such as
Benzo(ghi)perylene, while HDVs predominantly emit the lighter PAHs, such as
fluoranthene and pyrene. Furthermore, diesel engines are known to have significantly
higher elemental carbon (EC) emissions than gasoline-powered vehicles (Schauer et al.,
1999; 1996; Schauer, 2003). It may be possible, therefore, that information on emissions
of high and low molecular weight PAHs, hopanes, steranes, and EC from motor vehicles
can be used to distinguish HDV and LDV contributions to ambient samples in chemical
97

mass balance source apportionment calculations (Fraser et al., 2003; McGaughey et al.,
2004). Another potential vehicular marker is the so-called unresolved complex mixture
(UCM) of hydrocarbons, which appears as an unresolved hump underlying identifiable
compound peaks in typical GC/MS traces (Hays et al., 2004; Simoneit, 1984). The ion
spectra of the UCM look very similar to that of typical motor oil (Schauer et al., 1999).
To the extent that vehicle derived UCM can be accurately quantified in emission and
ambient samples, it may serve as an additional indicator of vehicular particulate
emissions.

A few previous studies have measured size-fractionated PAH, oxy-PAH, and
organonitrates over 5-24 hour periods (Allen et al., 1996; 1997; 1998; Garnes et al.,
2002). However, most ambient and emissions samples intended for organic speciation are
not size-resolved, due to limitations in collecting sufficient particle mass for trace species
analysis. A high-volume slit impactor developed by Misra et al. (2002) allows for
separation of particles based on aerodynamic diameter with a cut-point of 0.18 µm and a
flow rate of 450 lpm. The impactor, with a high volume after-filter, has been
successfully deployed to collect separate ultrafine (<0.18 µm) and accumulation mode
(0.18 – 2.5 µm) ambient samples in Los Angeles (Fine et al., 2004).

The current study was conducted within two bores of the Caldecott tunnel in Orinda, CA
during August and September of 2004. The high-volume slit impactor was deployed to
collect size-resolved PM (ultrafine and accumulation mode) for speciated organic
analysis of vehicular emissions by GC/MS. The emissions characterization for bulk
98

species such as mass, elemental carbon (EC), organic carbon (OC) and trace elements,
from this campaign has been reported previously (Geller et al., 2005). This work provides
additional detailed information on organic tracers of real-world vehicular sources found
in the two different PM size ranges. The first reported size-segregated emission factors
for organic tracers, other than PAH, are calculated for both light-duty vehicles (LDV) and
heavy-duty vehicles (HDV). Emission factor results for PM
2.5
are compared to previous
tunnel and dynamometer studies. Combined with other source profiles, including that of
vehicular cold-start emissions, the results of this study can be used in future chemical
mass balance calculations to assess the relative contribution of PM sources to ambient
samples.

4.3 Chapter 4: METHODS
4.3.1 Tunnel sampling
A detailed description of the tunnel environment, traffic characteristics and sampling
procedure is described by Geller et al. (2005). Briefly, the 1.1-kilometer long Caldecott
Tunnel in Orinda, CA includes three two-lane bores with a 4.2% incline from west to east
(Geller et al., 2005; Allen et al., 2001; Kirchstetter et al., 1999a; 1999b). Bores 1 and 3
allow both light-duty vehicles (LDV) and heavy-duty vehicles (HDV), while bore 2 is
restricted to LDV traffic only. Traffic flows from west-to-east in Bore 1, east-to-west in
Bore 3, and the direction of traffic switches from westward in the morning to eastward in
the afternoon and evening in Bore 2. Field sampling was conducted in the afternoon in
Bores 1 and 2 for four days each. The sampling period occurred from approximately 12
99

PM to 6PM, when all traffic in the two bores traveled eastward, during August and
September of 2004.

4.3.2 Traffic characterization
The traffic volume and vehicle characteristics in the tunnel during the sampling campaign
are also described by Geller et al. (2005). Vehicle counts broken down by axle class are
reported in Table 4.1. Although Bore 2 is legally restricted to light-duty traffic, some
medium-duty diesels and an occasional heavy-duty three-axle vehicle passed through.
The method for attributing 2-axle/6-tire vehicles to gasoline and diesel fuel is given in
Geller et al. (2005). As a percentage of the total vehicles counts, diesel vehicles are an
order of magnitude less prevalent in Bore 2 than in Bore 1. During the sampling
campaign, heavy-duty vehicles comprised an average of 3.8% of the total vehicles in bore
1 and less than 0.4% of vehicles in bore 2.

TABLE 4.1. Traffic volume in the Caldecott tunnel

Axle Class
Bore Date
3+ axles 2-axle, 6 tire 2-axle, 4 tire
% HD Diesel
8/23/2004 1 29 4041 0.38
8/24/2004 3 38 4113 0.54
8/25/2004 0 20 3982 0.25
2
8/26/2004 0 28 4028 0.34

8/30/2004 49 102 3013 3.2
8/31/2004 76 109 2482 4.9
9/1/2004 65 88 2951 3.5
1
9/2/2004 66 66 2741 3.4

100

4.3.3 Pollutant measurement and sample collection
Both gaseous and particulate pollutant concentrations in the tunnel bores were measured
with various continuous and time-integrated instruments, approximately 50 m from the
tunnel exit. Pollutant levels at the entrance were measured with an identical set of
instrumentation located 50 m from the tunnel entrance. Since this location was also inside
the tunnel, it is not a pure ambient background sample, considering that it includes
roadway emissions from the first 50 m of the tunnel. Emission factors are calculated
based on the difference in concentrations between exit and entrance samples over a
known, fixed distance of roadway between sampling locations.

The primary sampling apparatus used for PM collection was a custom built, high-volume
(450 lpm) sampler designed to separate and collect coarse (Dp > 2.5 µm), accumulation
(0.18 < Dp < 2.5 µm) and ultrafine mode (Dp < 0.18 µm) aerosols. This sampler is
described in greater detail by Misra et al. (2002). It allows collection of particles with
aerodynamic diameters greater than about 180 nm onto quartz-fiber impaction strips. A
preceding impaction stage with a 2.5 µm aerodynamic diameter collects coarse particles.
Due to low concentrations of organic tracers in the coarse mode, no coarse data is
presented here. Downstream of the ultrafine impactor, a commercially available 8 x 10
inch high-volume filter holder contains a Quartz-fiber filter (Pallflex
®
Tissuquartz
TM

2500QAT-UP-8x10, Pall Corp.) to collect the ultrafine PM fraction. Field blanks for
quartz filters contained negligible levels of the compounds quantified in this study.
Quartz filters and substrates were pre-baked at 550
o
C for 12 hours and stored in baked
aluminum foil prior to deployment (Geller at al., 2005; Fine et al., 2004).
101

4.3.4 Organic Speciation Analysis
Methods for the quantification of individual organic compounds in ambient particulate
matter were based on the procedures initially developed by Mazurek et al. (1987) and
advanced further by others (Fine et al., 2004; Schauer et al., 1999; 2002; Sheesley et al.,
2003). The quartz filter samples from the high-volume sampler were cut into smaller
portions and combined in an annealed glass jar with a Teflon-lined lid. Samples were
then spiked with known amounts of isotope labeled internal standard compounds,
including three deuterated PAH, two deuterated alkanoic acids, deuterated cholestane,
deuterated cholesterol, and C
13
labeled levoglucosan. Solvent extraction of all samples
was performed with three successive 10-minute mild sonications in 50 mL of a 9:1
mixture of HPLC-grade dichloromethane and methanol. The 50 mL extracts from each
sonication step were then combined and reduced in volume to approximately 10 mL by
rotary evaporation at 35
0
C under a slight vacuum. The remaining volume was filtered
through a baked quartz-fiber filter in a filtration assembly consisting of only stainless
steel and Teflon components. The samples were then reduced to approximately 1 mL
under pure nitrogen evaporation before the samples were split into two separate fractions.
One fraction was derivatized for organic acid analysis, the results of which are not
presented here. The other fraction was further reduced to a specified volume ranging
from 50 to 200 µL by evaporation under pure nitrogen. The final target volume was
determined based on the amount of organic carbon mass in each sample (Fine et al.,
2004; Fraser et al., 2003).

102

The underivatized samples were analyzed by auto-injection into a GC/MSD system (GC
model 5890, MSD model 5973, Agilent). A 30 m x 0.25 mm DB-5MS capillary column
(Agilent) was used with a 1 µL splitless injection. Along with the samples, a set of
authentic quantification standard solutions were also injected and used to determine
response factors for the compounds of interest. Overall, 100 compounds are included in
this set of standard mixtures in known concentrations, including known amounts of the
same isotope labeled compounds used to spike the samples. Each standard compound is
assigned a response factor relative to one of the internal standards with a similar retention
time and structure. Between one and three of the most prevalent ions in the spectrum for
each compound were selected for peak integration. These response factors were then
applied to the compounds identified in the sample extracts relative to the internal spike
compounds. While some compounds are quantified based on the response of that
compound in the standard mixtures, others for which matching standards were not
available, are quantified using the response factors of compounds with similar structures
and retention times. UCM quantification was based on the total ion current (TIC)
response of standard alkanes with similar retention times. Analytical errors for these
methods have been reported to be no more than 25% (Fine et al., 2004; Zheng et al.,
2002; Sheesley et al., 2003).

4.3.5 Emission factors
Fuel-based emission factors relating total carbon emissions in the tunnel (primarily in the
form of CO
2
) to the carbon content of fuel are computed as described in detail by
Kirchstetter et al. (1999a)

and were applied in the previous paper from this sampling
campaign (Geller et al., 2005). Briefly, pollutant concentrations are expressed as mass
emitted per unit mass of fuel burned by the following equation:

c p
w
CO CO
P
E
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
∆ + ∆
∆
=
] [ ] [
] [
10
2
3
…(4.1)

where E
p
is the emission factor (g emitted per kg fuel burned) for pollutant P, ∆[P] is the
increase in the concentration of pollutant P (µg/m
3
) above the tunnel entrance location,
∆[CO
2
] and ∆[CO] are the increases in the concentrations of CO
2
and CO (µg of
Carbon/m
3
) above the tunnel entrance, and w
c
is the weight fraction of carbon in the fuel
(Geller et al., 2005; Kirchstetter et al.,1999a).

In order to derive HDV and LDV specific emission factors, ∆[P], ∆[CO
2
], and ∆[CO]
must be apportioned between HDV and LDV contributions. Ignoring the very low
numbers of diesel vehicles in bore 2, ∆[P]
LDV
, ∆[CO
2
]
LDV
, and ∆[CO]
LDV
are assumed to
be equal to the measured concentrations in bore 2. ∆[CO
2
]
HDV
is calculated based on fuel
consumption rates, fuel densities, vehicle counts and w
c
as described in Kirchstetter et al.
(1999a). The calculation to determine the pollutants emitted by HDV essentially reduces
to the following equation

∆[P]
HDV
= ∆[P]
bore 1
– X ∆[P]
bore 2
…(4.2)

which subtracts the LDV emissions as determined in bore 2 from those in bore 1. In the
Kirchstetter et al. (1999a) method, which was applied here, the factor X is based on
measured CO levels and vehicles counts, resulting in a value of 0.86 in our study.
103

104
Based on the above calculations, the main source of error for the HDV apportionment is
the value of X and the uncertainty in the measured levels of species for which LDVs emit
more than HDVs (when ∆[P]
bore 1
~ X ∆[P]
bore 2
). The main source of error in LDV
emissions estimates derives from the few diesel vehicles that passed through bore 2.
Since the presence of diesel vehicles in bore 2 does not significantly affect the HDV
emissions estimates (<10%), the calculated HDV emissions factors can be used to assess
the impact of Bore 2 diesel vehicles on calculated LDV emission factors.

4.4 Chapter 4: RESULTS AND DISCUSSION
4.4.1 Tunnel concentrations
Table 4.2a and 4.2b presents the average mass concentrations and standard deviations of
the measured species in Bore 2 and Bore 1 respectively at both the ends in the tunnel in
the ultrafine and accumulation size modes. The measured concentrations of each organic
species were found to be higher at the east end than the west end, confirming significant
contributions from the vehicles in the tunnel. Most of the organic species were measured
to be an order of magnitude higher in ultrafine mode than the accumulation mode; an
expected result given the smaller sized primary particles emitted by vehicles. However,
tunnel concentrations of particulate mass were about equivalent or at least of the same
order of magnitude in both the size fractions. Tables 4.2a and 4.2b also indicate that Bore
1 concentrations of the organic species are significantly greater than the respective Bore 2
concentrations, showing the relatively higher contribution of HDVs in Bore 1 to
emissions of these species.

