University of Southern California Dissertations and Theses

Exposure assessment and source apportionment of size fractions of airborne particulate matter

(USC Thesis Other)

Exposure assessment and source apportionment of size fractions of airborne particulate matter

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

EXPOSURE ASSESSMENT AND SOURCE APPORTIONMENT OF SIZE
FRACTIONS OF AIRBORNE PARTICULATE MATTER

by

Mohammad Arhami

____________________________________________________________________

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 2009

Copyright 2009 Mohammad Arhami

ii
Dedication

T To o m my y F Fa am mi il ly y

iii
Acknowledgements

Studying deep into science and discovering the new sights of it is not an easy endeavor
and does not come without lots of sacrifices from a group of individuals, and it is how a
doctor of philosophy is granted. My Mother, Masomeh Parchamdar, and my Father,
Ahmad Arhami, which their least sacrifice to mention is enduring their distanced son for
about 6 years so I could follow my great wishes. Their inspiring compassion, sincerity,
support and encouragement is nothing that I can describe or thank with words. My
brother, Dr. Ashkan Arhami, who has already walked through the same pathway in
knowledge, has always been my best mentor, support and friend. My lovely sisters,
Moein and Negin Arhami, with their pure feelings have always been an encouragement,
cheerfulness and emotional support along my way.

This thesis would not be possible without support of my advisor, whom I would like to
express my greatest gratitude to. Despite all the hardship, he has prepared and organized
one of the best research opportunity and potential in the world to investigate in this field.
Prof. Constantinos Sioutas, I would never forget your appreciative passion, effort and
support.

I would also like to thank other members of guidance committee, Dr Ronal C Henry and
Dr. Dennis Phares for providing thoughtful suggestions on my research work as well as
dissertation. My sincerely thank to the member of our research team: Dr. María Cruz
Minguillón, Dr. Thomas Kun, Dr. Philip Fine, Dr. Andrea Polidori, Dr. Markus
iv
Sillanpää, Dr. Shaohua Hu, Dr. Subhasis Biswas and Zhi Ning, for their valuable
assistance and guidance in various projects

I would like to thank Dr. Ralph J. Delfino from department of Epidemiology, of
University of California, Irvine and his team for their technical support, consultant and
occupation thorough the projects. I would like to acknowledge Dr. James J. Schauer for
his technical support and his staff at Wisconsin State Lab of Hygiene (WSLH) for
chemical analysis of the collected samples.

This project was supported by National Institute of Environmental Health Sciences,
National Institutes of Health, the California Air Resources Board, U.S. Environmental
Protection Agency and Southern California Particle Center, which I would like to
acknowledge.

Finally, I would like to express my heartiest thank to all my friends and relatives who
have supported me throughout my life to this point.

Mohammad Arhami
Los Angeles, 01/13/09

v
Table of Contents

Dedication ii
Acknowledgements iii
List of Tables ix
List of Figures xi
Abstract xv
Chapter 1. Introduction 1
1.1. Background 1
1.1.1. Air Pollution and Ambient Particulate Matter 1
1.1.2. Health Effects of Particulate Matter 4
1.2. Rationale of the Proposed Research 6
1.3. Thesis Overview 12
1.4. Chapter 1 References 17

Chapter 2. Effect of Sampling Artifacts and Operating Parameters on the
Performance of a Semi-continues Particulate EC/OC Monitor 21
2.1 Abstract 21
2.2 Introduction 22
2.3 Experimental Methods 26
2.3.1 Semi-continuous OC/EC Field Instruments 26
2.3.2 Sampling Description 28
2.3.2.1 Precision Evaluation 30
2.3.2.2 Denuder Breakthrough Determination 31
2.3.2.3 Artifact Measurements 32
2.3.2.4 Analysis Protocol Comparison 33
2.3.2.5 Comparison of Quasi-Ultrafine Particles with
Accumulation Mode Particles 35
2.4 Results and Discussion 37
2.4.1 Precision Evaluation 37
2.4.2. Denuder breakthrough 40
2.4.3 Sampling Artifacts 41
2.4.4 Temperature profiles 49
2.4.5 Size-fractionated Measurements 51
2.5 Chapter 2 References 56

vi
Chapter 3. Indoor/Outdoor Relationships, Trends and Carbonaceous
Content of Fine Particulate Matter in Retirement Homes of the Los
Angeles Basin 62
3.1. Abstract 62
3.2. Introduction 63
3.3. Methods 66
3.3.1. Study Design 66
3.3.2. Instrumentation 67
3.3.3. Data Analysis 68
3.4. Results and Discussion 74
3.4.1. Particle and Gaseous Measurements 74
3.4.2. Primary OC and SOA Estimations Outdoors 80
3.4.3. Air Exchange Rate Estimates 89
3.4.4. Infiltration Factor Estimates 90
3.4.5. Indoor and Outdoor Contributions to Measured
Indoor Species concentrations 92
3.5. Conclusions 99
3.6. Chapter 3 References 101

Chapter 4. Associations between Personal, Indoor, and Residential
Outdoor Pollutant Concentrations for Exposure assessment to Size
Fractionated PM 107
4.1. Abstract 107
4.2. Introduction 108
4.3. Methods 111
4.3.1. Study Design 111
4.3.2. Instrumentation 113
4.3.3. Data Analysis 114
4.3.4. Mixed Models 118
4.4. Results 120
4.4.1. Data Overview 120
4.4.2. Outdoor - Outdoor Associations 122
4.4.3. Indoor - Indoor Associations 126
4.4.4. Outdoor – Personal Associations 129
4.4.5. Indoor – Personal Associations 134
4.4.6. Regional and Seasonal Correlations and Comparison
with other Studies 141
4.5. Conclusions 144
4.6. Chapter 4 References 146

Chapter 5. Organic Compound Characterization and Source
Apportionment of Indoor and Outdoor Quasi-ultrafine PM in Retirement
Homes of the Los Angeles Basin 150
5.1. Abstract 150
5.1. Introduction 151
vii
5.3. Methods 154
5.3.1. Sampling sites and schedule 154
5.3.2. Sampling method and chemical analyses 155
5.3.3. Source Apportionment 157
5.3.4. Data Analysis 159
5.4. Results and Discussion 160
5.4.1. Overview 160
5.4.2. Outdoor Organic Species and Seasonal Variability 162
5.4.3. Indoor-Outdoor Organics 165
5.4.4 Spatial Variability 172
5.4.5. Source Contribution Estimations 175
5.5. Chapter 5 References 181

Chapter 6. Size-Segregated Inorganic and Organic Components of PM in the
Communities of the Los Angeles Harbor 188
6.1. Abstract 188
6.2. Introduction 189
6.3. Experimental Method 193
6.3.1. Sampling Sites 193
6.3.2. Sampling Description 196
6.3.3. Gravimetric and Chemical Analysis 197
6.3.4. Chemical Mass Closure (CMC) 199
6.4. Results and Discussion 201
6.4.1. Overview of the Data 201
6.4.2. Chemical Mass Closure (CMC) 203
6.4.3. Organic Species Concentrations 208
6.4.4. Spatial Variance of Size Fractionated PM and its
Components 216
6.4.5. Elemental Constituents of PM 220
6.5. Summary and Conclusion 226
6.6. Chapter 6 References 227

Chapter 7. Seasonal and Spatial Variations of Sources of Fine and Quasi-
ultrafine Particulate Matter in Neighbourhoods near the Los Angeles-Long
Beach Harbour 234
7.1. Abstract 234
7.2. Introduction and Objectives 235
7.3. Methodology 237
7.3.1. Sampling sites and schedule 237
7.3.2. Sampling methods 239
7.3.3. Gravimetric and chemical analyses 239
7.3.4. CMB model methodologies 240
7.4. Results and Discussion 245
7.4.1. Chemical composition of PM in winter and summer 245
7.4.2. Source apportionment 255
viii
7.5. Conclusions 261
7.6. Chapter 7 References 265

Chapter 8. Conclusions and Future Research Directions 271
8.1. Summary and Conclusion 271
8.2. Chapter 8 References 287

Bibliography 288

ix
List of Tables

Table 2.1 Temperature profiles used for EC/OC analysis 34
Table 2.2 Average denuder breakthrough values, which were subtracted
from all denuded samples (all values in µgC/m
3
) 41
Table 2.3 Comparison between measured carbonaceous components of
PM
2.5
by different methods (units in µgC/m
3
) 50
Table 3.1 Average ± 1σ (standard deviation), minimum (min) and
maximum (max) of all hourly particle and gas data obtained for all groups
(G) and phases (P) of CHAPS. 76
Table 3.2 Correlation between particle concentrations (PM
2.5
and PN) gas
levels (CO and NO
X
) for all indoor and outdoor data collected during
CHAPS. 82
Table 3.3 By regressing (Deming regression) OC on EC using only
outdoor data dominated by primary emissions we estimated the
characteristic primary OC/EC ratios (a = slope), non-combustion primary
OC (b = intercept) and coefficient of determination (R
2
) for each month of
CHAPS. a and b were then used to estimate outdoor primary OC and SOA
concentrations, and the percentage contribution of SOA to measured
outdoor OC. 85
Table 3.4 By regressing (Deming regression) outdoor hourly overnight
OC, EC, PM
2.5
and PN concentrations over the correspondent indoor data
we determined the infiltration factor (F
inf
= slope), the background source
strength (intercept) and the coefficient of determinations (R
2
) for these
four particle species during CHAPS (see text for details). 97
Table 4.1 Estimated air exchange rate (AER) and infiltration factors F
inf

over the studied sites and phases of the study 117
Table 4.2 Descriptive statistics for residential outdoor, indoor and personal
concentrations 121
Table 5.1 Studied sites PM concentrations, meteorology and air exchange
rates 161
Table 6.1 The chemical components used in the mass closure studies 200
Table 6.2 Meteorological data during the sampling campaign at sampling
sites 202
x
Table 6.3 Measured n-alkanes and calculated carbon preference index
(CPI) for quasi-UF and accumulation mode particles at all sampling sites 210
Table 6.4 Pearson number and P-values of correlation between V and Ni
with EC and OC in different size fractions of PM 225
Table 7.1 Regression statistics parameters 244
Table 7.2 Correlation of fine particulate OC, EC, V, Ni and S over a)
winter and b) summer campaign (R is Pearson number and S is the slope
of linear correlation). 254

xi
List of Figures

Figure 1.1. Typical particle size distribution by mass and number showing
different size modes 3
Figure 2.1 Inlet configurations implemented for sampling, A) bare, B)
denuded, C) filtered, D) denuded/filtered and E) quasi-ultrafine. 30
Figure 2.2 Test of instrumental precision results for a) OC measurements,
b) EC measurements. 38
Figure 2.3 The comparison between the bare OC (OC
A
) and the particulate
OC obtained from Teflon filters method (OC
actual
) concurrently. 43
Figure 2.4 Comparison between OC
artifact
and OC
actual
determined by
Teflon filter method (configuration A vs. C), denuder method
(configuration A vs. B) and concurrent usage of Teflon filter and denuder
(configuration C vs. B). 43
Figure 2.5 Calculated OC
actual
from bare configuration measurements
(applying the relationship found with Teflon filter method) compared to
OC
actual
measured with a denuder, a) time series data including the
uncorrected bare OC (OC
A
), and b) correlation plot. 48
Figure 2.6 The diurnal pattern of carbonaceous component of particles in
quasi-ultrafine and accumulation mode, a) OC and b) EC. 53
Figure 2.7 The ratio of particulate EC, OC
1
and OC
2-4
to particulate OC in
quasi-ultrafine and accumulation modes. 54
Figure 3.1 Hourly diurnal variations for particle and gas data collected
during the first and the second phase of CHAPS at site A (G1P1 and
G1P2, respectively). The slope (S), intercept (I) and Pearson correlation
coefficient (R) for indoor versus outdoor concentrations are also reported. 77
Figure 3.2 Hourly diurnal variations for particle and gas data collected
during the first and the second phase of CHAPS at site B (G2P1 and
G2P2, respectively). The slope (S), ntercept (I) and Pearson correlation
coefficient (R) for indoor versus outdoor concentrations are also reported. 81
xii
Figure 3.3 Particulate OC and EC semi-continuous carbon measurements
made from 07/06/05 to 07/31/05 at site A (G1P1). Black rectangles
represent measurements with a moderate or high probability of SOA
formation. Grey triangles are measurements dominated by primary
emissions. The regression line, equation and coefficient of determination
(R2) were obtained by Deming regression of measurements labeled
“PRIMARY”. Similar scatter plots were obtained for each month of
CHAPS. 83
Figure 3.4 Time averaged diurnal pattern for estimated primary OC and
SOA concentrations during G1P1 (typical for summertime conditions) (a)
and G2P2 representative of wintertime conditions) (b). The corresponding
measured CO and O
3
concentrations are also reported. 86
Figure 3.5 Calculated indoor concentrations of indoor origin (Cig) for OC
(5a), EC (5b), PM2.5 (5c) and PN (5d) expressed as a percentage of the
corresponding measured indoor concentrations (Cin), and averaged
throughout G1P1, G2P1, G1P2 and G2P2 (black columns). The lowest
possible Cig estimations for the same species (grey columns) were
obtained by assuming Finf =1. Error bars represent + 1σ (1 standard
deviation) of all Cig estimates obtained within each group (G) and phase
(P). 93
Figure 3.6 Estimated indoor primary OC and indoor SOA concentrations
of outdoor origin (“Cog Primary OC” and “Cog SOA”, respectively)
expressed as a percentage of the corresponding measured indoor
concentrations (Cin), and averaged throughout G1P1, G2P1, G1P2 and
G2P2. Estimated indoor OC concentrations of indoor origin (Cig OC) are
also reported. 98
Figure 4.1 Mixed model and Spearman correlation results for outdoor-
indoor associations 124
Figure 4.2 Mixed model and Spearman correlation results for indoor-
indoor associations 127
Figure 4.3 Mixed model and Spearman correlation results for outdoor
pollutants with: a) personal quasi-UF PM, b) personal accumulation mode
PM and c) coarse PM 132
Figure 4.4 Mixed model and Spearman correlation results for indoor
pollutants with: a) personal quasi-UF PM, b) personal accumulation mode
PM and c) coarse PM 137

xiii
Figure 4.5 Spearman’s correlation coefficients (R) for the associations
between personal PM
2.5
concentrations and outdoor (residential or
ambient) particle/gaseous levels in the summer (a) and winter (b). The R
values calculated in this work at the San Gabriel Valley and Riverside
sites (personal vs residential outdoor) were compared to those obtained by
Sarnat et al. in Baltimore (2001) and Boston (2005) (personal vs ambient).
Error bars are referred to standard deviation of individual values 143
Figure 5.1 Outdoor concentrations of a) PAH’s, b) Hopanes and Steranes,
c) n-Alkanes and d) Acids. The presented values are average
concentrations across all sites and error bars are standard deviation of
these averages at each site. 164
Figure 5.2 Concentration of total a) PAH’s, b) Hopanes and Steranes, c) n-
Alkanes and d) Acids. Dots are average of concentrations across all the
sites and error bars are standard deviation of these averages at each site 168
Figure 5.3 Correlation coefficient and indoor and outdoor ratios of a)
PAHs, b) Hopanes and Steranes, c) n-Alkanes and d) Acids, values are
averaged over the sites and bars are the standard deviation over the sites
standard deviation, hence indicating the possibility of indoor sources for
these species. 170
Figure 5.4 Coefficient of variance (CV) for indoor and outdoor organic
groups for: a) warmer and b) colder period of the study 173
Figure 5.5 Source apportionment of quasi-UF PM in the four sites and
during the two sampling periods 176
Figure 6.1 Sampling sites locations 194
Figure 6.2 Particle mass concentrations in the quasi-UF, accumulation and
coarse mode measured at six sampling sites. Error bars represent standard
deviations, which are calculated for components on a weekly basis 203
Figure 6.3 The contributions of nine chemical component-groups to the
mass of quasi-UF, accumulation mode, fine and coarse particles measured
at the six sampling sites. The chemical mass closure for fine particles is
based on the sum of the concentrations measured in quasi-UF and
accumulation mode 205
Figure 6.4 Concentration of PAHs (classified by molecular weight),
Hopanes, steranes and Levoglucosan in a) quasi-UF and b) accumulation
mode. Error bar represent the uncertainties in organics concentrations 214
xiv
Figure 6.5 Relationship between hopanes and elemental carbon; Straight
lines represent measured ratios in a previous study of urban PM
2.5
in the
proximity of freeways (Phuleria et al. 2007) 215
Figure 6.6 Coefficient of variances (CV) with standard deviation (SD) of
selected chemical components at three size fractions: a) quasi-UF mode,
b) accumulation mode and c) coarse mode. Error bars represent standard
deviations 219
Figure 6.7 Size fractionated results of a) concentration ranges and
coefficient of variances (CV) and b) crustal enrichment factor for selected.
Error bars represent standard deviations. 222
Figure 6.8 Vanadium concentrations a) plotted versus nickel
concentrations and b) measured in quasi-ultrafine and accumulation mode
at all the sites 223
Figure 6.9 Relationships between vanadium and sulfur concentrations for
(a) quasi-UF and (b) accumulation fractions 225
Figure 7.1. Overall comparison of measured PM species in winter and
summer: a) mass concentrations of fine, quasi-ultrafine and accumulation
mode PM, b) EC, OC, nitrate, sulfate and ammonium concentrations in
fine particulate mode, error bars are the standard deviations of average
measured concentration over the studied sites. 247
Figure 7.2. Average organic species concentration of fine particles in
winter and summer: (a) n-alkanes; (b) PAH; (c) Hopanes and steranes and
(d) Organic acids, error bars are the standard deviations of average
measured concentration over the studied sites. 250
Figure 7.3. Source apportionment to total (a) fine and (b) quasi-ultrafine
OC (µg/m
3
) in winter and summer at the seven sampling sites. 257
Figure 7.4. Source apportionment to total (a) fine and (b) quasi-ultrafine
PM (µg/m
3
) in winter and summer at the seven sampling sites. 260
Figure 7.5. Source apportionment to ambient concentration of species in
fine fraction used as fitting species in CMB model in (a) winter and (b)
summer. 262

xv
Abstract

The aim of this thesis is to enhance the knowledge on exposure to size fractions of
airborne particulate matter and their components and to find more intensive information
on sources of indoor and outdoor size fractionated particles. In the first part of the study,
the physical and chemical characteristics of indoor, outdoor, and personal quasi-ultrafine
(<0.25μm), accumulation (0.25-2.5 μm), and coarse (2.5-10 μm) mode particles and
gaseous pollutant were measured at two phases (warmer and colder phase) of four
different retirement communities in Southern California in 2005-2007. Overall, the
magnitude of indoor and outdoor measurements was similar, due to high influence of
outdoor sources on indoor particle and gas levels. Secondary organic aerosol showed to
be able to comprise a major fraction of organic carbon (more than 40% were estimated at
some phases). Outdoor and indoor concentrations of gaseous pollutant were more
positively correlated to personal quasi-UF particles than larger size fractions. Indoor
sources were not significant contributors to personal exposure of PM, which is
predominantly influenced by primary emitted pollutants of outdoor origin. Vehicular
sources had the highest contribution to PM
0.25
among the apportioned sources for both
indoor and outdoor particles at all sites. The contribution of mobile sources to indoor
levels was similar to their corresponding outdoor estimates, thus even if people generally
spend most of their time indoors, a major portion of the submicron

particles to which they
are exposed to, comes from outdoor mobile sources.

xvi
In the following, we characterized the physicochemical properties and sources of size
fractionated PM and their spatial and seasonal variability at the Los Angeles-Long Beach
harbor community, which is the busiest harbor in the US and the fifth in the world. The
major mass contributions in the quasi-UF fraction were particulate organic matter, non-
sea salt sulfate and elemental carbon; in the accumulation mode fraction were non-sea
salt sulfate, sea salt, particulate organic matter and nitrate; and in the coarse fraction were
sea salt and insoluble soil. In general, PM and its components in accumulation mode
showed relatively lower spatial variability compare to the quasi-UF and the coarse
modes. The vehicular sources accounted for almost all of quasi-ultrafine PM and more
than 50% fine PM, whereas ship contribution was lower than 5% of total PM mass. Our
results clearly indicate that, although ship emissions can be significant, PM emissions in
the area of the largest US harbor are dominated by vehicular sources.

The results obtained in this study have been/will be used to examine the relationships
between outdoor (or ambient), indoor and personal measurements of atmospheric
particulate air pollution and health outcomes and to link health effects to certain sources
of particulate matter. Such information would be highly valuable for targeting control
strategies that protect human health and life.

1
Chapter 1.
Introduction

1.1. BACKGROUND

1.1.1. Air Pollution and Ambient Particulate Matter
Air pollution is the presence of any substances in sufficient quantities to cause harm to
environment, human, plant, animal life or property (Bishop, 1957). The air pollutant can
have natural origin such as smoke, fumes, ash, and gases from volcanoes and forest fires
or can be anthropogenic such as combustion emitted pollutant. The air pollutants are in
form of gas, solid and liquid and their pathway involves a sequence of events: the
generation of pollutants, their release from the source, transport and removal from the
atmosphere and their effects on human beings, materials and ecosystems (Flagan and
Seinfeld, 1988). Ambient particulate matters (PM) are a major portion of the air pollutant
which refers to suspended solid and/or liquid aerosols in the atmosphere. Ambient
particulate matter varies greatly in their ability to affect visibility, climate, health and life
quality (Hinds, 1999). PM can be directly emitted into the atmosphere or formed
indirectly by chemical reactions the term primary and secondary PM are referred to the
aerosol made from former and later process respectively. National Ambient Air Quality
Standards (NAAQS) set primary and secondary standards for ambient particulate matters
to protect environment and human health from adverse effect of these pollutants.

2
Particles have different chemical composition, sizes and shapes (e.g. spheres, cylinders,
plates, or combinations of these shapes). The size of the particles is one of the major
characteristic particles relevant to their behavior and health outcome which is usually
identified by aerodynamic diameter. Aerodynamic diameter is diameter of a sphere that
behaves aerodynamically like the actual particle (Heinsohn et al.1999). Aerosol has a size
range of larger than a single small molecule (about 0.002 µm in diameter) and smaller
than about 500 µm. Particulate matters are divided into different groups based on their
aerodynamic diameter. Particles with aerodynamic diameter bigger than 10 microns are
not usually point of interest in the environmental and health studies because of their short
life time in the atmosphere and their deposition efficiency of about 100% in nose (Hinds,
1999). PM
10
is generally defined as all particles equal to and less than 10 microns in
aerodynamic diameter which are inhale-able and important in air pollution studies. These
particles are grouped into two different size ranges, PM
2.5
with an aerodynamic diameter
of 2.5 µm or less and particles with aerodynamic diameter between PM
2.5
and PM
10
, these
particles are named “fine” and “coarse” PM respectively. This grouping is because they
showed different behavior emission sources, formation mechanism, chemical
composition, residence times in the atmosphere, distance traveled due to atmospheric
transport processes and removal mechanisms and different standards were set to them to
protect public health and environment. Fine particles has a high residence time of order
of days to weeks and can travel long distances, while most coarse particles typically
deposit to the earth within minutes to hours and within tens of kilometers from the
emission source (Hinds, 1999). The fine particles are grouped into two groups: particles
3
with a diameter of less than about 0.1 micrometers 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 (Whitby and Sverdrup 1980 and Ibald-Mulli et al., 2002). Typical
particle size distribution is presented in Figure 1.1. Ultrafine particles dominate the
number distribution while accumulation mode particles dominate the mass distribution.

Figure 1.1. Typical particle size distribution by mass and number showing different size
modes

These particles according to their sizes have different sources. Coarse particles are
generated mechanically by crushing or grinding operations and are often dominated by
crustal material and resuspended dusts from soil, paved or unpaved roads, construction
and mining activities, farming, transportation and sea salt breeze. Accumulation mode
particles are mainly direct products from combustion processes, formed by
photochemical reaction or condensation of the gases or coagulation of the smaller
4
particles in the atmosphere as well as wood combustion and high temperature processes
such as smelters and steel mills, contribute to accumulation mode particle emissions.
Nucleation and automobiles emit most of the ultrafine particles and they can be produced
by both natural and anthropogenic sources (Kavouras et al. 1998; Whitby and Svendrup
1980; Kotzick and Niessner 1999).

Different size range particles have different removal mechanisms. The removal of coarse
particles is due to gravitational deposition while diffusion is the primal cause of removal
of ultrafine particles. The accumulation mode particles are too large to be removed by
diffusion and too small to be settling down by gravity and generally has a long life time
in the atmosphere.

1.1.2. Health Effects of Particulate Matter
Associations of daily ambient air pollution and PM with mortality (Samet et al., 2000)
and cardiovascular hospital admissions (Morris, 2001) as well as respiratory hospital
admissions and mortality (EPA, 1996) and its adverse health outcome have been shown
in numerous studies (Dockery et al., 1993, Pope et al., 1995). Initially total suspended
solids in 1970 were considered as one of the eight criteria air pollutant. Studies proved
more correlation between smaller size particles and adverse health effect and in 1987, as
the result TSP was replaced by PM
10
with 50µm/m
3
and 150µm/m
3
in annual and daily
mean standards respectively. More studied showed the need of a standard for PM
2.5
, so
U.S. EPA added the annual and daily mean standard of 15 µm/m
3
and 65µm/m
3
for fine
5
particulate air pollutant in 1997. The fine particles travel deeper to the alveolar region
due to their high penetration efficiency characteristic, while the coarse particles can be
removed more easily by impaction or settling in upper respiratory tract (Hinds, 1999).

Recent studies have demonstrated that UF particles (less than ~100 microns in diameter)
are toxic and capable of penetrating cellular membranes and causing cell damage. This is
due to major characteristics of ultrafine particles, including, high particle number, high
pulmonary deposition efficiency, and a surface chemistry involving a high surface area
that can carry adsorbed or condensed toxic air pollutants (oxidant gases, organic
compounds and transition metals). These toxic air pollutants have all been identified as
having pro-inflammatory effects. Ultrafine particles coated with neutralized strong acids
may, upon deposition, cause tissue damage due to their acidity (Ferin et al., 1991). Trace
metals transferred to the lung on ultrafine particles could catalyze the formation of
oxidants within the lung which in turn produce tissue damage (Ghio et al., 1996). Heyder
et al,(1996) and Peters et al., (1997) have shown strong association between adverse
health effects and ultrafine exposure.

A large proportion of urban ultrafine particles in southern California is made up of
primary combustion products from mobile source emissions (particularly diesel and
automobile exhaust), and includes organic compounds, elemental carbon (EC) and metals
(Kim et al. 2001). Experimental data show that compared with larger particles, ultrafine
PM is better able to avoid phagocytosis by alveolar macrophages and gain entry to
6
pulmonary interstitial sites, including the endothelium. Therefore, ultrafine PM may
induce pulmonary inflammation at both epithelial and interstitial sites, as well as enter the
circulation to reach other target sites, including cardiovascular tissue (Oberdorster 2001).
Additionally, diesel exhaust particles (DEP) in ultrafine mode have been shown to induce
a broad polyclonal expression of cytokines and chemokines in respiratory epithelium
possibly due to the action of polycyclic aromatic hydrocarbons (PAH) and related
compounds that lead to the production of cytotoxic reactive oxygen species (ROS) (Nel
et al., 1998; 2001).

1.2. RATIONALE OF THE PROPOSED RESEARCH
As it was stated earlier, epidemiological studies have shown significant exposure-
response relationships for the adverse health effects in association with particulate matter
(Pope and Dockery 2006). Particulate matter chemical composition and properties (such
as mass, number, surface area and especially size) influence the adverse health effect and
toxicity of particulate matter. Due to insufficient knowledge about the composition and
properties of particulate matter and their exposure it has been difficult to address which
component of particulate influences the health risk and what air quality regulation should
be adopted. If health effects can be linked to certain sources of particulate matter, such
information would be highly valuable for targeting control strategies.

The size of the particles has a strong influence on the type and intensity of health effect
caused. Fine PM has been more strongly associated with mortality and morbidity,
7
although coarse particles have also been associated with respiratory hospital admissions
(Brunekreef and Forsberg 2005). Ultrafine particles penetrate deep into the alveolar
region of the respiratory system and have the ability to translocation in other parts of the
human body (Elder et al. 2006). Toxicological data suggest that these particles are more
strongly associated with cardiovascular and respiratory health outcomes (Araujo et al.
2007) compared to larger particles. Moreover, quasi-ultrafine PM (PM
0.25
; particles with
an aerodynamic diameter smaller that 0.25 µm) had the strongest and most significant
association with circulating biomarkers of inflammation, antioxidant activity, and platelet
activation measured in an study subjects, which used some findings from current research
(Delfino et al, 2008). So far, there is little research to support this finding (reviewed by
(Delfino et al. 2005; Weichenthal et al. 2007). Another problem is that the importance of
particle size and chemistry has been limited by reliance on government monitoring of
particle mass at two size cuts, PM
10
and PM
2.5
. Size fractionated indoor, outdoor and
personal PM exposure measurement and exposure studies will be highly desirable to
address this need.

Although air quality standards have been established for outdoor / ambient environments,
a significant portion of human exposures to PM occurs indoors, where people spend
around 85-90% of their time (Klepeis et al. 2001). One difficulty in identifying causal
pollutant components driving associations to adverse health effect has been due to using
the air pollution data from outdoor (ambient) sites which has led to exposure
misclassification. So understanding the exposure to indoor and personal PM and its
8
component is very important. Fundamental uncertainty and disagreement persist
regarding the composition, behavior and sources of indoor and personal PM so more
studies are required in order to characterize and mitigate indoor exposure. To the best of
our knowledge, only few studies on indoor PM source apportionment have been
conducted in the past few years. These were mainly focused on examining the influence
of outdoor sources on the measured outdoor concentrations of fine PM without any
further size fractionation (Olson et al. 2008). Furthermore, there are no studies on sources
and composition of indoor sub-micron particles. In addition not many continues and semi
continues measurement of indoor PM and its component are obtained. Furthermore,
recent epidemiologic studies have linked exposure to secondary organic aerosol (SOA) to
respiratory inflammation through the generation of reactive oxygen species. Also, volatile
and non-volatile PM
2.5
components are characterized by different infiltration factor and
indoor sources of PM
2.5
and OC might be significant. Thus, the composition of indoor
and outdoor particles is different and outdoor PM
2.5
concentrations may not adequately
represent personal exposure to PM
2.5
in indoor environments. Thus, investigating the
composition and sources of both indoor and outdoor PM and their relationships is
desired.

Ascertaining the true risk associated with exposure to PM is difficult, mainly because the
concentrations of ambient particles and those of their gaseous co-pollutants are often well
correlated, and estimates of the health risks associated with PM exposure may be
confounded by these gaseous species (Sarnat et al. 2000; Green et al. 2002; Sarnat et al.
9
2005). The National Research Council (1998) listed the investigation of the potential
confounding effect of gaseous co-pollutants on PM health effects as one of their research
priorities.

The Los Angeles Basin is a megalopolis of about 15 million inhabitants, and has one of
the most polluted atmospheres in the US due to the contributions of a multitude of traffic
and other combustion sources. One of the areas of particular concern regarding PM
pollution is the communities near the Los Angeles-Long Beach harbor which constitutes
the busiest harbor in the US and the fifth in the world, and therefore the area is affected
by several PM sources. The potential for complex pollutant concentration gradients and
high exposure conditions cannot be identified by conventional monitoring approaches.
Accordingly, it is crucial to assess the exposure gradient of the community in the
surrounding environment. Some studies have shown that using only community PM
average concentrations to determine the health effects resulting from PM exposure may
lead to non accurate results and therefore it is important to measure the variability of PM
levels and sources within a community (Jerrett et al. 2005). For the development and
implementation of PM policies that will be protective of the environment and human
health, regulators require scientific knowledge of the strengths, spatial distribution and
variability of the major sources of this pollutant. This information allows to design
effective mitigation strategies on the local- and meso-scale level, and to evaluate human
exposure to this pollutant and thus assess its health-related risks (Watson et al. 2002;
Hopke et al. 2006).
10

Studies in Los Angeles Basin examining atmospheric aerosols at multiple locations
across the basin have been conducted since the early 1970s (Cass et al. 2000;
Christoforou et al. 2000; Hughes et al. 1999; Russell and Cass, 1986; Sardar et al. 2005).
Most of these campaigns in Los Angeles have included only a few days or a week or two
of sampling or were not concurrent at all the sites. However, concurrent and more
extensive sampling in such a complex urban air basin would be highly desirable. In
context of source apportionment, previous studies have been carried out to identify PM
10

sources in the aforementioned area (Kleeman et al. 1999; Manchester-Neesvig et al.
2003). These studies were spatially constrained by the fact that they were based on data
collected in one sampling site in Long Beach. Moreover, source apportionment of fine
and ultrafine fractions has not been conducted in this area. Nonetheless, there are not
many studies on the micro-environmental spatial variations of chemical components and
physical characteristics of particles in such complex environments.

My endeavor in this thesis is to enhance the knowledge on exposure to size fractions of
airborne particulate matter and their components, to find more intensive information on
sources of indoor and outdoor size fractionated particles and their variations. The results
obtained in this study have been/will be used to examine the relationships between
indoor, outdoor and personal measurements of atmospheric particulate air pollution and
health outcomes and to link health effects to certain sources of particulate matter. Such
11
information would be highly valuable for targeting control strategies. To this end, the
specific aims to address the abovementioned needs are:

- Improve the measurement methods of the particulate carbonaceous content, in order to
get an insight in the characteristics of fine and ultrafine particles for better assessing the
human exposure in this study

- Find the relationships between indoor and outdoor PM
2.5
, its components and their
seasonal variations as well as their association with gaseous co-pollutants

- Discover the contributions of primary OC and SOA to measured outdoor OC

- Evaluate the relative importance of indoor and outdoor PM sources to measured indoor
OC, EC, PM
2.5
and PN concentrations

- Evaluate the associations between indoor, outdoor, and personal size-fractionated PM
and its components of both indoor and outdoor origin

- To assess the role of gaseous co-pollutants as surrogates of personal size-fractionated
PM

exposures
- To evaluate the organic composition of quasi-ultrafine PM (PM
0.25
) in both indoor and
outdoor environments throughout the calendar year
12

- To identify the most important sources of these sub-micrometer particles

- To quantify their contribution to the total PM mass concentrations in both indoor and
outdoor environments

- Characterize the chemical composition of ultrafine, accumulation mode and coarse
particles across the Los Angeles-Long Beach harbor community

- Provide new insight into the variation of size-segregated PM and its properties and
chemical composition over the harbor area

- Identify and quantify size fractionated particulate matter sources in the Los Angeles-
Long Beach harbor area, and identify, if any, the spatial and seasonal differences in PM
patterns and composition

1.3. THESIS OVERVIEW
The thesis consists of 8 chapters with Chapter 1 being the introduction. This section
provides a general overview and background on air pollution, significance and
characteristics of particles and their related health effects. It also describes the rationale
of this thesis and outlines a brief layout.

13
In Chapter 2 the performance of new state-of –the-art Sunset Lab semi-continuous
EC/OC field analyzer was assessed and evaluated in the field. The methods of examining
artifacts which cause overestimation or under estimation in OC measurements were
analyzed and improved. This instrument was used in rest of the study to get an insight in
the characteristics of fine and ultrafine particles to better assess the human exposure to
the carbonaceous components of particles.

Chapters 3, 4 and 5 are focused on exposure assessment to size fractionated particulate
matter and their composition and sources. These chapters were conducted within the
Cardiovascular Health and Air Pollution Study (CHAPS), a multi-disciplinary project
whose goals were to investigate the effects of micro-environmental exposures to PM on
cardiovascular outcomes in elderly retirees affected by coronary heart disease (CHD).
The elderly population with CHD is likely to be among the most vulnerable to the
adverse effects of particulate air pollutants. In this study the physical and chemical
characteristics of indoor, outdoor, and personal quasi-ultrafine (<0.25μm), accumulation
(0.25-2.5 μm), and coarse (2.5-10 μm) mode particles were studied at two phases
(warmer and colder phase) in four different retirement communities in southern
California between 2005 and 2007. Personal coarse, accumulation, and quasi-ultrafine
PM samples were collected for 67 elderly retirees with a history of coronary artery
disease. All participants were 71 years of age or older, nonsmokers, and with no home
exposure to environmental tobacco smoke (ETS). Each subject was followed for two 5-
day sampling periods during the 2 different phases of the study. Concurrent to personal
14
sampling, daily size fractionated PM samples were collected. In addition, real time
concentration of fine particulate matter mass, OC, and EC, particle number (PN), ozone
(O
3
), carbon monoxide (CO) and nitrogen oxides (NO and NO
2
) were measured at indoor
and outdoor of these communities. Indoor and outdoor samples were analyzed to find
their chemical composition and toxicological properties. This study provided one of the
most extensive data set of its type for air pollution studies.

In chapter 3 the indoor/outdoor relationship, trends and carbonaceous content of fine
particulate matter were intensively analyzed. The continues indoor and outdoor measured
data in CHAPS were used to provide new insight into: a) the relationships between
indoor and outdoor PM
2.5
measurements, its components and their seasonal variations as
well as their association with gaseous co-pollutants, b) the contributions of primary OC
and SOA to measured outdoor OC and c) the relative importance of indoor and outdoor
PM sources to measured indoor OC, EC.

Chapter 4 evaluates the association between indoor, outdoor, and personal size-
fractionated PM, OC, EC, PN, O
3
, CO, NO, NOx, and other important pollutants of both
indoor and outdoor origin. Furthermore, the role of gaseous co-pollutants as surrogates of
personal size-fractionated PM

exposures was assessed in this section.

Chapter 5 focuses on the quasi-ultrafine fraction of PM in both indoor and outdoor
environments of the retirement communities. The main objectives of this study are: a) to
15
evaluate the organic composition of quasi-ultrafine PM in both indoor and outdoor
environments throughout the calendar year, b) to identify the most important sources of
these sub-micrometer particles, and c) to quantify their contribution to the total PM mass
concentrations in both indoor and outdoor environments. The significant of this results
magnifies since in an earlier investigation, also part of CHAPS study (not a part of this
thesis) (Delfino et al. 2008), we reported that indoor PM of outdoor origin (mostly from
combustion sources) was more significantly associated with systemic inflammation,
platelet activation, and decreases in erythrocyte antioxidant activity than uncharacterized
indoor PM that included particles of indoor origin.

In Chapter 6 and 7 we characterize the physicochemical properties and sources of size
fractionated PM and their spatial and seasonal variability. Size fractionated PM samples
were collected concurrently at 7 sites in the southern Los Angeles basin for two different
phases throughout the year. The studied region was the Los Angeles Ports complex
consisting of the port of Long Beach and the port of Los Angeles which together is the
busiest harbor in the US and the fifth in the world.

The objective of Chapter 6 is to characterize the chemical composition of ultrafine,
accumulation mode and coarse particles across this community. Results from the
gravimetric and chemical analysis are verified by means of chemical mass closure
(CMC). Subsequently, the paper focuses on organic species and elemental components
and their distribution in PM size fractions among the sites. These results provide new
16
insight into the variation of size-segregated chemical composition of PM over the studied
area.

Chapter 7 identify and quantify fine and quasi-ultrafine particulate matter sources in the
Los Angeles-Long Beach harbor area, and identify, if any, the spatial and seasonal
differences in PM patterns and composition. The results from this study will provide
useful information for control strategies and will assist future toxicological studies that
are planned in this area.

Chapter 8 comprises of summary and major conclusions of the thesis extracted from
individual chapters. Finally, the potential research areas based on the findings of this
work are addressed.

17
1.4. CHAPTER 1 REFERENCES
Araujo, J. A., B. Barajas, M. Kleinman, X. P. Wang, B. J. Bennett, K. W. Gong, M.
Navab, J. Harkema, C. Sioutas, A. J. Lusis and A. Nel (2007). Ambient particulate
pollutants in the ultrafine range promote atherosclerosis and systemic oxidative stress,
Arteriosclerosis Thrombosis and Vascular Biology 27(6): E39-E39.

Birch, M. E. and Cary, R. A. (1996). Elemental carbon-based method for monitoring
occupational exposures to particulate diesel exhaust. Aerosol Science and Technology 25:
221-241.

Bishop, C.A. (1957). EJC policy statement on Air Pollution and its Control. Chem. Eng.
Progr. 53(11):146.

Brunekreef, B. and B. Forsberg (2005). Epidemiological evidence of effects of coarse
airborne particles on health, European Respiratory Journal 26(2): 309-318.
Chow, J. C., Watson, J. G.,(2002). Journal of Geophysical Research-Atmospheres.

Chow, J. C., Watson, J. G., Pritchett, L. C., Pierson, W. R., Frazier, C. A., Purcell, R. G.,
(1993). Atmospheric Environment Part a-General Topics, 27, 1185-1201.

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, Spengler J.D., Ware J.H., Fay M.E., Ferris B.G.,
Speizer F.E. (1993). An Association between Air Pollution and Mortality in Six U.S.
Cities. N Engl J Med, 329(24):1753-9.

Delfino, R. J., C. Sioutas and S. Malik (2005). Potential role of ultrafine particles in
associations between airborne particle mass and cardiovascular health, Environmental
Health Perspectives 113(8): 934-946.

Delfino, R. J., N. Staimer, T. Tjoa, A. Polidori, M. Arhami, D. L. Gillen, M. T.
Kleinman, N. D. Vaziri, J. Longhurst, F. Zaldivar and C. SioutaS (2008). Circulating
biomarkers of inflammation, antioxidant activity, and platelet activation are associated
with primary combustion aerosols in subjects with coronary artery disease,
Environmental Health Perspectives 116(7): 898-906.

Elder, A., R. Gelein, V. Silva, T. Feikert, L. Opanashuk, J. Carter, R. Potter, A. Maynard,
J. Finkelstein and G. Oberdorster (2006). Translocation of inhaled ultrafine manganese
oxide particles to the central nervous system, Environmental Health Perspectives 114(8):
1172-1178.

18
EPA. Air quality criteria for particulate matter., (1996)., Washington DC, Office of
Research and Development, EPA/600/P95/001b, Vol. III of III.

Ferin, J.; Oberdorster, G.; Soderholm, S. C. and R. Gelein, (1991). Journal of Aerosol
Medicine Deposition Clearance and Effects in the Lung, 4, 57-68.
Flagan, R.C. and Seinfeld, J.H. (1988). Fundamentals of air pollution. Prentice Hall. New
Jersey.

Ghio, A.J., Stonehuerner, J., Pritchard, R.J., Piantadosi, C.A., Quigley, D.R., Dreher,
K.L., Costa, D.L. (1996). Humic-Like Substances in Air Pollution Particulates Correlate
with Concentrations of Transition Metals and Oxidant Generation. Inhalation
Toxicology.8 (5): 479-494.

Green, L. C., E. A. C. Crouch, M. R. Ames and T. L. Lash (2002). What's wrong with the
National Ambient Air Quality Standard (NAAQS) for fine particulate matter (PM2.5)?,
Regulatory Toxicology and Pharmacology 35(3): 327-337.

Heinsohn, R.J. and Kabel, R.L. (!999). Sources and Control of Air Pollution. Prentice
Hall, New Jersey.

Heyder, J. Brand, P., Heinrich, J., Peters, A., Scheuh, G., Tuch, T. and Wichmann, E,
(1996). Size distribution of ambient particles and its relevance to human health. Presented
at the 2
nd
Colloquium on Particulate Air Pollution and Health, Park City, Utah, 1-3 May.
Hinds, W.C.,(1999). Aerosol technology: Properties, Behavior and Measurement of
Airborne Particles. New York: John Wiley & Sons, Inc.

Hopke, P. K., K. Ito, T. Mar, W. F. Christensen, D. J. Eatough, R. C. Henry, E. Kim, F.
Laden, R. Lall, T. V. Larson, H. Liu, L. Neas, J. Pinto, M. Stolzel, H. Suh, P. Paatero and
G. D. Thurston (2006). PM source apportionment and health effects: 1. Intercomparison
of source apportionment results, Journal of Exposure Science and Environmental
Epidemiology 16(3): 275-286.

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.

Jerrett, M., R. T. Burnett, R. J. Ma, C. A. Pope, D. Krewski, K. B. Newbold, G. Thurston,
Y. L. Shi, N. Finkelstein, E. E. Calle and M. J. Thun (2005). Spatial analysis of air
pollution and mortality in Los Angeles, Epidemiology 16(6): 727-736.

Kavouras, I., Mihalopoulos, N., and Stephanou, E. (1998). Formulation of Atmospheric
Particles from Organic Acids Produced by Forests. Nature. 395:683-686.

19
Kim, S., P. A. Jaques, M. C. Chang, T. Barone, C. Xiong, S. K. Friedlander and C.
Sioutas (2001). Versatile aerosol concentration enrichment system (VACES) for
simultaneous in vivo and in vitro evaluation of toxic effects of ultrafine, fine and coarse
ambient particles - Part II: Field evaluation, Journal of Aerosol Science 32(11): 1299-
1314.

Kleeman, M. J., L. S. Hughes, J. O. Allen and G. R. Cass (1999). Source contributions to
the size and composition distribution of atmospheric particles: Southern California in
September 1996, Environmental Science & Technology 33(23): 4331-4341.

Klepeis, N. E., W. C. Nelson, W. R. Ott, J. P. Robinson, A. M. Tsang, P. Switzer, J. V.
Behar, S. C. Hern and W. H. Engelmann (2001). The National Human Activity Pattern
Survey (NHAPS): a resource for assessing exposure to environmental pollutants, Journal
of Exposure Analysis and Environmental Epidemiology 11(3): 231-252.

Kotzick, R. and Niessner, R. (1997). The Effects of Aging Processes on Critical
Supersaturation Ratios of Ultrafine Carbon Aerosols. Atmos. Environ. 33:2669-2677.

Manchester-Neesvig, J. B., J. J. Schauer and G. R. Cass (2003). The distribution of
particle-phase organic compounds in the atmosphere and their use for source
apportionment during the southern California children's health study, Journal of the Air
& Waste Management Association 53(9): 1065-1079.

Morris R.D., Naumova E.N., Munasinghe R.L., (1995). Ambient air pollution and
hospitalization for congestive heart failure among elderly people in seven large US cities.
Am J Public Health, 85(10):1361-5.

National Research Council (1998). Research Priorities for Airborne Particulate Matter.
Immediate Priorities and a Long-Range Research Portfolio. National Academy Press.

Nel A.E., Diaz-Sanchez D., Li N. (2001) The role of particulate pollutants in pulmonary
inflammation and asthma: evidence for the involvement of organic chemicals and
oxidative stress. Curr Opin Pulm Med, 7(1):20-6.

Nel A.E., Diaz-Sanchez D., Ng D., Hiura T., Saxon A., (1998). Enhancement of allergic
inflammation by the interaction between diesel exhaust particles and the immune system,
J Allergy Clin Immunol, 102:539-54.

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).

20
Oberdorster, G. (2001). Pulmonary effects of inhaled ultrafine particles, International
Archives of Occupational and Environmental Health 74(1): 1-8.

Olson, D. A., J. Turlington, R. V. Duvall, S. R. Vicdow, C. D. Stevens and R. Williams
(2008). Indoor and outdoor concentrations of organic and inorganic molecular markers:
Source apportionment of PM2.5 using low-volume samples, Atmospheric Environment
42(8): 1742-1751.

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.

Pope, C. A. and D. W. Dockery (2006). Health effects of fine particulate air pollution:
Lines that connect, Journal of the Air & Waste Management Association 56(6): 709-742.

Robinson, J., Nelson, W.C. (1995). National Human Activity Pattern Survey Data Base:
USEPA, Research Triangle Park, N.C.
Sardar, S. B., Fine, P. M., Sioutas, C., (2005). J. of Geoph. Research-Atmospheres, 110.

Samet, J.M., Zeger S.L., Dominici, F., Curriero, F.,. Cursac, I, Dockery, D.W., Schwartz,
J., Zanobetti, A., (2000). The National Morbidity, Mortality, and Air Pollution Study.
Part II: Morbidity and mortality from air pollution in the United States. Health Effects
Institute Research Report 94(Pt 2):5-79.

Sarnat, J. A., K. W. Brown, J. Schwartz, B. A. Coull and P. Koutrakis (2005). Ambient
gas concentrations and personal particulate matter exposures - Implications for studying
the health effects of particles, Epidemiology 16(3): 385-395.

Sarnat, J. A., P. Koutrakis and H. H. Suh (2000). Assessing the relationship between
personal particulate and gaseous exposures of senior citizens living in Baltimore, MD,
Journal of the Air & Waste Management Association 50(7): 1184-1198.

Vedal, S., (1997). Ambient particles and health: lines that divide. Journal of Air and
Waste Management Association 47,551–581.

Watson, J. G., T. Zhu, J. C. Chow, J. Engelbrecht, E. M. Fujita and W. E. Wilson (2002).
Receptor modeling application framework for particle source apportionment,
Chemosphere 49(9): 1093-1136.

Weichenthal, S., A. Dufresne and C. Infante-Rivard (2007). Indoor ultrafine particles and
childhood asthma: exploring a potential public health concern, Indoor Air 17(2): 81-91.

Whitby, K.T., Sverdrup, G.M. (1980). California aerosols: Their physical and chemical
characteristics. Advanced Environmental Science and Technology, 8, 477-525.
21
Chapter 2.
Effect of Sampling Artifacts and Operating Parameters on the
Performance of a Semi-continues Particulate EC/OC Monitor

2.1 ABSTRACT
The carbonaceous component of atmospheric particulate matter (PM) is considered very
important with respect to the observed adverse health effects of PM. Elemental carbon
(EC) is emitted from incomplete combustion occurring in sources such as diesel engines
and biomass burning, while organic carbon (OC) is a component of particles emitted
from almost every primary particle source. A significant fraction of particulate organic
carbon can also be secondary organic aerosol (SOA), formed by atmospheric
photochemical reactions of organic vapor precursors. The OC and EC components of PM
have traditionally been measured off-line subsequent to daily, time-integrated particle
collection on filters. However, the sub-daily or hourly variability of EC and OC can help
to assess the variability of sources, ambient levels, and human exposure. In this study,
the performance of the Sunset Laboratory Inc. semi-continuous EC/OC monitor was
assessed in a field setting. The monitors were deployed near downtown Los Angeles, in
a location representing typical urban pollution. An inter-monitor comparison showed
high precision (R
2
of 0.98 and 0.97 for thermal OC and EC, respectively). By changing
the inlet configurations of one of the monitors (adding a denuder, a Teflon filter, or both),
the influences of positive and negative sampling artifacts were investigated. The positive
artifact was found to be relatively large (7.59 µg/m
3
on average), more than 50% of
measured OC, but it was practically eliminated with a denuder. The negative artifact was
much smaller (less than 20% of the positive artifact) and may be neglected in most cases.
A comparison of different temperature profiles, including a fast 3-minute analysis using
22
optical EC correction, showed good agreement among methods. Finally, a novel
configuration using a size selective inlet impactor removing particles greater than 250 nm
in diameter allowed for semi-continuous size-fractionated EC/OC measurements. More
EC was observed in the sub-250 nm particle range, and similar levels of OC were seen in
both the sub- and super-250 nm PM ranges. Evolution of OC at different temperatures of
the thermal analysis showed higher volatility of OC in larger particles.

2.2 INTRODUCTION
While the link between ambient fine particle mass (PM
2.5
) and adverse health outcomes
has now been repeatedly established (NRC 2004), it is still not fully understood which
properties of airborne particles are most responsible for these observations. Various
studies have implicated sulfate (Clarke et al. 2000; Batalha et al. 2002), toxic elements
such as vanadium (Saldiva et al. 2002), silicon (Wellenius et al. 2003), iron, nickel and
zinc (Burnett et al. 2000), elemental carbon (EC) (Mar et al. 2000; Metzger et al. 2004),
organic compounds such as polycyclic aromatic hydrocarbons (Dejmek et al. 2000),
ultrafine particles (diameters less than ~180 nm) (Oberdörster 2001; Li et al. 2003), wood
smoke (Tesfaigzi et al. 2002), and diesel exhaust (Seagrave et al. 2004), to name only a
few. Therefore, accurate and convenient instruments, which measure detailed particle
characteristics, are necessary to better assess ambient concentrations and human
exposures. Continuous or semi-continuous monitors, providing data on hourly or sub-
hourly time scales, are generally preferred over off-line analyses. Such monitors can not
only capture important short-term variations in particle properties, but also can prove
23
more economical to operate by reducing sampling site visits and eliminating the need for
laboratory facilities and analysis costs.

The carbonaceous component, elemental carbon (EC), organic carbon (OC) and
carbonate (Birch and Cary 1996; Chow and Watson 2002), has been considered one of
the most relevant PM fractions with respect to observed adverse health outcomes.
Carbonate does not comprise a significant portion of PM
2.5
, and is not suspected of being
toxicologically active. Elemental carbon, similar to black carbon or refractory carbon
(Chow et al. 1993; Birch and Cary 1996), is emitted from incomplete combustion
occurring in sources such as diesel engines and biomass burning. It has been shown to
produce adverse health responses when inhaled in both laboratory and ambient studies
(Mar et al. 2000; Oberdörster et al. 2002; Metzger et al. 2004). Organic carbon is a
component of particles emitted from almost every known primary particle source
(Hildemann et al. 1994), but also can consist of secondary organic aerosol (SOA) (Griffin
et al. 2002) which lead to low-volatility products. Both primary and secondary OC
consist of hundreds of organic species, many of which are known to be toxic (i.e. PAH,
nitro-PAH, etc.) (Schauer et al. 1996; Reisen and Arey 2005). The particle size
distributions of both EC and OC are generally shifted to lower particle diameters relative
to the total PM mass size distribution (Hughes et al. 1999).

The OC and EC components of PM have traditionally been measured off-line subsequent
to particle collection on filters (Chow et al. 1993; Birch and Cary 1996). Numerous
analytical methods have been developed, including thermal evolution techniques that heat
24
the filter to high temperatures and measure the total carbon that evolves off the filter.
During heating, a portion of organic carbon pyrolizes to form elemental carbon. Some of
the methods use both non-oxidizing and oxidizing atmospheres, and by optically
monitoring filter appearance, attempt to correct for this pyrolysis (Chow et al. 1993;
Birch and Cary 1996), and several studies have examined the accuracy of these methods
(Birch 1998; Chow et al. 2001; Schmid et al. 2001; Schauer et al. 2003; Chow et al.
2004). For the pyrolysis corrected techniques, it was found that the temperature profile
(NIOSH vs. IMPROVE methods) and the laser configuration, thermal optical reflectance
(TOR) vs. thermal optical transmittance (TOT) affected the results. All off-line analysis
techniques are applied to time-integrated filters that typically collect PM for 24 hours or
longer. However, these methods do not provide potentially useful information on the
variability of EC and OC found in sub-daily or hourly data. Such data provided on finer
temporal scales can help to assess the variability of sources, ambient levels and human
exposure to EC and OC.

Collecting particles on filters, whether part of an on-line or off-line measurement,
potentially leads to sampling artifacts. A positive organic carbon artifact arises from
organic vapor adsorption onto quartz-fiber filter material and previously collected
particles (matrix), leading to an overestimation of particle phase OC (McDow and
Huntzicker 1990; Subramanian et al. 2004). A negative artifact can be caused by
volatilization of organic particle-phase semi-volatile compounds from the particles into
the gas phase, leading to an underestimation of OC (Subramanian et al. 2004).

25
To address this need, several in-situ continuous or semi-continuous particle measurement
instruments have been developed for the measurement of EC, OC, or both. Black carbon
can be measured continuously with Aethalometer, which measure the absorption of
single-wavelength light through a filter collecting airborne particles (Hansen et al. 1984).
Thermal evolution carbon monitors have been deployed in the field as well (Lim et al.
2003; Bae et al. 2004). The Sunset Laboratories Inc. semi-continuous EC/OC monitor
was evaluated in a field study in St. Louis (Bae et al. 2004). In that study, OC levels
were shown to agree very well with off-line OC measurements of 24-hour time-integrated
filters using the laboratory based Sunset Lab analyzer (R
2
= 0.90, slope = 0.93). EC
comparisons showed less agreement, most likely due to the very low ambient EC
concentrations encountered at that sampling site. Since these instruments require an
analysis cycle during which sample is not collected, this study used two monitors to
sample alternating hours to achieve full 24-hour collection. The study also showed,
however, that a single instrument sampling every other hour also yields good agreement
with 24-hour time-integrated offline methods.

The objective of our study was to further assess the performance of the Sunset Lab semi-
continuous EC/OC monitor in a field setting. Unlike the St Louis study, two identical
and collocated instruments were run concurrently on a cycle consisting of 45 minutes of
sampling and a 15 minute analysis period. The monitors were deployed near downtown
Los Angeles at the Southern California Supersite Particle Instrumentation Unit (PIU)
trailer. The location is about 100 m downwind of a major freeway, is surrounded by
multi-story buildings, is near a construction area, and thus represents a good urban
26
pollution mix (Sardar et al. 2005). The collocated identical configurations allowed for
the evaluation of the inter-monitor precision and the effects of monitor maintenance such
as filter changes. By changing the inlet configurations of one of the monitors (adding a
denuder, a Teflon filter, or both), the influences of positive and negative sampling
artifacts were investigated. The temperature profiles were also varied between
instruments, including a fast 4-minute analysis using an optical EC calibration rather than
the thermal EC measurements. Finally, a novel configuration using a size selective inlet
impactor removing particles greater than 250 nm in diameter allowed for semi-
continuous size-fractionated EC/OC measurements. Observations of the evolution of OC
at different temperatures of the thermal analysis also provided data on the relative
volatility of OC in particles of different sizes.

2.3 EXPERIMENTAL METHODS
2.3.1 Semi-continuous OC/EC Field Instruments
Two identical OC/EC field instruments (Model 3F, Sunset Laboratory, Inc., Portland,
OR) were deployed for monitoring the carbonaceous components of PM between
December 2004 and May 2005. These instruments provide for automated sample
collection and analysis of OC and EC on a semi-continuous basis (Bae et al. 2004).
Samples are collected by drawing a sample flow of 8 l/min through two round 16 mm
quartz filters, which are mounted back to back in an oven inside the instrument. After
sample collection, the sample remains in the oven where it is heated in two different
atmospheres. In the first part of the analysis, the oven is purged with Helium and the
temperature is increased in multiple steps based on the programmed temperature profile.
27
The evolved organic carbon flows through a manganese dioxide (MnO
2
) oxidizing oven
and all carbon is transformed to carbon dioxide (CO
2
) (Bae et al. 2004; Jeong et al.
2004). The CO
2
is then quantified by a self-contained non-disperse infrared (NDIR)
detector system (Manual 2004). The oven is cooled prior to the second part of the
analysis, when the oven is purged with a mixture of 10% Oxygen in Helium and the
sample is again heated in steps (Bae et al. 2004; Jeong et al. 2004). During this stage, all
remaining carbon on the filter, including elemental carbon, is oxidized, flows through the
MnO
2
oven, and is detected by NDIR as CO
2
.

During the first part of analysis a fraction of organic compounds may pyrolyze and form
EC (Bae et al. 2004; Jeong et al. 2004). This pyrolitic conversion is monitored by
continuous measurement of the light absorbance of a red laser (wavelength of 660 nm)
passed through the filter. The light absorbance increases as some OC is pyrolized to EC
during the first analysis stage, then the absorbance declines as EC (from both pyrolized
OC and sampled particles) is oxidized and leaves the filter during the second stage. The
point at which the laser absorbance equals the initial value is used as the split point
between OC and EC (Bae et al. 2004). CO
2
detected before the split point is considered
OC, and CO
2
detected after the split point is considered EC (Turpin et al. 1990; Birch and
Cary 1996).

The instruments also provide an optical determination of EC. The laser transmission is
measured before and after the analysis cycle, and the difference is related to EC
concentration via calibration. A pre-determined calibration factor, based on numerous
28
ambient measurements is used to convert laser attenuation to EC mass on the filter (Jeong
et al. 2004). This optical EC is subtracted from the thermally measured total carbon (TC
= EC + OC) to determine a parameter known as optical OC. All the EC and OC results
presented in this study are thermal OC and EC unless stated otherwise.

Since the same quartz filters are re-used in every subsequent sampling and analysis cycle,
some refractory inorganic particle components not removed in the heating process will
accumulate on the filters. This is observed in the diminished initial laser transmittance
through the filter over several days of sampling. The effect of using a week-old filter
versus a fresh filter on the measured OC and EC was examined in this study and shown
to be negligible. However, the filters were changed once a week as recommended by the
manufacturer. The instruments were initially calibrated by injecting 1 cm
3
of calibration
gas into the analyzers two times during the analysis period. The stability of the analyzers
was checked by the same method later during the study period. Good internal
instrumental stability was observed, as was also shown in a previous study (Bae et al.
2004).

2.3.2 Sampling Description
The instruments were operated in a sampling trailer, and sampled ambient air from a
common inlet located on its roof. Inside the trailer, the common intake flow was split
between the two instruments. Each instrument was operated downstream of its own
PM
2.5
cyclone (provided by the manufacturer) at a flow rate of 8 l/min. The analyzers
were run concurrently in coordinated hourly cycles, which included a 45-minute
29
sampling period and a 15-minute analysis period. A previous study using the same
monitors showed good agreement between measurements made every other hour and 24-
hour time-integrated off-line methods with R
2
= 0.89 and a slope of 0.94 for OC (Bae et
al. 2004). Thus, missing half of the sampling time did not significantly bias 24-hour
results. By extension, the 15-minutes of analysis during which sampling is interrupted
should not significantly bias what is subsequently referred to as hourly readings.

The sampling location was on the University of Southern California campus at the
Southern California Supersite Particle Instrumentation Unit (PIU) trailer. This site is
located near downtown Los Angeles with a major freeway located about 100 m upwind
(Sardar et al. 2005). The site is surrounded by several multistory buildings and is near a
construction area. The air at this site represents a typical urban mix of mobile, industrial,
and construction sources (Sardar et al. 2005). An Aethalometer (Model AE-21
(UV+BC), Thermo Andersen, Smyrna, GA) was deployed at the same location and
measured black carbon (BC) in 5-minute averages.

The different sampling configurations used in this study are presented in Figure 2.1. Two
of these five configurations (configuration A to E) were run concurrently during each of
the sampling periods. The OC and EC measured via these configurations are referred to
as OC
A
, OC
B
, …OC
E
and EC
A
, EC
B
, …EC
E
based on the inlet configuration employed.
All configurations used the manufacturer supplied PM
2.5
cyclone

30

Figure 2.1 Inlet configurations implemented for sampling, A) bare, B) denuded, C) filtered,
D) denuded/filtered and E) quasi-ultrafine.

2.3.2.1 Precision Evaluation
The precision of the instruments was tested via side-by-side operation, using only the
cyclone on the inlet (bare configuration, Figure 2.1a). Each instrument collected 182
hourly samples in January 2005 and the OC and EC measured with the two analyzers
were compared. Also, an evaluation of the optical EC and OC measurements were
compared to their thermal counterparts using the instruments in the bare configuration.
As shown in previous studies, the BC measurements from the Aethalometer are often
comparable to EC measurements (Hansen et al. 1984; Turpin et al. 1990; Lavanchy et al.
31
1999; Ballach et al. 2001; Lim et al. 2003). The hourly averages of BC measured by
Aethalometer were also compared to the EC results of the Sunset Laboratory monitors.