105
TABLE 4.2a. Mean mass concentration in Bore 2 (in ng/m
3
)

Accumulation mode Ultrafine mode
East West East West Species measured
Mean SD Mean SD Mean SD Mean SD

PM Mass
*
16.970

2.241 7.743 1.829 8.633 0.965 2.573 0.626
EC
*
0.688 0.337 0.095 0.089 8.147 0.609 2.049 0.528

Fluoranthene 0.115 0.033 0.032 0.008 0.763 0.120 0.220 0.048
Acephenanthrylene

0.014 0.003 0.003 0.001 0.118 0.009 0.021 0.006
Pyrene 0.168 0.051 0.043 0.009 1.157 0.201 0.333 0.079
Methyl substituted MW 202 PAH

0.128 0.113 0.029 0.025 1.132 0.705 0.279 0.254
Benzo(ghi)fluoranthene

0.121 0.040 0.021 0.014 1.042 0.241 0.236 0.105
Benz(a)anthracene 0.120 0.033 0.021 0.006 1.112 0.208 0.178 0.078
Chrysene/Triphenylene 0.142 0.047 0.029 0.008 1.243 0.283 0.236 0.098
Methyl substituted MW 228 PAH

0.075 0.064 0.018 0.014 0.820 0.305 0.117 0.106
Benzo(k)fluoranthene 0.097 0.019 0.014 0.003 0.987 0.347 0.133 0.038
Benzo(b)fluoranthene 0.128 0.035 0.023 0.005 1.259 0.357 0.180 0.055
Benzo(j)fluoranthene

0.016 0.002 0.002 0.001 0.243 0.079 0.017 0.006
Benzo(e)pyrene 0.114 0.026 0.019 0.003 1.176 0.362 0.170 0.056
Benzo(a)pyrene

0.089 0.011 0.012 0.003 1.281 0.370 0.125 0.028
Perylene 0.015 0.008 0.000 0.001 0.180 0.055 0.012 0.009
Indeno(cd)pyrene 0.077 0.020 0.013 0.004 0.978 0.320 0.115 0.031
Benzo(ghi)perylene 0.186 0.052 0.032 0.008 2.524 0.692 0.333 0.096
Indeno(cd)fluoranthene 0.023 0.007 0.004 0.001 0.324 0.111 0.037 0.012
Dibenz[a,h]anthracene

0.011 0.003 0.001 0.001 0.079 0.018 0.013 0.003
Coronene 0.070 0.024 0.015 0.004 1.337 0.227 0.127 0.042

22,29,30-trisnorhopane 0.072 0.040 0.020 0.001 0.487 0.108 0.131 0.042
22,29,30-trisnorneohopane 0.065 0.028 0.021 0.003 0.435 0.164 0.123 0.036

106

TABLE 4.2a Continued…

Accumulation mode Ultrafine mode
East West East West Species measured
Mean SD Mean SD Mean SD Mean SD

17a(H)-21b(H)-29-norhopane

0.163 0.091 0.049 0.004 1.141 0.294 0.259 0.067
18a(H)-29-norneohopane

0.037 0.019 0.010 0.007 0.252 0.052 0.059 0.014
17a(H)-21b(H)-hopane 0.147 0.068 0.045 0.013 1.112 0.270 0.257 0.054
17b(H),21a(H)-moretane 0.010 0.004 0.001 0.002 0.087 0.014 0.019 0.003
22S, 17a(H),21b(H)-homohopane 0.065 0.039 0.020 0.003 0.411 0.124 0.095 0.023
22R, 17a(H),21b(H)-homohopane 0.055 0.038 0.020 0.003 0.331 0.077 0.074 0.022
22S, 17a(H),21b(H)-bishomohopane 0.040 0.020 0.013 0.005 0.206 0.070 0.066 0.008
22R, 17a(H),21b(H)-bishomohopane 0.031 0.017 0.008 0.003 0.163 0.040 0.041 0.009
22S, 17a(H),21b(H)-trishomohopane 0.027 0.021 0.007 0.005 0.142 0.021 0.035 0.007
22R, 17a(H),21b(H)-trishomohopane

0.016 0.007 0.005 0.003 0.087 0.022 0.027 0.005
20R+S, abb-cholestane 0.013 0.005 0.012 0.002 0.085 0.016 0.031 0.012
20R, aaa-cholestane 0.073 0.036 0.026 0.005 0.450 0.103 0.148 0.056
20R+S, abb-ergostane 0.026 0.007 0.006 0.008 0.148 0.048 0.045 0.014
20R+S, abb-sitostane

0.107 0.054 0.029 0.019 0.619 0.163 0.146 0.045
UCM 793.386 452.601 217.609 18.094 4426.087 1169.775 1038.072 365.281
*
expressed in µg/m
3

107
TABLE 4.2b. Mean mass concentration in Bore 1 (in ng/m
3
)

Accumulation mode Ultrafine mode
East West East West Species measured
Mean SD Mean SD Mean SD Mean SD

PM Mass
*
28.690

4.469 16.510 2.607 28.860 5.868 4.328 2.041
EC
*
2.370 1.742 0.134 0.129 27.346 4.503 1.865 0.532

Fluoranthene 0.851 0.159 0.033 0.006 4.644 1.191 0.269 0.068
Acephenanthrylene

0.155 0.042 0.003 0.001 0.990 0.239 0.025 0.008
Pyrene 1.379 0.246 0.040 0.006 8.076 2.119 0.344 0.086
Methyl substituted MW 202 PAH

1.140 0.179 0.050 0.007 9.172 2.175 0.495 0.095
Benzo(ghi)fluoranthene

0.498 0.021 0.027 0.002 4.054 0.410 0.297 0.065
Benz(a)anthracene 0.352 0.017 0.018 0.002 3.223 0.494 0.181 0.032
Chrysene/Triphenylene 0.379 0.020 0.032 0.004 2.915 0.504 0.292 0.060
Methyl substituted MW 228 PAH

0.242 0.022 0.024 0.003 2.145 0.082 0.170 0.046
Benzo(k)fluoranthene 0.237 0.069 0.014 0.004 2.356 0.309 0.141 0.012
Benzo(b)fluoranthene 0.353 0.084 0.020 0.005 3.145 0.385 0.193 0.014
Benzo(j)fluoranthene

0.053 0.016 0.001 0.001 0.599 0.099 0.020 0.016
Benzo(e)pyrene 0.315 0.081 0.017 0.003 3.113 0.527 0.176 0.026
Benzo(a)pyrene

0.213 0.041 0.009 0.003 2.741 0.622 0.150 0.035
Perylene 0.054 0.051 0.000 0.000 0.495 0.042 0.012 0.014
Indeno(cd)pyrene 0.117 0.032 0.011 0.002 1.146 0.139 0.109 0.014
Benzo(ghi)perylene 0.329 0.049 0.027 0.003 3.825 0.789 0.299 0.045
Indeno(cd)fluoranthene 0.040 0.005 0.004 0.001 0.507 0.126 0.036 0.005
Dibenz[a,h]anthracene

0.020 0.023 0.001 0.001 0.105 0.010 0.017 0.004
Coronene 0.073 0.025 0.015 0.002 1.065 0.248 0.121 0.016

22,29,30-trisnorhopane 0.217 0.003 0.017 0.001 0.877 0.094 0.117 0.023

108

TABLE 4.2b Continued…

Accumulation mode Ultrafine mode
East West East West Species measured
Mean SD Mean SD Mean SD Mean SD

22,29,30-trisnorneohopane

0.185 0.023 0.022 0.002 0.784 0.052 0.130 0.020
17a(H)-21b(H)-29-norhopane

0.476 0.021 0.043 0.007 2.212 0.179 0.248 0.052
18a(H)-29-norneohopane

0.094 0.015 0.008 0.006 0.432 0.073 0.073 0.020
17a(H)-21b(H)-hopane 0.414 0.045 0.048 0.007 2.005 0.249 0.237 0.045
17b(H),21a(H)-moretane 0.019 0.013 0.002 0.002 0.140 0.024 0.018 0.012
22S, 17a(H),21b(H)-homohopane 0.176 0.021 0.023 0.001 0.775 0.132 0.096 0.015
22R, 17a(H),21b(H)-homohopane 0.144 0.018 0.017 0.001 0.685 0.138 0.075 0.008
22S, 17a(H),21b(H)-bishomohopane 0.114 0.026 0.013 0.003 0.468 0.113 0.058 0.015
22R, 17a(H),21b(H)-bishomohopane 0.091 0.025 0.011 0.002 0.318 0.059 0.036 0.005
22S, 17a(H),21b(H)-trishomohopane 0.061 0.005 0.010 0.003 0.293 0.051 0.004 0.009
22R, 17a(H),21b(H)-trishomohopane

0.042 0.008 0.008 0.002 0.156 0.076 0.006 0.012
20R+S, abb-cholestane 0.027 0.010 0.009 0.013 0.115 0.016 0.034 0.009
20R, aaa-cholestane 0.187 0.027 0.024 0.005 0.778 0.085 0.146 0.021
20R+S, abb-ergostane 0.071 0.021 0.006 0.007 0.235 0.024 0.041 0.029
20R+S, abb-sitostane

0.300 0.035 0.034 0.005 1.262 0.176 0.162 0.036
UCM 2521.784 195.182200.072 48.166 10093.186 1230.612 1546.339 221.702
*
expressed in µg/m
3

109
Table 4.3 shows the Pearson correlation coefficients between the measured mass
concentrations of certain organic species and classes in both the size modes. The
measurements made at the tunnel entrance can also be considered samples heavily
influenced by traffic emissions. Therefore, all concentration data from individual days,
from both bores and both ends of the tunnel, are included in the correlation calculations.
The inter-species correlation among all the measured hopanes and steranes (Hop-Ster) is
very high with a minimum r of 0.86 and an average r of 0.96 in the ultrafine mode
(minimum r = 0.72, average r = 0.91 in the accumulation mode). Hence, the sum of the
hopanes and steranes is used for comparison rather than individual species. Lower
molecular weight polycyclic aromatic hydrocarbons (PAHs) and their methyl derivatives
have been found to predominantly come from HDVs, while higher molecular weight
PAHs such as Benzo(ghi)perylene (BgP) and Coronene (Cor) have been attributed to
LDVs (Marr et al., 1999; Miguel et al., 1998; Gross et al., 2000). Based on these previous
findings, we have grouped the PAHs based on their molecular weights in three groups;
PAHs MW = 226+228 (sum of the PAHs with molecular weights 226 and 228); PAHs
MW = 252 and PAHs MW ≥ 276. Coronene and Benzo(ghi)perylene are included
individually since they have been proposed as indicators of LDV emissions. It should be
noted that this grouping of individual species does not change the correlations
significantly, since the species within each group are high correlated with one another.

In general elemental carbon (EC) is found to have good correlation with all the other
parameters presented in the Table 4.3 in both the ultrafine and accumulation modes. The

111

TABLE 4.3. Pearson correlation coefficient between mass concentrations of various measured species in ultrafine and
accumulation modes

EC PAH = 226+228 MW
1
PAH = 252 MW
2
BgP Cor PAH >= 276 MW
3
Sum Hop-Ster
4
UCM
Ultrafine mode
EC

1.00
PAH = 226+228 MW 0.98 1.00
PAH = 252 MW

0.95 0.98 1.00
BgP 0.84 0.91 0.96 1.00
Cor 0.56 0.64 0.73 0.881.00
PAH >= 276 MW 0.79 0.86 0.92 0.99 0.93 1.00
Sum Hop-Ster 0.96 0.97 0.95 0.91 0.74 0.89 1.00
UCM 0.96 0.99 0.99 0.940.70 0.90 0.96 1.00

Accumulation mode
EC 1.00
PAH = 226+228 MW 0.78 1.00
PAH = 252 MW

0.83 0.97 1.00
BgP 0.65 0.95 0.91 1.00
Cor 0.36 0.72 0.64 0.871.00
PAH >= 276 MW 0.59 0.92 0.88 1.00 0.90 1.00
Sum Hop-Ster 0.76 0.99 0.96 0.94 0.73 0.91 1.00
UCM 0.80 0.99 0.96 0.930.70 0.90 0.99 1.00
1
Sum of Benzo(ghi)fluoranthene, Benz(a)anthracene and Chrysene/Triphenylene

2
Sum of Benzo(k)fluoranthene,Benzo(b)fluoranthene,Benzo(j)fluoranthene,Benzo(e)pyrene,Benzo(a)pyrene and Perylene
3
Sum of Indeno(cd)pyrene, Benzo(ghi)perylene, Indeno(cd)fluoranthene and Coronene
4
Sum of Hopanes and Steranes

112
correlation is greater in the ultrafine mode than the accumulation mode, as EC and the
other species are predominantly found in the ultrafine mode where non-vehicular
background contributions are negligible. The lowest correlations with EC are found for
the heavier PAHs. Diesel HDVs are known to emit much higher amounts of EC, while
gasoline powered LDVs have been shown to emit relatively high amounts of heavier
PAHs. The lower correlations can thus be attributed to two different sources of these
species. Higher correlations between EC and the lighter PAHs are consistent with the
previous work showing that HDVs emit relatively more light PAHs than LDVs.

0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
EC (µg/m
3
)
PAH = 226+228 MW (ng/m
3
)
Bore 2E
Bore 2W
Bore1E
Bore1W
y = 0.3355x + 0.4904
R
2
= 0.9451
0.0
0.5
1.0
1.5
2.0
0.0 1.0 2.0 3.0 4.0

FIGURE 4.1 Correlation between mass concentrations of EC and Sum PAHs with MW
226 and 228 in ultrafine mode

Figure 4.1 further demonstrates the correlation between EC and lighter PAHs (MW = 226
+ 228). It should be noted that the levels observed at the east end of Bore 2 are higher

113
Figure 4.2 a

0.0
5000.0
10000.0
15000.0
20000.0
25000.0
30000.0
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0
Sum Hop-Ster (ng/m
3
)
UCM (ng/m
3
)
Bore 2E
Bore 2W
Bore1E
Bore1W
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
0.0 1.0 2.0 3.0

Figure 4.2 b

0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Sum Hop-Ster (ng/m
3
)
UCM (ng/m
3
)
Bore 2E
Bore 2W
Bore1E
Bore1W
0.0
200.0
400.0
600.0
800.0
0.20.3 0.40.5 0.6

FIGURE 4.2 Correlation between mass concentrations of Sum Hop-Ster and UCM in a)
ultrafine mode; b) accumulation mode

than those that can be attributed to the few (0.4%) diesel vehicles that passed through
Bore 2. The EC and light PAH concentrations in Bore 2 are approximately 25% that of
the Bore 1, but the HDV traffic in Bore 2 was 10% of that observed in Bore 1. Although

114
there was more traffic in Bore 2, the high EC and lighter PAH levels in bore 2 cannot be
attributed completely to HDV. This may indicate that the fleet of gasoline vehicles also
emits EC and lighter PAHs, albeit in lower amounts per vehicle, but with a similar ratio
as that observed for HDV. It is possible that that the formation mechanisms of these two
species are similar in both HDV and LDV engines. The west end concentrations depicted
in the figure inset appear more enriched in EC than the east end, but they lie within the
error of the best-fit linear regression parameters.

Both the unresolved complex mixture (UCM) and the hopanes and steranes derive from
the same petroleum source (lubricating oil), and thus a very high correlation is observe
between the two in both size ranges (Schauer et al., 1999; 2002), Figures 4.2a and 4.2b
display the scatter-plots demonstrating this correlation in both size modes. Again, all
daily data points from both bores and tunnel ends are included. The high correlation
between UCM and the hopanes/steranes imply a similar origin (lubricating oil
components), and suggest that the particulate emission processes responsible for these
two potential tracers are the same in both HDV and LDV.

4.4.2 Size resolved emission factors
Table 4.4 presents the size resolved emission factors with standard deviations attributed
to LDVs and HDVs for the measured organic markers. All of the emissions factors of the
organic markers predominantly exist in ultrafine mode in both vehicle classes. Higher
molecular weight PAHs such as BgP (9.63 ± 2.76mg/kg fuel burned) and Cor (5.32 ±

115
1.00 mg/kg fuel burned) are the most abundant species emitted from LDVs, along with
medium weight Benzo(a)pyrene (BaP) (5.08 ± 1.56 mg/kg fuel burned) in the ultrafine
mode. Similarly, in the accumulation mode, the highest emission factors from LDVs are
found for BgP (0.68+-0.22 mg/kg fuel burned). Conversely, HDVs emit more of the
lighter molecular weight PAHs (i.e. Fluoranthene, Pyrene, and methyl substituted
Flu/Pyr) in both the ultrafine and accumulation size modes relative to the heavier PAHs.
However high BgP emissions were also calculated in both modes from HDVs. Even
though HDV PAH emission factors are generally larger than those for LDV, LDV
emissions are enriched in heavier PAH relative to total emitted mass or total PAH
emissions, while HDV emit more light PAH relative to total emissions. Comparison of
these calculated emission factors to earlier studies measuring PM
2.5
emissions, is
presented later in the text.