2.3.2.2 Denuder Breakthrough Determination
A carbon-paper denuder (provided by the manufacturer) was used to remove gas-phase
OC that is known to cause positive adsorption artifacts (Turpin et al. 2000). The
efficiency of a denuder can be less than 100%, allowing some organic gases to penetrate
through the denuder (breakthrough) (Subramanian et al. 2004). The denuder
breakthrough was measured by installing a 47 mm Teflon filter (PTFE, Gelman, 2µm
pore, Ann Arbor, MI) followed by the carbon-paper denuder upstream of the samplers
(Figure 2.1d). The Teflon filter removes the particles and the denuder removes organic
vapors from air stream (Turpin et al. 2000; Bae et al. 2004; Subramanian et al. 2004). EC
measurements under this configuration (EC
D
) were practically zero, demonstrating
complete removal of particles by the Teflon filter. Thus, the measured value of organic
carbon (OC
D
) is due to organic gases penetrating through the denuder and adsorbing on
the quartz filter. Since the measured OC
D
values were fairly consistent (see Results and
Discussion section), the average of the breakthrough level was subtracted from all
subsequent OC measurements using the denuder. The denuder breakthrough value found
is specific to this type of denuder; different breakthrough values are expected if other
denuder types are used. The effect of the age of the carbon paper strips in the denuder on
breakthrough was assessed by side-by-side comparison of a denuder with fresh strips and
one with two-month old strips. Using configuration D on both instruments, 24 samples
were collected in February 2005 and analyzed. The results did not show a significant
32
change after denuder strips were changed. Denuder strips were deployed a maximum of
three months before replacement with fresh strips.

2.3.2.3 Artifact Measurements
Two different methods were used for examining the magnitude of positive and negative
sampling artifacts: a denuder method and a filter method. For the denuder method, an
instrument with the denuder setup (configuration B, Figure 2.1b) was run side-by-side
with the other instrument in the bare configuration A (Figure 2.1a) in January 2005. The
denuder removes the organic vapors that may cause a positive adsorption artifact (Bae et
al. 2004; Subramanian et al. 2004). However, it may increase the magnitude of negative
volatilization artifacts since lowered organic vapor pressures favor volatilization of
organic carbon from particles already collected on the filter (Eatough 1990; Turpin et al.
1994). The measured organic carbon by the denuder configuration (OC
B
), after correction
for breakthrough, would be equal to actual particulate OC (OC
actual
) minus the negative
artifact. The measured organic carbon via the bare measurement (OC
A
) is OC
actual
plus
the positive artifact since no significant negative artifact is expected for the bare
configuration during such short sampling periods (Subramanian et al. 2004). Thus, the
difference between OC
A
and OC
B
is an estimate of the positive artifact plus the negative
artifact, and is referred to as the total artifact determined via the denuder method
(OC
artifact,denuder
= OC
A
- OC
B
).

In the filter method, a 47 mm Teflon filter was installed downstream of the cyclone of
one of the EC/OC analyzers (configuration C, Figure 2.1c) and run side-by-side with the
33
other instrument in the bare configuration in December 2004. The Teflon filter prevents
particles from entering the instrument so that the measured carbon content is entirely due
to adsorbed gas-phase organics (Turpin et al. 2000; Bae et al. 2004; Subramanian et al.
2004). It is therefore a direct measure of the positive artifact as determined with the filter
method (OC
artifact,filter
= OC
C
) (Bae et al. 2004; Subramanian et al. 2004). Near zero EC
C

levels confirmed the effectiveness of the filter. The measured OC
C
was subtracted from
the concurrent OC
A
and the results are taken as actual particulate OC (OC
actual
= OC
A
-
OC
C
). Teflon filters were changed about once a week even though it was shown (see
below) that the amount of loading on the Teflon filter did not significantly affect the
results. Additional artifact measurements were made by sampling with configurations B
and C concurrently.

2.3.2.4 Analysis Protocol Comparison
Three different temperature profiles, a modified-NIOSH protocol, a modified-IMPROVE
protocol, and a FAST-ramp protocol, were employed for analyzing samples and the inter-
comparability was assessed. Temperature profiles and purge gases in each analysis stage
of these methods are presented in Table 2.1. In the modified-NIOSH and modified-
IMPROVE methods the first temperature ramp consists of four heating steps in a helium
atmosphere. The modified-NIOSH method is adapted from the NIOSH temperature
profile (Birch and Cary 1996; NIOSH 1996) and was used by Schauer et al.(Schauer et
al. 2003). The modified-IMPROVE method is adapted from the IMPROVE protocol
(Chow et al. 1993; Schauer et al. 2003). The methods differ only in the temperatures
34

Table 2.1 Temperature profiles used for EC/OC analysis

Gas Hold time (s)
Modified-NIOSH
Temperature (°C)
Modified-IMPROVE
Temperature (°C)
He 10 No heating No heating
He 60 310 120
He 60 480 250
He 60 615 450
He 90 840 550
He 35 No heating No heating
He+Ox 35 550 550
He+Ox 105 850 850

Gas Hold time (s)
FAST-ramp
Temperature (°C)

He+Ox 10 No heating
He+Ox 210 850

used in the He atmosphere of the first analysis part. The FAST-ramp method requires an
analysis step of only 4 minutes and takes advantage of the optical measurements to
enable this shorter analysis time. In this method, the sample is heated up quickly to
850 °C in a 10% oxygen in helium atmosphere in only one step (see Table 2.1) and TC is
35
quantified from the total NDIR response. EC is measured optically, based on initial and
final laser transmittance, and is then used to determine OC via subtraction from TC. Not
only does a faster analysis reduce the time when sampling is interrupted, it also increases
the sensitivity dramatically since all carbon evolves in one narrow peak. Thus, it may
allow for shorter sub-hourly sampling times.

First the modified-NIOSH protocol was compared to modified-IMPROVE protocol via
side-by-side operation of the instruments employing these two methods. Then the FAST-
ramp temperature profile was compared to the modified-NIOSH profile. For all the
temperature profile comparisons, denuders were used on both monitors (configuration B)
since, as described later, this was shown to provide near artifact free sampling. Except
for these temperature protocol comparisons, all other samples in this study were analyzed
using the modified-NIOSH protocol. In both the modified-NIOSH and modified
IMPROVE methods, the four temperature steps in the He atmosphere allow for division
of OC into different NDIR response peaks representing different volatility fractions of
OC (Kirchstetter et al. 2001). These four OC peaks are designated and recorded as peak 1
to peak 4 (OC
1
to OC
4
). For the purposes of this study, OC
2
, OC
3
, and OC
4
were summed
(OC
2-4
) and considered a less volatile OC fraction compared to OC
1
, a more volatile OC
fraction.

2.3.2.5 Comparison of Quasi-Ultrafine Particles with Accumulation Mode Particles
Two collocated instruments provided the opportunity for simultaneous semi-continuous
EC/OC measurements of different PM size fractions. The PM0.25 stage of a Sioutas™
36
impactor (SKC Inc, Eighty Four, PA) (Misra et al. 2002; Singh et al. 2003) was operated
downstream of the PM2.5 cyclone of one of the instruments to remove particles greater
than 250 nm in aerodynamic diameter (configuration E, Figure 2.1e). The rationale for
these experiments was to obtain near continuous, concurrent measurements of the
concentrations of the ultrafine (“freshly” emitted”) and accumulation (“aged”) mode PM.
The Sioutas™ impactor was chosen because of its ability to remove super-250 nm
particles at a flow rate that matches that of the Sunset Laboratory monitor with a low
pressure drop (i.e., 1 kPa), an essential requirement for any sampler to be used as a pre-
selective particle inlet in conjunction with this monitor. Considering that the upper size
cuts that have been traditionally used to define the ultrafine mode (100–180 nm) are
somewhat lower than the cutpoint of the Sioutas™ impactor (Kim et al. 2002;
Chakrabarti et al. 2004; Fine et al. 2004), particles less than 250 nm are designated quasi-
ultrafine (UF) for the purposes of this paper (OCE = OCuf, ECE = ECuf). A denuder
was used downstream of the impactor to minimize sampling artifacts. The particles
between 0.25 and 2.5 µm are defined here as accumulation mode particles. The
instrument with the UF inlet configuration was run concurrently with an instrument with
the denuder configuration B, which collected PM
2.5
. The measurements with UF inlet
configuration were subtracted from the concurrent measurements with denuder
configuration to obtain accumulation mode values (OCacc = OCB - OCE, ECacc = ECB
- ECE ).

37
2.4 RESULTS AND DISCUSSION

2.4.1 Precision Evaluation
The time series plots of OC and EC measured concurrently by two collocated Sunset
Laboratory semi-continuous OC/EC field anNalyzers using the bare configuration (A) are
shown in Figure 2.2. Thermal OC measurements between the two instruments were very
highly correlated with a correlation coefficient, R
2
, of 0.98, a slope of 1.01±0.02, and y-
intercept of 0.12 ± 0.16 µgC/m
3
. The R
2
, slope, and y-intercept of measured EC with the
two instruments were 0.97, 0.82 ± 0.02 and 0.2 ± 0.04 µgC/m
3
respectively. The results
show excellent inter-instrument precision for OC, but a systematic bias in EC reflected in
the slope of 0.82. The reason for this discrepancy might be a result of a systematic
difference in the split point determination between OC and EC, and as the EC values are
a much lower fraction of TC, EC is affected to a greater degree than OC. The split point
between OC and EC of the instrument with overall higher EC measurements (instrument
#2) occurred on average 17 s before the split point of the other instrument (instrument
#1). Also, there were periods with relatively low levels of EC during the comparison,
which were close to the detection limit of the instrument (0.2 µgC/m
3
according to
manufacturer), which may cause additional uncertainty in the readings. At some of these
low EC levels when the EC level was lower than the detection limit, the instrument was
unable to properly detect the split between EC and OC. In this case, the split point
between OC and EC for at least one of the monitors occurred at the end of analyzing
period, which resulted in artificially lower EC measurements (practically zero EC levels).
Therefore, the EC measurements for which the split point occurred at the end of
38

Figure 2.2 Test of instrumental precision results for a) OC measurements, b) EC
measurements.
(a)
(b)
39
analyzing period were considered outliers and excluded from the analysis. This
occurrence was infrequent, resulting in exclusion of less than 5% of data. Total carbon
measured by the two instruments correlated well with each other, with R
2
and slope of
0.99 and 0.94±.0.02 respectively. Slightly lower TC measurements occurred on the same
instrument with the overall higher split point. The lower TC measurements in this unit
combined with the effect of the higher split point, resulted in lower EC measurements.
For OC, these two effects approximately cancel each other out since lower EC now
corresponds to higher OC.

The ability of the analyzers to measure EC (and by subtraction OC) optically was also
assessed by comparing the thermal data of each instrument to its own optical data. The R
2

and slope of thermal versus optical OC correlation were 0.98 and 1.04 ± 0.02 for one
unit, and 0.99 and 0.98 ± 0.02 for the other unit. Comparisons of thermal and optical EC
yielded an R
2
and slope of 0.97 and 0.77 ± 0.04 for one unit and 0.97 and 0.98 ± 0.05 for
the second instrument. These results indicate very strong correlation between optical and
thermal measurements. However, in one of the instruments, the optical EC values seem to
be higher compared to thermal EC, and because optical OC is determined by subtraction,
the optical OC values are lower than thermal OC on the same instrument. As optical EC
measured by two units correlated well with each other with a slope close to one (R
2
and
slope of 1.00 and 1.07±0.03 respectively) the observed difference in optical and thermal
EC measurements in one of the units is caused by the lower thermal EC measurements of
this unit mentioned above. The EC concentrations measured thermally with each unit
40
were also compared to BC measurements with the Aethalometer. The R
2
and slope of
correlation between BC and EC were 0.96 and 1.39 ± 0.06, respectively, for one unit and
0.95 and 1.17 ± 0.05 for the other. This indicates a high correlation between EC and BC
measurements which was also shown in several previous studies (Hansen et al. 1984;
Turpin et al. 1990; Lavanchy et al. 1999; Ballach et al. 2001; Lim et al. 2003), but
systematically lower EC measurements than BC.

2.4.2. Denuder breakthrough
Denuder breakthrough was assessed using configuration D (Figure 2.1d). The average
OC, EC, OC1 and OC2-4 measured with this configuration are presented in Table 2.2.
The fact that no EC was measured implies the perfect removal of particles by Teflon
filter, so the measured value of OC originated solely from the organic gases penetrating
through the denuder and adsorbing to the quartz filter (Turpin et al. 2000; Bae et al. 2004;
Subramanian et al. 2004). The average of OC measurements was 0.82 ± 0.31 µgC/m3
and the R
2
between the bare OC measurements and OCD was 0.01, with a slope of
0.01±0.07, which together indicate a fairly constant degree of breakthrough adsorption,
unrelated to ambient particulate OC levels. The average denuder breakthrough of 0.82
µgC/m3 was therefore subtracted from all the OC measurements obtained by using the
denuder. The average denuder breakthroughs for OC1 and OC2-4 were 0.43 ± 0.10
µgC/m3 and 0.37 ± 0.17 µgC/m3 respectively. As was done for total OC, the average
OC1 breakthrough was subtracted from all the OC1 measurements and the average OC2-
4 breakthrough was subtracted from OC
2-4
measurements of samples collected with a
denuder upstream. The sum of OC
1
and OC
2-4
breakthrough is smaller than the average of
41
total OC breakthrough because of the small amount of pyrolyzed OC included in total OC
but not in OC peaks 1-4.

Table 2.2 Average denuder breakthrough values, which were subtracted from all denuded
samples (all values in µgC/m
3
)

Parameter Average Standard Deviation
OC 0.82 0.31
EC 0.00 0.00
OC
1
0.43 0.10
OC
2-4
0.37 0.17

2.4.3 Sampling Artifacts
Using the filter method to determine the OC sampling artifact as described above, the
positive artifact (OCC = OCartifact,filter) ranged from 5.1 to 8.9 µgC/m3, while the
concurrent OCA from the bare configuration ranged from 7.2 to 24.7 µg/m3. The
average ECC was less than 10-2 µgC/m3, which demonstrated the high efficiency of
particle removal by the Teflon filter. The results of actual particulate OC (OCactual =
OCA - OCC) obtained by Teflon filter method and concurrent bare OC measurements
(OCA) are shown in Figure 2.3. A fairly high correlation with R
2
of 0.89 was found
between OCactual and OCA with a high non-zero intercept. Since the bare OCA consists
of both positive artifacts and particulate OC, the high correlation and slope near unity
show that the variation of OCA is driven by variations in particulate OC. An
42
approximate estimate of the level of positive artifact is indicated by the intercept at 6.4
µgC/m3.

Operating one instrument with a denuder configuration (B) concurrently with the other
instrument with the bare configuration (A) provides another measure of magnitude of
sampling artifacts. In this case, the artifact will include potentially enhanced negative
artifacts caused by the denuder (Eatough 1990; Turpin et al. 1994; Subramanian et al.
2004). If an initial assumption is made that the negative artifact is negligible, then OC
actual

= OCB, and the positive artifact is then OCartifact,denuder = OCA - OCB. As shown,
the filter method arrives at the same parameters (OCactual = OCA - OCC,
OCartifact,filter = OCC) and the results from both artifact determination methods are
plotted in Figure 2.4. The amount of positive artifact does not correlate well with the
actual particulate OC (R
2
= 0.26), and only a slight increase in positive artifact was
observed for increasing actual OC level (slope =0.22). The average of the positive
artifacts measured with both methods was 7.59 ± 1.52 µgC/m3, with average
OCartifact,denuder and OCartifact,filter of 8.14 ± 1.49 µgC/m3 and 6.86 ± 1.21 µgC/m3
respectively. The average positive artifact determined using denuder method is about 1.3
µgC/m3 higher than the artifact using the filter method, but within the standard deviation
of the measurements. This difference could be due to the negative artifact associated with
the denuder, which would lead to an overestimation of the positive artifact by that
method. However, it should also be noted that the measurements using the two methods
were conducted over two different sampling periods, with higher particulate OC levels
during the denuder method sampling.
43

Figure 2.3 The comparison between the bare OC (OC
A
) and the particulate OC obtained
from Teflon filters method (OC
actual
) concurrently.

Figure 2.4 Comparison between OC
artifact
and OC
actual
determined by Teflon filter method
(configuration A vs. C), denuder method (configuration A vs. B) and concurrent usage of
Teflon filter and denuder (configuration C vs. B).
44
The average bare OC during the denuder method measurements was 16.24 µgC/m3
compared to11.14 µgC/m3 during the filter method. Higher OC levels during the
denuder method sampling period could lead to slightly higher artifacts due to possibly
higher organic vapor concentrations on more polluted days, and/or possible matrix
adsorption effects (Kirchstetter et al. 2001). The observed differences between the two
methods does provide a rough upper estimate of the negative artifact of approximately 1
µgC/m3, less than 20% of the positive artifact on average. Other studies have
demonstrated similar results for negative artifacts on 24 hour samples e.g. less than 10%
of particulate OC by Subramanian et al.(Subramanian et al. 2004). However, higher
negative artifacts (up to 80% of particulate OC) were also measured in other studies,
which indicates a wide range of negative artifacts based on site and sampling conditions
(Eatough et al. 1993; Modey et al. 2001; Anderson et al. 2002; Ding et al. 2002;
Subramanian et al. 2004). Also shown in Figure 2.4 are results from measurements with
one of the instruments operating with a Teflon filter (configuration C) concurrently with
the other instrument operating with a denuder (configuration B). The measured OCC
(OCartifact,filter) is plotted as function of the measured OCB (OCactual). An average
positive artifact of 6.33±1.34 µgC/m3 was observed, which was consistent with our
results using the filter and denuder methods separately, as can be seen in Figure 2.4.

The positive artifact was relatively high compared to actual particulate OC, comprising
approximately 50% of OC
A
on average. This large value for positive artifacts is
attributable to the short 45-minute sampling time. Organic gases will adsorb on the filter
until the filter is fully saturated (Subramanian et al. 2004). The longer sampling continues
45
after saturation occurs, the lower the positive artifact will be relative to actual particulate
OC. Thus, a 24-hour sample would have a lower artifact relative to actual particulate OC
than a 45-minute sample, assuming filter saturation is reached within 24-hours. The high
percentage of artifact observed here indicates that the bare configuration of these
instruments cannot directly measure actual particulate OC reliably.

If the sampling time is long enough for the quartz filter to saturate with adsorbed organic
vapors, then the artifact mass will remain constant (Subramanian et al. 2004). For a short
sampling time of 45 min, it is assumed that gas-phase organic concentrations do not vary
sufficiently to cause additional adsorption or volatilization of organic material due to
changing vapor pressures of these gases. As stated above, the average 7.59 µgC/m3
positive artifact was measured for a 45-minute sampling period. This corresponds to an
adsorption artifact on the filter of 0.68 µgC/cm2 (2 back-to-back 16 mm filters, 8 l/min
and a 45 min sample). In a previous study in the Los Angeles basin, the average
measured positive artifact was 1.3 µg/m3 for 24 hr sampling on 37 mm quartz filters with
30 l/min flow rate (Sardar et al. 2005). This corresponds to 5.22 µgC/cm2, which is
about 7.7 times the artifact value determined in this study. In another study in the same
basin, an average positive artifact of 2.17 µgC/m3 was obtained (Kim et al. 2001), this
time based on a flow rate of 20 l/min, 24 h sampling and using 47 mm filters. This value
corresponds to 3.6 µgC/cm2 of positive OC artifact, which is about 5.3 times the artifacts
measured here. Comparison of our results with both of these studies indicates that
probably the saturation condition was not achieved in 45 min of sampling. In order to
verify whether saturation was achieved, the samples were collected for 165 min of
46
sampling period and analyzed in 15 min (total cycle of 3 h) using configurations B and C
concurrently. The average positive artifacts of 54 samples collected in May 2005, was
4.51±0.94 µgC/m3, corresponding to a 1.62 µgC/cm2 of positive OC artifact, which is
2.4 times higher than the artifacts measured for a sampling period of 45 min. This result
further indicates that the filters were not saturated within 45 min. While the sampling
period was increased about 3.7 times, the artifacts were enhanced only 2.4 times,
suggesting that the adsorption rate of gas-phase organics slows as sampling continues. In
a previous study in Pittsburgh (Subramanian et al. 2004) an average positive artifact of
0.53 µg/m3 and 0.71 µg/m3 were found respectively for 24 h and 4–6 h of sampling with
47mm quartz filters and a 16.7 l/min flow rate , corresponding to filter artifacts of
0.75 µgC/cm2 and 0.21 µgC/cm
2
, respectively. In that study, filter saturation was not
achieved after 6 hours of sampling. As in our case, the artifact concentration on the filter
increased with increasing sampling time, but the adsorption rate slows down as it
approaches saturation conditions. Differences in the magnitude of the positive artifacts
and adsorption rates at different locations indicate that the amount of OC artifact can vary
significantly with sampling conditions. This is probably due to differences in
concentrations of gas-phase organics at different sites and seasons.

An over-correction of positive artifacts using the filter method was predicted in previous
studies (Chow et al. 1996; Subramanian et al. 2004) due to organic particulate matter
collected on the Teflon filter volatilizing and then adsorbing to the quartz filter
downstream. This effect was examined by operating two instruments side-by-side using
47
configuration C, initially both having new Teflon filters. After about 60 hours, sufficient
time for Teflon filters to be heavily loaded with particulate matter, the Teflon filter on
one of the instruments was changed while the other one remained unchanged, then 20
samples were collected on each instrument. The average difference between OC
measured by the two instruments before the filter change was low (0.55 ± 0.52 µgC/m3)
and nearly identical to the difference after the filter change (0.58 ± 0.57 µgC/m3). The
similarity indicates that this effect may not be important for the current study, and that an
overestimation of positive artifacts by using a loaded Teflon filter upstream has not
occurred.

The relationship between particulate OC obtained by Teflon method and bare OC
A

(shown in Figure 2.3) may provide a correction to estimate actual particulate OC in cases
when the denuder is not deployed. The validity of this correction is demonstrated in
Figure 2.5, where particulate OC, calculated from OC
A
and the linear relationship in
Figure 2.3, is compared to particulate OC measured concurrently with a denuder (OC
B
,
ignoring the negligible negative artifact). The time series (Figure 2.5a) confirms the good
agreement. Figure 2.5b compares the calculated and measured OC in a scatter plot,
showing a high correlation with slope of 0.98±0.08. This good agreement shows that it
may be possible to obtain good particulate OC results in a bare configuration, using a

48

Figure 2.5 Calculated OC
actual
from bare configuration measurements (applying the
relationship found with Teflon filter method) compared to OC
actual
measured with a denuder,
a) time series data including the uncorrected bare OC (OC
A
), and b) correlation plot.

(a)
(b)
49
previously derived correction. The derived linear relationship between the particulate OC
and the bare OC may be specific to particular sampling site, instrument configuration,
and/or sampling duration. The excellent agreement in Figure 2.5b also shows that the
negative artifact caused by using a denuder is minor and does not significantly effect the
particulate OC measurements. Therefore, the denuder configuration provides the best
measure of actual particulate OC without significant associated artifacts.

2.4.4 Temperature profiles
Table 2.3 shows the statistical analysis of measurements made with the modified-
IMPROVE and the FAST-ramp methods run concurrently with the modified-NIOSH
method. The OC analyzed by modified-IMPROVE protocol showed a good agreement
with the OC measured by using the modified-NIOSH protocol with an R2 of 0.93 and
slope of 1.04, consistent with a pervious study by Chow et al.(Chow et al. 2001). The
main difference between methods is the maximum temperature reached in the first
analysis stage; 550°C for the modified-IMPROVE and 850°C in the modified-NIOSH
method. This shows that a temperature of 550°C is enough to evolve almost all of the
OC. The EC temperature steps in both methods are the same, so any observed difference
is the result of a difference between the split points affected by potentially more pyrolysis
of OC at the higher temperatures of the modified-NIOSH method. Considering the R2 of
0.92 and slope of 1.05 for EC measurements, and similar results for OC and TC, the two
methods compare very well under the conditions of our experiments.
50
Table 2.3 Comparison between measured carbonaceous components of PM
2.5
by different
methods (units in µgC/m
3
)

Protocol
# of
samples
Compared
Parameter
R
2

Slope
(95% intervals)
Intercept
(95% intervals)
Modified-IMPROVE
vs.
Modified NIOSH
114
OC 0.93 1.04 (0.99,1.10) 0.09 (-0.11,0.30)
EC 0.92 1.05 (1.11,1.00) 0.24 (0.17,0.31)
TC 0.95 1.08 (1.04,1.13) 0.13 (-0.10,0.35)
FAST-ramp vs.
Modified NIOSH
236
OC 0.92 0.90 (0.87,0.94) 0.05 (-0.2,0.09)
EC 0.98 1.38 (1.35,1.4) -0.14 (-0.17,-0.11)
Optical OC 0.91 1.13 (1.08, 1.18) -0.59 (-0.42,-0.77)
Optical EC 1.00 0.99 (0.99,1.00) -0.08(-0.09,-0.07)
TC 0.98 1.08 (1.05,1.10) -0.59 (-0.71,-0.47)

The OC measured using FAST-ramp temperature program (TC – optical EC) also
correlated well with concurrent thermal OC measurements by the modified-NIOSH
method with a correlation coefficient of 0.92. The slope of 0.90 (0.87,0.94) indicates
slightly lower OC measurements compared to the modified-NIOSH method. The EC
measurements in contrast were higher than those of the modified-NIOSH method with a
slope of 1.38 but a high correlation coefficient of 0.98. TC measurements were also fairly
consistent, with an R2 of 0.98 and slope of 1.08. The FAST-ramp relies on the optical EC
measurement to determine both EC and OC levels. The optical EC measurement is based
on a manufacturer calibration, derived from relating differences in laser transmission to
thermally measured EC levels over many samples using the modified NIOSH profile.
51
Comparing the optical EC measurements via the FAST method with optical EC
measurements via NIOSH method, an R
2
of 1.00 and slope of 0.99 (0.99,1.00) were
obtained. This excellent correlation is a result of using the same procedure for
determining optical EC in both instruments in either of the methods. The systematic bias
between optical and thermal EC measurements of about 30 % in one of the units (used for
the modified-NIOSH method in this comparison) observed earlier during the instrumental
precision tests can explain most of the difference between EC by the FAST-ramp and
thermal EC by modified-NIOSH. The OC measurements by the FAST method are also
highly correlated with optical OC via NIOSH method with R2 and slope of 0.91 and 1.13
respectively, which are close to the correlation between TC measurements of these two
methods (R2 = 0.98 and slope =1.08). This indicates that the difference between these
two optical OC values mainly originates from the difference between TC measurements,
since optical EC values showed to be similar. The high correlation between EC measured
with the NIOSH and FAST protocol suggest that a new calibration would bring the
results into better agreement. Given these encouraging results, the shorter analysis time in
the FAST-ramp method will potentially allow for nearly continuous sampling or shorter
sampling periods.

2.4.5 Size-fractionated Measurements
The instrument with a quasi-ultrafine inlet and denuder configuration (configuration E)
was run concurrently with the other instrument with a PM2.5 denuder configuration
(configuration B), and 387 hourly samples were collected in March and May of 2005.
The hourly OC measured in the quasi-ultrafine (UF) mode ranged from 0.03 to
52
5.80 µgC/m3 with average of 1.59 µgC/m3. Similar results were found in the
accumulation mode, with OC ranging from 0.07 to 8.49 µgC/m3 with an average of
1.37 µgC/m3. The hourly EC in the quasi-ultrafine mode varied from 0.32 to
5.20 µgC/m3 with an average of 1.16 µgC/m3. EC in the accumulation mode was
significantly less, varying from 0.0 to 2.78 µgC/m3 with an average of 0.49 µgC/m3.
The average diurnal variations of particulate OC and EC in the both size ranges are
presented in Figure 2.6. The diurnal variations of particulate EC as well as OC in these
two size-fractions generally track each other well. The significant morning peak indicates
the effect of morning rush hour. The UF concentrations of OC reached the maximum
about 1 h earlier than the accumulation mode concentrations. A possible explanation is
that UF particles are freshly emitted particles, originating directly from nearby emissions
of mobile sources, and thus have diurnal patterns that follow traffic volume. By contrast,
accumulation mode PM, which may have been emitted earlier as smaller particles in
locations upwind of our sampling site, may be reaching the site after aging in the
atmosphere, a process that allows for condensation of organic vapors onto pre-existing
particles and thus an increase in particle size. The OC1 concentration (more volatile OC)
in the UF mode varied between 0.01 and 3.26 µgC/m3 with an average of 0.67 µgC/m3,
while OC1 in the accumulation mode was higher, ranging from 0.07 to 4.78 µgC/m3 with
an average of 0.96 µgC/m3. The OC2-4 (less volatile OC) in the UF mode ranged from
0.02 to 2.54 µgC/m3 with an average of 0.93 µgC/m3, with lower values in the
accumulation mode between 0.0 and 3.68 µgC/m3 with the average of 0.41 µgC/m3. The
average ratios of OC1 and OC2-4 to total particulate OC in the UF and accumulation
53

Figure 2.6 The diurnal pattern of carbonaceous component of particles in quasi-
ultrafine and accumulation mode, a) OC and b) EC.

(a)
(b)
54
modes are presented in Figure 2.7. The average EC/OC ratios of particles in the UF and
accumulation mode are also displayed in the same Figure. The considerably higher EC to
OC ratio in the UF mode is due to the different sources and formation process of the two
particle size ranges. EC from mobile sources (in the form of soot) is emitted primarily in
smaller particles (Kittelson 1998; Kleemann 1999). While OC is also emitted in smaller
particles from mobile sources, a portion of accumulation mode OC is formed by the
condensation of organic gases which were either directly emitted from mobile sources or
formed by photochemical secondary reactions (Kleemann 1999). The higher OC1/OC
and lower OC2-4/OC in the accumulation mode compared to the UF mode indicates
higher OC volatility in the accumulation mode. This is consistent with OC condensation

Figure 2.7 The ratio of particulate EC, OC
1
and OC
2-4
to particulate OC in quasi-ultrafine
and accumulation modes.
55

In this mode since both photochemical products and condensable vapors from vehicles
are often semi-volatile species which will partition to pre-existing particle surface area
(Kleemann 1999).

The results show that the semi-continuous EC/OC field analyzer is a reliable instrument
for the measurement of the carbonaceous component of PM. The positive artifacts were
almost constant and relatively high for the short sampling time of 45 min; more than 50%
of un-denuded OC concentrations could be attributed to artifacts. These artifacts were
virtually eliminated with the use of a denuder. EC and OC measurements using different
temperature profiles, i.e., the modified-NIOSH, modified-IMPROVE, and FAST-ramp
for analyzing the samples were highly correlated with one another. The FAST-ramp
method offers the potential for reducing the time and increasing the sensitivity of the
analysis step, thus allowing for more continuous measurements and shorter sampling
periods. Finally, the inlets of the EC/OC analyzers can be easily modified to sample
different particle size fractions. Thus, multiple instruments allow for time-resolved, size-
fractionated measurements of the carbonaceous components of PM.
56
2.5 CHAPTER 2 REFERENCES
Anderson, R. R., D. V. Martello, P. C. Rohar, B. R. Strazisar, J. P. Tamilia, K. Waldner,
C. M. White, W. K. Modey, N. F. Mangelson and D. J. Eatough (2002). Sources and
composition of PM2.5 at the National Energy Technology Laboratory in Pittsburgh
during July and August 2000, Energy & Fuels 16(2): 261-269.
Bae, M. S., J. J. Schauer, J. T. DeMinter, J. R. Turner, D. Smith and R. A. Cary (2004).
Validation of a semi-continuous instrument for elemental carbon and organic carbon
using a thermal-optical method, Atmospheric Environment 38(18): 2885-2893.
Ballach, J., R. Hitzenberger, E. Schultz and W. Jaeschke (2001). Development of an
improved optical transmission technique for black carbon (BC) analysis, Atmospheric
Environment 35(12): 2089-2100.
Batalha, J. R. F., P. H. N. Saldiva, R. W. Clarke, B. A. Coull, R. C. Stearns, J. Lawrence,
G. G. K. Murthy, P. Koutrakis and J. J. Godleski (2002). Concentrated ambient air
particles induce vasoconstriction of small pulmonary arteries in rats, Environmental
Health Perspectives 110(12): 1191-1197.
Birch, M. E. (1998). Analysis of carbonaceous aerosols: interlaboratory comparison,
Analyst 123(5): 851-857.
Birch, M. E. and R. A. Cary (1996). Elemental carbon-based method for monitoring
occupational exposures to particulate diesel exhaust, Aerosol Science and Technology
25(3): 221-241.
Burnett, R. T., J. Brook, T. Dann, C. Delocla, O. Philips, S. Cakmak, R. Vincent, M. S.
Goldberg and D. Krewski (2000). Association between particulate- and gas-phase
components of urban air pollution and daily mortality in eight Canadian cities, Inhalation
Toxicology 12: 15-39.
Chakrabarti, B., M. Singh and C. Sioutas (2004). Development of a near-continuous
monitor for measurement of the sub-150 nm PM mass concentration, Aerosol Science
and Technology 38: 239-252.
Chow, J. C. and J. G. Watson (2002). PM2.5 carbonate concentrations at regionally
representative Interagency Monitoring of Protected Visual Environment sites, Journal of
Geophysical Research-Atmospheres 107(D21).
Chow, J. C., J. G. Watson, L. W. A. Chen, W. P. Arnott and H. Moosmuller (2004).
Equivalence of elemental carbon by thermal/optical reflectance and transmittance with
different temperature protocols, Environmental Science & Technology 38(16): 4414-
4422.
57
Chow, J. C., J. G. Watson, D. Crow, D. H. Lowenthal and T. Merrifield (2001).
Comparison of IMPROVE and NIOSH carbon measurements, Aerosol Science and
Technology 34(1): 23-34.
Chow, J. C., J. G. Watson, Z. Q. Lu, D. H. Lowenthal, C. A. Frazier, P. A. Solomon, R.
H. Thuillier and K. Magliano (1996). Descriptive analysis of PM(2.5) and PM(10) at
regionally representative locations during SJVAQS/AUSPEX, Atmospheric Environment
30(12): 2079-2112.
Chow, J. C., J. G. Watson, L. C. Pritchett, W. R. Pierson, C. A. Frazier and R. G. Purcell
(1993). The DRI Thermal Optical Reflectance Carbon Analysis System - Description,
Evaluation and Applications in United-States Air-Quality Studies, Atmospheric
Environment Part a-General Topics 27(8): 1185-1201.
Clarke, R. W., B. Coull, U. Reinisch, P. Catalano, C. R. Killingsworth, P. Koutrakis, I.
Kavouras, G. G. K. Murthy, J. Lawrence, E. Lovett, J. M. Wolfson, R. L. Verrier and J. J.
Godleski (2000). Inhaled concentrated ambient particles are associated with hematologic
and bronchoalveolar lavage changes in canines, Environmental Health Perspectives
108(12): 1179-1187.
Dejmek, J., I. Solansky, I. Benes, J. Lenicek and R. J. Sram (2000). The impact of
polycyclic aromatic hydrocarbons and fine particles on pregnancy outcome,
Environmental Health Perspectives 108(12): 1159-1164.
Ding, Y. M., Y. B. Pang and D. J. Eatough (2002). High-volume diffusion denuder
sampler for the routine monitoring of fine particulate matter: I. Design and optimization
of the PC-BOSS, Aerosol Science and Technology 36(4): 369-382.
Eatough, D. J., A. Wadsworth, D. A. Eatough, J. W. Crawford, L. D. Hansen and E. A.
Lewis (1993). A Multiple-System, Multichannel Diffusion Denuder Sampler for the
Determination of Fine-Particulate Organic Material in the Atmosphere, Atmospheric
Environment Part a-General Topics 27(8): 1213-1219.
Eatough, D. J. A., N.; Cottam, M.; Gammon, T.; Hansen, L.D.; Lewis, E.A.; Farber, R.J.
(1990). Loss of semi-volatile organic compounds from particles during sampling on
filters. Transaction of Visibility and Fine Particles. C. V. Mathai. Pittsburgh, PA, Air and
Waste Management Association: 146-156.
Fine, P. M., B. Chakrabarti, M. Krudysz, J. J. Schauer and C. Sioutas (2004). Diurnal
variations of individual organic compound constituents of ultrafine and accumulation
mode particulate matter in the Los Angeles basin, Environmental Science and
Technology 38(5): 1296-1304.
Griffin, R. J., D. Dabdub, M. J. Kleeman, M. P. Fraser, G. R. Cass and J. H. Seinfeld
(2002). Secondary organic aerosol - 3. Urban/regional scale model of size- and
composition-resolved aerosols, Journal of Geophysical Research-Atmospheres 107(D17).
58
Hansen, A. D. A., H. Rosen and T. Novakov (1984). The Aethalometer - an Instrument
for the Real-Time Measurement of Optical-Absorption by Aerosol-Particles, Science of
the Total Environment 36(JUN): 191-196.
Hildemann, L. M., D. B. Klinedinst, G. A. Klouda, L. A. Currie and G. R. Cass (1994).
Sources of Urban Contemporary Carbon Aerosol, Environmental Science & Technology
28(9): 1565-1576.
Hughes, L. S., J. O. Allen, M. J. Kleeman, R. J. Johnson, G. R. Cass, D. S. Gross, E. E.
Gard, M. E. Galli, B. D. Morrical, D. P. Fergenson, T. Dienes, C. A. Noble, P. J. Silva
and K. A. Prather (1999). Size and composition distribution of atmospheric particles in
southern California, Environmental Science & Technology 33(20): 3506-3515.
Jeong, C. H., D. W. Lee, E. Kim and P. K. Hopke (2004). Measurement of real-time
PM2.5 mass, sulfate, and carbonaceous aerosols at the multiple monitoring sites,
Atmospheric Environment 38(31): 5247-5256.
Kim, B. M., J. Cassmassi, H. Hogo and M. D. Zeldin (2001). Positive organic carbon
artifacts on filter medium during PM2.5 sampling in the South Coast Air Basin, Aerosol
Science and Technology 34(1): 35-41.
Kim, S., S. Shen, C. Sioutas, Y. F. Zhu and W. C. Hinds (2002). Size distribution and
diurnal and seasonal trends of ultrafine particles in source and receptor sites of the Los
Angeles basin, Journal of the Air & Waste Management Association 52(3): 297-307.
Kirchstetter, T. W., C. E. Corrigan and T. Novakov (2001). Laboratory and field
investigation of the adsorption of gaseous organic compounds onto quartz filters,
Atmospheric Environment 35(9): 1663-1671.
Kittelson, D. B. (1998). Engines and Nanoparticles: A Review, Journal of Aerosol
Science 29(5--6): 575-588.
Kleemann, M. J. (1999). Source contributions to the size and composition distribution of
atmospheric particles: Southern California in September 1996, Environmental science
and technology 33(23): 4331-4341.
Lavanchy, V. M. H., H. W. Gäggeler, S. Nyeki and U. Baltensperger (1999). Elemental
carbon (EC) and black carbon (BC) measurements with a thermal method and an
aethalometer at the high-alpine research station Jungfraujoch, Atmospheric Environment
33(17): 2759-2769.
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.
59
Lim, H. J., B. J. Turpin, E. Edgerton, S. V. Hering, G. Allen, H. Maring and P. Solomon
(2003). Semicontinuous aerosol carbon measurements: Comparison of Atlanta Supersite
measurements, Journal of Geophysical Research-Atmospheres 108(D7).
Manual (2004). SEMI-CONTINUOUS OCEC CARBON AEROSOL ANALYZER,
Sunset Laboratory Inc.
Mar, T. F., G. A. Norris, J. Q. Koenig and T. V. Larson (2000). Associations between air
pollution and mortality in Phoenix, 1995-1997, Environmental Health Perspectives
108(4): 347-353.
McDow, S. R. and J. J. Huntzicker (1990). Vapor Adsorption Artifact in the Sampling of
Organic Aerosol - Face Velocity Effects, Atmospheric Environment Part a-General
Topics 24(10): 2563-2571.
Metzger, K. B., P. E. Tolbert, M. Klein, J. L. Peel, W. D. Flanders, K. Todd, J. A.
Mulholland, P. B. Ryan and H. Frumkin (2004). Ambient air pollution and cardiovascular
emergency department visits, Epidemiology 15(1): 46-56.
Misra, C., M. Singh, S. Shen, C. Sioutas and P. A. Hall (2002). Development and
evaluation of a personal cascade impactor sampler (PCIS), Journal of Aerosol Science
33(7): 1027-1047.
Modey, W. K., Y. Pang, N. L. Eatough and D. J. Eatough (2001). Fine particulate
(PM2.5) composition in Atlanta, USA: assessment of the particle concentrator-Brigham
Young University organic sampling system, PC-BOSS, during the EPA supersite study,
Atmospheric Environment 35(36): 6493-6502.
NIOSH (1996). Elemental carbon (diesel particulate): Method 5040. NIOSH Manual of
Analytical Methods. Cincinnati.
NRC (2004). Research Priorities for Airborne Particulate Matter: IV. Continuing
Research Progress. Committee on Research Priorities for Airborne Particulate Matter.
Washington DC, National Research Council.
Oberdörster, G. (2001). Pulmonary effects of inhaled ultrafine particles, International
Archives of Occupational and Environmental Health 74(1): 1-8.
Oberdörster, G., Z. Sharp, V. Atudorei, A. Elder, R. Gelein, A. Lunts, W. Kreyling and
C. Cox (2002). Extrapulmonary translocation of ultrafine carbon particles following
whole-body inhalation exposure of rats, Journal of Toxicology and Environmental
Health-Part A 65(20): 1531-1543.
Reisen, F. and J. Arey (2005). Atmospheric reactions influence seasonal PAH and nitro-
PAH concentrations in the Los Angeles basin, Environmental Science & Technology
39(1): 64-73.
60
Saldiva, P. H. N., R. W. Clarke, B. A. Coull, R. C. Stearns, J. Lawrence, G. G. K.
Murthy, E. Diaz, P. Koutrakis, H. Suh, A. Tsuda and J. J. Godleski (2002). Lung
inflammation induced by concentrated ambient air particles is related to particle
composition, American Journal of Respiratory and Critical Care Medicine 165(12):
1610-1617.
Sardar, S. B., P. M. Fine and C. Sioutas (2005). Seasonal and spatial variability of the
size-resolved chemical composition of particulate matter (PM10) in the Los Angeles
Basin, Journal of Geophysical Research-Atmospheres 110(D7).
Schauer, J. J., B. T. Mader, J. T. Deminter, G. Heidemann, M. S. Bae, J. H. Seinfeld, R.
C. Flagan, R. A. Cary, D. Smith, B. J. Huebert, T. Bertram, S. Howell, J. T. Kline, P.
Quinn, T. Bates, B. Turpin, H. J. Lim, J. Z. Yu, H. Yang and M. D. Keywood (2003).
ACE-Asia intercomparison of a thermal-optical method for the determination of particle-
phase organic and elemental carbon, Environmental Science & Technology 37(5): 993-
1001.
Schauer, J. J., W. F. Rogge, L. M. Hildemann, M. A. Mazurek and G. R. Cass (1996).
Source apportionment of airborne particulate matter using organic compounds as tracers,
Atmospheric Environment 30(22): 3837-3855.
Schmid, H., L. Laskus, H. J. Abraham, U. Baltensperger, V. Lavanchy, M. Bizjak, P.
Burba, H. Cachier, D. Crow, J. Chow, T. Gnauk, A. Even, H. M. ten Brink, K. P. Giesen,
R. Hitzenberger, E. Hueglin, W. Maenhaut, C. Pio, A. Carvalho, J. P. Putaud, D. Toom-
Sauntry and H. Puxbaum (2001). Results of the "carbon conference" international aerosol
carbon round robin test stage I, Atmospheric Environment 35(12): 2111-2121.
Seagrave, J., C. Knall, J. D. McDonald and J. L. Mauderly (2004). Diesel particulate
material-binds and concentrates a proinflammatory cytokine that causes neutrophil
migration, Inhalation Toxicology 16: 93-98.
Singh, M., C. Misra and C. Sioutas (2003). Field evaluation of a personal cascade
impactor sampler (PCIS), Atmospheric Environment 37(34): 4781-4793.
Subramanian, R., A. Y. Khlystov, J. C. Cabada and A. L. Robinson (2004). Positive and
negative artifacts in particulate organic carbon measurements with denuded and
undenuded sampler configurations, Aerosol Science and Technology 38: 27-48.
Tesfaigzi, Y., S. P. Singh, J. E. Foster, J. Kubatko, E. B. Barr, P. M. Fine, J. D.
McDonald, F. F. Hahn and J. L. Mauderly (2002). Health effects of subchronic exposure
to low levels of wood smoke in rats, Toxicological Sciences 65(1): 115-125.
Turpin, B. J., R. A. Cary and J. J. Huntzicker (1990). An Insitu, Time-Resolved Analyzer
for Aerosol Organic and Elemental Carbon, Aerosol Science and Technology 12(1): 161-
171.
61
Turpin, B. J., J. J. Huntzicker and S. V. Hering (1994). Investigation of organic aerosol
sampling artifacts in the Los Angeles basin, Atmospheric Environment 28(19): 3061-
3071.
Turpin, B. J., P. Saxena and E. Andrews (2000). Measuring and simulating particulate
organics in the atmosphere: problems and prospects, Atmospheric Environment 34(18):
2983-3013.
Wellenius, G. A., B. A. Coull, J. J. Godleski, P. Koutrakis, K. Okabe, S. T. Savage, J. E.
Lawrence, G. G. K. Murthy and R. L. Verrier (2003). Inhalation of concentrated ambient
air particles exacerbates myocardial ischemia in conscious dogs, Environmental Health
Perspectives 111(4): 402-408.

62
Chapter 3.
Indoor/Outdoor Relationships, Trends and Carbonaceous Content of
Fine Particulate Matter in Retirement Homes of the Los Angeles Basin

3.1. ABSTRACT
Hourly indoor and outdoor fine particulate matter (PM
2.5
), organic and elemental carbon
(OC and EC, respectively), particle number (PN), ozone (O
3
), carbon monoxide (CO) and
nitrogen oxides (NO
X
) concentrations were measured at two different retirement
communities in the Los Angeles area as part of the Cardiovascular Health and Air
Pollution Study (CHAPS). Site A (group 1, or G1) was operated from 07/06/2005 to
08/20/2005 (phase 1, or P1) and from 10/19/2005 to 12/10/2005 (P2), while site B (G2)
was operated from 08/24/2005 to 10/15/2005 (P1) and from 01/04/2006 to 02/18/2006
(P2). Overall, the magnitude of indoor and outdoor measurements was similar, probably
because of the major influence of outdoor sources on indoor particle and gas levels.
However, G2 showed a substantial increase in indoor OC, PN and PM
2.5
between 06:00
and 09:00 am, probably from cooking. The contributions of primary and secondary OC
(SOA) to measured outdoor OC were estimated from collected OC and EC
concentrations using EC as a tracer of primary combustion-generated OC (i.e. “EC tracer
method”). The study average outdoor SOA accounted for 40% of outdoor particulate OC
(40-45% in the summer and 32-40% in the winter). Air exchange rates (AER; h
-1
) and
infiltration factors (F
inf
; dimensionless) at each site were also determined. Estimated F
inf

and measured particle concentrations were then used in a single compartment mass
balance model to assess the contributions of indoor and/or outdoor sources to measured
indoor OC, EC, PM
2.5
and PN. The average percentage contributions of indoor SOA of
63
outdoor origin to measured indoor OC were about 35% (during G1P1 and G1P2) and
about 45% (for G2P1 and G2P2). On average, 36 (G2P1) to 44% (G1P1) of measured
indoor OC was comprised of outdoor-generated primary OC.
3.2. INTRODUCTION
Numerous epidemiological studies have found associations between atmospheric aerosol
concentrations and both acute and chronic adverse respiratory and cardiovascular effects
(USEPA, 2004). Exposure to fine PM (PM
2.5
) and its components have also been
investigated in many toxicological studies on a) human volunteers exposed to
concentrated outdoor PM under controlled conditions (Ghio and Devlin, 2001), b) in-vivo
laboratory animal studies and c) in-vitro tissue studies using well characterized particles
containing individual compounds or source mixtures (Dye et al., 2001). PM
2.5
properties
and components that are believed to be responsible for the observed adverse health
effects include: mass, surface area, size, metals, acids, organic compounds, elemental
carbon (EC), sulfate and nitrate salts, peroxides, soot and bioaerosols (USEPA, 2004;
McClellan, 2004).
The air quality standards established by the USEPA in 1997 were primarily based upon
epidemiological studies conducted at stationary outdoor monitoring sites. However, a
significant portion of human exposures to PM
2.5
occurs indoors where people spend
approximately 85-90% of their time (Robinson et al., 1995; Klepeis et al., 2001). Thus,
understanding the composition, behavior and origin of indoor PM2.5 is important to
exposure characterization and mitigation. Typically, indoor PM
2.5
consists of ambient
64
(outdoor) particles that have infiltrated indoors, particles emitted indoors (primary), and
particles formed indoors (secondary) from precursors emitted both indoors and outdoors
(Wschler and Shields, 1997; Weschler 2004; Meng, 2005). Because of indoor sources
such as cooking, smoking, gas stoves, cleaning, washing, and other human activities,
PM
2.5
concentrations can be substantially higher indoors than outdoors (USEPA, 2004;
Weschler and Shields, 1997). A few recent studies have demonstrated that indoor sources
make a substantial contribution to the indoor concentrations of PM
2.5
and its components,
often higher than 50% (USEPA, 2004; Meng et al., 2005; Wallace, 1996; Polidori et al.
2006). Since outdoor particles can enter the building envelope by convective flow (e.g.
open windows) or by diffusional flow/infiltration (e.g. cracks and fissures), outdoor PM
2.5

is also a major contributor to indoor particle concentrations (USEPA, 2004; Thatcher et
al., 1995; Abt et al. 2000). Recent epidemiologic panel studies have demonstrated the
usefulness of separating total personal particle exposures into their ambient (outdoor
origin) and non-ambient (indoor-generated) components. Typically, only associations
between adverse health outcomes and ambient particle exposures have been found (Ebelt
at al., 2005; Koenig et al., 2005).
Organic compounds make an important but poorly understood contribution to indoor and
outdoor PM
2.5
, and are believed to be a key factor in causing adverse health effects
1
.
They consist of organic carbon (OC) and EC and are comprised of hundreds of individual
compounds with different physical and chemical properties. While EC is produced only
during incomplete combustion and emitted directly in the particle phase, indoor and
outdoor OC are both emitted from combustion sources (primary organic aerosols) and
65
formed from semi- and low volatility products of chemical reactions involving reactive
organic gases (secondary organic aerosols, or SOA) (Turpin et al., 2000). Quantifying the
SOA contribution to measured OC both indoors and outdoors is important to linking the
organic PM concentration to its emissions and precursors, and to developing effective
control strategies for PM.
The present work was funded by the National Institute of Health (NIH) and was
conducted within the Cardiovascular Health and Air Pollution Study (CHAPS), a multi-
disciplinary project whose goals are to investigate the effects of micro-environmental
exposures to PM on cardiovascular outcomes in elderly retirees affected by coronary
heart disease (CHD). The elderly population with CHD is likely to be among the most
vulnerable to the adverse effects of particulate air pollutants.
In this paper, hourly indoor and outdoor PM
2.5
, OC, EC, particle number (PN), ozone
(O
3
), carbon monoxide (CO) and nitrogen oxides (NO
X
) concentrations were measured at
two different retirement communities in the Los Angeles area and used to provide new
insight into: a) the relationships between indoor and outdoor PM
2.5
, its components and
their seasonal variations as well as their association with gaseous co-pollutants, b) the
contributions of primary OC and SOA to measured outdoor OC and c) the relative
importance of outdoor and indoor PM sources to measured indoor OC, EC, PM
2.5
and PN
concentrations. The results obtained in this paper will be used to determine personal
exposure to outdoor-infiltrated PM
2.5
and its particulate components and to indoor-
generated PM
2.5
and its particulate components in elderly retirees with a history of CHD.
66
3.3. METHODS

3.3.1. Study Design
As a part of CHAPS, the physical and chemical characteristics of indoor and outdoor
PM
2.5
were investigated at two different retirement communities in southern California.
Site A for subject group 1 (G1) was located about 30 miles east of downtown Los
Angeles, in a residential area, approximately 2 miles away from any major freeways and
close to a construction site. Site B for subject group 2 (G2) was located about 5 miles east
of downtown Los Angeles, approximately 0.1 miles south of a major freeway. Two 6-
week sampling campaigns were conducted at each site; site A (G1) was operated from
07/06/2005 to 08/20/2005 (Phase 1, or P1) and from 10/19/2005 to 12/10/2005 (Phase 2,
or P2), while site B (G2) was operated from 08/24/2005 to 10/15/2005 (P1) and from
01/04/2006 to 02/18/2006 (P2). Thus, we were able to study the seasonal variations in the
indoor/outdoor relationships of PM
2.5
and its components.
Two identical sampling stations were installed at each location, one indoors and one
outdoors. The indoor sampling station at site A was located in a recreational area of the
first community’s main building, adjacently to a construction site where work was
ongoing. The indoor sampling area at site B was situated in the dinning room of the
second community’s main building. At both sites the outdoor station, set-up inside a
movable trailer, was positioned within 300 m from the indoor station.
67
3.3.2. Instrumentation
At both indoor and outdoor sampling areas a water-based condensation particle counter
(CPC Model 3785, TSI Inc, Shoreview, MN), providing continuous (1-min) PN
concentrations (operating flow-rate = 1 lpm), and a semi-continuous OC_EC analyzer
(Model 3F, Sunset Laboratory Inc., Tigard, OR) were operated side-by-side. The two
CPCs were examined at the USC lab before being deployed in the field and showed high
internal precision. The OC_EC analyzers were placed downstream of a PM
2.5
cyclone
and collected samples at an approximate flow-rate of 8 lpm. Particulate OC and EC were
measured in hourly cycles (i.e. sampling time = 45-min; analysis time = 15-min). A
multi-channel parallel carbon plate diffusion denuder (provided by the manufacturer) was
placed upstream of the OC_EC instrument to remove most of the organic vapors in the
sampled air. The setup and the standard operating procedure for the semi-continuous
carbon analyzer are described in more details in Arhami et al., 2006. A modified-NIOSH
analysis protocol was used here to evolve particulate OC and EC. This protocol consists
of four temperature steps in the He-analysis segment and allows for the separation of
particulate OC into four response peaks representing different volatility fractions of OC
(Birch and Cary, 1996; Kirchstetter, 2001). These four OC peaks are designated and
recorded as peak 1 to peak 4 (OC
1
to OC
4
). For the purposes of this study, OC
2
, OC
3
, and
OC
4
were summed (OC
2-4
) and considered as the least volatile OC fraction, while OC
1

represented the most volatile OC fraction. The internal precision of the two OC_EC
analyzers (examined prior the beginning of CHAPS by running them side by side) was
high (R
2
of 0.98 and 0.97 for thermal OC and EC, respectively). A detailed description of
all quality control and quality assurance analyses performed with the semi-continuous
68
carbon analyzer is reported in the Supplemental Information along with the
corresponding results.
Hourly PM
2.5
mass concentrations were measured by Beta-Attenuation Mass Monitors
(BAM, Model 1020, Met One instruments Inc., OR) at a flow-rate of 16.7 lpm. Two
BAMs were used at each of the indoor and outdoor sampling stations in order to examine
the uncertainty of the collected data. Continuous (1-min) NO and NO
2
measurements
were obtained both indoors and outdoors by using Thermo Environmental NOx
Analyzers (Model 42, Thermo Environmental instruments Inc, Franklin, MA). Dasibi
Carbon Monoxide Analyzers (Model 3008, Dasibi Environmental Corp, Glendale, CA)
were implemented to measure continuous (1-min) indoor and outdoor CO levels.
Continuous (1-min) outdoor ozone (O
3
) concentrations were also monitored at each
sampling station by using API Ozone Analyzers (Model 400A, Teledyne Technologies
Inc, Los Angeles, CA).
3.3.3. Data Analysis
To match the OC, EC and PM
2.5
measurements, only hourly arithmetic averages of the
highly resolved PN and gaseous co-pollutant (CO, NOx, O
3
) concentrations were
considered. Then, a comprehensive indoor and outdoor database was constructed for each
group (G) and phase (P) of CHAPS to analyze the relationships between measured indoor
and outdoor particulate and gaseous species, and to facilitate the overall data analysis.
69
The contributions of primary OC and SOA to measured outdoor OC were estimated from
collected OC and EC concentrations using EC as a tracer of primary combustion-
generated OC (i.e. “EC tracer method”) (Turpin et al., 1995; Lim et al., 2003; Cabada et
al., 2004; Polidori et. al., 2006). This method assumes that primary OC and EC are
emitted from the same combustion sources. Data-points characterized by high CO and
NO peaks, mainly observed during rush hour traffic, were used to identify periods
dominated by primary sources, when SOA is less likely to be formed. By regressing the
OC and EC data collected during these time-periods the characteristic primary OC/EC
ratio for each month of CHAPS was determined. Because a conventional linear least-
squares regression assumes that there are uncertainties only in the dependent variable, a
Deming linear least-squares regression (Deming, 1943; Cornbleet et al., 1979)

was used
instead, and the uncertainties in OC and EC were assumed equal. Thus, primary OC
(OC
pri
) and SOA can be estimated by the following expressions:
OC
pri
= a x EC + b (3.1)
SOA = OC – OC
pri
, (3.2)
where, a = (OC/EC)
pri
= characteristic primary OC/EC ratio for the study area, and b =
non-combustion primary OC. Typically, the SOA values estimated through this method
vary with season and location and are generally higher during the afternoon hours of
summertime photochemical smog episodes (e.g. in the Los Angeles basin) and at
locations that are recipients of long distance transport (e.g., the eastern US).
70
The indoor-outdoor air exchange rates (AER; h
-1
) at each site were estimated from indoor
CO measurements collected during periods affected by a dominant indoor source. Only
time-periods when the CO concentration peaked at values significantly higher than the
background CO level and was followed by a non-source period (mostly observed in the
morning and probably associated with cooking activities) were considered in our
calculations. Assuming an exponential decay of particles, that AER and outdoor
concentrations are constant during the decay period, and that indoor concentrations are
well mixed, then:
C
t
= e
-(AER+k)t
C
0
(3.3)
or
ln C
t
= -(AER+k)t + ln C
0
(3.4)
where, C
t
is the indoor CO concentration after time t (after the decay period), C
0
is the
initial peak CO concentration (right after CO emission) and k is the indoor loss rate for
particles or gases (h
-1
) (Abt et al., 2000). Since k is rather negligible for CO, it was
possible to estimate the AERs for the two sites directly from the above-mentioned eq
(3.4) by regressing ln C
t
over ln C
0
.
71
The infiltration factor (F
inf
, defined as the equilibrium fraction of ambient particles that
penetrate indoors and remain suspended (Long et al., 2001)
is a key determinant of the
indoor concentrations of particulate species. F
inf
is described by the following eq:
F
inf
=
P(AER)/(AER+k) (3.5)
where, P is the penetration coefficient (dimensionless). F
inf
for PM
2.5
varies with particle
composition, particle size and volatility, surface to volume ratio of the indoor sampling
location and indoor air-speed. F
inf
is typically highest for non-volatile species such as EC
(Lunden et al., 2003; Sarnat et al., 2006). In order to estimate F
inf
for OC, EC, PM
2.5
and
PN two different techniques were used: 1) an analysis of the indoor/outdoor
concentration ratios, and 2) the recursive model (RM) developed by Allen et al., 2003. In
the first approach hourly indoor/outdoor ratios (I/O) for each particulate species were
determined at times when no indoor particle sources, such as cooking or cleaning, were
likely to be present (i.e. only I/O ratios ≤ 1 were considered) . Daily F
inf
estimates were
then obtained by averaging these segregated hourly I/O ratios. Mean F
inf
for each group
and phase of the study were also determined by averaging the corresponding daily values.
To verify these results the same analysis of the I/O concentration ratios was then repeated
by using only nighttime data (from 00:00 to 06:00 am), for at this time resident activities
causing indoor particle generation were expected to be minimal. Conversely, the RM
method, which has been recently validated for estimating F
inf
for PM
2.5
from hourly light
scattering data (Allen et al., 2006)

states that, for a particular species of interest, the
average indoor concentration during hour t (
in
t
C ) is equal to the sum of a fraction of the
72
average outdoor concentration during the same hour (
out
t
C ), a fraction of the average
indoor concentration remaining from the previous hour (
in
t
C
1 −
), and the contribution from
indoor sources (
in
t
S ):

in
t
in
t
out
t
in
t
S C a C a C + + =
−1 2 1

(3.6)
where

( )
2 inf 1
1 a F a − =

(3.7)
and

=
2
a
( ) [ ] t k AER ∆ + − exp

(3.8)
Algorithms are used to identify and minimize the influence of hours when the indoor
concentration is influenced by indoor sources, thus eliminating the
in
t
S term from eq (3.6):

in
t
out
t
in
t
C a C a C
1 2 1 −
+ =

(3.9)
The coefficients a
1
and a
2
are then estimated via multiple linear regression of eq (3.9)
and F
inf
is calculated from a
1
and a
2
using the following relationship:
73

2
1
inf
1 a
a
F
−
=

(3.10)
Finally, a single compartment mass balance model (Meng et al., 2005; Wallace, 1996;
Polidori et al. 2006)

was used to assess the mean contributions of indoor and outdoor
sources to measured indoor OC, EC, PM
2.5
and PN concentrations. Under the assumption
of perfect instantaneous mixing and that the factors affecting the indoor concentrations
are constant or change slowly with time, the steady state indoor concentration of any
particulate species can be described by the following eq:
(3.11)
where, C
in
is the indoor concentration of the species of interest (µg/m
3
), C
out
is the
corresponding outdoor concentration (µg/m
3
), F
inf
is the corresponding infiltration factor
(estimated for each species as described previously; dimensionless), C
ig
is the indoor-
generated concentration for the same species found indoors and C
og
is the outdoor-
generated concentration for the same species found indoors. Typically, in the mass
balance model, F
inf
is given by eq (3.5) and C
ig
is expressed by Qi/V(a+k), where Q
i
is the
indoor source strength (µg/h), and V is the house volume (m
3
).
in
P(AER)C
out
C
= +
k AER
+
=
ig og ig out
C C C C F
+ = +
inf
k AER
+
i
V Q /
in
P(AER)C
out
C
= +
k AER
+
k AER
+
=
ig og ig out
C C C C F
+ = +
inf ig og ig out
C C C C F
+ = +
inf
k AER
+
i
V Q /
k AER
+
k AER
+
i
V Q /
i
V Q /
74
3.4. RESULTS AND DISCUSSION

3.4.1. Particle and Gaseous Measurements
The minimum, maximum, average and standard deviation of all hourly particle and gas
data obtained for all groups (G) and phases (P) of CHAPS are presented in Table 3.1.
Overall, the magnitude of indoor measurements was similar to that of outdoor
measurements for most phases of the study, which highlights the major effect of outdoor
sources on indoor levels (Meng et al., 2005; Polidori et al., 2006; Naumova et al., 2002;
Naumova et al., 2003). Although at site B (G2) a wider range in indoor PM
2.5
, PN and
OC concentrations was observed compared to the outdoor levels, the average indoor and
outdoor concentrations were still comparable. The difference between indoor and outdoor
average concentrations was lowest for CO and NOx (except NOx in G2P1), which
suggests the absence of an important indoor source of these two gases and that their
penetration losses were not significant. Conversely, the differences between indoor and
outdoor particle levels were higher, probably because of the presence of indoor sources
and/or changes in concentration due to transportation of particles indoors from outdoors.
The average hourly diurnal variations for data collected during the first phase of CHAPS
at site A (G1P1; from 07/06/2005 to 08/22/2005) are shown in Figure 3.1. Generally,
indoor and outdoor particle and gas concentrations tracked each other well, with a better
agreement for gases. Slightly higher OC levels were measured indoors, mainly because of
indoor contributions of the most volatile OC fraction (OC
1
; not shown). Indoor EC, PM
2.5

and PN concentrations slightly increased from 10:00 am to 12:00 pm, probably because
75
of the emission contributions of a construction site located right outside the indoor
sampling area. Significant EC, PN, PM
2.5
, CO and NOx peaks occurred concurrently
outdoors and indoors during the morning rush hour traffic, suggesting that outdoor
primary pollutants were important contributors to the indoor air. Indoor and outdoor OC
concentrations increased from 12:00 to 04:00 pm probably as a result of photochemical
OC formation in the afternoon. This mechanism of particle generation may also explain
the increase in outdoor PN and PM
2.5
between 03:00 and 05:00 pm, not observed for EC,
CO and NOx.
The average indoor and outdoor diurnal patterns at site A during the second phase of the
study (G1P2; from 10/17/2005 to 12/13/2005), generally followed the same trends as
those observed during G1P1 (Figure 3.1), which strengthen the hypothesis that at this
location the majority of indoor particles and gases originated from outdoor sources.
During G1P2 the CO and NO
X
outdoor concentrations increased at midnight, most likely
because of a lowered mixing height. This caused a subsequent increase in indoor gaseous
levels.
Measurement results at site B during the first phase of CHAPS (G2P1, from 08/23/2005
to 10/15/2005) showed a substantial morning peak in indoor OC, PN and PM
2.5
(Figure3.2) between 06:00 and 09:00 am, probably from cooking activities in the kitchen
adjacent the indoor sampling site where breakfast, lunch and dinner were all cooked at
this time by using gas stoves/ovens. Interestingly, cooking did not affect EC, CO and
NO
X
, whose indoor and outdoor levels were mostly influenced by morning rush hour

76
Table 3.1 Average ± 1σ (standard deviation), minimum (min) and maximum (max) of all hourly particle and gas data obtained for all
groups (G) and phases (P) of CHAPS.

77

Figure 3.1 Hourly diurnal variations for particle and gas data collected during the first and
the second phase of CHAPS at site A (G1P1 and G1P2, respectively). The slope (S),
intercept (I) and Pearson correlation coefficient (R) for indoor versus outdoor
concentrations are also reported.

78
traffic. With the exception of these morning peaks, indoor and outdoor particle and gas
concentrations tracked each other well, although indoor levels were generally lower than
outdoor levels. This suggests that indoor sources of OC, PN and PM
2.5
were not
significant other than during the morning cooking events, and that indoor EC mainly
originated from outdoor sources. Indoor cooking affected the concentrations of the least
and the most volatile OC fractions (OC
1
and OC
2-4
, respectively) equally (not shown).
Similar indoor and outdoor trends in particle and gas concentrations, including a morning
increase in OC, PN and PM
2.5
due to cooking, were also observed in the second phase of
the study at site B (G2P2, from 01/04/2006 to 02/21/2006; Figure 3.2). Indoor and
outdoor diurnal patterns for EC, CO and NOx were virtually identical (no significant
indoor sources) and peaked in the morning during rush hour traffic. Ground-level
concentrations of all particulate and gaseous species increased significantly at midnight
because of a decrease in the mixing height and an increase in the atmospheric stability,
typical of wintertime conditions. Indoor particle mass levels were lower during G2P2
than during G2P1; the reasons for this discrepancy are unclear.
Despite the substantial morning increase in indoor OC, PN and PM
2.5
at site B due to
cooking, the average indoor concentrations of these three species were not much higher
than those measured outdoors (Table 3.1). This was the result of the smaller contribution
of indoor sources during the rest of the day, which lowered the average indoor
concentrations. Also, these observations are consistent with the low AER and F
inf
values
estimated in this work and discussed in a subsequent section.
79
Figures 1 and 2 also include the slope (S), intercept (I) and Pearson correlation coefficient
(R) for indoor versus outdoor concentrations of all measured particulate and gaseous
species and for all groups and phases of CHAPS. At site B, where indoor cooking
significantly affected PM
2.5
, PN and OC concentrations, the morning data (05:00 to 09:00
am) were not considered in the calculation of S, I and R. Indoor gas concentrations
usually showed the highest degree of correlation with the corresponding outdoor levels
with S close to 1 in most cases, confirming that indoor NOx and CO were mostly of
outdoor origin. For OC, EC, PM
2.5
and PN a positive I was obtained, suggesting the
presence of significant indoor background concentrations for all particulate species.
Indoor OC, OC
1
and OC
2-4
measurements correlate well with the corresponding outdoor
data, confirming the important effect of outdoor OC sources on indoor levels. A high R
for the EC indoor/outdoor correlations was always observed, for indoor sources of EC
were negligible at both sites.
The highest slopes for EC were found during G1P1, probably because of a substantial
outdoor contribution from the diesel vehicles operating at the construction site located
right outside the indoor sampling station and between the retirement community building
and the outdoor monitoring site. Therefore, the concentration of EC impacting the
building shell was greater than that measured by the outdoor site. This, rather than an
indoor source of EC, is thought to explain the indoor EC being higher than the outdoor
EC for G1P1. Representative hourly diurnal variations of the PM
2.5
, OC and EC
concentrations (µg/m
3
) (non-averaged data) measured during each group (G) and phase
(P) of CHAPS are reported in the Supplemental Information.
80
The correlation between gas levels, PM
2.5
and PN concentrations for indoor and outdoor
data are presented in Table 3.2. Overall, the results show a weak Pearson correlation
coefficient between particles and gases indoors. However, some modest correlations (R ~
0.4-0.5) between PN and NOx and between PN and CO are observed outdoors during
most phases of CHAPS, probably because these three species were mainly emitted from
the same source (e.g. motor vehicles emission). These correlations are slightly higher at
site B, which was located closer to a freeway.
3.4.2. Primary OC and SOA Estimations Outdoors
Figure 3.3 shows all semi-continuous outdoor OC and EC values obtained on July 2005
(G1P1). Data points were segregated into measurements dominated by primary emissions
(grey) and measurements affected by SOA formation (black). Similar plots were obtained
for each month of the study. By regressing OC on EC using only data dominated by
primary emissions we estimated the characteristic primary OC/EC ratios (a in eq 3.1) and
non-combustion primary OC (b in eq 3.1) for each month during CHAPS (Table 3.3).
Carbon data corresponding to time periods affected by rain were not considered in the
determination of a and b because of the possibility of differential wet scavenging (Lim et
al., 2003; Cabada et al., 2004). The average coefficient of determination (R
2
) for all
monthly regressions was 0.87 ± 0.06 (1σ), which adds confidence to our results.
Thus, using eqs 3.1 and 3.2 and the estimated a and b values monthly averaged outdoor
primary OC and SOA concentrations were obtained (Table 3). Polidori et al., 2006.
calculated that the variability in primary OC and SOA determined by using the method
81

Figure 3.2 Hourly diurnal variations for particle and gas data collected during the first and
the second phase of CHAPS at site B (G2P1 and G2P2, respectively). The slope (S), ntercept
(I) and Pearson correlation coefficient (R) for indoor versus outdoor concentrations are also
reported.

82
Table 3.2 Correlation between particle concentrations (PM
2.5
and PN) gas levels (CO and
NO
X
) for all indoor and outdoor data collected during CHAPS.

PN-CO PM
2.5
-CO PN-NO
X
PM
2.5
-NO
X

IN OUT IN OUT IN OUT IN OUT
G1P1 0.37 0.21 0.22 0.05 0.33 0.22 0.10 0.15
G1P2 0.07 0.38 0.40 0.28 0.15 0.49 0.04 0.07
G2P1 0.28 0.45 0.32 0.21 0.40 0.47 0.15 0.07
G2P2 0.11 0.49 0.37 0.44 0.19 0.57 0.15 0.35

described above is around 10%. This estimate of precision only includes “model”
uncertainties and it would be higher if measurement uncertainties were also taken into
account. Typically, uncertainties for primary and secondary OC calculations are on the
order of 10-40% (Cabada et al., 2004; Polidori et al., 2006).
During CHAPS the average SOA concentration was 2.50 ± 1.94 (1σ) µgC/m
3
, which
represents 40% ± 22 (1σ) of the study average particulate OC. These results are likely to
be representative of the entire San Gabriel Valley, for the SOA estimations presented in
this work only refer to outdoor data and none of the two CHAPS sites was affected by
83
0
4
8
12
16
20
0 2 4 6 8
EC (µgC/m
3
)
OC (µgC/m
3
)
y = 1.64x + 0.29
R
2
= 0.89
SOA
PRIMARY
0
4
8
12
16
20
0 2 4 6 8
EC (µgC/m
3
)
OC (µgC/m
3
)
y = 1.64x + 0.29
R
2
= 0.89
SOA
PRIMARY
SOA
PRIMARY

Figure 3.3 Particulate OC and EC semi-continuous carbon measurements made from
07/06/05 to 07/31/05 at site A (G1P1). Black rectangles represent measurements with a
moderate or high probability of SOA formation. Grey triangles are measurements
dominated by primary emissions. The regression line, equation and coefficient of
determination (R2) were obtained by Deming regression of measurements labeled
“PRIMARY”. Similar scatter plots were obtained for each month of CHAPS.

any specific local sources other than traffic emissions. The highest monthly average SOA
concentration (3.30 µgC/m
3
, or 40% of measured OC) was estimated between 02/01/06
and 02/16/06 (G2P2), while the lowest average SOA concentration (1.88 µgC/m
3
, or 35%
of measured OC) was estimated between 11/01/05 and 12/09/05 (G1P2) (see Table 3 for
details). Interestingly, the summertime percentage contributions of SOA to particulate
OC obtained during CHAPS (40-45%) are higher than those estimated by Turpin and
Huntzicker in Claremont, CA (where SOA exceeded 40% of the daily OC only during the
afternoon photochemical smog episodes), but are in good agreement with those obtained
in Atlanta by Lim and Turpin, 2002 (44%) and in the Pittsburgh area, PA, by Cabada et
al., 2004 (35%) and Polidori et al., 2006 (38%). In each case a similar decision strategy
was used to identify time periods dominated by primary OC and to estimate SOA. Thus,
84
these recent results are quite consistent and suggest that in the summertime SOA
represents a substantial fraction of measured OC both in the East and in the West of the
United States. Figure 3.4a shows the time averaged diurnal pattern for estimated primary
OC and SOA concentrations during G1P1 (typical of summertime conditions). The
corresponding measured outdoor CO and O
3
concentrations are also reported. As
expected, primary OC, CO and NO (not shown) peaked between 05:00 and 11:00 am
because of rush hour traffic, while SOA and O
3
peaked between 01:00 and 07:00 pm
because of the high photochemical activity occurring locally. CO and NO are considered
tracers of local and regional combustion, while O
3
is used as a tracer for photochemical
reactions. During the wintertime, both the average SOA concentration (2.36 µgC/m
3
in
January, and 3.30 µgC/m
3
in February) and the average percentage contributions of SOA
to particulate OC (32% in January and 40% in February; G2P2) were comparable to the
corresponding average values estimated in the summertime (2.18 µgC/m
3
or 43% of
measured OC in July, and 2.16 µgC/m
3
or 40% of measured OC in August; G1P1) (Table
3), but had very different diurnal patterns. These wintertime SOA results are significantly
higher than those reported for the San Joaquin Valley, CA, by Strader et al., 1999, where
SOA formation accounted for roughly 25% of wintertime OC, and for Pittsburgh, PA,

85
Table 3.3 By regressing (Deming regression) OC on EC using only outdoor data dominated by primary emissions we estimated the
characteristic primary OC/EC ratios (a = slope), non-combustion primary OC (b = intercept) and coefficient of determination (R
2
) for
each month of CHAPS. a and b were then used to estimate outdoor primary OC and SOA concentrations, and the percentage
contribution of SOA to measured outdoor OC.

From To a b (µgC/m
3
) R
2

Primary OC
(µgC/m
3
)
SOA
(µgC/m
3
)
SOA
(%)
G1P1 07/06/05 07/31/05 1.64 0.29 0.89 2.64 2.18 43
G1P1 08/01/05 08/19/05 2.27 0.00 0.87 3.06 2.16 40
G2P1 08/23/05 09/30/05 2.08 0.19 0.86 3.39 3.01 45
G2P1 10/01/05 10/14/05 2.21 0.04 0.94 4.95 3.10 42
G1P2 10/17/05 10/31/05 2.04 0.10 0.93 2.88 2.55 45
G1P2 11/01/05 12/09/05 1.86 0.00 0.85 3.18 1.88 35
G2P2 01/05/06 01/31/06 2.46 0.00 0.91 4.62 2.36 32
G2P2 02/01/06 02/16/06 2.39 0.18 0.74 4.32 3.30 40

86

Figure 3.4 Time averaged diurnal pattern for estimated primary OC and SOA
concentrations during G1P1 (typical for summertime conditions) (a) and G2P2
representative of wintertime conditions) (b). The corresponding measured CO and O
3

concentrations are also reported.

87
where the average wintertime SOA concentration was 24% of measured OC (Polidori et
al., 2006). The time averaged diurnal pattern for estimated primary OC and SOA
concentrations during G2P2 (representative of wintertime conditions) is shown in Figure
3.4b. The corresponding measured CO and O
3
concentrations are also reported.
Typically, the concentrations of primary OC, CO and NO (not shown) tracked one
another well across the day and peaked in the early morning (between 05:00 and 11:00
am; because of rush hour traffic) and late at night (between 08:00 pm and 02:00 am; most
likely because of increased stability and low mixing heights). The average primary OC
concentrations were higher during G2P2 (4.62 µgC/m
3
and 4.32 µgC/m
3
for January and
February, respectively) than during G1P1 (2.64 µgC/m
3
and 3.06 µgC/m
3
for July and
August, respectively) suggesting that primary combustion sources of OC were dominant
in the wintertime. Periods of high wintertime SOA concentrations (as high as 9-12
µgC/m
3
) typically occurred in the late afternoon or at night (Figure 3.4b). Strader et al.,
2005 suggested that under suitable conditions (clear skies, low horizontal winds, and low
mixing height) SOA concentrations to levels as high as 15-20 µgC/m
3
could be produced
in the wintertime, mainly due to the oxidation of aromatics. Species such as toluene,
xylenes, trimethylbenzenes, naphthalenes, and 1,3,5-trimethylbenzene are predicted to
produce roughly 75% of all SOA under these conditions.
The meteorological conditions observed during G2P2 (especially in February) were
extremely favorable for SOA production with afternoon temperatures that reached 30
0
C
on several occasions and nighttime temperatures as low as 5
0
C. Under these
circumstances, if highly reactive SOA precursors were accumulated within the San
88
Gabriel Valley, significant amounts of SOA could be formed in the afternoon because of
photochemical activity, and at night because of a shifting of the gas-particle partitioning
equilibrium towards the particulate phase due the temperature decrease (Pankow, 1994).
In addition, a decrease in the mixing height could also contribute to the accumulation of
SOA precursors at night, accelerating the rate of SOA formation. However, it has to be
recognized that these high wintertime SOA values could be, at least in part, an artifact of
the EC-tracer method, for the primary OC/EC ratio is in fact not constant, as assumed by
the method, but varies between sources and is influenced by meteorology, diurnal
fluctuations in emissions, and the influence of local sources. For example, an
overestimation of the SOA concentrations could occur when the influence of a large
primary source with no temporal regularity and a high OC/EC ratio (e.g., wood burning)
were not taken into account in the determination of the primary OC/EC ratio. In fact, the
OC/EC ratio for biomass burning can be greater than 10 (Hays, 2002). Although CO was
considered as a primary combustion tracer in the determination of the primary OC/EC
ratios, the possibility that a fraction of the estimated wintertime SOA is really primary
OC from biomass combustion cannot be entirely ruled out. The contribution of other
processes such as nighttime chemistry and fog/cloud processing to SOA formation
remains uncertain.
The fall period (most of G2P1 and G1P2 measurements) was characterized by average
SOA concentrations ranging between 3.10 µgC/m
3
(10/01/05 to 10/14/05) and 1.88
µgC/m
3
(11/01/05 to 12/09/05), corresponding to 42 and 35% of measured OC,
respectively (Table 3). The daily primary OC and SOA concentration dynamics for G2P1
89
were more comparable to those observed in the summer (G1P1), with similar afternoon
O
3
and SOA maxima, but slightly higher nighttime SOA increases and higher CO and
primary OC morning and nighttime peaks (not shown). Likewise, G1P2 concentration
dynamics were more comparable to those observed in the winter (G2P2), but with smaller
nighttime SOA increases and smaller CO and primary OC morning and nighttime peaks
(not shown).
3.4.3. Air Exchange Rate Estimates
The average AER for each group and phase (0.25 h
-1
± 0.04 (1σ), 0.28 h
-1
± 0.06, 0.33 h
-1

± 0.07 and 0.31 h
-1
± 0.10 for G1P1, G2P1, G1P2 and G2P2, respectively) were quite
constant throughout the year and similar for both G1 and G2 retirement communities.
The generally low estimated AERs are consistent with the structural characteristics of the
sampling sites (G1 was a recreational area and G2 a dining hall, both in the middle of the
retirement homes), the low number of open windows and doors, and the presence of
central air conditioners. These results are comparable to overnight AER high-resolution
(3-min) measurements obtained by Sarnat et al., 2006 in 17 Southern California homes
using a constant sulfur hexafluoride (SF
6
) source in conjunction with SF
6
monitors. By
using the same methodology, the median summertime AER measured in Pennsylvania
residences were ~0.30 h
-1
for air-conditioned homes and ~ 2 h
-1
for non-air-conditioned
homes (Suh, 1994). The R
2
for the regression lines used to calculate the AERs presented
here (see eq 3.4) was always higher than 0.9, which adds confidence to our results.
90
3.4.4. Infiltration Factor Estimates
The average F
inf
estimates (calculated by both the I/O concentration ratio and the RM
approaches) for OC, EC, PM
2.5
and PN concentrations for each group (G) and phase (P)
of CHAPS are reported in Table 3.4. In general, for G1 and G2 the F
inf
results were
similar across P1 (summer and fall) and P2 (fall and winter), which is consistent with no
seasonal changes in home dynamics and ventilation conditions as indicated by the rather
constant AERs calculated throughout the study and discussed above. The average F
inf

results were highest for EC (ranges across methods were 0.70-0.82, 0.67-0.74, 0.77-0.80
and 0.64-0.69 for G1P1, G2P1, G1P2 and G2P2, respectively) and OC (ranges were 0.83-
0.98, 0.74-0.77, 0.82-0.87 and 0.61-0.67 for G1P1, G2P1, G1P2 and G2P2, respectively).
For EC, this is likely due to the fact that EC is non-volatile, is found mostly in the 0.1-0.4
µm range (Miguel et al., 2004; Sardar et al., 2005)

and, thus, is capable of infiltrating
through the building envelope with great efficiency. The equally high F
inf
estimated for
OC suggests that the particle size range of this important PM
2.5
component was probably
similar to that of EC, and that it was mainly comprised of organic compounds with
relatively low vapor pressure. This is also consistent with our observations throughout the
study that a substantial fraction of outdoor OC consisted of SOA, whose size distribution
is generally concentrated in the lower sizes of the accumulation mode (Zhang et al.,
2005) (characterized by a nighttime F
inf
of ~ 0.7 (Long et al., 2001, Sarnt et al., 2006),
and is typically comprised of highly polar organics (Saxena et al., 1996; Kiss et al., 2002;
Carlton et al., 2006). It should be noted that lower F
inf
(OC) values may have been
obtained if our indoor sampling sites had been located in environments characterized by a
higher surface to volume ratio (S/V) (e.g. a fully furnished apartment as opposed to a
91
recreational area or a dining room), for the depositional loss rate (k) increases with
increasing S/V of the studied indoor location (Thatcher et al., 2002). During CHAPS, for
particulate species that are known to be comprised of both semi-volatile and volatile
compounds (e.g. OC) this might translate in an overestimation of the corresponding F
inf
.
A somewhat lower average F
inf
was obtained for PM
2.5
(ranges across methods were
0.52-0.74, 0.45-0.60, 0.52-0.62 and 0.38-0.45 for G1P1, G2P1, G1P2 and G2P2,
respectively) reflecting the possible effects of volatile and semi–volatile species on F
inf
.
For example, particulate compounds such as ammonium nitrate, which accounts for 35–
49% of the outdoor PM
2.5
mass in the Los Angeles basin (Chow et al., 1994; Christoforou
et al., 2000; Kim et al., 2000; Tolocka, 2001), volatilize upon building entry and
contribute to lower the average F
inf
of PM
2.5
. The average F
inf
for ammonium nitrate
reported by Sarnat et al., 2006 in a recent study conducted in Southern California homes
was 0.18.
Finally, the F
inf
estimates for PN concentration were 0.59-0.69, 0.46-0.55, 0.77-0.80 and
0.54-0.63 during G1P1, G2P1, G1P2 and G2P2, respectively. We hypothesize that the
somewhat lower F
inf
(PN) calculated at site B (G2) were caused, at least in part, by a
higher fraction of sub-100 nm particles in the sampled aerosol due to the close proximity
(less than one mile) of this site to a major highway. Lower F
inf
values for PN at site B
(G2) are consistent with the lower penetration of sub-100 nm particles indoors due to
diffusional losses (Long et al., 2001, Sarnt et al., 2006)

as well as losses due to
evaporation of volatile species associated with this size range (Zhu et al., 2005).
92
With the exception of F
inf
for PN concentration, the average F
inf
results for OC, EC and
PM
2.5
estimated during CHAPS are in good agreement with those obtained in several
previous studies conducted in other parts of the United States (Polidori et a., 2006;
Lunden et al., 2003; Sarnat et al., 2006). Because F
inf
varies with both aerosol
composition (e.g. changes in the outdoor concentration of labile species such as
ammonium nitrate) and the AER, diurnal variations of these variables are likely to affect
particle infiltration. However, the standard deviation of the daily averaged F
inf
estimates
for OC, EC, PN, and PM
2.5
(also reported in Table 3.4) were small within each group and
phase of the study, probably because of the structural characteristic of the G1 and G2
sampling sites (e.g. presence of central air conditioners and low number of open windows
and doors). Most importantly, our findings indicate that PM
2.5
and its carbonaceous
components (e.g. OC and EC, both of which comprise a substantial portion of the PM
2.5
mass
11
) are characterized by different F
inf
values. This implies that the composition of
indoor and outdoor particles is different and that ambient PM
2.5
concentrations may not
adequately represent personal exposures to outdoor-infiltrated PM
2.5
in indoor
environments (Polidori et al., 2006; Ebelt et al., 2005; Lunden et al., 2003; Naumova et
al., 2002; Naumova et al., 2003).
3.4.5. Indoor and Outdoor Contributions to Measured Indoor Species
concentrations
By multiplying the measured outdoor 1-hr OC, EC, PM
2.5
and PN concentrations (C
out
)
by the corresponding average F
inf
estimates reported in Table 3.4, we determined the
indoor contribution of outdoor origin for each particulate species (C
og
) and for each group
93

Figure 3.5 Calculated indoor concentrations of indoor origin (Cig) for OC (5a), EC (5b),
PM2.5 (5c) and PN (5d) expressed as a percentage of the corresponding measured indoor
concentrations (Cin), and averaged throughout G1P1, G2P1, G1P2 and G2P2 (black
columns). The lowest possible Cig estimations for the same species (grey columns) were
obtained by assuming Finf =1. Error bars represent + 1σ (1 standard deviation) of all Cig
estimates obtained within each group (G) and phase (P).

(G) and phase (P) of CHAPS. The resulting indoor contributions of indoor origin (C
ig
)
were then estimated by subtracting C
og
from C
in
on a sample-by-sample basis. Figure 3.5
shows the calculated C
ig
concentrations for OC (5a), EC (5b), PM
2.5
(5c)

and PN
concentrations (5d) expressed as a percentage of the corresponding measured indoor
concentrations (C
in
), and averaged throughout G1P1, G2P1, G1P2 and G2P2. Columns
refers to C
ig
values obtained by using F
inf
estimates from the indoor/outdoor ratio method
considering all hourly I/O data ≤ 1 (black), the I/O method accounting only for nighttime
ratios ≤ 1 (grey), and the RM approach (darker grey).
94
Our results indicate that, on average, 16 to 26% (1.06-1.63), 18 to 20% (1.69-1.80), 20 to
23% (1.17-1.33) and 13 to 17% (1.03-1.23) of measured indoor OC was emitted or
formed indoors during G1P1, G2P1, G1P2 and G2P2, respectively (the corresponding
ranges of average indoor-generated OC concentrations in µgC/m
3
are reported in
parenthesis) (Figure 3.5a). These calculations suggest that although the G2 indoor site
was characterized by higher indoor morning OC peaks due to cooking, the overall
contribution of indoor sources to measured indoor OC was actually higher at the G1 site.
These results are lower than those obtained by Polidori et al., 2006 during the
Relationship of Indoor, Outdoor and Personal Air (RIOPA) study, where the average C
ig
for OC varied between 40 and 70%. The lower C
ig
OC estimates obtained here are
consistent with the prevailing use of central air conditioning at both G1 and G2 indoor
sites, and may also be due to differences in home dynamics between the RIOPA and the
CHAPS sampling locations (personal residences and common areas for retirees,
respectively) and exposure groups. The CHAPS subjects consisted of retirees with
compromised health, whose indoor activity levels are likely to be much lower than those
of the RIOPA group.
The average percentages of measured indoor EC that was generated indoors were 17 to
25% (0.37-0.48), 11 to 16% (0.16-0.22), 21 to 23% (0.27-0.30) and 20-22% (0.23-0.27)
for G1P1, G2P1, G1P2 and G2P2, respectively (ranges of average indoor-generated EC
in µgC/m
3
in parenthesis) (Figure 3.5b). These values are quite close to the detection
limit for EC for semi-continuous carbon measurements, typically around 0.15-0.35
µgC/m
3
(as determined by Lim et al.
21
), and suggest that indoor sources of EC were not
95
an important contributor to measured indoor EC during CHAPS. These outcomes are
consistent with indoor/outdoor EC ratios close to or slightly lower than unity obtained in
several previous studies conducted both in California (Geller et al., 2002; Na et al., 2006)

and around the world (Funasaka et al., 2000; Ho et al., 2004).
The mass balance model results also showed that on average 6 to 21% (1.85-5.33), 24 to
38% (5.02-7.47), 42 to 51% (8.26-10.31) and 21 to 30% (2.82-4.03) of measured indoor
PM
2.5
was emitted or formed indoors during G1P1, G2P1, G1P2 and G2P2, respectively
(the corresponding ranges of average indoor-generated PM
2.5
concentrations in µg/m
3
are
reported in parenthesis) (Figure 3.5c). These outcomes are somewhat difficult to interpret
and suggest that the seasonal emission/formation of indoor PM
2.5
from indoor sources
was highly variable. It is important to recognize that the PM
2.5
concentrations measured
indoors during G2P2 were unusually low compared to the corresponding outdoor PM
2.5
concentrations and to the G2P1 PM
2.5
data. Whether or not this was due to a
malfunctioning of the indoor BAMs or to seasonal changes in home dynamics and
ventilation conditions between G2P1 and G2P2 remains unclear.
The average percentage of measured indoor PN concentration that was emitted/formed
indoors were 14 to 22% (2235-3169), 17 to 26% (4618-5493), 17 to 19% (3258-3527)
and 13 to 21% (6841-8010) for G1P1, G2P1, G1P2 and G2P2, respectively (ranges of
average indoor-generated PN/cm
3
reported in parenthesis) (Figure 3.5d). These results
suggest that the PN concentration of indoor origin increased from summer to fall (at the
G1 site) and from fall to winter (at the G2 site). The seasonal increase in C
ig
for PN
96
concentration was probably due to the use of indoor fan heaters during the wintertime. A
recent study conducted by He et al., 2004, in 15 Australian houses demonstrated that the
use of a fan heater elevates the indoor sub-micrometer PN concentration levels by more
than five times over a 48-hr period but does not affect significantly the levels of indoor
PM
2.5
mass. Other indoor activities such as cooking might have increased the indoor
levels of PN concentrations by a substantial amount.
By using the same mass balance approach, we also estimated the average amount of
outdoor SOA and outdoor primary OC that penetrated inside G1 and G2 indoor sites (C
og

SOA and C
og
primary OC, respectively) during each phase of CHAPS. For these
calculations we assumed that F
inf
for SOA was equal to 0.86, the average summertime
value for OC during G1P1 (see Table 3.4). As illustrated in Figure 3.6, the average
percentage contribution of indoor SOA of outdoor origin to measured indoor OC, C
og

SOA (%), was rather constant throughout the study within each group, varying from 33%
(1.89) to 35% (1.60) for G1P1 and G1P2, respectively, and from 46% (2.60) to 45%
(2.34) for G2P1 and G2P2, respectively (the corresponding average concentrations in
µgC/m
3
are reported in parenthesis). When varying F
inf
for SOA of ± 0.1, the
corresponding average C
og
SOA (%) for all groups and phases fluctuated by 5% or less.
Figure 3.6 also shows that, on average, 44%, 36%, 42% and 40% of measured indoor OC
was comprised of outdoor-generated primary OC during G1P1, G2P1, G1P2 and G2P2,
respectively. These C
og
primary OC (%) values were determined from the mass balance
equation as follows:
97
(3.12)
To the best of our knowledge, these results are among the first to quantify the
contributions of outdoor-generated SOA and primary OC to indoor OC and to
demonstrate their importance in indoor environments. These outcomes will be used by
CHAPS investigators to clarify the links between exposure to PM
2.5
of indoor and
outdoor origin and its effects on cardiovascular outcomes. In the Los Angeles basin

Table 3.4 By regressing (Deming regression) outdoor hourly overnight OC, EC, PM
2.5
and
PN concentrations over the correspondent indoor data we determined the infiltration factor
(F
inf
= slope), the background source strength (intercept) and the coefficient of
determinations (R
2
) for these four particle species during CHAPS (see text for details).
Species F
inf
Background source* R
2

G1P1
OC 0.62 1.17 0.60
EC 0.68 0.02 0.74
PN 0.60 -126.83 0.73
PM
2.5
0.64 -0.17 0.61
G2P1
OC 0.59 1.07 0.71
EC 0.72 0.05 0.88
PN 0.49 849.46 0.84
PM
2.5
0.62 1.27 0.85
G1P2
OC 0.65 0.06 0.75
EC 0.79 0.05 0.90
PN 0.60 1568.40 0.86
PM
2.5
0.63 1.56 0.89
G2P2
OC 0.66 0.05 0.71
EC 0.80 0.08 0.87
PN 0.48 1264.40 0.84
PM
2.5
0.60 0.85 0.94
og
C =
primary OC (%) 100 −
ig
C OC (%)
og
C SOA (%) −
og
C =
primary OC (%) 100 −
ig
C OC (%)
og
C SOA (%) −
98

* OC and EC in µgC/m
3
; PN in ptcl#/cm
3
; PM
2.5
in µg/m
3

Figure 3.6 Estimated indoor primary OC and indoor SOA concentrations of outdoor origin
(“Cog Primary OC” and “Cog SOA”, respectively) expressed as a percentage of the
corresponding measured indoor concentrations (Cin), and averaged throughout G1P1,
G2P1, G1P2 and G2P2. Estimated indoor OC concentrations of indoor origin (Cig OC) are
also reported.

outdoor primary OC particles are mainly emitted from motor-vehicle exhausts, are mostly
found in the ultra-fine mode, are comprised of well known carcinogenic
components/species such as diesel particles and polycyclic aromatic hydrocarbons
(PAHs), and are more likely to deposit in the lower airways than coarse particles (Daigle
et al., 2003; Kaiser et al., 2005; Nel , 2005). On the other hand, a growing body of
evidence is suggesting that exposure to SOA (mostly comprised of highly polar organic
compounds) are linked to respiratory inflammation through the generation of reactive
oxygen species (ROS) (Nel, 2005; Xiao et al., 2003).
0
20
40
60
80
100
G1P1 G2P1 G1P2 G2P2
Cig OC Cog Primary OC Cog SOA
Cig, Cog (%)
0
20
40
60
80
100
G1P1 G2P1 G1P2 G2P2
Cig OC Cig OC Cog Primary OC Cog Primary OC Cog SOA Cog SOA
Cig, Cog (%)
99
The single compartment mass balance model presented in this work allows for a
straightforward estimation of the C
ig
and C
og
concentrations for a given F
inf
value.
Although the uncertainties inherent in the approach proposed here must be acknowledged
(e.g. F
inf
was considered to be constant within each group and phase of the study), the
estimated C
ig
and C
og
values for OC, EC, PM
2.5
, PN, SOA and primary OC seem
reasonable when compared to the relatively limited data in the available literature.
3.5. CONCLUSIONS
This study was conducted in the Los Angeles basin at two retirement communities.
Measured indoor and outdoor concentrations of PM
2.5
, OC, EC, PN, O
3
, CO and NO
X

were generally comparable, although at G2 a substantial peak in indoor OC, PN and
PM
2.5
(probably from cooking) was typically observed between 06:00 and 09:00 am. The
study average percentage contribution of outdoor SOA to outdoor particulate OC
(representative for the San Gabriel Valley) was 40%, and varied between 40-45% in the
summer (during G1P1) and 32-40% in the winter (during G2P2). Quantifying the SOA
contribution to measured OC is important: a) to test evolving predictive SOA models, b)
to link the organic PM concentration to its emissions and precursors, and c) to develop
effective control strategies for PM. The low AERs (0.25 to 0.33 h
-1
) calculated for G1
and G2 are consistent with the structural characteristics of the sampling sites, the low
number of open windows and doors and the presence of central air conditioners. F
inf

values were determined for OC, EC, PM
2.5
and PN by using different methods, including
the recursive model (RM) developed by Allen et al., 2003. Here the RM approach,
recently validated for estimating F
inf
for PM
2.5
from light scattering data, has been
100
applied to OC, EC and PN data for the first time. F
inf
estimates were highest for EC (a
non-volatile species mostly found in the 0.1-0.4 µm range (Miguel et al., 2004; Sardar et
al., 2005) and also for OC (probably because of the substantial percentage mass
contribution from SOA throughout CHAPS). Lower F
inf
values were obtained for PM
2.5
and PN, for these compounds are comprised of both volatile and non-volatile inorganic
and organic components. Estimated F
inf
and measured particle concentrations were then
used in a single compartment mass balance model to assess the mean contributions of
indoor and outdoor sources to measured indoor OC, EC, PM
2.5
and PN. We found that
13-17% (G2P2) to 16-26% (G1P1) of measured indoor OC was emitted or formed
indoors. Although the G2 indoor site was characterized by higher indoor morning OC
peaks due to cooking, the overall contribution of indoor sources to measured indoor OC
was higher at the G1 site. These results are consistent with low indoor activity levels at
both retirement communities and with the prevailing use of central air conditioning. Our
modeling results also showed that the measured indoor PM
2.5
emitted or formed indoors
was highly variable (from 6-21% at G1P1 to 42-51% at G1P2). The average percentage
contribution of indoor SOA of outdoor origin to measured indoor OC varied from about
35% (at site 1) to about 45% (at site 2). Also, outdoor-generated primary OC comprised,
on average, 36 to 44% of measured indoor OC during G2P1 and G1P1, respectively.
These results are among the first to quantify the contributions of outdoor-generated SOA
and primary OC to indoor OC and to demonstrate their importance in indoor
environments. The outcomes presented here will be used by CHAPS investigators to
determine the relationship between cardiovascular outcomes and hourly retirement
community exposures by each resident to PM
2.5
of indoor and outdoor origin.
101
3.6. CHAPTER 3 REFERENCES
Abt, E., H. H. Suh, P. Catalano and P. Koutrakis (2000). Relative contribution of outdoor
and indoor particle sources to indoor concentrations, Environmental Science &
Technology 34(17): 3579-3587.
Allen, R., Larson, T., Sheppard, L., Wallace, L., Liu L.-J.S (2003). Use of Real-Time
Light Scattering Data to Estimate the Contribution of Infiltrated and Indoor-Generated
Particles to Indoor Air, Environmental Science and Technology., 37: 3484-3492.
Allen, R., Wallace, L., Larson, T., Sheppard, L., Liu L.-J.S (2006). Evaluation of the
Recursive Model Approach for Estimating Particulate Matter Infiltration Efficiencies
Using Continuous Light Scattering Data, Journal of Exposure Science and
Environmental Epidemiology., In Press.
Arhami, M., Kuhn,T., Fine, P.M., Delfino, R.J. (2006). Sioutas, C Effects of Sampling
Artifacts and Operating Parameters on the Performance of a Semicontinuous Particulate
Elemental Carbon/Organic Carbon Monitor, Environ. Sci and Technol., 40: 945-954.
Birch, M.E. and Cary, R.A. (1996). Elemental carbon-based method for monitoring
occupational exposures to particulate diesel exhaust, Aerosol Sci. Technol., 25, 221-241.
Cabada J.C, Pandis S.N., Subramanian R., Robinson A.L., Polidori A., Turpin B. (2004).
Estimating the Secondary Organic Aerosol Contribution to PM2.5 Using the EC Tracer
Method, Aerosol Sci. and Technol. 38(S1):140–155.
Carlton, A.G., Turpin, B.J., Lim, H-J. (2006). Altieri, K.E., Seitzinger, S. Link between
isoprene and secondary organic aerosol (SOA): Pyruvic acid oxidation yields low
volatility organic acids in clouds, Geophysical Research Letters., 33 (6): Art. No.
L06822.
Chow, J.C., Watson, J.G., Fujita, E.M., Lu, Z.Q., Lawson, D.R., Ashbaugh, L.L. (1994).
Temporal and Spatial Variations of PM
2.5
and PM
10
Aerosol in the Southern California
Air-Quality Study, Atmos. Environ., 28, 2061-2080.
Christoforou, C.S., Salmon, L.G., Hannigen, M.P. (2000). Solomon, P.A., Cass, G.R.
Trends in Fine Particle Concentration and Chemical Composition in Southern California,
J. Air & Waste Manage. Assoc., 50, 43-53.
Cornbleet, P.J. and Gochman, N. (1979). Incorrect least-squares regression coefficients in
method-comparison analysis, Clin. Chem., 25, 432-438.
Daigle, C.C., Chalupa, D.C., Gibb, F.R., Morrow, P.E., Oberdorster, G., Utell, M.J.
(2003). Ultrafine particle deposition in humans during rest and exercise. Inhal. Toxicol.,
15:539–552.
Deming, W.E. (1943). Statistical Adjustment of Data, Wiley, New York, NY..
102
Dye, J.A., Lehmann, J.R., McGee, J.K., Winsett, D.W., Ledbetter, A.D., Everitt, J.I.,
Ghio, A.J., Costa, D.L. (2001). Acute pulmonary toxicity of particulate matter filter
extracts in rats: coherence with epidemiological studies in Utah Valley residents,
Environmental Health Perspective. 109:395-403.
Ebelt, S. T., Wilson, W. E., Brauer, M. (2005). Exposure to the ambient and non-
ambient components of particulate matter: a comparison of health effects,
Epidemiology. 16: 396-405.
Funasaka, K., Miyazaki, T., Tsuruho, K., Tamura, K., Mizuno, T., Kuroda, K. (2000).
Relationship between indoor and outdoor carbonaceous particulates in roadside
households. Environ. Sci. Technol. 110:127-134.
Geller, M.D., Chang, M.H., Sioutas, C., Ostro, B.D., Lipsett, M.J. (2002). Indoor/outdoor
relationship and chemical composition of fine and coarse particles in the southern
California deserts, Atmospheric Environment. 36 (6): 1099-1110.
Ghio, A.J., Devlin, R.B. (2001). Inflammatory lung injury after bronchial instillation of
air pollution particles, American Journal of Respiratory Critical Care Medicine. 164:704-
708.
Hays, M.D., Geron, C.D., Linna, K.J., Smith, N.D., Schauer, J. J. (2002). Speciation of
Gas-Phase and Fine Particle Emissions from Burning of Foliar Fuels. Environ. Sci.
Technol. 36:2281-2295.
He, C.R., Morawsca, L.D., Hitchins, J., Gilbert, D. (2004). Contribution from Indoor
Sources to Particle Number and Mass Concentrations in Residential Houses, Atmospheric
Environment. 38 (21): 3405-3415.
Ho, K.F., Cao, J.J., Harrison, R.M., Lee, S.C., Bau, K.K. (2004). Indoor/outdoor
relationships of organic carbon (OC) and elemental carbon (EC) in PM
2.5
in roadside
environment of Hong Kong. Atmos. Environ. 38:6327-6335.
Kaiser, J. (2005). Mounting evidence indicts fine-particle pollution, Science. 308 (5722):
633-633.
Kim, B.M., Teffera, S., Zeldin, M.D. (2000). Characterization of PM
2.5
and PM
10
in the
South Coast air Basin of Southern California: Part 1 - Spatial Variations, J. Air & Waste
Manage. Assoc. 50, 2034-2044.
Kirchstetter, T. W., Corrigan, C. E., Novakov, T. (2001). Laboratory and field
investigation of the adsorption of gaseous organic compounds onto quartz filters, Atmos.
Environ. 35, 1663-1671.
Kiss, G., Varga, B., Galambos, I., I. Ganszky. (2002). Characterization of water-soluble
organic matter isolated from atmospheric fine aerosol, J. Geophys. Res. 107, D21, 8339.
103
Klepeis, N.E., Nelson, W.C., Ott, W.R., Robinson, J.P., Tsang, A.M., Switzer, P., Behar,
J.V., Hern, S.C., Engelmann, W.H. (2001). The National Human Activity Pattern Survey
(NHAPS): A resource for assessing exposure to environmental pollutants. J. Exposure
Anal. Environ. Epi. 11:231-252.
Koenig, J. Q., Mar, T. F., Allen, R. W., Jansen, K., Lumley, T., Sullivan, J. H., Trenga,
C. A., Larson, T., Liu, L. J. (2005). Pulmonary effects of indoor- and outdoor-generated
particles in children with asthma. Environ. Health Perspect. 113: 499-503.
Lim, H. J., Turpin, B. J. (2002). Origins of Primary and Secondary Organic Aerosol in
Atlanta: Results of Time-Resolved Measurements During the Atlanta Supersite
Experiment, Environ. Sci. Technol. 36:4489–4496.
Lim, H.J., Turpin, B.J., Russell, L.M., Bates, T.S. (2003). Organic and elemental carbon
measurements during ACE-Asia suggest a longer atmospheric lifetime for elemental
carbon, Environmental Science and Technology. 37 (14): 3055-3061.
Long, C., Suh, H., Catalano, P., Koutrakis, P. (2001). Using Time- and Size- Resolved
Particulate Data to Quantify Indoor Penetration and Deposition Behavior, Environ. Sci.
Technol. 35, 2089-2099.
Lunden, M.M., Revzan, K.L., Fischer, M.L., Thatcher, T.L., Littlejohn, D., Hering, S.V.,
Brown, N.J. (2003). The transformation of outdoor ammonium nitrate aerosols in the
indoor environment. Atmos. Environ. 37:5633-5644.
McClellan, R.O. (2002). Setting Ambient Air Quality Standards for Particulate Matter,
Toxicology. 181-182, 329-347.
Meng, Q.Y., Turpin, B.J., Korn, L., Weisel, C.P., Morandi, M., Colome, S., Zhang, J.,
Stock, T., Spektor, D., Winer, A., Zhang, L., Lee, J.H., Giovanetti, R., Cui, W., Kwon, J.,
Alimokhtari, S., Shendell, D., Jones, J., Farrar, C., Maberti, S. (2005). Infuence of
ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations:
Analyses of RIOPA data. J. of Exposure Analysis and Environmental Epidemiology. 15:
17-28.
Miguel, A.H., Eiguren-Fernandez, A., Jaques, P.A., Froines, J.R., Grant, B.L., Mayo,
P.R., Sioutas, C. (2004). Seasonal Variation of the Particle Size Distribution of
Polycyclic Aromatic Hydrocarbons and of Major Aerosol Species in Claremont,
California, Atmos. Environ. 38, 3241-3251.
Na, K., Cocker, D.R. (2006). Organic and Elemental Carbon Concentrations in Fine
Particulate Matter in Residences, Schoolrooms, and Outdoor Air in Mira Loma,
California, Atmospheric Environment. 39 (18): 3325-3333.
104
Naumova Y.Y., Eisenreich S.J., Turpin B.J., Weisel C.P., Morandi M.T., Colome S.D.,
Totten L.A., Stock T.H., Winer A.M., Alimokhtari S., Kwon J., Shendell D., Jones J.,
Maberti S., and Wall S.J. (2002). Polycyclic aromatic hydrocarbons in the indoor and
outdoor air of three cities in the US. Environ Sci Technol. 36: 2552–2559.
Naumova Y.Y., Offenberg J.H., Eisenreich S.J., Meng Q.Y., Polidori A., Turpin B.J.,
Weisel C.P., Morandi M.T., Colome S.D., Stock T.H., Winer A.M., Alimokhtari S.,
Kwon J., Maberti S., Shendell D., Jones J., and Farrar C. (2003). Gas/particle distribution
of polycyclic aromatic hydrocarbons in coupled outdoor/indoor atmospheres. Atmos
Environ. 37: 703–719.
Nel, A. (2005). Air pollution-related illness: effects of particles, Science. 309 (5739):
1326-1326.
Pankow, J.F. (1994). An absorption model of gas/particle partitioning of organic
compounds in the atmosphere, Atmos. Environ. 28, 185-188.
Polidori, A., Turpin, B.J., Lim, H-J., Cabada, J.C., Subramanian,

R., Pandis, S.N.,
Robinson, A.L. (2006). Local and Regional Secondary Organic Aerosol: Insight from a
Year of Semi-Continuous Carbon Measurements at Pittsburgh, Aerosol Science and
Technology. 40: 861-872.
Polidori, A., Turpin, B.J., Meng, Q.Y., Lee, J-H., Weisel, C., Morandi, M., Colome, S.,
Stock, T., Winer, A., Zhang, J., Kwon, J., Alimokhtari, S., Shendell, D., Jones, J., Farrar,
C., Maberti, S. (2006). Fine Organic Particulate Matter Dominates Indoor-Generated
PM
2.5
in RIOPA Homes, Journal of Exposure Analysis and Environmental Epidemiology.
16: 321-331.
Polidori, A., Turpin, B.J., Meng, Q.Y., Lee, J-H., Weisel, C., Morandi, M., Colome, S.,
Stock, T., Winer, A., Zhang, J., Kwon, J., Alimokhtari, S., Shendell, D., Jones, J., Farrar,
C., Maberti, S. (2006). Fine Organic Particulate Matter Dominates Indoor-Generated
PM
2.5
in RIOPA Homes, Journal of Exposure Analysis and Environmental Epidemiology.
16: 321-331.
Robinson, J., Nelson, W.C. (1995). National Human Activity Pattern Survey Data Base:
USEPA, Research Triangle Park, NC.
Sardar, S.B., Fine, P.M., Sioutas, C. (2005). Seasonal and Spatial Variability of the Size-
Resolved Chemical Composition of Particulate Matter (PM
10
) in the Los Angeles Basin,
Journal of Geophysical Research-Atmosphere. 110 (D7): D07S08.
Sarnat, S.E., Coull, B.A., Ruiz, P.A., Koutrakis, P., Suh, H.H. (2006). The Influences of
Ambient Particle Composition and Size on Particle Infiltration in Los Angeles, CA,
Residences, J. Air & Waste Manage. Assoc. 56:186–196.
105
Saxena, P., and Hildemann, L. (1996). Water-Soluble Organics in Atmospheric Particles:
A Critical Review of the Literature and Application of thermodynamic s to Identify
Candidate Compounds, J. Atmos. Chem. 24: 57–109.
Strader, R., Lurmann, F., Pandis, S., (1999). Evaluation of Secondary Organic Aerosol
Formation in Winter, Atmos. Environ. 33:4849–4863.
Suh, H.H., Koutrakis, P., Spengler, J.D. (1994). The Relationship Between Airborne
Acidity and Ammonia in Indoor Environments, J. Expos. Anal. Environ. Epidemiol. 4, 1-
23.
Thatcher, T.L., Lai, A.C., Moreno-Jackson, R., Sextro, R.G., Nazaroff. W.W. (2002).
Effects of room furnishings and air speed on particle deposition rates indoors,
Atmospheric Environment. 36:1811–1819.
Thatcher, T.L., Layton, D.W. (1995). Deposition, resuspension, and penetration of
particles within a residence. Atmos. Environ. 29:1487-1497.
Tolocka, M.P., Solomon, P.A., Mitchell, W., Norris, G.A., Gemmill, D.B., Wiener, R.W.,
Vanderpool, R.W., Homolya, J.B., Rice, J. (1999). East Versus West in the u.s.:
Chemical Characteristics of PM2.5 During the Winter of Aerosol Sci. Technol. (2001),
34, 88-96.
Turpin, B. J., Huntzicker, J. J. (1995). Identification of Secondary Organic Aerosol
Episodes and Quantitation of Primary and Secondary Organic Aerosol Concentrations
During SCAQS, Atmos. Environ. 29:3527–3544.
Turpin, B.J., Saxena, P., Andrews, E. (2000). Measuring and simulating particulate
organics in the atmosphere: problems and prospects, Atmospheric Environment. 34 (18):
2983-3013.
USEPA, (2004). Air Quality Criteria for Particulate Matter, U.S, Environmental
Protection Agency, Research Triangle Park.
Wallace L. (1996). Indoor particles: a review, J. Air Waste Manag. Assoc. 46: 98–126.
Weschler, C.J. (2004). Chemical reactions among indoor pollutants: What we've learned
in the new millennium. Indoor Air, Suppl. 7:184-194.
Weschler, C.J., Shields, H.C. (1997). Potential reactions among indoor pollutants.
Atmos. Environ. 31:3487-3495.
Xiao, G.G., Wang, M.Y., Li, N., Loo, J.A., Nel, A.E. (2003). Use of proteomics to
demonstrate a hierarchical oxidative stress response to diesel exhaust particle chemicals
in a macrophage cell line, Journal of Biological Chemistry. 278 (50): 50781-50790.
106
Zhang, Q., Worsnop, D.R., Canagaratna, M.R., Jimenez, J.L. (2005). Hydrocarbon-like
and Oxygenated Organic Aerosols in Pittsburgh: Insight into Sources and Processes of
Organic Aerosols, Atmospheric Chemistry and Physics. 5: 3289-3311.
Zhu, Y.F., Hinds, W.C., Krudysz, M., Kuhn, T., Froines, J., Sioutas, C. (2005).
Penetration of Freeway Ultrafine Particles into Indoor Environments, Journal of Aerosol
Science. 36 (3): 303-322.
107
Chapter 4.
Associations between Personal, Indoor, and Residential Outdoor
Pollutant Concentrations for Exposure assessment to Size Fractionated
PM

4.1. ABSTRACT
The physical and chemical characteristics of indoor, outdoor, and personal quasi-ultrafine
(<0.25μm), accumulation (0.25-2.5 μm), and coarse (2.5-10 μm) mode particles were
studied at four different retirement communities in southern California between 2005 and
2007. Linear mixed effects models and Spearman’s correlation coefficients were then
used to elucidate the relationships among size segregated PM levels, their particle
components, and gaseous co-pollutants. Seasonal and spatial differences in the
concentrations of all measured species were evaluated at all sites based on p-values for
product terms. Outdoor quasi-UF and, to a lesser extent, accumulation mode particles
were the two fractions that best correlated with outdoor concentrations of CO, NO
2
, NOx
(during both phases of the study) and O
3
(only during the warmer months). Outdoor and
indoor concentrations of CO, NO
2
and NOx were more positively correlated to personal
quasi-UF particles than larger size fractions. In spite of these findings, it seems unlikely
that these gaseous co-pollutants could confound epidemiologic associations between
quasi-UF particles and adverse health effects. Overall, measured gaseous co-pollutants
were weak surrogates of personal exposure to accumulation mode PM, at least for
subjects with similar exposure profiles and living in similar urban locations. Indoor
sources were not significant contributors to personal exposure of accumulation and quasi
UF PM, which is predominantly influenced by primary emitted pollutants of outdoor
108
origin. Correlations between personal coarse mode PM and both outdoor and indoor
gaseous co-pollutant concentrations were weak at all sites and during all seasons.

4.2. INTRODUCTION
Several epidemiological and toxicological studies have found positive associations
between levels of atmospheric fine particulate matter (PM
2.5
; Dp = aerodynamic diameter
≤ 2.5 µm) and acute or chronic adverse health effects

(Pope and Dockery 2006). The PM
components that are believed to be responsible for the adverse health outcomes include
transition metals, organic species (often reported using surrogate measurements such as
organic and elemental carbon, or OC and EC, respectively), sulfate and nitrate salts, and
bioaerosols (McClellan 2002; Chakrabarti et al. 2004). In addition, PM properties such as
mass, number, surface area and, especially, size are important in evaluating particle
toxicity. Besides having the highest potential to penetrate deeply into the human lungs,
ultra-fine (UF) particles (Dp ≤ 0.10 µm) collected in urban environments include
particularly toxic species such as polycyclic aromatic hydrocarbons (PAHs). In a recent
study conducted by Ntziachristos et al.(Ntziachristos et al. 2007), size-fractionated
ambient PM samples (i.e. quasi-UF, Dp ≤ 0.25µm, accumulation, Dp ≤ 2.5µm, and
coarse, 2.5 µm ≤ Dp ≤ 10 µm, mode particles) were collected at four different locations
in the Los Angeles basin, and analyzed for their chemical composition and redox
potential (an indicator of particle toxicity). The quasi-UF fraction had the highest redox
activity on a per-PM mass basis, which was correlated with the higher mass fractions of
109
particulate OC and PAHs (some of which are well-known carcinogens(Kaiser 2005; Nel
2005) for this size range.

Although the link between PM exposure and adverse health effects has become widely
accepted, ascertaining the true risk associated with exposure to PM is difficult, mainly
because the concentrations of ambient particles and those of their gaseous co-pollutants
(e.g. carbon monoxide, CO, nitrogen oxides, NOx = NO + NO
2
, ozone, O
3
, and sulfur
dioxide, SO
2
) are often well correlated, and estimates of the health risks associated with
PM exposure may be confounded by these gaseous species (Sarnat et al. 2000; Green et
al. 2002; Sarnat et al. 2005). The National Research Council(Press 1998) listed the
investigation of the potential confounding effect of gaseous co-pollutants on PM health
effects as one of their research priorities.

Sarnat et al. explored these confounding effects both in Baltimore, MD(Sarnat et al.
2000), and in Boston, MA(Sarnat et al. 2005), using integrated (24-hr) ambient and
personal exposure data (i.e. PM
2.5
, O
3
, NO
2
, and SO
2
concentrations) collected in the
summertime and in the wintertime. In both cities, ambient gaseous levels were more
strongly correlated with personal exposure to PM
2.5
and SO
4
2-
, than with their respective
personal gas exposures. Only the results obtained in Boston showed an occasional
association between ambient PM
2.5
and personal O
3
and NO
2
. Although the strength of
these cross-pollutant associations was not as substantial as between ambient and personal
PM
2.5
, these findings suggest that at times, ambient pollutant gases can also serve as
110
surrogates for personal exposures to PM
2.5
. In addition, these studies showed that
between-subject differences might exist in the strength of the personal-ambient
association for gases, probably due to differences in house characteristics and ventilation
conditions (Rojas-Bracho et al. 2000; Sarnat et al. 2000; Polidori et al. 2007). While in
Boston personal-ambient gaseous correlations were moderately strong, in Baltimore
ambient gas concentrations were not associated with their respective personal exposures.
This implies that changes over time for some gaseous pollutants (O
3
, for example)
measured at central sites reflect corresponding changes in personal exposures only at
some locations.

In this study, we expanded the work by (Sarnat et al. 2005) by including size segregated
PM data in our analysis. Associations between indoor, outdoor, and personal size-
fractionated PM and OC, EC, particle number (PN), O
3
, CO, NO, NOx, and other
important pollutants of both indoor and outdoor origin were evaluated, and the role of
gaseous co-pollutants as surrogates of personal size-fractionated PM

exposures assessed.
Data were collected at four retirement communities of the Los Angeles Basin during the
Cardiovascular Health and Air Pollution Study (CHAPS), a multi-disciplinary project
whose goals are to investigate the effects of micro-environmental exposures to PM on
cardiovascular outcomes in elderly retirees affected by coronary artery disease (Delfino
et al. 2008).

111
4.3. METHODS

4.3.1. Study Design
The physical and chemical characteristics of indoor, outdoor, and personal quasi-UF,
accumulation, and coarse PM were studied at four different retirement communities in
southern California between 2005 and 2007. Three of these communities were in the San
Gabriel Valley, CA (here referred to as sites San Gabriel 1, San Gabriel 2 and San
Gabriel 3) and the fourth in Riverside, CA. Site San Gabriel 1 was located about 50 Km
east of downtown Los Angeles, in a residential area, approximately 3 Km away from any
major freeways and close to a construction site. Site San Gabriel 2 was situated about 8
Km east of Los Angeles, approximately 300 m south of a major freeway (see Polidori et
al. (Polidori et al. 2007), for details). Site San Gabriel 3 was about 55 Km east of
downtown Los Angeles, 2.5 Km away from 2 busy freeways and in close proximity (150
m) of a major street. The Riverside site was about 110 Km east of Los Angeles and 15
Km southeast of downtown Riverside. The closest freeway and a major street were
approximately 3 and 1 Km away, respectively, and downwind of the site. The abundant
vegetation surrounding this last community may be a potential source of precursors of
biogenically generated PM (e.g. organic particles from the photo-oxidation of terpenes).

Two 6-week sampling campaigns were conducted at each location. Phase 1 (P1) of each
campaign was conducted during a warmer season (including summer and early fall),
whereas phase 2 (P2) was conducted during a cooler season (including late fall and
112
winter). Site San Gabriel 1 was operated from 07/06/2005 to 08/20/2005 (P1) and from
10/19/2005 to 12/10/2005 (P2); sampling at site San Gabriel 2 was conducted from
08/24/2005 to 10/15/2005 (P1) and from 01/04/2006 to 02/18/2006 (P2); site San Gabriel
3 was operated from 07/05/2006 to 8/17/2006 (P1) and from 10/18/2006 to 12/01/2006
(P2); sampling at site Riverside was conducted from 8/23/2006 to 10/13/2006 (P1) and
from 01/04/2007 to 02/16/2007 (P2). Thus, we were able to study the seasonal variations
in indoor, outdoor, and personal relationships between size-segregated PM and its
components.

Two identical sampling stations were installed at each site, one indoors and one outdoors.
The indoor sampling station at site San Gabriel 1 was located in a recreational area of the
first community’s main building, adjacently to a construction site where work was
ongoing. The indoor sampling area at site San Gabriel 2 was situated in the dining room
of the community’s central building. The indoor station at site San Gabriel 3 was set up
in a recreational area of the main retirement community complex, adjacent to a gym and
to an activity room. The indoor area at the Riverside site was located in the hallway of the
main building with a dining room, activity room and numerous apartment units nearby.
At all sites the outdoor station, set-up inside a movable trailer, was positioned within 300
m from the indoor station.

Personal PM concentrations were measured for 67 elderly retirees (18, 14, 17, and 18
subjects at sites San Gabriel 1, 2, 3, and Riverside, respectively) with a history of
113
coronary artery disease. All subjects, except one, also participated in at least some part
the epidemiologic part of this study including ambulatory ECG and blood pressure
monitoring. All participants were 71 years of age or older, nonsmokers, and with no
home exposure to environmental tobacco smoke (ETS). Each subject was followed for
two 5-day sampling periods during the 2 phases of the study. Throughout this time, size
fractionated PM levels were measured daily. A more detailed description of the study
design can be found in Delfino et al. (Delfino et al. 2008)

4.3.2. Instrumentation
Continuous (1-min) PN concentrations were measured using water-based condensation
particle counters (CPC Model 3785, TSI Inc, Shoreview, MN) at both indoor and outdoor
sampling stations. Indoor and outdoor particulate OC and EC were measured in hourly
cycles (i.e. sampling time = 45-min; analysis time = 15-min) by mean of 2 semi-
continuous OC_EC analyzers (Model 3F, Sunset Laboratory Inc., Tigard, OR). Hourly
PM
2.5
mass concentrations were measured by Beta-Attenuation Mass Monitors (BAM,
Model 1020, Met One instruments Inc., OR). Continuous (1-min) NO and NO
2

measurements were obtained both indoors and outdoors by using Thermo Environmental
NOx Analyzers (Model 42, Thermo Environmental instruments Inc, Franklin, MA).
Dasibi Carbon Monoxide Analyzers (Model 3008, Dasibi Environmental Corp, Glendale,
CA) were implemented to measure continuous (1-min) indoor and outdoor CO levels.
Continuous (1-min) outdoor ozone (O
3
) concentrations were also monitored at each
sampling site by using API Ozone Analyzers (Model 400A, Teledyne Technologies Inc,
114
Los Angeles, CA). For more details about the continuous/semi-continuous instruments
employed during the sampling campaign see Polidori et al. (Polidori et al. 2007)
In addition, integrated (24-h) size segregated indoor and outdoor particle samples were
collected at all sites by means of Sioutas
TM
Personal Cascade Impactors (SKC Inc, Eighty
Four, PA) (Misra et al. 2002; Singh et al. 2003) from Monday to Friday. Coarse,
accumulation, and quasi-UF (PM<0.25 m) mode PM were sampled on Zefluor filters (3
µm pore-size, Pall Life Sciences, Ann Arbor MI) and analyzed gravimetrically using a
microbalance (Mettler-Toledo, Columbus, OH; weight uncertainty ± 2 µg). Personal
environmental monitors (PEM) were deployed concurrently with the indoor and outdoor
PM samplers to obtain integrated (24-h) personal PM exposure data. Each PEM consists
of an inlet, a Sioutas™ impactor and a Leland Legacy pump (SKC Inc, Eighty Four, PA)
operating at 9 lpm, all enclosed in a personal carry-on bag, then assigned to each subject.

4.3.3. Data Analysis
In order to match all continuous (or semi-continuous) measurements to the corresponding
filter based data, only daily averages of the concurrently measured PM
2.5
, OC, EC, PN,
NOx, CO and O
3
concentrations were considered. The contributions to outdoor OC by
primary OC (OCpri; emitted directly from combustion sources such as vehicular exhaust
and wood smoke) and secondary organic aerosol (SOA; formed from semi- and low-
volatility products of chemical reactions involving reactive organic gases) were estimated
from measured OC and EC concentrations using EC as a tracer of primary combustion-
generated OC (i.e. “EC tracer method”; see Polidori et al.(Polidori et al. 2007)). Because
115
one of the aims of this study is to evaluate the effects of outdoor air pollutants on indoor
and personal exposure, we estimated air exchange rates (AER; from CO measurements
during periods affected by a dominant indoor source; Abt et al.
16
) and infiltration factors
(Finf; defined as the equilibrium fraction of the outdoor species of interest that penetrate
indoors and remain suspended) at each site.
Assuming an exponential decay of particles, that AER (hr
-1
) and outdoor concentrations
are constant during the decay period, that CO is conservative, and that indoor
concentrations are well mixed, then:
C
t
= e
-(AER)t
C
0
(4.1)
where, C
t
is the indoor CO concentration after time t (after the decay period), C
0
is the
initial peak CO concentration (right after CO emission). The infiltration factor (F
inf
,
defined as the equilibrium fraction of ambient particles that penetrate indoors and remain
suspended
17
)
is described by the following eq:
F
inf
=
P(AER)/(AER+k) (4.2)
where, P is the penetration coefficient (dimensionless). F
inf
for OC, EC, PM
2.5
and PN
were calculated using the recursive model (RM) developed by Allen et al.
18
. In this
method, for a particular species of interest, the average indoor concentration during hour
is equal to the sum of a fraction of the average outdoor concentration during the same
hour, a fraction of the average indoor concentration remaining from the previous hour,
and the contribution from indoor sources. More details on the F
inf
and AERs estimation
methods are described in Polidori et al. (Polidori et al. 2007). The average AERs
calculated during CHAPS at the 4 retirement communities ranged from 0.21 to 0.4 hr
-1

116
(Table 4.1). The generally low estimated AERs are consistent with the structural
characteristics of the sampling sites, the low number of open windows and doors, and the
presence of central air conditioners. The average F
inf
results were highest for EC (0.64-
0.82) and OC (0.60-0.98) compared to those of PM
2.5
(0.38-0.57) and PN (0.41-0.78). In
general, the F
inf
results were similar across P1 (summer and fall) and P2 (fall and winter),
which is consistent with no seasonal changes in home dynamics and ventilation
conditions as indicated by the rather constant AERs calculated throughout the study.
Estimated Finf and measured particle concentrations across all hours were then used in a
single compartment mass balance model to assess the contributions of indoor and outdoor
sources to measured indoor EC, OCpri, SOA, and PN (Wallace 1996; Meng et al. 2005;
Polidori et al. 2007). The average amounts of indoor- and outdoor-generated PM and PM
species (here denoted with “ig” and “og” subscripts, respectively) inside the studied
homes were determined by this approach.