Hopanes and steranes are emitted from both types of vehicles, but the relative emission
factors are higher for HDVs. The predominant species in both size modes and from both
types of vehicles are 17a(H)-21b(H)-29-norhopane and 17a(H)-21b(H)-hopane. In
general, the LDVs emission factors are an order of magnitude less than that of the HDVs
for all the hopanes and steranes. UCM shows the same trends with size and vehicle type
as hopanes and steranes, which is expected due their similar origins in lubricating oil.

TABLE 4.4 Emission factors (in mg/kg fuel burned) attributable to LDVs and HDVs in ultrafine and accumulation mode

LDV HDV
Ultrafine Accumulation Ultrafine Accumulation
Sample ID
Mean SD Mean SD Mean SD Mean SD

Fluoranthene

2.390.43 0.130.37 114.90 21.93

33.16 4.50
Acephenanthrylene 0.430.050.050.0125.916.93 1.234.18
Pyrene 3.63 0.550.68 0.20 206.47 60.03 36.237.10
Methyl substituted MW 202 PAH 3.75 2.34 0.44 0.40 233.54 61.53 29.52 5.10
Benzo(ghi)fluoranthene 3.55 0.780.440.14 89.9911.04 0.55 11.32
Benz(a)anthracene 4.110.760.430.1365.6913.80 0.467.30
Chrysene/Triphenylene 4.43 1.030.500.19 51.5215.64 0.527.33
Methyl substituted MW 228 PAH 3.09 0.95 0.25 0.22 40.19 3.00 4.94 0.61
Benzo(k)fluoranthene 3.751.400.360.0943.419.37 1.954.47
Benzo(b)fluoranthene 4.751.350.460.1559.3411.17 2.347.11
Benzo(j)fluoranthene 0.990.340.060.0011.293.27 0.441.17
Benzo(e)pyrene 4.421.40 0.110.41 60.80 6.3615.28 2.29
Benzo(a)pyrene 5.081.56 0.040.34 46.76 4.0418.55

1.13
Perylene 0.74 0.060.24 0.04 9.92 1.761.371.31
Indeno(cd)pyrene 3.791.36 0.090.28 8.524.20 0.871.49
Benzo(ghi)perylene 9.632.760.680.2247.90

23.47 1.404.96
Indeno(cd)fluoranthene 1.26 0.440.080.036.533.670.590.15
Dibenz[a,h]anthracene 0.290.070.040.020.920.31 0.640.48
Coronene

5.32 0.241.00 0.09 5.74 0.68 0.38
22,29,30-trisnorhopane 1.56 0.360.230.17 13.272.46 0.134.53
22,29,30-trisnorneohopane 1.38 0.630.200.12 11.28 1.023.650.67
17a(H)-21b(H)-29-norhopane 3.87 1.07 0.50 0.39 35.33 4.009.820.46
18a(H)-29-norneohopane 0.85 0.200.120.06 5.63 1.861.810.47
17a(H)-21b(H)-hopane 3.76 1.060.450.26 30.216.15 1.188.15

116
117
TABLE 4.4 Continued…

LDV HDV
Ultrafine Accumulation Ultrafine Accumulation Sample ID
Mean SD Mean SD Mean SD Mean SD

17b(H),21a(H)-moretane

0.30 0.060.040.02 1.88 0.530.480.07
22S, 17a(H),21b(H)-homohopane 1.39 0.50 0.20 0.17 11.91 3.61 3.37 0.62
22R, 17a(H),21b(H)-homohopane 1.13 0.33 0.16 0.18 11.38 3.89 2.81 0.54
22S, 17a(H),21b(H)-bishomohopane 0.61 0.33 0.12 0.10 8.51 3.46 2.32 0.79
22R, 17a(H),21b(H)-bishomohopane 0.53 0.14 0.10 0.07 5.22 1.58 1.79 0.77
22S, 17a(H),21b(H)-trishomohopane 0.47 0.11 0.09 0.07 5.75 1.64 0.99 0.09
22R, 17a(H),21b(H)-trishomohopane 0.27 0.08 0.05 0.02 3.98 1.52 0.73 0.19
20R+S, abb-cholestane 0.24 0.10 0.01 0.01 1.02 0.47 0.44 0.23
20R, aaa-cholestane 1.33 0.36 0.21 0.14 10.89 1.99 3.58 0.78
20R+S, abb-ergostane 0.45 0.27 0.09 0.03 3.07 1.09 1.43 0.75
20R+S, abb-sitostane

2.08 0.56 0.34 0.20 20.33 4.67 5.83 0.96
UCM 14893.03 2531.005365.50 2036.95 165124.63 38365.16 53779.135552.17

118
Figure 4.3 shows the ratio of emission factors between HDV and LDV of the measured
organic markers for both the size ranges. The HDV/LDV emission factor ratio of lighter
PAHs (MW <= 216) is very high (60 to 90) as compared to the higher molecular weight
ones (MW >= 276) (< 10) for both the size ranges. The results demonstrate that lower
molecular weight PAHs are emitted in relatively higher amounts by HDVs than the
higher molecular weight PAHs. While HDVs emit much more PM mass and PAHs
overall, the distribution of the PAHs with molecular weight is very different between
HDV and LDV. Such differences may prove useful in source apportionment calculations
attempting to distinguish the contributions of diesel and gasoline vehicles to ambient
samples. Hopanes, steranes and UCM are emitted from both LDVs and HDVs with
similar species distributions. The relative emission factors between HDVs and LDVs lie
in relatively narrow ranges, with ratios of 6-14 for the ultrafine mode and 12-21 for the
accumulation mode. These ratios are more similar to the medium MW PAH than for the
lighter or heavier PAHs. It can also be seen that this ratio is higher in all cases for the
accumulation mode relative to the ultrafine mode, indicating HDV emissions are shifted
to larger particle sizes than LDVs.

To further demonstrate the particle size partitioning for these organic species, Figure 4.4a
and 4.4b present the correlation between the ultrafine and accumulation mode emission
factors of PAHs and hopanes and steranes from LDVs and HDVs. The high correlations
for HDV show that high emissions of particular species in one mode are accompanied by
high emissions in the other mode, indicating similar size distributions for these species.

0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Fluoranthene
Acephenanthrylene
Pyrene
Methyl substituted MW 202 PAH
Benzo(ghi)fluoranthene
Benz(a)anthracene
Chrysene/Triphenylene
Methyl substituted MW 228 PAH
Benzo(k)fluoranthene
Benzo(b)fluoranthene
Benzo(j)fluoranthene
Benzo(e)pyrene
Benzo(a)pyrene
Perylene
Indeno(cd)pyrene
Benzo(ghi)perylene
Indeno(cd)fluoranthene
Dibenz[a,h]anthracene
Coronene
22,29,30-trisnorhopane
22,29,30-trisnorneohopane
17a(H)-21b(H)-29-norhopane
18a(H)-29-norneohopane
17a(H)-21b(H)-hopane
17b(H),21a(H)-moretane
22S, 17a(H),21b(H)-homohopane
22R, 17a(H),21b(H)-homohopane
22S, 17a(H),21b(H)-bishomohopane
22R, 17a(H),21b(H)-bishomohopane
20R, aaa-cholestane
20R+S, abb-ergostane
20R+S, abb-sitostane
UCM
Organic chemical constituents
HDV/LDV Emission Factor ratio
Ultrafine Accumulation
PAHs < 226 MW
PAHs > 276 MW
PAHs = 252 MW
PAHs = 226
+ 228 MW
FIGURE 4.3 HDV/LDV emission factor ratios for the measured organics species
119

120
Figure 4.4 a
y = 0.2661x + 0.3276
R
2
= 0.9656
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
0.0 50.0 100.0 150.0 200.0 250.0 300.0
Ultrafine mode (µg/kg fuel burned)
Accumulation mode ( µg/kg fuel burned)
PAH <= 216
PAH = 226 + 228
PAH = 252
PAH >= 276
Hopanes and Steranes
y = 0.1502x-0.6495
R
2
= 0.9393

Figure 4.4 b

y = 0.1242x + 0.023
R
2
= 0.969
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.0 2.0 4.0 6.0 8.0 10.0 12.0
Ultrafine mode (µg/kg fuel burned)
Accumulation mode ( µg/kg fuel burned)
PAH <= 216
PAH = 226 + 228
PAH = 252
PAH >= 276
Hopanes and Steranes
y = 0.0732x + 0.063
R
2
= 0.7265

FIGURE 4.4 Correlation between ultrafine and accumulation mode PAHs, Hopanes and
Steranes for a) HDVs; b) LDVs

121
The slopes less than unity show that most of the species are in the ultrafine mode, but the
higher slope for the hopanes and steranes relative to the PAHs indicates that the size
distribution of PAHs is shifted to smaller particle sizes than the size distribution of
hopanes and steranes. Note also that the ratio of accumulation to ultrafine mode HDV
emission factors for UCM is 0.33, closer to the hopanes/ steranes slope of 0.27 than the
PAH slope of 0.15. For the LDVs, there is more scatter in the PAH emission factor size
distributions, but the hopanes/steranes are again shifted to the larger particles relative to
most PAHs.

In the case of HDVs, all the PAHs, regardless of molecular weight, are found to partition
between ultrafine and accumulation modes in similar ways. But it is apparent in figure 4b
that for LDVs, the lighter PAHs tend to be more associated with larger particles than the
heavier PAH, suggesting that the volatility of a particular species affects its size
distribution. This observation is in agreement with earlier reported studies that the more
volatile PAH tend to partition into higher particle size fractions (Marr et al., 1999; Miguel
et al., 1998). As suggested in Figure 4.4, all HDV emission factors result in higher slopes
than LDVs, indicating that HDV emissions are shifted to larger sizes relative to LDV
emissions. This is somewhat expected given the agglomerate nature of soot particles
emitted from diesel engines which can have larger aerodynamic diameters than the
nucleation mode gasoline emissions (Kittelson et al., 1998; Kleeman et al., 2000).

122
4.4.3 Comparison with other studies
Table 5.5a and 5.5b compare the LDV and HDV PM
2.5
emission factors from this study
(accumulation plus ultrafine modes) to other earlier studies, including tunnel and chassis
dynamometer studies. It is immediately evident that there are large differences in
reported emission factors from various studies. Testing an individual vehicle, or even
averaging over several vehicles, can lead to very different results than a tunnel study that
includes an average over thousands of vehicles. Miguel et al. (1998)

and Marr et al.
(1999) have reported PAHs emissions from the Caldecott tunnel measured in 1996 and
1997 respectively. Miguel et al. (1998) have reported PM
1.3
emission factors for
particulate PAHs and it can be seen that for the LDVs, there is a modest decrease in the
PAHs emission factors between 1996 and the current 2004 study. This may be due to
better control technologies, but is more likely due to the removal of older high-emitting
vehicles from the on-road fleet during the seven years between the measurements.

Reformulated gasoline (RFG) was introduced in the San Francisco Bay Area in 1996,
which resulted in significant reductions in overall pollutant emissions. Kirchstetter et al.
(1999b) have attributed this reduction in pollutant emissions from vehicles to both fleet
turnover and the introduction of RFG. The 1997 study by Marr et al. (1999) in the same
tunnel also showed a slight reduction in PAHs emissions as compared to the previous
1996 study by Miguel et al. (1998). For HDVs, the lighter PAHs emissions were lower in
this study as compared to both of the 1996 and 1997 studies. However, in both of these
studies they did not attribute the higher molecular weight PAHs to HDVs, while we

TABLE 4.5a. Comparison of particle phase PM
2.5
emission factors (in mg/kg fuel burned) attributable to LDVs

Zielinska et al., 2004
1
Schauer et al., 2002
3
Rogge et al., 1993
4
Organic compounds
This
study
Cat. Non-cat.
2
Cat. Non-cat. Cat. Non-cat.
Marr et
al.,
1999
5
Miguel et
al., 1998
6
PAHs
Fluoranthene 2.75 416.44 2759.67 0.78 1,711.71 22.52 543.92 8 10.3
Acephenanthrylene 0.48 0.14 650.90
Pyrene 4.17 195.27 1568.46 0.87 2,443.69 28.15 349.10 9.00 13.80
Methyl substituted MW 202 PAH 4.19 356.24 5260.04 2,623.87 47.30 1203.83
Benzo(ghi)fluoranthene 3.98 0.71 527.03 14.64 278.15
Benz(a)anthracene 4.54 37.02 573.35 1.09 584.46 21.40 831.08 4.80 8.80
Chrysene/Triphenylene 4.92 54.38 828.15 2.32 586.71 42.79 628.38 7.00 8.60
Methyl substituted MW 228 PAH 3.34 2.73 1,159.91 38.29 1615.99
Benzo(k)fluoranthene 4.12 0.93 368.24 22.52 458.33 2.50 3.00
Benzo(b)fluoranthene 5.21 23.17 476.63 420.05 32.66 426.80 7.60 8.10
Benzo(j)fluoranthene 1.06 0.10 17.12 6.08 67.57
Benzo(e)pyrene 4.84 13.93 293.81 1.69 430.18 22.52 515.77
Benzo(a)pyrene 5.42 12.74 328.91 0.24 461.71 21.40 489.86 6.40 8.30
Perylene 0.80 0.35 6.76 157.66
Indeno(cd)pyrene 4.07 10.36 243.95 4.91 1,036.04

5.29 72.07
Benzo(ghi)perylene

10.3116.24428.05

52.931637.39 20.70 18.00
Indeno(cd)fluoranthene 1.35 19.14367.129.007.50
Dibenz[a,h]anthracene 0.342.10 47.00 3.7293.4716.201.30
Coronene 5.56 7.14 84.44 1,137.39

12.39 1177.93
Hopanes and Steranes
22,29,30-trisnorhopane 1.80 0.07 9.03 777.03
22,29,30-trisnorneohopane 1.57 6.02 286.82 0.43 1,407.66 76.58 33.78
17a(H)-21b(H)-29-norhopane 4.38 3.57 235.41 0.21 3,175.68 120.50 66.44
17a(H)-21b(H)-hopane 4.21 0.14 22.40 0.37 3,614.86 204.95 101.35
17b(H),21a(H)-moretane 0.34 0.21 39.05