The following four associations (models) were then used to study the relationships
between pollutant concentrations and size segregated PM levels:

 Associations between outdoor co-pollutant concentrations and outdoor size
fractionated PM levels (“outdoor-outdoor associations”).
 Associations between indoor co-pollutant concentrations and indoor size
fractionated PM levels (“indoor-indoor associations”)
117
 Associations between outdoor particle and gaseous species concentrations and
personal exposure to size fractionated PM (“outdoor-personal associations”).
 Associations between indoor particle and gaseous species concentrations and
personal exposures to size fractionated PM (“indoor-personal associations”).

Linear mixed-effects regression parameters (slopes, or S) and Spearman’s correlation
coefficients (R) were calculated for the above mentioned correlations.

Table 4.1 Estimated air exchange rate (AER) and infiltration factors F
inf
over the studied
sites and phases of the study

AER, h
-1
F
inf

(mean ± SD) OC EC PN PM
2.5

Phase 1
(summer
and fall)
San Gabriel 1 0.25 ± 0.04 0.98 0.82 0.66 0.52
San Gabriel 2 0.28 ± 0.06 0.74 0.74 0.46 0.45
San Gabriel 3 0.40 ± 0.12 0.67 0.68 0.41 0.42
Riverside 0.21 ± 0.06 0.60 0.72 0.57 0.49
Phase 2
(fall and
winter)
San Gabriel 1 0.33 ± 0.07 0.87 0.79 0.78 0.52
San Gabriel 2 0.31 ± 0.10 0.61 0.64 0.55 0.38
San Gabriel 3 0.26 ± 0.08 0.81 0.75 0.43 0.57
Riverside 0.31 ± 0.09 0.90 0.82 0.45 0.41

118
4.3.4. Mixed Models
Data were analyzed with linear mixed-effect models, which expand the capabilities of
linear regression by accounting for the correlation present in repeated measures data. In
matrix notation, the mixed-effects model can be expressed as:

Y = X β + Z υ + ε

(4.3)

where Y is a vector of outcomes, X is a known matrix of covariates, β is a vector of
fixed effect parameters, and ε is a vector of normally distributed errors. These terms
parallel the standard linear regression model. To account for correlation within subjects,
the linear mixed-effects model includes an additional covariate matrix Z and a vector of
subject-specific random effects, υ,

(Laird and Ware 1982). In our models, random
intercepts were estimated for each subject, and an autoregressive covariance structure
was selected based on best fit from Akaike’s information criteria. Residual and influence
diagnostics were used to identify potentially outlying and influential observations. To
allow for a fair comparison of the regression slopes, all regression parameter estimates
were standardized by the interquartile range (IQR) of the considered independent variable.
Thus, the mixed model coefficients for the dependent variable correspond to an IQR
change in the independent variable.

In addition to linear mixed-effects models, Spearman’s correlation coefficients (R) were
reported as an additional indicator of the association strength among indoor, outdoor, and
119
personal data for the studied associations. Subjects having fewer than 5 valid days of
observations in any of the size fractions (out of 10 per size fraction) were excluded from
the analysis. Prior to regression and correlation analyses, outliers were detected at three
standard deviations beyond the mean and individually inspected for influence in
regression models.

All data collected at sites San Gabriel 1 to 3 were combined and considered to be
representative of the entire San Gabriel Valley, and compared to the measurements
generated at the Riverside site. This division was based on the observation that diurnal
and seasonal patterns of indoor and outdoor PM pollutants in Riverside (in particular OC,
OCpri, and SOA) were distinctly different from those recorded at the other 3 sites. The
San Gabriel Valley sites are closer to downtown Los Angeles, are impacted to a higher
degree by fresh traffic emissions and, thus, exemplify the characteristics of typical
“source” sites. Conversely, site Riverside is designated as a “receptor” location, where
the aerosol is mostly comprised of advected, aged and photochemically processed
particles from the central Los Angeles area, as well as of some local emissions
22
.
Finally, seasonal differences in phase-average concentrations of all measured species
were estimated at all sites, and assessed using p-values for product terms between phase
and the predictor. These “Phase Interaction” terms allowed us to study possible temporal
variations in the associations between the variables considered during phase 1 (summer
and early fall) and phase 2 (late fall and winter). Similarly, regional interactions between
120
the San Gabriel Valley and the Riverside sites were calculated to highlight possible
spatial differences in the considered associations.

4.4. RESULTS

4.4.1. Data Overview
Mean personal and mean indoor PM mass concentrations were similar at all sites, during
all phases and for all size fractions (Table 4.2), probably because most subjects spent the
majority of their time indoors. Mean outdoor PM levels were higher than the
corresponding personal and indoor concentrations across all size modes (especially for
accumulation and coarse mode particles), sites and phases of the study (Table 4.2). This
suggests that the overall loss of outdoor particles during penetration through the building
envelope was higher than the particle generation from indoor sources. Fine PM levels
(Table 4.1) were calculated in two different ways, by combining the filter based quasi-UF
and accumulation mode PM gravimetric masses, and from continuous BAM
measurements. The resulting Fine PM concentrations were always within 1.7 µg/m
3

across all sites and during all phases of the study. The estimated average indoor
concentrations of outdoor origin of fine PM, OC, EC and PN (Fineog, OCog, ECog and
CPCog, respectively) were higher than the correspondent mean indoor generated levels
(Fineig, OCig, ECig and CPCig, respectively), which confirms that outdoor sources were
the most important contributors to the measured indoor pollutant concentrations.

121
Table 4.2 Descriptive statistics for residential outdoor, indoor and personal concentrations
Mean ± SD IQR
1
Mean ± SD IQR
1
Mean ± SD IQR
1
Mean ± SD IQR
1
CO (ppm) Outdoor 0.59 ± 0.23 0.37 0.69 ± 0.31 0.36 0.31 ± 0.13 0.25 0.12 ± 0.11 0.17
Indoor 0.64 ± 0.23 0.29 0.78 ± 0.35 0.37 0.46 ± 0.11 0.16 0.60 ± 0.11 0.17
NO
2
(ppb) Outdoor 32.55 ± 9.47 12.20 33.33 ± 9.05 10.72 15.25 ± 5.83 7.14 15.37 ± 7.14 9.42
Indoor 25.70 ± 9.99 16.21 31.02 ± 9.51 15.09 11.41 ± 2.48 3.89 13.03 ± 2.93 4.09
NO
x
(ppb) Outdoor 48.36 ± 21.07 21.77 68.63 ± 35.59 39.06 18.52 ± 7.29 10.08 18.77 ± 9.58 12.47
Indoor 41.02 ± 21.39 25.45 69.32 ± 37.71 40.77 15.58 ± 3.97 5.84 19.66 ± 5.65 6.81
O
3
(ppb) Outdoor 31.52 ± 10.98 15.65 18.31 ± 7.13 7.77 38.05 ± 10.40 13.82 28.69 ± 6.26 8.62
Quasi-UF PM Personal 8.52 ± 4.21 4.09 8.77 ± 7.83 4.30 6.97 ± 3.10 5.04 4.58 ± 2.75 2.60
Outdoor 9.87 ± 3.02 4.13 9.95 ± 4.40 5.46 11.42 ± 4.92 7.06 7.29 ± 3.54 6.49
Indoor 8.93 ± 2.86 3.77 8.89 ± 3.66 4.62 8.99 ± 3.26 4.24 6.05 ± 2.95 2.47
Accum. mode PM Personal 6.03 ± 3.69 4.43 5.01 ± 4.87 3.75 4.86 ± 3.16 3.64 1.98 ± 1.64 1.30
Outdoor 13.30 ± 6.05 8.01 10.94 ± 9.23 10.39 9.75 ± 6.34 8.65 5.07 ± 3.81 4.61
Indoor 7.31 ± 3.41 4.42 6.28 ± 4.72 5.14 4.55 ± 2.36 1.49 2.21 ± 1.68 1.17
Coarse PM Personal 3.33 ± 2.40 2.68 3.42 ± 4.80 2.11 3.14 ± 1.90 2.34 1.61 ± 1.01 0.89
Outdoor 11.48 ± 3.61 4.35 8.58 ± 5.21 5.89 12.52 ± 6.95 7.95 4.62 ± 2.55 3.15
Indoor 2.62 ± 2.91 3.16 4.14 ± 5.49 3.93 2.88 ± 2.64 1.94 2.85 ± 1.40 1.54
Fine PM Personal 14.54 ± 6.60 7.87 13.77 ± 10.93 8.20 11.77 ± 5.50 8.13 6.50 ± 3.66 3.53
Outdoor 24.48 ± 8.30 10.56 20.10 ± 13.75 18.35 22.06 ± 7.94 12.52 11.57 ± 7.65 11.85
Indoor 15.09 ± 5.95 9.88 12.78 ± 7.21 8.94 9.30 ± 2.83 3.94 5.31 ± 2.31 3.30
Fine
og
2
Indoor 12.73 ± 6.02 10.43 9.95 ± 7.54 8.86 7.50 ± 2.52 4.56 3.79 ± 2.17 3.20
Fine
ig
3
Indoor 3.42 ± 2.10 1.81 4.85 ± 3.39 5.24 3.39 ± 1.21 1.31 2.53 ± 1.46 1.12
PN Outdoor 12100 ± 4717 5646 17357 ± 6424 10334 6947 ± 1077 1133 9574 ± 2038 3243
Indoor 8910 ± 7120 8383 13866 ± 7930 11196 4676 ± 907 997 4373 ± 969 1343
PNig
3
Indoor 3021 ± 4112 2386 4311 ± 5912 4336 1239 ± 847 662 970 ± 849 1086
PNog
2
Indoor 6459 ± 3621 5757 9695 ± 4147 7782 3504 ± 487 569 3412 ± 627 533
OC Outdoor 6.24 ± 1.90 2.60 7.70 ± 3.55 6.94 10.50 ± 1.14 1.31 15.39 ± 1.74 2.90
Indoor 5.62 ± 1.42 1.70 6.78 ± 3.12 5.72 8.46 ± 1.68 2.47 14.27 ± 1.95 3.11
OCig
3
Indoor 1.16 ± 0.70 1.15 1.21 ± 1.10 1.32 2.32 ± 1.65 2.55 1.41 ± 0.52 0.69
OCog
2
Indoor 4.49 ± 1.28 1.49 5.69 ± 2.99 5.76 6.14 ± 0.65 0.85 13.64 ± 1.44 2.37
OCpri
4
Outdoor 2.85 ± 0.82 1.11 5.13 ± 3.27 6.17 8.23 ± 1.09 1.65 11.25 ± 0.78 0.97
OCpri
og
2,4
Indoor 2.19 ± 0.80 1.04 4.07 ± 2.28 4.11 4.42 ± 0.68 1.07 10.07 ± 0.61 0.87
SOA
5
Outdoor 2.81 ± 1.45 1.93 2.76 ± 1.43 2.13 2.28 ± 0.90 1.19 4.14 ± 1.55 1.98
SOAog
2,5
Indoor 2.38 ± 1.23 1.53 2.37 ± 1.22 1.83 1.82 ± 0.72 1.05 3.56 ± 1.33 1.70
EC Outdoor 1.66 ± 0.44 0.55 1.71 ± 0.74 1.09 1.22 ± 0.48 0.56 0.82 ± 0.31 0.39
Indoor 1.39 ± 0.44 0.65 1.32 ± 0.54 0.83 1.37 ± 0.23 0.21 1.01 ± 0.25 0.41
ECig
3
Indoor 0.31 ± 0.23 0.25 0.25 ± 0.20 0.15 0.48 ± 0.26 0.35 0.34 ± 0.10 0.14
ECog
2
Indoor 1.08 ± 0.30 0.42 1.10 ± 0.49 0.73 0.89 ± 0.29 0.37 0.65 ± 0.24 0.31
1 IQR = Interquartile range
2 og = Outdoor generated penerated indoor
3 ig = Indoor generated
4 OCpri = Primary fraction of particulate organic carbon
5 SOA = Secondary organic aersol
Riverside
Phase 2 Phase 1
Particulate Mass
( µg/m
3
)
Phase 2 Phase 1
San Gabriel Valley
Organic Carbon
( µg/m
3
)
Elemental Carbon
( µg/m
3
)
Particles Number
(ptcl #/cm
3
)
Gases Concentration

*
* Calculated as the sum of quasi-UF and
accumulation mode PM integrated measurements
(personal Fine PM), and from continuous BAM
data (indoor and outdoor Fine PM).
122
The San Gabriel Valley sites were closer to freeways than the Riverside site (located in a
desert region) and were impacted by higher levels of CO, NO
2
and NO
x
, mainly emitted
from primary combustion sources (e.g. motor-vehicle emissions). Conversely, OC and O
3

levels were generally higher in Riverside. The latter site was approximately 110 Km east
(and downwind) of downtown Los Angeles, with prevailing westerly winds blowing from
the Pacific Ocean. The plume of pollutants generated in the Los Angeles area includes
several reactive organic species that are likely to form OC through secondary processes
(i.e. SOA formation) as the air mass ages and is transported eastwards. The higher
average OC, O
3
and SOA levels and the smaller diurnal OC variation in Riverside
(compared to the characteristic afternoon increase in OC, O
3
and SOA in the San Gabriel
Valley) confirm that the latter is a typical receptor area, where the contribution of SOA to
total measured OC is substantial. In addition, the vegetation surrounding the Riverside
community is a potential source of biogenic gas-phase precursors, which form secondary
organic aerosols through photochemical reactions (e.g., photochemical oxidation of
terpenes (Kanakidou et al. 2005).

4.4.2. Outdoor - Outdoor Associations
Outdoor quasi-UF PM was positively (and moderately) correlated with CO, NO
2
and
NO
x
at all locations and during all seasons, mainly because all of these species are
emitted by the same combustion sources, and also because their atmospheric transport
and removal is affected by similar meteorological processes (Chen et al. 1999; Wallace
2000) (Figure 4.1). Outdoor quasi-UF PM was positively correlated with O
3
only
123
during phase 1 (warmer season), both in the San Gabriel Valley (R = 0.17; Regression
slope = 1.5 µg/m
3
[95% confidence interval = 0.4 to 2.6]) and, especially, in Riverside
(R = 0.58; Slope = 4.2 µg/m
3
[1.8 to 6.6]). For an interquartile increase in the average
O
3
concentration measured in Riverside during phase 1, the correspondent quasi-UF
PM increased by 4.2 µg/m
3
, reflecting the important contribution of photochemical
processes to the production of SOA. The same associations were negative and non-
significant during phase 2 (cooler months) (R=-0.16; S = -1.0 µg/m
3
[-2.1 to 0.2] in the
San Gabriel Valley; R=-0.02; S = -1.0 µg/m
3
[-2.6 to 0.7] in Riverside). Based on p-
values for product terms, the prediction of quasi-UF particles by gaseous co-pollutants
was significantly different between phase 1 and phase 2 in Riverside, and between
phase 1 in Riverside and phase 1 in the San Gabriel Valley (see the “Regional
Interaction” column in Figure 4.1 for details, and note the scale difference for slopes in
phase 1 for Riverside).

Accumulation mode particles had generally smaller correlations and mixed model
slopes with CO, NO
2
and NO
x
compared to quasi-UF PM, probably because their
outdoor levels are influenced by a combination of primary and secondary sources.
However, these associations were stronger in phase 2, when the production of
accumulation mode particles and their corresponding co-pollutants from primary
combustion sources is typically the highest and the contribution of secondary formation
mechanisms to the measured PM mass are decreased.

124

Figure 4.1 Mixed model and Spearman correlation results for outdoor-outdoor associations

125
With the exception of phase 2 in the San Gabriel Valley and phase 1 in Riverside,
positive and moderately strong correlations between coarse particles and CO, NO
2
and
NO
x
were observed at all sites. Although not consistent, this association is not
surprising, since coarse PM is emitted from re-suspended soil and road dust, and the
latter mechanism shares the same anthropogenic sources responsible for the production
of these gaseous co-pollutants. The product term models for phase showed a significant
phase interaction at all sites (p < 0.01 = *** in Figure 4.1), indicative of the seasonal
effect of primary sources on the associations between outdoor coarse PM and the
corresponding gaseous co-pollutants. Interestingly, slopes for the prediction of coarse
particles by these gases were greater in phase 2 than phase 1 in Riverside. The opposite
situation was observed in the San Gabriel Valley.

Strong and positive associations between outdoor EC and outdoor CO, NO
2
and NO
x
were found both in the San Gabriel Valley and in the Riverside (average R = 0.45 to
0.91 and average S = 0.4 to 0.8 µg/m
3
). Similarly, PN and OC concentrations were well
correlated with primary gaseous co-pollutants levels measured at all sites and during all
phases. EC consists of graphite-like material emitted from the incomplete combustion
of organic fuels (Chow et al. 1993; Birch and Cary 1996). Combustion sources are also
the major contributor to the ambient PN and OC concentrations, although a substantial
amount of OC is also formed from secondary processes. A significant phase interaction
for PN was observed at the San Gabriel Valley sites, but not in Riverside. PN
concentrations were both higher and more strongly associated with CO, NO
2
and NO
x

126
during the period of air stagnation (phase 2). This finding highlights the seasonal effect
of primary sources on the associations between PN and primary gaseous co-pollutants.

4.4.3. Indoor - Indoor Associations
Indoor quasi-UF particles were strongly and positively associated with indoor gaseous
co-pollutants at all sites and during all phases (R values ranged from 0.34 to 0.72 for
this size fraction) (Figure 4.2). The strength and regression slopes of these correlations
are similar to the corresponding outdoor-outdoor associations (Figure 4.1).

Accumulation mode particles were more weakly associated with gaseous co-pollutants
than quasi-UF PM, probably because this size fraction contains a higher percentage by
mass of SOA that might condense on existing indoor particles, or volatilize in the
indoor environment (depending on the vapor pressure of the considered organic species
and/or the home characteristics). It is well known that changes in the physical and
chemical properties of fine PM occur as they penetrate indoors from outdoors. For
example, ammonium nitrate (NH
4
NO
3
), which accounts for 35–50% of the outdoor
PM
2.5
mass in the Los Angeles basin (Christoforou et al. 2000; Kim et al. 2000; Sardar
et al. 2005), volatilizes upon building entry and results in altering the PM size and
chemical composition indoors.

In most cases, indoor coarse PM was not correlated with indoor gaseous co-pollutants
because, unlike CO, NO
2
and NO
X
, the coarse fraction is characterized by very low

127

Figure 4.2 Mixed model and Spearman correlation results for indoor-indoor associations

128
infiltration indoors (Finf), and may also be emitted from re-suspension of existing
particles deposited on indoor surfaces and floors (Chakrabarti et al. 2004). The product
term analysis showed a significant phase interaction at the Riverside site for indoor
quasi-UF and accumulation mode PM, implying a seasonal variability in the
associations between these two fractions and the corresponding indoor gaseous co-
pollutants (Figure 4.2).

Indoor OC was weakly associated with indoor gases, except during phase 1 (warmer
months) at the San Gabriel Valley site, where strong positive associations (R = 0.69 to
0.81; Average regression slopes = 0.6 to 1.4 µg/m
3
) were found. These overall weak
associations could be explained by changes in the OC characteristics as it penetrates
indoors from outdoors, or by the presence of indoor OC sources, such as cooking,
particularly for the retirement community here referred to as group 2 (see Polidori et
al.(Polidori et al. 2007), for details). A significant phase interaction between indoor EC
and indoor gaseous co-pollutants was observed at all locations, similarly to the findings
for outdoor-outdoor associations. This indoor correlation may be because EC, CO, NO
2

and NOx all share the same primary combustion sources coupled with the size range of
EC particles (0.1-0.3 µm)(Sardar et al. 2005) that allows them to penetrate indoors with
great efficiency
31
. Moreover, EC is formed indoors in negligible amounts (Geller et al.
2002; Na and Cocker 2005; Polidori et al. 2007).

129
In Riverside, correlations between indoor PN and indoor gaseous species were weaker
than the correspondent outdoor-outdoor associations, probably due to the presence of
indoor sub-micrometer PN particles from fan heaters (particularly during phase 2),
which tend to increase PN concentrations, but not the overall PM mass level (He et al.
2004).

4.4.4. Outdoor – Personal Associations
At all sites, personal quasi-UF particles correlation with outdoor quasi-UF PM (Figure
4.3a), were weaker compare to corresponding correlations for accumulation mode
particles (discussed below). Weaker quasi-UF correlations is consistent with the
relatively low penetration of sub-100 nm particles indoors due to diffusional
losses
31
(Long et al. 2001; Sarnat et al. 2006) as well as losses due to evaporation of
volatile species associated with this size range (Zhu et al. 2005).

This is further confirmed by the relatively small average regression slopes for the
personal-outdoor associations (S = 0.1 to 1.4 µg/m
3
). Personal quasi-UF particles were
not well correlated with either the larger outdoor PM fractions, or OC and most of its
components (e.g. SOA and OC
pri
), but were positively associated with EC and tracers
of primary combustion (CO and NOx). The low correlations (R) between personal
quasi-UF PM and OC
pri
might be because OC
pri
includes components with a relatively
high vapor pressure, which volatilize upon building entry. Most importantly, this data

130
a) Quasi-UF PM

Figure 4.3 Mixed model and Spearman correlation results for outdoor pollutants with: a) personal quasi-UF PM, b) personal
accumulation mode PM and c) coarse PM

131

b) Accumulation mode PM

Figure 4.3 Continued

132

c) Coarse PM

Figure 4.3 Continued
133
suggest that personal exposures to quasi-UF PM were significantly associated with EC
at all CHAPS sites except during phase 2 in Riverside. This has important implications
for the potential health effects of personal PM because EC is a good surrogate for
diesel particles and a well known carcinogenic compound class (IARC 2005).

Outdoor accumulation mode PM (as well as PM
2.5
) were well correlated with personal
accumulation mode particles (R-values ranged between 0.38 and 0.73), and the
magnitude of the regression parameter estimates was also relatively high. In particular,
an interquartile increase in outdoor accumulation mode PM was associated with
increases in personal accumulation mode particles of 1.9 and 3.1 µg/m
3
(for phases 1
and 2, respectively) in the San Gabriel Valley, and of 1.6 and 1.0 µg/m
3
(for phases 1
and 2, respectively) in Riverside (see Figure 4.3b for details). This size fraction is not
as easily lost by diffusion/coagulation as quasi-UF PM when entering indoors, or by
inertial deposition mechanisms (like coarse particles) and, hence, has a high penetration
efficiency. Outdoor OC and its sub-fractions, were generally not well associated with
personal accumulation mode particles. However, in the cooler seasons, stronger
correlations (and generally higher regression slopes) were found with EC and other
combustion products, such as CO, NO
2
, and NOx. Based on product term models, a
significant phase interaction (p < 0.01) was found at the Riverside site for the
prediction of personal accumulation mode particles by outdoor PM
2.5
, highlighting the
seasonal effect of primary sources on these associations. between these two PM size
fractions. Overall, personal coarse mode particles were not well correlated with any of
134
the outdoor PM fractions/components, or outdoor gases, suggesting that none of these
species was a good surrogate for exposure to personal coarse PM. This is because of
the lower F
inf
values for larger particle sizes, coupled with the fact that indoor coarse
particles are mostly generated by mechanical processes and/or re-suspended from
previously deposited particles on indoor surfaces. P-values showed no significant phase
or regional interactions for personal coarse particles at the San Gabriel Valley and
Riverside communities (see the “phase interactions” and “regional interactions”
columns in Figure 4.3c), which implies that factors affecting the associations between
personal coarse PM and its outdoor gaseous and particle co-pollutants were similar at
all sites and phases.

4.4.5. Indoor – Personal Associations
Personal and indoor quasi-UF PM levels were best correlated in the San Gabriel Valley
(R = 0.38; average regression slope = 1.6 µg/m
3
[0.8 to 2.3] in the warmer season; R =
0.40; slope = 2.3 µg/m
3
[0.7 to 4.0] in the cooler months) than in Riverside (Figure 4.4a).
At the San Gabriel Valley sites, moderate correlations and significant regression
parameter estimates were also observed between personal quasi-UF particles and fine PM.
Associations between personal quasi-UF particles and indoor OC (and its sub-
components) varied with location (from strong and positive in the San Gabriel Valley, to
poor and negative in Riverside). Although there are different possible mechanisms
leading to the production of particulate OC (e.g. photochemical reactions, condensation
of organic vapors on existing particles, and primary emission from combustion sources),

135

a) Quasi-UF PM

Figure 4.4 Mixed model and Spearman correlation results for indoor pollutants with: a) personal quasi-UF PM, b)
personal accumulation mode PM and c) coarse PM

136

b) Accumulation mode PM

Figure 4.4 Continued

137

c) Coarse PM

Figure 4.4 Continued
138

organic particles in the quasi-UF mode are generally formed by combustion processes
(Fine et al. 2004; Sardar et al. 2005), which are more dominant in the San Gabriel Valley
than in Riverside. Indoor EC and outdoor generated EC found indoors (ECog) were better
associated with quasi-UF personal PM than indoor generated EC (ECig), indicating that
indoor sources of EC were not substantial. For each interquartile increase in ECog the
personal quasi-UF PM concentration increased of 0.9 and 2.4 µg/m
3
(during phases 1 and
2, respectively) in the San Gabriel Valley, and of 1.6 and 0.2 µg/m
3
(during phases 1 and
2, respectively) in Riverside (see Figure 4.4a for details). Similarly, indoor concentrations
of outdoor origin of other important particle species, such as fine PM, OC, and OCpri
(“Fine
og
”, “OCog”, and “OCpri(og)”, respectively; see Figure 4.4a) were generally
strongly and positively correlated with personal quasi-UF PM, compared to their
corresponding indoor concentrations of indoor origin (“Fine
ig
” and “OCig”, respectively;
“OCpri(ig)”, estimates were not reported). This observation suggests that indoor sources
were probably not significant contributors to personal exposure, which is predominantly
influenced by primary emitted pollutants produced/emitted outdoors. Indoor gaseous co-
pollutants (most likely originating outdoors) were more positively correlated to quasi-UF
particles (R = 0.21 to 0.34; average regression slopes = 0.4 to 2.3 µg/m
3
) than larger size
fractions (discussed below), likely due to similarities in sources, transportation and
deposition mechanisms for these two classes of pollutants. Positive and fairly well
correlation of indoor (and outdoor) concentrations of CO, NO
2
and NOx with personal
quasi-UF PM levels, reflects the possibility that these gaseous co-pollutants could
confound epidemiologic associations between quasi-UF particles and adverse health
139

effects. However, this seems questionable because CO is neither a respiratory irritant nor
a moderator of immune response in the respiratory tract (Sarnat et al. 2000), and NO
2
is
probably not responsible for adverse health effects despite, given its low concentrations
in personal and ambient air (Delfino et al. 2006; Delfino et al. 2008). Thus, we speculate
that the observed association between personal quasi-UF PM concentrations and
measured indoor co-pollutant levels do not reflect the corresponding personal-personal
relationships (personal gaseous measurements were not performed during CHAPS).

At all sites and during all seasons, personal accumulation mode particles were well
associated with indoor accumulation PM and with PM
2.5
(Figure 4.4b). Moreover,
personal accumulation mode PM was more strongly correlated with Fineog, OCog
(only at the San Gabriel sites) and ECog than with the corresponding indoor levels of
indoor origin (Fineig, OCig, and ECig, respectively). In contrast, personal
accumulation mode PM was always negatively associated to SOAog. These results
reinforce the idea that outdoor primary emission sources are of great importance in
terms of personal exposure to PM
2.5
. These findings support the epidemiologic results
reported by Delfino et al. (Delfino et al. 2008) for the present panel of elderly people
with a history of coronary artery disease. They concluded that the strongest
associations were found between indoor PM of outdoor origin and increases in blood
biomarkers of systemic inflammation and platelet activation, and decreases in
erythrocyte antioxidant activity. In the same study, little evidence for biomarker
associations with secondary PM
2.5
, SOA or total OC was found, while robust
140

associations with emission sources of primary PM
2.5
, OC and quasi-UF particles were
observed. This is consistent with the weak and often negative correlations between
personal quasi-UF and accumulation mode PM and indoor SOA of outdoor origin
(SOAog) obtained in our work.

Indoor CO, NO
2
and NOx levels were not as well correlated to personal accumulation
PM concentrations as quasi-UF mode particles, except during phase 2 at Riverside (R=
0.37 to 0.45; see Figure 4.4b). Generally, indoor CO and NO
2
were better associated
with personal accumulation PM exposure than indoor NO
x
. Overall, this suggests that
the measured gaseous co-pollutants are weak surrogates of personal exposure to
accumulation mode PM, at least for subjects with similar exposure profiles and living
in similar urban locations. It is worth emphasizing that, while quasi-UF PM is mostly
produced from primary combustion sources, accumulation mode particles originate
from a combination of primary and secondary emissions. This might explain the
relatively weaker correlations observed earlier between personal accumulation PM and
outdoor CO (and EC, a tracer of primary combustion). For NO
2
(a rather non reactive
gas), indoor sources may also weaken its association with personal PM.

Personal coarse mode particles were more strongly associated with all indoor PM
fractions in the San Gabriel Valley than in Riverside, although the strength of this
correlation was rather low at all sites (Figure 4.4c). Associations between personal
coarse PM and indoor EC (as well as indoor OC) were generally small. Indoor CO,
141

NO
2
and NO
x
showed relatively poor associations with personal coarse PM at all sites,
except in Riverside during the cooler months (R= 0.34 to 0.42; average regression
slopes = 0.3 to 0.4 µg/m
3
).

4.4.6. Regional and Seasonal Correlations and Comparison with Other Studies
To the best of our knowledge, this study is one of the most extensive analysis to date of
seasonal correlations between indoor, outdoor, and personal size-segregated PM
concentrations and the corresponding gaseous levels/PM components. Despite the
uniqueness of this data analysis, we considered it important to compare our results to
those obtained from similar studies conducted previously in the U.S., in an effort to
clarify the role of CO, NO
2
, NOx, and O
3
as surrogates of human exposure to PM
2.5
.
Figure 4.5 shows Spearman’s correlation coefficients (R) for the associations between
personal PM
2.5
levels and the outdoor (residential or ambient) concentrations of PM
2.5
,
CO, NO
2
, NOx, and O
3
, both in the warmer (Figure 4.5a) and cooler (Figure 4.5b)
months. R values calculated at the San Gabriel Valley and Riverside sites (personal vs
residential outdoor) were plotted against those obtained by Sarnat et al. in Baltimore
(Sarnat et al. 2000) and Boston (Sarnat et al. 2005) (personal vs ambient).

At all studied locations, personal PM
2.5
was well correlated with outdoor/ambient PM
2.5,
both in the warmer and the cooler seasons. This is also consistent with results from other
recent longitudinal studies on PM exposure (Ebelt et al. 2000; Sarnat et al. 2000;
Williams et al. 2000; Sarnat et al. 2005). Of particular interest are the moderately strong
142

associations observed at all sites between personal PM
2.5
and NO
2
(in both phases; see
Figures 5a and 5b), personal PM
2.5
and O
3
(only in the phase 1; Figure 4.5a), and PM
2.5
and

CO (only in the phase 2; Figure 4.5b). Variability in these correlations reflects the
effects of seasonal changes on the formation mechanisms of PM.

In the warmer months, a considerable fraction of personal exposure to PM
2.5
might be
related to particles originating from photochemical activities, while in the cooler months
personal PM
2.5
levels are predominantly influenced by combustion processes. In addition,
results from all studies indicate that outdoor/ambient NO
2
and O
3
(in the warmer season)
and outdoor/ambient CO and NO
2
(in the cooler season) are moderately correlated with
personal PM
2.5
exposure and, in each location, there may be differences in the strength of
the personal-ambient associations, probably due to geographical variability in housing
characteristics and ventilation conditions (Sarnat et al. 2000; Sarnat et al. 2005; Polidori
et al. 2007).

Therefore, it might be incorrect to assume that outdoor/ambient gas measurements are
consistent surrogates for personal PM
2.5
exposures, because correlations between personal
PM
2.5
and outdoor/ambient CO, NO
2
, NOx, and O
3
vary by both season and location
(Figures 5a and 5b). Results from time-series epidemiologic studies that include both
gaseous and particulate pollutant concentrations in the models should be interpreted with
caution. As noted by Sarnat et al. (Sarnat et al. 2000), if ambient co-pollutant
concentrations are surrogates (as opposed to confounders) of PM, using multiple particles
143

a) Summertime personal PM
2.5
vs outdoor species associations
-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
Spearman Correlation Coefficient
PM
2.5
CO
NO
2
NO
x
O
3

b) Wintertime personal PM
2.5
vs outdoor species associations
-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00
Spearman Correlation Coefficient
Median San Gabriel (outdoor-personal) Median Riverside (outdoor-personal)
Median Baltimore (ambient-personal) Median Boston (ambient-personal)
PM
2.5
CO
NO
2
NO
x
O
3
San Gabriel Standard Deviation Riverside Standard Deviation

Figure 4.5 Spearman’s correlation coefficients (R) for the associations between personal
PM
2.5
concentrations and outdoor (residential or ambient) particle/gaseous levels in the
summer (a) and winter (b). The R values calculated in this work at the San Gabriel Valley
and Riverside sites (personal vs residential outdoor) were compared to those obtained by
Sarnat et al. in Baltimore (2001) and Boston (2005) (personal vs ambient). Error bars are
referred to standard deviation of individual values
144

would lead to an erroneous significant association for the collinear co-pollutants.
Previous environmental and occupational studies showed that O
3
and NO
2
exposures may
elicit adverse health effects (Delvin et al. 1997; Devlin et al. 1997; Gong et al. 1998;
Geyh et al. 2000; Mosqueron et al. 2002), thus have the potential to act as confounders of
personal exposure to PM
2.5
. However, two recent studies by Delfino et al. have suggested
that NO
2
might be a surrogate of not only particle species, but also volatile and
semivolatile compounds that may have important health effects independent of particle
bound components (Delfino et al. 2006; Delfino et al. 2008).

4.5. CONCLUSIONS
Our modeling results have shown that outdoor and indoor levels of CO, NO
2
and NOx
were better correlated with measured indoor, outdoor and personal quasi-UF PM levels
than accumulation mode and coarse mode PM. This better correlation is due to more
similarity in sources and transportation mechanism. Indoor concentrations of outdoor
origin of important carbonaceous species such as EC, OC, and OCpri (“ECog, “OCog”,
and “OCpri(og), respectively) were more strongly correlated with personal quasi-UF and
accumulation mode PM, than their corresponding indoor concentrations of indoor origin
(“ECog”, “OCig”, and “OCpri(ig)”). This is because indoor sources were probably not
significant contributors to personal exposure of accumulation and quasi UF PM, which is
predominantly influenced by primary pollutants produced/emitted outdoors. These results
are important, because other CHAPS investigators have suggested that traffic-related
emission sources of PM
2.5
OCpri, and quasi-UF particles lead to increases in systemic
145

inflammation, platelet activation, and decreases in erythrocyte antioxidant activity in
elderly people with a history of coronary artery disease.

Overall, our data analysis suggests that investigating the correlations among size-
segregated indoor, outdoor and personal PM, their specific components, and concurrently
measured gaseous co-pollutants is a challenging endeavor. These associations depend on
a number of factors that vary in space and time, such as: the relative contribution of UF,
accumulation and coarse mode PM to the measured PM concentrations, the seasonal
variability of primary and secondary emission sources, the presence of indoor sources of
PM and gaseous co-pollutants (e.g. cooking), home characteristics (e.g. ventilation
conditions and household characteristics), and proximity to the emission sources. The
analysis of these associations is further complicated by the amount of time spent indoors
(highly variable among subjects, especially in the warmer season), which is also a critical
component in determining exposure. Thus, results from time-series epidemiologic studies
that include both gaseous and particulate pollutant concentrations in the models should be
interpreted with caution. Future research should focus on how these specific factors affect
the strength of between-pollutant associations for individuals living in different locations.

146

4.6. CHAPTER 4 REFERENCES
Birch, M. E. and R. A. Cary (1996). Elemental carbon-based method for monitoring
occupational exposures to particulate diesel exhaust, Aerosol Science and Technology
25(3): 221-241.
Chakrabarti, B., M. Singh and C. Sioutas (2004). Development of a near-continuous
monitor for measurement of the sub-150 nm PM mass concentration, Aerosol Science
and Technology 38: 239-252.
Chen, C., D. P. Chock and S. L. Winkler (1999). A simulation study of confounding in
generalized linear models for air pollution epidemiology, Environmental Health
Perspectives 107(3): 217-222.
Chow, J. C., J. G. Watson, L. C. Pritchett, W. R. Pierson, C. A. Frazier and R. G. Purcell
(1993). The DRI Thermal Optical Reflectance Carbon Analysis System - Description,
Evaluation and Applications in United-States Air-Quality Studies, Atmospheric
Environment Part a-General Topics 27(8): 1185-1201.
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.
Delfino, R. J., N. Staimer, D. Gillen, T. Tjoa, C. Sioutas, K. Fung, S. C. George and M. T.
Kleinman (2006). Personal and ambient air pollution is associated with increased exhaled
nitric oxide in children with asthma, Environmental Health Perspectives 114(11): 1736-
1743.
Delfino, R. J., N. Staimer, T. Tjoa, A. Polidori, M. Arhami, D. L. Gillen, M. T. Kleinman,
N. D. Vaziri, J. Longhurst, F. Zaldivar and C. SioutaS (2008). Circulating biomarkers of
inflammation, antioxidant activity, and platelet activation are associated with primary
combustion aerosols in subjects with coronary artery disease, Environmental Health
Perspectives 116(7): 898-906.
Delvin, E. E., V. Lopez, E. Levy and D. Menard (1997). Control of apolipoprotein
synthesis by calcitriol and clofibrate in human fetal jejunum., Gastroenterology 112(4):
A870-A870.
Devlin, R. B., L. J. Folinsbee, F. Biscardi, G. Hatch, S. Becker, M. C. Madden, M.
Robbins and H. S. Koren (1997). Inflammation and cell damage induced by repeated
exposure of humans to ozone, Inhalation Toxicology 9(3): 211-235.
Ebelt, S. T., A. J. Petkau, S. Vedal, T. V. Fisher and M. Brauer (2000). Exposure of
chronic obstructive pulmonary disease patients to particulate matter: Relationships
between personal and ambient air concentrations, Journal of the Air & Waste
Management Association 50(7): 1081-1094.
147

Fine, P. M., B. Chakrabarti, M. Krudysz, J. J. Schauer and C. Sioutas (2004). Diurnal
variations of individual organic compound constituents of ultrafine and accumulation
mode particulate matter in the Los Angeles basin, Environmental Science and
Technology 38(5): 1296-1304.
Geller, M. D., M. H. Chang, C. Sioutas, B. D. Ostro and M. J. Lipsett (2002).
Indoor/outdoor relationship and chemical composition of fine and coarse particles in the
southern California deserts, Atmospheric Environment 36(6): 1099-1110.
Geyh, A. S., J. P. Xue, H. Ozkaynak and J. D. Spengler (2000). The Harvard Southern
California chronic ozone exposure study: Assessing ozone exposure of grade-school-age
children in two Southern California communities, Environmental Health Perspectives
108(3): 265-270.
Gong, H., R. Wong, R. J. Sarma, W. S. Linn, E. D. Sullivan, D. A. Shamoo, K. R.
Anderson and S. B. Prasad (1998). Cardiovascular effects of ozone exposure in human
volunteers, American Journal of Respiratory and Critical Care Medicine 158(2): 538-
546.
Green, L. C., E. A. C. Crouch, M. R. Ames and T. L. Lash (2002). What's wrong with the
National Ambient Air Quality Standard (NAAQS) for fine particulate matter (PM2.5)?,
Regulatory Toxicology and Pharmacology 35(3): 327-337.
He, C. R., L. D. Morawska, J. Hitchins and D. Gilbert (2004). Contribution from indoor
sources to particle number and mass concentrations in residential houses, Atmospheric
Environment 38(21): 3405-3415.
IARC (2005). Overall Evaluations of Carcinogenicity to Humans (World Health
Organization), International Agency for Research on Cancer (IARC).
Kaiser, J. (2005). Mounting evidence indicts fine-particle pollution, Science 307(5717):
1858-1861.
Kanakidou, M., J. H. Seinfeld, S. N. Pandis, I. Barnes, F. J. Dentener, M. C. Facchini, R.
Van Dingenen, B. Ervens, A. Nenes, C. J. Nielsen, E. Swietlicki, J. P. Putaud, Y.
Balkanski, S. Fuzzi, J. Horth, G. K. Moortgat, R. Winterhalter, C. E. L. Myhre, K.
Tsigaridis, E. Vignati, E. G. Stephanou and J. Wilson (2005). Organic aerosol and global
climate modelling: a review, Atmospheric Chemistry and Physics 5: 1053-1123.
Kim, S., C. Sioutas, M. C. Chang and H. Gong (2000). Factors affecting the stability of
the performance of ambient fine-particle concentrators, Inhalation Toxicology 12: 281-
298.
Laird, N. M. and J. H. Ware (1982). Random-Effects Models for Longitudinal Data,
Biometrics 38(4): 963-974.
148

Long, C. M., H. H. Suh, P. J. Catalano and P. Koutrakis (2001). Using time- and size-
resolved particulate data to quantify indoor penetration and deposition behavior (vol 35,
pg 2089, 2001), Environmental Science & Technology 35(22): 4584-4584.
McClellan, R. O. (2002). Setting ambient air quality standards for particulate matter,
Toxicology 181: 329-347.
Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. F. J. Zhang,
T. Stock, D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S.
Alimokhtari, D. Shendell, J. Jones, C. Farrar and S. Maberti (2005). Influence of ambient
(outdoor) sources on residential indoor and personal PM2.5 concentrations: Analyses of
RIOPA data, Journal of Exposure Analysis and Environmental Epidemiology 15(1): 17-
28.
Misra, C., M. Singh, S. Shen, C. Sioutas and P. A. Hall (2002). Development and
evaluation of a personal cascade impactor sampler (PCIS), Journal of Aerosol Science
33(7): 1027-1047.
Mosqueron, L., I. Momas and Y. Le Moullec (2002). Personal exposure of Paris office
workers to nitrogen dioxide and fine particles, Occupational and Environmental
Medicine 59(8): 550-555.
Na, K. and D. R. Cocker (2005). Organic and elemental carbon concentrations in fine
particulate matter in residences, schoolrooms, and outdoor air in Mira Loma, California,
Atmospheric Environment 39(18): 3325-3333.
Nel, A. (2005). Air pollution-related illness: Effects of particles, Science 308(5723): 804-
806.
Ntziachristos, L., Z. Ning, M. D. Geller, R. J. Sheesley, J. J. Schauer and C. Sioutas
(2007). Fine, ultrafine and nanoparticle trace element compositions near a major freeway
with a high heavy-duty diesel fraction, Atmospheric Environment 41(27): 5684-5696.
Polidori, A., M. Arhami, C. Sioutas, R. J. Delfino and R. Allen (2007). Indoor/outdoor
relationships, trends, and carbonaceous content of fine particulate matter in retirement
homes of the Los Angeles basin, Journal of the Air & Waste Management Association
57(3): 366-379.
Pope, C. A. and D. W. Dockery (2006). Health effects of fine particulate air pollution:
Lines that connect, Journal of the Air & Waste Management Association 56(6): 709-742.
Press, N. A. (1998). Research Priorities for Airborne Particulate Matter. Immediate
Priorities and a Long-Range Research Portfolio, National Research Council
149

Rojas-Bracho, L., H. H. Suh and P. Koutrakis (2000). Relationships among personal,
indoor, and outdoor fine and coarse particle concentrations for individuals with COPD,
Journal of Exposure Analysis and Environmental Epidemiology 10(3): 294-306.
Sardar, S. B., P. M. Fine, P. R. Mayo and C. Sioutas (2005). Size-fractionated
measurements of ambient ultrafine particle chemical composition in Los Angeles using
the NanoMOUDI, Environmental Science & Technology 39(4): 932-944.
Sardar, S. B., P. M. Fine and C. Sioutas (2005). Seasonal and spatial variability of the
size-resolved chemical composition of particulate matter (PM10) in the Los Angeles
Basin, Journal of Geophysical Research-Atmospheres 110(D7).
Sarnat, J. A., K. W. Brown, J. Schwartz, B. A. Coull and P. Koutrakis (2005). Ambient
gas concentrations and personal particulate matter exposures - Implications for studying
the health effects of particles, Epidemiology 16(3): 385-395.
Sarnat, J. A., P. Koutrakis and H. H. Suh (2000). Assessing the relationship between
personal particulate and gaseous exposures of senior citizens living in Baltimore, MD,
Journal of the Air & Waste Management Association 50(7): 1184-1198.
Sarnat, S. E., B. A. Coull, J. Schwartz, D. R. Gold and H. H. Suh (2006). Factors
affecting the association between ambient concentrations and personal exposures to
particles and gases, Environmental Health Perspectives 114(5): 649-654.
Singh, M., C. Misra and C. Sioutas (2003). Field evaluation of a personal cascade
impactor sampler (PCIS), Atmospheric Environment 37(34): 4781-4793.
Wallace, L. (1996). Indoor particles: a review, Air and Waste management Association
46: 98-126.
Wallace, L. (2000). Correlations of personal exposure to particles with outdoor air
measurements: a review of recent studies., Aerosol Sci. 32: 15-25.
Williams, R., J. Creason, R. Zweidinger, R. Watts, L. Sheldon and C. Shy (2000). Indoor,
outdoor, and personal exposure monitoring of particulate air pollution: the Baltimore
elderly epidemiology-exposure pilot study, Atmospheric Environment 34(24): 4193-4204.
Zhu, Y. F., W. C. Hinds, M. Krudysz, T. Kuhn, J. Froines and C. Sioutas (2005).
Penetration of freeway ultrafine particles into indoor environments, Journal of Aerosol
Science 36(3): 303-322.

150
Chapter 5.
Organic Compound Characterization and Source Apportionment of
Indoor and Outdoor Quasi-ultrafine PM in Retirement Homes of the
Los Angeles Basin

5.1. ABSTRACT
Quasi-ultrafine PM (PM
0.25
) and its components were measured in indoor and outdoor
environments at four retirement communities in the Los Angeles basin, CA, as part of the
Cardiovascular Health and Air Pollution Study (CHAPS). The present paper focuses on
the characterization of the sources, organic constituents and indoor and outdoor
relationships of quasi-ultrafine PM. In contrary to n-alkanes and n-alkanoic acid, the
average indoor/outdoor ratio of most of the measured PAHs, hopanes and steranes were
close to- or slightly lower than 1, and indoor-outdoor correlation coefficients (R) were
always positive and for most of these components moderate to strong (median R was 0.60
for PAHs and 0.74 for hopanes and steranes). This suggests that indoor PAHs, hopane
and steranes were mainly from outdoor origin, whereas indoor n-alkanes and n-alkanoic
acide were significantly influenced by indoor sources.

The Chemical Mass Balance (CMB) model was applied to both indoor and outdoor
speciated chemical measurements of quasi-ultrafine PM. Vehicular sources had the
highest contribution to PM
0.25
among the apportioned sources for both indoor and outdoor
particles at all sites (on average 24-47%). The contribution of mobile sources to indoor
levels was similar to their corresponding outdoor estimates.

151
In an earlier investigation, also part of this study, we reported that indoor infiltrated
particles from mobile sources are more strongly correlated with the adverse health effects
observed in the elderly subjects living in the studied retirement communities compared to
indoor measured particles. The contribution of mobile sources to indoor levels was
similar to their corresponding outdoor estimates, thus illustrating the significance of these
sources on indoor PM concentrations. A major implication of these findings is that, even
if people (particularly the elderly population of our study) generally spend most of their
time indoors, a major portion of the PM
0.25
particles to which they are exposed comes
from outdoor mobile sources.

5.2. INTRODUCTION
Positive associations between exposure to atmospheric particulate matter (PM) and
adverse health effects have been shown by numerous epidemiological and toxicological
studies (Pope and Dockery 2006). Particle size is an important parameter affecting the
percentage of the inhaled aerosol that deposit in the human lung as well as the deposition
site. Generally, smaller particles penetrate into the deeper regions of the human
respiratory tract after being inhaled. Thus, fine PM (PM
2.5
; particles with an aerodynamic
diameter smaller that 2.5 µm) has been more strongly associated with mortality and
morbidity, although coarse particles (PM
2.5-10
; aerodynamic diameter between 2.5 and 10
µm) have also been associated with respiratory hospital admissions (Brunekreef and
Forsberg 2005). Ultrafine particles, tenuously defined as those with diameters less than
0.1 – 0.2 µm (see more discussion on the appropriate cutpoint of the ultrafine PM mode
152
in (Sioutas et al. 2005) penetrate deep into the alveolar region of the respiratory system
and have the ability to translocation in other parts of the human body (Elder et al. 2006).
Toxicological data suggest that these particles are more strongly associated with
cardiovascular and respiratory health outcomes (Araujo et al. 2007) compared to larger
particles. So far, there is little direct epidemiologic research to support this (reviewed by
(Delfino et al. 2005); (Weichenthal et al. 2007). Along with particle size, chemical
composition influences the toxicity of PM. Thus, exposure to several types of highly
toxic organic particle components [including quinones, polycyclic aromatic hydrocarbons
(PAHs), polychlorinated biphenyls and other organochlorine compounds] may result in
acute effects (Li et al. 2003).

Although air quality standards have been established for outdoor / ambient environments,
a significant portion of human exposures to PM occurs indoors, where people spend
around 85-90% of their time (Klepeis et al. 2001). Hence, it is important to understand
the composition and sources of both indoor and outdoor PM and their relationships.
Indoor PM consists of outdoor particles that have infiltrated indoors, particles emitted
indoors (primary), and particles formed indoors (secondary) from precursors emitted both
indoors and outdoors (Weschler 2004). To the best of our knowledge, only few studies on
indoor PM source apportionment have been conducted in the past few years. These were
mainly focused on examining the influence of outdoor sources on the measured outdoor
concentrations of fine PM (without any further size fractionation), although indoor
measurements of organic tracers that are typically used for source apportionment were
153
also conducted (Olson et al. 2008). Few studies have demonstrated that indoor sources
can contribute up to 50% to the indoor concentrations of fine PM and its components
(Wallace 1996; Meng et al. 2005), whereas other studies reported lower contributions (6-
22%) (Yli-Tuomi et al. 2008).

The present papers has been conducted as part of the Cardiovascular Health and Air
Pollution Study (CHAPS), a multidisciplinary project funded by the National Institutes of
Health (NIH) and designed to investigate the effects of micro-environmental exposures to
PM on cardiovascular outcomes in elderly retirees affected by coronary artery disease.
The elderly population with coronary artery disease is likely to be among the most
vulnerable to the adverse effects of particulate air pollutants. In an earlier investigation,
also part of this study (Delfino et al. 2008), we reported that indoor PM of outdoor origin
(mostly from combustion sources) was more significantly associated with systemic
inflammation, platelet activation, and decreases in erythrocyte antioxidant activity than
uncharacterized indoor PM that included particles of indoor origin. The present study
focuses on the quasi-ultrafine fraction of PM (PM
0.25
; particles with an aerodynamic
diameter smaller that 0.25 µm) in both indoor and outdoor environments at four distinct
retirement communities of the Los Angeles Basin, which were study sites of the CHAPS
project. The main objectives of this study are: a) to evaluate the organic composition of
quasi-ultrafine PM (PM
0.25
) in both indoor and outdoor environments throughout the
calendar year, b) to identify the most important sources of these sub-micrometer particles,
and c) to quantify their contribution to the total PM mass concentrations in both indoor
154
and outdoor environments. The study focuses on PM
0.25
because we have shown in earlier
publications from the CHAPS that this size range had the strongest and most significant
association with circulating biomarkers of inflammation, antioxidant activity, and platelet
activation measured in the study subjects (Delfino et al, 2008). Furthermore, to best of
our knowledge, there are no other studies of sources and composition of indoor ultrafine
or quasi-ultrafine particles. The results described in this paper will be used by the
CHAPS investigators to evaluate associations between indoor and outdoor PM sources to
cardiovascular outcomes.

5.3. METHODS

5.3.1. Sampling sites and schedule
Indoor and outdoor PM measurements were conducted at four retirement communities in
southern California between 2005 and 2007. Three of these communities were in the San
Gabriel Valley, CA (sites San Gabriel 1, 2 and 3) and the fourth in Riverside, CA. Site
San Gabriel 1 was located in a residential area about 50 km east of downtown Los
Angeles, approximately 3 km away from a major freeway. Site San Gabriel 2 was about 8
km east of Los Angeles, approximately 300 m away of a major freeway. Site San Gabriel
3 was situated about 55 km east of downtown Los Angeles, 2.5 km from two busy
freeways and 150 m away from a major street. Site Riverside was located about 110 km
east of Los Angeles, 15 km southeast of downtown Riverside, 3 km away from the
closest freeway and 1 km from a major street (downwind of the site). The abundant
155
vegetation surrounding this last community may be a potential source of precursors of
biogenically generated PM (Rogge et al. 1993).

Two identical sampling stations were installed at each site, one indoors and one outdoors.
The indoor sampling station at site San Gabriel 1 was set-up in a recreational area of the
community’s main building, adjacently to a construction site. San Gabriel 2 indoor
station was located in the dining room of the community’s central building (see Polidori
et al., 2007, for further details on sites San Gabriel 1 and 2). The indoor station at site San
Gabriel 3 was in a recreational area of the main retirement community complex. The
indoor station at site Riverside was in the hallway of the main building with a dining
room, activity room and numerous apartment units nearby. Outdoor sampling equipment
was set-up inside a movable trailer, positioned within 300 m from the indoor station at all
sites.

Two 6-week sampling campaigns were conducted at each location, one during summer
and early fall (warmer phase) and one throughout late fall and winter (colder phase); see
Arhami et al. (2009) for more details.

5.3.2. Sampling method and chemical analyses
24-h size segregated PM samples were collected daily from Monday to Friday by means
of Sioutas
TM
Personal Cascade Impactors (SKC Inc, Eighty Four, PA). Coarse,
accumulation, and quasi-ultrafine mode PM were sampled on Zefluor (3 µm pore-size,
156
Pall Life Sciences, Ann Arbor MI) filters (although the present study focuses only in the
quasi-ultrafine fraction). The PM mass concentration was determined gravimetrically by
weighing filters in a controlled temperature and relative humidity room, using a
microbalance (Mettler-Toledo, Columbus, OH; weight uncertainty ± 2 µg).

Filters were composited weekly (including 5 daily collected samples-from Monday to
Friday) for chemical analyses. Composites were cut into 3 sections: 1 half and 2 quarter
sections. The one half section was analysed for 92 different organic compounds using
Gas Chromatography/Mass Spectrometry (GC/MS) (Stone et al. 2008). The first quarter
section of such composited filters were digested with concentrated acid using microwave
digestion and then analyzed by high resolution Inductively Coupled Plasma Mass
Spectrometer (HR-ICPMS, Finnigan Element 2) to determine 52 trace elements (Herner
et al. 2006). The second quarter was analyzed for water soluble organic carbon (WSOC)
and used for a reactive organic species (ROS) assay. A General Electric Instrument
(Sievers Total Organic Carbon, TOC; GE, Inc.) was used to determine WSOC
concentrations. ROS will be used and described in other CHAPS papers.

Hourly elemental and organic carbon (EC and OC, respectively) levels in the fine PM
fraction were measured using semi-continuous OC_EC analyzer (Model 3F, Sunset
Laboratory Inc). The quasi-ultrafine EC was estimated from the measured fine EC using
a factor of 0.70 ± 0.18 (quasi-ultrafine EC = fine EC × 0.70), which is based on recent
size distribution data obtained over a 3 year period at 10 locations of the Los Angeles
157
Basin (Arhami et al. 2006; Minguillon et al. 2008; Arhami et al. 2009). Dasibi Carbon
Monoxide Analyzers (Model 3008, Dasibi Environmental Corp, Glendale, CA) were
implemented to measure continuous (1-min) indoor and outdoor CO levels.

5.3.3. Source Apportionment
The Chemical Mass Balance (CMB) model (version CMB8.2 from the US Environmental
Protection Agency) was used for the apportionment of the total measured ambient
organic carbon (OC). In order to apportion the most important quasi-ultrafine PM
sources, all model results were converted to equivalent PM based on the correspondent
OC/PM ratio of each of the considered sources. OC/PM ratios were calculated for source
profiles assuming that quasi-ultrafine PM = Elemental Carbon (EC) + Organic Matter
(OM) and that OM=1.4*OC (Turpin et al. 2000; Polidori et al. 2008). Other contributors
to PM considered in this study are: sulfate, estimated from S concentrations assuming
that all measured S by ICP-MS was in the form of fully neutralized ammonium sulfate
(Arhami et al. 2009); sea spray, calculated based on total Na as an estimate of water
soluble Na concentrations and using a multiplication factor of 3.248 (Simoneit 1986);
resuspended dust, calculated from Si, Al, Ca, Fe and K concentrations, assuming they
appear predominantly as oxides (Brook et al. 1997) and Si was not measured but
estimated assuming Si=3*Al (Sillanpaa et al. 2006); and SOA estimations were based on
measurements of WSOC. In polluted regions, compounds comprising water soluble
organic carbon (WSOC) are either mainly emitted from biomass burning sources
(Docherty et al. 2008) or formed via secondary atmospheric processes (Weber et al.
158
2007). Thus, measured WSOC concentrations, minus the OC fraction apportioned to
biomass burning (from CMB output), was multiplied by a factor of 2.5 µgOM/µgOC
(Turpin and Lim 2001; Polidori et al. 2008) to convert it to SOA. The resulting SOA was
then adjusted to account for the water-insoluble organic carbon (WISOC) fraction,
assuming that 20% of the SOA concentration was water insoluble (Kondo et al. 2007).
The uncertainty associated with this method is discussed in the results section.

A careful selection of OC sources is critical for the correct application of the CMB
model, as demonstrated in previous sensitivity studies (Subramanian et al. 2006; Sheesley
et al. 2007). Hence, after evaluating all potential pollutant emissions in the study area, the
sources considered in this work were: light duty and heavy-duty vehicles (LDV and HDV
respectively; (Kuhn et al. 2005; Ntziachristos et al. 2007; Phuleria et al. 2007), biomass
burning in the Western US (Fine et al. 2004), and ocean vessels (Rogge et al. 1997;
Agrawal et al. 2008). Vehicular profiles correspond to roadway data from studies carried
out at the CA-110 and I-710 freeways in Los Angeles and, thus, they represent emissions
from a mixture of vehicular sources (Phuleria et al. 2007). Other typical OC sources were
included in the first modeling attempts, but were found to be non-quantifiable (meat
cooking and natural gas) or their contribution was very low (candle smoke and cigarette
smoke, with contributions <1% of OC). A set of fitting species was chosen based on: a)
their chemical stability (Schauer et al. 1996), b) availability of their concentrations in
different source profiles and in ambient data, and c) previous studies that identified
markers for different sources (Schauer et al. 1996; Simoneit 1999; Schauer and Cass
159
2000). Thus, the following species were used as fitting species: EC,
Benzo(k)fluoranthene, Benzo(e)pyrene, Benzo(b)fluoranthene, Benzo(ghi)perylene,
Coronene, 17α(H)-22,29,30-Trisnorhopane, 17α(H)-21β(H)-Hopane, 22S-Homohopane,
22R-Homohopane, Sitostane, Levoglucosan, Vanadium and Nickel.

Each model result was evaluated using the regression statistics parameters accompanying
each CMB model output: correlation coefficient (R
2
) and χ
2
, which were within the
desired ranges (0.81-1.00 and 0.0-5.7, respectively). The chi-square is the weighted sum
of squares of the differences between the calculated and measured fitting species
concentrations (for more details and description of statistical parameters, see CMB8.2
manual by the US Environmental Protection Agency). Weekly source contributions were
estimated and the results were averaged over each phase of study at each site.

5.3.4. Data Analysis
A number of relevant organic species were not detectable at several sites or during
particular phases of the study. In order to make a fair comparison between sites and time
phases, half of the detection limit for each species was used as its concentration with half
of the detection limit as uncertainty (the detection limit varied from 1.7e-5 to 0.06 ng/m
3

for different species). CO levels were used to estimate indoor-outdoor air exchange rates
(AER; h
-1
) at each site using a well established procedure developed by (Abt et al. 2000)
(see (Polidori et al. 2007) for further details).

160
5.4. RESULTS AND DISCUSSION

5.4.1. Overview
The recorded meteorological data highlight the overall climatological stability of the Los
Angeles basin, characterized by moderate differences in terms of temperature (T) and
relative humidity (RH%) between colder and warmer phases (Table 5.1). These moderate
meteorological variations may cause low variability in pollutants levels throughout the
year. The average T and RH% were lower in Riverside than at the three San Gabriel sites.
During the warmer phase of the study, the indoor and outdoor areas were generally
characterized by similar T, whereas during the colder phases the average T was ~10ºC
higher indoors than outdoors. The higher differences between indoor and outdoor T
values in the colder phase can potentially cause higher variability between indoor and
outdoor concentrations compared to the warmer phase, especially for volatile (or semi-
volatile) components. The air exchange rates (AER) at different sites were relatively
similar to each other throughout the monitoring phases (Table 5.1), suggesting an overall
similarity in home characteristics among different communities (Polidori et al. 2007).
The magnitude of the AERs was generally low (0.21-0.4 h
-1
), and consistent with the low
number of open windows and doors, the presence of central air conditioners, and the
overall structural characteristics of the studied retirement homes.

161
Table 5.1 Studied sites PM concentrations, meteorology and air exchange rates

San Gabriel 1 9.9 ± 2.2 10.3 ± 1.6 25.1 ± 2.2 26.1 ± 1.2 60 ± 6 0.25 ± 0.04
San Gabriel 2 9.3 ± 1.8 9.8 ± 1.6 21.5 ± 2.1 23.6 ± 0.9 58 ± 15 0.28 ± 0.06
San Gabriel 3 10.4 ± 2.3 6.6 ± 1.3 25.9 ± 2.8 23.3 ± 1.1 58 ± 11 0.40 ± 0.12
Riverside 11.5 ± 3.0 8.7 ± 2.3 21.1 ± 4.0 24.9 ± 1.7 53 ± 15 0.21 ± 0.06
San Gabriel 1 8.8 ± 1.8 9.6 ± 3.1 15.4 ± 2.8 23.4 ± 1.2 58 ± 19 0.33 ± 0.07
San Gabriel 2 10.4 ± 2.6 9.4 ± 2.6 14.9 ± 2.1 23.9 ± 0.8 49 ± 14 0.31 ± 0.10
San Gabriel 3 10.7 ± 2.0 7.1 ± 2.6 16.6 ± 3.6 24.7 ± 1.2 55 ± 10 0.26 ± 0.08
Riverside 7.2 ± 2.2 6.0 ± 1.5 11.2 ± 2.8 25.4 ± 0.7 42 ± 22 0.31 ± 0.09
Average ± Standard Deviation of weekly data are reported
*
AER: Air Exchange Rate
Warmer Phase
Colder Phase
Outdoor
Humidity
(%) Indoor outdoor
Quasi-UF PM
(µg/m3)
Temperature
(°C)
outdoor Indoor
AER
*
(h
-1
)

The average outdoor quasi-ultrafine mass concentrations at all sites varied from 9.3 to
11.5 µg/m
3
in the warmer phases and from 8.9 to 10.7 µg/m
3
in the colder phases thus
indicating relatively low seasonal variability. Similar to outdoor quasi-ultrafine PM
levels, indoor levels were consistent throughout the year at all the sites. Mean indoor
concentrations were generally lower than- or similar to the corresponding outdoor
concentrations (average indoor levels were 63-107% of their outdoor values). This
suggests that particle loss due to penetration of outdoor particles indoors, deposition of
infiltrated PM on indoor surfaces, and/or evaporation of semi-volatile particle
components is generally greater than or similar to the amount of PM generated indoors.

162
5.4.2. Outdoor Organic Species and Seasonal Variability
As shown in Figure 5.1a, the outdoor PAHs concentrations were similar in the warmer
and the colder phases. However, medium and high molecular weight PAHs levels were
slightly higher in the colder months compared to the warmer periods. PAHs are mainly
products of incomplete combustion, including vehicular emissions (Manchester-Neesvig
et al. 2003). The higher colder phase levels could be attributed to the influence of cold
start spark-ignition from gasoline-powered vehicles, which emit higher amounts of high
molecular weight PAHs, such as benzo(ghi)perylene and coronene, than hot-start
conditions (Miguel et al. 1998; Fine et al. 2004; Lough et al. 2007). The average seasonal
changes in hopanes and steranes were also quite small (Figure 5.1b), a result that can be
explained by the low seasonal variability in the emission rates of their main source, i.e.,
engine lubricating oil of mobile sources (Rogge et al. 1993; Rogge et al. 1996; Schauer et
al. 1996), which are independent of the driving conditions (e.g. cold-start, hot-start or
steady state; (Schauer et al. 2002). Moreover, the low seasonal variability may reflect the
low volatility of these organic species. A major portion of the analyzed n-alkanes was
characterized by substantially higher and more variable concentrations in the colder
months over the studied sites (Figure 5.1c), possibly because of the enhanced
condensation of gas phase n-alkanes onto existing particles (Fraser et al. 1997; Kuhn et
al. 2005). Conversely, the lower n-alkanes concentrations observed in the warmer phases
could be due to volatilization of their most volatile fraction (Fraser et al. 1997; Kuhn et
al. 2005) and to variations in the emission sources of these compounds.

163
a)
0
0.05
0.1
0.15
0.2
0.25
0.3
Phenanthrene
Anthracene
Fluoranthene
Pyrene
Benzo[ghi]fluoranthene
Cyclopenta[cd]pyrene
Benz[a]anthracene
Chrysene
Benzo[b]fluoranthene
Benzo[k]fluoranthene
Benzo[e]pyrene
Benzo[a]pyrene
Indeno[1,2,3-
cd]pyrene
Benzo[ghi]perylene
Coronene
PAHs (ng/m
3
)
Warmer Phase
Colder Phase
Low MW PAHs Medium MW PAHs High MW PAHs
Indeno[1,2,3-cd]pyrene

b)
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
17α[H]-22,29,30-
Trisnorhopane
17β[H]-21α[H]-
30-Norhopane
17α[H]-21β[H]-
Hopane
22S-
Homohopane
22R-
Homohopane
22S-
Bishomohopane
22R-
Bishomohopane
αββ-20R-C27-
Cholestane
αββ-20S-C27-
Cholestane
ααα-20S-C27-
Cholestane
αββ-20R-C29-
Sitostane
αββ-20S-C29-
Sitostane
Hopanes and Steranes (ng/m
3
)
Warmer Phase
Colder Phase
Hopanes Steranes
17α[H]-22,29,30-Trisnorhopane
17β[H]-21α[H]-30-Norhopane .
17α[H]-21β[H]-Hopane .
22S-Homohopane
22R-Homohopane
22S-Bishomohopane
22R-Bishomohopane
αββ-20R-C27-Cholestane
αββ-20S-C27-Cholestane
ααα-20S-C27-Cholestane
αββ-20R-C29-Sitostane
αββ-20S-C29-Sitostane

Figure 5.1 Outdoor concentrations of a) PAH’s, b) Hopanes and Steranes, c) n-
Alkanes and d) Acids. The presented values are average concentrations across all
sites and error bars are standard deviation of these averages at each site.

164
c)

0
2
4
6
8
10
12
14
16
18
Tetracosane
Pentacosane
Hexacosane
Heptacosane
Octacosane
Nonacosane
Triacontane
Hentriacontane
Dotriacontane
Tritriacontane
Tetratriacontane
Pentatriacontane
Hexatriacontane
Heptatriacontane
Octatriacontane
Nonatriacontane
Tetracontane
n-Alkanes (ng/m
3
)
Warmer Phase
Colder Phase

d)

0
20
40
60
80
100
Octanoic acid
Decanoic acid
Dodecanoic
acid
Tetradecanoic
acid
Pentadecanoic
acid
Hexadecanoic
acid
Heptadecanoic
acid
Octadecanoic
acid
Palmitoleic
acid
Oleic acid
Phthalic acid
Acids (ng/m
3
)
Warmer Phase
Colder Phase
max=140.1 max=199.6

Figure 5.1 Continued
165
Hexadecanoic, octadecanoic and phthalic acids were the most dominant measured acids
in quasi-UF PM (Figure 5.1d). Phthalic acid concentration in the warmer months was on
average more than 2 times higher than in the colder periods. This variability is probably
due to relatively higher photo-oxidation rates of organic gases in warmer conditions
(Rogge et al. 1991; Pandis et al. 1993; Robinson et al. 2007).

5.4.3. Indoor-Outdoor Organics
Figures 2 and 3 present the relationship between indoor and outdoor concentrations for
the studied organic compounds in the quasi-ultrafine particle range. Figure 5.2 shows the
average indoor and outdoor levels of PAHs, hopanes and steranes, n-alkanes and organic
acids at each site and phase of the study. Figure 5.3 shows the indoor / outdoor (IN /
OUT) ratio and correlation coefficient of measured organic species for different phases of
the study. The average outdoor level of the sum of all PAHs were lowest in Riverside
(0.5 ng/m
3
) and highest at San Gabriel 2 site (1.5 ng/m
3
). The Riverside site was the most
distant from the primary combustion sources (i.e. freeways and busy roadways), while
the San Gabriel 2 site was closest to a major freeway (within 300 m) among all the sites.
Typically, the average concentrations of the sum of all measured PAHs were similar, but
slightly lower indoors than outdoors. Accordingly, the average IN / OUT ratio of most of
the measured PAHs was close to or lower than 1 and correlation coefficients were always
positive and generally high for most of the components (median R for all components =
0.60). These results suggest that most of the indoor PAHs in the quasi-ultrafine mode
were of outdoor origin (mostly from motor-vehicle emissions), which is consistent with
166
previous studies (Ohura et al. 2004). PAHs generated by tobacco smoke were not
expected indoors, since all of the studied retirement communities were non-smoking
residences. Few individual PAH components, such as phenanthrene, anthracene and
benz(a)anthracene, showed slightly higher than 1 average IN / OUT ratios and a
relatively high high standard deviation, hence indicating the possibility of indoor sources
(e.g. natural gas appliances) for these species.

Similarly to PAHs, the sum of all measured hopanes and steranes concentrations was
slightly higher outdoors than indoors at all sites. Average IN / OUT ratios were close to 1
(min=0.83, max=1.31 and median=0.94), accompanied by relatively high R values
(median of R for all components = 0.74). As in the case of PAHs, these results highlight
the dominant influence of outdoor sources to the measured indoor concentrations of
hopanes and steranes, and the insignificant contributions from indoor sources. There were
no clear seasonal patterns for the corresponding IN / OUT ratios and R values. As
hopanes and steranes are more stable species compared to PAHs, the effect of
temperature differences between indoor and outdoor environments on their indoor and
outdoor associations becomes less significant. Similar relationships between the indoor-
outdoor concentrations of hopanes and steranes (and also PAHs) were found in a recent
study conducted in Tampa, FL (Olson et al. 2008).

The average indoor concentrations of the sum of all measured n-alkanes were typically
higher than the corresponding outdoor levels. Exceptionally high indoor levels with large
167
a)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
PAHs (ng/m
3
)
Warmer Phase Colder Phase
Indoor
Outdoor
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside

b)

0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Hopanes and Steranes (ng/m
3
)
Warmer Phase Colder Phase
Indoor
Outdoor
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside

Figure 5.2 Concentration of total a) PAH’s, b) Hopanes and Steranes, c) n-Alkanes
and d) Acids. Dots are average of concentrations across all the sites and error bars
are standard deviation of these averages at each site
168

c)
0
20
40
60
80
100
120
140
160
180
n-Alkanes (ng/m
3
)
Warmer Phase Colder Phase
Indoor
Outdoor
0.44(± 0.28)
0.17(± 0.22)
0.12(± 0.19)
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside

d)
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
Organic Acids (ng/m
3
)
Warmer Phase Colder Phase
Indoor
Outdoor
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside
San Gabriel 1
San Gabriel 2 .
San Gabriel 3 .
Riverside

Figure 5.2 Continued
169
-1
-0.5
0
0.5
1
R
R Warmer Phase
R Colder Phase
Low MW PAHs Medium MW PAHs High MW PAHs
0
0.5
1
1.5
2
2.5
3
3.5
Phenanthrene
Anthracene
Fluoranthene
Pyrene
Benzo[ghi]fluoranthene
Cyclopenta[cd]pyrene
Benz[a]anthracene
Chrysene
Benzo[b]fluoranthene
Benzo[k]fluoranthene
Benzo[e]pyrene
Benzo[a]pyrene
Indeno[1,2,3-cd]pyrene
Benzo[ghi]perylene
Coronene
Indoor/Outdoor
In/Out Warmer Phase
In/Out Colder Phase
Indeno[1,2,3-cd]pyrene
0
0.5
1
1.5
2
Indoor/Outdoor
In/Out Warmer Phase
In/Out Colder Phase
17β[H]-21α[H]-30-Norhopane.
17α[H]-21β[H]-Hopane.
22S-Homohopane
22R-Homohopane
22S-Bishomohopane
22R-Bishomohopane
αββ-20R-C27-Cholestane
αββ-20S-C27-Cholestane
ααα-20S-C27-Cholestane
αββ-20R-C29-Sitostane
αββ-20S-C29-Sitostane
-1
-0.5
0
0.5
1
R
R Warmer Phase
R Colder Phase
Hopanes Steranes
a)

b)

Figure 5.3 Correlation coefficient and indoor and outdoor ratios of a) PAHs, b)
Hopanes and Steranes, c) n-Alkanes and d) Acids, values are averaged over the sites
and bars are the standard deviation over the sites standard deviation, hence
indicating the possibility of indoor sources for these species.
170
0.1
1
10
100
Tetracosane
Pentacosane
Hexacosane
Heptacosane
Octacosane
Nonacosane
Triacontane
Hentriacontane
Dotriacontane
Tritriacontane
Tetratriacontane
Pentatriacontane
Hexatriacontane
Heptatriacontane
Octatriacontane
Nonatriacontane
Tetracontane
Indoor/Outdoor
(Log Scale)
In/Out Warmer Phase
In/Out Colder Phase
v
-1
-0.5
0
0.5
1
R
R Warmer Phase
R Colder Phase
0.1
1
10
100
Octanoic acid
Decanoic acid
Dodecanoic
acid
Tetradecanoic
acid
Pentadecanoic
acid
Hexadecanoic
acid
Heptadecanoic
acid
Octadecanoic
acid
Palmitoleic
acid
Oleic acid
Phthalic acid
Indoor/Outdoor
(Log Scale)
In/Out Warmer Phase
In/Out Colder Phase
-1
-0.5
0
0.5
1
R
R Warmer Phase
R Colder Phase

c)

d)

Figure 5.3 Continued

171
standard deviation were shown during the colder phase at San Gabriel 3 and Riverside
sites. Most of the IN / OUT ratios of n-alkanes were much higher than 1 (up to 23 during
the colder phase). R values were not always positive and, on average, were much lower
than those found for PAHs, hopanes and steranes (min=-0.14, max=0.79 and
median=0.41). The high IN / OUT ratios and low R values are indicative of the
significant influence of indoor sources of n-alkanes. Considerably higher indoor n-
alkanes levels compared to the outdoor levels were also found in a previous study (Olson
et al. 2008). A variety of PM sources such as cooking, household products, dust, smoking
and candle burning are known as indoor sources of n-alkanes (Fine et al. 1999; Schauer et
al. 1999; Kleeman et al. 2008). The average indoor and outdoor concentrations of n-
alkanes at the San Gabriel 3 site were substantially higher than those of at other sites.

Similarly to n-alkanes, average indoor concentrations of the sum of all measured n-
alkanoic acids were higher than the corresponding outdoor levels. The indoor
concentrations were substantially enriched (i.e, more than 3 times of outdoor levels) with
a large variation in indoor concentrations during both phases at San Gabriel 1 and 2 sites.
IN / OUT ratios of measured organic acids were higher than 1 (except for octanoic acid in
the colder months) with average IN / OUT ratios of 4.8 (IN/OUT ratios of up to ~30 were
found for oleic acid in the warmer phases). The high IN / OUT ratios were accompanied
by low R-values (median R values = 0.19; lowest R = -0.27 for oleic acid in the warmer
phases), indicating the influence of indoor sources for organic acids. Cooking is a major
indoor source of these acids, and oleic and palimitoleic acid had often been used as
172
biomarkers of food cooking (Robinson et al. 2006). Throughout the study, no specific
seasonal trends were observed for the association between indoor and outdoor organic
acids.

The carbon preference index (CPI), i.e., the ratio of the concentrations of odd-carbon-to-
even-carbon n-alkanes, is a parameter used to differentiate between anthropogenic and
biogenic source contributions to PM (Simoneit 1986). Previous studies have shown that
n-alkanes originating from anthropogenic sources have a CPI close to 1, whereas the CPI
is generally higher than 2 when the biogenic sources are dominant (Simoneit 1986). The
average indoor and outdoor CPIs at sampling sites varied from 0.60 to 0.95, suggesting
that anthropogenic emissions (originated from fossil fuel) are the dominating sources in
the studied area.

5.4.4 Spatial Variability
The coefficient of variance, which is also called coefficient of variation, (CV = standard
deviation / mean) was determined for several measured components to investigate their
spatial variation over the studied area (Figure 5.4). A limitation to the calculated
coefficient of variations is that the sampling at different sites were not concurrent which
can affect the calculated CVs in some extent, however CV was calculated separately for
colder and warmer phases to reduce this effect. In general, colder phases were
characterized by a higher CV values compared to the warmer phases, probably due to the

173
a)
0 0.2 0.4 0.6 0.8 1 1.2 1.4
PAH s (Low MW)
PAHs (Medium MW)
PAHs (High MW)
Hopanes
Steranes
n-Alkanes
Selected
Organic Acids
WSOC
Mass
Outdoor
Indoor

b)
0 0.2 0.4 0.6 0.8 1 1.2 1.4
PAH s (Low MW)
PAHs (Medium MW)
PAHs (High MW)
Hopanes
Steranes
n-Alkanes
Selected
Organic Acids
WSOC
Mass
Outdoor
Indoor

Figure 5.4 Coefficient of variance (CV) for indoor and outdoor organic groups for:
a) warmer and b) colder period of the study

174
lower regional atmospheric mixing and increased atmospheric stability during cold
meteorological conditions. Quasi-UF mass concentrations showed relatively low
variability over the studied sites for both indoors and outdoors (CV= 0.09 to 0.22).
Outdoor WSOC was less variable in the warmer phases compared to colder phases. That
can be explained by the seasonal variability of the main WSOC sources, i.e. secondary
atmospheric processes and biomass burning (Weber et al. 2007). Secondary quasi-UF OC
formation is expected to be higher in warmer periods due to higher photochemical
activity, whereas higher biomass burning formation of OC is expected in colder
conditions in the absence of wildfire. Biomass burning has localized effects on OC
compared to secondary photo-oxidation formation of OC, which can influence the
concentrations of several carbonaceous compounds on a regional scale. Although
particulate OC formation by biomass burning is generally not significant (at least in the
absence of wildfires) in the polluted Los Angeles basin (Minguillon et al. 2008), it may
still affect the spatial variability of WSOC levels. Regarding indoor CVs of WSOC, the
relatively high values in the warmer months may be due to the high levels of indoor
WSOC at the San Gabriel 1 and 2 sites during these periods. The spatial variance (CV) of
outdoor hopanes and steranes was 0.83 and 0.97, respectively, of the highest among all
organic species. This variability originates from differences in the local traffic sources
and their influence on each sampling site. Spatial variability in indoor and outdoor PAHs
was similar, due to the significant contribution of outdoor originated PAHs to indoor PM.
175
5.4.5. Source Contribution Estimations
The results of the source apportionment of quasi-ultrafine particle mass are presented in
Figure 5.5. Vehicular sources, including both HDV and LDV, showed the highest
contribution for both indoor and outdoor particles at all sites (on average 1.67-4.86 µg/m
3

or 24-47% of the quasi-UF mass). Estimations of the HDV contribution were higher than
those from LDV. This could be due to the location of the study sites, all situated in the
eastern region of Los Angeles, where the traffic fleet has a higher fraction of HDV
compared to the typical urban area of Los Angeles. The average percentage contribution
of HDV to the total vehicle fleet (HDV + LDV) at the I-10, I-210 and I-60 freeways in
eastern Los Angeles counties (San Bernardino and Riverside counties), where most of the
sites were located, was ~10 to 15%, which is ~2-3 times higher than the corresponding
percentage in Los Angeles urban area (values were estimated from data obtained from the
Caltrans website; http://traffic-counts.dot.ca.gov). As we stated in the methods section,
the LDV source profile used for CMB analysis is from a study conducted near the I-110
freeway, between downtown Los Angeles and Pasadena, CA (Phuleria et al. 2007).
However, in the study areas, the emissions of EC from older vehicles can be higher than
that at the I-110 (Schauer et al. 2002). This could bias the results obtained by the CMB
analysis by underestimating the LDV contribution, because in this model, EC is a key
determinant for discriminating between HDV and LDV. Therefore, higher EC levels
emitted from old LDV can artificially increase the source contribution estimations for
HDV.

176
0
1
2
3
4
5
6
7
8
9
10
San Gabriel 1 IN
San Gabriel 1 OUT
San Gabriel 2 IN
San Gabriel 2 OUT
San Gabriel 3 IN
San Gabriel 3 OUT
Riverside IN
Riverside OUT
San Gabriel 1 IN
San Gabriel 1 OUT
San Gabriel 2 IN
San Gabriel 2 OUT
San Gabriel 3 IN
San Gabriel 3 OUT
Riverside IN
Riverside OUT
µg/m
3
SOA
rs-Dust
nss-Sulfate
SS
SHIP
BB
LDV
HDV
Warmer Phase Colder Phase

SOA = secondary organic aerosol
rs-Dust = resuspended dust
nss-Sulfate = non sea salt sulfate
SS = sea salt
BB = biomass burning
LDV = light duty vehicles
HDV = heavy duty vehicles

Figure 5.5 Source apportionment of quasi-UF PM in the four sites and during the two
sampling periods

177
The relative contribution of biomass burning to the measured indoor and outdoor quasi-
UF mass was low (on average, from 0.06 to 0.87 µg/m
3
, across all sites and phases of the
study, with the exception of San Gabriel 2 site). The differences between indoor and
outdoor contributions were low, except at the San Gabriel 2 site in the warmer phase.
Average outdoor biomass burning contribution was 1.4 to 3.8 times higher in the colder
phases compared to the warmer phase. Sea spray and ship emission contributions to PM
were negligible, as it is expected for sites located far away from the ocean (median sea
spray and ship contributions were 0.12 and 0.08 µg/m
3
, respectively, over all sites and
phases). Estimated indoor and outdoor non-sea salt sulfate contributions tracked each
other at most of sites, suggesting that a substantial portion of indoor sulfate originates
from outdoors (median = 0.72µg/m
3
indoors and 0.74µg/m
3
outdoors). Outdoor non-sea
salt sulfate was, on average, 33% higher in warmer phases compared to colder phases,
confirming the secondary origin of this pollutant (Rodhe 1999).

Candle and cigarette smoke sources were also used as an input for the CMB model, but
the resulting contributions were negligible (no more than 0.02 µg/m
3
at all sites and
during all phases of the study). Our CMB model was not able to apportion the
contribution of cooking using common meat cooking source profiles (Schauer et al. 1999;
Kleeman et al. 2008). This does not necessarily imply that the contribution of cooking to
the quasi-UF particle mass was negligible, but it can indicate that the cooking source
profile used is not representative of the specific food cooking emissions at our study sites.
The influence of food cooking emissions on indoor PM is evidenced by the elevated
178
levels of indoor organic acids such as oleic acid and palimitoleic acid, frequently used as
biomarkers of food cooking (Robinson et al. 2006). The same study also showed that
significant inconsistencies exist between ambient data and published source profiles for
cooking, which makes it difficult to obtain reliable estimates of the relative contribution
of cooking to ambient PM. Resuspended dust contributions were 0.02 to 1.66 µg/m
3
to
indoor, and 0.27 to 2.17 µg/m
3
to outdoor quasi-ultrafine PM across all sites. Road dust
and indoor activities are respectively main outdoor and indoor sources of resuspended
dust. Warmer phase resuspended dust was on average more than 100% higher than
corresponding colder phase levels over all outdoor stations. Estimated SOA accounted for
0.23 to 1.62 µg/m
3
of the indoor and outdoor quasi-ultrafine PM at all sites and phases.
At some sites (e.g. colder phase of San Gabriel 2), estimated SOA concentrations were
higher indoors than outdoors (up to ~3 times), which can be partially due to the formation
of secondary particles in indoor environments from reactions of household products with
ozone and to a lesser extent hydroxyl radicals (Destaillats et al. 2006; Weschler and
Nazaroff 2008). The average SOA contribution in the warmer phase was about 2 times
higher than during the colder phase at all outdoor sites, which highlights the important
role of photo-oxidation in the formation of SOA. The average un-apportioned fraction of
quasi-UF PM was 33 ± 15% among all sites and phases. A fraction of this un-apportioned
mass could be attributed to ammonium nitrate, which was not measured, but could
account for as much as 2-3 µg/m
3
in the PM
0.25
mass concentrations, especially in the
Riverside area (Kleeman et al. 1999; Hughes et al. 2002; Sardar et al. 2005). Moreover,
there are uncertainties associated with the calculations of SOA. Part of this uncertainty
179
originates from the multiplication factor used to convert WSOC to SOA (2) and from the
assumed fraction of WISOC in SOA (20%), as they both can vary with time and location;
(Turpin and Lim 2001; Docherty et al. 2008). A study carried out in Tokyo showed that 6
to 26% of summer oxygenated organic carbon was water-insoluble (Kondo et al. 2007),
whereas water-insoluble SOA fractions as large as 60% have recently been reported for
urban environments (Favez et al. 2008). In a recent study by (Docherty et al. 2008) SOA
was reported to comprise 45 to 90% of the organic fine aerosol mass in the Los Angeles
basin.

Lastly, the estimated indoor LDV and HDV source contributions were similar to those
calculated outdoors during both phases and at all retirement communities. This is
indicative of the significant role of outdoor mobile sources on indoor environments and
illustrates the high indoor infiltration of particles generated by mobile sources. This
finding has important exposure and health implications considering that in an earlier
publication generated from the CHAPS study, we found that traffic-related particles had
much stronger associations with adverse health effects in the elderly retirees of the
studied communities compared to uncharacterized indoor particles (Delfino et al. 2008).

5.5. CONCLUSIONS
The mass and chemical composition of indoor and outdoor quasi-UF PM levels generally
did not show a clear seasonal pattern. However, the concentrations of most n-alkanes and
n-alkanoic acid were higher in the colder periods and in the warmer months, respectively.
180
No major seasonal differences were found for PAHs, hopanes and steranes. High
influence of outdoor sources (mainly vehicular sources) and insignificant contributions
from indoor sources were observed for PAHs, hopanes and steranes. By contrast, indoor
sources (e.g. cooking) impacted the measured indoor concentrations of n-alkanes and
organics acids significantly. Inside some studied retirement communities we observed
evidence of secondary organic aerosol formation, probably from reactions of household
products with indoor ozone.

Vehicular sources showed the highest contribution among the apportioned sources for
both indoor and outdoor particles at all sites (on average 24-47% of the quasi-UF mass).
The contribution of mobile sources to indoor levels was similar to their corresponding
outdoor estimates, thus illustrating the significance of these sources on indoor PM
concentrations. A major implication of these findings is that, even if people (particularly
the elderly retired population of our study) generally spend most of their time indoors, a
major portion of the PM
0.25
particles to which they are exposed comes from outdoor
mobile sources. The significance of this conclusion is supported by the fact that indoor
infiltrated particles from mobile sources were more strongly associated with the adverse
health effects observed in the elderly subjects living in the studied retirement
communities compared to uncharacterized indoor particles.

181
5.6. CHAPTER 5 REFERENCES
Abt, E., H. H. Suh, P. Catalano and P. Koutrakis (2000). Relative contribution of outdoor
and indoor particle sources to indoor concentrations, Environmental Science &
Technology 34(17): 3579-3587.
Agrawal, H., Q. G. J. Malloy, W. A. Welch, J. W. Miller and D. R. Cocker (2008). In-use
gaseous and particulate matter emissions from a modern ocean going container vessel,
Atmospheric Environment 42(21): 5504-5510.
Araujo, J. A., B. Barajas, M. Kleinman, X. P. Wang, B. J. Bennett, K. W. Gong, M.
Navab, J. Harkema, C. Sioutas, A. J. Lusis and A. Nel (2007). Ambient particulate
pollutants in the ultrafine range promote atherosclerosis and systemic oxidative stress,
Arteriosclerosis Thrombosis and Vascular Biology 27(6): E39-E39.
Arhami, M., T. Kuhn, P. M. Fine, R. J. Delfino and C. Sioutas (2006). Effects of
sampling artifacts and operating parameters on the performance of a semicontinuous
particulate elemental carbon/organic carbon monitor, Environmental Science &
Technology 40(3): 945-954.
Arhami, M., M. Sillanpää, S. Hu, M. R. Olson, J. J. Schauer and C. Sioutas (2009). Size-
Segregated Inorganic and Organic Components of PM in the Communities of the Los
Angeles Harbor, Aerosol Science and Technology 43(2): 145-160.
Brook, J. R., T. F. Dann and R. T. Burnett (1997). The relationship among TSP, PM(10),
PM(2.5), and inorganic constituents of atmospheric particulate matter at multiple
Canadian locations, Journal of the Air & Waste Management Association 47(1): 2-19.
Brunekreef, B. and B. Forsberg (2005). Epidemiological evidence of effects of coarse
airborne particles on health, European Respiratory Journal 26(2): 309-318.
Delfino, R. J., C. Sioutas and S. Malik (2005). Potential role of ultrafine particles in
associations between airborne particle mass and cardiovascular health, Environmental
Health Perspectives 113(8): 934-946.
Delfino, R. J., N. Staimer, T. Tjoa, A. Polidori, M. Arhami, D. L. Gillen, M. T.
Kleinman, N. D. Vaziri, J. Longhurst, F. Zaldivar and C. SioutaS (2008). Circulating
biomarkers of inflammation, antioxidant activity, and platelet activation are associated
with primary combustion aerosols in subjects with coronary artery disease,
Environmental Health Perspectives 116(7): 898-906.
Destaillats, H., M. M. Lunden, B. C. Singer, B. K. Coleman, A. T. Hodgson, C. J.
Weschler and W. W. Nazaroff (2006). Indoor secondary pollutants from household
product emissions in the presence of ozone: A bench-scale chamber study, Environmental
Science & Technology 40(14): 4421-4428.
182
Docherty, K. S., E. A. Stone, I. M. Ulbrich, P. F. DeCarlo, D. C. Snyder, J. J. Schauer, R.
E. Peltier, R. J. Weber, S. N. Murphy, J. H. Seinfeld, B. D. Grover, D. J. Eatough and J.
L. Jiimenez (2008). Apportionment of Primary and Secondary Organic Aerosols in
Southern California during the 2005 Study of Organic Aerosols in Riverside (SOAR-1),
Environmental Science & Technology 42(20): 7655-7662.
Elder, A., R. Gelein, V. Silva, T. Feikert, L. Opanashuk, J. Carter, R. Potter, A. Maynard,
J. Finkelstein and G. Oberdorster (2006). Translocation of inhaled ultrafine manganese
oxide particles to the central nervous system, Environmental Health Perspectives 114(8):
1172-1178.
Favez, O., J. Sciare, H. Cachier, S. C. Alfaro and M. M. Abdelwahab (2008). Significant
formation of water-insoluble secondary organic aerosols in semi-arid urban environment,
Geophysical Research Letters 35(15): -.
Fine, P. M., G. R. Cass and B. R. T. Simoneit (1999). Characterization of fine particle
emissions from burning church candles, Environmental Science & Technology 33(14):
2352-2362.
Fine, P. M., G. R. Cass and B. R. T. Simoneit (2004). Chemical characterization of fine
particle emissions from the fireplace combustion of wood types grown in the Midwestern
and Western United States, Environmental Engineering Science 21(3): 387-409.
Fine, P. M., B. Chakrabarti, M. Krudysz, J. J. Schauer and C. Sioutas (2004). Diurnal
variations of individual organic compound constituents of ultrafine and accumulation
mode particulate matter in the Los Angeles basin, Environmental Science & Technology
38(5): 1296-1304.
Fraser, M. P., G. R. Cass, B. R. T. Simoneit and R. A. Rasmussen (1997). Air quality
model evaluation data for organics .4. C-2-C-36 non-aromatic hydrocarbons,
Environmental Science & Technology 31(8): 2356-2367.
Herner, J. D., P. G. Green and M. J. Kleeman (2006). Measuring the trace elemental
composition of size-resolved airborne particles, Environmental Science & Technology
40(6): 1925-1933.
Hughes, L. S., J. O. Allen, L. G. Salmon, P. R. Mayo, R. J. Johnson and G. R. Cass
(2002). Evolution of nitrogen species air pollutants along trajectories crossing the Los
Angeles area, Environmental Science & Technology 36(18): 3928-3935.
Kleeman, M. J., L. S. Hughes, J. O. Allen and G. R. Cass (1999). Source contributions to
the size and composition distribution of atmospheric particles: Southern California in
September 1996, Environmental Science & Technology 33(23): 4331-4341.
183
Kleeman, M. J., M. A. Robert, S. G. Riddle, P. M. Fine, M. D. Hays, J. J. Schauer and M.
P. Hannigan (2008). Size distribution of trace organic species emitted from biomass
combustion and meat charbroiling (vol 42, pg 3059, 2008), Atmospheric Environment
42(24): 6152-6154.
Klepeis, N. E., W. C. Nelson, W. R. Ott, J. P. Robinson, A. M. Tsang, P. Switzer, J. V.
Behar, S. C. Hern and W. H. Engelmann (2001). The National Human Activity Pattern
Survey (NHAPS): a resource for assessing exposure to environmental pollutants, Journal
of Exposure Analysis and Environmental Epidemiology 11(3): 231-252.
Kondo, Y., Y. Miyazaki, N. Takegawa, T. Miyakawa, R. J. Weber, J. L. Jimenez, Q.
Zhang and D. R. Worsnop (2007). Oxygenated and water-soluble organic aerosols in
Tokyo, Journal of Geophysical Research-Atmospheres 112(D1): -.
Kuhn, T., S. Biswas and C. Sioutas (2005). Diurnal and seasonal characteristics of
particle volatility and chemical composition in the vicinity of a light-duty vehicle
freeway, Atmospheric Environment 39(37): 7154-7166.
Kuhn, T., M. Krudysz, Y. Zhu, P. M. Fine, W. C. Hinds, J. Froines and C. Sioutas
(2005). Volatility of indoor and outdoor ultrafine particulate matter near a freeway,
Journal of Aerosol Science 36(3): 291-302.
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.
Lough, G. C., C. G. Christensen, J. J. Schauer, J. Tortorelli, E. Mani, D. R. Lawson, N.
N. Clark and P. A. Gabele (2007). Development of molecular marker source profiles for
emissions from on-road gasoline and diesel vehicle fleets, Journal of the Air & Waste
Management Association 57(10): 1190-1199.
Manchester-Neesvig, J. B., J. J. Schauer and G. R. Cass (2003). The distribution of
particle-phase organic compounds in the atmosphere and their use for source
apportionment during the southern California children's health study, Journal of the Air
& Waste Management Association 53(9): 1065-1079.
Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. F. J. Zhang,
T. Stock, D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S.
Alimokhtari, D. Shendell, J. Jones, C. Farrar and S. Maberti (2005). Influence of ambient
(outdoor) sources on residential indoor and personal PM2.5 concentrations: Analyses of
RIOPA data, Journal of Exposure Analysis and Environmental Epidemiology 15(1): 17-
28.
Miguel, A. H., T. W. Kirchstetter, R. A. Harley and S. V. Hering (1998). On-road
emissions of particulate polycyclic aromatic hydrocarbons and black carbon from
gasoline and diesel vehicles, Environmental Science & Technology 32(4): 450-455.
184
Minguillon, M. C., M. Arhami, J. J. Schauer and C. Sioutas (2008). Seasonal and spatial
variations of sources of fine and quasi-ultrafine particulate matter in neighborhoods near
the Los Angeles-Long Beach harbor, Atmospheric Environment 42(32): 7317-7328.
Ntziachristos, L., Z. Ning, M. D. Geller and C. Sioutas (2007). Particle concentration and
characteristics near a major freeway with heavy-duty diesel traffic, Environmental
Science & Technology 41(7): 2223-2230.
Ohura, T., T. Amagai, T. Sugiyama, M. Fusaya and H. Matsushita (2004). Characteristics
of particle matter and associated polycyclic aromatic hydrocarbons in indoor and outdoor
air in two cities in Shizuoka, Japan, Atmospheric Environment 38(14): 2045-2054.
Olson, D. A., J. Turlington, R. V. Duvall, S. R. Vicdow, C. D. Stevens and R. Williams
(2008). Indoor and outdoor concentrations of organic and inorganic molecular markers:
Source apportionment of PM2.5 using low-volume samples, Atmospheric Environment
42(8): 1742-1751.
Pandis, S. N., A. S. Wexler and J. H. Seinfeld (1993). Secondary Organic Aerosol
Formation and Transport .2. Predicting the Ambient Secondary Organic Aerosol-Size
Distribution, Atmospheric Environment Part a-General Topics 27(15): 2403-2416.
Phuleria, H. C., R. J. Sheesley, J. J. Schauer, P. M. Fine and C. Sioutas (2007). Roadside
measurements of size-segregated particulate organic compounds near gasoline and diesel-
dominated freeways in Los Angeles, CA, Atmospheric Environment 41(22): 4653-4671.
Polidori, A., M. Arhami, C. Sioutas, R. J. Delfino and R. Allen (2007). Indoor/outdoor
relationships, trends, and carbonaceous content of fine particulate matter in retirement
homes of the Los Angeles basin, Journal of the Air & Waste Management Association
57(3): 366-379.
Polidori, A., B. J. Turpin, C. I. Davidson, L. A. Rodenburg and F. Maimone (2008).
Organic PM2.5: Fractionation by polarity, FTIR spectroscopy, and OM/OC ratio for the
Pittsburgh aerosol, Aerosol Science and Technology 42(3): 233-246.
Pope, C. A. and D. W. Dockery (2006). Health effects of fine particulate air pollution:
Lines that connect, Journal of the Air & Waste Management Association 56(6): 709-742.
Robinson, A. L., N. M. Donahue, M. K. Shrivastava, E. A. Weitkamp, A. M. Sage, A. P.
Grieshop, T. E. Lane, J. R. Pierce and S. N. Pandis (2007). Rethinking organic aerosols:
Semivolatile emissions and photochemical aging, Science 315(5816): 1259-1262.
Robinson, A. L., R. Subramanian, N. M. Donahue, A. Bernardo-Bricker and W. F. Rogge
(2006). Source apportionment of molecular markers and organic aerosol. 3. Food cooking
emissions, Environmental Science & Technology 40(24): 7820-7827.
185
Rodhe, H. (1999). Human impact on the atmospheric sulfur balance, Tellus Series a-
Dynamic Meteorology and Oceanography 51(1): 110-122.
Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass and B. R. T. Simoneit
(1993). Sources of Fine Organic Aerosol .2. Noncatalyst and Catalyst-Equipped
Automobiles and Heavy-Duty Diesel Trucks, Environmental Science & Technology
27(4): 636-651.
Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass and B. R. T. Simoneit
(1993). Sources of Fine Organic Aerosol .4. Particulate Abrasion Products from Leaf
Surfaces of Urban Plants, Environmental Science & Technology 27(13): 2700-2711.
Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass and B. R. T. Simoneit
(1996). Mathematical modeling of atmospheric fine particle-associated primary organic
compound concentrations, Journal of Geophysical Research-Atmospheres 101(D14):
19379-19394.
Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass and B. R. T. Simoneit
(1997). Sources of fine organic aerosol .8. Boilers burning No. 2 distillate fuel oil,
Environmental Science & Technology 31(10): 2731-2737.
Rogge, W. F., L. M. Hildemann, M. A. Mazurek, G. R. Cass and B. R. T. Simonelt
(1991). Sources of Fine Organic Aerosol .1. Charbroilers and Meat Cooking Operations,
Environmental Science & Technology 25(6): 1112-1125.
Sardar, S. B., P. M. Fine and C. Sioutas (2005). Seasonal and spatial variability of the
size-resolved chemical composition of particulate matter (PM10) in the Los Angeles
Basin, Journal of Geophysical Research-Atmospheres 110(D7): -.
Schauer, J. J. and G. R. Cass (2000). Source apportionment of wintertime gas-phase and
particle-phase air pollutants using organic compounds as tracers, Environmental Science
& Technology 34(9): 1821-1832.
Schauer, J. J., M. J. Kleeman, G. R. Cass and B. R. T. Simoneit (1999). Measurement of
emissions from air pollution sources. 1. C-1 through C-29 organic compounds from meat
charbroiling, Environmental Science & Technology 33(10): 1566-1577.
Schauer, J. J., M. J. Kleeman, G. R. Cass and B. R. T. Simoneit (2002). Measurement of
emissions from air pollution sources. 5. C-1-C-32 organic compounds from gasoline-
powered motor vehicles, Environmental Science & Technology 36(6): 1169-1180.
Schauer, J. J., W. F. Rogge, L. M. Hildemann, M. A. Mazurek and G. R. Cass (1996).
Source apportionment of airborne particulate matter using organic compounds as tracers,
Atmospheric Environment 30(22): 3837-3855.
186
Sheesley, R. J., J. J. Schauer, M. Zheng and B. Wang (2007). Sensitivity of molecular
marker-based CMB models to biomass burning source profiles, Atmospheric
Environment 41(39): 9050-9063.
Sillanpaa, M., R. Hillamo, S. Saarikoski, A. Frey, A. Pennanen, U. Makkonen, Z.
Spolnik, R. Van Grieken, M. Branis, B. Brunekreef, M. C. Chalbot, T. Kuhlbusch, J.
Sunyer, V. M. Kerminen, M. Kulmala and R. O. Salonen (2006). Chemical composition
and mass closure of particulate matter at six urban sites in Europe, Atmospheric
Environment 40: S212-S223.
Simoneit, B. R. T. (1986). Characterization of organic-constituents in aerosols in relation
to their origin and transports A review, Int. J. Environ. Anal. Chem 23: 207-237.
Simoneit, B. R. T. (1999). A review of biomarker compounds as source indicators and
tracers for air pollution, Environmental Science and Pollution Research 6(3): 159-169.
Sioutas, C., R. J. Delfino and M. Singh (2005). Exposure assessment for atmospheric
ultrafine particles (UFPs) and implications in epidemiologic research, Environmental
Health Perspectives 113(8): 947-955.
Stone, E. A., D. C. Snyder, R. J. Sheesley, A. P. Sullivan, R. J. Weber and J. J. Schauer
(2008). Source apportionment of fine organic aerosol in Mexico City during the
MILAGRO experiment 2006, Atmospheric Chemistry and Physics 8(5): 1249-1259.
Subramanian, R., N. M. Donahue, A. Bernardo-Bricker, W. F. Rogge and A. L. Robinson
(2006). Contribution of motor vehicle emissions to organic carbon and fine particle mass
in Pittsburgh, Pennsylvania: Effects of varying source profiles and seasonal trends in
ambient marker concentrations, Atmospheric Environment 40(40): 8002-8019.
Turpin, B. J. and H. J. Lim (2001). Species contributions to PM2.5 mass concentrations:
Revisiting common assumptions for estimating organic mass, Aerosol Science and
Technology 35(1): 602-610.
Turpin, B. J., P. Saxena and E. Andrews (2000). Measuring and simulating particulate
organics in the atmosphere: problems and prospects, Atmospheric Environment 34(18):
2983-3013.
Wallace, L. (1996). Indoor particles: A review, Journal of the Air & Waste Management
Association 46(2): 98-126.
Weber, R. J., A. P. Sullivan, R. E. Peltier, A. Russell, B. Yan, M. Zheng, J. de Gouw, C.
Warneke, C. Brock, J. S. Holloway, E. L. Atlas and E. Edgerton (2007). A study of
secondary organic aerosol formation in the anthropogenic-influenced southeastern United
States, Journal of Geophysical Research-Atmospheres 112(D13): -.
187
Weichenthal, S., A. Dufresne and C. Infante-Rivard (2007). Indoor ultrafine particles and
childhood asthma: exploring a potential public health concern, Indoor Air 17(2): 81-91.
Weschler, C. J. (2004). Chemical reactions among indoor pollutants: what we've learned
in the new millennium, Indoor Air 14: 184-194.
Weschler, C. J. and W. W. Nazaroff (2008). Semivolatile organic compounds in indoor
environments, Atmospheric Environment 42: 9018-9040.
Yli-Tuomi, T., T. Lanki, G. Hoek, B. Brunekreef and J. Pekkanen (2008). Determination
of the sources of indoor PM2.5 in Amsterdam and Helsinki, Environmental Science &
Technology 42(12): 4440-4446.

188
Chapter 6.
Size-Segregated Inorganic and Organic Components of PM in the
Communities of the Los Angeles Harbor

6.1. ABSTRACT
The Los Angeles Ports complex consists of the port of Long Beach and the port of Los
Angeles. Due to the high levels of particulate matter (PM) emitted from many sources in
the vicinity of these ports and to their projected massive expansion, the Harbor area will
be the focus of future governmental regulations. This study aims to characterize the
physicochemical properties of PM at locations influenced by port-affiliated sources. PM
samples were collected concurrently at six sites in the southern Los Angeles basin for a
7-weeks period between March and May 2007. Four sites were set-up within the
communities of Wilmington and Long Beach; one site was located at a background
location near the harbors of the Los Angeles port; the sixth site, near downtown Los
Angeles, was chosen to represent a typical urban area. Coarse (PM2.5-10), accumulation
(PM0.25-2.5), and quasi-ultrafine (PM0.25) mode particles were collected at each site.
Samples were analyzed for organic and elemental carbon content (OC and EC,
respectively), organic species, inorganic ions, water soluble and total elements. The
carbon preference index (CPI) for quasi-UF and accumulation mode particles varied from
0.65 to 1.84 among sites, which is in the range of previous findings in areas with high
influence of anthropogenic sources. In sites located close to harbor the average n-Alkanes
and PAHs levels were respectively about 3 and 5 times higher than their corresponding
levels at a site located in vicinity of harbor, but upwind of most of local sources. The
ratio of hopanes to EC and hopanes to OC over all the sites were in the range of previous
189
roadside measurements near freeways with variable volumes of diesel truck traffic. High
overall correlation of vanadium with nickel (R=0.9) and a considerable gradient of
vanadium concentrations with distance to the port, suggest marine vessels as the major
sources of these elements.

6.2. INTRODUCTION
Epidemiological studies have shown significant exposure-response relationships for the
adverse health effects in association with particulate mass concentrations (Samoli et al.
2005; Atkinson et al. 2001). Fine particles (aerodynamic diameter, D
p
< 2.5 µm; PM
2.5
)
have been more strongly associated with mortality and morbidity than coarse particles
(2.5 µm < D
p
< 10 µm; PM
2.5-10
) (WHO 2003) whereas coarse particles have been
associated more strongly with respiratory hospital admissions (Brunekreef and Forsberg
2005). Ultrafine particles (D
p
< 0.1 µm; PM
0.1
) are of particular interest in health-related
studies due to their high number concentration in urban environments and ability to
penetrate deep into the alveolar region of the lung (Delfino et al. 2005). Recent
toxicological studies suggest that adverse responses per unit mass are associated more
strongly with ultrafine particles than fine or coarse particles (Donaldson et al. 2002; Li et
al. 2003; Oberdörster et al. 2001; Xia et al. 2004).

Along with the particle size, the chemical composition influences the toxicity of
particulate matter (Oberdörster 1996; Anderson 2000). There are several types of highly
toxic organic compounds in the atmosphere including quinones, polycyclic aromatic
190
hydrocarbons (PAHs), polychlorinated biphenyls and other organochlorine compounds,
which may have acute effects (USEPA 2004; Li et al. 2003). Pollen spores and proteins
(in the super micron fraction) are known allergens, whereas some components of bacteria
and viruses are biologically generated toxins (Lighty et al. 2000). Carbonaceous matter
and transition metals such as iron, copper, chromium and vanadium have been shown to
generate reactive oxygen species and can contribute to oxidative DNA damage (Dreher et
al. 1997). Secondary inorganic ions and sea salt as well as soil-related material are
thought to be relatively benign, but they may affect the toxicity or bioavailability of other
particulate components. In addition, endotoxins (mostly in coarse PM) are believed to
play an important role in the development of organic-dust related diseases (e.g. Salonen
et al. 2004). Because the atmospheric aerosols form a highly multi-component system, it
is very difficult to discern a clear association between adverse health effect and specific
chemical components. If health effects can be linked to certain sources of particulate
matter, such information would be highly valuable for targeting control strategies.

The Los Angeles Basin is a megalopolis of about 15 million inhabitants, and has one of
the most polluted atmospheres in the US due to the contributions of a multitude of traffic
and other combustion sources. Studies in Los Angeles Basin examining atmospheric
aerosols at multiple locations across the basin have been conducted since the early 1970s
(Cass et al. 2000; Christoforou et al. 2000; Hughes et al. 1999; Russell and Cass, 1986).
Most of these campaigns in Los Angeles have included only a few days or a week or two
of sampling. Recently, a more comprehensive, longer term campaign was operated by the
191
Southern California Particle Center and Supersite (SCPCS), and the results are
summarized by Sardar et al. (2005a). The focus of that campaign was to determine the
size- fractionated and chemically speciated PM concentrations in the so-called “source”
and “receptor” regions of the Los Angeles basin. The former are the central and western
parts of the basin. In these regions, aerosols are mainly produced by primary emissions.
By contrast, aged aerosols transported from the “source” regions dominate the eastern
part of the basin, i.e. the “receptor” areas. PM samples in that campaign were not
collected concurrently at the different sites, but were often apart by several years. To a
certain degree, the consistent meteorology in Southern California may allow for seasonal
comparisons between sites even if samples have been collected in different years.
However, concurrent sampling in such a complex urban air basin would be highly
desirable.

The focus of the present study is the area of the Los Angeles- Long Beach harbor. This is
a unique area and quite disparate from the overall description of source and receptor sites
of the basin that were discussed in the previous section. The Los Angeles port complex
consisting of the port of Long Beach and port of Los Angeles is the busiest harbor in the
US and ranks fifth in the world. In addition to the harbor activities (e.g. Marine vessels,
heavy-duty trucks, locomotives, cargo handling equipments and harbor crafts), the local
PM sources include some of the most heavily traveled freeways in southern California
(CA-110, I710 plus local street traffic) and multiple petroleum refineries and other
industrial facilities. Many smaller industrial and commercial businesses are also located
192
within the community. The Alameda Corridor runs through the eastern portion of the
community. Thus, the surrounding area of the Los Angeles – Long Beach harbor
constitutes arguably the most complex emission source scenario in California, and
provides the potential for complex pollutant concentration gradients and high exposure
conditions that cannot be identified by conventional monitoring approaches. Accordingly,
it is crucial to assess the exposure gradient of the community in the surrounding
environment. Nonetheless, there are not many studies on the micro-environmental spatial
variations of chemical components and physical characteristics of particles in such
complex environments.

The objective of this study is to characterize the chemical composition of ultrafine,
accumulation mode and coarse particles across this community. Results from the
gravimetric and chemical analysis are verified by means of chemical mass closure (CMC)
(Sillanpää et al. 2006). Subsequently, the paper focuses on organic species and elemental
components and their distribution in PM size fractions among the sites. These results
provide new insight into the variation of size-segregated chemical composition of PM
over the studied area.

193
6.3. EXPERIMENTAL METHOD

6.3.1. Sampling Sites
Size-segregated PM samples were collected concurrently at five sampling sites in the LA
port area and one additional site further north (at USC), serving as a representative site of
the urban Los Angeles air quality, sufficiently far from the harbor, thus not immediately
impacted by the local sources of that area. Sampling was conducted daily from Monday
to Friday over a 7-week period from March – May of 2007. The sampling site locations
are shown in Figure 6.1 and their specifications and major potential emission sources are
described in detail in subsequent paragraphs.

Site 1 and 2 were located in the communities of Wilmington to the west of Long Beach
area. Site 1 was about 1.5 km east (mostly downwind) of a major freeway (CA-110) and
about 1.5 km north of Pacific Ocean coast. The site was situated in an open field, at the
intersection of a major street and a local residential street.

Site 2 was about 3 km north of the coast. It was located in the backyard of a single-family
house in a residential area, at the intersection of two busy major streets. It was also next
to the Alameda corridor, a 20 mile (32 km) freight rail "expressway", directly connecting
the national rail system near downtown Los Angeles, to the ports of Los Angeles and
Long Beach, running parallel to Alameda Street.

194

Figure 6.1 Sampling sites locations
S6
S1 to S5
S1
S3
S5
S2
S4
195
Site 3 and 4 were located within the communities of Long Beach. Site 3 was about 3 km
north of the coast with a major highway (CA-1) in less than 1 km to the south (hence
upwind) of the site. The sampling site was located inside a semi-industrial area with one
and two-story buildings around it.

Site 4 was about 7 km north of the coast and 1 km downwind (east) of a major freeway
(I-710), which has the highest ratio (up to 25%) of heavy-duty diesel vehicles in the Los
Angeles highway network (Ntziachristos et al. 2007a). Another major freeway (I-405)
was located about 1 km to the south of this site. The samplers were placed on the rooftop
of a one-story building right beside a major street. Industrial, commercial and residential
one- and two-story buildings were surrounding the site. This site was thus influenced by
an urban and industrial mix of sources.

Site 5 was located in Long Beach at a pier extending about 1.5 km toward Pacific Ocean
upwind of Long Beach harbor. The sampling site was located in an open area at the end
of the pier, which was surrounded by ocean at three sides, and a small tree-planted area
on one side. This site served as the background area of Long Beach harbor. The
maximum distance between Sites 1 to 5 was about 8 km.

Site 6 was located on the University of Southern California campus at the Southern
California Supersite Particle Instrumentation Unit (PIU) trailer near downtown Los
Angeles. The PIU is about 40 km north of the coast, 150 m east of a major freeway (CA-
196
110) and adjacent to a six level parking structure and various construction sites. This site
represents a typical urban mix of downtown Los Angeles (Arhami et al. 2006) and was
used for comparison to the harbor sites 1-5.

Sites 1-5 were located between latitudes of N33 ° 44’ to N33° 48’ and between
longitudes of W118° 13’ to W118° 15’ and Site 6 were located at N34° 1’ and W118° 17.

6.3.2. Sampling Description
Two collocated Sioutas™ impactors (SKC Inc, Eighty-Four, PA), operated at a flow rate
of 9 lpm (Misra et al. 2002; Singh et al. 2003), were implemented at each site to collect
size fractionated PM samples. Zefluor filters (3 µm pore, Pall Life Sciences, Ann Arbor
MI) were used in one impactor and quartz fiber filters (Pall Life Sciences, Ann Arbor MI)
in the other impactor. Coarse (aerodynamic diameter, D
P
≥2.5m ; PM
2.5-10
),
accumulation mode (D
p
= 0.25-2.5 µm; PM
0.25-2.5
) and quasi-ultrafine (D
p
< 0.25 µm;
PM
0.25
) particles were collected at each site. The impactors were placed inside
temperature-controlled enclosures. The sampling inlet for each impactor was about 3 m
in length (above the enclosure) and 0.95 cm in diameter and the vertical height of the
inlet point from ground was at least 5 m. All sampling stations were mounted to the
ground, except for Site 4, where the sampler enclosure was placed on the rooftop of a
one-story building. Due to technical and weather-related problems, samples were not
collected or were disregarded for week 7 at Sites 2, 4 and 5.

197
Local weather data, including temperature, relative humidity, wind speed and direction,
were measured every minute at all sampling sites using a weather station (Vantage Pro2,
Davis Instruments Corp., Hayward, CA). The meteorological data were not available for
the weeks 1 to 5 at Site 1.

Prior to sampling, quartz filters were baked at 550ºC for a minimum of 12 hours and
Zefluor filters were cleaned using sequential flow through leaches of 2N HCl, 2N HNO3
and high purity water. After cleaning the filters were air dried in a laminar flow hood in a
trace metals clean room. At the end of each sampling period, all the filters and substrates
were placed in petri dishes and quartz fiber filters were wrapped with aluminum foil. All
the samples were kept frozen until chemical analysis.

6.3.3. Gravimetric and Chemical Analysis
Aerosol mass was determined by weighing the Zefluor filters before and after sampling
with a microbalance (MT 5, Mettler-Toledo Inc., Highstown, NJ) having a sensitivity of
0.001 mg. The samples were allowed for equilibration in the weighing room at a relative
humidity of 40–45% and a temperature of 22–24 ºC for about 24 hours before weighing.
The stability of mass readings was verified by weighting the laboratory blank filters
before, after and during each weighing session. The electrostatic charges of substrate and
filter materials were eliminated with a static neutralizer (500uCi Po210, NRD LLC,
Grand Island, NY) before each weighing.

198
The chemical components of PM samples were analyzed at Wisconsin State Lab of
Hygiene at University of Wisconsin-Madison. Daily (24 hr) samples from the each
impactor were combined into weekly composites and analyzed for chemical speciation.
Both the Zefluor and quartz fiber filters were cut into four equal parts. One quarter of the
quartz fiber filters was analyzed by an Ion Chromatography (IC) for inorganic ions
including chloride, nitrate, phosphate, sulfate, sodium, ammonium and potassium (Kerr et
al. 2004). The second quarter of the quartz fiber filters was analyzed by the Thermal
Evolution/Optical Transmittance (TOT) analysis (Schauer et al. 2003; Birch and Cary
1996) to determine the elemental and organic carbon (EC and OC) levels. The third set of
the quartz fiber filters was composited for the whole 7-week period at each site and
analyzed by a Gas Chromatography/Mass Spectrometry (GC/MS) for 92 different organic
compounds (Zheng et al. 2002; Chowdhury, 2007; Schauer et al. 1999). The remaining
quarter of quartz filter samples was archived for future analysis. Similarly, one of the four
sets of Zefluor filters were composited for the whole 7-week period at each site and for
each PM range and analyzed by Inductively Coupled Plasma Mass Spectrometer (ICP-
MS) to determine 52 trace elements (Herner et al. 2006). The second set of Zefluor filters
was also composited for the whole 7-week period at each site and analyzed for water-
soluble elemental content. Samples were leached in high purity water and analyzed using
a magnetic sector inductively coupled plasma mass spectrometer (HR-ICPMS, Finnigan
Element 2). Three internal standards, gallium, indium and bismuth were used for the HR-
ICPMS analysis along with authentic standards for each quantified elements. The
remaining samples were archived for future toxicity analysis.
199
6.3.4. Chemical Mass Closure (CMC)
The sum of mass concentrations obtained from nine chemical components was used to
assess the extent to which the gravimetrically measured particulate mass could be
reconstructed from the sum of the measured chemical components (Sillanpää et al. 2006).
The chemical constituents were grouped into nine components as follows: ammonium
(NH
4
+
), nitrate (NO
3
-
), non-sea salt sulfate (nss-SO
4
2-
), soil-derived compounds (crustal
soil), sea salt, other elements, elemental carbon (EC), particulate organic matter (POM)
and unidentified matter (UM). Table 6.1 summarizes the formula for calculating the
grouped chemical components. The nss-SO
4
2-
was calculated from the measured SO
4
2-
,
Na
+
and a standard seawater parameter; Sea salt concentrations were also estimated by
multiplying Na
+
concentrations by a standard seawater parameter (3.25) (Brewer 1975).
Si, Al, Ca, Fe and K, appear predominantly as oxides, used to calculate the soil-derived
components (Brook et al. 1997). Sum of other elements, which include all the trace
elements except major soil and sea salt elements, represents mostly the metal content of
the particles. These elements are generated by a great diversity of sources, including
metal industry, automotive vehicles (by abrasion of brakes, clutch etc.) and dust
resuspension (containing minor soil elements). The POM was obtained by multiplying
the thermo-optically measured OC by a factor of 1.4 (Turpin and Lim 2001; Russell
2003), which roughly converts the carbon mass of organic compounds to its total mass by
including other elements. It should be noted that there are uncertainties associated with
use of 1.4 as a multiplier for converting measured carbon to organic carbon, leading to
uncertainties in estimating the total PM mass by means of CMC. The average organic
200
molecular weight per carbon weight is subject to change with location, season, and time,
due to changes in the particular organic mixture (Turpin and Lim 2001). A higher
multiplication factor than 1.4 is expected for more oxygenated organic compounds
(Sardar et al. 2005b), which would be more prevalent in areas impacted by aged aerosols.
In a previous study by Turpin and Lim (Turpin and Lim 2001), the range of 1.6±0.2 was
recommended to be used for urban areas, including that of Los Angeles. Thus, the factor
of 1.4, which was used in this study, lies in the suggested range and has been used in
previous recent studies in Los Angeles basin (e.g. Sardar et al. 2005b) as well as other
urban areas (e.g. Sillanpää et al. 2006). The UM was obtained by subtracting the
reconstructed mass (i.e., the sum of calculated chemical components) from the
gravimetrically measured aerosol mass.

Table 6.1 The chemical components used in the mass closure studies

Component Abbreviation Formula
Non-sea–salt sulphate
a
Nss-SO
4
2-
[Nss-SO
4
2-
]=[SO
4
2-
]-0.246 x [Na
+
]
Nitrate NO
3
-

Ammonium ion NH
4
+

Sea salt
a
SS [SS]=3.248 x [Na
+
]
Water-soluble soil
b
WSS [WSS]=[Fe
2
O
3
]+[Al
2
O
3
]+[CaO]+[K
2
O]
Water-insoluble soil
c
WIS [WIS]=[Fe
2
O
3
]
IS
+[SiO
2
]
IS
d
+[Al
2
O
3
]
IS
+[CaO]
IS
++[K
2
O]
IS

Other elements OE [OE]=Sum of the analysed elements excluding soil
elements
Elemental carbon

EC
Particulate organic matter

POM POM=1.4 x [OC]
Unidentified matter UM [UM]=[PM
x
]-[Σ identified components of PM
x
]
a
Brewer (1975).
b
WSS is based on ICP-MS extraction of the soluble portion of these species
c
WIS is based on ICP-MS extraction of the total - soluble portion of these species.
d
[Si] has been estimated from [Al], [Si] = 3 x [Al] (Sillanpää et al, 2006).

201
6.4. RESULTS AND DISCUTION

6.4.1. Overview of the Data
Weather station data collected during the sampling campaign at each site are presented in
Table 6.2. Site 5, which was located at the pier, was somewhat colder (average T=14.7
ºC), windier (average WS=3.0 m/s) and more humid (average RH=75%) than the other
sites, as one would expect. Averaged meteorological data over the sampling period were
similar across the other sites, with the average temperature, relative humidity and wind
speed varying in the ranges of 16.6-19.1 ºC, 52-63% and 0.8-2.3 m/s, respectively. These
meteorological data reaffirm the overall climatological stability of Los Angeles and show
that weather conditions did not have a considerable effect on differences of the PM and
its components between the sampling sites. In most of the sites, the prevailing wind was
from the southwest, except at Sites 1 and 3, where a major part of wind directed from
north and northwest, respectively. It should be mentioned that meteorological data was
available for only the weeks 6-7 of sampling at Site 1.
The mean mass concentrations of three PM size ranges measured at the sampling sites are
presented in Figure 6.2. The campaign mean PM
10
concentrations varied in the range of
13.9-26.8 µg/m
3
. The lowest PM
10
concentrations were measured at Site 1 and Site 5
whereas the concentrations at the other sites were ≥ 22.8 µg/m
3
. The mass distribution
between three measured size ranges was relatively different among sampling sites. Quasi-
UF particles were the dominant PM fraction (49%) at Site 1, whereas Site 6 was

202
Table 6.2 Meteorological data during the sampling campaign at sampling sites

Site
Temperature
('C)
RH (%)
Wind Speed
(m/s)
Wind Roses
Wind Speed (m/s)
Average 19.1 57 1.4
St Dev 5.4 21 1.2
Min. 12.2 12 0.0
Max 35.9 87 5.0
Average 17.6 58 0.8
St Dev 4.6 19 0.7
Min. 9.2 12 0.0
Max 36.9 86 4.7
Average 16.6 63 1.5
St Dev 4.0 21 1.3
Min. 8.5 10 0.0
Max 34.2 92 6.5
Average 16.6 63 2.3
St Dev 4.4 26 1.3
Min. 9.2 3 0.4
Max 35.5 100 7.4
Average 14.7 75 3.0
St Dev 1.6 16 1.9
Min. 10.9 24 0.0
Max 21.6 93 8.8
Average 17.3 52 1.5
St Dev 4.2 22 0.8
Min. 10.9 5 0.1
Max 35.0 89 3.9
5
6
1
2
3
4

N
NW NE
W E
SW SE
S
N
NW NE
W E
SW SE
S

N
NW NE
W E
SW SE
S

N
NW NE
W E
SW SE
S

N
NW NE
W E
SW SE
S

N
NW NE
W E

SW SE
S

203

PM
0
2
4
6
8
10
12
14
16
18
20
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
µ
g/m
3
Ultrafine
Accumulation
Coarse

Figure 6.2 Particle mass concentrations in the quasi-UF, accumulation and coarse mode
measured at six sampling sites. Error bars represent standard deviations, which are
calculated for components on a weekly basis

dominated by accumulation mode particles (37%). The coarse particles had the highest
contribution to PM
10
in the other four sites (37% to 50%).

6.4.2. Chemical Mass Closure (CMC)
The mass closure results of different particle size ranges at each sampling site are shown
in Figure 6.3. The major components of each size fraction and their contributions are
described below.