123

124

TABLE 4.5a Continued…

Zielinska et al., 2004
1
Schauer et al., 2002
3
Rogge et al., 1993
4
Organic compounds This study
Cat. Non-cat.
2
Cat. Non-cat. Cat. Non-cat.
al., 1999
Marr et
5
Miguel et
al., 1998
6
22S, 17a(H),21b(H)-homohopane 1.59 1.19 116.04 2,916.67 93.47 34.91
22R, 17a(H),21b(H)-homohopane 1.29 0.84 84.83 61.94 21.40
22S, 17a(H),21b(H)-bishomohopane 0.73 0.49 65.44 2,207.21 52.93 16.89
22R, 17a(H),21b(H)-bishomohopane 0.64 0.42 44.48 39.41 12.39
22S, 17a(H),21b(H)-trishomohopane 0.56 0.28 49.87
22R, 17a(H),21b(H)-trishomohopane 0.31 0.14 31.81
20R+S, abb-cholestane 0.24 1.61 94.84 1,914.41 94.59 40.54
20R, aaa-cholestane 1.53 1,340.09 104.73 45.05
20R+S, abb-ergostane 0.54 1,565.32 88.96 46.17
20R+S, abb-sitostane 2.42 1.19 92.70 1,531.53 128.38 60.81

1
Run on California unified driving cycle (UDC); 5 catalyst equiped vehicles (1993-1996 model year); 2 non-catalyst (1976 model year black
emitter and 1990 model year white emitter)
2
Average of black emitter and white emitter gasoline vehicles
3
Run on cold-start FTP urban driving cycle; 9 catalyst equipped ith average model year 1990 (1981-1994) and 2 1970 model year non-catalyst vehicles
4
Run on cold-start FTP urban driving cycle; 5 catalyst equipped vehicles (model year 1977-1983) and 6 non-catalyst (model year 1965-1976)
5
1997 data from Caldecott tunnel with %HD Diesel = 4.3
6
1996 data from Caldecott tunnel with %HDVs = 4.7

TABLE 4.5b. Comparison of particle phase PM
2.5
emission factors (in mg/kg fuel burned) attributable to HDVs and mixed
tunnel fleets

HDVs Mixed tunnel fleet
Organic compounds
This study
Zielinska et
al., 2004
1
Schauer et
al., 1999
2
Rogge et al.,
1993
3
Marr et al.,
1999
4
Miguel et
al., 1998
5
Chellam et
al., 2005
6*
Fraser et
al., 1998
7*
PAHs
Fluoranthene

136.8332.51

143.3632.93

480.00

749.00

18.90

4.85
Acephenanthrylene 30.09 41.03 1.48
Pyrene 242.7044.24224.1657.24690.00986.0021.378.35
Methyl substituted MW 202 PAH 263.06 31.25

205.17 32.42 27.74
Benzo(ghi)fluoranthene 101.30 50.15 17.48 26.53
Benz(a)anthracene 72.99 2.76 19.66 9.12 140.00 180.00 5.62 28.14
Chrysene/Triphenylene 58.85 7.71 39.51 25.08 66.00 140.00 20.16 32.05
Methyl substituted MW 228 PAH

45.12 16.57

6.84 90.09
Benzo(k)fluoranthene 47.87 6.84 2.80 59.00 8.94 21.28
Benzo(b)fluoranthene 66.46 3.59 7.35 25.00

90.00

11.44
Benzo(e)pyrene 67.16 2.08 6.59 13.5629.22
Benzo(a)pyrene

50.80 6.79 3.29 126.005.32 24.64
Perylene 11.24 2.53

3.11 5.25
Indeno(cd)pyrene 10.01 0.91 5.32 41.21
Benzo(ghi)perylene 52.86 2.15

4.05

10.96

137.62
Indeno(cd)fluoranthene 14.00
Dibenz[a,h]anthracene 1.28 0.14 7.38
Coronene 1.941.27
Hopanes and Steranes
22,29,30-trisnorhopane 17.80 8.66 2.51 15.99 24.24
22,29,30-trisnorneohopane 14.93 102.88 6.94 58.51 15.24 37.71
17a(H)-21b(H)-29-norhopane 45.15 5.61

28.62 100.30

45.05 73.53
18a(H)-29-norneohopane 7.44 11.05 21.01
17a(H)-21b(H)-hopane 38.36 7.23 28.88 238.60 53.98 110.42

125
126

TABLE 5.5b (Continued)

HDVs Mixed tunnel fleet
Organic compounds
This
study
Zielinska et
al., 2004
1
Schauer et al.,
1999
2
Rogge et al.,
1993
3
Marr et al.,
1999
4
Miguel et
al., 1998
5

Chellam et
al., 2005
6*
Fraser et
al., 1998
7*
17b(H),21a(H)-moretane 2.24 4.20
22S, 17a(H),21b(H)-homohopane

15.29 48.65 96.25 18.34 46.73
22R, 17a(H),21b(H)-homohopane 14.18 33.17 97.01 14.96 31.38
22S, 17a(H),21b(H)-bishomohopane 10.84 49.99 57.50 10.89 29.36
22R, 17a(H),21b(H)-bishomohopane 7.01 31.52 40.53 8.11 18.85
22S, 17a(H),21b(H)-trishomohopane 6.74 14.66

22R, 17a(H),21b(H)-trishomohopane 3.72 8.74
20R+S, abb-cholestane 1.46 28.53 1.98 100.30 15.35
20R, aaa-cholestane 14.47 3.01 108.92 23.70
20R+S, abb-ergostane 4.50 7.98 109.93 30.03
20R+S, abb-sitostane

26.16 31.71

6.61

160.59 28.82

UCM 218903.76

104863.22

1
Run on California unified driving cycle (UDC); 4 diesel vehicles (model year 1991-2000)

2
Run on hot-start FTP urban driving cycle; 2 medium duty diesel trucks
3
2 diesel trucks (1987 model year)
4
1997 data from Caldecott tunnel with %HDVs = 4.3
5
1996 data from Caldecott tunnel with %HDVs = 4.7
6
2000 Houston tunnel study; %HDVs = 3.4
7
1993 Los Angeles tunnel study with average 1986 model year vehicles; % HDVs = 2.7 and % non-catalyst vehicles = 3.6
*
Tunnel study with mixed fleet, hence mixed fleet (LDV+HDV) emision factors are reported

127

attribute significant emissions of the higher PAHs to HDVs. Zielinska et al. (2004),

also
measured heavier PAH (Coronene and BgP in the emissions from diesel vehicles), and
Rogge et al. (1993), also found BgP in diesel emissions.

The other tunnel studies are from Chellam et al. (2005) in a Houston tunnel and Fraser et
al. (1998) in a Los Angeles tunnel. Both of these studies reported mixed fleet (LDVs +
HDVs) emission factors for the organic markers. The PAH emission factors from the
Houston tunnel are generally between our LDVs and HDVs emission factors, with the
exception of Dibenz(a,h)anthracene, which had a higher emission factor from the
Houston tunnel than that of what was attributed to HDVs here. However, the
hopanes/steranes emissions in the Houston tunnel are very similar to our results for HDV
from the Caldecott tunnel.

Differences may also be due to different vehicle types, fuel blends, tunnel characteristics,
or average speeds. Also, the Houston tunnel study occurred at different times over
different days whereas the current study sampled over the same time period every day.
The Van Nuys tunnel study in 1993 by Fraser et al. (1998) shows higher levels of higher
molecular weight PAHs emissions as well as higher hopanes/steranes emissions
compared to our study. The lighter PAHs, however, are much less as compared to our
HDVs contributions. Again, there are many possible reasons for these discrepancies,
including higher numbers of older or non-catalytic cars, different vehicle driving
conditions and fuel composition.

128

Shown in Table 5.5a are three chassis dynamometer studies done in 1993, 2002 and 2004
for LDVs. Compared to the Schauer et al. (2002)

study, our LDV emission factor values
fall in between the catalyst and non-catalyst emissions for all the measured species, as
one would expect in a tunnel with mostly catalyst but some non-catalyst (or
malfunctioning catalyst) vehicles as well. The higher catalyst emissions from Rogge et al.
(1993) may be due to the pre-reformulation gasoline and possibly less advanced control
technologies at the time of that study. Zielinska et al. (2004) fine PAHs emission factors
for the catalyst-equipped vehicles are higher than the LDVs emission factor reported in
this study. Since all of the dynamometer testing occurs over specified driving cycles,
often including cold-start and multiple accelerations, it is not surprising that the
emissions of certain species will be higher.

For the HDVs, the dynamometer studies by Zielinska et al. (2004), and Rogge et al.
(1993) resulted in generally lower PAH emissions, whereas a few of the hopanes and
steranes are significantly higher in theses previous studies. A similar study by Schauer et
al. (1999), however, is very close to our results for the lighter PAHs and the hopanes and
steranes, but no higher molecular weight PAHs were reported.

As stated earlier, the main source of error for the HDV apportionment is the value of X,
which relates to the uncertainty in CO and CO
2
measurements, vehicle counts and the
assumption that gasoline and diesel emit CO in similar amounts elative to fuel
consumption. Also, the uncertainty in the measured levels of species for which LDVs

129

emit more than HDVs can induce additional error. For an estimated upper bound 15%
total uncertainty in X the maximum error in the HDV emission factors across all species
due to this calculation is less than 10%. The main source of error in LDV emissions
estimates derives from the few diesel vehicles (less than 10% that of in bore 1) that
passed through bore 2. The presence of diesel vehicles in bore 2 does not significantly
affect the HDV emissions estimates which therefore can be used to assess the impact of
Bore 2 diesel vehicles on calculated LDV emission factors. For the worst case of the
lightest PAH emission factors, the diesel vehicles in bore 2 can add as much as 40%
uncertainty to the LDV results in the ultrafine mode. For all the other organic species, the
error introduced by this issue is generally less than 10%. Due to the extremely low
counts of HDV in bore 2, and the uncertainty in the 2-axle/6-tire split assumptions, it is
not feasible to correct LDV emissions factors based on our HDV data. The use of
previously measured fuel economies, as well as the assumed classification of 2-axle/6-tire
trucks as gasoline or diesel, can contribute to additional but minor uncertainties in the
apportionment of emissions to LDVs and HDVs (Kirchstetter et al., 1999a).

130

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136
Chapter 5: Roadside measurements of size-segregated particulate
organic compounds near gasoline and diesel-dominated freeways in Los
Angeles, CA*

*Phuleria H.C; Sheesley R.; Fine P.M.; Schauer J.J; Sioutas C. Roadside measurements of size-
segregated particulate organic compounds near gasoline and diesel-dominated freeways in Los
Angeles, CA. Submitted for publication to Atmospheric Environment, January 2007.

5.1 Chapter 5: ABSTRACT
Individual organic compounds such as hopanes and steranes (originating in lube oil) and
selected polycyclic aromatic compounds (PAHs) (generated via combustion) found in
particulate emissions from vehicles have proven useful in source apportionment of
ambient particulate matter. Detailed information on the size-segregated (ultrafine and
accumulation mode) chemical characteristics of organic particulate matter during the
winter season originating from a pure gasoline traffic freeway (CA-110), and a mixed
fleet freeway with the highest fraction of heavy-duty diesel vehicles in the state of
California (I-710) is reported in this study. Hopanes and steranes as well as high
molecular weight PAHs such as benzo(ghi)perylene (BgP) and coronene levels are found
comparable near these freeways, while elemental carbon (EC) and lighter molecular
weight PAHs are found much elevated near I-710 compared to CA-110. The roadway
organic speciation data presented here is compared with the emission factors measured in
the Caldecott tunnel, Berkeley CA (Phuleria et al., 2006) for light duty vehicles (LDVs)
and heavy-duty vehicles (HDVs). Very good agreement is observed between CA-110
measurements and LDV emission factors (EFs) as well as I-710 measurements and
corresponding reconstructed EFs from Caldecott tunnel for hopanes and steranes as well
as heavier PAHs such as BgP and coronene. Our results, therefore, suggest that the
137
emission factors for hopanes and steranes obtained in tunnel environments, where
emissions are averaged over a large vehicle-fleet, enable reliable source apportionment of
ambient particulate matter (PM), given the overall agreement between the roadway vs
tunnel concentrations of these species.

5.2 Chapter 5: INTRODUCTION
Several epidemiological studies have shown positive associations between adverse health
effects and fine particles (diameter < 2.5 µm) (Pope and Dockery, 2006; Schwartz et al.,
2002) as well as ultrafine particles (with aerodynamic diameter <100 nm) (Peters et al.,
1997). Although the causal mechanisms are still uncertain, particle characteristics such as
particle chemistry, number and size have been linked to the associated health effects.

In urban environments, vehicular emissions are the major sources of ultrafine and fine
particles (Schauer et al., 1996; Sternbeck et al., 2002), which are of particular interest
because of their potentially toxic components, such as PAHs and trace metallic elements,
their high number and surface area relative to larger particles, and their ability to
penetrate cell membrane and alleviated cardiovascular responses (Nel et al., 2006). A
number of health studies have demonstrated the adverse health effects of diesel exhaust
particles (Diaz-Sanchez et al., 2003; Mauderly, 1994; Weingartner et al., 1997). Children
living near freeways have been shown to have increased prevalence of asthma and
symptoms of airway allergies (Brunekreef et al., 1997; Kleinman et al., 2005; McDonald
et al., 2004).

138
Vehicular emissions have been examined using a number of different methods including
roadside measurements (Biswas et al., 2006; Hitchins et al., 2000; Kuhn et al., 2005a;
2005b; Zhu et al., 2002a; 2002b; 2004), on-road chase experiments (Canagaratna et al.,
2004; Kittelson, 1998), laboratory dynamometer studies (Cadle et al., 1997; Rogge et al.,
1993; Schauer et al., 1999; 2002; Zielinska et al., 2004), and measurements inside of
roadway tunnels (Chellam et al., 2005; Geller et al., 2005; Kirchstetter et al, 1999;
Phuleria et al., 2006). Emission measurements based on dynamometer and chase
experiments offer the advantage of precise and controlled testing conditions, which
enable the evaluation of emissions control technologies over different driving conditions
and cycles, including cold-start (Schauer et al., 2003). However, these tests cannot
capture the large variation in engine type, age of the vehicles and maintenance history
due to the high cost and complexity of such tests, and thus may not provide a good
representation of the in-use vehicle fleet on the road. Such studies may also not account
for particle aging effects, the mixing of emissions from different vehicles (Weingartner et
al., 1997), and non-tailpipe emissions from tire wear, break-wear, and re-suspended road
dust (Allen et al., 2001).