Quasi-ultrafine mode. The POM was the dominant component of quasi-UF particles,
followed by nss-sulfate and EC. The highest POM contribution was observed at Site 4
(50%) whereas its contribution was lowest at Site 3 (27%). The organic carbon consists
204
of both primary OC and secondary OC. The primary OC, emitted mainly from various
combustion sources, is dominated by a mode around 0.1 to 0.2 m (Maricq, 2007 ;
Hildemann et al. 1991), which makes it fall mostly in the quasi-UF particle range of our
study. The generally high correlation between OC and EC (R
2
=0.66) in quasi-UF mode
indicates that the majority of OC in that range is attributed to primary sources. The nss-
sulfate is a secondary aerosol component that is predominantly formed in the atmosphere
through the oxidation of sulfur dioxide (Rodhe 1999). The nss-sulfate contribution to
total mass of quasi-UF varied in the range of 8.3-17%. Elemental carbon (EC) consists of
graphite-like species and is formed by incomplete combustion of organic material
(Seinfeld and Pandis 1998). The EC contributed 7.0-11% to the quasi-UF particle mass.
Insoluble soil elements, ammonium, sea salt and nitrate, contributed 1.6-9.0% to the
quasi-UF particle mass. The contribution of sea salt (7.4%) at Site 5 was more than two
times higher than that at the other sites. Recent studies have shown that sea spray can
generate primary particles even in the ultrafine range (O’Dowd and De Leeuw 2007).
Elements associated with soluble soil had only a small impact (<0.60%) on the quasi-UF
particle mass. The gravimetric mass was somewhat higher than the reconstructed PM
0.25

mass in each sampling site. The unidentified matter in Sites 1, 4, 5 and 6 ranged from
6.7% to 26%, which is within the methodological uncertainties as well as the
uncertainties associated with the multiplication factor for OC. A higher fraction of
unidentified matter was found in Site 2 (36%) and Site 3 (45%). The discrepancy may be
due to the following factors: 1) the actual OC-to-POM ratio may differ from the applied
factor (1.4) and it can vary between the sites and different size ranges; 2) as discussed
205
Quasi-UF
0
20
40
60
80
100
120
140
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
% of PM Mass
Accumulation
0
20
40
60
80
100
120
140
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
% of PM Mass
Coarse
0
20
40
60
80
100
120
140
160
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
% of PM Mass
POM(=1.4xOC) EC non-sea-salt sulfate
Nitrate Ammonium ion Sea salt
Soluble soil Insoluble soil Other elements

Figure 6.3 The contributions of nine chemical component-groups to the mass of quasi-UF,
accumulation mode, fine and coarse particles measured at the six sampling sites. The
chemical mass closure for fine particles is based on the sum of the concentrations measured
in quasi-UF and accumulation mode
206
earlier, the sample collected on Zefluor filters was used for mass measurement, while the
major chemical components of reconstructed mass were quantified from the samples
collected on quartz fiber filters. The cutoff size (D
50
) between quasi-UF and
accumulation mode may also be slightly lower for the quartz fiber filter than the Zefluor
filter, since other collection mechanisms in addition to inertial impaction play a role
while using the porous impaction substrates (Saarikoski et al. 2008).

It should be noted that positive artifact due to adsorption of gaseous organic vapors on
the quartz after-filter used to collect quasi-UF particles might have occurred during our
sampling. In previous studies in the Los Angeles basin (Kim et al. 2001; Sardar et al.
2005), it was shown that the positive organic artifacts approached a nearly constant
saturation value of 1-1.5 µg/m
3
of OC for 24 hr sampling periods at similar flow rates to
those of our impactors, suggesting that these artifacts may be more important at lower
PM mass concentrations than those reported in our study. Moreover, possible negative
artifacts caused by volatilization of these organic vapors over a 24 hr sampling period
may compensate for some of the adsorption artifacts (Sardar et al. 2005). Given the likely
occurrence of these positive artifacts, however, the statements made in previous
paragraphs regarding quasi-ultrafine OC (and POM) concentrations need to be qualified
and put into perspective.

Accumulation mode. The major chemical components found in the accumulation mode
were nss-sulfate, sea salt, POM and nitrate—each of them accounted for 15-30% of the
mass. The contributions of these major components were rather evenly distributed over
207
the studied area. In contrast to quasi-UF and coarse particles, the accumulation mode
particles remain longer in the atmosphere due to weak removal mechanics, which enables
them to transport far from their sources and to disperse evenly through an urban area
(Seinfeld and Pandis 1998). Both ammonium and insoluble soil accounted roughly for
10% of the accumulation mode mass. The contributions of EC and soluble soil were in
the magnitude of a few percent, whereas that of other elements was less than half percent.
In the site located at the pier (Site 5), the EC contribution was about half compared to that
measured at the other sites. This site can be influenced by some primary EC emissions,
mainly in quasi-UF mode, from nearby ships. The reconstructed mass was close to or
higher than the measured mass (92-128%) in accumulation mode, contrary to quasi-UF
mode. The relatively higher reconstructed mass may be explained by the differences in
cutoff sizes mentioned above hence a part of large ultrafine particles were collected on
quartz fiber substrate while bypassed this stage in Zefluor sampling. In addition the actual
OC-to-POM ratio may differ from the applied factor (1.4), which results in the under- or
overestimation of the particulate organic matter.

Coarse particles. Contrary to fine particles, coarse particles are typically composed of
windblown dust, large sea salt particles from sea spray and mechanically generated
anthropogenic particles, as well as biogenic particles (e.g. pollen, fungal spores and
bacteria). The major contributors to coarse particles mass were sea salt (31-55%) and
insoluble soil (11-47%), followed by nitrate (14-23%) and POM (8-18%). The coarse
particles in Site 5 were heavily dominated by sea salt (55%). Due to their size, these
208
larger sea salt particles cannot be transported far from the source, which can be seen from
the low sea salt concentration at Site 6. Among secondary inorganic ions, nitrate had
clearly the highest contribution to coarse PM. Unlike the case of nitrate in fine particles,
coarse particulate nitrate is predominantly formed in the reactions between nitric acid and
sea salt or mineral compounds (Kerminen et al. 1998; Pio and Lopes 1998). Soluble nss-
sulfate, soluble soil, ammonium, EC and other elements had a minor contribution to this
PM size fraction (<3%). Excluding Site 1, the fraction of unidentified matter ranged from
-0.9 % to 22%, which is within the methodological uncertainties. An underestimation in
the reconstructed mass calculations is partly due to the assumption that the soil-related
elements exist only as oxides, but other mineral compounds are likely present in the
coarse PM fraction. The reconstructed coarse mass for Site 1 samples was 43% higher
than the gravimetrically measured mass. This bias is likely due to the very low mass
concentration of coarse particles (2.2 µg/m
3
) at Site 1, which causes relatively high
uncertainty in the result.

6.4.3. Organic Species Concentrations
Table 6.3 presents the size-fractionated concentrations of n-alkanes. n-Alkanes
concentrations varied from 6.4 to 43.3 ng/m
3
in quasi-UF and from 2.3 to 27.4 ng/m
3
in
accumulation mode particles at all six sites. Fraser et al. (1997) has shown that n-alkanes
are emitted from both anthropogenic sources (mainly combustion process) and biogenic
sources (e.g. plants and bacterial activities). Site 5 had substantially lower levels of fine
particulate n-alkanes (8.7 ng/m
3
) than Sites 1 to 4 (26.0-63.8 ng/m
3
). Site 5 was located
209
upwind of local anthropogenic sources (e.g. vehicular sources), while Sites 1 to 4 were in
the same vicinity as Site 5, but downwind of local anthropogenic sources. Site 2 had the
highest particulate n-alkanes levels compare to other sites (63.8 ng/m
3
in fine mode),
which indicates a significant influence of local combustion sources (likely surface street
traffic, with frequent vehicle acceleration- deceleration) on this site.

Carbon preference index (CPI), i.e., the ratio of the concentrations of odd-carbon-to-
even-carbon n-alkanes, is an indicator to differentiate between anthropogenic and
biogenic source contribution to PM (Simoneit 1986). Previous studies have shown that n-
alkanes originating from anthropogenic sources have a CPI close to unity, whereas the
CPI is generally higher ( ≥ 2) when the biogenic sources are domina nt (Simoneit 1986).
The calculated CPIs at the six sampling sites are presented in Table 6.3. The CPIs varied
from 0.74 (Site 2) to 1.36 (Site 6) in quasi-UF mode particles, and from 0.65 (Site 1) to
1.84 (Site 5) in the accumulation mode. These values confirm that anthropogenic
emissions are the dominating sources of fine particulate matter emissions in the studied
area. The relatively high CPI in accumulation mode at Site 5 (background site) implies a
lower influence of anthropogenic sources. However, the low CPI of 0.9 in quasi-UF
mode at this site could be due to primary emission of quasi-UF particles from ships
located upwind of that site.

210
Σn-alkanes (ng/m
3
)
C
max
CPI
a
Quasi-Ultrafine Site 1 20.66 C26 (1.24) 0.77
Site 2 43.28 C28 (4.54) 0.74
Site 3 19.67 C25 (2.78) 0.89
Site 4 17.84 C24 (2.75) 0.81
Site 5 6.42 C24 (1.08) 0.90
Site 6 8.41 C25 (1.15) 1.36
Accumulation Site 1 27.38 C34 (2.68) 0.65
Site 2 20.47 C24 (1.47) 0.81
Site 3 22.41 C31 (1.83) 0.85
Site 4 8.11 C29 (1.10) 0.79
Site 5 2.29 C27 (0.41) 1.84
Site 6 4.52 C25 (0.62) 1.35
a
CPI = ΣC(2n+1)/ΣC(2n), n = 7-20
Table 6.3 Measured n-alkanes and calculated carbon preference index (CPI) for quasi-UF
and accumulation mode particles at all sampling sites

PAHs are also products of incomplete combustion of different organic matter and their
composition and emission rate are dependent on combustion processes and atmospheric
conditions (Manchester-Neesvig et al. 2003). The PAH concentrations in quasi-UFP and
accumulation mode are shown in Figure 6.4. In general, PAH concentrations were higher
in quasi-UFP mode compared to accumulation mode, since they are species of primarily
emitted aerosols, as it was shown in previous studies (Fine et al. 2004; Phuleria et al.
2007). Tunnel studies suggested that heavy molecular weight PAHs (e.g. coronene) are
primarily originated from poorly operated gasoline-powered vehicles (Miguel 1998;
Lough et. al. 2006; Phuleria et al. 2006). The highest coronene concentration was
observed at Site 2 (0.07 ng/m
3
), suggesting a possible impact from poorly operating
gasoline powered engines associated with the local street traffic in that site. The transient
operation of the vehicle engines, which is characteristic of urban surface street driving
conditions (compared to the more even cruise-mode driving conditions of the freeways)
may have also contributed to the higher concentrations of almost all organic species
211
coming from combustion in that site. It should be noted that coronene was close to or
under the detection limit of the GC/MS method in quasi-ultrafine particles at Sites 4, 5
and 6 and in accumulation mode at all the sites. Site 5 had the lowest level of particulate
PAHs (0.1 ng/m
3
) since it was upwind of most of combustion sources. The low, but still
measurable levels of PAHs at this site may originate from ship and boat traffic upwind of
this site. The fine particulate PAHs concentrations at the studied sites ranged from 0.1 to
0.4 ng/m
3
, and were close to but somewhat lower than the range reported in previous
studies (Manchester-Neesvig et al. 2003; Ning et al. 2007; Lough et al. 2006). It should
be noted the PAH levels in this study represent concentrations averaged over an entire
day. However, diurnal variation of different molecular weight PAHs are generally
substantial, especially during the morning rush hour, when they can be up to 5-6 times
higher than the rest of the day (Ning et al. 2007).

Hopanes and steranes appear in the particulate emissions of gasoline- and diesel-powered
vehicles due to their presence in engine lubricating oil (Rogge et al 1993 and 1996;
Schauer et al. 1996 and 2002). These compounds are used as organic markers of
vehicular emissions (Fine et al. 2004; Schauer et al. 1996, 1999, 2000 and 2002).
Nonetheless, marine vessels may contribute to the urban concentrations of hopanes and
steranes (Peter et al. 1992). More than 96% of the detected hopanes and all the detected
steranes were contained in quasi-UFP mode (Figure 6.4). Sites 1, 2 ,3 and 5, which were
located within 3 km of the coast, had higher hopanes and steranes levels than site 4 (~7
km from the coast) and Site 6 (~40 km from the coast). This gradient of hopanes and
212
steranes concentrations may be due to impact of marine vessels in addition to that of
vehicular emissions in each site. For example, non-negligible hopanes and steranes were
measured at Site 5 (0.7 ng/m
3
of fine hopanes + steranes), although this site was clearly
located upwind of most known vehicular sources in that area. The higher concentrations
of hopanes and steranes were measured at Site 2 than all other sites (3.6 ng/m
3
of fine
hopanes + steranes), which indicates a high vehicular emission from either gasoline or
diesel-powered vehicles in addition to marine vessels emissions affecting this site. The
total fine particulate hopanes and steranes concentrations at the studied sites ranged from
0.1 to 3.6 ng/m
3
and were consistent with the measured concentrations in previous studies
in the Los Angeles area (Manchester-Neesvig et al. 2003; Fine et al. 2004).

The relative ratio of sum of hopanes to EC has been used in previous studies to
distinguish between the influence of diesel and gasoline powered vehicles (Manchester-
Neesving et al. 2003; Fine et al. 2004). Diesel powered vehicles contribute generally to
hopanes and steranes concentrations and higher amounts of EC, whereas gasoline
powered vehicles contribute to hopanes and steranes and smaller amounts of EC
(Manchester-Neesvig et al. 2003; Schauer et al; 1999; Fraser et al. 1997). Figure 6.5
shows the measured hopanes and EC levels (points) at all the sites compared to average
levels measured near I-710 freeway (with the highest ratio of diesel powered vehicles in
the entire sate of California), shown as a solid line, and average levels measured near I-
110 freeway (less than 3% diesel powered vehicles), shown as a dashed line (Phuleria et
al. 2007). The plotted data points represent the fine fraction particles (i.e., the sum of
213
quasi-ultrafine and accumulation mode concentrations). Site 5, which is the background
site, had the lowest level of EC compare to the other sites, as it was anticipated. For the
rest of the sites located close to harbor, the data points lie closer to the previously
measured EC –to- hopanes ratio for diesel vehicle emissions, except for Site 2. Site 2 had
the highest hopanes level accompanied with a relative high EC level. As it was discussed
earlier, Site 2 is not situated in an area with an obvious high diesel vehicle impact, unlike
Sites 1, 3, 4 and 6. The high hopane, sterane and EC levels maybe attributed to poorly
maintained gasoline vehicles impacting that site as well as to the transient (frequent
acceleration and abrupt stops as well as accelerating from stop lights) driving on the busy
arterial roads that surround this site. When gasoline-powered vehicles perform hard
accelerations, they temporarily reduce the efficiency of the catalytic converter, due to
deviations from the stoichiometric fuel-air ratio (Maricq et al. 1999). Under the more
steady-state conditions such as freeway driving, catalytic converters remove CO, NO,
volatile organic compounds, and UFP with high efficiency.

The average ratio of hopanes/OC over all the sampling sites was 0.21±0.17 (ng/ µg),
which agrees reasonably well with data measured near highways with either highly
impact from heavy-duty vehicles (I-710, hopanes/OC = 0.42 ng/ µg), or from gasoline
vehicles (I-110, hopanes/OC = 0.35 ng/ µg) (Phuleria et al. 2007). These ratios support the
notion that vehicular sources are the major OC contributors. The OC/EC ratios varies

214
a) Quasi-ultrafine
PAHs
0.00
0.05
0.10
0.15
0.20
0.25
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Concentration (ng/m3)
PAH(MW=202+228)
PAH(MW=252)
Coronene
Levoglucosan
0
1
2
3
4
5
6
7
8
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Levoglucosan
Hopanes and Steranes
0.00
0.40
0.80
1.20
1.60
2.00
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Hopanes
Steranes

b) Accumulation mode
0.00
0.05
0.10
0.15
0.20
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Concentration (ng/m3)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
0
1
2
3
4
5
6
7
8
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6

Figure 6.4 Concentration of PAHs (classified by molecular weight), Hopanes, steranes and Levoglucosan in a) quasi-UF and b)
accumulation mode. Error bar represent the uncertainties in organics concentrations
215
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Hopanes (ng/m3)
EC(
µ
g/m3)
Measured Fine Particles
I710 (High Diesel Ratio)
I110 (High Gasoline Ratio)
S1
S5
S4
S3
S6
S2

Figure 6.5 Relationship between hopanes and elemental carbon; Straight lines represent
measured ratios in a previous study of urban PM
2.5
in the proximity of freeways (Phuleria et
al. 2007)

from 3.4 (Site 3) to 6.1 (Site 1), which lie within the ranges of those measured near the I-
710 (OC/EC = 2.0) and near the I-110 freeway (OC/EC=7.7) (Phuleria et al 2007),
inferring impacts from both diesel powered vehicles and gasoline vehicles. Levoglucosan
is a pyrolysis product of cellulose and it is regarded as a good tracer for wood burning
emissions (Schauer et al. 2000; Simoneit et al. 1999; Fraser et al. 1997). The quasi-UF-
to-accumulation mode ratio of levoglucosan varied substantially across the sites, which
suggests that local wood burning dominates the levels of levoglucosan in the investigated
area (Figure 6.4). Site 2 and 4 had substantially higher levoglucosan levels than the other
sites; however, levoglucosan at Site 2 mainly appeared in the accumulation mode (6.1
ng/m
3
in accumulation and 0.6 ng/m
3
in quasi-UF mode), while at Site 4 it was measured
216
in quasi-UFP mode (4.5 ng/m
3
in quasi-UF and 1.6 ng/m
3
in the accumulation mode).
The levels of the fine PM levoglucosan during the sampling campaign (1.6 to 6.7 ng/m
3
)
were comparable to those reported by Manchester-Neesvig et al. (2003) and Fine et al.
(2004) in Los Angeles. The levoglucosan- to- OC ratios for PM
2.5
varied from 0.6 ng/ µg
(Site 1) to 2.0 ng/ µg (Site 2 and 4), which are far below the ratio reported for PM
2.5

influenced by biomass burning (about 44.0 ng/ µg; Sheesley et al. 2007). These low ratios
suggest that biomass burning is not a major source for particulate organic matter for the
harbor communities.

6.4.4. Spatial Variance of Size Fractionated PM and its Components
The coefficient of variance (CV = standard deviation / mean) was determined for several
measured species within each PM size range to investigate the spatial variation of these
chemical components. To that end, during each week, mean concentrations across the six
sampling sites (and standard deviations) were determined for PM, OC, EC and ions data
where the concentrations were available concurrently at all the sites. The PAHs were
classified into three groups on the basis of their molecular weight (MW) (Phuleria et al.
2007). The crustal material consisted of Al, Ca, Ti and Fe. Organic species with more
than one missing data point (mainly due to concentrations lower than the detection limit)
were excluded from the CV analysis. Figure 6.6 shows the CV of species for the three
PM size ranges. PM mass in accumulation mode showed a relatively lower spatial
variability (CV = 0.25 ± 0.06) compared to the quasi-UFP mode (CV = 0.47 ± 0.16) and
the coarse mode (CV = 0.46 ± 0.01). The accumulation mode particles have higher
217
residence times in the atmosphere, are subject to more intense atmospheric mixing and
are thus more homogeneously dispersed over a large area. Conversely, the ultrafine and
coarse mode PM tend to have local maxima closer to their emission sources and are
removed faster from the atmosphere by diffusion and gravitational deposition (Seinfeld
and Pandis 1998). In the quasi-UF particles, sodium and chloride are the most
heterogeneously dispersed ions, with CV of 0.88 and 0.91, respectively. The high CV is
mainly driven by the substantially higher concentration of these species at the
background site (Site 5), which corresponds to high impact of sea salt generated by
bubble bursting processes. EC, which is mainly emitted from diesel-powered vehicles,
displayed relative high CV, which is indicative of the high variation of the influence of
diesel vehicles on the six sites. By contrast, OC, which originates from most combustion
sources (e.g. gasoline and diesel engine vehicles and wood smoke) showed relatively low
CV values. Moreover, a fraction of OC can also be attributed to secondary formation
processes in the atmosphere, which are regional in nature and result in a relatively
homogenous dispersion of OC. Sulfate had the lowest CV values (hence the most
homogeneous distribution over the investigated area) due to its mostly secondary origin.
Steranes and hopanes showed also a high spatial variance (CV= 0.80 and 0.91,
respectively), indicating a large variation in the impact of local traffic and marine sources
on the different sites. The relative higher CV for these species was driven by the levels
measured at Site 2, where the concentrations of hopanes and steranes were about 2-3
times higher than the rest of the sampling sites. PAHs had a low CV (0.2-0.3), at the
same level with the CV of OC, and unlike the rest of the organic species in that size range.
218

Elements

Elements

Figure 6.6 Coefficient of variances (CV) with standard deviation (SD) of selected chemical
components at three size fractions: a) quasi-UF mode, b) accumulation mode and c) coarse
mode. Error bars represent standard deviations
219

Elements

Figure 6.6 Continued

In the accumulation mode particles, levoglucosan was the most spatially heterogeneous
species, with a CV of 1.1. The high variation was mainly due to substantially higher
concentration at Site 2 than the other sites, probably due to the presence of a local wood-
burning source near Site 2. The majority of the other species in accumulation mode are
rather evenly distributed over the sites compared to other modes, with CV values varying
from 0.2-0.6. In the coarse mode particles, levoglucosan and n-alkanes are the most
spatially heterogeneous species with (CV =1.1 and 1.2, respectively). Potassium and EC
also showed relatively high spatial variances (0.9 and 0.7, respectively). Crustal elements
and inorganic ions in that size range had CV values varying from 0.5-0.7.
220
6.4.5. Elemental Constituents of PM
The concentration ranges of selected elements and their coefficient of variance (CV) over
all the sites and for different size fractions are shown in Figure 6.7a. Na and S were the
most abundant elements in all three size fractions, followed by Ca, Mg, K, Fe and Al.
The calculated CV in the quasi-UF particles varied from 0.22 (P) to 1.63 (Cr); in the
accumulation mode, they varied from 0.12 (Ni) to 0.81 (Cd); and in the coarse mode,
they varied from 0.40 (Zn) to 0.79 (Sb). Figure 6.7b presents the size fractionated Upper
Continental Crust (UCC) enrichment factors (EFs) (Ntziachristos et al. 2007b) for
selected elements. The concentration of each element was normalized to Al and then was
divided by the relative abundance of the same element over Al in UCC (Taylor and
McLennan 1985). EFs in Figure 6.7b are averaged over the six sites; the error bars,
denoting one standard deviation, suggest that the variability in EFs among sites was
rather small. Trace elements were sorted in a decreasing order of their EF in fine PM
mode. EFs close to 1 indicate crustal origin, while higher EFs indicate anthropogenic
origin for a given element. For almost all the elements, the EF values were higher in the
fine mode compare to the coarse mode, as the fine PM mostly originates from the
anthropogenic sources. The lower coarse mode EFs were observed for Al, Fe, Ti, K, Mn,
Cs, and K, indicating that these airborne species are products of resuspended soil dust.
Even the fine PM fraction of these species had generally lower EFs compare to other
elements, indicating again a crustal origin. High EFs for Sb, S, Cd, Mo, Zn, Pb and Cu
were found in all the size fractions with higher values in fine mode (both quasi-ultrafine
and accumulation mode). Most of these elements are generated from vehicular sources
221
and some are ingredients of lube oil (Ntziachristos et al. 2007b). Cu, Sb and Ba originate
from vehicle brake abrasion (Sanders et al. 2003; Sternbeck et al. 2002). The
concentrations of these three species were highly correlated across all size ranges, with
R
2
values ranging from 0.90-0.95 for Ba vs Cu and Sb vs Ba, respectively, confirming
their common source; in fact, EFs for the coarse mode of these species are substantially
higher than 1, supporting this argument that these elements are not products of soil dust
resuspension. Mo is a component of lube-oil combustion since it is used as additives in
oils. Zn is mostly a product of tire attrition (Singh et al. 2002). Pb is attributed to wheel
weights and gasoline exhaust in small amount (Sternbeck et al. 2002). High EFs of Sb,
Mo, S, Sn, Pb, Zn, Cu, Ba, V and Ni were also reported at other urban locations
(Ntziachristos et al. 2007b; Lin et al. 2005; Birmili et al. 2006). The high EF values for
Na can be attributed to sea breeze as it is expected in coastal areas. High EFs were found
for V and Ni, as these elements are generated by fuel oil combustion, mainly by marine
vessels (Isakson et al. 2001; Lu et al. 2006; Cass and McRae 1983). Vanadium-to-nickel
ratio has been used to distinguish between different emission sources so that the ratios
higher than 1.5 mainly indicate fuel oil combustion, ratios around 1 indicate industrial
sources (Isakson and Persson et al. 2001) and smaller ratios indicate diesel and gasoline
engine emissions (Lin et al. 2005). Figure 6.8a presents the scatter plot of vanadium
versus nickel in the fine particle modes. Excluding Site 6 (at USC) a strong association
between the two species was obtained across all the sites (r = 0.90). This association
suggests a common dominant source. A high slope of regression line (V/Ni = 4.2)

222
a)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Sb
S
Cd
Mo
Zn
Pb
V
Cu
Ni
Na
La
Ba
Cr
Li
Ce
Mg
Co
Sr
K
Cs
Mn
Ca
P
Fe
Rb
Ti
Al
CV
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
Concentration (ng/m
3
)
Quasi-UF
Accumulation
Coarse
Concentration CV

b)
Crustal Enrichment Factor
1
10
100
1000
10000
Sb
S
Cd
Mo
Zn
Pb
V
Cu
Ni
Na
La
Ba
Cr
Li
Ce
Mg
Co
Sr
K
Cs
Mn
Ca
P
Fe
Rb
Ti
Al
Species
Index
PM2.5
Coarse
Anthropogenic Crustal

Figure 6.7 Size fractionated results of a) concentration ranges and coefficient of variances
(CV) and b) crustal enrichment factor for selected. Error bars represent standard
deviations.
223
Vanadium
0.E+00
4.E-03
8.E-03
1.E-02
2.E-02
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6
Concentration (
µ
g/m
3
)
Accumulation
quasi-UF
Fine Particles
y = 4.166x + 0.005
R = 0.90
0.E+00
1.E-02
2.E-02
0.E+00 1.E-03 2.E-03 3.E-03
Ni ( µg/m
3
)
V (
µ
g/m
3
)
Site 6
Site 4 Site 2
Site 3
Site 1
Site 5
a)

b)

Figure 6.8 Vanadium concentrations a) plotted versus nickel concentrations and b)
measured in quasi-ultrafine and accumulation mode at all the sites

indicates a fuel oil combustion source, (i.e. vessels). The distinctly lower ratio at Site 6
(V/Ni = 1.9) suggests the influence of other important local Ni source(s).

Figure 6.8b presents the variation of the vanadium concentrations measured over all the
sampling sites. More than 80% of vanadium concentrations were in the quasi-UF mode.
Site 5 and Site 1 showed the highest vanadium concentrations, followed by Site 3 and
Site 2—all these sites were located within 3km from the coast. Relative to the vanadium
concentration measured at the coastal site (Site 5), the concentrations dropped to about
224
62% at Site 4 (7km from the coast) and 49% at Site 6 (40 km from the coast). This
gradient further confirms the major contribution of marine vessels to vanadium
concentrations. Figure 6.9 presents the scatter plots and correlation coefficients of
vanadium versus sulfur at all sites. Vanadium and sulfur were highly correlated in the
quasi-UF mode (r=0.72), whereas there was lack of correlation in the accumulation mode
(r=0.03). These results suggest that sulfur in quasi-UF mode mainly comes from similar
sources to vanadium, i.e., mainly bunker-fuel combustion from marine vessels (Lin et al.
2005). By contrast, sulfur in accumulation mode is mainly in the form of ammonium
sulfate which is produced by secondary reactions, hence the low correlation with V.
There was no significant correlation (p<0.05) between V and Ni with OC and EC in
different PM size fractions (Table 6.4). The weak association further supports the
argument that marine vessels are probably not the major contributor to particulate EC and
OC; even in areas proximal to the largest US harbor, the carbonaceous content of PM is
generally emitted from vehicular sources. The V/OC ratio varied from 1.8 ng/ g (Site 6,
Los Angeles Downtown Site) to 7.1 ng/ µg (at Site 5, the background site, influenced by
marine vessels). The Ni/OC ratio varied from 2.0 ng/ µg (Site 5) to 12.0 ng/ µg (Site 4).
Yuan et al. (2006) reported V/OC and Ni/OC for residual oil combustion of about 160
and 70 ng/ µg, respectively. This corroborates the minor overall contribution of residual
oil combustion sources to the measured EC and OC concentrations. Slightly higher, but
still not significant correlations were found between Ni and EC-OC in the accumulation
mode, which may be due to the contribution of vehicular sources to airborne Ni in
addition to marine vessels.
225
a) Quasi-ultrafine
y = 0.03x - 0.01
R = 0.72
0.00
0.01
0.02
0.7 0.8 0.9 1 1.1
S ( µg/m
3
)
V (
µ
g/m
3
)

b) Accumulation
y = 2E-05x + 0.002
R = 0.03
0.000
0.001
0.002
0 2 4 6 8
S ( µg/m
3
)
V (
µ
g/m
3
)

Figure 6.9 Relationships between vanadium and sulfur concentrations for (a) quasi-UF and
(b) accumulation fractions

Table 6.4 Pearson number and P-values of correlation between V and Ni with EC and OC
in different size fractions of PM

V Ni V Ni V Ni
R -0.08 -0.17 0.15 0.58 -0.08 0.12
P-Value 0.88 0.75 0.78 0.23 0.88 0.82
R -0.40 -0.57 -0.25 0.39 -0.66 -0.43
P-Value 0.44 0.24 0.63 0.45 0.16 0.39
OC
Accumulation Mode PM
2.5
Quasi-ultrafine
EC
226
6.5. SUMMARY AND CONCLUSION
Size fractioned PM and its chemical compositions were investigated in the communities
of Los Angeles harbor. The major mass contributions in the quasi-UF fraction were
particulate organic matter (POM), nss-sulfate and EC; in the accumulation mode fraction
were nss-sulfate, sea salt, POM and nitrate; and in the coarse fraction were sea salt and
insoluble soil. In general, PM and its components in accumulation mode showed
relatively lower spatial variability compare to the quasi-UF and the coarse modes. The
carbon preference index (CPI) for quasi-UF and accumulation mode particles varied from
0.65 to 1.84 among sites, which is in the range of previous findings in areas with high
influence of anthropogenic sources. In sites located close to harbor, the average n-
Alkanes and PAHs levels were respectively about 3 and 5 times higher than their
corresponding levels at a site located in vicinity of harbor, but upwind of most of local
sources. The ratio of hopanes to EC and hopanes to OC over all the sites were in the
range of previous roadside measurements near freeways with variable volume of diesel
truck traffic. High overall correlations of vanadium with nickel (R=0.9), as well as a
considerable gradient of vanadium concentrations with distance from the coast, suggests
marine vessels as the major sources of these elements.

227
6.6. CHAPTER 6 REFERENCES
Allen, J. O., Hughes, L. S., Salmon, L. G., Mayo, P. R., Johnson, R. J., and Cass, G. R.
(2000). Characterization and evolution of primary and secondary aerosols during PM2.5
and PM10 episodes in the south Coast Air Basin, Report A-22 to the Coordinating
Research Council (CRC).

Anderson, H.R. (2000). Differential epidemiology of ambient aerosols, Philosophical
Transactions of the Royal Society of London. Series A, Mathematical and Physical
Sciences (1775): 2771-2785.

Arhami, M., Kuhn, T., Fine, P.M., Delfino, R.J., and Sioutas, C. (2006). Effects of
Sampling Artifacts and Operating Parameters on the Performance of a Semi-continuous
Particulate Elemental Carbon/Organic Carbon Monitor, Env. Sci. Technol. 40:945-954.

Atkinson, R.W., Anderson, H.R., Sunyer, J., Ayres, J., Baccini, M., Vonk, J.M.,
Boumghar, A., Forastiere, F., Forsberg, B., Touloumi, G., Schwartz, J. and Katsouyanni,
K. (2001). Acute effects of particulate air pollution on respiratory admissions - Results
from APHEA 2 project Source: American J. Resp. Critical Care Med. 164 (10): 1860-
1866.

Birch, M.E., and Cary, R.A. (1996). Elemental carbon-based method for monitoring
occupational exposures to particulate diesel exhaust, Aerosol Sci. Tech. 25:221-241.

Birmili, W., Allen, A.G., Bary, F., and Harrison, R.M. (2006). Trace metal concentrations
and water solubility in size-fractionated atmospheric particles and influence of road
traffic, Environ. Sci. Technol. 40:1144–1153.

Brewer P.G. (1975). Minor elements in sea water, Chemical Oceanography. 1:417-425.

Brook J.F., Dann T.F. and Burnett R.T. (1997). The relationship among TSP, PM
10
,
PM
2.5
, and inorganic constituents of atmospheric particulate matter at multiple Canadian
locations, J. Air & Waste Manage. Assoc. 47:2-19.

Brunekreef, B. and Forsberg, B. (2005). Epidemiological evidence of effects of coarse
airborne particles on health, Europ. Resp. J. 26 (2): 309-318.

Cass, G.R. and McRae, G.J. (1983). Source-receptor reconciliation of routine air
monitoring data for trace metals: an emission inventory assisted approach, Environ. Sci.
Technol. 17 (3): 129-139.

Cass, G.R., Hughes, L.A., Bhave, P., Kleeman, M.J., Allen, J.O., Salmon, L.G. (2000).
The chemical composition of atmospheric ultrafine particles, Philosophical Transactions
of the Royal Soc. of London Series A-Math. Phys. and Eng. Sci. 358 (1775): 2581-2592.
228

Christoforou, C.S., Salmon, L.G., Hannigan, M.P., Solomon, P.A. and Cass, G.R. (2000).
Trends in fine particle concentration and chemical composition in Southern California, J.
Air Waste Manag. 50 (1): 43-53.

Chowdhury Z., Zheng M., Schauer J.J., Sheesley R.J., Salmon L.G., Cass G.R. and
Russell A.G. (2007). Speciation of ambient fine organic carbon particles and source
apportionment of PM2.5 in Indian cities, J. Geophys. Res. 112:D15303.

Delfino, R.J., Sioutas, C., Malik, S. (2005). Potential role of ultrafine particles in
associations between airborne particle mass and cardiovascular health, Environ Health
Perspec. 113 (8): 934-946.

Donaldson, K., Brown, D., Clouter, A., Duffin, R., MacNee, W., Renwick, L., Tran, L.,
Stone, V. (2002). The pulmonary toxicology of ultrafine particles, J. of Aerosol
Medicine—Deposition Clearance and Effects in the Lung, 15 (2):213–220.

Dreher, K.L., Jaskot, R.H., Lehmann, J.R., Richards, J.H., McGee, J.K., Ghio, A.J. and
Costa, D.L. (1997). Soluble transition metals mediate residual oil fly ash induced acute
lung injury, J. Tox. Environ. Health, (3): 285-305.

Fine, P. M., Chakrabarti, B., Krudysz, M., Schauer, J. J. and Sioutas, C. (2004). Seasonal,
spatial, and diurnal variations of individual organic compound constituents of ultrafine
and accumulation mode PM in the Los Angeles basin, Environ. Sci. Technol. 38:1296-
1304.

Fraser, M.P., Cass, G.R., Simoneit, B.R.T. and Rasmussen, R.A. (1997). Air quality
model evaluation data for organics C2-C36 non-aromatic hydrocarbons, Environ. Sci.
Technol. 31:2356-2367.

Herner J.D., Green P.G. and Kleeman M.J. (2006). Measuring the trace elemental
composition of size-resolved airborne particles, Environ. Sci. Technol. 40 (6):1925–1933.

Hildemann, L.M., Markowski, G.R., Jones, M.C. and Cass, G.R.(1991). Submicrometer
aerosol mass distributions of emissions from boilers, fireplaces, automobiles, diesel
trucks, and meat cooking operations. Aerosol Sci. Technol. 14:138–152.

Hughes, L.S., Allen, J.O., Kleeman, M.J., Johnson, R.J., Cass, G.R., Gross, D.S., Gard,
E.E., Galli, M.E., Morrical, B.D., Fergenson, D.P., Dienes, T., Noble, C.A., Silva, P.J.,
Prather, K.A. (1999). Size and composition distribution of atmospheric particles in
southern California, Environ. Sci. Technol. 33 (20): 3506-3515.

229
Isakson, J., T. A. Persson, and Lindgren, E.S. (2001). Identification and assessment of
ship emissions and their effects in the harbor of Gothenburg, Sweden, Atmos. Environ.
35:3659-3666.

Kerminen V.-M., Teinilä K., Hillamo R. and Pekkanen T. (1998). Substitution of chloride
in sea-salt particles by inorganic and organic anions, J. Aerosol Sci. 29:929-942.

Kerr S.C., Schauer J.J. and Rodger B. (2004). Regional haze in Wisconsin: sources and
the spatial distribution, J. Environ. Eng. and Sci. 3:213–222.

Kim, B. M., Cassmassi, J., Hogo, H. and Zeldin, M. D. (2001), Positive organic carbon
artifacts on filter medium during PM2.5 sampling in the South Coast Air Basin, Aerosol
Sci. Technol. 34(1): 35–41.

Kim, S., Shen, S., Sioutas, C., Zhu, Y. F., and Hinds, W. C. (2002). Size distribution and
diurnal and seasonal trends of ultrafine particles in source and receptor sites of the Los
Angeles Basin, J. Air Waste Manag. Assoc. 52:297-307.

Lin, C.C., Chen, S.J., Huang, K.L., Hwang, W.I., Chang-Chien, G.P., and Lin, W.Y.
(2005). Characteristics of metals in nano/ultrafine/fine/coarse particles collected beside a
heavily trafficked road, Environ. Sci. Technol. 39(21):8113–8122.

Lighty, J.S., Veranth, J.M., Sarofim, A.F. (2000). Combustion aerosols: Factors
governing their size and composition and implications to human health, J. of air waste
manage. Assoc. 50 (9):1565-1618.

Li, N., Sioutas, C., Cho, A., Schmitz, D., Misra, C., Sempf, J., Wang, M.Y., Oberley, T.,
Froines, J. and Nel, A. (2003). Ultrafine particulate pollutants induce oxidative stress and
mitochondrial damage; Environ. Health Perspec., 111:455-460.

Lough, G. C., Schauer, J.J. and Lawson, D.R. (2006). Day-of-week trends in
carbonaceous aerosol composition in the urban atmosphere, Atmos. Sci. 40:4137-4149.

Lu, G., Brook, J.R., Alfarra, M.R., Anlauf, K., Leaitch, W.R., Sharma, S., Wang, D.,
Worsnop, D.R., and Phinney, L. (2006). Identification and characterization of inland ship
plumes over
Vancouver, BC. Atmos. Environ. 40:2767–2782.

Manchester-Neesvig, J.B., Schauer, J.J. and Cass, G.R. (2003). The distribution of
particle-phase organic compounds in the atmosphere and their use for source
apportionment during the Southern California Children’s Health Study, J. Air Waste
Manag. Assoc. 53:1065-1079.

230
Maricq, M. M., Podsiadlik, D. H. and Chase, R. E. (1999). Gasoline Vehicle Particle Size
Distributions: Comparison of Steady State, FTP, and US06 Measurements, Environ. Sci.
Technol., 33 (12):2007 -2015.

Maricq, M. M. (2007). Chemical characterization of particulate emissions from diesel
engines: A review, J. Aerosol Sci. 38(11):1079-1118.

Misra, C., Singh, M., Shen, S., Sioutas, C. and Hall, P. A. (2002). Development and
evaluation of a personal cascade impactor sampler (PCIS). J. Aerosol Sci. 33: 1027-1047.

Ning, Z., Geller, M.D., Moore, K.F., Sheesley, R., Schauer, J.J. and Sioutas, C. (2007).
Daily variation in chemical characteristics of urban ultrafine aerosols and inference of
their sources, Environ Sci. Technol. 41(17): 6000-6006.

Ntziachristos, L., Ning, Z., Geller M.D. and Sioutas, C. (2007a). Particle concentration
and characteristics near a major freeway with heavy-duty diesel traffic, Environ. Sci.
Technol. 41(7): 2223-2230.

Ntziachristos, L., Ning, Z., Geller, M. D., Sheesley, J. J., Schauer, J. J., Sioutas, C.
(2007b). Fine, ultrafine and nanoparticle trace element compositions near a major
freeway with a high heavy-duty diesel fraction, Atmos. Environ. 41:5684–5696.

Oberdörster, G. (1996). Significance of particle parameters in the evaluation of exposure-
dose-response relationships of inhaled particles, Particulate Sci .Technol. 14:135-151.

Oberdörster, G. (2001). Pulmonary effects of inhaled ultrafine particles, International
Archives of Occupational and Environ. Health, 74:1-8.

O’Dowd, C.D., De Leeuw, G., (2007). Marine aerosol: a review of the current knowledge,
Philosophical Transactions of the Royal Society A-Mathematical Physical and
Engineering Sci. 365(1856):1753-1774.

Pandis, S. N., Harley, R. A., Cass, G. R., Seinfeld, J. H. (1992). Secondary organic
aerosol formation and transport, Atmos. Environ. 26:2269-2282.

Peters, K. E., Scheuerman, L., Lee, C. Y., Moldowan, J. M., Reynolds, R. N. and Peńa,
M. M. (1992). Effect of refinery processes on biological markers, Energy and Fuel,
6:560-577.

Pio, C.A. and Lopes, D.A. (1998). Chlorine loss from marine aerosol in a coastal
atmosphere, J. Geophys. Res. 103:25263-25272.

231
Phuleria, H.C., Geller, M.D., Fine, P.M., Sioutas, C. (2006). Size-resolved emissions of
organic tracers from light- and heavy-duty vehicles measured in a California roadway
tunnel, Environ. Sci. Technol. 40:4109–4118.

Phuleria, H.C., Sheesley, R.J., Schauer, J.J., Fine, P.M. and Sioutas, C. (2007). Roadside
measurements of size-segregated particulate organic compounds near gasoline and diesel-
dominated freeways in Los Angeles, CA, Atmos. Environ. 41:4653–4671.

Rodhe, H. (1999). Human impact on the atmospheric sulfur balance, Tellus, 51:110-122.

Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R. and Simoneit, B. R. T.
(1993). Sources of fine organic aerosol. 2. Noncatalyst and catalyst-equipped
automobiles and heavy-duty diesel trucks, Environ. Sci. Technol. 27:636-651.

Rose, D., Wehner, B., Ketzel, M., Engler, C., Voigtländer, J., Tuch, T. and Wiedensohler,
A. (2006). Atmospheric number size distributions of soot particles and estimation of
emission factors. Atmos. Phys. Chem. 6:1021-1031.

Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R. and Simoneit, B.R.T. (1996).
Mathematical modeling of atmospheric fine particle-associated primary organic
compound concentrations, J. Geophys. Research. 101 (D14): 19379-19394.

Russell, A.G. and Cass, G.R. (1986). Verification of a mathematical model for aerosol
nitrate and nitric acid formation and its use for control measure source, Atmos. Environ.
(10): 2011-2025.

Russell, L.M. (2003). Aerosol organic-mass-to-organic-carbon ratio measurements.
Environ. Sci. Technol., 37:2982-2987.

Samoli, E., Analitis, A., Touloumi, G., Schwartz, J., Anderson, H.R., Sunyer, J., Bisanti,
L., Zmirou, D., Vonk, J.M., Pekkanen, J., Goodman, P., Paldy, A., Schindler, C. and
Katsouyanni, K. (2005). Estimating the exposure-response relationships between
particulate matter and mortality within the APHEA multicity project, Environ. Health
Perspect. 113 (1): 88-95.

Saarikoski, S., Frey A., Mäkelä, T. and Hillamo, R. (2008). Size distribution
measurement of carbonaceous particulate matter using a low pressure impactor with
quartz fiber substrates. Aerosol Sci. Technol., in press.

Salonen, R.O., Halinen, A.I., Pekkanen, A.S., Hirvonen, M.R., Sillanpaa, M., Hillamo, R.,
Shi, T.M., Borm, P., Sandell, E., Koskentalo, T. and Aarnio, P. (2004). Chemical and in
vitro toxicologic characterization of wintertime and springtime urban-air particles with an
aerodynamic diameter below 10 m in Helsinki, Scandinavian J. Work and Environ.
Health, 30: 80-90.
232

Sanders, P.G., Xu, N., Dalka, T.M., and Maricq, M.M. (2003). Airborne brake wear
debris: size distributions, composition, and a comparison of dynamometer and vehicle
tests, Environ. Sci. Technol. 37:4060–4069.

Sardar, S.B., Fine, P.M., Mayo, P.R. and Sioutas C. (2005a). Size-fractionated
measurements of ambient ultrafine particle chemical composition in Los Angeles using
the NanoMOUDI, Environ. Sci. Technol. 39:932-944.

Sardar, S.B., Fine, P.M. and Sioutas C. (2005b). Seasonal and spatial variability of the
size-resolved chemical composition of particulate matter (PM10) in the Los Angeles
Basin, J. Geophys. Research. 110:D07S08.doi:10.1028/2004JD004627.

Schauer, J. J., Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and
Simoneit, B. R. T. (1996). Source apportionment of airborne particulate matter using
organic compounds as tracers, Atmos. Environ. 30:3837-3855.

Schauer, J. J., Kleeman, M. J., Cass, G. R., and Simoneit, B. R. T. (1999). Measurement
of emissions from air pollution sources 1. C1 through C29 organic compounds from meat
charbroiling, Environ. Sci. Technol. 33:1566– 1577.

Schauer, J. J. and Cass, G. R. (2000). Source apportionment of wintertime gas-phase and
particle-phase air pollutants, Environ. Sci. Technol. 34:1821- 1832.

Schauer, J. J., Kleeman, M. J., Cass, G. R., and Simoneit, B. R. T. (2002). Measurement
of emissions from air pollution sources. 5. C1-C32 organic compounds from gasoline-
powered motor vehicles, Environ. Sci. Technol. 36:1169-1180.

Schauer, J.J., Mader, B.T., Deminter, J.T., Heidemann, G., Bae, M.S., Seinfeld, J.H.,
Flagan, R.C., Cary, R.A., Smith, D., Huebert, B.J., Bertram, T., Howell, S., Kline, J.T.,
Quinn, P., Bates, T., Turpin, B., Lim, H.J., Yu, J.Z., Yang, H. and Keywood, M.D.
(2003). ACE—Asia intercomparison of a thermal–optical method for the determination
of particle-phase organic and elemental carbon, Environ. Sci. Technol. 37:993–1001.

Schauer, J.J., Christensen, C.G., Kittelson, D.B., Johnson, J. P., Watts, W.F. (2008).
Impact of Ambient Temperatures and Driving Conditions on the Chemical Composition
of Particulate Matter Emissions from Non-Smoking Gasoline-Powered Motor Vehicles,
Aerosol Sci. Technol. In press.

Seinfeld, J.H. and Pandis, S.N. (1998). Atmospheric chemistry and physics: From air
pollution to climate change, John Wiley, New York.

Sheesley, R. J., Schauer, J. J., ,Zheng, M., and Wang, B. (2007). Sens. of molec. marker-
based CMB models to biomass burning source profiles, Atmos. Environ. 41:9050–9063.
233

Sillanpaa, M., Hillamo, R., Saarikoski, S., Frey, A., Pennanen, A., Makkonen, U.,
Spolnik, Z., Van Grieken, R., Branis, M., Brunekreef, B., Chalbot, M. C., Kuhlbusch, T.,
Sunyer, J., et al. (2006). Chemical composition and mass closure of particulate matter at
six urban sites in Europe, Atmos. Environ. 40:S212–S223.

Simoneit, B. R. T. (1986). Characterization of organic-constituents in aerosols in relation
to their origin and transports A review, Int. J. Environ. Anal. Chem. 23:207-237.

Simoneit, B.R.T. (1999). A review of biomarker compounds as source indicators and
tracers for air pollution, Environ Sci. Poll. Res. 6 (3): 159-169.

Singh, M., Jaques, P.A., Sioutas, C. (2002). Size distribution and diurnal characteristics
of particle-bound metals in source and receptor sites of the Los Angeles Basin, Atmos.
Environ. 36:1675–1689.

Singh, M., Misra, C. and Sioutas, C. (2003). Field evaluation of a personal cascade
impactor sampler (PCIS). Atmos. Environ. 37:4781-4793.

Sternbeck, J., Sjodin, A., and Andreasson, K. (2002). Metal emissions from road traffic
and the influence of resuspension, Atmos. Environ. 36:4735–4744.

Turpin, B. J. and Lim, H. J. (2001). Species Contributions to PM2.5 Mass c
Concentrations, Aerosol Sci. Technol. 35:602-610.

Taylor, S.R., and McLennan, S.M. (1985). The Continental Crust: its composition and
evolution, Blackwell Scientific Publications, Oxford, Boston, Palo Alto, Victoria.

World Health Organization (WHO) (2003). Health aspects of air pollution with
particulate matter, ozone and nitrogen dioxide. Report EUR/03/5042688 of working
group, Bonn, Germany, 13-15 January 2003. Copenhagen, Denmark.

Xia, T, Korge, T, Weiss, J., Li, N. , Venkatesan, I., Sioutas, C. and Nel, A. (2004).
Quinones and aromatic chemical compounds in particulate matter induce mitochondrial
dysfunction: Implications for ultrafine particle toxicity, Environ. Health. Perspec. 112
(14): 1347-1358.

Yuan, Z, Lau, A. K. H., Zhang, H., Yu, J. Z., Louie, P. K. K., and Funge, J. C. H. (2006).
Identification and spatiotemporal variations of dominant PM10 sources over Hong Kong,
Atmos. Environ. 40:1803–1815.

Zheng, M., Cass, G. R., Schauer, J.J., and Edgerton, E.S. (2002). Source apportionment
of PM2.5 in the southeastern United States, Environ. Sci. Technol. 36:2361– 2371.
234

Chapter 7.
Seasonal and Spatial Variations of Sources of Fine and Quasi-ultrafine
Particulate Matter in Neighbourhoods near the Los Angeles-Long
Beach Harbour

7.1. ABSTRACT
The Los Angeles – Long Beach harbor is the busiest port in the US. Levels of particulate
matter (PM) are relatively high in this area, since it is affected by multiple PM sources. A
Chemical Mass Balance (CMB) model was applied to speciated chemical measurements
of quasi-ultrafine and fine particulate matter from seven different sites. Winter
measurements were obtained during a 7-week period between March and May 2007, and
summer measurements corresponded to a 6-week period between July and September
2007. Four of the sites were located within the communities of Wilmington and Long
Beach, two sites were located at a background area in the harbor of Los Angeles and
Long Beach, and one more site was located further downwind, near downtown Los
Angeles, representing urban downtown LA, influenced by mostly traffic sources. The
samples were analyzed for organic (OC) and elemental (EC) carbon content, organic
species, inorganic ions, water soluble and total elements. The sources included in the
CMB model were: light duty vehicles (LDV), heavy duty vehicles (HDV), road dust
(RD), biomass burning and ship emissions. The model predictions of the LDV and HDV
source contributions accounted, on average, for 83% of total fine OC in winter and for
70% in summer, whereas ship emissions contribution was lower than 5% of total OC at
all sites. In the quasi-ultrafine mode, the vehicular sources accounted for 118% in winter
and 103% in summer. Spatial variation of source contributions was not very pronounced
235

with the exception of some specific sites. In terms of total fine PM, vehicular sources
together with road dust explain up to 54% of the mass, whereas ship contribution is lower
than 5% of total fine PM mass. Our results clearly indicate that, although ship emissions
can be significant, PM emissions in the area of the largest US harbor are dominated by
vehicular sources.

7.2. INTRODUCTION AND OBJECTIVES
A large number of epidemiological studies have shown a relationship between exposure
to ambient particulate matter (PM) and adverse effects on human health (Dockery et al.,
1993; Atkinson et al., 2001; Samoli et al., 2005). The size of the particles has a strong
influence on the type and intensity of health effect caused. Thus, fine particles
(aerodynamic diameter, D
p
< 2.5 µm; PM
2.5
) have been more strongly associated with
mortality and morbidity, whereas coarse particles (2.5 µm < D
p
< 10 µm; PM
2.5-10
) have
been associated with respiratory hospital admissions (Brunekreef and Forsberg, 2005).
Ultrafine particles (D
p
< 0.1 µm; PM
0.1
) penetrate deep into the alveolar region of the
respiratory system (Delfino et al., 2005) and recent toxicological studies suggest that
some adverse effects are associated more strongly with ultrafine particles than fine or
coarse particles (Oberdorster, 2001; Donaldson et al., 2002; Li et al., 2003). Moreover,
some studies have shown that using only community PM average concentrations to
determine the health effects resulting from PM exposure may lead to non accurate results
and therefore it is important to measure the variability of PM levels and sources within a
community (Jerrett et al., 2005).
236

The communities near the Los Angeles-Long Beach harbor are of particular concern
regarding PM pollution given that this harbor constitutes the busiest harbor in the US and
the fifth in the world, and therefore the area is affected by several PM sources. In addition
to the PM sources associated with harbor activities (marine vessels, heavy-duty trucks,
locomotives, cargo handling equipments and harbor crafts), these communities are
affected by other PM sources including road traffic from nearby freeways and local
streets, multiple petroleum refineries and other industrial facilities. Thus, the surrounding
area of the Los Angeles-Long Beach represents a highly complex urban airshed, and
requires the application of sophisticated analytical and statistical tools to identify and
quantify the major PM sources at different sites within the area.
For the development and implementation of PM policies that will be protective of the
environment and human health, regulators require scientific knowledge of the strengths,
spatial distribution and variability of the major sources of this pollutant. This information
allows to design effective mitigation strategies on the local- and meso-scale level, and to
evaluate human exposure to this pollutant and thus assess its health-related risks (Watson
et al., 2002; Hopke et al., 2006).

In this context, previous studies have been carried out to identify PM
10
sources in the
aforementioned area (Kleeman et al., 1999; Manchester-Neesvig et al., 2003). These
studies were spatially constrained by the fact that they were based on data collected in
one sampling site in Long Beach. Moreover, source apportionment of fine and ultrafine
237

fractions has not been conducted in this area. To the authors’ knowledge, ship emissions’
contribution to ambient PM has not been quantified before by means of receptor models
and very few studies exist on source apportionment of the ultrafine PM fraction (Toner et
al., 2008).

Hence, the objective of this study is to identify and quantify fine (PM
2.5
) and quasi-
ultrafine (PM
0.25
) particulate matter sources in the Los Angeles-Long Beach harbor area,
and identify, if any, the spatial and seasonal differences in PM patterns and composition.
The results from this study will provide with useful information for control strategies and
will assist future toxicological studies that are planned in this area.

7.3. METHODOLOGY

7.3.1. Sampling sites and schedule
Size-segregated PM samples were collected concurrently at seven sampling sites. Sites 1
and 2 were located in the community of Wilmington to the west of Long Beach area. Site
1 was about 1.5 km east (mostly downwind) of a major freeway (CA-110) at the
intersection of a major street and a local residential street. Site 2 was at the intersection of
two busy major streets, and next to the Alameda corridor, a 20 mile (32 km) freight rail
"expressway" connecting the national rail system to the port. Site 3 and 4 were located
within the communities of Long Beach. Site 3 was 1 km downwind of a major highway
(CA-1), inside a semi-industrial area. Site 4 was 1 km downwind (east) of a major
238

freeway (I-710) with the highest ratio (up to 25%) of heavy-duty diesel vehicles in the
Los Angeles highway network (Ntziachristos et al., 2007), and 1 km to the north of
another major freeway (I-405). This site was influenced also by industrial sources. Sites 5
and 7 were located in an open area at the end of the pier of Long Beach and Los Angeles,
respectively, upwind of harbor activities, hence serving as the background sites of the
harbor.

Site 6 was located at the Southern California Particle Center (SCPC) Particle
Instrumentation Unit (PIU) trailer near downtown Los Angeles. The PIU is about 40 km
north of the coast, 150 m east of a major freeway (CA-110). This site represents a typical
urban mix of downtown Los Angeles (Arhami et al., 2006) and was used as a reference to
which the measurements in the harbor area would be contrasted. More details about these
sites are described elsewhere (Arhami et al., 2008).

Sampling was conducted daily from Monday to Friday over a 7-week period from March
to May 2007 (hereafter referred to as the winter period) and a 6-week period from July to
September 2007 (hereafter refereed to as summer period). Due to technical and weather-
related problems, samples were not collected or were disregarded for week 7 at Sites 2, 4
and 5 in the wintertime. Moreover, sampling was not possible at Site 7 during the winter
period.

239

7.3.2. Sampling methods
Only a brief description of the sampling methods is included in this paper. A detailed
description can be found elsewhere (Arhami et al., 2008). Two collocated Sioutas™
impactors (SKC Inc, Eighty-Four, PA; (Misra et al., 2002; Singh et al., 2003)), operated
at a flow rate of 9 lpm, were used in each site to collect size fractionated PM samples. In
this study we only report data in the accumulation (Dp = 0.25-2.5 µm; PM
0.25-2.5
) and
quasi-ultrafine (Dp < 0.25 µm; PM
0.25
) modes, although coarse (Dp = 2.5- 10 µm) PM
were also collected and analyzed during these campaigns. Zefluor and quartz fiber filters
were used as particle collection media.

Local weather data were measured every minute at all sampling sites by means of a
weather station. The average temperature, relative humidity and wind speed varied in the
ranges of 14.7-19.1 ºC, 52-75% and 0.8-3.0 m/s in winter period and 20.9-23.9 ºC, 58-
82% and 0.5-2.2 m/s in summer period over all the sites. These meteorological data show
that, on average, the winter period was 4.2-6.4 ºC colder and that humidity and wind
speed do not change much thorough the year. Generally, the prevailing wind was from
southwest, where the ocean is located.

7.3.3. Gravimetric and chemical analyses
Mass concentration was determined by weighing the Zefluor filters before and after
sampling with a microbalance (MT 5, Mettler-Toledo Inc., Highstown, NJ) having a
sensitivity of 0.001 mg. A 24 hr equilibrating period under controlled temperature and
240

relative humidity was allowed prior to each filter weighing. A detailed description can be
found elsewhere (Arhami et al., 2008).
Each filter was divided into 4 fractions for chemical analyses. Weekly composites (from
daily samples) of quartz fiber filters were used for analyses of inorganic ions (chloride,
nitrate, phosphate, sulfate, sodium, ammonium and potassium) by ion chromatography,
and of elemental and organic carbon by the Thermal Evolution/Optical Transmittance
(TOT) analysis. Seven (winter) or six (summer) weekly composites of quartz filters were
used for analyses of 92 different organic compounds by Gas Chromatography/Mass
Spectrometry (GC/MS). Seven (winter) or six (summer) week composites of Zefluor
filters were used for analyses of 52 trace elements in soluble and insoluble fractions by
means of a magnetic sector inductively coupled plasma mass spectrometer (HR-ICPMS,
Finnigan Element 2). The remaining samples were used for toxicity analysis (results
reported elsewhere (Hu et al., 2008)).

7.3.4. CMB model methodologies
General description of the model
The chemical mass balance receptor model has been widely used to determine source
contribution estimates for PM
10
and PM
2.5
(Watson et al., 1994; Schauer et al., 1996;
Samara et al., 2003). Differently from other source apportionment models, CMB model
requires knowledge of the emission sources and their chemical profiles, i.e. the fractional
abundances of chemical species in the source emissions, in addition to the ambient data.
In this paper we used the version CMB8.2 from the US Environmental Protection
241

Agency (US EPA), applied for apportionment to total OC (instead of total PM). Thus, the
concentration of a general constituent i at a receptor site k, c
ik
, can be expressed as:
ik
m
j
jk ij ik
s a c ε + =
∑
=1
(7.1)
Where a
ij
is the relative concentration of chemical constituent i in the emissions form
source j; s
jk
is the increment to total OC mass concentration at receptor site k originating
from source j; and ε
ik
is the error term with zero mean and standard deviation.

Source profiles
A careful selection of OC/PM sources is critical for the application of the CMB model, as
demonstrated in previous sensitivity studies (Subramanian et al., 2006; Robinson et al.,
2006; Sheesley et al., 2007; Lough et al., 2007). Hence, after evaluating the
characteristics of the area, a selection of PM/OC sources was considered to run the CMB
model. Source profiles were referenced to total OC (concentration ratio between the
specific species and OC in the emission source), since the CMB model was run to
apportion total OC as stated above. The sources considered were: road dust specific for
the Long Beach area (Schauer, 1998; Amato et al., 2008) (only for the fine PM fraction);
biomass burning characteristic from West US (Fine et al., 2004) for both fine and quasi-
ultrafine fractions, although the source profile was only available for the fine fraction, it
was assumed to be the same for the quasi-ultrafine fraction based on results from
Kleeman et al. (2008); ocean vessels (Rogge et al., 1997; Agrawal et al., 2008) for fine
and quasi-ultrafine fractions (again the quasi-ultrafine profile was assumed to be the same
with the fine fraction); and light duty and heavy-duty vehicles, for fine and quasi-
242

ultrafine fractions (Kuhn et al., 2005a; Ntziachristos et al., 2007; Phuleria et al., 2007).
The vehicular profiles correspond to roadway profiles (instead of the more commonly
used exhaust profiles) from studies carried out in the CA-110 and I-710 freeways in Los
Angeles, which are the closest freeways to the study area, and thus provide the most
suitable and representative traffic source profiles for this area.

Other typical OC sources were not considered for different reasons. Meat cooking was
not considered because cholesterol (main marker for this source) was not detected in
most part of our ambient data set; nevertheless, its contribution was expected to be low in
comparison with the rest of the sources in the area. Vegetative detritus and natural gas
contributions were included in the first attempts but were found not quantifiable,
moreover their contribution is expected to be very low since there is no obvious reason
to believe that they have a substantial contribution to the air quality burden in that area.
Refinery emissions were not included in the model due to lack of appropriate information
on suitable profiles for refineries in the Long Beach area.

Fitting species
A set of fitting species was chosen based on: a) their chemical stability (Schauer et al.,
1996); b) availability of their concentrations in the different source profiles and in the
ambient data, and; c) previous studies that identify markers for different sources (Schauer
et al., 1996; Rogge et al., 1997; Simoneit, 1999; Schauer and Cass, 2000; Cooper, 2001;
Fine et al., 2004; Phuleria et al., 2007; Sheesley et al., 2007; Agrawal et al., 2008).
243

Thus, the following species were used as fitting species: EC, Benzo(e)pyrene, Coronene,
17α(H)-22,29,30-Trisnorhopane, 17α(H)-21β(H)-Hopane, 22S-Homohopane, 22R-
Homohopane, Cholestane, Sitostane, Levoglucosan, Aluminum, Vanadium and Nickel.
When some of the species were not detected at some of the sites, they were not used as
fitting species for the specific case.