More realistic estimates of vehicle emissions, under actual on-road driving conditions,
are possible using air-quality measurements in highway tunnels and near-roadside
measurements. Roadway tunnel studies measure the cumulative contribution of the
emissions from a large population of the on-road vehicle fleet mix under real-world
driving conditions, thus more closely approximate actual on-road emissions than
dynamometer tests. Limitations of tunnel measurements include sampling only the
139
specific driving conditions of the tunnel (thus missing cold-start emissions), and under
dilution, temperature, and humidity conditions that may differ from ambient. Similar to
tunnel studies, roadside studies provide the opportunity for PM measurements under
actual ambient conditions and a representative large vehicle fleet. They also can provide
the real exposure to vehicular emissions near - or within the freeway environment where
people spend a significant amount of time during commute.

Several studies have utilized gas chromatography-mass spectrometry (GC-MS) methods
for identification and quantification of individual organic compounds in atmospheric
particulate matter (PM) samples (Fine et al., 2004; Fraser et al., 1998; 1999; 2003a;
Phuleria et al, 2006; Schauer et al., 1996; Simoneit, 2002; Venkataraman et al., 1994).
Many of these compounds or combinations of compounds are unique to different primary
PM sources and have been used to apportion ambient PM using chemical mass balance
(CMB) methods (Fraser et al., 2003a; 2003b; Schauer et al., 1996; 2000). These methods
rely on representative and up-to-date source profiles for successful application. Hopanes
and steranes, which are found in the lubricating oils employed by both gasoline and
diesel powered motor vehicles, have been frequently used as organic tracers of primary
vehicular particle sources (Cass, 1998; Schauer et al., 1996). High molecular weight
PAHs such as BgP have been reported as a tracer of gasoline-powered vehicles, while
diesel vehicles predominantly emit the lighter PAHs (Miguel et al., 1998; Zielinska et al.,
2004).

140
Assessment of the relative contributions of diesel and gasoline vehicles to the overall PM
urban load in the United States remains an elusive scientific undertaking (Gertler, 2005).
While some studies using organic molecular markers to trace emissions have attributed
greater contribution to ambient PM from diesel vehicles (Fraser et al., 2003a; Schauer et
al., 1996; Zheng et al., 2002), others incorporating cold-start and poorly maintained
gasoline engine source profiles attribute larger contributions to gasoline vehicles. These
inconsistencies in sources attribution emanate largely from the differences in source
profile selection, seasonal differences in emissions and the time and place of the studies
(Fraser et al., 2003a; Gertler, 2005). From a regulatory standpoint, it is very important to
accurately segregate diesel and gasoline particulate emissions and thus, more
representative and robust real-world source profiles for these classes of vehicles are
desired.

The primary goal of this study is to provide detailed information on the chemical
characteristics of organic PM originating from a pure gasoline traffic freeway, and a
mixed fleet freeway with the highest fraction of heavy-duty diesel vehicles in the state of
California. Size-resolved PM samples (ultrafine and accumulation mode) were collected,
and speciated organic tracer concentrations were measured using GC-MS methods. A
comparison is made between the roadway organic speciation data presented here with the
emission factors measured in the Caldecott tunnel, Berkeley, CA for LDVs and HDVs, in
order to validate their use in source apportionment models.

5.3 Chapter 5: METHODS
5.3.1. Sampling locations
Freeway site
Background site

FIGURE 5.1a. Sampling locations near CA-110 Freeway

Field samples were collected near the CA-110 freeway as well as near the I-710 freeway
in Los Angeles, CA. A detailed description of the CA-110 freeway environment, traffic
characteristics and sampling procedure is described by Kuhn et al. (2005a). Briefly,
measurements were conducted at the CA-110, between downtown Los Angeles and
Pasadena, CA as shown in figure 5.1a. On this stretch of the freeway, only light-duty
vehicles are permitted thus affording a unique opportunity of studying emissions from
pure light-duty traffic under ambient conditions. The study took place in January 2005,
from about 12 p.m. to 7 p.m. every day, capturing the evening rush-hour traffic. Samples
were collected from two different sites, one of which was very close to the three
northbound traffic lanes, at a distance of around 2.5 m from the edge of the freeway. The
other site was chosen to characterize background aerosols at a greater distance to the
freeway (~150 m).
141

Detailed description of the I-710 freeway measurements can be found elsewhere (Biswas
et al., 2006; Ntziachristos et al., 2006) and only a brief description follows. The sampling
location (see figure 5.1b) was directly adjacent to the roadway, at approximately 10 m
from the centerline of the freeway, with no other immediate sources either upwind or
downwind. Background measurements were conducted at Downey, CA, a location 1.6

Background site
Freeway site

FIGURE 5.1b. Sampling locations near I-710 Freeway

km southeast (hence downwind) of the freeway, at the facilities of Rancho Los Amigos
Rehabilitation Center. The sampling campaign took place during February and March of
2006, and from 11am -7 pm each day, similar to the CA-110 measurements.

5.3.2. Traffic characterization
Traffic volume and average speed data in each freeway were obtained from the California
Department of Transportation. Manual and videotaped counts were also taken for one
142
143
minute out of every five minutes during selected sampling intervals. Estimates for counts
broken down by vehicle type were done by analyzing the videotapes and counting the
number of axles per vehicle. The average traffic density on the CA-110 freeway was
about 4,500-5,400 vehicles per hour. Traffic speed was fairly constant during the day
and, even during evening rush hour no significant slowing of traffic was observed (Kuhn
et al., 2005a). The traffic volume and vehicle characteristics on the I-710 freeway during
the sampling campaign are described by Ntziachristos et al. (2006). Heavy-duty vehicles
comprised an average of 17 % of the total vehicles on the I-710. Total traffic counts on
this freeway are also very high, with approximately 10,000-11,000 vehicles per hour
passing the sampling location.

5.3.3. Pollutant measurement and sample collection
Gaseous and particulate pollutant concentrations near the freeways were measured with
various continuous and time-integrated instruments. PM collection was accomplished
with a custom built, high-volume (450 lpm) sampler designed to separate and collect
coarse (Dp > 2.5 µm), accumulation (0.18 < Dp < 2.5 µm) and ultrafine mode (Dp < 0.18
µm) aerosols (Misra et al., 2002). The sampler collects particles with aerodynamic
diameters greater than about 0.18 µm onto quartz-fiber impaction strips. A preceding
impaction stage with a 2.5 µm aerodynamic diameter cut-point removes coarse particles.
Downstream of the ultrafine impactor, an 8 x 10 inch high-volume filter holder
containing Quartz-fiber filters (Pallflex
®
Tissuquartz
TM
2500QAT-UP-8x10, Pall Corp.)
is used to collect the ultrafine PM fraction. Field blanks for quartz filters contained
negligible levels of the compounds quantified in this study. Quartz filters and substrates
144
were pre-baked at 550
o
C for 8 hours and stored in baked aluminum foil prior to
deployment (Phuleria et al., 2006). For gravimetric measurements, two micro-orifice
uniform deposit impactors (MOUDI, MSP, Inc., Minneapolis, MN) sampled concurrently
at 30 lpm at the freeway and background sites. 4.7 cm PTFE filters were used as
impaction substrates for coarse and accumulation mode PM, and a 3.7 cm PTFE filter
was used to collect ultrafine PM. Similar to high volume sampler, the MOUDI was used
to collect coarse (10 < Dp < 2.5 µm), accumulation (2.5 < Dp < 0.18 µm) and ultrafine
(Dp < 0.18 µm) size particles.

5.3.4. Organic Speciation Analysis
Methods for the quantification of individual organic compounds in ambient particulate
matter were based on earlier established solvent extraction methods (Schauer et al., 1999;
2002; Sheesley et al., 2003). Procedures for sample extraction and molecular
quantification for the organic tracers is described in detail elsewhere (Phuleria et al.,
2006) and only a brief summary is presented here. The quartz filter samples from the
high-volume sampler were spiked with known amounts of isotope labeled internal
standard compounds, including three deuterated PAHs, two deuterated alkanoic acids,
deuterated cholestane, deuterated cholesterol, and C
13
labeled levoglucosan. Samples
were extracted in dichloromethane and methanol and were combined and reduced in
volume to approximately 1 mL by rotary evaporation followed by pure nitrogen
evaporation. One fraction of the extracts was derivatized for organic acid analysis, the
results of which are not presented here. The other fraction was further reduced to a
specified volume ranging from 50 to 200 µL by evaporation under pure nitrogen. The
145
final target volume was determined based on the amount of organic carbon mass in each
sample (Phuleria et al., 2006).

The underivatized samples were analyzed by auto-injection into a GC/MSD system (GC
model 5890, MSD model 5973, Agilent). A 30 m x 0.25 mm DB-5MS capillary column
(Agilent) was used with a splitless injection. Along with the samples, a set of authentic
quantification standard solutions were also injected and used to determine response
factors for the compounds of interest. While some compounds are quantified based on
the response of a matching compound in the standard mixtures, others for which
matching standards were not available are quantified using the response factors of
compounds with similar structures and retention times. Analytical errors for these
methods have been reported to be no more than 25% (Fine et al., 2004; Sheesley et al.,
2003).

5.4. Chapter 5: RESULTS AND DISCUSSION
5.4.1. Mean measured organic species concentrations
The ambient concentrations of each organic species at the CA-110 were found to be
higher near the freeway site compared the background site (Table 5.1a). However, the
difference is not very significant (i.e., on the order of 20-30%) for most of the chemical
species, PM mass, and EC (Table 5.2), indicating a significant impact of freeway
emissions at the distant site. The traffic impact on that site is also manifested by the
somewhat higher CO
2
concentrations measured during our study (427 ± 44 ppm)
compared to typical background CO
2
levels of 375-380 ppm (Ntziachristos et al., 2006).
146
The relatively similar levels for these PM species are in contrast to the influence of
freeways on nearby sites based on particle numbers as well as gaseous co pollutants such
as CO, NO
x
that originate from traffic sources (Zhu et al., 2002a; 2002b; 2004). Given the

TABLE 5.1a. Mean mass concentration (in ng/m
3
) of the organic tracers measured near
CA-110 Freeway

Ultrafine mode Accumulation mode
Freeway Background Freeway Background Organic Species
MeanSD Mean SD MeanSD Mean SD
PAHs
Pyrene 0.3280.1030.238 0.1260.0580.024 0.048 0.006
Benzo(ghi)fluoranthene 0.1710.030 0.141 0.0750.0370.018 0.028 0.004
Benz(a)anthracene 0.1080.068 0.096 0.0700.0290.020 0.017 0.003
Chrysene 0.2200.0710.181 0.1080.0470.023 0.036 0.010
Benzo(k)fluoranthene 0.1490.078 0.125 0.0900.0350.013 0.025 0.008
Benzo(b)fluoranthene 0.2110.095 0.183 0.1330.0440.017 0.035 0.009
Benzo(j)fluoranthene 0.0160.021 0.019 0.0190.0050.004 0.004 0.001
Benzo(e)pyrene 0.2100.0870.179 0.1210.0460.016 0.035 0.010
Benzo(a)pyrene 0.1720.1280.159 0.1320.0430.017 0.034 0.011
Perylene 0.0320.0260.029 0.0240.0080.003 0.006 0.002
Indeno(cd)pyrene 0.1630.1110.145 0.1160.0380.014 0.032 0.009
Benzo(ghi)perylene 0.4540.306 0.412 0.3310.0840.030 0.074 0.023
Indeno(cd)fluoranthene 0.0460.038 0.047 0.0430.0110.004 0.010 0.003
Dibenz(ah)anthracene 0.0130.011 0.012 0.0100.0040.001 0.004 0.001
Coronene 0.2750.1950.271 0.2300.0470.020 0.045 0.017
Hopanes and Steranes
22,29,30-Trisnorhopane 0.3730.117 0.295 0.1920.0460.018 0.040 0.016
22,29,30-Trisnorneohopane 0.4880.130 0.362 0.2290.0540.016 0.044 0.019
17 α(H)-21 α(H)-30-Norhopane1.2010.505 0.951 0.7250.1510.059 0.129 0.046
18 α(H)-29-Norneohopane 0.3350.159 0.246 0.2010.0370.016 0.027 0.009
17 α(H)-21 β(H)-Hopane 1.7670.983 1.362 1.1370.1860.078 0.148 0.064
22S-Homohopane 0.5360.2490.401 0.3270.0690.031 0.056 0.021
22R-Homohopane 0.4740.2380.366 0.3110.0660.030 0.054 0.026
22S-Bishomohopane 0.2820.1160.230 0.1720.0440.020 0.037 0.015
22R-Bishomohopane 0.2540.1380.196 0.1560.0340.014 0.026 0.012
22S-Trishomohopane 0.2080.0720.171 0.1270.0410.021 0.029 0.014
22R-Trishomohopane 0.1320.0690.099 0.0780.0220.013 0.018 0.006
20(R+S), αββ-Cholestane 0.0720.023 0.056 0.0340.0170.005 0.014 0.007
20R, ααα-Cholestane 0.3870.151 0.294 0.1790.0800.028 0.074 0.038
20(R+S), αββ-Ergostane 0.1020.044 0.102 0.0650.0250.009 0.024 0.011
20(R+S), αββ-Sitostane 0.4840.198 0.385 0.2920.1140.044 0.090 0.036
147
TABLE 5.1b. Mean mass concentration (in ng/m
3
) of the organic tracers measured near
I-710 Freeway

Ultrafine mode Accumulation mode
Freeway Background Freeway Background Organic species
Mean SD Mean SD Mean SD Mean SD
PAHs
Pyrene 0.590 0.201 0.041 0.020 0.025 0.009 0.010 0.004
Benzo(ghi)fluoranthene 0.446 0.184 0.060 0.028 0.035 0.014 0.021 0.006
Benz(a)anthracene 0.307 0.138 0.011 0.001 0.011 0.002 0.010 0.004
Chrysene 0.264 0.123 0.057 0.028 0.030 0.014 0.015 0.010
Benzo(k)fluoranthene 0.126 0.067 0.032 0.017 0.012 0.002 0.015 0.011
Benzo(b)fluoranthene 0.194 0.062 0.078 0.016 0.051 0.020 0.039 0.011
Benzo(j)fluoranthene 0.062 0.004 ND ND ND
Benzo(e)pyrene 0.250 0.108 0.075 0.022 0.037 0.014 0.031 0.015
Benzo(a)pyrene 0.147 0.085 0.057 0.025 0.024 0.010 0.029 0.010
Perylene 0.044 NA ND ND ND
Indeno(cd)pyrene 0.233 0.098 0.088 0.023 ND ND
Benzo(ghi)perylene 0.531 0.198 0.190 0.064 0.020 0.008 0.030 0.011
Indeno(cd)fluoranthene NR NR NR NR
Dibenz(ah)anthracene ND ND ND ND
Coronene 0.292 0.115 0.111 0.030 0.009 NA 0.018 0.007
Hopanes and Steranes
22,29,30-Trisnorhopane 0.257 0.098 0.062 0.028 0.025 0.012 0.014 NA
22,29,30-Trisnorneohopane NR NR NR NR
17 α(H)-21 α(H)-30-Norhopane 0.614 0.249 0.157 0.057 0.086 0.014 0.048 0.008
18 α(H)-29-Norneohopane NR NR NR NR
17 α(H)-21 β(H)-Hopane 0.434 0.215 0.111 0.044 0.058 0.016 0.037 0.004
22S-Homohopane 0.192 0.103 0.049 0.023 0.030 0.005 0.018 0.005
22R-Homohopane 0.159 0.096 0.049 0.023 0.025 0.013 0.015 0.007
22S-Bishomohopane 0.139 0.088 0.048 NA 0.009 NA 0.014 NA
22R-Bishomohopane 0.073 0.020 0.019 NA 0.019 NA 0.009 NA
22S-Trishomohopane NR NR NR NR
22R-Trishomohopane NR NR NR NR
20(R+S), αββ-Cholestane * 0.282 0.106 0.095 0.028 0.026 0.015 0.023 NA
20R, ααα-Cholestane 0.250 0.122 0.081 0.045 0.029 0.020 0.026 NA
20(R+S), αββ-Ergostane * 0.330 0.192 0.036 0.009 0.023 NA ND
20(R+S), αββ-Sitostane * 0.376 0.229 0.080 0.087 0.055 0.030 0.040 NA
ND = Not detected; NR = Not reported; NA = Not applicable
* R and S isomers measured separately and summed together

smaller sized primary particles emitted by vehicles, most of the organic species were
measured to be an order of magnitude higher in ultrafine than the accumulation mode.
148
The dominant PAHs near the CA-110 freeway are BgP, coronene and pyrene. 17 α(H)-
21β(H)-29-norhopane and 17 α(H)-21 β(H)-hopane are the pre-dominant hopanes in both
size modes and at both sites. Similar to PAHs, hopanes and steranes are measured in
comparable amounts at freeway and background sites near CA-110 in both size modes
(Figure 5.2a-b). Unlike the CA-110, the concentrations of EC and several organic species
are substantially higher at the proximal site of the I-710 freeway compared to the urban
background site (Figure 5.3b; Table 5.2), possibly a result of the longer distance to that
background site (~1.6 km downwind of the freeway) compared to the background site at
the CA-110. The CO
2
level in the urban background site is also close to its typical
background levels (Table 5.2).

TABLE 5.2. Mean concentrations of the meteorological and bulk-chemical parameters
measured near CA-110 and I-710 Freeway

CA-110 I-710
Freeway Background Freeway Background Parameters
Mean SD Mean SD Mean SD Mean SD
CO
2
(ppm) 476 39 427 44 430 28 383 10
T (
0
C) 21.6 3.0 21.3 3.4 18.9 2.9 18.0 2.2
RH (%) 50.7 14.2 50.1 15.3 46.0 11.8 45.3 7.0
PM
2.5
( µg/m
3
) 20.0 11.2 15.7 5.6 15.4 5.1 12.0 6.0
EC
2.5
( µg/m
3
) 1.8 1.2 1.4 0.9 3.3 0.6 0.7 0.3
OC
2.5
( µg/m
3
) 14.9 5.2 11.4 6.6 6.9 1.8 5.4 1.6

Lighter PAHs are an order of magnitude higher near the freeway site, while heavier
PAHs, such as BgP and coronene as well as hopanes and steranes are about 3-4 times
higher near I-710 freeway site compared to background measurements for ultrafine PM.
The accumulation mode PM concentrations are comparable at both locations, and account

149
y = 0.7661x + 0.0086
R
2
= 1.00
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
Freeway (ng/m
3
)
Background (ng/m
3
)

y = 0.8201x + 0.0001
R
2
= 0.99
0.00
0.04
0.08
0.12
0.16
0.20
0.00 0.05 0.10 0.15 0.20 0.25
Freeway (ng/m
3
)
Background (ng/m
3
)

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Freeway (ng/m
3
)
Background (ng/m
3
)
PAHs < 228 MW
PAHs = 252 MW
PAHs > 276 MW
y = 0.8604x + 0.0017
R
2
= 0.98

0.00
0.02
0.04
0.06
0.08
0.10
0.00 0.02 0.04 0.06 0.08 0.10 0.12
Freeway (ng/m
3
)
Background (ng/m
3
)
PAHs < 228 MW
PAHs = 252 MW
PAHs > 276 MW
y = 0.8526x - 0.0016
R
2
= 0.97

Figure 5.2 Correlation of organic species between freeway and background sites near CA-110 in a) ultrafine

size mode for hopanes and steranes;
b) accumulation size mode for hopanes and steranes; c) ultrafine

size mode for PAHs; and d) accumulation size mode for PAHs. Uncertainty in the
measurements is presented in terms of standard error (SE).
b
d
a
c

150
on average for 10% or less of the total PM
2.5
concentrations of these organic species.
Pyrene, benzo(ghi)fluoranthene, benzo(a)anthracene, BgP and coronene are the pre-
dominant PAHs near the freeway. Benzo(b)fluoranthene has the highest concentrations in
the accumulation mode at the freeway as well as background sites. Hopanes and steranes
are about 3-4 times higher at the freeway than the background site for most species in
ultrafine size mode (Figure 5.2a); however, similar levels are observed in the
accumulation mode between the two sites (Figure 5.2b).

Figures 5.2a-d illustrate the impact of CA-110 freeway emissions to the background site.
The very high correlation (~ 1) and slopes (~0.8) of the plotted linear regressions suggest
that emissions from gasoline vehicles on CA-110 are the predominant and possibly the
single source of PAHs and hopanes and steranes at a distance 150 m away from the
freeway. Also the mass of the organics tracers is dominated by the ultrafine mode (90%)
at both the sites.

Similarly, Figures 5.3a-d present the correlations between the concentrations of PAHs
and hopane and steranes in different PM size mode at the I-710 freeway and background
sites. Unlike the case of the CA-110, we find a lower linear regression slope between the
proximal and distant sites for these species. High correlation, but smaller slope for
hopanes and steranes, indicates that background site is influenced by the freeway
emissions, but to a lower degree, as a result of higher atmospheric dilution, given its
distance from the freeway. The ultrafine mode concentrations of PAHs and hopanes-
151
y = 0.235x + 0.0101
R
2
= 0.92
0.00
0.04
0.08
0.12
0.16
0.20
0.00 0.20 0.40 0.60 0.80
Freeway (ng/m
3
)
Background (ng/m
3
)

y = 0.537x + 0.0051
R
2
= 0.88
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.00 0.02 0.04 0.06 0.08 0.10
Freeway (ng/m
3
)
Background (ng/m
3
)

0.00
0.05
0.10
0.15
0.20
0.25
0.00 0.20 0.40 0.60 0.80
Freeway (ng/m
3
)
Background (ng/m
3
)
PAHs < 228 MW
PAHs = 252 MW
PAHs > 276 MW
y = 0.3673x - 0.003
R
2
= 0.97

0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Freeway (ng/m
3
)
Background (ng/m
3
)
PAHs < 228 MW
PAHs = 252 MW
PAHs > 276 MW
y = 0.51x + 0.0138
R
2
= 0.84

Figure 5.3 Correlation of organic species between freeway and background sites near I-710 in a) ultrafine

size mode for hopanes and steranes; b)
accumulation size mode for hopanes and steranes; c) ultrafine

size mode for PAHs; and d) accumulation size mode for PAHs. Uncertainty in the
measurements is presented in terms of SE. Also, the correlation coefficient for PAHs is calculated for PAHs > 252 MW only (Figure c and d).
d
b a
c

152
steranes are roughly 0.25-0.37 those of the freeway site, and about 50% lower that those
of the freeway site in the accumulation mode. The degree of correlation and linear
regression slopes between the freeway and background sites for both ultrafine and
accumulation PM mode are quite similar between higher MW PAHs and hopanes-
steranes, whereas the degree of correlation decreases for the more volatile, lighter PAHs.
This finding is consistent with previous experimental (Biswas et al., 2006; Kuhn et al.,
2005a; 2005b) and theoretical (Zhang et al., 2005) studies on the fate of semi-volatile
labile species emitted from vehicles as they move away from the freeway, indicating a
greater decrease in concentration with distance from the freeway due to evaporative
losses in addition to atmospheric dilution.

5.4.2. Comparison of CA-110 and I-710 measurements
Direct comparison in the PAH and hopanes- steranes levels at CA-110 and I-710 freeway
sites reveals that higher concentrations are observed near the CA-110. While HDVs
generally emit one order of magnitude higher hopanes and steranes than LDVs
(excluding poor maintenance high emitters and non-cat gasoline driven vehicles)
(Phuleria et al; 2006; Schauer et al., 2002; Zielinska et al., 2004), the higher levels at CA-
110 (which is a pure gasoline freeway) may be due to lower atmospheric dilution and
high background influence compared to I-710. CO
2
concentrations, which have been used
as a measure of atmospheric dilution ratio (Ntziachristos et al., 2006), are quite similar at
the I-710 freeway site and the CA-110 background site, and substantially higher at the
CA-110 freeway site, suggesting significantly higher atmospheric dilution during the

153
field experiments at the I-710 (Table 2). While CO
2
levels are starkly different in these
two studies, temperature and relative humidity are similar between these measurements.
Based on the above discussion, a direct comparison of the measurements near these two
freeways may not be appropriate without taking into account the higher atmospheric
dilution conditions during the measurements at I-710. In order to account for the different
background sources and different degrees of atmospheric dilution, the concentrations of
hopanes-steranes and PAHs at the freeway sites were adjusted by subtracting the
background and normalized according to equation (5.1)

Normalized Pollutant concentration = (P
FW
– P
BG
) / (CO
2 FW
– CO
2 BG
) …(5.1)

where, P
FW
and P
BG
are the organic species concentration measured at freeway and
background sites and CO
2 FW
and CO
2 BG
are the corresponding CO
2
concentrations
measured at the freeway and background sites respectively. Assuming insignificant CO
levels, this normalization effectively provides a roadway emission rate per unit of fuel.

These normalized concentrations (or pseudo-emission rates) are shown in Figures 5.4a
and 5.4b. Similar normalized concentrations for hopanes and steranes can be observed for
CA-110 and I-710 freeways (Figure 5.4a), especially given the uncertainty in the
measurements. This implies that even at the I-710, with a HDV fraction of about 20%,
the overall levels of hopanes and steranes are dominated by gasoline vehicles. Heavy
MW PAHs also have similar normalized concentrations on these freeways (Figure 5.4b).

154
0.000
0.005
0.010
0.015
0.020
0.025
22,29,30-Trisnorhopane
22,29,30-Trisnorneohopane
17a(H)-21a(H)-30-Norhopane
18a(H)-29-Norneohopane
17a(H)-21b(H)-Hopane
22S-Homohopane
22R-Homohopane
22S-Bishomohopane
22R-Bishomohopane
22S-Trishomohopane
22R-Trishomohopane
20(R+S), abb-Cholestane
20R, aaa-Cholestane
20(R+S), abb-Ergostane
20(R+S), abb-Sitostane
Hopanes and Steranes
Normalized conc (ng/m
3
/ppm CO
2
)
I-710
CA-110

0.000
0.005
0.010
0.015
0.020
0.025
EC
Pyrene
Benzo(ghi)fluoranthene
Benz(a)anthracene
Chrysene
Benzo(k)fluoranthene
Benzo(b)fluoranthene
Benzo(j)fluoranthene
Benzo(e)pyrene
Benzo(a)pyrene
Perylene
Indeno(cd)pyrene
Benzo(ghi)perylene
Indeno(cd)fluoranthene
Dibenz(ah)anthracene
Coronene
PAHs
Normalized conc (ng/m
3
/ppm CO
2
)
I-710
CA-110
0.058+0.031

Figure 5.4 Comparison of measured - a) hopanes and steranes (normalized to ∆CO
2
); and
b) PAHs and EC (normalized to ∆CO
2
) - between CA-110 and I-710 in PM
2.5
size mode.
Error bars represent SE.
5.4a
5.4b

155
As the levels of species at the background site were comparable to the freeway site in
CA-110 (Table 5.1a), subtracting them resulted in larger uncertainty. The difference in
the absolute levels of these PAHs, however, is not as striking, possibly because heavy
molecular weight PAHs are shown to be emitted more in creep and transient driving
modes (Shah et al., 2005) as well as cold start conditions (Cadle et al., 1999; Schauer et
al., 2003), compared to the cruise driving conditions of our study at both I-710 and CA-
110. In contrast to hopanes- steranes and heavy PAHs, the emission of EC and lighter
PAHs are dominated by diesel-powered vehicles (Figure 5.4b), implying a significantly
higher contribution of diesel vehicles to the ambient concentrations of these species.

5.4.3. Chemical profiles of organic markers
The concentrations of vehicular molecular markers measured in this study are compared
with the emission factors measured in a similar study conducted at the Caldecott roadway
tunnel in Berkeley, CA (Phuleria et al., 2006). Size-segregated, fuel-based emission
factors (EFs) were calculated for LDVs and HDVs using exactly the same sampling
system for PM collection as that in the present study. Since this study has focused on
organic molecular markers only, the chemical profiles of these organic markers are
presented as a fraction of total carbon (TC) (in units of ng/ µg TC). LDV emission factors
(normalized to TC) from the Caldecott tunnel are compared with the pure gasoline
vehicle emissions (normalized to TC) on the CA-110. For a similar comparison of the I-
710 measurements, reconstructed EFs were obtained using equation 5.2:

156
EF
Reconstructed
= 0.8 x EF
LDV
+ 0.2 x EF
HDV
…(5.2)

where, EF
Rreconstructed
represented the expected mixed-fleet emission factor for the 20%
HDV fraction on I-710 based on EF
LDV
and EF
HDV
, LDV and HDV emission factors
measured in the Caldecott tunnel, respectively. The reconstructed EFs were calculated to
account for the 20% HDV fraction measured at I-710 based on our traffic counts during
this study period. The reconstructed EFs normalized to TC (in ng of organic species per
µg of TC) are compared with those of normalized measurements from the I-710.