Evaluation of the accuracy of the results
Each model result was evaluated by using the regression statistics parameters
accompanying each CMB model output (Table 7.1). These are: the percent of measured
ambient OC mass (%mass) accounted for by the sum of the source contribution estimates;
the correlation coefficient (R
2
) i.e. the variance in ambient species concentrations
explained by the calculated species concentrations; the χ
2
representing the weighted sum
of squares of the differences between calculated and measured fitting species
concentrations. The R
2
and χ
2
were within the desired ranges (0.8-1 and 0-5, respectively)
for the fine fraction. The % mass in summer was relatively low at some sites, probably
due to the higher contribution of secondary organic compounds to the overall PM mass in
the summer time. On the other hand, for the quasi-ultrafine fraction some parameters are
not within the desirable ranges due to potential sampling artifacts associated with the
reported OC concentrations both for ambient data and for the source profiles (Olson and
Norris, 2005; Arhami et al., 2006; Polidori et al., 2006). These are typically due to the
inadvertent collection of some vapor phase OC when using quartz filters for particle
244

Table 7.1 Regression statistics parameters

Period Site Quasi-ultrafine Fine
R
2
χ
2
%mass R
2
χ
2
%mass
winter
S1 0.92 2.5 124 ± 19 0.96 1.7 100 ± 19
S2 0.82 4.6 162 ± 28 0.90 3.5 130 ± 26
S3 0.80 5.9 115 ± 18 0.90 3.8 85 ± 17
S4 0.90 4.2 60 ± 10 0.98 0.7 65 ± 12
S5 0.93 1.6 144 ± 25 0.96 1.2 94 ± 19
S6 0.92 3.3 60 ± 10 0.91 4.1 62 ± 10
summer
S1 0.97 1.1 102 ± 16 0.97 1.9 73 ± 14
S2 0.94 1.9 72 ± 13 0.97 1.4 55 ± 12
S3 0.97 0.9 73 ± 13 0.98 1.1 58 ± 11
S4 0.92 2.2 91 ± 16 0.93 2.9 72 ± 14
S5 0.91 2.3 141 ± 30 0.92 2.4 126 ± 31
S6 0.90 3.4 82 ± 16 0.92 2.8 81 ± 16
S7 0.94 1.7 156 ± 33 0.93 2.1 119 ± 29

sampling. Previous studies in Los Angeles (Kim et al., 2001; Sardar et al., 2005) showed
that the positive artifact for a 24 hours period is on the order of 1-1.5 μg/m
3
of OC, thus
on the order of 20% of our measured OC concentrations. Given the possible occurrence
of these positive artifacts in our study as well as in previous studies creating source
emissions profiles, statements made in the following paragraphs regarding quasi-ultrafine
OC concentrations need to be qualified and put into perspective. The accumulation mode
OC concentrations were determined by collecting particles via impaction, which
diminishes the importance of this artifact.
245

Calculation of the source apportionment to total PM
Direct results from the model provide us with the source apportionment to ambient OC
mass. To better asses the impact of the sources on total ambient PM, OC results were
converted to PM based on the OC/PM ratio in each of the sources. For the quasi-ultrafine
sources, the OC/PM ratio reported was higher that 1, which has no physical meaning,
hence it was calculated assuming that PM=EC+OM and that OM=1.4*OC (Turpin et al.,
2000; Arhami et. al., 2008). To account for secondary inorganic aerosol contributions to
PM, sulfate, nitrate and ammonium concentrations apportioned by the considered sources
were subtracted from the ambient concentrations, and the difference was attributed
mainly to secondary aerosol. The sea spray sulfate (calculated from Na concentration)
was also subtracted. Sea spray contribution was calculated based on Na concentration
(after subtracting Na apportioned by the considered sources) and a factor (3.248)
including the chlorine and sulfate. The contribution from ‘other organics’ was calculated
from the unexplained OC mass assuming it is mostly in the form of secondary OC and
hence multiplied by a factor of two to be converted into organic matter (El-Zanan et al.,
2005).

7.4. RESULTS AND DISCUSSION

7.4.1. Chemical composition of PM in winter and summer
The overall fine, quasi-ultrafine and accumulation mode PM mass were in relatively
similar ranges in summer and winter (Figure 7.1). Higher OC levels in summer (Figure
246

7.1) can be due to the additional contribution of more intense photochemical activity
leading to the formation of secondary OC (Seinfeld and Pandis, 1998). The average
OC/EC ratios over all the sampling sites were 4.0±1.1 in winter and 5.7±1.0 in summer.
The 30% higher OC/EC ratios in summer can be attributed to the higher OC levels
because of the contribution of secondary OC as stated before. In a previous study the
average ratio of OC/EC measured near highways with either highly impact from heavy-
duty vehicles (I-710) or from gasoline vehicles (I-110) were 2.0 and 7.7, respectively
(Phuleria et al., 2007). Our ratios are within this range, which could infer the high impact
of vehicular sources on ambient OC concentrations.

Sum of all measured n-alkanes over the sampling sites in fine mode were 33.6±21.4
ng/m
3
in winter and 17.9±7.0 in summer, reflecting 88% of higher winter period
concentrations (Figure 7.2). The wintertime concentration of n-alkanes were 132% higher
in accumulation mode and 62% higher in quasi-ultrafine compared to summer, showing a
higher prevalence in accumulation mode. The lower levels of n-alkanes concentration in
summer could be due to volatilization of the particulate phase into the gas phase (Kuhn et
al., 2005b) and to variations in the emission sources of these compounds. Furthermore,
more enhanced condensation of gas phase n-alkanes during the winter time onto the
larger surface area of accumulation mode particles will result in increased concentrations
of that size range in the winter (Kuhn et al., 2005b).

247

a)
0
2
4
6
8
10
12
14
16
18
20
Fine Quasi-ultrafine Accumulation
winter
summer
µg/m
3

b)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
winter
summer
OC EC NO
3
-
SO
4
2-
NH
4
+
µg/m
3

Figure 7.1. Overall comparison of measured PM species in winter and summer: a) mass
concentrations of fine, quasi-ultrafine and accumulation mode PM, b) EC, OC, nitrate,
sulfate and ammonium concentrations in fine particulate mode, error bars are the standard
deviations of average measured concentration over the studied sites.

248

The sums of all measured fine particulate polycyclic aromatic hydrocarbons (PAH) were
0.32±0.15 ng/m
3
in winter and 0.23±0.08 in summer over all the studied sites (Figure 7.2).
PAH are mainly products of incomplete combustion of organic matter (Manchester-
Neesvig et al., 2003). The composition and emission rate of PAH are dependent on
combustion processes, atmospheric conditions (Manchester-Neesvig et al., 2003), gas
particle partitioning and deposition (Mader and Pankow, 2002; Kuhn et al., 2005b). Also
oxidizing gases such as ozone, nitrogen oxides, hydrogen peroxide can react with PAH
and lower their concentrations (Grosjean et al., 1983), and these reactions are generally
more pronounced in summer time. Hence, the slightly lower concentration of PAH in
summer could be due to combine effect of volatilization and reaction with oxidizing
gases. However, the decline in PAH levels is not as sharp as for n-alkanes levels, which
could be due to the lower volatility of PAH compare to n-alkanes.

The sum of all measured hopanes and steranes were 0.86±1.00 ng/m
3
in winter and
0.78±0.23 ng/m
3
in summer in the fine PM fraction (Figure 7.2), thus quite similar during
both seasons. This similarity may be due to the lower volatility of hopanes and steranes
compared to PAH and n-alkanes, which showed a difference of 28% and 47%,
respectively, between summer and winter.

249

a) n-Alkanes
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Tetradecane
Pentadecane
Hexadecane
Octadecane
Nonadecane
Eicosane
Heneicosane
Docosane
Tricosane
Tetracosane
Pentacosane
Hexacosane
Heptacosane
Octacosane
Nonacosane
Triacontane
Hentriacontane
Dotriacontane
Tritriacontane
Tetratriacontane
Pentatriacontane
Hexatriacontane
Heptatriacontane
Octatriacontane
Nonatriacontane
Tetracontane
ng/m
3
winter
summer

b) PAHs
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
Phenanthrene
Fluoranthene
Acephenanthrylene *
Pyrene
Benz(a)anthracene
Benzo(k)fluoranthene
Benzo(e)pyrene
Benzo(a)pyrene
Coronene
ng/m
3
winter
summer

Figure 7.2. Average organic species concentration of fine particles in winter and summer: (a)
n-alkanes; (b) PAH; (c) Hopanes and steranes and (d) Organic acids, error bars are the
standard deviations of average measured concentration over the studied sites.

250

c) Hopane and Steranes
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
17A(H)-22,29,30-Trisnorhop
17B(H)-21A(H)-30-Norhopa
17A(H)-21B(H)-Hopane
22S-Homohopane *
22R-Homohopane *
22S-Bishomohopane *
22R-Bishomohopane *
22S-Trishomohopane*
22R-Trishomohopane*
ABB-20R-C27-Cholestane
ABB-20S-C27-Cholestane
AAA-20S-C27-Cholestane *
ABB-20R-C28-Ergostane
ABB-20S-C28-Ergostane *
ABB-20R-C29-Sitostane
ABB-20S-C29-Sitostane *
ng/m
3
winter
summer

d) Acids
0
10
20
30
40
50
60
70
Tetradecanoic
acid (M)
Hexadecanoic
acid (M)
Octadecanoic
acid (M)
ng/m
3
winter
summer

Figure 7.2. Continued
251

Particulate hopanes and steranes mainly come from lubricating oil of both diesel powered
and gasoline-powered vehicles. In order to verify the origin of these compounds, EC
concentration can be used as a reference, as EC is mainly attributed to diesel powered
vehicles (Rogge et al., 1993; Schauer et al., 1996; Schauer et al., 2002). The average
hopane to EC ratio over all the sampling sites were 0.82±0.71 ng/µg in winter and
1.35±0.55 ng/µg in summer, which lie within the ranges of those measured near the I-710
(0.64 ng/µg) and near the I-110 freeways (2.23 ng/µg) (Phuleria et al., 2007), inferring
impacts from both diesel and gasoline powered vehicles. Moreover, hopane to OC ratios
were 0.21±0.17 ng/µg in winter and 0.24±0.09 ng/µg in summer. These ratios are also
reasonably comparable to roadway ratios measured near the I-110 (0.42 ng/µg) and I-710
(0.35 ng/µg) freeways (Phuleria et al., 2007). The impact of road traffic emissions will be
confirmed and quantify by means of the CMB model in subsequent sections. The sum of
concentrations of n-alkanoic acids in fine PM was 39.9±51.6 ng/m
3
in winter and
81.5±54.1 ng/m
3
in summer. The average concentration of Tetradecanoic acid (C18),
Hexadecanoic acid (C16) and Octadecanoic acid (C14), which are among the most
abundant measured organic acids (Ning et al., 2007) are presented in Figure 7.2. The
average summer time concentration of fine mode Tetradecanoic acid, Hexadecanoic acid
and Octadecanoic acid were respectively 2.1, 6.5 and 1.9 times of their corresponding
concentrations in winter. In addition to food cooking, which is known to be a source of
particulate organic acids, secondary photo-oxidation of organic gases and semi-volatile
species volatilizing from particulate phase are reported to be a major source of these acids
252

(Rogge et al., 1991; Pandis et al., 1993; Robinson et al., 2007). The effect of secondary
formation on the total concentration of these acids is thus evident.

The average concentrations of levoglucosan in fine PM measured in our sites were
3.6±2.2 ng/m
3
in winter and 2.2±1.0 ng/m
3
in summer. This compound is generated by
pyrolysis of cellulose which is generally attributed to biomass burning emissions
(Simoneit, 1999; Schauer and Cass, 2000; Fine et al., 2004). The relatively low measured
levels indicate that biomass burning is not a major source of OC in the study area, as
already pointed out by a previous study (Arhami et al., 2008) and confirmed by CMB
model later in the present work.

Regarding inorganic compounds, average particulate sulfate and ammonium
concentrations were 28% and 32% higher in summer than in winter due to the
condensation of gaseous sulfuric acid from the oxidation of sulfur oxide gases through
photochemical mechanisms (Hidy, 1994). A decline of 53% in nitrate concentrations in
the summer reflects the partitioning of ammonium nitrate to its gaseous precursors with
increasing temperature (Schaap et al., 2004) (Figure 7.1). From the group of trace
elements analyzed, vanadium and nickel levels are considered more relevant in the study
area and therefore discussed in detail due to their relationship with vessel emissions. V
and Ni are mainly generated from fuel oil combustion (Cass and McRae, 1983), which in
the study area can be assumed to be coming from ship emissions and refineries. A strong
association between the two species in the fine mode was obtained across all the sites in
253

both winter and summer (r = 0.90 in winter and 0.89 in summer), with V/Ni slopes of 4.2
and 2.9 in winter and summer, respectively (Table 7.2). Vanadium concentration is
highest at coastal sites (sites 5 and 7) and declines 55-62% at site 6 (USC, 40 km away
from the coast). This gradient indicates the major contribution of marine vessels to
vanadium concentrations, which will be confirmed with CMB model results explained
later.

In wintertime, vanadium and sulfur were highly correlated in the quasi-ultrafine mode
(r=0.72), whereas there was lack of correlation in the accumulation mode (r=0.03). These
results suggest that sulfur in quasi-ultrafine mode mainly comes from similar sources to
vanadium, i.e. marine vessels, , on the other hand in the accumulation mode it mainly has
photochemical origin causing low correlation with V (Arhami et al., 2008). In summer
time vanadium and sulfur were less correlated in the quasi-ultrafine mode (r=0.46) and
similarly to wintertime there was no correlation in the accumulation mode (r=0.09)
(Table 7.2). The low correlation in summer time even in lower mode particles can be
attributed to higher overall spatial mixing and enhanced dispersion due to meteorological
conditions in summer time.

There was no significant correlation between V and Ni with OC and EC in either summer
or wintertime (p>0.05). The weak association further supports the notion that marine
vessels are probably not the major contributor to particulate EC and OC as will be
confirmed by the results from CMB model.
254

Table 7.2 Correlation of fine particulate OC, EC, V, Ni and S over a) winter and b) summer
campaign (R is Pearson number and S is the slope of linear correlation).

a) Winter
OC EC V Ni S
R 0.58 -0.67 -0.44 -0.48
S 1.39 -0.10 -0.38 0.00
R -0.11 -0.08 -0.06
S -0.01 -0.03 0.00
R 0.90 0.24
S 4.17 0.01
R 0.54
S 0.00
R
S
Dependent variable
Independent Variable
OC
EC
S
V
Ni

b) Summer
OC EC V Ni S
R 0.71 -0.41 -0.30 0.28
S 3.11 -0.11 -0.25 0.00
R -0.09 0.08 0.48
S -0.01 0.02 0.00
R 0.89 0.51
S 2.96 0.01
R 0.55
S 0.00
R
S
Independent Variable
Dependent variable
OC
EC
S
V
Ni

255

7.4.2. Source apportionment
The collective contributions of the selected sources (LDV, HDV, road dust, ship
emissions and biomass burning) calculated by means of CMB model accounts for
55±12% to 130±26% of measured ambient fine OC. The proximity of the total to 100%
indicates that the selected sources are the main sources of organic matter in the study
sites. The lower values (higher proportion of unaccounted OC) correspond to the summer
period, probably not accounting for the higher contribution of secondary organic
compounds in summer than in winter (Gelencser et al., 2007), which cannot be
apportioned by the CMB model, given that it uses primary chemical profiles.

The total contribution of vehicular sources accounts for 52-120% of ambient fine OC at
the harbor sites (Sites 1 to 4) in winter (Figure 7.3). These contributions are in general
higher than in summer (42-60%) due to the higher stagnation of the atmosphere in winter,
causing the accumulation of pollutants. The contributions of LDV and HDV are quite
similar between sites located in the harbor area, with the exception of the higher LDV
contribution at S2 in winter, probably due to the major influence of stop-and-go traffic at
this site, which is more noticeable in winter with respect to summer owing to the higher
stagnation. Moreover, contributions of light duty vehicles at the background sites (sites 5
and 7) in summer are relatively higher in comparison to the rest of the sites, and in
comparison to the winter contribution at site 5 (site 7 data are not available for the winter).
This could be due to the more enhanced mixing of pollutants in the atmosphere in the
summer, which would increase the effect of traffic sources even in the coastal areas, as
256

well as to the additional contribution of off-road sources with similar chemical profile to
light duty vehicles to these sites. These additional off-road sources could be recreational
marine vessels, which would be used more often in the summertime, and therefore with
higher impact on the sites nearest to the coast (background sites 5 and 7). The distinction
between these on-road and off-road sources is difficult to be made by means of the CMB
model.

The contribution of road dust is similar at all sites, explaining at most 13% of ambient
fine OC either in summer or winter, with the exception of site 6 in summer, with a
contribution of 18% to ambient fine OC. This can be explained by the proximity of that
site to major freeways and sporadic higher wind speeds and drier conditions in summer
than in winter, which would enhance resuspension of road dust. In the harbor sites, the
summer-winter difference is not as pronounced because of the higher relative humidity in
these areas due to their proximity to the ocean. Biomass burning contributes less than
1.5% to ambient fine OC. Finally, the contribution of ship emissions to ambient fine OC
is also very low, between 0.04-0.07 µg/m
3
(1.1- 2.4 % of ambient fine OC) at the harbor
sites, and slightly higher at the background harbor sites (0.05-0.09 µg/m
3
, 2.3-4.1% of
ambient fine OC). These results are consistent with previous findings in Hong Kong,
showing that vessel contributions to ambient PM are very low (Yuan et al., 2006). Hence,
despite the proximity to the harbor, the levels of particulate organic matter in the study
area are controlled by traffic emissions rather than by ship emissions.

257

a)
0
1
2
3
4
5
OC (µg/m
3
)
Undetermined OC
Ships
Biomass burning
Road dust
HDV
LDV
w in sum w in sum w in sum w in sum w in sum w in sum w in sum
S1 S2 S3 S4 S5 S7 S6

b)
0
1
2
3
4
5
OC (µg/m
3
)
Undetermined OC
Ships
Biomass burning
HDV
LDV
w in sum w in sum w in sum w in sum w in sum w in sum w in sum
S1 S2 S3 S4 S5 S7 S6

Figure 7.3. Source apportionment to total (a) fine and (b) quasi-ultrafine OC (µg/m
3
) in
winter and summer at the seven sampling sites.

258

Source contribution obtained for the quasi-ultrafine fraction of OC shows that vehicular
sources are clearly predominant in this fraction (Figure 7.3). Although road dust was not
included as a quasi-ultrafine source as stated before, the contribution from this source is
expected to be very low according to the recorded Aluminum levels (i.e, the fitting
species for road dust source). Moreover, the total contribution of the considered sources
account for 60±10 to 162±28% of total quasi-ultrafine OC, which indicates that no major
sources are missing. The ratios of the LDV to HDV contributions in the quasi-ultrafine
fraction at each of the sites are similar, but slightly higher than those found for the fine
PM fraction, indicating that LDV have a higher impact on the quasi-ultrafine fraction.
Only at the urban site (site 6), the ratio LDV/HDV contribution is higher for the fine
fraction, indicating that HDV may have a higher impact on the quasi-ultrafine fraction at
this site.

As discussed in the methodology section, source contribution to ambient PM was
obtained from OC contributions and by considering secondary inorganic compounds
(Figure 7.4). The vehicular sources (heavy and light duty vehicles) together with the road
dust account for 24-54% of total fine PM at the harbor sites. This percentage increases up
to 48% at site 6 in summer, 62% of which is due to road dust, which can be explained by
sporadic increased wind speed and drier conditions in the summer, as noted earlier. The
contributions of ‘other organics’ are higher in summer than in winter, and are assumed to
be mostly in the form of secondary organic compounds. The contribution of sea spray
accounts for 3.6-16% of total fine PM at the harbor sites, with higher values at the
259

background harbor sites (8-18% of ambient fine PM) due to their proximity to the ocean.
As already discussed above, levels of nitrate are higher in winter due to the volatilization
of nitrate at higher temperatures (Schaap et al., 2004), whereas levels of sulfate are higher
in summer due to the higher photochemical activity that forms partially or fully
neutralized ammonium sulfate salts (Hidy, 1994).

The total contribution of vehicular sources accounts for 36 to 100% of quasi-ultrafine PM
in winter, whereas in summer it is in the range of 24-41%. The remaining mass is
explained mainly by the contributions of secondary organic matter and sulfate. The
contribution of sea spray and nitrate in the quasi-ultrafine fraction is very low compared
to the fine fraction. Ship emissions contributions, although low, are similar in the quasi-
ultrafine and fine fractions, indicating that these emissions are mainly in the quasi-
ultrafine fraction, because they represent freshly emitted aerosols from a combustion
source, as shown elsewhere (Petzold et al., 2008).

Results obtained in this study are consistent with previous results carried out in the same
area for PM
10
fraction in 1995 (Manchester-Neesvig et al., 2003). Hence, in the present
study vehicular sources (heavy duty + light duty vehicles) contribute to ambient fine OC
levels by 1.6-2.3 µg/m
3
in the area of Long Beach (excluding site 2, with a very high
contribution of light duty vehicles due to its specific location in a crossroad with traffic
lights). These values are comparable to 1.8 µg/m
3
of total PM
10
OC found by
Manchester-Neesvig et al. (Manchester-Neesvig et al., 2003) in winter. Vehicular
260

a)
0
2
4
6
8
10
12
14
16
18
PM2.5 (µg/m
3
)
Undetermined
Ammonium
Nitrate
Sulfate
Sea Spray
Other organics
Ships
Biomass burning
Road dust
HDV
LDV
w in sum w in sum w in sum w in sum w in sum w in sum w in sum
S1 S2 S3 S4 S5 S7 S6

b)
0
2
4
6
8
10
12
PM0.25 (µg/m
3
)
Undetermined
Ammonium
Nitrate
Sulfate
Sea Spray
Other organics
Ships
Biomass burning
HDV
LDV
w in sum w in sum w in sum w in sum w in sum w in sum w in sum
S1 S2 S3 S4 S5 S7 S6

Figure 7.4. Source apportionment to total (a) fine and (b) quasi-ultrafine PM (µg/m
3
) in
winter and summer at the seven sampling sites.

261

contributions in summer season are also comparable, 1.3-1.8 µg/m
3
in our study and 1.4
µg/m
3
according to Manchester-Neesvig et al. (Manchester-Neesvig et al., 2003).

Regarding the source apportionment of different PM species, hopanes are mainly
apportioned by light duty vehicles in the harbor area as well as in the marine background
sites. Only in the urban site near downtown in winter they are apportioned to a major
degree by heavy-duty vehicles (Figure 7.5). Benzo(e)pyrene is apportioned by LDV and
HDV at all sites. In contrast, EC mainly comes from HDV. We should note,
nevertheless , the relatively high contribution of LDV to ambient EC at the background
sites. As we argued earlier, this could be due to the additional contribution of other off-
road sources in the harbor with similar chemical profiles to LDV. Finally, the rest of the
fitting species used are very specific to a given source, hence they are apportioned by
more than 70% by one single source: levoglucosan from biomass burning, Al from road
dust and V from ship emissions. The undetermined mass for Ni in summer is relatively
high, which indicates that there are probably additional sources of this element during
this season.

7.5. CONCLUSIONS
Some seasonal variations have been found regarding the concentration of different
compounds: higher OC levels recorded in summer can be due to the additional
contribution of more intense photochemical activity leading to the formation of
secondary OC; higher levels of n-alkanes in winter than in summer are due to
262

a) b)
0%
20%
40%
60%
80%
100%

Sites 1-4
0%
20%
40%
60%
80%
100%
Undetermined
Ships
Biomass burning
Road dust
HDV
LDV
Sites 1-4

0%
20%
40%
60%
80%
100%

Site 5
0%
20%
40%
60%
80%
100%
Undetermined
Ships
Biomass burning
Road dust
HDV
LDV
Sites 5 and 7

0%
20%
40%
60%
80%
100%
EC
Benzo(e)pyrene
Coronene
17α ( H) - 22,29,30- T r i s nor hopane
17α ( H) - 21β ( H) - Hopane
22S-Homohopane
22R-Homohopane
Cholestane
Sitostane
Levoglucosan
Al
V
Ni

Site 6

0%
20%
40%
60%
80%
100%
EC
Benzo(e)pyrene
Coronene
17α ( H) - 22,29,30- T r i s nor hopane
17α ( H) - 21β ( H) - Hopane
22S-Homohopane
22R-Homohopane
Cholestane
Sitostane
Levoglucosan
Al
V
Ni
Undetermined
Ships
Biomass burning
Road dust
HDV
LDV
Site 6

Figure 7.5. Source apportionment to ambient concentration of species in fine fraction used
as fitting species in CMB model in (a) winter and (b) summer.

volatilization; higher levels of PAH in winter than in summer are due to volatilization and
reaction with oxidizing gases; higher concentrations of n-alkanoic acids in summer than
in winter evidence the effect of secondary formation; higher sulfate and ammonium
263

concentrations in summer are due to the condensation of gaseous sulfuric acid from the
oxidation of sulfur oxide gases through photochemical mechanisms; lower nitrate levels
in summer reflect the increase in the partitioning of ammonium nitrate to its gaseous
precursors with increasing temperature.

The source contribution to OC is dominated by the vehicular sources (42-120% of total
fine OC). The contribution of road dust is at most 13% of ambient fine OC and that of
biomass burning is lower than 1.5%. The quasi-ultrafine mode is also dominated by
vehicular sources. The contribution of ship emissions to ambient OC is less than 5% in
both quasi-ultrafine and fine fractions. Hence, despite the proximity to the harbor, the
levels of particulate organic matter in the study area are controlled by traffic emissions
rather than by ship emissions.

The source contribution to total PM is also dominated by the vehicular sources, hence
heavy and light duty vehicles together with road dust account for 24-54% of total fine
PM and for 24-100% of total quasi-ultrafine PM. The remaining mass is explained by the
contributions of secondary organic matter, secondary inorganic compounds (sulfate,
nitrate and ammonium) and, in the case of fine PM, also by sea spray. The contribution of
sea spray accounts for 3.6-16% of total fine PM, with higher values at the background
harbor sites (8-18% of ambient fine PM) due to their proximity to the ocean. Ship
emissions contributions, although low, are similar in the quasi-ultrafine (0.12-0.33 µg/m
3
)
264

and fine (0.18-0.42 µg/m
3
) fractions, indicating that these emissions are mainly in the
quasi-ultrafine fraction.
265

7.6. CHAPTER 7 REFERENCES
Agrawal, H., Malloy, Q. G. J., Welch, W. A., Miller, J. W., Cocker, D. R., 2008. In-Use
Gaseous and Particulate Matter Emissions from a Modern Ocean Going Container Vessel.
Atmospheric Environment, in press, doi:10.1016/j.atmosenv.2008.02.053
Amato, F., Pandolfi, M., Viana, M., Querol, X., Alastuey, A., Moreno, T., 2008. Spatial
and chemical patterns of PM10 in road dust deposited in urban environment.
Atmospheric Environment, submitted.
Arhami, M., Kuhn, T., Fine, P. M., Delfino, R. J.,Sioutas, C., 2006. Effects of sampling
artifacts and operating parameters on the performance of a semicontinuous particulate
elemental carbon/organic carbon monitor. Environmental Science & Technology 40, 945-
954.
Arhami, M., Sillanpää, M., Hu, S., Olson, M. R., Schauer, J. J., Sioutas, C., 2008. Size-
Segregated Inorganic and Organic Components of PM in the Communities of the Los
Angeles Harbor. Aerosol Science and Technology, accepted for publication.
Atkinson, R. W., Anderson, H. R., Sunyer, J., Ayres, J., Baccini, M., Vonk, J. M.,
Boumghar, A., Forastiere, F., Forsberg, B., Touloumi, G., Schwartz, J.,Katsouyanni, K.,
2001. Acute effects of particulate air pollution on respiratory admissions - Results from
the APHEA 2 project. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL
CARE MEDICINE 164, 1860-1866.
Brunekreef, B.,Forsberg, B., 2005. Epidemiological evidence of effects of coarse
airborne particles on health. European Respiratory Journal 26, 309-318.
Cass, G. R.,McRae, G. J., 1983. Source Receptor Reconciliation of Routine Air
Monitoring Data for Trace-Metals - an Emission Inventory Assisted Approach.
Environmental Science & Technology 17, 129-139.
Cooper, D. A., 2001. Exhaust emissions from high speed passenger ferries. Atmospheric
Environment 35, 4189-4200.
Delfino, R. J., Sioutas, C.,Malik, S., 2005. Potential role of ultrafine particles in
associations between airborne particle mass and cardiovascular health. Environmental
Health Perspectives 113, 934-946.
Dockery, D. W., Pope, C. A., Xu, X. P., Spengler, J. D., Ware, J. H., 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, 1753-1759.
Donaldson, K., Brown, D., Clouter, A., Duffin, R., MacNee, W., Renwick, L., Tran,
L.,Stone, V., 2002. The pulmonary toxicology of ultrafine particles. Journal of Aerosol
Medicine-Deposition Clearance and Effects in the Lung 15, 213-220.
266

El-Zanan, H. S., Lowenthal, D. H., Zielinska, B., Chow, J. C.,Kumar, N., 2005.
Determination of the organic aerosol mass to organic carbon ratio in IMPROVE samples.
Chemosphere 60, 485-496.
Fine, P. M., Cass, G. R.,Simoneit, B. R. T., 2004. Chemical characterization of fine
particle emissions from the fireplace combustion of wood types grown in the Midwestern
and Western United States. Environmental Engineering Science 21, 387-409.
Gelencser, A., May, B., Simpson, D., Sanchez-Ochoa, A., Kasper-Giebl, A., Puxbaum,
H., Caseiro, A., Pio, C.,Legrand, M., 2007. Source apportionment of PM2.5 organic
aerosol over Europe: Primary/secondary, natural/anthropogenic, and fossil/biogenic
origin. Journal of Geophysical Research-Atmospheres 112, -.
Grosjean, D., Fung, K.,Harrison, J., 1983. Interactions of Polycyclic Aromatic-
Hydrocarbons with Atmospheric Pollutants. Environmental Science & Technology 17,
673-679.
Hidy, G.M., 1994. Atmospheric sulphur and nitrogen oxides. Academic Press, San Diego,
California.
Hopke, P. K., Ito, K., Mar, T., Christensen, W. F., Eatough, D. J., Henry, R. C., Kim, E.,
Laden, F., Lall, R., Larson, T. V., Liu, H., Neas, L., Pinto, J., Stolzel, M., Suh, H.,
Paatero, P.,Thurston, G. D., 2006. PM source apportionment and health effects: 1.
Intercomparison of source apportionment results. Journal of Exposure Science and
Environmental Epidemiology 16, 275-286.
Jerrett, M., Burnett, R. T., Ma, R. J., Pope, C. A., Krewski, D., Newbold, K. B., Thurston,
G., Shi, Y. L., Finkelstein, N., Calle, E. E.,Thun, M. J., 2005. Spatial analysis of air
pollution and mortality in Los Angeles. Epidemiology 16, 727-736.
Kim, B. M., Cassmassi, J., Hogo, H.,Zeldin, M. D., 2001. Positive organic carbon
artifacts on filter medium during PM2.5 sampling in the South Coast Air Basin. Aerosol
Science and Technology 34, 35-41.
Kleeman, M. J., Hughes, L. S., Allen, J. O.,Cass, G. R., 1999. Source contributions to the
size and composition distribution of atmospheric particles: Southern California in
September 1996. Environmental Science & Technology 33, 4331-4341.
Kleeman, M. J., Robert, M. A., Riddle, S. G., Fine, P. M., Hays, M. D., Schauer, J. J.,
Hannigan, M. P., 2008. Size distribution of trace organic species emitted from biomass
combustion and meat charbroiling. Atmospheric Environment, in press,
doi:10.1016/j.atmosenv.2007.12.044.
Kuhn, T., Biswas, S.,Sioutas, C., 2005a. Diurnal and seasonal characteristics of particle
volatility and chemical composition in the vicinity of a light-duty vehicle freeway.
Atmospheric Environment 39, 7154-7166.
267

Kuhn, T., Krudysz, M., Zhu, Y., Fine, P. M., Hinds, W. C., Froines, J.,Sioutas, C., 2005b.
Volatility of indoor and outdoor ultrafine particulate matter near a freeway. Journal of
Aerosol Science 36, 291-302.
Li, N., Sioutas, C., Cho, A., Schmitz, D., Misra, C., Sempf, J., Wang, M. Y., Oberley, T.,
Froines, J.,Nel, A., 2003. Ultrafine particulate pollutants induce oxidative stress and
mitochondrial damage. Environmental Health Perspectives 111, 455-460.
Lough, G.C., Schauer, J.J., 2007. Sensitivity of source apportionment of urban particulate
matter to uncertainty in motor vehicle emissions profiles. Journal of the Air & Waste
Management Association 57, 10, 1200-1213.
Mader, B. T.,Pankow, J. F., 2002. Study of the effects of particle-phase carbon on the
gas/particle partitioning of sernivolatile organic compounds in the atmosphere using
controlled field experiments. Environmental Science & Technology 36, 5218-5228.
Manchester-Neesvig, J. B., Schauer, J. J.,Cass, G. R., 2003. The distribution of particle-
phase organic compounds in the atmosphere and their use for source apportionment
during the southern California children's health study. Journal of the Air & Waste
Management Association 53, 1065-1079.
Misra, C., Singh, M., Shen, S., Sioutas, C.,Hall, P. A., 2002. Development and evaluation
of a personal cascade impactor sampler (PCIS). Journal of Aerosol Science 33, 1027-
1047.
Ning, Z., Geller, M. D., Moore, K. F., Sheesley, R., Schauer, J. J.,Sioutas, C., 2007. Daily
variation in chemical characteristics of urban ultrafine aerosols and inference of their
sources. Environmental Science & Technology 41, 6000-6006.
Ntziachristos, L., Ning, Z., Geller, M. D.,Sioutas, C., 2007. Particle concentration and
characteristics near a major freeway with heavy-duty diesel traffic. Environmental
Science & Technology 41, 2223-2230.
Oberdorster, G., 2001. Pulmonary effects of inhaled ultrafine particles. International
Archives of Occupational and Environmental Health 74, 1-8.
Olson, D. A.,Norris, G. A., 2005. Sampling artifacts in measurement of elemental and
organic carbon: Low-volume sampling in indoor and outdoor environments. Atmospheric
Environment 39, 5437-5445.
Pandis, S. N., Wexler, A. S.,Seinfeld, J. H., 1993. Secondary Organic Aerosol Formation
and Transport .2. Predicting the Ambient Secondary Organic Aerosol-Size Distribution.
Atmospheric Environment Part a-General Topics 27, 2403-2416.
268

Petzold, A., Hasselbach, J., Lauer, P., Baumann, R., Franke, K., Gurk, C., Schlager, H.,
Weingartner, E., 2008. Experimental studies on particle emissions from cruising ship,
their characteristic properties, transformation and atmospheric lifetime in the marine
boundary layer. Atmospheric Chemistry and Physics, 8, 9, 2387-2403.
Phuleria, H. C., Sheesley, R. J., Schauer, J. J., Fine, P. M.,Sioutas, C., 2007. Roadside
measurements of size-segregated particulate organic compounds near gasoline and diesel-
dominated freeways in Los Angeles, CA. Atmospheric Environment 41, 4653-4671.
Polidori, A., Turpin, B. J., Lim, H. J., Cabada, J. C., Subramanian, R., Pandis, S.
N.,Robinson, A. L., 2006. Local and regional secondary organic aerosol: Insights from a
year of semi-continuous carbon measurements at Pittsburgh. Aerosol Science and
Technology 40, 861-872.
Robinson, A. L., Subramanian, R., Donahue, N. M., Bernardo-Bricker, A., Rogge, W. F.,
2006. Source apportionment of molecular markers and organic aerosol. 2. Biomass
smoke. Environmental Science and Technology, 40, 24, 7811-7819.
Robinson, A. L., Donahue, N. M., Shrivastava, M. K., Weitkamp, E. A., Sage, A. M.,
Grieshop, A. P., Lane, T. E., Pierce, J. R.,Pandis, S. N., 2007. Rethinking organic
aerosols: Semivolatile emissions and photochemical aging. Science 315, 1259-1262.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R.,Simoneit, B. R. T., 1993.
Sources of Fine Organic Aerosol .2. Noncatalyst and Catalyst-Equipped Automobiles and
Heavy-Duty Diesel Trucks. Environmental Science & Technology 27, 636-651.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R.,Simoneit, B. R. T., 1997.
Sources of fine organic aerosol .8. Boilers burning No. 2 distillate fuel oil. Environmental
Science & Technology 31, 2731-2737.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R.,Simonelt, B. R. T., 1991.
Sources of Fine Organic Aerosol .1. Charbroilers and Meat Cooking Operations.
Environmental Science & Technology 25, 1112-1125.
Samara, C., Kouimtzis, T., Tsitouridou, R., Kanias, G.,Simeonov, V., 2003. Chemical
mass balance source apportionment of PM10 in an industrialized urban area of Northern
Greece. Atmospheric Environment 37, 41-54.
Samoli, E., Analitis, A., Touloumi, G., Schwartz, J., Anderson, H. R., Sunyer, J., Bisanti,
L., Zmirou, D., Vonk, J. M., Pekkanen, J., Goodman, P., Paldy, A., Schindler,
C.,Katsouyanni, K., 2005. Estimating the exposure-response relationships between
particulate matter and mortality within the APHEA multicity project. Environmental
Health Perspectives 113, 88-95.
269

Sardar, S. B., Fine, P. M.,Sioutas, C., 2005. Seasonal and spatial variability of the size-
resolved chemical composition of particulate matter (PM10) in the Los Angeles Basin.
Journal of Geophysical Research-Atmospheres 110, -.
Schaap, M., Spindler, G., Schulz, M., Acker, K., Maenhaut, W., Berner, A., Wieprecht,
W., Streit, N., Muller, K., Bruggemann, E., Chi, X., Putaud, J. P., Hitzenberger, R.,
Puxbaum, H., Baltensperger, U.,ten Brink, H., 2004. Artefacts in the sampling of nitrate
studied in the "INTERCOMP" campaigns of EUROTRAC-AEROSOL. Atmospheric
Environment 38, 6487-6496.
Schauer, J. J.,Cass, G. R., 2000. Source apportionment of wintertime gas-phase and
particle-phase air pollutants using organic compounds as tracers. Environmental Science
& Technology 34, 1821-1832.
Schauer, J. J., Kleeman, M. J., Cass, G. R.,Simoneit, B. R. T., 2002. Measurement of
emissions from air pollution sources. 5. C-1-C-32 organic compounds from gasoline-
powered motor vehicles. Environmental Science & Technology 36, 1169-1180.
Schauer, J. J., Rogge, W. F., Hildemann, L. M., Mazurek, M. A.,Cass, G. R., 1996.
Source apportionment of airborne particulate matter using organic compounds as tracers.
Atmospheric Environment 30, 3837-3855.
Seinfeld, J.,Pandis, S. 1998. Atmospheric Chemistry and Physics, Wiley, New York.
Sheesley, R. J., Schauer, J. J., Zheng, M.,Wang, B., 2007. Sensitivity of molecular
marker-based CMB models to biomass burning source profiles. Atmospheric
Environment 41, 9050-9063.
Simoneit, B. R. T., 1999. A review of biomarker compounds as source indicators and
tracers for air pollution. Environmental Science and Pollution Research 6, 159-169.
Singh, M., Misra, C.,Sioutas, C., 2003. Field evaluation of a personal cascade impactor
sampler (PCIS). Atmospheric Environment 37, 4781-4793.
Subramanian, R., Donahue, N. M., Bernardo-Bricker, A., Rogge, W. F., Robinson, A. L.,
2006. Contribution of motor vehicle emissions to organic carbon and fine particle mass in
Pittsburgh, Pennsylvania: Effects of varying source profiles and seasonal trends in
ambient marker concentrations. Atmospheric Environment 40, 8002-8019.
Toner, S. M., Shields, L. G., Sodeman, D. A.,Prather, K. A., 2008. Using mass spectral
source signatures to apportion exhaust particles from gasoline and diesel powered
vehicles in a freeway study using UF-ATOFMS. Atmospheric Environment 42, 568-581.
Turpin, B. J., Saxena, P.,Andrews, E., 2000. Measuring and simulating particulate
organics in the atmosphere: problems and prospects. Atmospheric Environment 34, 2983-
3013.
270

Watson, J. G., Chow, J. C., Lu, Z. Q., Fujita, E. M., Lowenthal, D. H., Lawson, D.
R.,Ashbaugh, L. L., 1994. Chemical Mass-Balance Source Apportionment of Pm(10)
during the Southern California Air-Quality Study. Aerosol Science and Technology 21,
1-36.
Watson, J. G., Zhu, T., Chow, J. C., Engelbrecht, J., Fujita, E. M.,Wilson, W. E., 2002.
Receptor modeling application framework for particle source apportionment.
Chemosphere 49, 1093-1136.
Yuan, Z., Lau, A., K., H., Zhang, H., Yu, J. Z., Louie, P. K. K., Fung, J. C., H., 2006.
Identification and spatiotemporal variations of dominant PM10 sources over Hong Kong.
Atmospheric Environment 40, 1803-1815.
271
Chapter 8.
Conclusions and Future Research Directions

8.1. SUMMARY AND CONCLUSION
Although several epidemiological studies have shown adverse health effects of
atmospheric particulate matter (PM), it has been difficult to address which component of
particulate influences the health risk and what air quality regulation should be adopted.
This difficulty is due to insufficient knowledge about the composition and properties of
particulate matter and human exposure to them. Furthermore, if health effects can be
linked to certain sources of particulate matter, such information would be highly valuable
for targeting control strategies. A number of outstanding reasons that drives these
important deficiencies in knowledge and were targeted in this thesis are:

a) Because the atmospheric aerosols form a highly multi-component system, composition
and properties of aerosols and their variations over time and space can not be ascertained
by standard monitoring system.

b) The size of the particles has a strong influence on the type and intensity of health
effect caused. Particularly, ultrafine particles are shown to have more toxicity potential
and more strongly associated with cardiovascular and respiratory health outcomes
(Araujo et al. 2007) compared to larger particles. So far, there is little research to support
this finding (reviewed by Delfino et al. 2005; Weichenthal et al. 2007). Another problem
is that the importance of particle size and chemistry has been limited by reliance on
272
government monitoring of particle mass at two size cuts, PM
10
(<10 μm and PM
2.5
(<2.5
μm).

c) Although people spend around 85-90% of their time in indoor environment, air
pollution data from outdoor (ambient) sites have been used for air quality standards
which led to exposure misclassification. Accordingly, it is crucial to understand the
composition and sources of both indoor and outdoor PM, their relationships and the
association of indoor and outdoor concentrations with real personal exposure levels.
Besides, no studies on apportionment of the sources of submicron indoor PM have been
conducted.

d) Ascertaining the true risk associated with exposure to PM is difficult, mainly because
the concentrations of ambient particles and those of their gaseous co-pollutants are often
well correlated, and estimates of the health risks associated with PM exposure may be
confounded by these gaseous species (Sarnat et al. 2000; Green et al. 2002; Sarnat et al.
2005).

The air pollution of the community near Los Angeles-Long Beach harbor is one of the
areas of particular concern regarding PM pollution which constitutes the busiest harbor in
the US and the fifth in the world, and therefore the area is affected by several PM
sources. The potential for complex pollutant concentration gradients and high exposure
conditions cannot be identified by conventional monitoring approaches. Accordingly, it is
crucial to assess the exposure gradient of the community in the surrounding environment,
273
since using only community PM average concentrations to determine the health effects
resulting from PM exposure may lead to non accurate results. In this context, previous
studies have been carried out to identify PM sources, characterization and variations in
the aforementioned area (Kleeman et al. 1999; Manchester-Neesvig et al. 2003). These
studies were spatially constrained by the fact that they were based on data collected in
limited number of sampling site in Long Beach or were not concurrent. Moreover, source
apportionment of ultrafine fractions has not been conducted in this area. Nonetheless,
there are not many studies on the micro-environmental spatial variations of chemical
components and physical characteristics of particles in such complex environments. For
the development and implementation of PM policies that will be protective of the
environment and human health, regulators require scientific knowledge of the strengths,
spatial distribution and variability of the major sources of this pollutant. This information
allows to design effective mitigation strategies on the local- and meso-scale level, and to
evaluate human exposure to this pollutant and thus assess its health-related risks (Watson
et al. 2002; Hopke et al. 2006). Thus, concurrent and more extensive sampling and
studies in such a complex urban air basin is desirable.

The conducted studies to address the above mentioned knowledge deficiencies and needs
are summarized and their results and conclusions are briefed in the following. The
measurements and studies were conducted in the Los Angeles Basin which is a
megalopolis of about 15 million inhabitants and has one of the most polluted atmospheres
in the US due to the contributions of a multitude of traffic and other combustion sources.

274
In first part of the study (Chapter 2), the performance of the Sunset Laboratory Inc. semi-
continuous EC/OC monitor was assessed in a field setting and the influences of positive
and negative sampling artifacts were investigated. The carbonaceous component of
atmospheric particulate matter is considered very important with respect to the observed
adverse health effects of PM. The organic carbon (OC) and elemental carbon (EC)
components of PM have traditionally been measured off-line subsequent to daily, time-
integrated particle collection on filters. However, the sub-daily or hourly variability of
EC and OC can help to assess the variability of sources, ambient levels, and human
exposure. The semi continues monitors were deployed near downtown Los Angeles, in a
location representing typical urban pollution. The results show that the semi-continuous
EC/OC field analyzer is a reliable instrument for the measurement of the carbonaceous
component of PM. The positive artifacts were almost constant and relatively high for the
short sampling time of 45 min; more than 50% of un-denuded OC concentrations could
be attributed to artifacts. These artifacts were virtually eliminated with the use of a
denuder. The inlets of the EC/OC analyzers can be easily modified to sample different
particle size fractions. Thus, multiple instruments allow for time-resolved, size-
fractionated measurements of the carbonaceous components of PM. This instrument was
used in rest of the study to get an insight in the characteristics of fine and ultrafine
particles to better assess the human exposure to the carbonaceous components of
particles.

Following part of the study (Chapter 3, 4 and 5) focused on exposure assessment and
source characterization of size fractionated particulate matter and their. This part of the
275
study conducted within the Cardiovascular Health and Air Pollution Study (CHAPS), a
multi-disciplinary project whose goals were to investigate the effects of micro-
environmental exposures to PM on cardiovascular outcomes in elderly retirees affected
by coronary heart disease (CHD). The elderly population with CHD is likely to be among
the most vulnerable to the adverse effects of particulate air pollutants. In this study the
physical and chemical characteristics of indoor, outdoor, and personal quasi-ultrafine
(<0.25μm), accumulation (0.25-2.5 μm), and coarse (2.5-10 μm) mode particles were
measured in four different retirement communities in southern California between 2005
and 2007. Three of these communities were in the San Gabriel Valley, CA (sites San
Gabriel 1, 2 and 3 for groups G1, G2 and G3 respectively) and the fourth in Riverside,
CA (group G4). Two 6-week sampling campaigns were conducted at each location. Phase
1 (P1 or warmer phase) of each campaign was conducted during a warmer period
(including summer and early fall), whereas phase 2 (P2 or colder phase) was conducted
during a cooler period (including late fall and winter). The San Gabriel Valley sites were
closer to downtown Los Angeles and major freeways and Riverside site was further away
from downtown Los Angeles and any major freeways.

Personal size fractionated PM samples were collected daily for 67 elderly retirees with a
history of coronary artery disease. All participants were 71 years of age or older,
nonsmokers, and with no home exposure to environmental tobacco smoke. Each subject
was followed for two 5-day sampling periods during the 2 different phases of the study.
Concurrent to personal sampling, daily indoor and outdoor size fractionated PM samples
were collected. In addition, real time concentration of fine particulate matter mass, OC,
276
and EC, particle number (PN), ozone (O
3
), carbon monoxide (CO) and nitrogen oxides
(NO and NO
2
) were measured at indoor and outdoor of these communities. Indoor and
outdoor samples were analyzed to find their chemical composition and toxicological
properties. This study provided one of the most extensive data set of its type for air
pollution studies.

In Chapter 3 real time data collected at site San Gabriel 1 (G1) and San Gabriel 2 (G2)
during 2005 and 2006 were analyzed. Measured indoor and outdoor concentrations of
PM
2.5
, OC, EC, PN, O
3
, CO and NO
X
were generally comparable, although at San
Gabriel 2 a substantial peak in indoor OC, PN and PM
2.5
(probably from cooking) was
typically observed between 06:00 and 09:00am. The contributions of primary and
secondary OC (SOA) to measured outdoor OC were estimated from collected OC and EC
concentrations using EC as a tracer of primary combustion-generated OC (i.e. “EC tracer
method”). The study average outdoor SOA accounted for 40% of outdoor particulate OC
(40-45% in the summer and 32-40% in the winter). Air exchange rates (AER; h
-1
) and
infiltration factors (F
inf
; dimensionless) at each site were also determined. Estimated F
inf

and measured particle concentrations were then used in a single compartment mass
balance model to assess the contributions of indoor and/or outdoor sources to measured
indoor OC, EC, PM
2.5
and PN. The average percentage contributions of indoor SOA of
outdoor origin to measured indoor OC were about 35% at San Gabriel 1 and about 45%
at San Gabriel 2. On average, 36 to 44% of measured indoor OC was comprised of
outdoor-generated primary OC.

277
Associations between indoor, outdoor, and personal size-fractionated PM and OC, EC,
particle number (PN), O
3
, CO, NO, NOx, and other important pollutants of both indoor
and outdoor origin were evaluated, and the role of gaseous co-pollutants as surrogates of
personal size-fractionated PM

exposures assessed (Chapter 4). Linear mixed effects
models and Spearman’s correlation coefficients were then used to elucidate the
relationships among size segregated PM levels, their particle components, and gaseous
co-pollutants. Seasonal and spatial differences in the concentrations of all measured
species were evaluated at all sites based on p-values for product terms. Our modeling
results have shown that outdoor and indoor levels of CO, NO
2
and NOx were better
correlated with measured indoor, outdoor and personal quasi-UF PM levels than
accumulation mode and coarse mode PM. This better correlation is due to more similarity
in sources and transportation mechanism. Indoor concentrations of outdoor origin of
important carbonaceous species such as EC, OC, and OCpri were more strongly
correlated with personal quasi-UF and accumulation mode PM, than their corresponding
indoor concentrations of indoor origin. This is because indoor sources were probably not
significant contributors to personal exposure of accumulation and quasi UF PM, which is
predominantly influenced by primary pollutants produced/emitted outdoors. These results
are important, because other CHAPS investigators have suggested that traffic-related
emission sources of PM
2.5
OCpri, and quasi-UF particles lead to increases in systemic
inflammation, platelet activation, and decreases in erythrocyte antioxidant activity in
elderly people with a history of coronary artery disease. Overall, our data analysis
suggests that investigating the correlations among size-segregated indoor, outdoor and
personal PM, their specific components, and concurrently measured gaseous co-
278
pollutants is a challenging endeavor. These associations depend on a number of factors
that vary in space and time. Thus, results from time-series epidemiologic studies that
include both gaseous and particulate pollutant concentrations in the models should be
interpreted with caution.

Chapter 5 focuses on the characterization of the sources, organic constituents and indoor
and outdoor relationships of quasi-ultrafine PM. In contrary to n-alkanes and n-alkanoic
acid, the average indoor/outdoor ratio of most of the measured PAHs, hopanes and
steranes were close to- or slightly lower than 1, and indoor-outdoor correlation
coefficients (R) were always positive and for most of these components moderate to
strong (median R was 0.60 for PAHs and 0.74 for hopanes and steranes). This suggests
that indoor PAHs, hopane and steranes were mainly from outdoor origin, whereas indoor
n-alkanes and n-alkanoic acide were significantly influenced by indoor sources. The
Chemical Mass Balance (CMB) model was applied to both indoor and outdoor speciated
chemical measurements of quasi-ultrafine PM. Vehicular sources had the highest
contribution to PM
0.25
among the apportioned sources for both indoor and outdoor
particles at all sites (on average 24-47%). The contribution of mobile sources to indoor
levels was similar to their corresponding outdoor estimates. A major implication of these
findings is that, even if people (particularly the elderly retired population of our study)
generally spend most of their time indoors, a major portion of the PM
0.25
particles to
which they are exposed comes from outdoor mobile sources. The significance of this
conclusion is supported by the fact that indoor infiltrated particles from mobile sources
were more strongly associated with the adverse health effects observed in the elderly
279
subjects living in the studied retirement communities compared to uncharacterized indoor
particles.

In the final part of this study (Chapter 6 and 7) we characterize the physicochemical
properties and sources of size fractionated PM and their spatial and seasonal variability.
Size fractionated PM samples were collected concurrently at 7 sites in the southern Los
Angeles basin for two different phases throughout the year. The studied region was the
Los Angeles Ports complex consisting of the port of Long Beach and the port of Los
Angeles which together is the busiest harbor in the US and the fifth in the world. Due to
the high levels of particulate matter emitted from many sources in the vicinity of these
ports and to their projected massive expansion, the Harbor area will be the focus of future
governmental regulations. Four of the sites were located within the communities of
Wilmington and Long Beach, two sites were located at a background area in the harbor of
Los Angeles and Long Beach, and one more site was located further downwind, near
downtown Los Angeles, representing urban downtown LA, influenced by mostly traffic
sources. Winter measurements were obtained during a 7-week period between March and
May 2007, and summer measurements corresponded to a 6-week period between July
and September 2007. Coarse, accumulation, and quasi-ultrafine mode particles were
collected at each site. Samples were analyzed for organic and elemental carbon content,
organic species, inorganic ions, and water soluble and total elements.

Chapter 6 characterizes the chemical composition of ultrafine, accumulation mode and
coarse particles across this community. Results from the gravimetric and chemical
280
analysis are verified by means of chemical mass closure (CMC). The major mass
contributions in the quasi-UF fraction were particulate organic matter (POM), nss-sulfate
and EC; in the accumulation mode fraction were nss-sulfate, sea salt, POM and nitrate;
and in the coarse fraction were sea salt and insoluble soil. In general, PM and its
components in accumulation mode showed relatively lower spatial variability compare to
the quasi-UF and the coarse modes. The carbon preference index (CPI) for quasi-UF and
accumulation mode particles varied from 0.65 to 1.84 among sites, which is in the range
of previous findings in areas with high influence of anthropogenic sources. In sites
located close to harbor, the average n-Alkanes and PAHs levels were respectively about 3
and 5 times higher than their corresponding levels at a site located in vicinity of harbor,
but upwind of most of local sources. The ratio of hopanes to EC and hopanes to OC over
all the sites were in the range of previous roadside measurements near freeways with
variable volume of diesel truck traffic. High overall correlations of vanadium with nickel
(R=0.9), as well as a considerable gradient of vanadium concentrations with distance
from the coast, suggests marine vessels as the major sources of these elements. These
results provide new insight into the variation of size-segregated chemical composition of
PM over the studied area.

Finally (in Chapter 7) we identify and quantify fine and quasi-ultrafine particulate matter
sources in the Los Angeles-Long Beach harbor area, and the spatial and seasonal
differences in PM patterns and composition. A Chemical Mass Balance model was
applied to speciated chemical measurements of quasi-ultrafine and fine particulate matter
from seven different sites. The sources included in the CMB model were: light duty
281
vehicles (LDV), heavy duty vehicles (HDV), road dust (RD), biomass burning and ship
emissions. The model predictions of the LDV and HDV source contributions accounted,
on average, for 83% of total fine OC in winter and for 70% in summer, whereas ship
emissions contribution was lower than 5% of total OC at all sites. In the quasi-ultrafine
mode, the vehicular sources accounted for 118% in winter and 103% in summer. Spatial
variation of source contributions was not very pronounced with the exception of some
specific sites. In terms of total fine PM, vehicular sources together with road dust explain
up to 54% of the mass, whereas ship contribution is lower than 5% of total fine PM mass.
Our results clearly indicate that, although ship emissions can be significant, PM
emissions in the area of the largest US harbor are dominated by vehicular sources.

Some seasonal variations have been found regarding the concentration of different
compounds: higher OC levels recorded in summer can be due to the additional
contribution of more intense photochemical activity leading to the formation of
secondary OC; higher levels of n-alkanes in winter than in summer are due to
volatilization; higher levels of PAH in winter than in summer are due to volatilization and
reaction with oxidizing gases; higher concentrations of n-alkanoic acids in summer than
in winter evidence the effect of secondary formation; higher sulfate and ammonium
concentrations in summer are due to the condensation of gaseous sulfuric acid from the
oxidation of sulfur oxide gases through photochemical mechanisms; lower nitrate levels
in summer reflect the increase in the partitioning of ammonium nitrate to its gaseous
precursors with increasing temperature. The results from this study will provide with
282
useful information for control strategies and will assist future toxicological studies that
are planned in this area.

8.2. FUTURE RESEARCH DIRECTIONS
This thesis has put an effort to improve our scientific understanding of composition and
properties of particulate matter, their sources and human exposure to them. Considering
the complexity of PM metrics, several questions remained unanswered and should be
areas of active research in future. These research areas are listed below.

- It is still not definitely known which component of particulate influences the health
risk and what air quality regulation should be adopted and such information would be
highly valuable for targeting control strategies. Some epidemiological studies have been
performed (or are ongoing) to find the link of composition and properties found in this
study to health effect (e.g. Delfino et al., 2008). However, considering the importance of
ascertaining this link in protecting human health and life and current knowledge
deficiency, more studies in this area are required.

- A major portion of this thesis focused on finding the real exposure to particulate
matter, its composition and properties. In this regard, several findings were achieved
which were discussed in previous sections. However, more air pollution and personal
exposure studies are required to refine, verify and improve this finding for different
locations, home environments, and population groups.

283
- Toxicity analysis (e.g. ROS and DTT assays) of the samples that were collected in
this study to find the toxicity of different size fractions of PM, their correlations with
organics, elements and other constituents of PM or gaseous pollutants and their link to
different sources is recommended. This information will be very useful in targeting
control strategies and regulatory applications.

- More research to find accurate and convenient instruments, which measure detailed
particle characteristics, are necessary to better assess ambient concentrations and human
exposures. In particular, continuous or semi-continuous monitors, providing data on
hourly or sub-hourly time scales, are generally preferred over off-line analyses. Such
monitors can not only capture important short-term variations in particle properties, but
also can prove more economical to operate by reducing sampling site visits and
eliminating the need for laboratory facilities and analysis costs.

- It was shown that even if people spend most of their time indoors, a major portion
of the particles to which they are exposed can come from outdoor mobile sources. Also it
was found that indoor infiltrated particles from mobile sources were more strongly
associated with the adverse health effects observed in the elderly subjects living in the
studied retirement communities compared to uncharacterized indoor particles. Thus, it is
very important to implement more researches to improve the control technologies for
vehicles. Also, more research to come up with new technologies to reduce the infiltration
efficiency of particles to indoor environment is required. As an example filters have been
implemented on air conditioning units to address this need; however more studies are
284
required to find the efficiency of this system, improve it and come up with better
methods.

- The importance of particle size and real exposure has been limited by reliance on
government monitoring and standards of only outdoor (ambient) PM
10
and PM
2.5
.
Furthermore, the results of this study showed that the real exposure could vary with size
of PM and could be different from outdoor levels. More size fractionated indoor, outdoor
and personal PM exposure measurements and studies are necessary to improve the
current regulations. In particular, submicron particles are shown to have more toxicity
potential and more strongly associated with cardiovascular and respiratory health
outcomes (Araujo et al. 2007) compared to larger particles. Thus, more studies to
evaluate the need for new regulation for ultrafine particles are required.

- Studies are needed to advance mitigation technologies such as air purifier to remove
the particles in indoor environment where the people spend most of their time. To this
end, exposure studies to find the efficiency of available purifiers and technologies are
required to be utilized in advancing the current technologies for better efficiency.

- More studies are required to find personal level chemical composition of size
fractionated PM and to apportion the sources to personal levels. These results are very
important for control strategies, and the extensive personal samples collected and
archived during CHAPS (as a part of this thesis) can be used for such a study.

285
- Association of indoor, outdoor and personal levels depend on a number of factors
that vary in space and time, such as: the relative contribution of UF, accumulation and
coarse mode PM to the measured PM concentrations, the seasonal variability of primary
and secondary emission sources, the presence of indoor sources of PM and gaseous co-
pollutants (e.g. cooking), home characteristics (e.g. ventilation conditions and household
characteristics), and proximity to the emission sources. The analysis of these associations
is further complicated by the amount of time spent indoors (highly variable among
subjects, especially in the warmer season), which is also a critical component in
determining exposure. Thus, results from time-series epidemiologic studies that include
both gaseous and particulate pollutant concentrations in the models should be interpreted
with caution. Future research should focus on how these specific factors affect the
strength of between-pollutant associations for individuals living in different locations

- Most of conducted toxicity studies have been focused on toxicity of outdoor
generated particles, however not many studies are available on indoor generated particles
and more studies are recommended. In this regard, the correlation between indoor
generated particles which were estimated in this study and toxicity of indoor particles can
be assessed and compared to the same correlation for outdoor generated particles.

- As a part of this thesis a source apportionment analysis was conducted only on the
quasi-ultrafine fraction of samples collected during CHAPS. However, applying source
apportionment on fine and coarse mode particles and compare it to the results for quasi-
286
ultrafine mode is desirable. Also other models than CMB (such as PMF) to identify the
sources are suggested to compare with the results from this thesis.

- Studies to find better, more extensive and size fractionated source profiles are
required to improve future source apportionment studies.

- A portion of particles could not be apportioned to any sources, which in some cases
was more than 50% of particle mass (especially in ultrafine mode). Studies are required
on origin and sources of this unapportioned fraction or refining source apportionment
models to cover this fraction.

- There are other mega cities with complex air pollution environment around the
world, which not many studies have been conducted there and they have been among
most polluted city in the world (such as Tehran, Capital of Iran). Similar studies in these
cities are highly desirable and the results can be compared to our findings to find better
insight to the composition and properties of PM and their variation over space.

287
8.2. CHAPTER 8 REFERENCES
Araujo, J. A., B. Barajas, M. Kleinman, X. P. Wang, B. J. Bennett, K. W. Gong, M.
Navab, J. Harkema, C. Sioutas, A. J. Lusis and A. Nel (2007). Ambient particulate
pollutants in the ultrafine range promote atherosclerosis and systemic oxidative stress,
Arteriosclerosis Thrombosis and Vascular Biology 27(6): E39-E39.
Delfino, R. J., C. Sioutas and S. Malik (2005). Potential role of ultrafine particles in
associations between airborne particle mass and cardiovascular health, Environmental
Health Perspectives 113(8): 934-946.
Green, L. C., E. A. C. Crouch, M. R. Ames and T. L. Lash (2002). What's wrong with the
National Ambient Air Quality Standard (NAAQS) for fine particulate matter (PM2.5)?,
Regulatory Toxicology and Pharmacology 35(3): 327-337.
Hopke, P. K., K. Ito, T. Mar, W. F. Christensen, D. J. Eatough, R. C. Henry, E. Kim, F.
Laden, R. Lall, T. V. Larson, H. Liu, L. Neas, J. Pinto, M. Stolzel, H. Suh, P. Paatero and
G. D. Thurston (2006). PM source apportionment and health effects: 1. Intercomparison
of source apportionment results, Journal of Exposure Science and Environmental
Epidemiology 16(3): 275-286.
Kleeman, M. J., L. S. Hughes, J. O. Allen and G. R. Cass (1999). Source contributions to
the size and composition distribution of atmospheric particles: Southern California in
September 1996, Environmental Science & Technology 33(23): 4331-4341.
Manchester-Neesvig, J. B., J. J. Schauer and G. R. Cass (2003). The distribution of
particle-phase organic compounds in the atmosphere and their use for source
apportionment during the southern California children's health study, Journal of the Air
& Waste Management Association 53(9): 1065-1079.
Sarnat, J. A., K. W. Brown, J. Schwartz, B. A. Coull and P. Koutrakis (2005). Ambient
gas concentrations and personal particulate matter exposures - Implications for studying
the health effects of particles, Epidemiology 16(3): 385-395.
Sarnat, J. A., P. Koutrakis and H. H. Suh (2000). Assessing the relationship between
personal particulate and gaseous exposures of senior citizens living in Baltimore, MD,
Journal of the Air & Waste Management Association 50(7): 1184-1198.
Watson, J. G., T. Zhu, J. C. Chow, J. Engelbrecht, E. M. Fujita and W. E. Wilson (2002).
Receptor modeling application framework for particle source apportionment,
Chemosphere 49(9): 1093-1136.
Weichenthal, S., A. Dufresne and C. Infante-Rivard (2007). Indoor ultrafine particles and
childhood asthma: exploring a potential public health concern, Indoor Air 17(2): 81-91.

288
Bibliography

Abt, E., Suh, H. H., Catalano, P., and Koutrakis, P. (2000). Relative Contribution of
Outdoor and Indoor Particle Sources to Indoor Concentrations. Environmental Science &
Technology 34(17): 3579-3587.
Agrawal, H., Malloy, Q. G. J., Welch, W. A., Miller, J. W., and Cocker, D. R. (2008). In-
Use Gaseous and Particulate Matter Emissions from a Modern Ocean Going Container
Vessel. Atmospheric Environment 42(21): 5504-5510.
Allen, R., Larson, T., Sheppard, L., Wallace, L., Liu L.-J.S (2003). Use of Real-Time
Light Scattering Data to Estimate the Contribution of Infiltrated and Indoor-Generated
Particles to Indoor Air, Environmental Science and Technology., 37: 3484-3492.
Allen, R., Wallace, L., Larson, T., Sheppard, L., Liu L.-J.S (2006). Evaluation of the
Recursive Model Approach for Estimating Particulate Matter Infiltration Efficiencies
Using Continuous Light Scattering Data, Journal of Exposure Science and
Environmental Epidemiology., In Press.
Anderson, R. R., Martello, D. V., Rohar, P. C., Strazisar, B. R., Tamilia, J. P., Waldner,
K., White, C. M., Modey, W. K., Mangelson, N. F., and Eatough, D. J. (2002). Sources
and Composition of Pm2.5 at the National Energy Technology Laboratory in Pittsburgh
During July and August 2000. Energy & Fuels 16(2): 261-269.
Araujo, J. A., Barajas, B., Kleinman, M., Wang, X. P., Bennett, B. J., Gong, K. W.,
Navab, M., Harkema, J., Sioutas, C., Lusis, A. J., and Nel, A. (2007). Ambient Particulate
Pollutants in the Ultrafine Range Promote Atherosclerosis and Systemic Oxidative Stress.
Arteriosclerosis Thrombosis and Vascular Biology 27(6): E39-E39.
Arhami, M., Kuhn, T., Fine, P. M., Delfino, R. J., and Sioutas, C. (2006). Effects of
Sampling Artifacts and Operating Parameters on the Performance of a Semicontinuous
Particulate Elemental Carbon/Organic Carbon Monitor. Environmental Science &
Technology 40(3): 945-954.
Arhami, M., Sillanpää, M., Hu, S., Olson, M. R., Schauer, J. J., and Sioutas, C. (2009).
Size-Segregated Inorganic and Organic Components of Pm in the Communities of the
Los Angeles Harbor. Aerosol Science and Technology 43(2): 145-160.
Atkinson, R. W., Anderson, H. R., Sunyer, J., Ayres, J., Baccini, M., Vonk, J. M.,
Boumghar, A., Forastiere, F., Forsberg, B., Touloumi, G., Schwartz, J., and Katsouyanni,
K. (2001). Acute Effects of Particulate Air Pollution on Respiratory Admissions - Results
from the Aphea 2 Project. American Journal of Respiratory and Critical Care Medicine
164(10): 1860-1866.
289
Bae, M. S., Schauer, J. J., DeMinter, J. T., Turner, J. R., Smith, D., and Cary, R. A.
(2004). Validation of a Semi-Continuous Instrument for Elemental Carbon and Organic
Carbon Using a Thermal-Optical Method. Atmospheric Environment 38(18): 2885-2893.
Ballach, J., Hitzenberger, R., Schultz, E., and Jaeschke, W. (2001). Development of an
Improved Optical Transmission Technique for Black Carbon (Bc) Analysis. Atmospheric
Environment 35(12): 2089-2100.
Batalha, J. R. F., Saldiva, P. H. N., Clarke, R. W., Coull, B. A., Stearns, R. C., Lawrence,
J., Murthy, G. G. K., Koutrakis, P., and Godleski, J. J. (2002). Concentrated Ambient Air
Particles Induce Vasoconstriction of Small Pulmonary Arteries in Rats. Environmental
Health Perspectives 110(12): 1191-1197.
Birch, M. E. (1998). Analysis of Carbonaceous Aerosols: Interlaboratory Comparison.
Analyst 123(5): 851-857.
Birch, M. E., and Cary, R. A. (1996). Elemental Carbon-Based Method for Monitoring
Occupational Exposures to Particulate Diesel Exhaust. Aerosol Science and Technology
25(3): 221-241.
Brook, J. R., Dann, T. F., and Burnett, R. T. (1997). The Relationship among Tsp,
Pm(10), Pm(2.5), and Inorganic Constituents of Atmospheric Particulate Matter at
Multiple Canadian Locations. Journal of the Air & Waste Management Association
47(1): 2-19.
Brunekreef, B., and Forsberg, B. (2005). Epidemiological Evidence of Effects of Coarse
Airborne Particles on Health. European Respiratory Journal 26(2): 309-318.
Burnett, R. T., Brook, J., Dann, T., Delocla, C., Philips, O., Cakmak, S., Vincent, R.,
Goldberg, M. S., and Krewski, D. (2000). Association between Particulate- and Gas-
Phase Components of Urban Air Pollution and Daily Mortality in Eight Canadian Cities.
Inhalation Toxicology 12: 15-39.
Cabada J.C, Pandis S.N., Subramanian R., Robinson A.L., Polidori A., Turpin B. (2004).
Estimating the Secondary Organic Aerosol Contribution to PM2.5 Using the EC Tracer
Method, Aerosol Sci. and Technol. 38(S1):140–155.
Carlton, A.G., Turpin, B.J., Lim, H-J. (2006). Altieri, K.E., Seitzinger, S. Link between
isoprene and secondary organic aerosol (SOA): Pyruvic acid oxidation yields low
volatility organic acids in clouds, Geophysical Research Letters., 33 (6): Art. No.
L06822.
Chakrabarti, B., Singh, M., and Sioutas, C. (2004). Development of a near-Continuous
Monitor for Measurement of the Sub-150 Nm Pm Mass Concentration. Aerosol Science
and Technology 38: 239-252.
290
Chen, C., Chock, D. P., and Winkler, S. L. (1999). A Simulation Study of Confounding
in Generalized Linear Models for Air Pollution Epidemiology. Environmental Health
Perspectives 107(3): 217-222.
Chow, J. C., and Watson, J. G. (2002). Pm2.5 Carbonate Concentrations at Regionally
Representative Interagency Monitoring of Protected Visual Environment Sites. Journal of
Geophysical Research-Atmospheres 107(D21).
Chow, J. C., Watson, J. G., Chen, L. W. A., Arnott, W. P., and Moosmuller, H. (2004).
Equivalence of Elemental Carbon by Thermal/Optical Reflectance and Transmittance
with Different Temperature Protocols. Environmental Science & Technology 38(16):
4414-4422.
Chow, J. C., Watson, J. G., Crow, D., Lowenthal, D. H., and Merrifield, T. (2001).
Comparison of Improve and Niosh Carbon Measurements. Aerosol Science and
Technology 34(1): 23-34.
Chow, J. C., Watson, J. G., Lu, Z. Q., Lowenthal, D. H., Frazier, C. A., Solomon, P. A.,
Thuillier, R. H., and Magliano, K. (1996). Descriptive Analysis of Pm(2.5) and Pm(10) at
Regionally Representative Locations During Sjvaqs/Auspex. Atmospheric Environment
30(12): 2079-2112.
Chow, J. C., Watson, J. G., Pritchett, L. C., Pierson, W. R., Frazier, C. A., and Purcell, R.
G. (1993). The Dri Thermal Optical Reflectance Carbon Analysis System - Description,
Evaluation and Applications in United-States Air-Quality Studies. Atmospheric
Environment Part a-General Topics 27(8): 1185-1201.
Chow, J.C., Watson, J.G., Fujita, E.M., Lu, Z.Q., Lawson, D.R., Ashbaugh, L.L. (1994).
Temporal and Spatial Variations of PM
2.5
and PM
10
Aerosol in the Southern California
Air-Quality Study, Atmos. Environ., 28, 2061-2080.
Christoforou, C. S., Salmon, L. G., Hannigan, M. P., Solomon, P. A., and Cass, G. R.
(2000). Trends in Fine Particle Concentration and Chemical Composition in Southern
California. Journal of the Air & Waste Management Association 50(1): 43-53.
Clarke, R. W., Coull, B., Reinisch, U., Catalano, P., Killingsworth, C. R., Koutrakis, P.,
Kavouras, I., Murthy, G. G. K., Lawrence, J., Lovett, E., Wolfson, J. M., Verrier, R. L.,
and Godleski, J. J. (2000). Inhaled Concentrated Ambient Particles Are Associated with
Hematologic and Bronchoalveolar Lavage Changes in Canines. Environmental Health
Perspectives 108(12): 1179-1187.
Cornbleet, P.J. and Gochman, N. (1979). Incorrect least-squares regression coefficients in
method-comparison analysis, Clin. Chem., 25, 432-438.
Daigle, C.C., Chalupa, D.C., Gibb, F.R., Morrow, P.E., Oberdorster, G., Utell, M.J.
(2003). Ultrafine particle deposition in humans during rest and exercise. Inhal. Toxicol.,
15:539–552.
291
Dejmek, J., Solansky, I., Benes, I., Lenicek, J., and Sram, R. J. (2000). The Impact of
Polycyclic Aromatic Hydrocarbons and Fine Particles on Pregnancy Outcome.
Environmental Health Perspectives 108(12): 1159-1164.
Delfino, R. J., Sioutas, C., and Malik, S. (2005). Potential Role of Ultrafine Particles in
Associations between Airborne Particle Mass and Cardiovascular Health. Environmental
Health Perspectives 113(8): 934-946.
Delfino, R. J., Staimer, N., Gillen, D., Tjoa, T., Sioutas, C., Fung, K., George, S. C., and
Kleinman, M. T. (2006). Personal and Ambient Air Pollution Is Associated with
Increased Exhaled Nitric Oxide in Children with Asthma. Environmental Health
Perspectives 114(11): 1736-1743.
Delfino, R. J., Staimer, N., Tjoa, T., Polidori, A., Arhami, M., Gillen, D. L., Kleinman,
M. T., Vaziri, N. D., Longhurst, J., Zaldivar, F., and SioutaS, C. (2008). Circulating
Biomarkers of Inflammation, Antioxidant Activity, and Platelet Activation Are
Associated with Primary Combustion Aerosols in Subjects with Coronary Artery
Disease. Environmental Health Perspectives 116(7): 898-906.
Delvin, E. E., Lopez, V., Levy, E., and Menard, D. (1997). Control of Apolipoprotein
Synthesis by Calcitriol and Clofibrate in Human Fetal Jejunum. Gastroenterology 112(4):
A870-A870.
Deming, W.E. (1943). Statistical Adjustment of Data, Wiley, New York, NY..
Destaillats, H., Lunden, M. M., Singer, B. C., Coleman, B. K., Hodgson, A. T., Weschler,
C. J., and Nazaroff, W. W. (2006). Indoor Secondary Pollutants from Household Product
Emissions in the Presence of Ozone: A Bench-Scale Chamber Study. Environmental
Science & Technology 40(14): 4421-4428.
Devlin, R. B., Folinsbee, L. J., Biscardi, F., Hatch, G., Becker, S., Madden, M. C.,
Robbins, M., and Koren, H. S. (1997). Inflammation and Cell Damage Induced by
Repeated Exposure of Humans to Ozone. Inhalation Toxicology 9(3): 211-235.
Ding, Y. M., Pang, Y. B., and Eatough, D. J. (2002). High-Volume Diffusion Denuder
Sampler for the Routine Monitoring of Fine Particulate Matter: I. Design and
Optimization of the Pc-Boss. Aerosol Science and Technology 36(4): 369-382.
Docherty, K. S., Stone, E. A., Ulbrich, I. M., DeCarlo, P. F., Snyder, D. C., Schauer, J. J.,
Peltier, R. E., Weber, R. J., Murphy, S. N., Seinfeld, J. H., Grover, B. D., Eatough, D. J.,
and Jiimenez, J. L. (2008). Apportionment of Primary and Secondary Organic Aerosols
in Southern California During the 2005 Study of Organic Aerosols in Riverside (Soar-1).
Environmental Science & Technology 42(20): 7655-7662.
Dockery, D. W., Pope, C. A., Xu, X. P., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris,
B. G., and 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.
292
Donaldson, K., Brown, D., Clouter, A., Duffin, R., MacNee, W., Renwick, L., Tran, L.,
and Stone, V. (2002). The Pulmonary Toxicology of Ultrafine Particles. Journal of
Aerosol Medicine-Deposition Clearance and Effects in the Lung 15(2): 213-220.
Dye, J.A., Lehmann, J.R., McGee, J.K., Winsett, D.W., Ledbetter, A.D., Everitt, J.I.,
Ghio, A.J., Costa, D.L. (2001). Acute pulmonary toxicity of particulate matter filter
extracts in rats: coherence with epidemiological studies in Utah Valley residents,
Environmental Health Perspective. 109:395-403.
Eatough, D. J. A., N.; Cottam, M.; Gammon, T.; Hansen, L.D.; Lewis, E.A.; Farber, R.J.
(1990). Loss of Semi-Volatile Organic Compounds from Particles During Sampling on
Filters. Transaction of Visibility and Fine Particles. Mathai, C. V. Pittsburgh, PA, Air and
Waste Management Association: 146-156.
Eatough, D. J., Wadsworth, A., Eatough, D. A., Crawford, J. W., Hansen, L. D., and
Lewis, E. A. (1993). A Multiple-System, Multichannel Diffusion Denuder Sampler for
the Determination of Fine-Particulate Organic Material in the Atmosphere. Atmospheric
Environment Part a-General Topics 27(8): 1213-1219.
Ebelt, S. T., Petkau, A. J., Vedal, S., Fisher, T. V., and Brauer, M. (2000). Exposure of
Chronic Obstructive Pulmonary Disease Patients to Particulate Matter: Relationships
between Personal and Ambient Air Concentrations. Journal of the Air & Waste
Management Association 50(7): 1081-1094.
Ebelt, S. T., Wilson, W. E., Brauer, M. (2005). Exposure to the ambient and non-
ambient components of particulate matter: a comparison of health effects, Epidemiology.
16: 396-405.
Elder, A., Gelein, R., Silva, V., Feikert, T., Opanashuk, L., Carter, J., Potter, R.,
Maynard, A., Finkelstein, J., and Oberdorster, G. (2006). Translocation of Inhaled
Ultrafine Manganese Oxide Particles to the Central Nervous System. Environmental
Health Perspectives 114(8): 1172-1178.
El-Zanan, H. S., Lowenthal, D. H., Zielinska, B., Chow, J. C., and Kumar, N. (2005).
Determination of the Organic Aerosol Mass to Organic Carbon Ratio in Improve
Samples. Chemosphere 60(4): 485-496.
Favez, O., Sciare, J., Cachier, H., Alfaro, S. C., and Abdelwahab, M. M. (2008).
Significant Formation of Water-Insoluble Secondary Organic Aerosols in Semi-Arid
Urban Environment. Geophysical Research Letters 35(15): -.
Fine, P. M., Cass, G. R., and Simoneit, B. R. T. (1999). Characterization of Fine Particle
Emissions from Burning Church Candles. Environmental Science & Technology 33(14):
2352-2362.
293
Fine, P. M., Cass, G. R., and Simoneit, B. R. T. (2004). Chemical Characterization of
Fine Particle Emissions from the Fireplace Combustion of Wood Types Grown in the
Midwestern and Western United States. Environmental Engineering Science 21(3): 387-
409.
Fine, P. M., Chakrabarti, B., Krudysz, M., Schauer, J. J., and Sioutas, C. (2004). Diurnal
Variations of Individual Organic Compound Constituents of Ultrafine and Accumulation
Mode Particulate Matter in the Los Angeles Basin. Environmental Science and
Technology 38(5): 1296-1304.
Fine, P. M., Chakrabarti, B., Krudysz, M., Schauer, J. J., and Sioutas, C. (2004). Diurnal
Variations of Individual Organic Compound Constituents of Ultrafine and Accumulation
Mode Particulate Matter in the Los Angeles Basin. Environmental Science & Technology
38(5): 1296-1304.
Fraser, M. P., Cass, G. R., Simoneit, B. R. T., and Rasmussen, R. A. (1997). Air Quality
Model Evaluation Data for Organics .4. C-2-C-36 Non-Aromatic Hydrocarbons.
Environmental Science & Technology 31(8): 2356-2367.
Funasaka, K., Miyazaki, T., Tsuruho, K., Tamura, K., Mizuno, T., Kuroda, K. (2000).
Relationship Between Indoor and Outdoor Carbonaceous Particulates in Roadside
Households. Environ. Sci. Technol. 110:127-134.
Gelencser, A., May, B., Simpson, D., Sanchez-Ochoa, A., Kasper-Giebl, A., Puxbaum,
H., Caseiro, A., Pio, C., and Legrand, M. (2007). Source Apportionment of Pm2.5
Organic Aerosol over Europe: Primary/Secondary, Natural/Anthropogenic, and
Fossil/Biogenic Origin. Journal of Geophysical Research-Atmospheres 112(D23): -.
Geller, M. D., Chang, M. H., Sioutas, C., Ostro, B. D., and Lipsett, M. J. (2002).
Indoor/Outdoor Relationship and Chemical Composition of Fine and Coarse Particles in
the Southern California Deserts. Atmospheric Environment 36(6): 1099-1110.
Geyh, A. S., Xue, J. P., Ozkaynak, H., and Spengler, J. D. (2000). The Harvard Southern
California Chronic Ozone Exposure Study: Assessing Ozone Exposure of Grade-School-
Age Children in Two Southern California Communities. Environmental Health
Perspectives 108(3): 265-270.
Ghio, A.J., Devlin, R.B. (2001). Inflammatory lung injury after bronchial instillation of
air pollution particles, American Journal of Respiratory Critical Care Medicine. 164:704-
708.
Gong, H., Wong, R., Sarma, R. J., Linn, W. S., Sullivan, E. D., Shamoo, D. A.,
Anderson, K. R., and Prasad, S. B. (1998). Cardiovascular Effects of Ozone Exposure in
Human Volunteers. American Journal of Respiratory and Critical Care Medicine 158(2):
538-546.
294
Green, L. C., Crouch, E. A. C., Ames, M. R., and Lash, T. L. (2002). What's Wrong with
the National Ambient Air Quality Standard (Naaqs) for Fine Particulate Matter (Pm2.5)?
Regulatory Toxicology and Pharmacology 35(3): 327-337.
Griffin, R. J., Dabdub, D., Kleeman, M. J., Fraser, M. P., Cass, G. R., and Seinfeld, J. H.
(2002). Secondary Organic Aerosol - 3. Urban/Regional Scale Model of Size- and
Composition-Resolved Aerosols. Journal of Geophysical Research-Atmospheres
107(D17).
Grosjean, D., Fung, K., and Harrison, J. (1983). Interactions of Polycyclic Aromatic-
Hydrocarbons with Atmospheric Pollutants. Environmental Science & Technology
17(11): 673-679.
Hansen, A. D. A., Rosen, H., and Novakov, T. (1984). The Aethalometer - an Instrument
for the Real-Time Measurement of Optical-Absorption by Aerosol-Particles. Science of
the Total Environment 36(JUN): 191-196.
Hays, M.D., Geron, C.D., Linna, K.J., Smith, N.D., Schauer, J. J. (2002). Speciation of
Gas-Phase and Fine Particle Emissions from Burning of Foliar Fuels. Environ. Sci.
Technol. 36:2281-2295.
He, C. R., Morawska, L. D., Hitchins, J., and Gilbert, D. (2004). Contribution from
Indoor Sources to Particle Number and Mass Concentrations in Residential Houses.
Atmospheric Environment 38(21): 3405-3415.
Herner, J. D., Green, P. G., and Kleeman, M. J. (2006). Measuring the Trace Elemental
Composition of Size-Resolved Airborne Particles. Environmental Science & Technology
40(6): 1925-1933.
Hildemann, L. M., Klinedinst, D. B., Klouda, G. A., Currie, L. A., and Cass, G. R.
(1994). Sources of Urban Contemporary Carbon Aerosol. Environmental Science &
Technology 28(9): 1565-1576.
Ho, K.F., Cao, J.J., Harrison, R.M., Lee, S.C., Bau, K.K. (2004). Indoor/outdoor
relationships of organic carbon (OC) and elemental carbon (EC) in PM
2.5
in roadside
environment of Hong Kong. Atmos. Environ. 38:6327-6335.
Hopke, P. K., Ito, K., Mar, T., Christensen, W. F., Eatough, D. J., Henry, R. C., Kim, E.,
Laden, F., Lall, R., Larson, T. V., Liu, H., Neas, L., Pinto, J., Stolzel, M., Suh, H.,
Paatero, P., and Thurston, G. D. (2006). Pm Source Apportionment and Health Effects: 1.
Intercomparison of Source Apportionment Results. Journal of Exposure Science and
Environmental Epidemiology 16(3): 275-286.
295
Hughes, L. S., Allen, J. O., Kleeman, M. J., Johnson, R. J., Cass, G. R., Gross, D. S.,
Gard, E. E., Galli, M. E., Morrical, B. D., Fergenson, D. P., Dienes, T., Noble, C. A.,
Silva, P. J., and Prather, K. A. (1999). Size and Composition Distribution of Atmospheric
Particles in Southern California. Environmental Science & Technology 33(20): 3506-
3515.
Hughes, L. S., Allen, J. O., Salmon, L. G., Mayo, P. R., Johnson, R. J., and Cass, G. R.
(2002). Evolution of Nitrogen Species Air Pollutants Along Trajectories Crossing the Los
Angeles Area. Environmental Science & Technology 36(18): 3928-3935.
IARC (2005). Overall Evaluations of Carcinogenicity to Humans (World Health
Organization), International Agency for Research on Cancer (IARC).
Jeong, C. H., Lee, D. W., Kim, E., and Hopke, P. K. (2004). Measurement of Real-Time
Pm2.5 Mass, Sulfate, and Carbonaceous Aerosols at the Multiple Monitoring Sites.
Atmospheric Environment 38(31): 5247-5256.
Jerrett, M., Burnett, R. T., Ma, R. J., Pope, C. A., Krewski, D., Newbold, K. B., Thurston,
G., Shi, Y. L., Finkelstein, N., Calle, E. E., and Thun, M. J. (2005). Spatial Analysis of
Air Pollution and Mortality in Los Angeles. Epidemiology 16(6): 727-736.
Kaiser, J. (2005). Mounting Evidence Indicts Fine-Particle Pollution. Science 307(5717):
1858-1861.
Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J., Facchini, M. C.,
Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J., Swietlicki, E., Putaud, J. P.,
Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G. K., Winterhalter, R., Myhre, C. E. L.,
Tsigaridis, K., Vignati, E., Stephanou, E. G., and Wilson, J. (2005). Organic Aerosol and
Global Climate Modelling: A Review. Atmospheric Chemistry and Physics 5: 1053-1123.
Kim, B. M., Cassmassi, J., Hogo, H., and Zeldin, M. D. (2001). Positive Organic Carbon
Artifacts on Filter Medium During Pm2.5 Sampling in the South Coast Air Basin.
Aerosol Science and Technology 34(1): 35-41.
Kim, B.M., Teffera, S., Zeldin, M.D. (2000). Characterization of PM
2.5
and PM
10
in the
South Coast air Basin of Southern California: Part 1 - Spatial Variations, J. Air & Waste
Manage. Assoc. 50, 2034-2044.
Kim, S., Jaques, P. A., Chang, M. C., Barone, T., Xiong, C., Friedlander, S. K., and
Sioutas, C. (2001). Versatile Aerosol Concentration Enrichment System (Vaces) for
Simultaneous in Vivo and in Vitro Evaluation of Toxic Effects of Ultrafine, Fine and
Coarse Ambient Particles - Part Ii: Field Evaluation. Journal of Aerosol Science 32(11):
1299-1314.
Kim, S., Shen, S., Sioutas, C., Zhu, Y. F., and Hinds, W. C. (2002). Size Distribution and
Diurnal and Seasonal Trends of Ultrafine Particles in Source and Receptor Sites of the
Los Angeles Basin. Journal of the Air & Waste Management Association 52(3): 297-307.
296
Kim, S., Sioutas, C., Chang, M. C., and Gong, H. (2000). Factors Affecting the Stability
of the Performance of Ambient Fine-Particle Concentrators. Inhalation Toxicology 12:
281-298.
Kirchstetter, T. W., Corrigan, C. E., and Novakov, T. (2001). Laboratory and Field
Investigation of the Adsorption of Gaseous Organic Compounds onto Quartz Filters.
Atmospheric Environment 35(9): 1663-1671.
Kiss, G., Varga, B., Galambos, I., I. Ganszky. (2002). Characterization of water-soluble
organic matter isolated from atmospheric fine aerosol, J. Geophys. Res. 107, D21, 8339.
Kittelson, D. B. (1998). Engines and Nanoparticles: A Review. Journal of Aerosol
Science 29(5--6): 575-588.
Kleeman, M. J., Hughes, L. S., Allen, J. O., and Cass, G. R. (1999). Source Contributions
to the Size and Composition Distribution of Atmospheric Particles: Southern California
in September 1996. Environmental Science & Technology 33(23): 4331-4341.
Kleeman, M. J., Robert, M. A., Riddle, S. G., Fine, P. M., Hays, M. D., Schauer, J. J., and
Hannigan, M. P. (2008). Size Distribution of Trace Organic Species Emitted from
Biomass Combustion and Meat Charbroiling (Vol 42, Pg 3059, 2008). Atmospheric
Environment 42(24): 6152-6154.
Kleemann, M. J. (1999). Source Contributions to the Size and Composition Distribution
of Atmospheric Particles: Southern California in September 1996. Environmental science
and technology 33(23): 4331-4341.
Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P.,
Behar, J. V., Hern, S. C., and Engelmann, W. H. (2001). The National Human Activity
Pattern Survey (Nhaps): A Resource for Assessing Exposure to Environmental
Pollutants. Journal of Exposure Analysis and Environmental Epidemiology 11(3): 231-
252.
Koenig, J. Q., Mar, T. F., Allen, R. W., Jansen, K., Lumley, T., Sullivan, J. H., Trenga,
C. A., Larson, T., Liu, L. J. (2005). Pulmonary effects of indoor- and outdoor-generated
particles in children with asthma. Environ. Health Perspect. 113: 499-503.
Kondo, Y., Miyazaki, Y., Takegawa, N., Miyakawa, T., Weber, R. J., Jimenez, J. L.,
Zhang, Q., and Worsnop, D. R. (2007). Oxygenated and Water-Soluble Organic Aerosols
in Tokyo. Journal of Geophysical Research-Atmospheres 112(D1): -.
Kuhn, T., Biswas, S., and Sioutas, C. (2005). Diurnal and Seasonal Characteristics of
Particle Volatility and Chemical Composition in the Vicinity of a Light-Duty Vehicle
Freeway. Atmospheric Environment 39(37): 7154-7166.
297
Kuhn, T., Krudysz, M., Zhu, Y., Fine, P. M., Hinds, W. C., Froines, J., and Sioutas, C.
(2005). Volatility of Indoor and Outdoor Ultrafine Particulate Matter near a Freeway.
Journal of Aerosol Science 36(3): 291-302.
Laird, N. M., and Ware, J. H. (1982). Random-Effects Models for Longitudinal Data.
Biometrics 38(4): 963-974.
Lavanchy, V. M. H., Gäggeler, H. W., Nyeki, S., and Baltensperger, U. (1999).
Elemental Carbon (Ec) and Black Carbon (Bc) Measurements with a Thermal Method
and an Aethalometer at the High-Alpine Research Station Jungfraujoch. Atmospheric
Environment 33(17): 2759-2769.
Li, N., Sioutas, C., Cho, A., Schmitz, D., Misra, C., Sempf, J., Wang, M. Y., Oberley, T.,
Froines, J., and Nel, A. (2003). Ultrafine Particulate Pollutants Induce Oxidative Stress
and Mitochondrial Damage. Environmental Health Perspectives 111(4): 455-460.
Lim, H. J., Turpin, B. J. (2002). Origins of Primary and Secondary Organic Aerosol in
Atlanta: Results of Time-Resolved Measurements During the Atlanta Supersite
Experiment, Environ. Sci. Technol. 36:4489–4496.
Lim, H. J., Turpin, B. J., Edgerton, E., Hering, S. V., Allen, G., Maring, H., and
Solomon, P. (2003). Semicontinuous Aerosol Carbon Measurements: Comparison of
Atlanta Supersite Measurements. Journal of Geophysical Research-Atmospheres
108(D7).
Lim, H.J., Turpin, B.J., Russell, L.M., Bates, T.S. (2003). Organic and elemental carbon
measurements during ACE-Asia suggest a longer atmospheric lifetime for elemental
carbon, Environmental Science and Technology. 37 (14): 3055-3061.
Long, C. M., Suh, H. H., Catalano, P. J., and Koutrakis, P. (2001). Using Time- and Size-
Resolved Particulate Data to Quantify Indoor Penetration and Deposition Behavior.
Environmental Science & Technology 35(22): 4584-4584.
Lough, G. C., Christensen, C. G., Schauer, J. J., Tortorelli, J., Mani, E., Lawson, D. R.,
Clark, N. N., and Gabele, P. A. (2007). Development of Molecular Marker Source
Profiles for Emissions from on-Road Gasoline and Diesel Vehicle Fleets. Journal of the
Air & Waste Management Association 57(10): 1190-1199.
Lunden, M.M., Revzan, K.L., Fischer, M.L., Thatcher, T.L., Littlejohn, D., Hering, S.V.,
Brown, N.J. (2003). The transformation of outdoor ammonium nitrate aerosols in the
indoor environment. Atmos. Environ. 37:5633-5644.
Mader, B. T., and Pankow, J. F. (2002). Study of the Effects of Particle-Phase Carbon on
the Gas/Particle Partitioning of Sernivolatile Organic Compounds in the Atmosphere
Using Controlled Field Experiments. Environmental Science & Technology 36(23): 5218-
5228.
298
Manchester-Neesvig, J. B., Schauer, J. J., and Cass, G. R. (2003). The Distribution of
Particle-Phase Organic Compounds in the Atmosphere and Their Use for Source
Apportionment During the Southern California Children's Health Study. Journal of the
Air & Waste Management Association 53(9): 1065-1079.
Manual (2004). Semi-Continuous Ocec Carbon Aerosol Analyzer. Sunset Laboratory Inc.
Mar, T. F., Norris, G. A., Koenig, J. Q., and Larson, T. V. (2000). Associations between
Air Pollution and Mortality in Phoenix, 1995-1997. Environmental Health Perspectives
108(4): 347-353.
McClellan, R. O. (2002). Setting Ambient Air Quality Standards for Particulate Matter.
Toxicology 181: 329-347.
McClellan, R.O. (2002). Setting Ambient Air Quality Standards for Particulate Matter,
Toxicology. 181-182, 329-347.
McDow, S. R., and Huntzicker, J. J. (1990). Vapor Adsorption Artifact in the Sampling
of Organic Aerosol - Face Velocity Effects. Atmospheric Environment Part a-General
Topics 24(10): 2563-2571.
Meng, Q. Y., Turpin, B. J., Korn, L., Weisel, C. P., Morandi, M., Colome, S., Zhang, J.
F. J., Stock, T., Spektor, D., Winer, A., Zhang, L., Lee, J. H., Giovanetti, R., Cui, W.,
Kwon, J., Alimokhtari, S., Shendell, D., Jones, J., Farrar, C., and Maberti, S. (2005).
Influence of Ambient (Outdoor) Sources on Residential Indoor and Personal Pm2.5
Concentrations: Analyses of Riopa Data. Journal of Exposure Analysis and
Environmental Epidemiology 15(1): 17-28.
Metzger, K. B., Tolbert, P. E., Klein, M., Peel, J. L., Flanders, W. D., Todd, K.,
Mulholland, J. A., Ryan, P. B., and Frumkin, H. (2004). Ambient Air Pollution and
Cardiovascular Emergency Department Visits. Epidemiology 15(1): 46-56.
Miguel, A. H., Kirchstetter, T. W., Harley, R. A., and Hering, S. V. (1998). On-Road
Emissions of Particulate Polycyclic Aromatic Hydrocarbons and Black Carbon from
Gasoline and Diesel Vehicles. Environmental Science & Technology 32(4): 450-455.
Miguel, A.H., Eiguren-Fernandez, A., Jaques, P.A., Froines, J.R., Grant, B.L., Mayo,
P.R., Sioutas, C. (2004). Seasonal Variation of the Particle Size Distribution of
Polycyclic Aromatic Hydrocarbons and of Major Aerosol Species in Claremont,
California, Atmos. Environ. 38, 3241-3251.
Minguillon, M. C., Arhami, M., Schauer, J. J., and Sioutas, C. (2008). Seasonal and
Spatial Variations of Sources of Fine and Quasi-Ultrafine Particulate Matter in
Neighborhoods near the Los Angeles-Long Beach Harbor. Atmospheric Environment
42(32): 7317-7328.
299
Misra, C., Singh, M., Shen, S., Sioutas, C., and Hall, P. A. (2002). Development and
Evaluation of a Personal Cascade Impactor Sampler (Pcis). Journal of Aerosol Science
33(7): 1027-1047.
Modey, W. K., Pang, Y., Eatough, N. L., and Eatough, D. J. (2001). Fine Particulate
(Pm2.5) Composition in Atlanta, USA: Assessment of the Particle Concentrator-Brigham
Young University Organic Sampling System, Pc-Boss, During the Epa Supersite Study.
Atmospheric Environment 35(36): 6493-6502.
Mosqueron, L., Momas, I., and Le Moullec, Y. (2002). Personal Exposure of Paris Office
Workers to Nitrogen Dioxide and Fine Particles. Occupational and Environmental
Medicine 59(8): 550-555.
Na, K., and Cocker, D. R. (2005). Organic and Elemental Carbon Concentrations in Fine
Particulate Matter in Residences, Schoolrooms, and Outdoor Air in Mira Loma,
California. Atmospheric Environment 39(18): 3325-3333.
National Research Council (1998). Research Priorities for Airborne Particulate Matter.
Immediate Priorities and a Long-Range Research Portfolio. National Academy Press,
National Research Council
Naumova Y.Y., Eisenreich S.J., Turpin B.J., Weisel C.P., Morandi M.T., Colome S.D.,
Totten L.A., Stock T.H., Winer A.M., Alimokhtari S., Kwon J., Shendell D., Jones J.,
Maberti S., and Wall S.J. (2002). Polycyclic aromatic hydrocarbons in the indoor and
outdoor air of three cities in the US. Environ Sci Technol. 36: 2552–2559.
Naumova Y.Y., Offenberg J.H., Eisenreich S.J., Meng Q.Y., Polidori A., Turpin B.J.,
Weisel C.P., Morandi M.T., Colome S.D., Stock T.H., Winer A.M., Alimokhtari S.,
Kwon J., Maberti S., Shendell D., Jones J., and Farrar C. (2003). Gas/particle distribution
of polycyclic aromatic hydrocarbons in coupled outdoor/indoor atmospheres. Atmos
Environ. 37: 703–719.
Nel, A. (2005). Air Pollution-Related Illness: Effects of Particles. Science 308(5723):
804-806.
Ning, Z., Geller, M. D., Moore, K. F., Sheesley, R., Schauer, J. J., and Sioutas, C. (2007).
Daily Variation in Chemical Characteristics of Urban Ultrafine Aerosols and Inference of
Their Sources. Environmental Science & Technology 41(17): 6000-6006.
NIOSH (1996). Elemental Carbon (Diesel Particulate): Method 5040. Niosh Manual of
Analytical Methods. Cincinnati.
NRC (2004). Research Priorities for Airborne Particulate Matter: Iv. Continuing
Research Progress. Committee on Research Priorities for Airborne Particulate Matter.
Washington DC, National Research Council.
300
Ntziachristos, L., Ning, Z., Geller, M. D., and Sioutas, C. (2007). Particle Concentration
and Characteristics near a Major Freeway with Heavy-Duty Diesel Traffic.
Environmental Science & Technology 41(7): 2223-2230.
Ntziachristos, L., Ning, Z., Geller, M. D., Sheesley, R. J., Schauer, J. J., and Sioutas, C.
(2007). Fine, Ultrafine and Nanoparticle Trace Element Compositions near a Major
Freeway with a High Heavy-Duty Diesel Fraction. Atmospheric Environment 41(27):
5684-5696.
Oberdorster, G. (2001). Pulmonary Effects of Inhaled Ultrafine Particles. International
Archives of Occupational and Environmental Health 74(1): 1-8.
Oberdörster, G. (2001). Pulmonary Effects of Inhaled Ultrafine Particles. International
Archives of Occupational and Environmental Health 74(1): 1-8.
Oberdörster, G., Sharp, Z., Atudorei, V., Elder, A., Gelein, R., Lunts, A., Kreyling, W.,
and Cox, C. (2002). Extrapulmonary Translocation of Ultrafine Carbon Particles
Following Whole-Body Inhalation Exposure of Rats. Journal of Toxicology and
Environmental Health-Part A 65(20): 1531-1543.
Ohura, T., Amagai, T., Sugiyama, T., Fusaya, M., and Matsushita, H. (2004).
Characteristics of Particle Matter and Associated Polycyclic Aromatic Hydrocarbons in
Indoor and Outdoor Air in Two Cities in Shizuoka, Japan. Atmospheric Environment
38(14): 2045-2054.
Olson, D. A., and Norris, G. A. (2005). Sampling Artifacts in Measurement of Elemental
and Organic Carbon: Low-Volume Sampling in Indoor and Outdoor Environments.
Atmospheric Environment 39(30): 5437-5445.
Olson, D. A., Turlington, J., Duvall, R. V., Vicdow, S. R., Stevens, C. D., and Williams,
R. (2008). Indoor and Outdoor Concentrations of Organic and Inorganic Molecular
Markers: Source Apportionment of Pm2.5 Using Low-Volume Samples. Atmospheric
Environment 42(8): 1742-1751.
Pandis, S. N., Wexler, A. S., and Seinfeld, J. H. (1993). Secondary Organic Aerosol
Formation and Transport .2. Predicting the Ambient Secondary Organic Aerosol-Size
Distribution. Atmospheric Environment Part a-General Topics 27(15): 2403-2416.
Pankow, J.F. (1994). An absorption model of gas/particle partitioning of organic
compounds in the atmosphere, Atmos. Environ. 28, 185-188.
Phuleria, H. C., Sheesley, R. J., Schauer, J. J., Fine, P. M., and Sioutas, C. (2007).
Roadside Measurements of Size-Segregated Particulate Organic Compounds near
Gasoline and Diesel-Dominated Freeways in Los Angeles, Ca. Atmospheric Environment
41(22): 4653-4671.
301
Polidori, A., Arhami, M., Sioutas, C., Delfino, R. J., and Allen, R. (2007).
Indoor/Outdoor Relationships, Trends, and Carbonaceous Content of Fine Particulate
Matter in Retirement Homes of the Los Angeles Basin. Journal of the Air & Waste
Management Association 57(3): 366-379.
Polidori, A., Turpin, B. J., Davidson, C. I., Rodenburg, L. A., and Maimone, F. (2008).
Organic Pm2.5: Fractionation by Polarity, Ftir Spectroscopy, and Om/Oc Ratio for the
Pittsburgh Aerosol. Aerosol Science and Technology 42(3): 233-246.
Polidori, A., Turpin, B. J., Lim, H. J., Cabada, J. C., Subramanian, R., Pandis, S. N., and
Robinson, A. L. (2006). Local and Regional Secondary Organic Aerosol: Insights from a
Year of Semi-Continuous Carbon Measurements at Pittsburgh. Aerosol Science and
Technology 40(10): 861-872.
Polidori, A., Turpin, B.J., Meng, Q.Y., Lee, J-H., Weisel, C., Morandi, M., Colome, S.,
Stock, T., Winer, A., Zhang, J., Kwon, J., Alimokhtari, S., Shendell, D., Jones, J., Farrar,
C., Maberti, S. (2006). Fine Organic Particulate Matter Dominates Indoor-Generated
PM
2.5
in RIOPA Homes, Journal of Exposure Analysis and Environmental Epidemiology.
16: 321-331.
Pope, C. A., and Dockery, D. W. (2006). Health Effects of Fine Particulate Air Pollution:
Lines That Connect. Journal of the Air & Waste Management Association 56(6): 709-
742.
Press, N. A. (1998). Research Priorities for Airborne Particulate Matter. Immediate
Priorities and a Long-Range Research Portfolio, National Research Council
Reisen, F., and Arey, J. (2005). Atmospheric Reactions Influence Seasonal Pah and
Nitro-Pah Concentrations in the Los Angeles Basin. Environmental Science &
Technology 39(1): 64-73.
Robinson, A. L., Donahue, N. M., Shrivastava, M. K., Weitkamp, E. A., Sage, A. M.,
Grieshop, A. P., Lane, T. E., Pierce, J. R., and Pandis, S. N. (2007). Rethinking Organic
Aerosols: Semivolatile Emissions and Photochemical Aging. Science 315(5816): 1259-
1262.
Robinson, A. L., Subramanian, R., Donahue, N. M., Bernardo-Bricker, A., and Rogge,
W. F. (2006). Source Apportionment of Molecular Markers and Organic Aerosol. 3. Food
Cooking Emissions. Environmental Science & Technology 40(24): 7820-7827.
Robinson, J., Nelson, W.C. (1995). National Human Activity Pattern Survey Data Base:
USEPA, Research Triangle Park, NC.
Rodhe, H. (1999). Human Impact on the Atmospheric Sulfur Balance. Tellus Series a-
Dynamic Meteorology and Oceanography 51(1): 110-122.
302
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and Simoneit, B. R. T.
(1993). Sources of Fine Organic Aerosol .2. Noncatalyst and Catalyst-Equipped
Automobiles and Heavy-Duty Diesel Trucks. Environmental Science & Technology
27(4): 636-651.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and Simoneit, B. R. T.
(1993). Sources of Fine Organic Aerosol .4. Particulate Abrasion Products from Leaf
Surfaces of Urban Plants. Environmental Science & Technology 27(13): 2700-2711.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and Simoneit, B. R. T.
(1996). Mathematical Modeling of Atmospheric Fine Particle-Associated Primary
Organic Compound Concentrations. Journal of Geophysical Research-Atmospheres
101(D14): 19379-19394.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and Simoneit, B. R. T.
(1997). Sources of Fine Organic Aerosol .8. Boilers Burning No. 2 Distillate Fuel Oil.
Environmental Science & Technology 31(10): 2731-2737.
Rogge, W. F., Hildemann, L. M., Mazurek, M. A., Cass, G. R., and Simonelt, B. R. T.
(1991). Sources of Fine Organic Aerosol .1. Charbroilers and Meat Cooking Operations.
Environmental Science & Technology 25(6): 1112-1125.
Rojas-Bracho, L., Suh, H. H., and Koutrakis, P. (2000). Relationships among Personal,
Indoor, and Outdoor Fine and Coarse Particle Concentrations for Individuals with Copd.
Journal of Exposure Analysis and Environmental Epidemiology 10(3): 294-306.
Saldiva, P. H. N., Clarke, R. W., Coull, B. A., Stearns, R. C., Lawrence, J., Murthy, G. G.
K., Diaz, E., Koutrakis, P., Suh, H., Tsuda, A., and Godleski, J. J. (2002). Lung
Inflammation Induced by Concentrated Ambient Air Particles Is Related to Particle
Composition. American Journal of Respiratory and Critical Care Medicine 165(12):
1610-1617.
Samara, C., Kouimtzis, T., Tsitouridou, R., Kanias, G., and Simeonov, V. (2003).
Chemical Mass Balance Source Apportionment of Pm10 in an Industrialized Urban Area
of Northern Greece. Atmospheric Environment 37(1): 41-54.
Samoli, E., Analitis, A., Touloumi, G., Schwartz, J., Anderson, H. R., Sunyer, J., Bisanti,
L., Zmirou, D., Vonk, J. M., Pekkanen, J., Goodman, P., Paldy, A., Schindler, C., and
Katsouyanni, K. (2005). Estimating the Exposure-Response Relationships between
Particulate Matter and Mortality within the Aphea Multicity Project. Environmental
Health Perspectives 113(1): 88-95.
Sardar, S. B., Fine, P. M., and Sioutas, C. (2005). Seasonal and Spatial Variability of the
Size-Resolved Chemical Composition of Particulate Matter (Pm10) in the Los Angeles
Basin. Journal of Geophysical Research-Atmospheres 110(D7).
303
Sardar, S. B., Fine, P. M., and Sioutas, C. (2005). Seasonal and Spatial Variability of the
Size-Resolved Chemical Composition of Particulate Matter (Pm10) in the Los Angeles
Basin. Journal of Geophysical Research-Atmospheres 110(D7): -.
Sardar, S. B., Fine, P. M., Mayo, P. R., and Sioutas, C. (2005). Size-Fractionated
Measurements of Ambient Ultrafine Particle Chemical Composition in Los Angeles
Using the Nanomoudi. Environmental Science & Technology 39(4): 932-944.
Sardar, S.B., Fine, P.M., Sioutas, C. (2005). Seasonal and Spatial Variability of the Size-
Resolved Chemical Composition of Particulate Matter (PM
10
) in the Los Angeles Basin,
Journal of Geophysical Research-Atmosphere. 110 (D7): D07S08.
Sarnat, J. A., Brown, K. W., Schwartz, J., Coull, B. A., and Koutrakis, P. (2005).
Ambient Gas Concentrations and Personal Particulate Matter Exposures - Implications
for Studying the Health Effects of Particles. Epidemiology 16(3): 385-395.
Sarnat, J. A., Koutrakis, P., and Suh, H. H. (2000). Assessing the Relationship between
Personal Particulate and Gaseous Exposures of Senior Citizens Living in Baltimore, Md.
Journal of the Air & Waste Management Association 50(7): 1184-1198.
Sarnat, S. E., Coull, B. A., Schwartz, J., Gold, D. R., and Suh, H. H. (2006). Factors
Affecting the Association between Ambient Concentrations and Personal Exposures to
Particles and Gases. Environmental Health Perspectives 114(5): 649-654.
Sarnat, S.E., Coull, B.A., Ruiz, P.A., Koutrakis, P., Suh, H.H. (2006). The Influences of
Ambient Particle Composition and Size on Particle Infiltration in Los Angeles, CA,
Residences, J. Air & Waste Manage. Assoc. 56:186–196.
Saxena, P., and Hildemann, L. (1996). Water-Soluble Organics in Atmospheric Particles:
A Critical Review of the Literature and Application of thermodynamic s to Identify
Candidate Compounds, J. Atmos. Chem. 24: 57–109.
Schaap, M., Spindler, G., Schulz, M., Acker, K., Maenhaut, W., Berner, A., Wieprecht,
W., Streit, N., Muller, K., Bruggemann, E., Chi, X., Putaud, J. P., Hitzenberger, R.,
Puxbaum, H., Baltensperger, U., and ten Brink, H. (2004). Artefacts in the Sampling of
Nitrate Studied in The "Intercomp" Campaigns of Eurotrac-Aerosol. Atmospheric
Environment 38(38): 6487-6496.
Schauer, J. J., and Cass, G. R. (2000). Source Apportionment of Wintertime Gas-Phase
and Particle-Phase Air Pollutants Using Organic Compounds as Tracers. Environmental
Science & Technology 34(9): 1821-1832.
Schauer, J. J., Kleeman, M. J., Cass, G. R., and Simoneit, B. R. T. (1999). Measurement
of Emissions from Air Pollution Sources. 1. C-1 through C-29 Organic Compounds from
Meat Charbroiling. Environmental Science & Technology 33(10): 1566-1577.
304
Schauer, J. J., Kleeman, M. J., Cass, G. R., and Simoneit, B. R. T. (2002). Measurement
of Emissions from Air Pollution Sources. 5. C-1-C-32 Organic Compounds from
Gasoline-Powered Motor Vehicles. Environmental Science & Technology 36(6): 1169-
1180.
Schauer, J. J., Mader, B. T., Deminter, J. T., Heidemann, G., Bae, M. S., Seinfeld, J. H.,
Flagan, R. C., Cary, R. A., Smith, D., Huebert, B. J., Bertram, T., Howell, S., Kline, J. T.,
Quinn, P., Bates, T., Turpin, B., Lim, H. J., Yu, J. Z., Yang, H., and Keywood, M. D.
(2003). Ace-Asia Intercomparison of a Thermal-Optical Method for the Determination of
Particle-Phase Organic and Elemental Carbon. Environmental Science & Technology
37(5): 993-1001.
Schauer, J. J., Rogge, W. F., Hildemann, L. M., Mazurek, M. A., and Cass, G. R. (1996).
Source Apportionment of Airborne Particulate Matter Using Organic Compounds as
Tracers. Atmospheric Environment 30(22): 3837-3855.
Schmid, H., Laskus, L., Abraham, H. J., Baltensperger, U., Lavanchy, V., Bizjak, M.,
Burba, P., Cachier, H., Crow, D., Chow, J., Gnauk, T., Even, A., ten Brink, H. M.,
Giesen, K. P., Hitzenberger, R., Hueglin, E., Maenhaut, W., Pio, C., Carvalho, A.,
Putaud, J. P., Toom-Sauntry, D., and Puxbaum, H. (2001). Results of The "Carbon
Conference" International Aerosol Carbon Round Robin Test Stage I. Atmospheric
Environment 35(12): 2111-2121.
Seagrave, J., Knall, C., McDonald, J. D., and Mauderly, J. L. (2004). Diesel Particulate
Material-Binds and Concentrates a Proinflammatory Cytokine That Causes Neutrophil
Migration. Inhalation Toxicology 16: 93-98.
Seinfeld, J., and Pandis, S. (1998). Atmospheric Chemistry and Physics. Wiley, New
York.
Sheesley, R. J., Schauer, J. J., Zheng, M., and Wang, B. (2007). Sensitivity of Molecular
Marker-Based Cmb Models to Biomass Burning Source Profiles. Atmospheric
Environment 41(39): 9050-9063.
Sillanpaa, M., Hillamo, R., Saarikoski, S., Frey, A., Pennanen, A., Makkonen, U.,
Spolnik, Z., Van Grieken, R., Branis, M., Brunekreef, B., Chalbot, M. C., Kuhlbusch, T.,
Sunyer, J., Kerminen, V. M., Kulmala, M., and Salonen, R. O. (2006). Chemical
Composition and Mass Closure of Particulate Matter at Six Urban Sites in Europe.
Atmospheric Environment 40: S212-S223.
Simoneit, B. R. T. (1986). Characterization of Organic-Constituents in Aerosols in
Relation to Their Origin and Transports a Review. Int. J. Environ. Anal. Chem 23: 207-
237.
Simoneit, B. R. T. (1999). A Review of Biomarker Compounds as Source Indicators and
Tracers for Air Pollution. Environmental Science and Pollution Research 6(3): 159-169.
305
Singh, M., Misra, C., and Sioutas, C. (2003). Field Evaluation of a Personal Cascade
Impactor Sampler (Pcis). Atmospheric Environment 37(34): 4781-4793.
Sioutas, C., Delfino, R. J., and Singh, M. (2005). Exposure Assessment for Atmospheric
Ultrafine Particles (Ufps) and Implications in Epidemiologic Research. Environmental
Health Perspectives 113(8): 947-955.
Stone, E. A., Snyder, D. C., Sheesley, R. J., Sullivan, A. P., Weber, R. J., and Schauer, J.
J. (2008). Source Apportionment of Fine Organic Aerosol in Mexico City During the
Milagro Experiment 2006. Atmospheric Chemistry and Physics 8(5): 1249-1259.
Strader, R., Lurmann, F., Pandis, S., (1999). Evaluation of Secondary Organic Aerosol
Formation in Winter, Atmos. Environ. 33:4849–4863.
Subramanian, R., Donahue, N. M., Bernardo-Bricker, A., Rogge, W. F., and Robinson,
A. L. (2006). Contribution of Motor Vehicle Emissions to Organic Carbon and Fine
Particle Mass in Pittsburgh, Pennsylvania: Effects of Varying Source Profiles and
Seasonal Trends in Ambient Marker Concentrations. Atmospheric Environment 40(40):
8002-8019.
Subramanian, R., Khlystov, A. Y., Cabada, J. C., and Robinson, A. L. (2004). Positive
and Negative Artifacts in Particulate Organic Carbon Measurements with Denuded and
Undenuded Sampler Configurations. Aerosol Science and Technology 38: 27-48.
Suh, H.H., Koutrakis, P., Spengler, J.D. (1994). The Relationship Between Airborne
Acidity and Ammonia in Indoor Environments, J. Expos. Anal. Environ. Epidemiol. 4, 1-
23.
Tesfaigzi, Y., Singh, S. P., Foster, J. E., Kubatko, J., Barr, E. B., Fine, P. M., McDonald,
J. D., Hahn, F. F., and Mauderly, J. L. (2002). Health Effects of Subchronic Exposure to
Low Levels of Wood Smoke in Rats. Toxicological Sciences 65(1): 115-125.
Thatcher, T.L., Lai, A.C., Moreno-Jackson, R., Sextro, R.G., Nazaroff. W.W. (2002).
Effects of room furnishings and air speed on particle deposition rates indoors,
Atmospheric Environment. 36:1811–1819.
Thatcher, T.L., Layton, D.W. (1995). Deposition, resuspension, and penetration of
particles within a residence. Atmos. Environ. 29:1487-1497.
Tolocka, M.P., Solomon, P.A., Mitchell, W., Norris, G.A., Gemmill, D.B., Wiener, R.W.,
Vanderpool, R.W., Homolya, J.B., Rice, J. (1999). East Versus West in the u.s.:
Chemical Characteristics of PM2.5 During the Winter of Aerosol Sci. Technol. (2001),
34, 88-96.
306
Toner, S. M., Shields, L. G., Sodeman, D. A., and Prather, K. A. (2008). Using Mass
Spectral Source Signatures to Apportion Exhaust Particles from Gasoline and Diesel
Powered Vehicles in a Freeway Study Using Uf-Atofms. Atmospheric Environment
42(3): 568-581.
Turpin, B. J., and Lim, H. J. (2001). Species Contributions to Pm2.5 Mass
Concentrations: Revisiting Common Assumptions for Estimating Organic Mass. Aerosol
Science and Technology 35(1): 602-610.
Turpin, B. J., Cary, R. A., and Huntzicker, J. J. (1990). An Insitu, Time-Resolved
Analyzer for Aerosol Organic and Elemental Carbon. Aerosol Science and Technology
12(1): 161-171.
Turpin, B. J., Huntzicker, J. J. (1995). Identification of Secondary Organic Aerosol
Episodes and Quantitation of Primary and Secondary Organic Aerosol Concentrations
During SCAQS, Atmos. Environ. 29:3527–3544.
Turpin, B. J., Huntzicker, J. J., and Hering, S. V. (1994). Investigation of Organic
Aerosol Sampling Artifacts in the Los Angeles Basin. Atmospheric Environment 28(19):
3061-3071.
Turpin, B. J., Saxena, P., and Andrews, E. (2000). Measuring and Simulating Particulate
Organics in the Atmosphere: Problems and Prospects. Atmospheric Environment 34(18):
2983-3013.
USEPA, (2004). Air Quality Criteria for Particulate Matter, U.S, Environmental
Protection Agency, Research Triangle Park.
Wallace, L. (1996). Indoor Particles: A Review. Air and Waste management Association
46: 98-126.
Wallace, L. (1996). Indoor Particles: A Review. Journal of the Air & Waste Management
Association 46(2): 98-126.
Wallace, L. (2000). Correlations of Personal Exposure to Particles with Outdoor Air
Measurements: A Review of Recent Studies. Aerosol Sci. 32: 15-25.
Watson, J. G., Chow, J. C., Lu, Z. Q., Fujita, E. M., Lowenthal, D. H., Lawson, D. R.,
and Ashbaugh, L. L. (1994). Chemical Mass-Balance Source Apportionment of Pm(10)
During the Southern California Air-Quality Study. Aerosol Science and Technology
21(1): 1-36.
Watson, J. G., Zhu, T., Chow, J. C., Engelbrecht, J., Fujita, E. M., and Wilson, W. E.
(2002). Receptor Modeling Application Framework for Particle Source Apportionment.
Chemosphere 49(9): 1093-1136.
307
Weber, R. J., Sullivan, A. P., Peltier, R. E., Russell, A., Yan, B., Zheng, M., de Gouw, J.,
Warneke, C., Brock, C., Holloway, J. S., Atlas, E. L., and Edgerton, E. (2007). A Study
of Secondary Organic Aerosol Formation in the Anthropogenic-Influenced Southeastern
United States. Journal of Geophysical Research-Atmospheres 112(D13): -.
Weichenthal, S., Dufresne, A., and Infante-Rivard, C. (2007). Indoor Ultrafine Particles
and Childhood Asthma: Exploring a Potential Public Health Concern. Indoor Air 17(2):
81-91.
Wellenius, G. A., Coull, B. A., Godleski, J. J., Koutrakis, P., Okabe, K., Savage, S. T.,
Lawrence, J. E., Murthy, G. G. K., and Verrier, R. L. (2003). Inhalation of Concentrated
Ambient Air Particles Exacerbates Myocardial Ischemia in Conscious Dogs.
Environmental Health Perspectives 111(4): 402-408.
Weschler, C. J. (2004). Chemical Reactions among Indoor Pollutants: What We've
Learned in the New Millennium. Indoor Air 14: 184-194.
Weschler, C. J., and Nazaroff, W. W. (2008). Semivolatile Organic Compounds in Indoor
Environments. Atmospheric Environment 42: 9018-9040.
Weschler, C.J., Shields, H.C. (1997). Potential reactions among indoor pollutants.
Atmos. Environ. 31:3487-3495.
Williams, R., Creason, J., Zweidinger, R., Watts, R., Sheldon, L., and Shy, C. (2000).
Indoor, Outdoor, and Personal Exposure Monitoring of Particulate Air Pollution: The
Baltimore Elderly Epidemiology-Exposure Pilot Study. Atmospheric Environment
34(24): 4193-4204.
Xiao, G.G., Wang, M.Y., Li, N., Loo, J.A., Nel, A.E. (2003). Use of proteomics to
demonstrate a hierarchical oxidative stress response to diesel exhaust particle chemicals
in a macrophage cell line, Journal of Biological Chemistry. 278 (50): 50781-50790.
Yli-Tuomi, T., Lanki, T., Hoek, G., Brunekreef, B., and Pekkanen, J. (2008).
Determination of the Sources of Indoor Pm2.5 in Amsterdam and Helsinki.
Environmental Science & Technology 42(12): 4440-4446.
Zhang, Q., Worsnop, D.R., Canagaratna, M.R., Jimenez, J.L. (2005). Hydrocarbon-like
and Oxygenated Organic Aerosols in Pittsburgh: Insight into Sources and Processes of
Organic Aerosols, Atmospheric Chemistry and Physics. 5: 3289-3311.
Zhu, Y. F., Hinds, W. C., Krudysz, M., Kuhn, T., Froines, J., and Sioutas, C. (2005).
Penetration of Freeway Ultrafine Particles into Indoor Environments. Journal of Aerosol
Science 36(3): 303-322.

Abstract (if available)

Abstract The aim of this thesis is to enhance the knowledge on exposure to size fractions of airborne particulate matter and their components and to find more intensive information on sources of indoor and outdoor size fractionated particles. In the first part of the study, the physical and chemical characteristics of indoor, outdoor, and personal quasi-ultrafine (<0.25μm), accumulation (0.25-2.5 μm), and coarse (2.5-10 μm) mode particles and gaseous pollutant were measured at two phases (warmer and colder phase) of four different retirement communities in Southern California in 2005-2007. Overall, the magnitude of indoor and outdoor measurements was similar, due to high influence of outdoor sources on indoor particle and gas levels. Secondary organic aerosol showed to be able to comprise a major fraction of organic carbon (more than 40% were estimated at some phases). Outdoor and indoor concentrations of gaseous pollutant were more positively correlated to personal quasi-UF particles than larger size fractions. Indoor sources were not significant contributors to personal exposure of PM, which is predominantly influenced by primary emitted pollutants of outdoor origin. Vehicular sources had the highest contribution to PM0.25 among the apportioned sources for both indoor and outdoor particles at all sites. The contribution of mobile sources to indoor levels was similar to their corresponding outdoor estimates, thus even if people generally spend most of their time indoors, a major portion of the submicron particles to which they are exposed to, comes from outdoor mobile sources.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Assessing indoor and outdoor air quality by characterizing the physicochemical and toxicological properties of particulate matter using…

PDF

Size-resolved particulate matter (PM) in urban areas: toxico-chemical characteristics, sources, trends and health implications

PDF

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

PDF

Investigation of physico-chemical characteristics of particulate matter from vehicular sources

$Physico-chemical properties and source apportionment of size-fractionated airborne particulate matter in urban areas with implications for public health$

PDF

Physico-chemical properties and source apportionment of size-fractionated airborne particulate matter in urban areas with implications for public health

$Chemical and toxicological characteristics and historical trends of size-fractioned particulate matter from traffic-related emissions in Los Angeles$

PDF

Chemical and toxicological characteristics and historical trends of size-fractioned particulate matter from traffic-related emissions in Los Angeles

PDF

Analysis of sources and profiles of organic carbon in ambient particulate matter across fine and coarse sizes and introducing an optical technique for real-time urban dust measurement

PDF

Physico-chemical characteristics and sources of ambient PM mass and number concentrations and their associated toxicity, and development of novel techniques for high time-resolution measurement o...

PDF

Investigating the temporal trends, sources, and toxicity of ambient particulate matter (PM) in different metropolitan environments, and development of a novel aerosol generation setup for inhalat...

PDF

Impact of urban source emissions on ambient particulate matter (PM) composition and associated toxicity in local and regional scales using source appointment models

PDF

Influence of atmospheric processes on the physico-chemical properties of primary particles and their impact on public exposure

PDF

Particulate matter (PM) exposure for commuters in Los Angeles: chemical characterization and implications to public health

PDF

Ultrafine particle concentration inside vehicles: models for exposure assessment

PDF

Toxicological characteristics of particulate matter in an urban environment and their linkage to the source-specific constituents

PDF

Temporal, spatial and toxicological characteristics of coarse particulate matter in an urban area and relation to sources and regulations

PDF

Personal exposure to particulate matter PM2.5 sources during pregnancy and birthweight

PDF

Development of novel techniques for evaluating physical, chemical and toxicological properties of particulate matter in ambient air

PDF

Oxidative potential of urban atmospheric particles: spatiotemporal trends and associations with source-specific chemical components

PDF

Chemical and toxicological characteristics of particulate matter in urban environments with a focus on its sources, associated health impacts and mitigation policies

PDF

Investigating the role of urban emission sources on redox-active PM compounds and the chemical analysis of the standardized diesel exhaust particles

Asset Metadata

Creator Arhami, Mohammad (author)

Core Title Exposure assessment and source apportionment of size fractions of airborne particulate matter

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Environmental Engineering

Publication Date 04/15/2009

Defense Date 01/13/2009

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

Tag Air pollution,exposure,Los Angeles,OAI-PMH Harvest,particulate matter,size fractionated,source apportionment

Place Name Long Beach (city or populated place), Los Angeles (city or populated place)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sioutas, Constantinos (committee chair), Henry, Ronald C. (committee member), Phares, Dennis (committee member)

Creator Email arhami@usc.edu,ehsanarhami@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2085

Unique identifier UC1442987

Identifier etd-Arhami-2630 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-233833 (legacy record id),usctheses-m2085 (legacy record id)

Legacy Identifier etd-Arhami-2630.pdf

Dmrecord 233833

Document Type Dissertation

Rights Arhami, Mohammad

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