Figure 5.5a shows the normalized chemical profile of hopanes and steranes at the CA-110
freeway against those measured inside the pure LDV bore of the Caldecott tunnel. The
normalized mass concentrations of these molecular markers have virtually identical
chemical profiles in these two different environments. The normalized tunnel
concentrations are slightly lower than the corresponding normalized levels at the CA-110,
possibly because of the higher TC levels due to some positive artifacts in the ultrafine
size mode at the tunnel compared to the freeway. This is because higher concentrations of
gas-phase organics in the tunnel would result in overestimation of TC due to increased
adsorption of these species on the PM –collecting filters and therefore might be
responsible for the somewhat observed lower hopane-sterane/TC ratios in the Caldecott
tunnel compared to the CA-110 measurement. Similarly, Figure 5.5b, demonstrates
generally very similar chemical profiles measured at the I-710 freeway compared to the
reconstructed normalized concentrations from the Caldecott tunnel measurements.

157
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
22,29,30-Trisnorhopane
22,29,30-Trisnorneohopane
17a(H)-21a(H)-30-Norhopane
18a(H)-29-Norneohopane
17a(H)-21b(H)-Hopane
22S-Homohopane
22R-Homohopane
22S-Bishomohopane
22R-Bishomohopane
22S-Trishomohopane
22R-Trishomohopane
20(R+S), abb-Cholestane
20R, aaa-Cholestane
20(R+S), abb-Ergostane
20(R+S), abb-Sitostane
Hopanes and Steranes
Species mass (ng/ πg TC)
CA-110
Caldecott (LDV)

5.5a
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
22,29,30-Trisnorhopane
22,29,30-Trisnorneohopane
17a(H)-21a(H)-30-Norhopane
18a(H)-29-Norneohopane
17a(H)-21b(H)-Hopane
22S-Homohopane
22R-Homohopane
22S-Bishomohopane
22R-Bishomohopane
22S-Trishomohopane
22R-Trishomohopane
20(R+S), abb-Cholestane **
20R, aaa-Cholestane
20(R+S), abb-Ergostane **
20(R+S), abb-Sitostane **
Hopanes and Steranes
Species mass (ng/ g TC)
I-710
Caldecott (Reconstructed)
N
N
NR

5.5b
Figure 5.5.Chemical profile of hopanes and steranes (normalized to TC) in a) CA-110
and Caldecott (LDV) study and b) I-710 study and Reconstructed Caldecott study.

158
y = 1.0236x - 0.0015
R
2
= 0.89
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Caldecott (LDV) (ng/ ng SUM Hop-Sters)
CA-110 (ng/ ng SUM Hop-Sters)

5.6a
y = 1.1669x + 0.0056
R
2
= 0.94
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Caldecott (Reconstructed) (ng/ ng SUM Hop-Sters)
I-710 (ng/ ng SUM Hop-Sters

5.6b
Figure 5.6. Correlation of hopanes and steranes (normalized to Sum of hopanes and
steranes) in PM
2.5
size mode between a) CA-110 study and Caldecott (LDV) study; and
b) b) I-710 study and Reconstructed Caldecott study. Error bars represent SE.

Several studies have shown similar source profile of hopanes and steranes between
gasoline and diesel engines tested on dynamometer facilities (Cadle et al., 1999;
Zielinska et al., 2004) as well as between on road LDV and HDV fleet in tunnel
environment (Phuleria et al., 2006). Fraser et al. (1999) have also shown consistent

159
agreement between relative chemical distribution of hopanes and steranes measured in
ambient air vis-à-vis tunnel emission rate measurements. In order to illustrate the internal
consistency among individual hopane and steranes species, a correlation between the CA-
110 measurements (normalized to sum of hopanes and steranes) and LDV emission
factors (normalized to sum of hopanes and steranes) from the Caldecott tunnel study is
given in Figure 5.6a. Correlation between normalized I-710 measurements and
normalized reconstructed emission factors obtained from the Caldecott tunnel are shown
in Figure 5.6b. The very high correlations and slopes ~1 illustrate the conservative nature
of these organic markers and reaffirm their use as reliable markers of vehicular emissions.

This correlation is reflective of the natural distribution of hopanes-steranes found in the
crude oil stock used to make lubricating oil. It should be noted that the lower number of
data points in Figure 5b is due the fact that few hopanes are not reported and correlations
are only obtained for the available data. The data plotted in Figures 5.6a and 5.6b show
that the relative concentrations (normalized to sum of hopanes and steranes) of these
individual species is similar in vehicles powered by either gasoline or diesel engines,
consistent with the notion that that these species originate from lubricating oil (Fine et al.,
2004, Fraser et al., 1998). Zielinska et al (2004) have studied the fuel and oil properties
used in gasoline and diesel engines and showed that hopane and steranes are only present
in lubricating oil and not in the fuel.

160
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
Pyrene
Benzo(ghi)fluoranthene
Benz(a)anthracene
Chrysene/Triphenylene
Benzo(k)fluoranthene
Benzo(b)fluoranthene
Benzo(j)fluoranthene
Benzo(e)pyrene
Benzo(a)pyrene
Perylene
Indeno(cd)pyrene
Benzo(ghi)perylene
Indeno(cd)fluoranthene
Dibenz[a,h]anthracene
Coronene
PAHs
CA-110 (ng/ g TC)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
Caldecott (LDV) (ng/ g TC)
CA-110
Caldecott (LDV)

5.7a
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Pyrene
Benzo(ghi)fluoranthene
Benz(a)anthracene
Chrysene
Benzo(k)fluoranthene
Benzo(b)fluoranthene
Benzo(j)fluoranthene
Benzo(e)pyrene
Benzo(a)pyrene
Perylene
Indeno(cd)pyrene
Benzo(ghi)perylene
Indeno(cd)fluoranthene
Dibenz(ah)anthracene
Coronene
PAHs
I-710 (ng/ g TC)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Caldecott (Reconstructed) (ng/ g TC)
I-710
Caldecott (Reconstructed)
N
N

5.7b
Figure 5.7. Chemical profile of PAHs (normalized to TC) in PM
2.5
size mode in a) CA-
110 study and Caldecott (LDV) study; and b) I-710 study and Reconstructed Caldecott
study. Error bars represent SE.

161
Similar to the hopanes and steranes data, Figures 5.7a and 5.7b present the normalized
chemical profile of the measured PAHs at the CA-110 and I-710 against normalized EFs
measured for LDVs and HDVs from the Caldecott tunnel, respectively. (The
concentrations are normalized to TC measured in the respective studies). The normalized
PAH concentrations are about 2-3 times lower in the freeways than the corresponding
normalized concentrations of the tunnel for lighter PAHs, while for heavier PAHs the
chemical profiles become more comparable, especially for the I-710 data. Part of these
differences may arise from different vehicle operating conditions, such as engine load
and speed (considering that the Caldecott tunnel has a 4% grade), ambient temperature
and atmospheric dilution (affecting the ambient sites but not the tunnel measurements) as
y = 0.1046x + 0.0009
R
2
= 0.89
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16
Ultrafine(ng/ µg TC in PM
2.5
)
Accumulation (ng/ µg TC in PM
2.5
)
P AHs < 228 M W
P AHs = 252 M W
P AHs > 276 M W
Hop-Sters
y = 0.1764x + 0.0003
R
2
= 0.97

5.8a
Figure 5.8. Correlation of measured organic species (normalized to TC in PM
2.5
)
between ultrafine and accumulation size modes in a) CA-110 study; and b) Caldecott
(LDV) study. Error bars represent SE.

162
Figure 5.8 Continued…

y = 0.1239x + 0.0003
R
2
= 0.97
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16
Ultrafine (ng/ µg TC in PM
2.5
)
Accumulation (ng/ µg TC in PM
2.5
)
P AHs < 228 M W
P AHs = 252 M W
P AHs > 276 M W
Hop-Sters
y = 0.0704x + 0.0007
R
2
= 0.70

5.8b

well as catalytic converter efficiency in these two different environments (Schauer et al.,
2003). Since PAHs result from fuel combustion in the engines, they strongly depend on
the factors influencing combustion, including engine-load and ambient air temperature
(Kean et al., 1999). Furthermore, the gas-particle partitioning of semi-volatile PAHs can
be strongly influenced by ambient temperature (Mader and Pankow, 2000; Finlayson-
Pitts and Pitts, 2000), particularly for low molecular weight PAHs whose vapor pressures
are higher than heavier PAHs and the hopanes and steranes (Schauer et al., 2003). It
should be also noted that the plotted data in these figures reflect concentrations
normalized to TC, which themselves are also affected by engine operating parameters,
thus influence the fraction of carbon emitted as these organic markers. Photochemical
degradation of PAHs on the filter (Schauer C. et al., 2003) as well as in the atmosphere

163
(Zielinska, 2005; Finlayson-Pitts and Pitts, 2000) affect both the total carbon as well as
particulate phase PAHs concentrations measured near the roadway environments. In
tunnel studies, these phenomena are expected to be minimal. All of these factors may
compromise the use of PAHs as tracers of vehicular emissions.

The relationship between PAHs and hopanes- steranes (normalized to TC in PM
2.5
size
mode) in the ultrafine and accumulation size modes at the CA-110 and the corresponding
levels for the LDVs measured in Caldecott tunnel is shown in Figures 5.8a and 5.8b,
respectively. The normalization to TC is done in order to bring the units to same scales
between the freeway and reconstructed Caldecott studies, and does not affect the slope
and correlation coefficients of the linear regressions between these species. The high
correlations in the CA-110 as well as for LDVs at the Caldecott tunnel show that high
emissions of a PAHs and hopanes and steranes in one mode are accompanied by high
emissions in the other mode, thus indicating similar particle size distributions for these
species, consistently with results shown earlier (Figure 5.5a). Hopanes and steranes
appear to be distributed similarly in both tunnel and freeway environments (slope ~ 0.1
and R
2
> 0.9) between accumulation and ultrafine PM modes. In contrast, there appears
to be a shift towards the accumulation mode for PAHs at the CA-110 compared to the
Caldecott data, particularly for lighter PAHs. There may be several reasons for these
changes in size distribution observed between the two environments. Earlier studies have
reported that more volatile PAHs tend to partition into larger particle size fractions (Marr
et al., 1999; Miguel et al., 1998). Moreover, since the presence of poorly maintained

164
y = 0.1314x - 0.0002
R
2
= 0.83
0.000
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.010
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08
Ultrafine (ng/ µg TC in PM
2.5
)
Accumulation (ng/ µg TC in PM
2.5
)
P AHs < 228 M W
P AHs = 252 M W
P AHs > 276 M W
Hop-Sters

5.9a
y = 0.2283x + 0.0002
R
2
= 0.98
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.045
0.00 0.05 0.10 0.15 0.20 0.25 0.30
Ultrafine (ng/ µg TC in PM
2.5
)
Accumulation (ng/ µg TC in PM
2.5
)
P AHs < 228 M W
P AHs = 252 M W
P AHs > 276 M W
H op-Sters
y = 0.1641x - 0.0021
R2 = 0.9324

5.9b
Figure 5.9.Correlation of measured organic species (normalized to TC in PM
2.5
) between
ultrafine and accumulation size modes in a) I-710 study; and b) Reconstructed Caldecott
study. Error bars represent SE. Reconstructed Caldecott EFs are obtained using equation
(2) in the text.

165
gasoline vehicles (gross emitters such as white and black smokers) significantly affects
the total PAHs levels (Schauer et al., 2002; Zielinska et al., 2004), different fraction of
these high emitters in these studies might be partially responsible for the observed
differences.

The relationship between the ultrafine and accumulation size modes of PAHs, hopanes
and steranes (normalized to TC in PM
2.5
size mode) at the I-710 and their corresponding
reconstructed emissions factors from the tunnel measurements is shown in Figures 5.9a
and 5.9b, respectively. Unlike the CA-110 and Caldecott LDV measurements, some
differences in the size distribution of hopanes and steranes can be observed between I-
710 and reconstructed tunnel measurements. Hopanes and steranes are shifted more
towards the accumulation mode in the reconstructed emissions from the Caldecott study
(slope = 0.23) compared to those of the I-710 (slope = 0.13). Based on CO
2

measurements, the dilution factor ratio between the I-710 study and the Bore 1 of the
Caldecott tunnel (corresponding to HDV measurements) is on the order of 8-10
(Ntziachristos et al., 2006), which implies a stronger background influence at the I-710
site. Since hopanes and steranes come from both vehicle types, the more prevalent LDVs
in the background as well as the high number of LDVs on the I-710 tend to bring the size
distributions closer to those of LDVs. The fact that the freeway site measurements in the
accumulation mode are relatively similar to background concentrations in I-710 study
(Figure 5.3b, Table 5.1b), suggests that, while hopane and steranes in ultrafine mode are

166
primarily from fresh emissions in the I-710, the accumulation mode concentrations may
be also influenced by background emissions of LDVs in the surrounding urban area.

Comparing the PAHs distribution in two size modes (Figures 5.9a and 5.9b), the
influence of the urban background as well as the larger number of LDV cars (60% more
than on CA-110) at the I-710 is even more pronounced. The accumulation mode PAH
concentrations are affected significantly by local combustion sources, including LDVs (in
addition to the large number of cars already on I-710), which are more regional in nature
and likely influence the measurements near freeway site. In addition to the impact of
background sources, the higher dilution at the I-710 compared to the tunnel
measurements affects the gas-particle phase partitioning of semi volatile PAHs, and may
be responsible for the observed scatter in the distribution of PAHs in these size modes.
Compared to the CA-110 data, hopanes and steranes are partitioned more in the
accumulation mode, although in lower fractions compared to the Caldecott data, as noted
earlier. This reflects the influence of HDVs at the I-710 and is consistent with previous
studies showing higher emission rates in the accumulation mode by HDVs for these
species (Phuleria et al., 2006).

5.5. Chapter 5: SUMMARY AND CONCLUSIONS
The main objective of this study was to compare the concentrations of vehicular organic
markers measured near freeway environments to those based on emission factors
measured for LDVs and HDVs inside the Caldecott tunnel. Very good agreement is

167
observed between CA-110 measurements and tunnel-based LDV EFs for hopanes and
steranes as well as heavier PAHs. The reconstructed profiles from the Caldecott tunnel
also agree quite well with the I-710 measurements. The comparisons between the
chemical profiles of these organic species in environments affected by different
atmospheric dilution, meteorological conditions, and different fleet composition,
validates the emission factors measured in our earlier study in the Caldecott tunnel. PAH
concentrations are affected by various factors such as fuel type, engine-operating
parameters as well as meteorological conditions. However, high MW PAHs seem to be
less influenced and hence show better agreement between roadside and tunnel
measurements as compared to lighter PAHs. A potential limitation of roadway
measurements is their inability to capture cold-start emissions, which are significantly
higher than the prevailing cruise or transient mode emissions of this study as well as
those in roadway tunnels, and which affect the average emission profiles significantly,
especially in the winter season (Manchester-Neesvig et al., 2003; Shah et al., 2005).
Recognizing this limitation, our results suggest that in general, the emission factors for
hopanes and steranes obtained in tunnel environments, where emissions are averaged
over a large vehicle-fleet, enable reliable source apportionment of ambient PM, given the
overall agreement between the roadway vs tunnel concentrations of these species.

168
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Chapter 6: Conclusions and future research directions

6.1 SUMMARY
Recent focus of studies on health effects of ambient particulate matter (PM) have
suggested particle chemical composition in addition to particle size, shape and number
concentration responsible for observed health outcomes. Chemical composition of the
atmospheric particles, however, can have seasonal, temporal, spatial and size-dependent
variability. In addition, differences in ambient temperature, relative humidity, and
photochemical activity and source contributions affect the concentration and size
distribution of particular chemical species.

There are very significant differences in urban versus rural location; source versus
receptor sites in terms of PM exposure, which invite for characterizing the sources, and
develop methods of assessing their impact on air quality. Two of the events where we
studied the impact of specific sources were Long Beach harbor strike in October 2002
and Southern California wildfires in late October 2003. In the former case the airborne
PM contribution originating from cargo trucks was reduced while resulted
simultaneously in elevated idling ship emissions. In later case, however, there was high
level of PM emissions originating from wildfires during the fire episode. As against
uncontrolled wildfires, which produce particles in lower accumulation size ranges and
have more regional impact on air quality, emissions from gasoline and diesel driven
vehicles impact the air quality locally and more so in ultrafine and nano particle size
ranges.
174
Size fractionated speciated organic chemical analysis was attempted for emission
characterization from averaged PM emission sampled from gasoline and diesel vehicles
inside Caldecott tunnel. The tunnel off limits one bore to only light duty gasoline
vehicles and hence provided the opportunity to segregate and study the emission from
LDVs and HDVs. While the LDVs are shown to emit high molecular weight PAHs, the
relative emissions is dominated by light molecular weight PAHs and EC in the case
HDVs. Hopanes and steranes emissions, which emanate from lubricating oil, are found
to have similar chemical profile from both type of vehicles. HDVs. To understand
vehicle contribution to ambient PM near freeway environments where people spend a lot
of time in commute, measurements were carried out near CA-110 and I-710 freeways.

CA-110 measurements, which provided the pure LDV emissions, were compared against
mix-fleet (20% HD fraction) of vehicles on I-710 for the measured organic tracers near
these freeways. Successively the chemical profiles of the organic tracers obtained from
these freeway measurement were compared with the earlier obtained chemical profiles
from Caldecott tunnel measurements. Very good agreement is obtained between these
different studies, conducted in different environments, and hence provides the potential
use of these molecular marker EFs in CMB model calculations for source apportionment
over to single vehicle laboratory dynamometer measurements.

6.2 CONCLUSIONS
Preventing people from the deleterious and carcinogenic health effects of ambient air
pollution is the desired goal and motivation of any kind of air quality study. In order to
175
relate public health with ambient air pollution, mechanistic pathways of PM induced
health effects need to be established (Dockery and Pope, 2006). Since there is huge
variability in sources, exposed population, population sensitivity, meteorology as well as
measurement methods, extensive physicochemical characterization of airborne PM is
necessary to develop future emission control technologies as well as in focusing on more
specific PM component than mass.

Within the uncertainty in the measurements as stated in relevant sections of this thesis,
the research work presented here successfully demonstrate the association between
ambient PM, sources and the temporal, seasonal and spatial variability linked with these
measurements. The measured emission factors for vehicular organic tracers from on-
road fleet of gasoline and diesel vehicles using GCMS methods proved very useful and
presents the potential use of these molecular source profiles in CMB model calculations
for ultrafine and fine PM source apportionment. To date no single source profiles are
available for different ultrafine PM sources other than obtained in this work here and it
just presents a small step forward towards achieving these goals. The PM measurements
and data obtained in this study provides an increased understanding of the relationship
between sources and the ambient concentrations of their PM emissions and how physical
and chemical characteristics of the fine and ultrafine particles changes in different
environments, which is necessary in order to understand the exposure outcomes.

176
6.3 FUTURE WORK DIRECTIONS
Major research effort is underway to improve scientific understanding of airborne PM
and its effects on human health. The research effort is directed at reducing scientific and
technical uncertainties in the evidence related to regulation of airborne PM. For effective
emission-management strategies, and regulatory decision making processes,
development of measurement methods to characterize size distribution, chemical
composition and emission rates from different sources is essential. The required data
includes chemically speciated and size-resolved emission factors/ ratios for a sufficient
number of geographic locations and source types (NRC, 1999).

Similar to the study of spatial, seasonal and temporal differences in PM levels in
Southern California, different locations having different mix of sources, and
progressively evolving pollution control technologies would continue to be the focus of
air quality evaluation studies in future. The focus of the research work presented here is
on PM of primary source origin, however, secondary particle formation from precursor
gaseous species would be another potent area of future studies in perspective to the
organic gaseous emissions from different combustion sources including mobile vehicular
emissions. We studied the impact of wildfire emissions on local air quality in Los
Angeles Basin, however, as the particle originating from these fire episodes are in 100-
200 nm size ranges, they have higher residence time in atmosphere and would affect the
visibility and regional air quality, in general. How these particles of fire origin would
change characteristics in the atmosphere on a longer time scale would be another area of
future study.
177
EFs calculated in Caldecott tunnel are specific to the tunnel grade and vehicle operating
conditions. In different load and smooth/congested driving conditions the emission
factors would change (since catalytic converter efficiency is a strong function of catalyst
operating temperature) and one needs to be cautious before their use in CMB
applications and in data assimilation with other studies. On similar lines, the traffic was
observed very smooth in the Caldecott tunnel and steady state driving pattern was
observed. Since idling and transient operation of vehicle as well as cold-start emissions
from engines are significantly different, the relative contribution of these must be
included before applying tunnel based EFs in CMB models.

We observed strong impact of ambient dilution on the ambient levels of deferent species,
which suggest that it may not be appropriate to compare the strength of two sources
without accounting the difference in atmospheric dilution. The source profiles of LDVs
as well as HDVs (adjusted for dilution using CO
2
measurements), with respect to hopanes
and steranes, were observed similar in tunnel and freeway environments despite high
differences in atmospheric dilution. This important finding further invites the comparison
and assimilation of these data with other laboratory dynamometer measurements
involving limited vehicles. As stated earlier, LDV/HDV split for 6tire-2axle trucks is
assumed to remain same as observed in 1997 (Kirchstetter et al, 1999). This must be
accounted before attributing PM concentration to HD diesel vehicles and invites an
updated comprehensives mobile emission inventory. The uncertainty in few of the
organic marker measurements (especially lighter molecular weight PAHs) was about
178
50% and hence one must be cautious to attribute the impact of LDV and HDV on source
apportionment studies using these molecular markers with high uncertainties.

Most of our knowledge of the detailed chemical composition of PM emissions from
gasoline vehicles comes from the standard cold and hot start chassis dynamometer tests.
Standard cold start testing, however, does not truly represent the cold start conditions in
many locations during the winter. The accurate assessment of the impact of the vehicular
sources on ambient PM concentrations requires knowledge of the emission rates and
composition of the true vehicle fleet in a region (Schauer et al., 2003). The emission
factors derived for the diesel and gasoline vehicles from tunnel studies are representative
of the true on-road vehicle fleet but does not incorporate the cold-start emissions. The
nature of the organic carbon emissions for the cold start, steady state and hot UDC
cycles are different based on organic tracer analysis (Schauer et al., 2003; Shah et al.,
2005). Hence a potential area of research would be to incorporate LDV emission
measurements near multi-complex parking structure for cold-start conditions.

Studies that use organic molecular markers to trace emissions from both gasoline and
diesel powered vehicles suggests that diesel contribution attributable to ambient PM is
greater than that of the gasoline vehicles (Schauer et al., 1996; Zheng et al., 2002). Other
studies which incorporate cold-start emissions and poorly maintained gasoline vehicles
estimate that the gasoline contribution more than the diesel powered vehicles (Watson et
al., 2002; Fraser et al., 2003). However, reconciliation in these opposite findings can be
achieved by measuring average fleet emission profiles from the on-road vehicles. More
179
studies of similar nature but different fleet composition and driving characteristics would
be necessary for the development of comprehensive source profiles for different
exposure environments.

Despite the consistent wind patterns in Los Angeles, it is conceivable that variations in
wind speed and direction will inevitably occur during sampling, and additional sources,
other than the one under study, will contribute to the samples at each site. Merely
locating the samplers and concentrators near a source may not ensure that the sample will
be influenced by that source alone at all times. Therefore, the actual contribution of the
source of interest and other PM sources to each sample or exposure atmosphere should be
quantified using source apportionment techniques. Two approaches can be employed to
evaluate source contributions from source emissions data and ambient monitoring data:
source-oriented models and receptor-oriented models. The source-oriented models rely
profoundly on inventory estimates, which are often based on rough emission factors
(Fine, 2002). Receptor-oriented models, on the other hand provide an alternative to
source-oriented models and use the best combination of source contributions needed to
reconstruct the chemical composition of an ambient sample (Watson, 1984).

One useful method for assigning ambient particulate matter concentration increments to
the sources from which they originate, which has been already referenced many times
earlier in the text, is the chemical mass balance (CMB) technique. CMB methods using
individual organic compounds and trace elements have been successfully used to
apportion the mass of a given PM
2.5
sample to different PM sources (Schauer and Cass,
180
2000; Schauer et al., 1996; Zheng et al., 2002). In the CMB method, a mass balance is
constructed in which the concentration of specific chemical constituents in a given
ambient sample is described as arising from a linear combination of the relative chemical
compositions of the contributing sources.

The dominant sources of airborne fine organic particulate matter in Los Angeles were
found to be diesel engine exhaust, gasoline-powered vehicle exhaust, the effluent from
meat cooking operations, and wood smoke. Paved road dust was the next largest
contributor, followed by four smaller sources: tire wears debris, vegetative detritus,
natural gas combustion aerosol, and cigarette smoke. The remaining contributions to fine
particle mass concentrations in Los Angeles are due to sulfate, nitrate, and secondary
organic aerosols that are formed from gas phase precursors by atmospheric chemical
reactions and that cannot be attributed to specific primary particle emissions sources by
the organic chemical tracer techniques (Schauer et al., 1996; Cass, 1998; Schauer and
Cass, 2000).

As technology changes over time (e.g. as cleaner diesel engines; cleaner fuel and better
control technologies are introduced) source test data will need to be updated and the
hence the need for the assessment of revised source contributions arises. In addition,
there are no source profiles available today for ultrafine particle composition form
different sources. As understanding of PM characteristics has improved and hence the
promulgation of newer air quality standards, PM
2.5
mass standards would no longer be
enough to protect human health from increased ultrafine nano-size particle emissions
181
from vehicles and other stationary combustion sources and necessitate the need for
generation of ultrafine PM source profiles.

Accurate emission factors are the critical starting point for any atmospheric models.
Understanding the factors that influence particle emission factors from motor vehicles
will also help in future emissions control efforts. The information generated through this
study would add to existing source-exposure knowledge and shall serve as the basis for
linking emissions to local air quality and ultimately to health effects. Knowing the
relative toxicity of different PM sources will allow for more targeted and effective
regulatory strategies. Furthermore, these data on which PM sources are the most toxic,
combined with detailed chemical and physical characterization of PM from these sources
will allow for a narrower, more focused effort in identifying the biological mechanisms
of PM health effects.
182
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Abstract (if available)

Abstract Recent focus of studies on health effects of ambient particulate matter (PM) have suggested particle chemical composition in addition to particle size, shape and number concentration responsible for the observed health outcomes. However, chemical composition and size distribution of the atmospheric particles can be strongly affected by the differences in ambient temperature, relative humidity, photochemical activity and source contributions. This thesis is intended to demonstrate the importance of characterizing predominant PM sources from an exposure perspective and develop methods of assessing their impact on air quality in Southern California. A study of particle number concentration and size distribution showed seasonal and spatial variability in Southern California. While contribution of local vehicular emissions was most evident in winter, effects of long-range transport of particles and photochemical particle formation were enhanced during warmer periods. Ship emissions are found to be dominant source of lower accumulation and ultrafine particles near ports. During the wildfires in October 2003 in Southern California, PM10 (particulate matter with aerodynamic diameter 10 mm and less) levels were found highly elevated, while ozone concentrations dropped during the fire episode and these fire-borne particles were found to effectively penetrate indoors. To characterize the emission profiles from on-road diesel and gasoline vehicle-fleets, size-segregated PM samples were collected inside the Caldecott tunnel in Orinda, CA and analyzed for vehicular organic tracers such as hopanes and steranes, and polycyclic aromatic hydrocarbons (PAHs). In a separate study, detailed information on the chemical characteristics of organic PM originating from a pure gasoline and a diesel dominated mixed-traffic freeway is obtained.

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Asset Metadata

Creator Phuleria, Harish Chandra (author)

Core Title Measurement and methods of assessing the impact of prevalent particulate matter sources on air quality in southern California

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Environmental Engineering

Publication Date 05/03/2007

Defense Date 03/05/2007

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag air quality,gasoline and diesel emissions,OAI-PMH Harvest,organics tracers,particulate matter,seasonal spatial and temporal variability,southern California

Place Name California (states), USA (countries)

Language English

Advisor Sioutas, Constantinos (committee chair), Phares, Dennis (committee member), Pirbazari, Massoud M. (committee member)

Creator Email phuleria@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m471

Unique identifier UC1308188

Identifier etd-Phuleria-20070503 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-497043 (legacy record id),usctheses-m471 (legacy record id)

Legacy Identifier etd-Phuleria-20070503.pdf

Dmrecord 497043

Document Type Dissertation

Rights Phuleria, Harish Chandra

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu