University of Southern California Dissertations and Theses

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

(USC Thesis Other)

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

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content 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
by
Ramin Tohidi
A Dissertation Presented to
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ENVRIONMENTAL ENGINEERING)
May 2024
© 2024 Ramin Tohidi

ii
Dedication
To my parents and sister whose boundless love and sacrifices have crafted the foundation
upon which I stand and thrive
&
To my dearest Ati, my partner and best friend.
You are the heartbeat of my life and my unwavering source of strength. Your love and support
have illuminated my path through every challenge.

iii
Acknowledgments
I'd like to begin by extending my profound gratitude to Professor Constantinos Sioutas. No
matter where he was in the world, he was always available to provide almost immediate feedback
and suggestions. His consistent guidance, mentorship, and invaluable insights have been the
cornerstone of this research. Your unwavering support and constructive feedback have been
instrumental in shaping this work.
To the commendable team at the USC Aerosol Lab, both former and present members, your
invaluable assistance and collaboration throughout my doctoral journey at USC have been pivotal.
I owe a profound debt of gratitude to Dr. Sina Taghvaee, Dr. Milad Pirhadi, Dr. Abdulmalik
Altuwayjiri, and Dr. Vahid Jalali Farahani. They have not only been guiding lights in the realm of
research but have also been reliable friends and mentors. The lessons I've imbibed from them are
irreplaceable, and I remain eternally indebted.
Furthermore, my interactions with Mohammad Aldekheel, Mohammad Mahdi Badami, and
Yashar Aghaei have been truly enriching. Collaborating with them has been an absolute delight,
and I cherish every moment we spent delving deep into our projects. Our passionate discussions,
mutual support, deep-rooted friendships, and yes, even our spirited disagreements, have shaped
my journey at USC in more ways than I can articulate.
I would like to extend my heartfelt gratitude to my PhD candidacy and defense committee
members: Professor Rima Habre, Professor Kelly Sanders, Professor William Mack, and Professor
Burcin Becerik-Gerber. Their insightful feedback and constructive comments have been
invaluable throughout this journey.
Equally significant is the financial and technical support from various institutions. I am deeply
indebted to the United States National Institutes of Health for their generous funding that propelled
this research forward (grant numbers: R01AI065617, R01ES029395, and R01ES032806).

iv
Table of Contents
Dedication........................................................................................................................... ii
Acknowledgments.............................................................................................................. iii
List of Tables .................................................................................................................... vii
List of Figures.................................................................................................................. viii
Abstract.............................................................................................................................. xi
Chapter 1: Introduction.......................................................................................................1
1.1Background ......................................................................................................1
1.2 Objectives.........................................................................................................2
1.3 Overview ..........................................................................................................4
Chapter 2: Real-time measurements of mineral dust concentration in coarse
particulate matter (PM10-2.5) by employing a novel optical-based technique in Los
Angeles ................................................................................................................................5
2.1 Introduction ......................................................................................................5
2.2 Data and Methodology .....................................................................................8
2.2.1 Sampling location, period ...................................................................8
2.2.2 Chemical analysis .............................................................................10
2.2.3 Calculation of mineral dust concentration ........................................11
2.3Results and discussions..................................................................................14
2.3.1 Determination of the mineral dust absorption coefficient and
enrichment factor............................................................................................14
2.3.2 Calculation of the mineral dust mass absorption cross-section
17
2.3.3 Comparison of the real-time mineral dust mass concentration
and coarse PM mass concentrations...............................................................23

v
2.4 Summary and conclusions..............................................................................25
Chapter 3: Quantifying ambient concentrations of primary and secondary organic
aerosol in central Los Angeles using an integrated approach coupling source
apportionment with regression analysis.............................................................................27
3.1 Introduction ....................................................................................................27
3.2 Methodology ..................................................................................................29
3.2.1 Sampling location, period, and instrumentation ...............................29
3.2.2 Source apportionment analysis.........................................................31
3.2.2.1 PMF model......................................................................31
3.2.2.2 PMF input .......................................................................33
3.2.2.3 Linear regression analysis...............................................34
3.3Results and discussions..................................................................................34
3.3.1 PMF source apportionment results...................................................34
3.3.1.1 Number of factors...........................................................34
3.3.1.2 Factor identification........................................................37
3.3.2 Linear regression between SOA and O3 ...........................................41
3.3.3 Linear regression between POA and CO..........................................44
3.3.4 Linear Regression between EC and CO............................................45
3.3.5 Linear regression between EC and NO2 ...........................................47
3.4 Summary and Conclusions.............................................................................49
Chapter 4 : Investigation of organic carbon profiles and sources of coarse PM in Los
Angeles ..............................................................................................................................50
4.1 Introduction ....................................................................................................50
4.2 Methodology ..................................................................................................53
4.2.1 Site description and sampling time...................................................53
4.2.2 Experimental setup and data collection ............................................55
4.2.3 Source apportionment analysis.........................................................56
4.2.4 Auxiliary data....................................................................................57
4.3Results and discussion....................................................................................57

vi
4.3.1 Temporal and seasonal variability of coarse PM-bound OC
fractions..........................................................................................................57
4.3.2 PMF source apportionment results...................................................64
4.3.2.1 Overview.........................................................................64
4.3.2.2 Factor identification........................................................65
4.3.2.3 Source contributions to coarse OC .................................68
4.4 Summary and conclusions..............................................................................70
Chapter 5: Conclusions and Future works........................................................................72
5.1Conclusions....................................................................................................72
References.........................................................................................................................74

vii
List of Tables
Table 2-1. Average meteorological data for each period during the sampling
campaigns…. ...........................................................................................................8
Table 4-1. Seasonal variation (Mean±SD) of meteorological data for different seasons
during 2021. ...........................................................................................................53

viii
List of Figures
Figure 2-1. Schematic of the sampling setup. First line: using a coarse PM virtual
impactor (VI) to measure light absorption and collect coarse PM samples.
Second line: using a PM2.5 impactor to measure light absorption of PM2.5. ........9
Figure 2-2. The size distribution of particle mass with respect to particle size before
and after the virtual impactor (VI). Error bars are standard deviations. ................13
Figure 2-3. Absorption coefficient of ambient PM2.5 and concentrated coarse PM
at (a) 370 nm; and (b) 880 nm. ..............................................................................15
Figure 2-4. The logarithm of absorption coefficient, babs, for mineral dust in two
distinct wavelengths (i.e., 370 nm and 880 nm) with respect to Log (λ)...............17
Figure 2-5. Mass absorption cross-section of mineral dust. a) First method: using
typical crustal metals- Al, K, Fe, Ca, Mg, Ti and Si. b) Second method:
Assuming 12% Ca in mineral dust (each data point corresponds to a sampling
day). .......................................................................................................................18
Figure 2-6. Correlation between Al and Fe (a), and correlation between Al (b) and
Fe (c) with Ca measured on PM10 filters (each data point corresponds to a
sampling day).........................................................................................................20
Figure 2-7. The real-time mass concentration of mineral dust (a) and the average
mass concentration of mineral dust for each measurement period, and the
corresponding wind speed values (b). Error bars are standard deviations.............22
Figure 2-8. Comparison of the real-time mineral dust mass concentration and coarse
PM mass concentrations in Los Angeles (a) and correlation between the realtime mineral dust mass concentration and reported coarse PM mass
concentration by CARB.........................................................................................24
Figure 3-1. Location of the monitoring site in central Los Angeles (CELA)....................29
Figure 3-2. PMF-resolved factor profiles for (a) 2015; (b) 2017; and (c) 2019. ...............37

ix
Figure 3-3. The relative (fractional) contribution of PMF-resolved sources to
ambient OC in CELA over the years 2015, 2017, and 2019. ................................38
Figure 3-4. Absolute source contributions to ambient OC mass concentrations
during the years of 2015, 2017 and 2019 in CELA...............................................39
Figure 3-5. Linear regression between PMF-resolved SOA and O3 in: (a) 2015; (b)
2017; and (c) 2019 in central Los Angeles (CELA)..............................................43
Figure 3-6. Linear regression between EC and CO in: (a) 2015; (b) 2017; and (c)
2019 in central Los Angeles (CELA) ....................................................................46
Figure 3-7. Linear regression between EC and NO2 concentrations over the 2015-
2019 period in central Los Angeles (CELA).........................................................48
Figure 4-1. Location of the sampling site in Los Angeles.................................................54
Figure 4-2. Schematic of the experimental setup for coarse OC measurement using
coarse PM virtual impactors (VI)/concentrators....................................................55
Figure 4-3. Evaluation of the coarse particle concentration enrichment factor by
measuring particle mass concentration before and after the VIs. ..........................55
Figure 4-4. Time series of the mass concentrations of coarse PM-bound OC fractions
and elemental carbon during the study period. Green areas correspond to
episodes with RH≥90.............................................................................................58
Figure 4-5. Diurnal variations of relative humidity during winter, spring, and
summer. Error bars correspond to 1 standard error. ..............................................60
Figure 4-6. Average OC fractions mass concentration measured during weekdays
and weekends.........................................................................................................61
Figure 4-7. Average diurnal trends of elemental carbon and OC fractions. (a) winter,
(b) spring, (c) summer............................................................................................62
Figure 4-8. PMF-resolved factor profiles ..........................................................................65

x
Figure 4-9. Seasonal trends and contributions of the three PMF-resolved factors to
total OC and its fractions during the cold and warm phases in Los Angeles. .......69

xi
Abstract
Particulate matter (PM), recognized as a criteria pollutant, is linked to a range of adverse health
issues, including neurodegenerative disorders, cardiovascular diseases, and respiratory
inflammation. PM is regulated primarily based on two criteria: PM2.5 and PM10 mass
concentration standards. However, research has shown that ambient PM comprises various
chemical compositions, size ranges, and physical characteristics, emitted from different pollution
sources. Specifically, the measurement of coarse PM concentrations (PM with diameters larger
than 2.5 and smaller than 10 micrometers) poses a significant challenge due to the particles’
residence time in ambient air. Addressing this issue, this study proposes a novel, multi-faceted
approach, initiating with the creation of an innovative optical technique for the real-time
measurement of urban dust, with a focus on coarse PM. This technique offers a deeper
understanding of urban dust dynamics and its impact.
Furthermore, among different species of ambient PM, organic carbons (OC) are identified as
having higher toxicity. This thesis progresses to quantify the sources of PM2.5-bound OC in
central Los Angeles. Through a blend of source apportionment and regression analysis, the study
reveals detailed insights into the primary and secondary organic carbons (OC) sources in Los
Angeles and offers a straightforward way to estimate their concentration based on criteria gaseous
pollutants. This is a pivotal step toward exploring coarse OC and identifying its primary sources.
The latter part of this research examines the temporal and seasonal fluctuations of organic
carbon in coarse PM, pinpointing major sources such as resuspended road dust, vehicular
emissions, and secondary aerosols. These findings illuminate the intricate nature of urban air
pollution and emphasize the necessity for more nuanced air quality regulations that specifically
address sources within distinct size ranges. Overall, this research not only advances methodologies
in environmental PM analysis but also lays a solid groundwork for future endeavors in urban air
quality management.

1
Chapter 1: Introduction
1.1 Background
Numerous toxicological and epidemiological studies have provided compelling evidence
linking exposure to ambient particulate matter (PM) with adverse health outcomes, including
neurodegenerative effects as well as respiratory and cardiovascular diseases (Berger et al., 2018;
Davis et al., 2013; Gauderman et al., 2015; Mabahwi et al., 2014; Pope et al., 2004; San Tam et
al., 2015). As strong associations between different PM chemical components and distinct health
outcomes have been documented earlier (Crilley et al., 2017; Song et al., 2007; Taghvaee et al.,
2018b), many studies have focused on specific PM components to investigate the toxicity and
endpoint health impacts of ambient PM (Akhtar et al., 2010; Bae et al., 2017; Fang et al., 2016;
Saffari et al., 2015).
Particle size is one of the most important characteristics of particles, which defines their
formation and removal mechanism, the site of deposition in human respiratory system as well as
their atmospheric lifetime. Particles smaller than 10 micrometers have been of interest since bigger
particles have a relatively short lifetime and settle in the atmosphere even before penetrating into
human respiratory tract, while these particles may pass through the first defense line of the
respiratory system (i.e., nasal cavity) and enter blood circulation system (Seinfeld, 2006). Based
on PM physicochemical properties, we can divide PM into two main size ranges, PM10-2.5 (particles
with 2.5 ˂ ˂ 10 µm), and PM2.5 (particles with ˂ 2.5 µm).
In Urban environments like Los Angeles, a large portion of ambient PM consists of
carbonaceous aerosols (i.e., organic carbon (OC), elemental carbon (EC)) (Karanasiou et al.,
2011), with a high contribution to the toxicity of PM (Bates et al., 2019). OC originates from
various sources in PM2.5, such as tailpipe emissions (Hasheminassab et al., 2013; Heo et al.,
2013a), biomass burning (Bae et al., 2019b; Schauer et al., 1999), and secondary organic aerosols
(Heo et al., 2013a; Jimenez et al., 2009). But the underlying formation pathways and possible
sources of coarse PM are poorly understood and investigated.

2
In spite of recent advancements in PM sampling methods, most of them are time-consuming
and require expensive instrumentation and analytical costs for the chemical analysis of different
species. The variation in physical and chemical properties of PM (e.g., size, number, volatility,
surface area, and composition) make it difficult to solely focus on a specific size range or a
component of PM. New techniques with high time resolution are needed to provide the opportunity
to capture the temporal variation of PM with respect to the short-lived excursions or short-term
changes in meteorological conditions and PM sources. For example, dust particles can carry other
components of PM with higher toxicity. In addition to the health risks, dust episodes damage roads
and railroad infrastructure, promote desertification, reduce water quality, and increase soil salinity,
following devastating agricultural issues (Cao et al., 2015; Lee, 2018; Middleton, 2017).
Therefore, it is crucial to devise new methods and techniques to measure the real-time
concentration of different PM components.
1.2 Objectives
In recent years numerous studies have been carried out regarding the effects of PM2.5 on human
health because of its diverse physicochemical characteristics, various sources, and higher oxidative
capacity (Apte et al., 2018; Davidson et al., 2007; Pope et al., 2004), and has been targeted by
stringent regulatory policies in the US. These particles compose a significant amount of PM
number and surface area concentration and can deposit efficiently in lungs. On the other hand,
coarse PM deposits mainly in the upper respiratory system, especially in the nasal cavity (Garcia
and Kimbell, 2009; Tohidi et al., 2020). These particles generally do not reach the circulatory
system, but endotoxins, metals, and soluble compounds attached to coarse PM can easily leach
into nasal mucosa (Foster et al., 2018). Coarse PM compounds may trigger inflammatory
responses and the production of reactive oxygen species by the local immune system once reaching
the fluid lining the airways (Ljubimova et al., 2018; Woodward et al., 2017). Another route for the
coarse PM to permeate the brain is to deposit in the olfactory epithelium and leach the compounds
present on coarse PM to olfactory sensory neurons (Cheng et al., 2016).
Currently, coarse PM is regulated under the PM10, which consists of both coarse and PM2.5 with
different sources, removal mechanisms, chemical composition, and health impacts. Due to higher

3
settling velocity and short residence time, coarse PM mass concentration is more influenced by
local and short-lived sources, demanding a high time-resolution sampling. Thus, their chemical
composition and possible sources may differ from place to place. While numerous sampling
networks have been placed worldwide to measure and report PM2.5, coarse PM has been
undermined and less investigated. Given that emerging new technology may generate new sources
for air pollution and current regulations on PM2.5, it is desirable to investigate coarse PM
independently as a size range with distinct characteristics.
The first study had the following objectives:
• Separation of coarse PM from total PM
• Eliminating the noises of strong light absorbers to focus on dust particles
• Assessing the reliability of calculations and feasibility of using an optical-based method in
an environment impacted by vehicular emissions
• Estimating the mass concentration of dust particles based on their light absorption
properties
The second study had the following objectives:
• Evaluating the long-term trends in PM2.5-bound OC mass concentrations in Los Angeles.
• Identifying the main contributing sources to total OC and its volatility fractions from 2015
to 2019.
• Quantifying a ratio to predict the mass concentration of primary and secondary organic
aerosols based on criteria gaseous pollutants.
The last study had the following objectives:
• Enriching the concentration of coarse PM for high-time-resolution measurements
• Determining the total coarse PM-bound OC and its volatility fractions
• Analyzing the temporal and seasonal variation of coarse OC
• Identifying the major sources of coarse OC in Los Angeles

4
1.3 Overview
This thesis proposal presents my research during the past years under the supervision of
Professor Constantinos Sioutas, intending to provide a scientific basis for effective regulation of
OC particles and developing a new method to measure PM components in ambient. The thesis
proposal includes the following chapters:
Chapter 1 provides a general concept of urban particulate matter and its health impacts, and the
rationale of this investigation.
Chapter 2 presents a novel optical-based method to measure dust particle mass concentration
as the dominant component of coarse particles.
Chapter 3 explores the primary and secondary organic carbon variations during the past years
and their relationship with criteria gaseous pollutants, which gives insight on the prediction of
SOA and POC with respect to the gaseous pollutants.
Chapter 4 focuses on the temporal and seasonal variation of ambient coarse PM-bound organic
carbon. The results will help identify the primary sources of coarse OC in Los Angeles.
Chapter 5 concludes the findings of the present investigation and outlines the possible strategies
that would help establish cost-effective air quality standards to protect public health from CPM
exposure. It also identifies the limitations of the current investigation and provides suggestions for
future research on this subject.

5
Chapter 2: Real-time measurements of mineral dust concentration in coarse
particulate matter (PM10-2.5) by employing a novel optical-based technique in
Los Angeles
2.1 Introduction
Ambient dust particles have been identified as one of the most abundant types of aerosol
dominating PM10 in many places throughout the world, they have been associated with adverse
health effects such as respiratory and cardiovascular diseases by depositing in the respiratory
system (Hochgatterer et al., 2013; Middleton et al., 2008), and they have been considered in the
context of regulation (WHO, 2018). Inhaled dust particles induce a series of immune responses
and pathological mechanisms in the respiratory system that increase the oxidative stress in
respiratory epithelial cells, which act as the first physical barrier against dust particles (Goudie,
2014; Honda et al., 2014; Ren et al., 2014). Recent epidemiological studies have reported that dust
particles can transport bacteria, fungi, and heavy metals, increasing the possibility of instigating
and transmitting diseases (Ardon-Dryer et al., 2020; Goudie, 2020; Shotyk et al., 2016). Moreover,
mineral dust affects the Earth's radiative balance by scattering and absorbing solar radiation. Over
the last decades, dust particles have been considered to create a net cooling effect at the
surface (Evan et al., 2006). However, recent studies investigating light absorption characteristics
of mineral dust have shown that the warming impact of dust aerosols has been previously
underestimated, and dust aerosols can lead to a net positive radiative forcing and affect regional
and global climate (Adebiyi and Kok, 2020; Wu et al., 2018). For example, ambient mineral dust
can impose a cooling effect by reflecting solar radiation to space (Myhre et al., 2014) or warming
effects by depositing on ice sheets and increasing their ion content (Greilinger et al., 2018).
Despite the significance of mineral dust in terms of health risks and climate impacts, few near
real-time techniques can distinguish dust aerosol in the PM10 size range (Ealo et al., 2016; Pan et
al., 2017). For example, the Streaker sampler, Davis Rotating-drum Unit for Monitoring (DRUM)
sampler, and the Semi-continuous Elements in Aerosol Sampler (SEAS) have been used for
measurements of dust particles with hourly or sub-hourly resolutions (Bukowiecki et al., 2005;

6
Chen et al., 2016; Visser et al., 2015). More recently, technological developments such as
employing microfluidic paper-based analytical devices based on a distance-based detection motif
(Cate et al., 2015), using magnetic properties to detect particle-bound metals (e.g., Fe) (Li et al.,
2017), and operating a semi-continuous Xact 625 automate multi-metals monitor (Fang et al.,
2015; Phillips-Smith et al., 2017) have resulted in a more accurate and precise prediction of
ambient metal species. However, the main disadvantage of these techniques is that they are timeconsuming, expensive, and require considerable analytical resources. On the other hand, the online measurement of mineral dust levels using light absorption properties of aerosols offers an
exciting and robust approach. The on-line measurement of mineral dust provides higher temporal
resolution, which adds more information and details to the interpretation of embedded dust
particles in PM10. These techniques can capture the temporal trends of mineral dust more
accurately. For example, they provide a better insight into distinguishing the long-range
windblown mineral dust from locally-emitted re-suspended road dust in urban environments.
Time-resolved measurements of mineral dust concentration are especially important in a large
metropolitan area like Los Angeles, where significant differences in meteorological conditions
between daytime and nighttime could strongly impact the density and composition of coarse
particulate matter (PM) (Cheung et al., 2011).
Few studies investigated the possibility of using aerosol absorption characteristics to determine
its concentration in PM10, mainly using Aethalometer instruments that depend primarily on iron
compounds' absorption (Fialho et al., 2014; Fialho et al., 2006; Fialho et al., 2005). Zhang et al.
(2008a) measured iron loadings as a surrogate of minerals to determine carbonaceous fraction and
the contribution of light-absorbing components of ambient aerosols. Recently, Caponi et al. (2017)
conducted a chamber study on dust samples from various sources worldwide, focusing on lightabsorption properties of size-segregated dust aerosols. The authors determined optical properties
(e.g., mass absorption coefficient) by using multi-wavelength aethalometers combined with the
iron content analysis of samples. They reported values in the range of 0.037-0.135 m2
/g for PM10
and 0.095-0.71 m2
/g for PM2.5 at 320 nm wavelength. The measurement of mineral dust
concentration by optical-based technique might be challenging and associated with uncertainties
given that in urban areas, dust is usually internally mixed with black carbon (BC), which has a

7
significantly higher mass absorption cross-section (Ramanathan et al., 2001). BC is the strongest
light-absorbing component of PM2.5 (PM with aerodynamic diameter < 2.5 µm), dominating the
radiation absorption measured by on-line monitors, making it difficult to focus solely on the small
contribution of dust particles to light absorption. As an alternative approach, Drinovec et al. (2020)
enriched the concentration of coarse PM (particles with 2.5 ˂ ˂ 10 µm, as the typical size range
of mineral dust particles) to decrease the impact of light absorption by PM2.5-bound BC, resulting
in more efficient measurements of mineral dust absorption and mass concentration.
Unlike conventional filter-based approaches, in which intensive time and effort are required to
determine the time-integrated mass concentration of mineral dust, a real-time monitoring approach
has not been systematically explored. Additionally, limited peer-reviewed studies have examined
the discrepancies of different light absorbers in urban areas of the US that are heavily impacted by
traffic emissions (in particular, emissions of black carbon), thereby limiting the understanding of
the light absorption properties of different PM components. In this study, we demonstrated the
feasibility of using an optical-based method to measure the light absorption and the mass
concentration of mineral dust in central Los Angeles by implementing an approach similar to that
by Drinovec et al. (2020). A high-volume coarse PM virtual impactor (VI)/concentrator was
combined with two online aerosol light absorption monitors to measure aerosol absorption at two
different wavelengths. The concentration-enriched coarse particles were collected on Teflon filters
simultaneously and were chemically analyzed for each day's metals and crustal element content.
First, we evaluated the VI Enrichment Factor (EF) by measuring the particle size distributions at
the inlet of the VI and inside its minor flow. The enhanced absorption coefficient of mineral dust
was calculated by subtracting the absorption measured with the VI from that of PM2.5. The
correlation between a reference value for mineral dust concentrations measured by a filter-based
approach and mineral dust absorption determined by an optical-based approach yielded the mass
absorption cross-section of dust particles. These data were subsequently applied to calculate the
mineral dust mass concentrations with high temporal resolution.

8
2.2 Data and Methodology
2.2.1 Sampling location, period
Our field campaign took place at the Particle Instrumentation Unit (PIU) of the University of
Southern California, located 3 km south of downtown Los Angeles (LA). The PIU is in proximity
to the I-110 freeway and is impacted by various pollution sources, such as vehicle and industrial
emissions (Heo et al., 2013b; Mousavi et al., 2018a; Tohidi et al., 2021). Thus, the site is a suitable
representative of a typical urban area affected by vehicle emissions as well as re-suspended road
dust emissions in Los Angeles (Hasheminassab et al., 2020).
We tested our approach in three periods, summer (i.e., twelve days in July 2020), fall (i.e., six
days in October 2020), and winter (i.e., four days in February 2021). We acknowledge that our
sampling timeframe is limited to a few days each season, even though we aimed to investigate the
possibility of using the optical-based approach under variable meteorological conditions that might
affect mineral dust concentrations; for simplicity, hereafter, we call each period by the
corresponding season. Table 2-1 shows the average meteorological data, including temperature
and wind speed for the three investigated periods provided by the California Air Resources Board
(CARB) for the closest air monitoring site, approximately 3 km to the north of the PIU. Ambient
temperatures were predictably higher in the summer than winter, while the wind speed during the
summer period was lower, resulting in lower dispersion of ambient coarse aerosols. The recorded
relative humidity reached a minimum value of 20% in fall and peaked during summer with an
average of 70%.
Table 2-1. Average meteorological data for each period during the sampling campaigns.
Sampling Site Summer Fall Winter
Temperature (°C) 25.4±2.11 19.50±0.5 13.5±2.5
Wind speed (m/s) 2.00±0.11 3.73±0.45 3.60±0.43
Relative humidity (%) 70.0±2.33 30.7±10.2 60.2±6.7

9
Instrumentation
Figure 2-1 shows the schematic of the sampling setup for measuring light absorption and
collecting coarse PM samples. As shown in the figure, ambient air entered into the system after
passing through a coarse PM VI at the first line of our setup.
Black carbon has been identified as the strongest light-absorbing component of PM2.5,
dominating radiation absorption (Krasowsky et al., 2016), making it difficult to determine the dust
light absorption characteristics. To overcome this challenge, following the approach by Drinovec
et al. (2020), we employed a virtual impactor developed earlier by our group (Kim et al., 2001),
operating at a sampling flow rate of 110 L/min. Particles with an aerodynamic diameter larger than
the nominal 50% cut-point of the VI of approximately 1.7 µm are carried out by the minor flow
rate of 5 L/min, enriched in concentration ideally by the ratio of total to minor flows (~21), while
the remaining aerosol is drawn into the major flow of the VI at 105 L/min. The performance of
Figure 2-1. Schematic of the sampling setup. First line: using a coarse PM virtual impactor
(VI) to measure light absorption and collect coarse PM samples. Second line: using a PM2.5
impactor to measure light absorption of PM2.5.

10
this VI has been validated extensively in previous studies (Kim et al., 2001; Wang et al., 2013).
Two single-wavelength Aethalometers (Model AE51, AethLabs, USA) were employed
simultaneously using TFE-coated glass fiber filter to measure the light absorption of concentrated
coarse particles at 370 nm and 880 nm. The contrast in the light absorption properties of mineral
dust has been reported to be more pronounced compared with black carbon at the 370 nm channel
(Drinovec et al., 2017). We, therefore, estimated the mass concentration of mineral dust based on
measurements at this wavelength. In the parallel second line, ambient PM passed through a PM2.5
pre-selective inlet (a 90-degree elbow made of an aluminum tube with a diameter of 0.5 cm
(Pirhadi et al., 2020a)) with a flow rate of 5 lpm, and the aerosol light absorption was measured
using the aforementioned Aethalometer at 370 nm and 880 nm. The Aethalometer model AE51
follows the same basic procedures as the older models. This instrument collects samples by
drawing air continually on filter tape and measuring the light attenuation by comparing light
transmission through the loaded part and a blank portion of the filter (Drinovec et al., 2015; Ferrero
et al., 2011). It measures optical attenuation of samples instantaneously with a high time resolution
of 1s or 300s (Cheng and Lin, 2013). It should be noted that the Aethalometers were set to work
at a time resolution of 300s and a flow rate of 100 ml/min to ensure that the recorded data are not
associated with high uncertainty and noise levels (Targino et al., 2017).
2.2.2 Chemical analysis
In the minor flow of the VI, the concentrated coarse PM was also collected on Teflon filters
(37-mm, Pall Life Sciences, 2-μm pore, Ann Arbor, MI) to measure the concentrations of metals
and trace elements in that size range. A total of 22 Teflon filters were used to collect samples, 12
filters for summer, 6 for fall, and 4 for winter. The elemental content of filters (50 elements) was
quantified using the magnetic-sector inductively coupled plasma mass spectroscopy (ICP-MS)
method by means of low-level mixed acid microwave-assisted digestion of the whole filter (Lough
et al., 2005).
Iron (Fe) has been reported as a significant component of mineral dust (Almeida et al., 2005;
Perrino et al., 2014), and Calcium (Ca), Magnesium (Mg), Aluminum (Al), Silicon (Si), and
Potassium (K) have been documented as tracers of soil and dust (Heo et al., 2009; Thurston et al.,

11
2011; Zong et al., 2016). Furthermore, the presence of Titanium (Ti) in samples can be attributed
to mineral dust as well as re-suspended road dust (Altuwayjiri et al., 2022; Fitzgerald et al., 2015;
Song et al., 2006). Therefore, as an initial estimation, the concentration of mineral dust in coarse
PM was determined by multiplying typical crustal metals, including Al, K, Fe, Ca, Mg, Ti and Si,
by the proper factor to account for their common oxides, as follows (Chow et al., 1994):
= 1.89 + 1.21 + 1.43 + 1.4 + 1.66 + 1.7 + 2.14 2-1
Al was multiplied by a factor of 3.41 to estimate elemental Si mass concentrations since the
ICP-MS method used in this study does not measure Si (Hueglin et al., 2005).
2.2.3 Calculation of mineral dust concentration
Equation 2-2 calculates the light attenuation based on the proportion of a reference value to the
loaded filter spot with samples, where I0 is the light intensity that goes through a pristine portion
of the filter, and I is the transmission through the aerosol-laden spot on the 3 mm diameter filter:
ATN ≡ 100 (
0

) 2-2
The aerosol attenuation coefficient (bATN) of collected particles on filters is calculated using
Equation 2-3 (Weingartner et al., 2003):
b =

Δ
∆
2-3
where Q is the volumetric air flow rate of the instrument, set at 100 ml/min in this study, ΔATN
indicates the light attenuation variation during the time period of Δt on the sample spot area of A
(7.07 cm2 for AE51 model). Absorption coefficient (babs) was estimated from the attenuation
coefficient (bATN):
b = b

2-4

12
where C is the multiple scattering parameter (i.e., C=2.57). Drinovec et al. (2015) reported that the
multiple scattering parameter C strongly depends on the filter material used in the aethalometer,
C= 2.14 for quartz filters and C=2.57 for TFE-coated glass fiber filters. In addition, Ran et al.
(2016) proposed a C value of 2.52 for measurements using a TFE-coated glass fiber filter.
Based on the working principle of the VI, the concentration of PM10-2.5 was enriched by a factor
of 19, as we will show in the results section, while PM2.5 remained at their ambient air levels. We
also need to subtract the absorption signal of PM2.5 (dominated by black carbon) from the
absorption measured after the virtual impactor to determine the absorption caused solely by
mineral dust in coarse PM. Therefore, the mineral dust light absorption at both 370 and 880 nm
wavelengths was determined using the following equation:
, = �, − ,2.5�

2-5
where , and ,2.5 refer to the measured light absorption of concentrated coarse PM
downstream of the virtual impactor in the first line and PM2.5 in the second line, respectively.
In order to estimate the absorption of the mineral dust, the enrichment factor of the VI should
be considered in the calculations. In Equation 2-5, EF is the enrichment factor of coarse PM,
evaluated experimentally during the field campaign using an Optical Particle Sizer (Model 3330,
TSI, USA) before and after the VI. As shown in Figure 2-2, the enrichment of coarse particles
using our VI (with a cut-point diameter of 1.7 µm) was derived by performing several pre- and
post-VI tests using ambient aerosols at the sampling site and was found to be, on average, 19. This
value is in agreement with the design of this impactor and the expected theoretical EF of 21.

13
The absorption angstrom exponent (AAE) for mineral dust was determined based on the
following approach using light absorption values in two wavelengths (i.e., 370 nm and 880 nm)
(Ångström,1929):
, = . − 2-6
where , ,, and denote wavelength, the absorption coefficient, and a wavelengthindependent constant, respectively. Based on the above equation, by using linear interpolation
between �,� and () at 370 and 880 nm, the AAE is the slope between two known
points.
The mass absorption cross-section (MAC) of dust particles can be obtained from the following
equation (Yuan et al., 2021):
,370 = , ,370

2-7
Figure 2-2. The size distribution of particle mass with respect to particle size
before and after the virtual impactor (VI). Error bars are standard deviations.

14
where is the initial estimation of the mineral dust mass concentrations and determined in
section 2.2.3 from the metal and crustal content of collected coarse PM on filters for each sampling
day.
Finally, the high-time resolution measurements of the light absorption allow us to determine
the corrected mass concentration of mineral dust using the following equation (Drinovec et al.,
2020):
10−2.5 = �, − ,2.5�
× ,370
2-8
2.3 Results and discussions
2.3.1 Determination of the mineral dust absorption coefficient and enrichment factor
Figure 2-3 shows the time series of the absorption coefficient for two different aerosol size
fractions; 1) PM2.5, which is dominated by BC, and 2) concentrated coarse PM, which is mainly
composed of mineral dust particles. The depicted values were estimated using Equation 2-4 at two
wavelengths (i.e., 370 and 880 nm) for three time periods. In both wavelengths, significant
variations were observed in the absorption coefficients of ambient PM2.5 as well as concentrated
coarse PM. The light absorption values were generally higher in fall than in summer and winter,
corresponding to greater mineral dust concentrations in fall than in other seasons.

15
(a)
(b)
While the absorption coefficient in the PM2.5 line was closely related to that of the post-VI line
at the 880 nm channel, we observed high values and a weak correlation between the absorption
coefficient of the post-VI and PM2.5 lines at the 370 nm channel. As shown in Figure 2-3, the
Figure 2-3. Absorption coefficient of ambient PM2.5 and concentrated coarse PM at (a)
370 nm; and (b) 880 nm.

16
difference between absorption coefficients for PM2.5 and concentrated coarse PM is more evident
at the wavelength of 370 nm compared to 880 nm. Previous studies indicated that the absorption
coefficient of dust particles is highly dependent on their content in oxides, most notably iron. The
goethite accounts for up to 70% of the iron oxide mass, which controls light absorption in the UV
and visible spectrum (Alfaro et al., 2004; Formenti et al., 2014; Klaver et al., 2011). Due to the
higher absorbance of iron oxides at a shorter wavelength (Caponi et al., 2017; Lázaro et al., 2008),
our results showed that the absorption coefficient at 370 nm is approximately 7 times larger than
that at 880 nm.
The enhancement of aerosol light absorption after the VI is associated with the enrichment of
the PM coarse fraction. While the post-VI fraction of PM2.5 is similar to the ambient, coarse PM
is enriched in concentration. By normalizing the subtracted light absorption coefficients using the
EF, the average absorption coefficients of mineral dust were 2.7±1.6 Mm-1 at 370 nm and
0.41±0.16 Mm-1 at 880 nm wavelengths for the entire campaign period, which are in good
agreement with the reported values in Cyprus at 370 nm (~2 Mm-1
) and 880 nm wavelengths (~0.5
Mm-1
) (Drinovec et al., 2020).
The AAE expresses the absorption variation as a function of the wavelength, which depends on
the shape, chemical composition, and size of the particles (Li et al., 2016; Scarnato et al., 2013;
Schuster et al., 2006). AAE is considered one of the properties of different aerosol species, which
can be used to infer the presence of typical aerosols in the atmosphere (Giles et al., 2012). For
example, the AAE values close to 1 represent absorption by pure black carbon, while dust particles
generally have AAE values of around 2 (Kirchstetter et al., 2004; Lu et al., 2015; Russell et al.,
2010). AAE values also depend on the wavelength range considered for its determination (Russell
et al., 2010). As illustrated in Figure 2-4, the AEE value for mineral dust particles in central Los
Angeles is 2.18, which was calculated using Equation 2-6.

17
2.3.2 Calculation of the mineral dust mass absorption cross-section
We evaluated the ,370 in central Los Angeles based on the initial estimation of
the mineral dust mass concentrations as elaborated in section 2.2.2. Moreover, we also determined
filter-based mineral dust concentration by following Sciare et al. (2005) approach in which they
assumed that Calcium (Ca) constitutes 12% of mineral dust mass concentrations.
Figure 2-5 compares the results for the two proposed methods by examining the correlation
between the mass absorption cross-sections at 370 nm with mineral dust mass concentrations
corresponding to each sampling day. While both methods showed high correlation between the
mineral dust concentrations and the absorption coefficient, the first method exhibited stronger
correlation coefficient (R2 = 0.91) compared to the second approach (R2 = 0.82). Furthermore,
both methods yielded similar MAC values (i.e., 0.21 and 0.20 m2
/g, respectively) in central Los
Angeles for the entire campaign period with that of Drinovec et al. (2020) (i.e., 0.24 m2
/g) in
Cyprus. Due to the higher correlation coefficient, we decided to use the first method in our
calculations which we considered more robust.
Figure 2-4. The logarithm of absorption coefficient, babs, for mineral dust in two distinct
wavelengths (i.e., 370 nm and 880 nm) with respect to Log (λ).

18
(a)
(b)
Figure 2-5. Mass absorption cross-section of mineral dust. a) First method: using typical crustal
metals- Al, K, Fe, Ca, Mg, Ti and Si. b) Second method: Assuming 12% Ca in mineral dust (each
data point corresponds to a sampling day).

19
The MAC value for mineral dust was one to two orders of magnitude smaller compared to other
light-absorbing aerosols. Mousavi et al. (2018a) estimated MAC values for black carbon in the
Los Angeles basin at the 370 nm and 850 nm wavelengths. The quantified MAC values were
within the range of 16.5-20 m2
/g at 370 nm, while the corresponding values at 850 nm were
between 5.3-8.1 m2
/g. Similar MAC values were also reported in Milan (Mousavi et al., 2019).
The MAC values in Ontario were also in the range of 11.45 m2
/g and 10.70 m2
/g (Healy et al.,
2017). The values for brown carbon were mostly in the range of 2.0-7.0 m2
/g at 350 nm (Massabò
et al., 2016). The observed difference between reported and our MAC values of PM components
is consistent with the differences in light absorption properties of various aerosols.
To investigate the use of Al and Fe as the tracers of mineral dust, we examined the correlation
between Al and Fe with Ca collected on the Teflon filters in our sampling setup, which are shown
in Fig. 6. High correlation coefficient between Fe and Al (R2 = 0.95, Fig. 6 (a)) and equally high
correlation of these two metal elements with Ca (i.e., R2 ≥0.83, Fig. 6 (b) and (c)) indicate a
common source for soil dust particles in the area.
(a) (b)

20
(c)
The light absorption by mineral dust depends mainly on the absorption by iron (Alfaro et al.,
2004; Di Biagio et al., 2017). The most abundant crustal element measured on the filters was iron,
accounting for about 1.5% of the total mass of coarse PM. This value is lower than values reported
in other areas. For example, the iron was measured to contribute 3% in the Middle East (Linke et
al., 2006) and Sahara of 3.5% (Caponi et al., 2017), with dust MAC values of 0.09 m2
/g and 0.099
m2
/g, respectively, which are much lower than the MAC value estimated in the present study (i.e.,
0.21 m2
/g). In contrast, both the iron mass fraction and MAC values determined in our study were
close to those reported by Drinovec et al. (2020) in Cyprus, with coarse PM iron content and MAC
values of 1.9% and 0.24 m2
/g, respectively. Our AAE (i.e., 2.18) is lower compared to the reported
values in Middle East and Sahara (i.e., in the range of 2.9 to 4), which are mostly impacted by
frequent fresh dust events (Caponi et al., 2017; Fialho et al., 2005). This difference in the MAC
values and AAEs could be attributed to the contamination of mineral dust with black carbon in
urban areas, whose aerosols contain a higher content of atmospherically aged, traffic-induced resuspended road dust (Hasheminassab et al., 2014c). In addition, our AAE value is in line with
reported values in East Asia (i.e., the Aerosol Characterization Experiments (ACE) Asia program)
for an area impacted by both urban pollution and mineral dust components (AAE=2.2) (Bergstrom
Figure 2-6. Correlation between Al and Fe (a), and correlation between Al (b) and Fe (c) with
Ca measured on PM10 filters (each data point corresponds to a sampling day).

21
et al., 2007) and other published values in Kanpur (northern India), Ilorin (West Africa) and
Bahrain (Middle East) which were in the range of 1.9 to 2.2 (Eck et al., 2010; Russell et al., 2010).
Higher AAE values generally indicate mineral dust particles without substantial contamination
by mixing with black carbon, while lower values represent polluted dust internally mixed with BC
near pollution sources (Scarnato et al., 2015; Tian et al., 2018; Xia et al., 2021). Based on the
above discussion, it is more challenging to determine the mass concentration of mineral dust in
urban environments due to higher contamination with black carbon, which further underscores the
necessity of employing new approaches such as using coarse particle concentrators to decrease the
light absorption signal by black carbon.
According to Figure 2-7, the average mass concentration of mineral dust in central Los Angeles
was 11.90±6.8 µg/m3
. During the sampling campaign, the daily average minimum and maximum
were 4 and 27.73 µg/m3
, respectively. Our site estimations showed higher mass concentrations of
mineral dust during the fall and winter campaigns (i.e., 19.3±7.8 and 11.4±3.0 µg/m3
, respectively)
than in summer (i.e., 8.50±3.6 µg/m3
). This is likely due to the higher wind speeds and the lower
relative humidity during the colder periods of the year, leading to a higher resuspension of mineral
dust compared to the prevailing meteorological conditions in the summertime. Similar seasonal
variations in mineral dust concentrations have been detected in previous studies in Los Angeles.
For example, Pakbin et al. (2011) observed that crustal metal concentrations (e.g., Al, Ca, K, Ti,
Fe, Si, and Mg) in central Los Angeles peaked in October, while the concentrations remained lower
in summer. Cheung et al. (2011) also observed higher concentrations in fall and winter compared
to summer in the same area. The authors showed that PM sources from natural crustal elements
(e.g., Fe, Al, Ca, Si, Na, and Mg) were major contributors to coarse PM mass, while elements
linked to anthropogenic sources contributed to a lesser extent. The elevated concentrations in fall
and winter can be attributed to the corresponding differences between wind speeds (the black boxes
in Figure 2-7 (b)) and relative humidity during our sampling days in different seasons. Considering
the proximity of our site to a major interstate freeway (i.e., I-110), re-suspended road dust can have
a significant contribution to measured values, leading to higher mineral dust concentrations
favored by higher wind speeds as well as increased soil dryness. Previous studies in central Los

22
Angeles demonstrated that road dust is primarily composed of mineral material (most commonly
Al, Fe, Ca, K, and Ti), the concentrations of which are highest during seasons with higher
atmospheric mixing and higher traffic movement (Hasheminassab et al., 2020; Pakbin et al., 2011).
This means that even though the nature of these dust particles is not necessarily related to traffic
(e.g., non-tailpipe and tailpipe) emissions, traffic activities may increase the re-suspension of the
mineral dust aerosols that had already been deposited on the surface of the roads.
We should note some uncertainties in estimating mineral dust concentration, which mainly arise
from the instrumental uncertainty as well as the difference in chemical composition of mineral
dust particles. The calculation of the enrichment factor affects the determination of MAC value
and dust concentration. Based on the particle size distribution measurements, the VI did not
introduce any significant uncertainty into the estimation of the enrichment factor. It is worth noting
that several pre- and post-VI tests were done, and the variability (i.e., standard deviation) among
the tests, ranging between 8 and 10%, is considered the uncertainty of the selected EF. Since
mineral dust concentration depends on the iron PM content (Almeida et al., 2005; Perrino et al.,
Figure 2-7. The real-time mass concentration of mineral dust (a) and the average mass
concentration of mineral dust for each measurement period, and the corresponding wind speed
values (b). Error bars are standard deviations.

23
2014), Drinovec et al. (2020) reported that there might be up to 40% uncertainty in the ratio of
Fe/Ca, affecting the calculation of MAC values. Our comparison of the two different methods in
the initial estimation of the mineral dust mass concentrations showed that the first method (with a
correlation coefficient of R2 = 0.91 compared to 0.81 for the second method) can further lower the
uncertainty of measured dust concentrations.
2.3.3 Comparison of the real-time mineral dust mass concentration and coarse PM mass
concentrations
The mass concentrations of PM10 and PM2.5 were obtained from the nearest CARB monitoring
station in central Los Angeles (CELA) and averaged over each day in order to compare PM10-2.5
(i.e., coarse PM) with the real-time mineral dust mass concentrations estimated in this study
(Figure 2-8). Mineral dust is a major component of coarse PM in central Los Angeles, with an
average contribution of 48.0 ±19% to total reported coarse PM values at CELA by CARB for the
entire period of the campaign. The percent contributions of mineral dust in fall (59.03±11%) and
winter (59.34±11%) were notably higher than in summer (38.46±15%). Cheung et al. (2011)
reported that crustal materials were the most abundant category, accounting for an average
47.5±12% of the total coarse PM mass concentration ranging from over 50% in cold seasons to as
low as 38% during warm seasons in the Los Angeles basin. These results are in very good
agreement with our results determined by the optical-based approach.

24
(a)
(b)
Figure 2-8. Comparison of the real-time mineral dust mass concentration and coarse PM mass
concentrations in Los Angeles (a) and correlation between the real-time mineral dust mass
concentration and reported coarse PM mass concentration by CARB

25
In order to further investigate the validation of our results with source contribution of coarse
PM, we separated the results obtained during fall and winter from those during the summer. Even
though we observed a high correlation between coarse PM mass and dust concentrations for the
entire campaign (i.e., R2 =0.64), this correlation was stronger during the colder seasons (i.e., R2
=0.75), verifying that mineral dust is a major contributor to total ambient coarse particles during
these periods. While mineral dust was also a major contributor to total coarse PM and followed
the trends of coarse PM during our sampling days in the summer period, there was a weak
correlation between mineral dust concentrations and coarse PM reported by CARB (i.e., R2 =0.20).
This might be explained by the fact that sea salt particles are more prevalent in spring and summer
(12.7±9.7% of the total ambient coarse particles compared to 5.9±7.7% in fall and winter) due to
the strong prevailing onshore southwesterly winds in that period, which transport sea salt particles
along the coast to the Los Angeles (Cheung et al., 2011). Previous studies documented sodium
(Na) as a chemical marker of sea salt particles (Hughes et al., 2000; Oroumiyeh et al., 2022; Terzi
et al., 2010; White, 2008). Sodium mass concentrations were significantly higher (Pvalue < 0.05)
during the summer campaign (i.e., 5.4±3.5 µg/m3
) compared to the fall and winter campaigns (i.e.,
2.9±0.8 and 2.5±0.9 µg/m3
, respectively). However, our conclusions are restricted to a limited
number of samples, which may not represent the long-term and possibly seasonal variability of the
mineral dust concentration in the area, either because they may be affected by short-lived spikes
from different emission sources or influenced by temporary meteorological extremes. Therefore,
it is possible that the relatively higher contribution of sea salt particles decreases the correlation
coefficient between coarse PM mass and dust concentrations in summer.
2.4 Summary and conclusions
This study investigated a novel optical-based approach for estimating mineral dust mass
concentrations in central Los Angeles during three periods in summer, fall, and winter. Using
aethalometers at 370 and 880 nm, the real-time light absorption of PM was measured in two
parallel lines. We have shown the potential of this approach by demonstrating its applicability in
an urban environment impacted by black carbon emissions. Utilizing a virtual impactor, the light
absorption by coarse particles enriched by 19-fold was estimated by minimizing the impact of

26
black carbon as the dominant light absorber in ambient air. A site-specific MAC value was derived
based on the typical crustal metals and trace elements that comprise the complex aerosol mixture
in that area. We observed lower concentrations of mineral dust during the summer campaign with
an average of 8.50 µg/m3 compared to fall and winter of 19.3 and 11.4 µg/m3
, respectively, which
can be attributed to corresponding meteorological conditions in those periods. Although our
estimations cannot represent the entire season and are limited to only 22 days, the daily mineral
dust concentration trends are in very good agreement with the reported coarse PM mass
concentrations by CARB. We should stress that our results are limited to a specific timeframe (i.e.,
22 days in three seasons), and generalization of these findings to an entire year should be made
with caution and should be explored in long-term campaigns.

27
Chapter 3: Quantifying ambient concentrations of primary and secondary
organic aerosol in central Los Angeles using an integrated approach coupling
source apportionment with regression analysis
3.1 Introduction
Total particulate carbonaceous material, including organic carbon (OC), elemental carbon (EC),
and carbonate carbon (CC), constitutes a significant portion of the PM2.5 mass in different urban
environments (Karanasiou et al., 2011). Several studies in the literature have shown strong
associations between increased levels of carbonaceous aerosols in ambient air and severe impacts
on human health as well as the climate (Atkinson et al., 2015; Bates et al., 2019; Chylek et al.,
2006; Grahame and Schlesinger, 2010; Ning et al., 2008). EC is emitted into the atmosphere due
to incomplete combustion of carbonaceous fuels, biomass burning, and cooking (Herich et al.,
2011; Zotter et al., 2017). OC is chemically mixed with other elements and can either be in the
form of primary organic aerosol (POA) or secondary organic aerosols (SOA). POA is originated
directly from primary sources (e.g., traffic emissions and biomass burning), while SOA is formed
indirectly through photooxidation of volatile and semi-volatile organic compounds in the presence
of sunlight (Jimenez et al., 2009; Ng et al., 2007; Saylor et al., 2015).
Although investigating the formation mechanisms, emission rates, and ambient concentrations
of POAs and SOAs is important due to their distinct physiochemical characteristics and
documented health impacts (Delfino et al., 2010; Künzi et al., 2015; Liu et al., 2020a; Wang et al.,
2017; Xu et al., 2020a), there is no straightforward approach to determine and quantify their
concentrations in the atmosphere. Receptor models, including the chemical mass balance (CMB),
which requires a priori knowledge of primary sources, and PMF that needs a large number of
ambient samples compared to CMB model, have widely been used in the literature to identify the
contribution of POA and SOA sources to the total OC concentrations (Stone et al., 2008). It has
been reported that using constraints in the PMF model significantly reduces the rotational
ambiguity of the resolved solutions (Norris et al., 2009; Paatero et al., 2002). As the PMF software
has built-in constraints, it is sometimes deficient in finding appropriate factor profiles and

28
contributions (Norris et al., 2009; Paatero et al., 2002). In such cases, a priori information about
source contributions, source profiles, or chemical species ratios can serve as additional constraints
(Norris et al., 2009). Many studies (Amato and Hopke, 2012; Amato et al., 2009; Sturtz et al.,
2014) used measured profiles from potential source-types to constrain the PMF source factors,
resulting in better correspondence between the calculated and measured abundances. Bae et al.
(2019b) employed CMB and PMF models to determine PM2.5 source contributions to ambient OC
at two urban locations in California's San Joaquin Valley. Shirmohammadi et al. (2016) also
applied a hybrid molecular marker-based chemical mass balance (MM-CMB) model to investigate
the source contributions to PM0.25-bound and PM2.5-bound OC concentrations in central Los
Angeles (CELA).
Another approach to identifying the POA and SOA concentrations has been the EC/OC tracer
method in earlier studies (Cabada et al., 2004; Masiol et al., 2017; Yu et al., 2004). Lim and Turpin
(2002) investigated the OC and EC hourly data to determine the concentrations of POA and SOA
in Atlanta, GA. They reported that SOA contributed up to 46% of measured OC in Atlanta,
consistent with the observations in the Los Angeles basin. However, these studies are expensive
and time-consuming since they generally require notably large datasets from various species (e.g.,
organic compounds, metals and trace elements, inorganic ions, and gaseous pollutants) with
acceptable uncertainties in order to obtain statistically robust and physically interpretable results
from source apportionment models (Manousakas et al., 2015). They also require expensive
instrumentation and analytical costs for the chemical analysis of different species.
To overcome the above-mentioned challenges, in this study, we investigated the correlation
between carbonous species in the atmosphere obtained from the PMF model and concentration of
criteria gaseous pollutants reported by air quality agencies, as the means to estimate the 24-hr
average ambient concentrations of POA and SOA in CELA. We utilized the outputs of our
comprehensive PMF model for different years (i.e., 2015, 2017, and 2019) at CELA to derive
linear regressions between the carbonaceous species and criteria pollutants. The input to our model
was provided by the United States Environmental Protection Agency (US EPA) through the
Chemical Speciation Network (CSN).

29
3.2 Methodology
3.2.1 Sampling location, period, and instrumentation
Figure 3-1 shows the location of the monitoring site located in CELA (34°03′59.7″N,
118°13′36.8″W). CELA is in the heart of an 18 million urban area (i.e., Greater Los Angeles Area,
the largest urban area in the United States) and is impacted by various types of emission sources
such as vehicular and industrial (Heo et al., 2013b; Mousavi et al., 2018a; Shirmohammadi et al.,
2017). Previous studies have documented that CELA site is representative of a typical urban area
in Los Angeles (Hasheminassab et al., 2014a).
The comprehensive PM2.5 chemical composition data as well as different air pollutants used in
our study were obtained from the monitoring conducted by the US EPA as part of the Air Quality
System (AQS) and CSN database (US EPA, 2019a) for the entire years of 2015, 2017, and 2019
from January through December. In our study, we focused on these recent years (i.e., 2015-2019)
because the correlations between different particulate and gaseous pollutants strongly depend on
the emission sources in the area, and earlier studies have documented that due to the
Figure 3-1. Location of the monitoring site in central Los Angeles (CELA)

30
implementation of strict air quality regulations in California such as development of aftertreatment
technologies, chemical composition of air pollutants emitted from the sources has significantly
changed over the recent years (Biswas et al., 2009; Herner et al., 2011; Pakbin et al., 2009).
According to the CSN database, 24-hr time-integrated PM2.5 samples were collected on quartz
filters using the URG 3000N Carbon Sampler (URG-3000N Carbon Sampler, URG Inc., 3000N
(module C), USA) with an operational flow rate of 22 liters per minute and on
polytetrafluoroethylene (PTFE) and nylon filters employing a low volume Met One Speciation Air
Sampling System (SASS, Met One Instruments Inc.,131 OR, USA) with a flow rate of 6.7 liters
per minute (SCAQMD, 2014; SCAQMD, 2015). The concentration of EC, OC as well as OC
volatility fractions were measured utilizing the Desert Research Institute (DRI) thermal/optical
Carbon Analyzer (DRI thermal/optical carbon analyzer, Atmoslytic Inc., model 2001, USA)
applying the Interagency Monitoring of Protected Visual Environments (IMPROVE_A) thermal
protocols. The limits of detection (LOD) were equal to 0.45 μg/m3 and 0.06 μg/m3 for OC and EC,
respectively (Desert Research Institute, 2005). According to this protocol, the OC fractions of the
collected PM2.5 are gradually desorbed from quartz filters as temperatures are ramped through
different stages: OC1 (<140 °C), OC2 (140–280 °C), OC3 (280–480 °C). The OC fractions mainly
consist of semi-volatile organic compounds with different vapor pressures, and their volatility
decreases from OC1 to OC3 (Chow et al., 2007; Chow et al., 1993).
Inorganic Compendium Method IO-3.3 (US EPA, 1999) was used by applying the energy
dispersive X-ray fluorescence (EDXRF) to quantify the trace element and metal content of PTFE
filters. Ion chromatography (IC) was used to determine the content of inorganic ions in PM2.5
samples collected on nylon filters (US EPA, 1999). In addition to the abovementioned PM2.5
chemical components, the concentrations of carbon monoxide (CO) and ozone (O3) were
continuously recorded by means of non-dispersive infrared photometry (NDIR) analyzer (AQMS400, Focused Photonics Inc. ) and ultraviolet (UV) continuous monitor (49, Thermo
Environmental Instruments Inc.) (US EPA, 2019b), respectively, while the chemiluminescence
method was implemented for the determination of NO2 as indicated by the US EPA (Demerjian,
2000), which has been discussed in detail elsewhere (Maeda et al., 1980).

31
As the above-mentioned data are provided by the US EPA, highest standards of quality
assurance and control are employed in field and lab audits to ensure the quality of the data
(Solomon et al., 2014). The field audits consist of six parts: (1) determining detailed
responsibilities for the site operations, (2) safety inspection, (3) confirming the quality of the
selected site as well as the sampling tools according to EPA standards, (4) maintenance inspection
of the sampling site and logbooks, (5) quality insurance of the sample handling and proper chain
of custody, (6) validating appropriate procedures for storage and delivery. Moreover, all
laboratories analyzing the CSN samples are annually evaluated for their chemical analysis
reliability using performance evaluation (PE) samples provided by the National Analytical
Radiation Environmental Laboratory (NAREL). These PE samples include the filters and solutions
with a known quantity of the analyte loadings prepared by NAREL as a reference. Solutions of
anion and cation with known concentrations are also used for ion chromatography analysis.
Performance audit (PA) samples, including National Institute of Standards and Technology (NIST)
traceable metal weights, are also sent to analytical laboratories.
Regarding trace elements and metals, PM samples are analyzed independently by the US EPA’s
National Exposure Research Laboratory (NERL) EDXRF facility. Once the audited laboratories
analyze the filters, they are sent back to NERL for reanalysis to certify that the level of elements
on filters has not been affected by handling or delivery. In addition to the above-mentioned quality
assurance procedures, NAREL conducts on-site laboratory technical systems audits (Solomon et
al., 2014).
3.2.2 Source apportionment analysis
3.2.2.1 PMF model
PMF is a receptor model which has widely been used to identify the sources and quantify their
contributions to the target variable (here, ambient PM2.5-bound OC) (Paatero and Tapper, 1994;
Wang et al., 2019). This multivariate model is used for solving the chemical mass balance
equation:

32
= � +

=1
3-1
where , the mass concentration, refers to the ith sample and the jth species and number of
factors p. stands for the airborne mass concentration contributed by kth factor to ith sample.
indicates jth species resolved factor of each source. is the model residual error in ith sample
for jth specie.
The main goal of the PMF model is to find out the most reasonable factor profile and
contribution by minimizing the objective function, Q, based on the following equation:
where n and m represent the number of samples and species, refers to the uncertainty of the
measured mass concentration for the jth species and the ith sample.
The above-mentioned minimization is conducted by assigning non-negative values to the factor
profiles and contributions as the constraints of the optimization process (Norris et al., 2014). The
following equation was utilized to determine the uncertainties of the input species to our PMF
model (Paatero et al., 2014):
σ = �0.05 × � + 3-3
in which σ is the calculated uncertainty of the ith sample and the jth species. indicates the
detection limit assigned to the jth species.
The mass concentration of the species as well as the above-mentioned user-defined uncertainty,
were employed as input to the US EPA's PMF model version 5.0, and the OC concentration was
chosen as the “total variable”. The PMF runs were conducted using the robust mode in which the
impact of samples with significant uncertainties is minimized. To further validate the PMF outputs,
Q = ��

�
2
=1

=1
3-2

33
we performed different uncertainty analyses including the Bootstrap (BS), Displacement (DISP),
and BS-DISP (Bootstrap + Displacement) tests.
DISP analysis investigates the effects of rotational ambiguity by evaluating the largest range of
source profile values without a notable increase in PMF objective function (Q), and does not
capture the uncertainty of PMF solutions caused by random errors in the data. On the other hand,
BS analysis includes effects from random errors and partially includes effects of rotational
ambiguity. Unlike DISP and BS, BS-DISP analysis covers, to a great extent, the effects of random
errors and rotational ambiguity. Therefore, for modeling errors (e.g., variation of source profiles
with time, incorrect number of factors, etc.), DISP intervals are directly affected; however, BS
results are generally robust. In combined mode, the results of BS-DISP analysis are more robust
compared to DISP because the displacements in DISP analysis of BS-DISP are not as strong as
when performing DISP by itself. (Brown et al., 2015; Paatero et al., 2014; Reff et al., 2007).
Based on the results of the BS analysis, our PMF outputs were verified because for all the PMFresolved factors, around 90% of the results were re-mapped. Regarding the DISP analysis, our
PMF solutions were considered reliable without any rotational ambiguity due to the <1% drop in
the Q value and absence of any factor swap for the dQmax=4. Also, a sensitivity test for different
PMF runs with a different number of factors validated our number of factor selections (i.e., 5
factors).
3.2.2.2 PMF input
In the PMF model, different numbers of factors and various extra modeling uncertainty values
were investigated following a trial-and-error approach to identify the most interpretable and
statistically robust solution for emission sources contributing to the total OC mass concentrations.
The final number of factors (i.e., 5) were determined according to several criteria: 1) Strong
correlation (i.e., high linear regression R2 value) of predicted versus actual total OC mass
concentration, 2) Physically interpretable PMF-resolved source profiles, 3) Evaluation of the
uncertainty analyses on the PMF outputs (BS, DISP, and BS-DISP).

34
The optimum solution in our model included EC, OC, OCx (i.e., OC1, OC2, and OC3), sulfate
(SO4
2-
), O3, potassium ion to potassium ratio (K+/K), and metal elements such as zinc (Zn) titanium
(Ti), copper (Cu) and chromium (Cr). Numerous studies have documented EC, OC1, OC2, and OC3
as indicators of gasoline and diesel exhaust emissions (Cao et al., 2005; Schauer, 2003; Zong et
al., 2016), O3 and SO4 as chemical markers of the photochemical reactions and secondary aerosols
(Heo et al., 2015; Jacob, 1999; Taghvaee et al., 2018a), K+
/K as a frequently used tracer of biomass
burning (Lee et al., 2007; Zhu et al., 2017), Cu and Ti as surrogates of road dust and brake abrasion
particles (Adamiec et al., 2016; Harrison et al., 2012), and Cr as a marker of industrial activities
in the area (Mousavi et al., 2018b; Propper et al., 2015).
3.2.2.3 Linear regression analysis
The SOA concentrations were determined based on the contribution of the “SOA” factor to
total OC mass concentrations resolved by the PMF model. The POA concentrations were
calculated as the difference of total OC and PMF-resolved SOA mass concentrations. We then
conducted linear regression analysis between SOA and POA concentrations and different criteria
gaseous pollutants reported frequently by air quality monitoring stations. The time-integrated data
of the gaseous pollutants for different years were extracted from CSN database as mentioned
earlier. We also conducted linear regression analysis between EC and gaseous pollutants including
CO and NO2.
3.3 Results and discussions
3.3.1 PMF source apportionment results
3.3.1.1 Number of factors
Based on the correlation coefficient values between the predicted and actual total OC mass
concentration (R2 > 0.90), our PMF model quantified the contributions of 5 factors to the total OC
in CELA. As it will be elaborately discussed in the following sections, the PMF-resolved factors
were tailpipe emissions, non-tailpipe emissions, biomass burning, SOA, and local industrial
activities. The PMF-resolved factor profiles for the years 2015, 2017, and 2019 at our monitoring

35
site are shown in Figure 3-2. Figures 3-3 and 3-4 also represent the relative and absolute
contributions of the identified sources to the total OC mass concentrations for the study location.
(a)
(b)

36

37
(c)
3.3.1.2 Factor identification
Factor 1: Tailpipe emissions
The first factor was associated with ~50-60% loadings of EC and high loadings of OC1 (i.e.,
50-90%). This factor also demonstrated ~40-50% and ~30-40% contributions of OC2 and OC3,
respectively (Figure 3-2). EC is a well-known tracer of vehicular emissions (Mooibroek et al.,
2011; Zong et al., 2016), and OC1 has also been associated with tailpipe emissions (Cao et al.,
2006; Zong et al., 2016). Moreover, OC2 and OC3 have been reported as the significant
components of gasoline exhaust (Cao et al., 2006; Zhu et al., 2010), which corroborates the
Figure 3-2. PMF-resolved factor profiles for (a) 2015; (b) 2017; and (c) 2019.

38
vehicular origin of this factor. The factor also has a significant contribution to total OC mass in
CELA during the whole study period, accounting for ~35-45% of total OC mass in the investigated
site (Figure 3-3). Furthermore, the absolute contribution of this factor to total OC (Figure 3-4)
decreased significantly (Pvalue < 0.05) from ~1.5±0.20 µg/m3 to 1.0±0.10 µg/m3 over the 2015–
2019 period, which could be due to the adopted strict air quality regulations targeting tailpipe
emissions in California during the recent years as elaborately discussed elsewhere (Altuwayjiri et
al., 2021).
2015 2017 2019
Figure 3-3. The relative (fractional) contribution of PMF-resolved sources to ambient OC in
CELA over the years 2015, 2017, and 2019.
44%
33%
3%
18%
2%
37%
28% 3%
30%
1%
34%
28%
10%
19%
9%

39
Factor 2: Non-tailpipe emissions
The second factor was characterized by high loadings of Ti, Cu, and Zn (i.e., ~60-80%) (Figure
3-2), and contributed to a large portion of total OC mass concentration (i.e., 28-33%) according to
Figure 3-3. Previous studies documented Ti, Cu, and Zn as chemical tracers of particle brake wear,
tire wear, and engine abrasion (Adamiec et al., 2016; Harrison et al., 2012; Peltier et al., 2011;
Thorpe and Harrison, 2008). It should be noted that in recent years, electric vehicles (EVs) have
been replacing internal combustion engine vehicles. EVs are much heavier than other vehicles,
which increases the friction between their tires and road surfaces, resulting in higher re-suspension
of road dust particles (Timmers and Achten, 2016). Beddows and Harrison (2021) reported that
EVs have approximately 5% higher PM2.5 emission factors than euro-6 diesel and petrol
equivalents. Moreover, Farahani et al. (2021) showed an association between the growing use of
EVs and the increase in resuspended road dust emissions in the area. Therefore, a fraction of nontailpipe emissions could be attributed to EVs in the Los Angeles basin (Kapustin and Grushevenko,
Figure 3-4. Absolute source contributions to ambient OC mass concentrations during the years
of 2015, 2017 and 2019 in CELA

40
2020). As shown in Figure 3-3, the relative contribution of non-tailpipe particles to total OC were
28 ± 2.5% in 2015, 28 ± 2.2% in 2017, and 33 ±2.6% in 2019. According to Figure 3-4, the absolute
contributions of non-tailpipe emissions to total OC mass concentrations were comparable
throughout the study period (Pvalue of 0.12). Altuwayjiri et al. (2021) reported that the relative
(fractional) contribution of non-tailpipe emissions to the total OC mass increased during the 2005-
2015 period in CELA. Our findings also demonstrate a similar trend from 2015 to 2019, which is
most likely attributed to the lack of local regulations controlling non-exhaust emissions in
California.
Factor 3: Secondary organic aerosols (SOA)
The third factor demonstrated 80% contributions of sulfate and approximately between 45 and
75% contributions of O3, according to Figure 3-2, and it is another major contributor (>18%) to
total OC concentrations in CELA (Figure 3-3). Based on earlier studies, sulfate (in the form of
ammonium sulfate), O3, and SOA are formed through concurrent photochemical reactions
involving hydroxyl radicals (OH-
) in the ambient atmosphere (Carlton et al., 2009; Jacob, 1999);
thus, O3 and sulfate are regarded as surrogates of SOA formations in the atmosphere (Heo et al.,
2009; Taghvaee et al., 2018b). For example, Yuan et al. (2006) have illustrated significant
correlations between the SOA and secondary sulfate concentrations at multiple environments in
different seasons in Hong Kong. Moreover, it has been documented that organic and inorganic
secondary aerosols, including ammonium sulfate and SOA, are internally mixed in the atmosphere
(Harrison et al., 2016; Huang et al., 2014; Turpin et al., 1991). We, therefore, concluded that
“SOAs” is an appropriate label for this factor. According to Figure 3-3, the SOA fractional
contribution to total OC over the study period remained constant. However, our findings revealed
that the absolute contribution of this factor to total OC decreased overall from 2015 (~0.83 μg/m3
)
to 2019 (~0.46 μg/m3
) (Figure 3-4). This trend is likely due to the implementation of numerous air
quality regulations during the study period, which limited the emissions of primary organic
precursors of secondary aerosols. These regulations are elaborately discussed in sections 3.3.3 and
3.3.5.

41
Factor 4: Biomass burning
Other significant contributors to primary OA in urban areas are biomass burning and cooking
emissions (Crippa et al., 2013; Mohr et al., 2015; Shah et al., 2018; Sun et al., 2011). The fourth
factor is represented by high loadings of K+/K (i.e., ~70-80%). The ratio of K+/K has previously
been used as a tracer of biomass burning emissions in metropolitan environments, including CELA
(Jung et al., 2014; Soleimanian et al., 2019b; Yu et al., 2018). Furthermore, meat cooking sources
may also emit K+ (Simoneit, 2002). So it is conceivable that cooking emissions may also be
contributing to this factor since they are comparable in magnitude and chemical signature to
vehicular emissions of POA (e.g., Shah et al. (2018), Mohr et al. (2015), Sun et al. (2011)). While
it is common to expect higher contributions of biomass burning emissions to OC in winter, our
results showed comparable contributions during the warm and cold periods in CELA (Pvalue >
0.16). A potential reason for this observation may be attributed to the frequent summer time
wildfire events in the area (Okoshi et al., 2014; Warneke et al., 2012), counteracting the influence
of higher wood burning emissions during the winter time (Heo et al., 2013b; Lee et al., 2007). This
factor accounts for less than 10% of the total OC mass concentrations throughout the study period.
Factor 5: Local industrial activities
This factor has ~80-90% loading of Cr in its profile and has a negligible contribution to the
total OC over the investigated period. Previous studies in the literature have identified Cr as a
tracer of industrial activities (e.g., electroplating, refractory, metallurgy, and foundry industries)
(Mansha et al., 2012; Morrison and Murphy, 2010; Tositti et al., 2014). Similar to the observed
trend in tailpipe emissions, the absolute contributions of this factor to the total OC mass decreased
from 0.37 ± 0.1 µg/m3 to 0.06 ± 0.01 µg/m3 over the 2015-2019 period.
3.3.2 Linear regression between SOA and O3
The results of the regression analysis for 2015, 2017, and 2019 (Figure 3-5) displayed positive
associations between PMF-resolved SOA factor and O3 in CELA. Our findings revealed that
although the SOA concentration resolved by the PMF model varied significantly over the 2015-

42
2019 period, the average SOA/O3 values remained almost constant (approximately between ~ 47
and 55 µg/m3
/ppm) over the investigated period (i.e., 2015 to 2019).
According to the figure, there was a high correlation (R2 >0.74) between the PMF-resolved
SOA concentrations and O3 values, probably due to synchronized photochemical reactions
producing these species in the atmosphere (Carlton et al., 2009; Jacob, 1999). Numerous studies
in the literature reported that a significant portion of O3 and SOA are originated from the same
volatile organic compounds (VOCs) in different environments (Cui, 2013; Lin et al., 2015; Shin
and Jo, 2013; Wu et al., 2017). For example, Wu et al. (2017) investigated the O3 and SOA
formation potential from anthropogenic VOC emissions and reported that alkylbenzenes (i.e., a
known VOC from anthropogenic emissions) accounts for about 40-50% of the total ozone and
SOA formation in various locations in China during the year 2010. The California Research at the
Nexus of Air Quality and Climate Change (CalNex) campaign at Pasadena in Los Angeles county,
reported an increase in total particulate carbon when the primary emissions in the area were
transported to Pasadena and coincided with an increase in secondary pollutants such as
acetaldehyde (Bahreini et al., 2012). It has been shown that vehicle emissions (especially
emissions from gasoline vehicles) are the predominant source of light VOCs, including benzene
(Marr and Harley, 2002; Warneke et al., 2007) in California. Once emitted in the air, VOCs
undergo reactions with atmospheric oxidants and form SOA. In addition, in an air mass, O3
generation arises from OH reactions with CO and VOCs (Hayes et al., 2013). While there are
mitigating policies to decrease the vehicular VOCs, some recent studies pointed to the growing
importance of volatile chemical products (VCPs) as significant contributors to the formation of
ozone and SOA due to higher reactivity with OH (Li et al., 2018b; McDonald et al., 2018). Shah
et al. (2019) showed that the potential of SOA formation from VCPs is larger than that from
vehicles (with a ratio of 1.3) in urban environments. In a similar study in Los Angeles, McDonald
et al. (2018) reported that SOA formed from VCPs (e.g., personal care products) to vehicular
VOCs has a ratio of 1.4. Consequently, the similarity in formation mechanisms of SOA and ozone
provides a possible platform for quantifying the SOA concentration based on ozone
concentrations. Earlier studies in the Los Angeles basin showed that ozone exhibited higher
concentrations on weekends in comparison to weekdays, which stems from lower vehicular

43
emissions than non-methane VOC emissions on weekends, resulting in higher ozone production
and lower ozone destruction by nitrogen oxides (Pollack et al., 2012; Warneke et al., 2013).
Similarly, Heo et al. (2015) showed higher SOA formations on weekends than weekdays in CELA,
corroborating our correlations. These observations are expected to be similar to other urban areas
in the US, given that VOC emissions have similar composition and emission rates in the US and
have followed consistent trends over the past several years (Warneke et al., 2012; Warneke et al.,
2007).
Figure 3-5. Linear regression between PMF-resolved SOA and O3 in: (a) 2015; (b) 2017; and
(c) 2019 in central Los Angeles (CELA)

44
3.3.3 Linear regression between POA and CO
The contributions of POA to OC mass were estimated by subtracting the PMF-resolved SOA
concentrations from the total OC mass (Turpin and Huntzicker, 1995; Wu and Yu, 2016). A strong
correlation between PMF-derived POA concentrations and CO concentrations in CELA (R2
 >
0.70) during the study period is shown in Figure 3-6. The average POA/CO values ranged from
approximately 6.5 µg/m3
/ppm in 2015 to about 5 µg/m3
/ppm in 2019, in agreement with the trend
of tailpipe emission contribution to total OC mass in CELA. The ratio of POA/CO in this study is
lower than the values reported by an earlier study by de Gouw et al. (2008) in the northeastern
united states in 2004 (9.4 µg/m3
/ppm). Using a quadrupole mass filter, the authors collected their
organic matter (OM) onboard a ship and an aircraft employing an Aerodyne aerosol mass
spectrometer (AMS). The POA/CO ratios estimated in earlier studies at Tokyo also using an AMS
and in Zurich utilizing the solution of 6-factorial PMF were about 11 and 10.4 µg/m3
/ppm,
respectively (Lanz et al., 2007; Takegawa et al., 2006). In addition to the different instrumentation
and analytical methods employed to estimate POA, the POA/CO ratio is affected by the mixture
of specific POA sources in a given area (e.g., traffic vs biomass combustion), since they all emit
CO but in different proportions relative to POA, as well as by the time period during which these
studies were conducted since studies published earlier may not incorporate the impacts of
subsequent air pollution mitigation policies on the CO and POA levels. Therefore, in addition to
the different sampling methods, these other factors will need to be considered when comparing the
POA/CO ratios among various studies. A possible explanation of the variations between these
measured ratios is the implementation of various regulations in California targeting POA emission
sources. Of particular note is the LEV II (CARB 2019), which was implemented between 2004-
2010 and targeted vehicles with the model year 2004 and above (Lurmann et al., 2015). In addition,
other programs including financial incentives for cleaner port trucks (2007) have also been
implemented to mitigate the air pollution generated by mobile sources in the area (Haveman and
Thornberg, 2008; Lee et al., 2012). Hence, there could be reductions in POA faster than CO over
the years in various parts of the world, which is in agreement with the trend of tailpipe emission
contributions to total OC mass as discussed in previous sections. An equally important justification
is that the relationship between POA and CO is also expected to depend on meteorological factors

45
in different parts around the globe. This is because a major fraction of POA consists of semivolatile species that partition to particulate phase upon cooling in the atmosphere (Alam et al.,
2003), while CO is an inert non-reactive species, often used as a tracer of atmospheric dilution and
its mixing height (Gamage et al., 2020; Turnbull et al., 2006). Thus, meteorology plays a major
role on the POA/CO ratio, with colder temperatures increasing POA concentrations to a higher
degree than CO, so at colder climates one might observe a higher POA/CO ratio because the POA
concentration is affected not only by dilution but also by cooling which favors the partitioning of
semi-volatile organic species to the particle phase.
3.3.4 Linear Regression between EC and CO
Regression lines between CO and EC for CELA over the 2015–2019 period are illustrated in
Figure 3-7. According to the figure, we observed high correlation (R2
>0.70) between EC and CO
values in the study site, corroborating their common origins (i.e., combustion related emissions).
Previous studies reported that CO and EC are both products of incomplete combustion, and
indicators of combustion emissions (Kirchstetter et al., 1999; Subramanian et al., 2010). We also
observed that the EC/CO ratios in CELA during years 2015 and 2019 were comparable (around

46
2.3 µg/m3
/ppm), and consistent with the findings of Subramanian et al. (2010) who reported an
EC/CO ratio of 2.89±0.89 µg/m3
/ppm in the Mexico City Metropolitan Area.
Figure 3-6. Linear regression between EC and CO in: (a) 2015; (b) 2017; and (c) 2019 in central
Los Angeles (CELA)

47
3.3.5 Linear regression between EC and NO2
Figure 3-8 shows the daily correlation of EC and NO2 concentrations in the study area over the
2015-2019 period. According to the figure, the EC was highly correlated (R2 > 0.73) with NO2,
and EC/NO2 ratios were comparable (~0.04-0.06 µg/m3
/ppb) over the whole period (i.e., 2015-
2019), which is most probably because they are originating from the same sources. Previous
studies have documented road traffic (particularly diesel engines) and other combustion activities
as major sources of NO2 and EC (Afzal et al., 2012; Pepe et al., 2019; Zhang et al., 2018). In
concert with our observation, (Kim et al., 2004) reported 0.034 µg/m3
/ppb as the EC/NO2 ratio
during their experimental measurements in the vicinity of busy roadways in the San Francisco
metropolitan area, while Altuwayjiri et al. (2021) reported a ratio of 0.040 µg/m3
/ppb in the city
of Milan, Italy. It should be noted that there are possible factors for the variations of these estimated
ratios, such as temporal and spatial variations of measurements, various measurement
instrumentations, available sources in the study area, and established air quality regulations
including development of aftertreatment technologies as discussed earlier. In our results, we see
an increase of this ratio by ~50% from 2015 to 2019. This could be explained by the faster
reduction in NO2 than EC levels, due to regulatory policies targeting NO2 that have been
implemented in California in recent years (Brauer et al., 2008; Kim et al., 2004). For example, the
CA LEV-II program urged reduction of major pollutants (e.g., carbon monoxide (CO) and nitrogen
dioxide (NO2)) emitted from various vehicles (CARB, 2000; Hwang and Doniger, 2004).
Furthermore, CA LEV III is planning to further reduce the NO2 emissions by 73% by 2025 from
2012 emissions levels (CARB 2012). Henneman et al. (2021) studied the relationship between
various air pollutants and road proximity in the US over long time periods. They reported that NO2
decreased by ~0.29 ppb/yr during 2010-2019, while EC decreased at a relatively constant rate of

48
~0.002 μg/m3
/yr across 2000-2019 in the proximity of roadsides. They also suggested that NO2
emission sources, which are far from the roadside, have also declined their emissions since 2010.
Figure 3-7. Linear regression between EC and NO2 concentrations over the 2015-2019 period in
central Los Angeles (CELA)

49
3.4 Summary and Conclusions
In this study, the PMF model was implemented to determine the contributing sources to OC
mass concentration in central Los Angeles between 2015 and 2019. We then conducted a
regression analysis between elemental, and primary and secondary organic aerosol concentrations
resolved by the PMF model with criteria gaseous pollutants in CELA as the means to estimate the
24-hr concentrations of these carbonaceous species. Source apportionment results showed that
tailpipe emissions (38.3 ±4.2%), non-tailpipe emissions (29.7±2.4), and SOA (22.3 ±5.4) were the
three dominant sources of total OC concentration during the study period in CELA. Moreover, the
PMF results showed a decrease in the absolute source contribution of tailpipe emissions from
~1.5±0.20 µg/m3 to 1.0±0.10 µg/m3 over the 2015–2019 period, most likely due to the adopted
regulations in California. In addition, the regression analysis results revealed a strong correlation
between SOA and ozone (R2 >0.74), which is mainly related to the same precursors (i.e., VOCs)
and formation pathways (i.e., photochemical reactions in the atmosphere). The SOA/O3 ratios
ranged from 47-56 µg/m3
/ppm in the 2015-2019 period. POA concentrations were derived as the
difference between total OC and PMF-resolved SOA. According to the regression analysis, the
POA/CO ratios decreased from 6.5 to 5 µg/m3
/ppm from 2015 to 2019, which is the same as the
trend of tailpipe emissions to OC mass concentrations in CELA. Lastly, EC was highly correlated
with CO (R2 >0.70) and NO2 (R2 > 0.73) further corroborating that these species are emitted by
the same combustion sources. We should note that our results are based on the available data in a
specific site (i.e., CELA) and generalization of these findings to other areas should be done with
caution, because the number of factors and emission rates of their sources may vary between
different areas. Moreover, the concentrations of these carbonaceous species will also be affected
differently by prospective air quality mitigation strategies that might vary among different
locations and states.

50
Chapter 4 : Investigation of organic carbon profiles and sources of coarse PM
in Los Angeles
4.1 Introduction
Ambient coarse particulate matter (PM10-2.5) has been associated with adverse health effects,
including lower birth weight, increased rate of hospital admissions, and mortality (Brunekreef and
Forsberg, 2005; Chen et al., 2015; Ebisu et al., 2016; Qiu et al., 2012). Giannossa et al. (2022), by
investigating the oxidative potential of total PM and its sources in Italy using the dithiothreitol
(DTT) assay, showed that natural sources (e.g., re-suspended dust) in PM2.5 have a small
contribution to the oxidative DTT potential compared with that in coarse PM. The authors showed
that the DTT values of coarse PM peaked in summer whereas the DTT values of fine PM increased
during cold periods, suggesting that sources with distinct seasonality contribute differently to the
oxidative potential of each size fraction. The lack of explicit regulatory policies and a monitoring
network for coarse PM makes it necessary to investigate their behavior separately from fine and
ultrafine particles, which have different chemical compositions, predominant sources, and removal
mechanisms.
While PM2.5 and PM10-2.5 mass concentrations may both consist of inorganic ions, crustal
materials, trace elements, organic carbon (OC), and elemental carbon (EC) to some extent, primary
sources of coarse PM mass concentrations include re-suspended road dust and soil, sea spray, fly
ash and metallurgical process (Celo et al., 2021; Cheung et al., 2011; Soleimanian et al., 2019b;
Tohidi et al., 2022; Wong et al., 2022). Organic carbon has been identified as an important fraction
of PM by contributing up to 25% to the PM10 mass concentration and affecting PM physiochemical
and toxicological characteristics (Bae et al., 2019a; Cheung et al., 2011; Chung and Seinfeld, 2002;
Mauderly and Chow, 2008; Sardar et al., 2005). While OC concentration accounted for a
significant fraction of PM2.5 mass concentration (ranging between 40 to 65%), OC can also
contribute up to 25-30% to the PM10-2.5 mass in Los Angeles, depending on season (Cheung et al.,
2011; Ho et al., 2006; Zhang et al., 2020). Previous studies have identified fossil fuel combustion
as the primary source of organic carbon directly emitted into the ambient and partitioned from gas

51
to particle phase, which undergoes photochemical reactions to form secondary organic aerosols
(SOA) as the dominant sources of PM2.5-bound OC (Heo et al., 2013a; Zhang et al., 2009). A
minor fraction of OC, as well as SOA extending into the coarse PM, has remained unknown,
thereby complicating the identification and contribution of different sources to coarse OC. Cheung
et al. (2012) investigated the individual organic composition (i.e., PAHs, hopanes, n-alkanes,
organic acids) and other coarse particulate matter compounds in the Los Angeles Basin. The
authors concluded that soil and the associated biota might be one of the major contributors to the
organic constituent of coarse PM. In addition, Falkovich et al. (2004) demonstrated the redistribution of volatile and semi-volatile organic carbon onto dust particles and their potential
ability to be transported by coarse PM in an urban environment. Nevertheless, the understanding
of formation pathways and sources of coarse PM-bound OC is still limited.
The thermal-optical EC/OC method is widely used to determine ambient organic carbon
concentrations. Based on the implemented operational protocol and temperature plateaus, the
collected PM samples on quartz filters are gradually heated to a specific temperature to determine
the OC fractions (i.e., OC1, OC2, OC3, and OC4) and then to a higher temperature to determine the
EC levels. Previous studies showed that vehicular emissions and biomass burning have a higher
contribution to OC1 in PM2.5 (Moody et al., 2001; Phairuang et al., 2020; Tham et al., 2019; Tohidi
et al., 2021), whereas both OC2 and OC3 are linked to tailpipe emissions and incomplete
combustion of organic matter in urban environments (Jaén et al., 2021; Miguel et al., 2004; Wang
et al., 2009). The fraction of OC4 was mainly attributed to secondary origins related to
photochemical activities and chemical aging processes in the atmosphere in PM2.5 (Cao et al.,
2005; Kim and Hopke, 2004; Li et al., 2018a). EC is a well-established marker of fossil fuel
combustion in the total PM (Krudysz et al., 2008; Minguillón et al., 2012). Soleimanian et al.
(2019a) identified three primary PM2.5 sources contributing to total OC concentration and its
volatility fractions in the Los Angeles basin. The authors quantified the contributions of vehicular
emissions (~57%), secondary organic aerosols (~35%), and biomass burning (~8%) to total OC
and its fractions, while the OC1 was non-detectable in their measurements, the SOA contributed up
to 66% to OC4 mass concentration in the Los Angeles basin in PM2.5. Despite these insights, the
possible sources and contribution of OC fractions to coarse PM are still unclear. Previous studies

52
using the chemical mass balance (CMB) model showed that vehicular emissions and SOA are the
predominant sources of carbonaceous aerosols in coarse PM. This is a common characteristic for
many urban areas with large vehicular populations, such as Lahore, Pakistan and Yangon,
Myanmar, where vehicular exhausts are the largest OC sources, contributing about 50% and 25%
of ambient OC, respectively (Daher et al., 2012; Schauer et al., 1996; Sricharoenvech et al., 2020).
However, other sources quantified by the CMB model had smaller contributions, including
biomass burning, vegetative detritus, and coal combustions (Zhang et al., 2008b). The CMB model
has mainly been used to determine the source contributions to PM0.25, and PM2.5-bound OC
concentrations in Los Angeles. The previous studies in the area showed that the contributions of
vehicular emissions to PM2.5 and PM0.25 OC fractions were approximately 25% and 60% of total
OC, respectively (Hasheminassab et al., 2013; Shirmohammadi et al., 2016).
Additionally, the hourly monitoring of the total OC and its volatility fractions provides the
opportunity to capture the temporal variation of corresponding sources with respect to the shortlived excursions or short-term changes in meteorological conditions (Hamad et al., 2016).
Furthermore, source apportionment analyses, including positive matrix factorization (PMF),
greatly benefit from hourly time-resolved measurements of coarse PM-bound OC, as the results’
reliability and source identification depend on the number of data (Jeong et al., 2019; Sofowote et
al., 2019). Other statistical multivariate receptor models, such as CMB, need prior knowledge of
existing source profiles (Stone et al., 2008), which for coarse PM are sparse and/or highly
dependent on location (Abu-Allaban et al., 2003; Sricharoenvech et al., 2020).
To the best of our knowledge, none of the previous studies have used a receptor model to
apportion the sources of coarse OC in Los Angeles. The main goal of this study was to
identify/quantify the sources of coarse OC and its seasonal and temporal trends in central Los
Angeles. Semi-continuous measurements of coarse OC and its OC volatility fractions were
conducted in central Los Angeles during the winter, spring, and summer of 2021. The study of
formation mechanisms and primary sources of coarse OC has been challenging mainly due to
instrument limitations. To overcome this problem, two coarse PM virtual impactors
(VIs)/concentrators were used in parallel with an EC/OC field monitor downstream of both VIs.

53
The concentration-enriched coarse PM was analyzed for concentrations of EC, OC, and OC
volatility fractions. Source apportionment analysis was performed using the EC/OC monitor
measured values as an input to the positive matrix factorization (PMF) model.
4.2 Methodology
4.2.1 Site description and sampling time
The measurment took place at our sampling site, the PIU, located approximately 150 m to the
northeast of the I-110 freeway in downtown Los Angeles (Figure 4-1). This site is influenced by
southwesterly winds, which transport freshly emitted particles from both the freeway and local
traffic. Moreover, recent studies showed that this site represents an area impacted by a mixture of
primary and secondary organic aerosols (i.e., POA and SOA, respectively) in both PM2.5 and coarse
PM (Hasheminassab et al., 2014a; Heo et al., 2013a; Minguillón et al., 2008; Sowlat et al., 2016;
Tohidi et al., 2021).
The experimental campaign was conducted during three seasons: Winter (i.e., late-February
2021 to late-March 2021), Spring (i.e., mid-April 2021 to late-May 2021), and Summer (i.e., midAugust 2021 to late-September 2021) to capture the meteorological extremes in terms of relative
humidity (RH) and ambient temperature as well as variation of mixing height, all of which have a
considerable impact on concentrations of the examined chemical species (Hasheminassab et al.,
2014b; Mousavi et al., 2018b).
Table 4-1. Seasonal variation (Mean±SD) of meteorological data for different seasons during
2021.
Winter Spring Summer
Temperature (°C) 16.8±3.1 24.5±6.5 26.2±5.5
Wind speed (m/s) 1.0 ±0.9 1.9±1.1 1.8±1.1
Relative Humidity (%) 56.7±20.1 70.1±20.6 75.2±16.5

54
Table 4-1 shows the average seasonal variation of meteorological parameters (i.e., temperature,
wind speed, and relative humidity (RH)) during the study period. Temperatures were generally
lowest in the winter with relatively lower wind speeds compared to those of in warm seasons. The
average monthly RH was also lowest during the winter phase, probably due to the persistence of
evening winds from deserts to the north that are typical in that area and have been documented in
previous studies (Miguel et al., 2004; Moore et al., 2010; Pakbin et al., 2010). Relative to the
winter phase, the seasonal averages of meteorological parameters were similar across the spring
and summer phases.
Figure 4-1. Location of the sampling site in Los Angeles

55
4.2.2 Experimental setup and data collection
Figure 4-2 shows the schematic of the sampling setup for coarse OC measurements. Two
collocated virtual impactors (VIs)/concentrators were set up to enrich the concentration of coarse
PM coupled with an EC/OC thermal/optical carbon field analyzer (model-4, Sunset Laboratory
Inc., USA).
Figure 4-3. Schematic of the experimental setup for coarse OC measurement using coarse PM
virtual impactors (VI)/concentrators.
Figure 4-2. Evaluation of the coarse particle concentration enrichment factor by measuring
particle mass concentration before and after the VIs.

56
As shown in the figure, we employed two VIs, the performance of which has been validated
extensively in previous studies (Geller et al., 2005; Kim et al., 2001; Pirhadi et al., 2020a; Wang
et al., 2013), operating at a total sampling flow rate of 55 lpm with the major and minor flow rates
of each VI set at 50 lpm and 5 lpm, respectively. The VIs have a 50% cut-point of 1.7 µm and
concentrate particles larger than that size into the minor flow enriched in coarse PM by 10-fold
(ideally, the total to minor flow rate (Geller et al., 2005)). The inlet of the EC/OC monitor is the
stream of concentrated coarse PM combined downstream of the VIs with a total flow rate of 10
lpm. Coarse PM size distribution has been measured using an optical particle sizer (OPS) (Model
3330, TSI, USA) with a time resolution of 5 minutes. Figure 4-3 shows our evaluation of VIs
enrichment factor (EF) by means of OPS after carrying out several pre and post VIs tests. The
enrichment of coarse particles using our VIs was about 10-fold (using 50 lpm and 5 lpm as the
major and minor flows of two parallel VIs).
The concentrations of EC, OC, and OC volatility fractions were measured using a semicontinuous EC/OC field analyzer with a time resolution of 1 hour, adopting the new Interagency
Monitoring of Protected Visual Environments protocol (IMPROVE_A) introduced by Chow et al.
(2007), which slightly differs from the IMPROVE_TOR method (Chow et al., 2004; Watson et
al., 2005). Briefly, in the IMPROVE_A protocol, the collection time was 40 minutes plus 20
minutes for analysis in each cycle. The collected samples were heated stepwise to 140 °C, 280 °C,
480 °C, and 580 °C in a pure helium atmosphere to determine OC1, OC2, OC3, and OC4 and
subsequently higher than 580°C in the 98%helium/2%oxygen atmosphere to measure the EC
concentrations. This protocol is explicated elsewhere (Birch and Cary, 1996; Cheng et al., 2014;
Chow et al., 2007; Wang et al., 2020).
4.2.3 Source apportionment analysis
We used the PMF model to find the sources of ambient coarse OC and its volatility fractions in
LA. This method has been discussed in detail earlier in section 3.2.2.1
Moreover, Belis et al. (2020) proposed that receptor models can be used in conjunction with
chemical transport models since traffic and exhaust were the sources with the best results in the

57
PMF model. Although chemical transport models showed better results with dust and soil sources,
these models underestimated total PM during high pollution episodes mainly due to reconstructing
the organic fraction in PM.
4.2.4 Auxiliary data
In order to increase the reliability of the PMF model to identify factor profiles, specific gaseous
pollutants were extracted with a time resolution of 1 hour from online monitoring stations. The
hourly concentrations of NOx and NO2 (chemiluminescence, Model 200 Analyzer, Advanced
Pollution Instrumentation, Inc.), O3 (UV-absorption, 49, Thermo Environmental Instruments Inc.),
and CO (non-dispersive infrared photometry, AQMS-400, Focused Photonics Inc.) were obtained
from the California Air Resources Board (CARB) website for the nearest station to the PIU,
approximately 3 km to the north of the sampling site (US EPA, 2019). Meteorological parameters
(i.e., temperatures, relative humidity, and wind speeds) were also downloaded for the same
sampling station with a time resolution of 1 hour. Vehicle miles traveled (VMT), which refers to
the number of miles traveled by vehicles over a given period, was obtained from the Caltrans
(California Department of Transportation) website (Caltrans, 2022) on I-110-N and I-110-S
freeways which are the nearest monitoring points to our sampling site. We should mention that in
order to keep the consistency of the time resolution for all species and to focus on coarse OC, we
were unable to use other tracers in our study (e.g., metals or inorganic ions).
4.3 Results and discussion
4.3.1 Temporal and seasonal variability of coarse PM-bound OC fractions
Figure 4-4 shows the time series of coarse PM OC and OC volatility fractions (OCx) as well as
EC mass concentrations during the entire study period. The coarse PM-bound OC mass
concentrations were on average 0.98 ± 0.32, 1.27 ± 0.24, and 1.42 ± 0.26 µg/m3 during the winter,
spring, and summer phases, respectively (in the range of 0.57-1.90 µg/m3
).

58
The historical trends of coarse PM-bound OC mass concentrations denote comparable values
during the past two decades. Sardar et al. (2005) conducted 24-hr time-integrated sampling in
central Los Angeles by collecting size-fractioned samples once per week for an entire year in 2004.
The authors reported that the average of the total coarse PM-bound OC mass concentrations was
~1 µg/m3 in March (winter phase), ~1.40 µg/m3 in April and May (spring phase), and ~1.43 µg/m3
in August and September (summer phase). These values are consistent with Cheung et al. (2011),
reported organic matter (OM) mass concentration of ~2 µg/m3 (or equivalently OC=1.1 µg/m3
)
during the winter season at the university of southern California (USC) using personal cascade
impactor samplers and analyzing the samples once per week. According to previous studies in the
area, a factor of 1.8 has been used to convert OC to OM mass concentration (Cheung et al., 2011;
Daher et al., 2013; Hasheminassab et al., 2014b). By investigating the carbonaceous fraction of
both PM2.5 and PM10-2.5 at Gosan, Korea, a remote location in the East China Sea, Stone et al.
(2011) showed that the average coarse OC was about 0.75 μg/m3. These values are comparable
with our measured OC mass concentrations for different seasons. Given the significant reduction
in the concentrations of organic carbon in PM2.5 in the area, which has been mainly attributed to
the regulation of vehicular sources in the past decade in Los Angeles (Altuwayjiri et al., 2021;
Figure 4-4. Time series of the mass concentrations of coarse PM-bound OC fractions and
elemental carbon during the study period. Green areas correspond to episodes with RH≥90.

59
Tohidi et al., 2021), the relatively constant OC concentrations of coarse PM during the past two
decades need more attention, and rigorous investigations may assist regulatory agencies in
implementing more stringent policies on coarse PM to protect public health. It should be noted
that frequent rain during mid-March 2021 and March 20th-25th have also led to decreased levels
of coarse OC in the area due to the wet deposition, which is more pronounced for coarse particles.
According to Figure 4-4, OC2 with an average concentration of 0.70±0.2 µg/m3 was the most
dominant OC fraction accounting for 57%, followed by OC1 with a 27% contribution (0.33±0.1
µg/m3
) to total OC. The less volatile fractions, OC3 and OC4, accounted for a 16% contribution
(0.20±0.05 µg/m3
) to total OC. While total coarse OC mass concentration (i.e., the sum of all OCx)
followed an increasing trend (Pvalue<0.001) with the exception of rainy days (March 20th-25th) from
cold to warm seasons, EC showed no significant trend (Pvalue >0.05) during the same period. This
increase can be justified by higher re-suspension of dust particles in the coarse PM size range due
to the higher wind speed and lower relative humidity during daytime (Figure 4-5), leading to
enhanced levels of coarse PM-bound OC in the warm seasons. For the metropolitan area of Los
Angeles, where the impact of both tailpipe and non-tailpipe emissions are well established, the
local sources have been identified as the major contributor to the total PM (Hasheminassab et al.,
2014a; Heo et al., 2013a; Jalali Farahani et al., 2022; Minguillón et al., 2008; Sowlat et al., 2016;
Tohidi et al., 2021)
It is worth noting that OC1 showed a significant contribution to coarse PM, while it was mostly
non-detectable in PM2.5 in previous studies in the area (Altuwayjiri et al., 2021; Pirhadi et al.,
2020b; Soleimanian et al., 2019a), and its levels were below the detection limit of the
IMPROVE_A method for most of the days. The low concentrations might be attributed to the
higher volatility of OC1 compared with other OC fractions, which may cause significant sampling
artifacts resulting in substantial underestimations of its concentration when using time-integrated
aerosol samplers (Li et al., 2018a).

60
In the case of our experiments using the EC/OC analyzer, since the time interval between PM
collection and analysis is relatively short (almost 20 minutes on average), this process can provide
a more reliable measurement of OC1 mass concentration in comparison with the case of timeintegrated collection of PM samples for an entire day/several days, storing the filters, and lastly
conducting chemical analysis, all of which increase the possibility of evaporative losses of OC.
Furthermore, while the primary source of organic carbon emissions is typically related to the
incomplete combustion of fossil fuels as well as lubricant oils, the mass concentrations of different
OC fractions vary significantly in different size modes (Cheung et al., 2010; Worton et al., 2014;
Zhang et al., 2021), likely due to their different formation pathways. Previous studies have shown
a bimodal trend in carbonous substances peaking in both fine and coarse PM with a higher
contribution of relatively lower molecular weight and higher volatility organic carbons to the
coarse size range (Miguel et al., 2004; Zhang et al., 2021).
Figure 4-5. Diurnal variations of relative humidity during winter, spring, and summer. Error
bars correspond to 1 standard error.

61
Figure 4-6 presents the OC fractions mass concentration measured during weekdays and
weekends. While higher mass concentrations of fine OC have been reported during the weekdays
compared with weekends in Los Angeles due to higher tailpipe emissions (Hasheminassab et al.,
2014b), coarse PM-bound OCx mass concentrations were not statistically different between the
weekdays and weekends (Pvalues in the range of 0.20-0.80 for different OC fractions). This
observation suggests that anthropogenic sources may not be the major and dominant source of
coarse OC (compared with PM2.5-bound OC).
Figure 4-6. Average OC fractions mass concentration measured during weekdays and weekends

62
Figure 4-7 shows the diurnal variations in OCx and elemental carbon (EC) concentrations
during the study period. According to the figure, EC mass concentration was negligible in the
coarse PM size range (0.07±0.03 µg/m3
) compared with fine PM EC (i.e., 1.4 ± 0.4 µg/m3 based
on the most recent study in the area by Soleimanian et al. (2019a)). This observation is consistent
with previous studies reporting generally low concentrations of EC in the coarse PM size range
and higher contribution to ultrafine particles as fresh emissions due to higher engine combustion
temperature from diesel vehicles across the LA basin (Cheung et al., 2011; Huang and Yu, 2008;
Sardar et al., 2005).
Figure 4-7. Average diurnal trends of elemental carbon and OC fractions. (a) winter, (b) spring,
(c) summer.

63
The OC mass concentration was significantly higher (Pvalue<0.001) during mid-night and early
morning in comparison with mid-day during winter, while the diurnal trend of coarse PM-bound
OC remained comparable during the spring phase. Similar to total coarse PM mass concentration,
higher re-suspension of road dust particles due to turbulence induced by heavy-duty vehicles along
with lower mixing height can justify a higher nighttime concentration of coarse OC. In contrast,
coarse OC showed a different trend in summer by having significantly higher mid-day
concentrations than midnight and early morning, which can be attributed to the prevailing
meteorological conditions (i.e., lower relative humidity, higher temperature, and wind speed) in
the middle of a day. Moreover, the diurnal mass concentrations of OC fractions were comparable
during all seasons, except for OC1 mass concentration, which was significantly higher during
afternoon/evening (Pvalue<0.001) in the summer and OC4 with considerably higher levels
(Pvalue<0.01) during nighttime in all seasons. Additionally, the maximum EC mass concentration
was ~0.10-0.15 µg/m3 during the early morning coinciding with the morning rush hour traffic,
while its concentration decreased to ~0.02-0.04 µg/m3 during mid-day in the winter and spring.
Therefore, the diurnal variation in coarse EC concentration is almost identical to that of in fine EC.
On the other hand, EC mass concentration followed the diurnal trend of coarse OC in summer by
peaking during mid-day (~0.1 µg/m3
), likely due to atmospheric conditions as discussed earlier.
The average coarse EC levels in Los Angeles (0.07± 0.03 µg/m3
) were in the range of Yorkville,
GA (0.08 ± 0.06 µg/m3
) and significantly lower than coarse EC estimated in other parts of the US,
which are mainly impacted by industrial emissions, such as Houston, TX (0.44± 0.24 µg/m3),
Atlanta, GA (0.21 ± 0.13 µg/m3
), or Centerville, AL representative of a rural site (0.27 ± 0.16
µg/m3
) (Edgerton et al., 2009). We should note that the EC/OC measurements in the Edgerton et
al. (2009) study were performed by means of the Desert Research Institute (DRI) Carbon Analyzer
Model 2001 using the IMPROVE Thermal/Optical Reflectance (TOR) analytic method, which
compared to IMPROVE_A protocol both maintain the consistency in EC measurements for longterm samplings (Chow et al., 2007).

64
4.3.2 PMF source apportionment results
4.3.2.1 Overview
A 3-factor run yielded the most optimal results based on: 1) the results of BS, DISP, and BSDISP analyses, 2) high linear regression coefficient (i.e., R2
) between predicted values by the PMF
model and input values (i.e., matrix X), 3) contribution of primary source markers in the resolved
factors 4) temporal variation of OC fractions (seasonal and diurnal trends) 5) physically
interpretable source profiles as for the sampling site by implementing an extra modeling
uncertainty of 10% to account for errors that are beyond the instrumental or analytical
measurements. The PMF-resolved factors were vehicle emissions, secondary organic aerosols
(SOA), and re-suspended dust-bound OC, and their identification/interpretation is elaborated in
the following sections. Figure 4-8 shows our PMF-resolved factor profiles based on the hourly
average of the measured parameters. As discussed in section 4.2.1, temperature, wind speed, and
relative humidity were comparable during the spring and summer phases; therefore, the statistical
analysis and source contribution are presented by separating the winter (i.e., cold phase) from the
spring and summer (i.e., warm phase) to increase the robustness of our results.

65
4.3.2.2 Factor identification
Factor 1: Vehicular emissions
The first factor was associated with high loadings of EC (⁓92%), a well-established marker of
fossil fuel combustion both in fine and coarse PM (Krudysz et al., 2008; Minguillón et al., 2012)
and NO2 (⁓73%), which is also a tracer of vehicular emissions (Zong et al., 2016), and moderate
loadings of OC2 and OC3 (~49-58%). It is worth noting that based on previous studies, OC2 and
OC3 are dominated by tailpipe emissions in PM2.5 (Cao et al., 2006; Schauer, 2003; Soleimanian
et al., 2019a; Sowlat et al., 2016; Zhu et al., 2010), further corroborating fresh vehicular emissions
as the origin of this source profile. Thus, the first factor represents vehicular emissions.
Figure 4-8. PMF-resolved factor profiles

66
Factor 2: Secondary Organic Aerosol
The second factor demonstrated high contributions of O3 and RH (i.e., 60 and 83%,
respectively) and moderate loadings of OC4. Ozone has been used as a tracer of secondary aerosols
(Jacob, 1999; Liu et al., 2020b; Tohidi et al., 2021). Additionally, previous studies have shown a
positive association between RH and the rate of secondary aerosol formation, particularly at
nighttime from aqueous phase reactions in coarse PM (Hersey et al., 2011; Sowlat et al., 2016;
Venkatachari et al., 2005).
The underlying SOA formation pathways vary significantly with particle size. By investigating
the volume growth of ambient aerosols over several days and under different conditions in the
north China plain, Xu et al. (2020b) found that rapid SOA formation in coarse PM (mainly on dust
particles) is the dominant mechanism in promoting secondary aerosol concentrations during high
RH episodes. The growth of SOA within coarse PM contributed up to 71% of the total PM growth,
suggesting dust particles could serve as a medium for secondary organic aerosols for
heterogeneous reactions under favorable photochemical and RH conditions. Although dust events
from natural sources are rather rare in Los Angeles, this city is significantly impacted by trafficinduced re-suspension of dust particles and non-tailpipe sources on a daily basis (Jalali Farahani
et al., 2022).
According to previous studies regarding the different SOA formation pathways, high pollution
episodes can occur after relatively clean days with lower RH values followed by polluted days
with higher RH (Kuang et al., 2020; Zhao et al., 2013). As shown in Figure 4-4, during four time
periods characterized by higher RH values (the green areas correspond to episodes with RH≥90%),
the OC4 concentrations significantly increased after relatively clean days. Note that wind speeds
were comparable before and after these periods, ruling out any impact of re-suspended dust on the
increased concentrations. Nevertheless, mineral dust can increase SOA formation by forming solid
cores coated with aqueous shells, particularly in high RH episodes, which promotes heterogeneous
reactivity and thus leads to higher SOA formation (Ma et al., 2012; Tang et al., 2017).
Incorporating the SOA formation process on coarse PM with other pathways is essential in order

67
to obtain a more accurate estimate of SOA production during high RH episodes. Thus, SOA's
contribution to coarse OC should not be neglected.
OC4 also showed considerably higher nighttime concentrations (Pvalue<0.001) during our study
period, further supporting our PMF resolved factor that the increase of OC4 concentration in coarse
OC could have resulted from aqueous phase reactions accounting for SOA formation during higher
RH periods at nighttime (Figure 4-5).
Factor 3: Re-suspended dust-bound OC
Finally, the third factor demonstrated high loadings of wind speed and VMT (i.e., 95 and 70%,
respectively). According to the literature, higher wind speed can facilitate the re-suspension of soil
and dust particles (Sowlat et al., 2016). Similarly, higher traffic (in terms of VMT) can lead to
higher re-suspension of particles due to the traffic movement. Falkovich et al. (2004) investigated
the interaction of mineral dust particles with organic compounds. The authors observed organic
matter (which is usually found in the fine PM size range) on mineral dust (i.e., larger aerosol size)
through scanning electron microscopy (SEM) analysis. They revealed that the high volatility of
semi-volatile organics (e.g., PAHs as OC1) facilitates their adsorption onto dust particles, thereby
re-distributing them from submicron to larger particle size fractions. Cheung et al. (2012) showed
that biogenic tracers have a high correlation (R2 >0.68) with dust markers (i.e., Al and Ti),
suggesting that the re-suspended dust contains biological matter and vegetative debris in coarse
particles. Miguel et al. (2004) investigated the size distributions of OC and PAHs categorized
based on their volatility and molecular weight in Claremont, California, and showed that a
significant fraction of more volatile organic compounds was partitioned in the coarse PM mode,
mainly on dust particles, up to 25% of total PM mass in comparison to lower volatility organic
carbons. The authors’ findings are consistent with the high loadings of OC1 in the re-suspended
dust source profile in this study, which can be attributed to the vapor sorption by dust particles and
the higher volatility of OC1. In addition, their results revealed that organic carbon concentrations
were highly correlated with ambient temperature, peaking during the warm seasons, particularly
in the coarse mode. According to the abovementioned studies, the enhancement of OC in the coarse
fraction and its association with dust particles suggested that the presence of dust facilitated

68
atmospheric processes that caused fine particles to increase in size, including coating fine particles
with volatile compounds (i.e., OC1) or OC adsorption onto dust particles (Seinfeld et al., 2004).
During the past decades, while the contribution of mobile sources to air pollution significantly
decreased mainly due to stringent regulations on vehicular sources (Health Effects Institute, 2015),
the recent national VMT data reported by state agencies in all 50 states show an increasing trend
in the VMT as well as the number of vehicles in terms of both personal travel and commercial
travel (Lurmann et al., 2015; Williams et al., 2016). This is quite important as recent studies
showed that the contribution of non-tailpipe emissions (i.e., tire and brake wear and re-suspended
dust) significantly increased in the area (Altuwayjiri et al., 2021; Tohidi et al., 2021), resulting in
larger contributions to total PM. Farahani et al. (2021) attributed this increase to the growing trend
of using electric vehicles due to their heavier weight than internal combustion vehicles and
expressed it in terms of VMT. The authors showed a high correlation between VMT (which
represents the increased road traffic) and re-suspended road dust emissions. Moreover, numerous
studies in the literature have documented that a significant portion of traffic emissions is originated
from non-tailpipe sources (up to 70%), particularly re-suspended dust in coarse PM (Amato et al.,
2016; Bukowiecki et al., 2010; Habre et al., 2021; Jalali Farahani et al., 2022). Furthermore, Moore
et al. (2010) showed that coarse PM concentrations are positively correlated with wind speed for
all seasons except for winter, with the strongest in warm seasons, suggesting wind-induced dust
as one of the major sources of coarse PM. Thus, the third factor with high loadings of VMT, WS,
and OC1 was attributed to re-suspended dust-bound OC.
4.3.2.3 Source contributions to coarse OC
Figure 4-9 shows the relative contribution of the identified sources to coarse OC during the cold
and warm phases. As can be observed in the source contribution figure, vehicular emissions were
the dominant contributor to the total coarse OC concentrations (46%), followed by SOA (27%)
and re-suspended dust particles (27%) during the cold phase. While vehicular emissions’
contribution to total OC decreased to 26%, the other factor profiles contribution (SOA and resuspended dust) increased equally to 37% during the warm seasons. Based on previous PM2.5-
bound OC source apportionment studies in the area (Altuwayjiri et al., 2021; Soleimanian et al.,

69
2019a), the contribution of vehicular emissions to total OC was higher in cold seasons in PM2.5
mainly due to lower wind speeds and mixing heights prevailing during their investigated period,
which limit the horizontal and vertical dispersion of particles. Similarly, we detected a lower
contribution of vehicular emissions to total coarse OC during the spring and summer seasons.
Regarding the contribution of SOA to OC, higher photochemical activities in the warm season
increased the formation rate of SOA. Aside from that, higher relative humidity and increased
formation of SOA on dust particles intensified the impact of enhanced photochemical reactions in
the warm phase (as discussed in Factor 2). Finally, the contribution of "re-suspended dust bound
OC" to total OC increased during the warm phase since higher wind speeds (according to table 4-
1 and Figure 4-4) elevate the re-suspension rate of dust particles (Laidlaw and Filippelli, 2008).
Furthermore, the evaporation of volatile species and their re-distribution on coarse PM mode are
more evident in the seasonal variations of OC fractions. While vehicular emissions contribution to
coarse OC1 significantly decreased from the cold (~25%) to warm (~15%) phase, the re-suspended
dust contribution to coarse OC1 increased from ~67% during the cold to ~77% during the warm
phase.
Figure 4-9. Seasonal trends and contributions of the three PMF-resolved factors to total OC
and its fractions during the cold and warm phases in Los Angeles.

70
A similar increasing trend was observed for OC2 and OC3. During the investigated period, resuspended dust contribution to coarse OC2 increased by ~9%, while vehicular emissions
contribution to coarse OC2 decreased by 20%. These values were about 14% and 20% for OC3,
respectively. Interestingly, the contribution of vehicular emissions to OC2 was slightly higher than
OC3, presumably due to the higher volatility of OC2 (and, accordingly, the higher presence of fresh
emissions in OC2).
The seasonal contribution of SOA to OC4 concentrations increased from 45% in the cold phase
to 54% during the warm phase, which is consistent with higher RH values and temperatures during
the warm phase. Our findings revealed that the contribution of re-suspended dust to OC4 increased
under the typical atmospheric condition of warm seasons, which favors the partitioning of this
species in the gas phase and then their re-distribution onto dust particles.
4.4 Summary and conclusions
The concentrations of EC and OC volatility fractions were examined in coarse PM in the winter,
spring, and summer of 2021. Based on the analysis conducted by implementing two virtual
impactors that concentrated ambient coarse particles by 10-fold, our PMF-resolved factors
identified three distinct sources for coarse PM OC fractions in central Los Angeles. Vehicular
emissions were the major source of coarse OC in cold seasons (46% of total OC), and re-suspended
dust-bound OC and SOA were the primary contributors to coarse OC in warm seasons (37% of
total OC). The total OC concentrations showed an increasing trend from the cold (0.98 ± 0.32
µg/m3
) to warm (1.42 ± 0.26 µg/m3
) periods. This increase was mainly due to 1) re-distribution of
more volatile organic carbon species on dust particles following an increase of up to 77% in resuspended dust contribution to coarse OC1 in the warm phase, 2) heterogeneous SOA formation
involving dust particles as reaction surfaces for less volatile organic carbons under favorable high
RH conditions, leading to higher contribution of SOA to OC4 concentrations from 45% to 54%
during the warm phase. Our results showed that the OC4 concentration significantly increased
during high RH episodes (RH>90%) compared to other days. The same observation occurred
during the night (typically with higher RH compared to daytime), underscoring the effect of
aqueous phase reactions on SOA formation. In recent years, the historical trends of PM and OC

71
concentrations in central Los Angeles have indicated significant reductions in PM2.5 from
vehicular emissions. Comparing the temporal and seasonal variation of coarse PM-bound OC
concentration measured in the current study with the data acquired from earlier studies conducted
in the same sampling site, we observed comparable values over the past two decades. Although
the local sources have dominated the total PM emissions, trajectory modelings also can be used in
future studies to further evaluate the effects of transported air pollution from other sources near
Los Angeles.
Our results highlight the importance of organic particles (both anthropogenic and naturally
emitted) on coarse PM concentrations. We should note that, although analytical methods have been
developed, and more volatile and semi-volatile organic carbons are continuously being quantified,
the toxicological examination of these species should also be considered, especially in coarse PM.

72
Chapter 5: Conclusions and Future works
5.1 Conclusions
In the first study, I focused on developing and testing a novel optical-based approach to measure
mass concentrations of dust particles. Dust is the dominant particle with the highest contribution
to the mass concentration of coarse PM. Los Angeles is affected by vehicular emissions with high
levels of black carbon. To determine the dust mass concentration based on its optical properties,
first, we needed to eliminate the noises from other light absorbers. We demonstrated the feasibility
of using a simple method for estimating the light absorption and mass concentrations of mineral
dust based on the light-absorbing properties of these particles. Contrary to other methods, which
are extremely labor- and time-intensive, the findings and methodology presented in this study
provide a powerful tool to quantify mineral dust concentrations with high time-resolution and
investigate dust optical properties. This is particularly helpful in studies evaluating the heating
effect of dust aerosols in the atmosphere, which need to incorporate mineral dust properties into
the climate models. However, the long-term sources of mineral dust cannot be apportioned by
using the current method. The source contribution of mineral dust can be identified by coupling
and complementing our optical-based approach with various source apportionment tools. In
addition, further investigations in highly polluted environments that are impacted by both dust and
traffic-related emissions will strengthen the reliability of this method.
In the second study, I focused on sources and long-term trends of organic carbon in PM2.5,
which is one of the most toxic components of ambient PM. The correlations explored in this study
were intended to provide a simple paradigm for estimating the 24-hr average concentrations of
carbonaceous species (i.e., POA, SOA, and EC) based on the reported concentrations of criteria
gaseous pollutants. Moreover, my source apportionment analysis showed that the contribution of
tailpipe emissions to total OC mass concentration has significantly reduced during recent years
following the stringent regulations on PM2.5, while the contribution of non-tailpipe emissions was
almost comparable during the same period. Our results could easily be updated or revisited in the
future periodically to incorporate the effects of prospective legislative measures and mitigation
strategies on the concentrations of primary and secondary organic aerosols in Los Angeles.

73
In the last study, I focused on the seasonal and temporal variations of coarse OC and its
volatility fractions to identify their primary sources in LA. My findings showed that secondary
organic aerosols are one of the major contributors to coarse OC in warm seasons and by focusing
on PM2.5, we may underestimate the mass concentration of SOA in PM. As expected the resuspended dust showed high levels in warm seasons, which highlights the importance of coarse
OC in PM. Due to regulations on vehicular sources and curtailing the OC emissions in PM2.5, the
concentration of fine OC has significantly decreased, while coarse OC showed comparable
concentration during the past two decades. Therefore, more attention and rigorous investigations
may assist regulatory agencies in implementing more stringent policies on coarse PM to protect
public health.

74
References
CARB. “LEV III” Amendments to the California Greenhouse Gas and Criteria Pollutant Exhaust
and Evaporative Emission Standards and Test Procedures and to the On-Board Diagnostic
System Requirements for Passenger Cars, Light-Duty Trucks, and Medium-Duty
Vehicles,. 2012.
CARB. The California Low-Emission Vehicle Regulations. 2019;1.
US EPA. Air Quality System (AQS) API. 2019a.
US EPA. Compendium of Methods for the Determination of Inorganic Compounds in Ambient
Air: Determination of Metals in Ambient Particulate Matter Using X-Ray Fluorescence
(XRF) Spectroscopy. 1999.
US EPA. List of designated reference and equivalent methods. United State Environmental
Protection Agency-methods-list.pdf. 2019b.
Abu-Allaban M, Gillies JA, Gertler AW, Clayton R, Proffitt D. Tailpipe, resuspended road dust,
and brake-wear emission factors from on-road vehicles. Atmospheric environment 2003;
37: 5283-5293.
Adamiec E, Jarosz-Krzemińska E, Wieszała R. Heavy metals from non-exhaust vehicle emissions
in urban and motorway road dusts. Environmental monitoring and assessment 2016; 188:
1-11.
Adebiyi AA, Kok JF. Climate models miss most of the coarse dust in the atmosphere. Science
advances 2020; 6: eaaz9507.
Afzal A, Cioffi N, Sabbatini L, Torsi L. NOx sensors based on semiconducting metal oxide
nanostructures: progress and perspectives. Sensors and Actuators B: Chemical 2012; 171:
25-42.
Akhtar US, McWhinney RD, Rastogi N, Abbatt JP, Evans GJ, Scott JA. Cytotoxic and
proinflammatory effects of ambient and source-related particulate matter (PM) in relation
to the production of reactive oxygen species (ROS) and cytokine adsorption by particles.
Inhalation toxicology 2010; 22: 37-47.
Alam A, Shi JP, Harrison RM. Observations of new particle formation in urban air. Journal of
Geophysical Research: Atmospheres 2003; 108.
Alfaro S, Lafon S, Rajot J, Formenti P, Gaudichet A, Maille M. Iron oxides and light absorption
by pure desert dust: An experimental study. Journal of Geophysical Research:
Atmospheres 2004; 109.

75
Almeida S, Pio C, Freitas M, Reis M, Trancoso M. Source apportionment of fine and coarse
particulate matter in a sub-urban area at the Western European Coast. Atmospheric
Environment 2005; 39: 3127-3138.
Altuwayjiri A, Pirhadi M, Kalafy M, Alharbi B, Sioutas C. Impact of different sources on the
oxidative potential of ambient particulate matter PM10 in Riyadh, Saudi Arabia: A focus
on dust emissions. Science of The Total Environment 2022; 806: 150590.
Altuwayjiri A, Pirhadi M, Taghvaee S, Sioutas C. Long-term trends in the contribution of PM 2.5
sources to organic carbon (OC) in the Los Angeles basin and the effect of PM emission
regulations. Faraday Discussions 2021; 226: 74-99.
Amato F, Favez O, Pandolfi M, Alastuey A, Querol X, Moukhtar S, et al. Traffic induced particle
resuspension in Paris: Emission factors and source contributions. Atmospheric
Environment 2016; 129: 114-124.
Amato F, Hopke PK. Source apportionment of the ambient PM2. 5 across St. Louis using
constrained positive matrix factorization. Atmospheric Environment 2012; 46: 329-337.
Amato F, Pandolfi M, Escrig A, Querol X, Alastuey A, Pey J, et al. Quantifying road dust
resuspension in urban environment by multilinear engine: a comparison with PMF2.
Atmospheric Environment 2009; 43: 2770-2780.
Apte JS, Brauer M, Cohen AJ, Ezzati M, Pope III CA. Ambient PM2. 5 reduces global and regional
life expectancy. Environmental Science & Technology Letters 2018; 5: 546-551.
Ardon-Dryer K, Mock C, Reyes J, Lahav G. The effect of dust storm particles on single human
lung cancer cells. Environmental Research 2020; 181: 108891.
Atkinson RW, Mills IC, Walton HA, Anderson HR. Fine particle components and health—a
systematic review and meta-analysis of epidemiological time series studies of daily
mortality and hospital admissions. Journal of exposure science & environmental
epidemiology 2015; 25: 208-214.
Bae M-S, Schauer JJ, Lee T, Jeong J-H, Kim Y-K, Ro C-U, et al. Relationship between reactive
oxygen species and water-soluble organic compounds: Time-resolved benzene carboxylic
acids measurement in the coastal area during the KORUS-AQ campaign. Environmental
Pollution 2017; 231: 1-12.
Bae M-S, Schwab JJ, Park D-J, Shon Z-H, Kim K-H. Carbonaceous aerosol in ambient air: Parallel
measurements between water cyclone and carbon analyzer. Particuology 2019a; 44: 153-
158.
Bae M-S, Skiles MJ, Lai AM, Olson MR, de Foy B, Schauer JJ. Assessment of forest fire impacts
on carbonaceous aerosols using complementary molecular marker receptor models at two

76
urban locations in California's San Joaquin Valley. Environmental Pollution 2019b; 246:
274-283.
Bahreini R, Middlebrook A, De Gouw J, Warneke C, Trainer M, Brock C, et al. Gasoline emissions
dominate over diesel in formation of secondary organic aerosol mass. Geophysical
Research Letters 2012; 39.
Bates JT, Fang T, Verma V, Zeng L, Weber RJ, Tolbert PE, et al. Review of acellular assays of
ambient particulate matter oxidative potential: methods and relationships with
composition, sources, and health effects. Environmental science & technology 2019; 53:
4003-4019.
Beddows DC, Harrison RM. PM10 and PM2. 5 emission factors for non-exhaust particles from
road vehicles: Dependence upon vehicle mass and implications for battery electric
vehicles. Atmospheric Environment 2021; 244: 117886.
Belis CA, Pernigotti D, Pirovano G, Favez O, Jaffrezo J, Kuenen J, et al. Evaluation of receptor
and chemical transport models for PM10 source apportionment. Atmospheric environment:
X 2020; 5: 100053.
Berger K, Malig BJ, Hasheminassab S, Pearson DL, Sioutas C, Ostro B, et al. Associations of
source-apportioned fine particles with cause-specific mortality in California.
Epidemiology 2018; 29: 639-648.
Bergstrom RW, Pilewskie P, Russell PB, Redemann J, Bond TC, Quinn PK, et al. Spectral
absorption properties of atmospheric aerosols. Atmospheric Chemistry and Physics 2007;
7: 5937-5943.
Birch M, Cary R. Elemental carbon-based method for monitoring occupational exposures to
particulate diesel exhaust. Aerosol Science and Technology 1996; 25: 221-241.
Biswas S, Verma V, Schauer JJ, Sioutas C. Chemical speciation of PM emissions from heavy-duty
diesel vehicles equipped with diesel particulate filter (DPF) and selective catalytic
reduction (SCR) retrofits. Atmospheric Environment 2009; 43: 1917-1925.
Brauer M, Lencar C, Tamburic L, Koehoorn M, Demers P, Karr C. A cohort study of traffic-related
air pollution impacts on birth outcomes. Environmental health perspectives 2008; 116:
680-686.
Brown SG, Eberly S, Paatero P, Norris GA. Methods for estimating uncertainty in PMF solutions:
Examples with ambient air and water quality data and guidance on reporting PMF results.
Science of the Total Environment 2015; 518: 626-635.
Brunekreef B, Forsberg B. Epidemiological evidence of effects of coarse airborne particles on
health. European respiratory journal 2005; 26: 309-318.

77
Bukowiecki N, Hill M, Gehrig R, Zwicky CN, Lienemann P, Hegedüs F, et al. Trace metals in
ambient air: Hourly size-segregated mass concentrations determined by synchrotron-XRF.
Environmental science & technology 2005; 39: 5754-5762.
Bukowiecki N, Lienemann P, Hill M, Furger M, Richard A, Amato F, et al. PM10 emission factors
for non-exhaust particles generated by road traffic in an urban street canyon and along a
freeway in Switzerland. Atmospheric Environment 2010; 44: 2330-2340.
Cabada JC, Pandis SN, Subramanian R, Robinson AL, Polidori A, Turpin B. Estimating the
secondary organic aerosol contribution to PM2. 5 using the EC tracer method special issue
of aerosol science and technology on findings from the fine particulate matter supersites
program. Aerosol Science and Technology 2004; 38: 140-155.
Caltrans. Caltrans Performance Measurement System (PeMS),
https://pems.dot.ca.gov/?dnode=search&content=cnt_search&view=p#33.972406,-
118.269196,11., 2022.
Cao H, Amiraslani F, Liu J, Zhou N. Identification of dust storm source areas in West Asia using
multiple environmental datasets. Science of The Total Environment 2015; 502: 224-235.
Cao J, Lee S, Ho K, Fung K, Chow JC, Watson JG. Characterization of roadside fine particulate
carbon and its eight fractions in Hong Kong. Aerosol and Air Quality Research 2006; 6:
106-122.
Cao J, Wu F, Chow J, Lee S, Li Y, Chen S, et al. Characterization and source apportionment of
atmospheric organic and elemental carbon during fall and winter of 2003 in Xi'an, China.
Atmospheric Chemistry and Physics 2005; 5: 3127-3137.
Caponi L, Formenti P, Massabó D, Biagio CD, Cazaunau M, Pangui E, et al. Spectral-and sizeresolved mass absorption efficiency of mineral dust aerosols in the shortwave spectrum: a
simulation chamber study. Atmospheric Chemistry and Physics 2017; 17: 7175-7191.
CARB. LEV II and CAP 2000 Amendments: Final Regulation Order., 2000.
Carlton A, Wiedinmyer C, Kroll J. A review of Secondary Organic Aerosol (SOA) formation from
isoprene. Atmospheric Chemistry and Physics 2009; 9: 4987-5005.
Cate DM, Noblitt SD, Volckens J, Henry CS. Multiplexed paper analytical device for
quantification of metals using distance-based detection. Lab on a Chip 2015; 15: 2808-
2818.
Celo V, Yassine MM, Dabek-Zlotorzynska E. Insights into Elemental Composition and Sources
of Fine and Coarse Particulate Matter in Dense Traffic Areas in Toronto and Vancouver,
Canada. Toxics 2021; 9: 264.

78
Chen B, Stein AF, Castell N, Gonzalez-Castanedo Y, de la Campa AS, De La Rosa J. Modeling
and evaluation of urban pollution events of atmospheric heavy metals from a large Cusmelter. Science of the Total Environment 2016; 539: 17-25.
Chen R, Zhao Z, Sun Q, Lin Z, Zhao A, Wang C, et al. Size-fractionated particulate air pollution
and circulating biomarkers of inflammation, coagulation, and vasoconstriction in a panel
of young adults. Epidemiology 2015; 26: 328-336.
Cheng H, Saffari A, Sioutas C, Forman HJ, Morgan TE, Finch CE. Nanoscale particulate matter
from urban traffic rapidly induces oxidative stress and inflammation in olfactory
epithelium with concomitant effects on brain. Environmental health perspectives 2016;
124: 1537-1546.
Cheng Y-H, Lin M-H. Real-time performance of the microAeth® AE51 and the effects of aerosol
loading on its measurement results at a traffic site. Aerosol and Air Quality Research 2013;
13: 1853-1863.
Cheng Y, He K-b, Duan F-k, Du Z-y, Zheng M, Ma Y-l. Ambient organic carbon to elemental
carbon ratios: Influence of the thermal–optical temperature protocol and implications.
Science of the total environment 2014; 468: 1103-1111.
Cheung K, Daher N, Kam W, Shafer MM, Ning Z, Schauer JJ, et al. Spatial and temporal variation
of chemical composition and mass closure of ambient coarse particulate matter (PM10–
2.5) in the Los Angeles area. Atmospheric environment 2011; 45: 2651-2662.
Cheung K, Ntziachristos L, Tzamkiozis T, Schauer J, Samaras Z, Moore K, et al. Emissions of
particulate trace elements, metals and organic species from gasoline, diesel, and biodiesel
passenger vehicles and their relation to oxidative potential. Aerosol Science and
Technology 2010; 44: 500-513.
Cheung K, Olson MR, Shelton B, Schauer JJ, Sioutas C. Seasonal and spatial variations of
individual organic compounds of coarse particulate matter in the Los Angeles Basin.
Atmospheric environment 2012; 59: 1-10.
Chow JC, Watson JG, Chen L-WA, Arnott WP, Moosmüller H, Fung K. Equivalence of elemental
carbon by thermal/optical reflectance and transmittance with different temperature
protocols. Environmental science & technology 2004; 38: 4414-4422.
Chow JC, Watson JG, Chen L-WA, Chang MO, Robinson NF, Trimble D, et al. The IMPROVE_A
temperature protocol for thermal/optical carbon analysis: maintaining consistency with a
long-term database. Journal of the Air & Waste Management Association 2007; 57: 1014-
1023.

79
Chow JC, Watson JG, Fujita EM, Lu Z, Lawson DR, Ashbaugh LL. Temporal and spatial
variations of PM2. 5 and PM10 aerosol in the Southern California air quality study.
Atmospheric Environment 1994; 28: 2061-2080.
Chow JC, Watson JG, Pritchett LC, Pierson WR, Frazier CA, Purcell RG. The DRI thermal/optical
reflectance carbon analysis system: description, evaluation and applications in US air
quality studies. Atmospheric Environment. Part A. General Topics 1993; 27: 1185-1201.
Chung SH, Seinfeld JH. Global distribution and climate forcing of carbonaceous aerosols. Journal
of Geophysical Research: Atmospheres 2002; 107: AAC 14-1-AAC 14-33.
Chylek P, Dubey M, Lesins G. Greenland warming of 1920–1930 and 1995–2005. Geophysical
Research Letters 2006; 33.
Crilley LR, Lucarelli F, Bloss WJ, Harrison RM, Beddows DC, Calzolai G, et al. Source
apportionment of fine and coarse particles at a roadside and urban background site in
London during the 2012 summer ClearfLo campaign. Environmental Pollution 2017; 220:
766-778.
Crippa M, DeCarlo P, Slowik J, Mohr C, Heringa M, Chirico R, et al. Wintertime aerosol chemical
composition and source apportionment of the organic fraction in the metropolitan area of
Paris. Atmospheric Chemistry and Physics 2013; 13: 961-981.
Cui H-X. Estimation of the formation potential of ozone and secondary organic aerosol in
Shanghai in spring. Huan jing ke xue= Huanjing kexue 2013; 34: 4529-4534.
Daher N, Hasheminassab S, Shafer MM, Schauer JJ, Sioutas C. Seasonal and spatial variability in
chemical composition and mass closure of ambient ultrafine particles in the megacity of
Los Angeles. Environmental Science: Processes & Impacts 2013; 15: 283-295.
Daher N, Ruprecht A, Invernizzi G, De Marco C, Miller-Schulze J, Heo JB, et al. Characterization,
sources and redox activity of fine and coarse particulate matter in Milan, Italy. Atmospheric
Environment 2012; 49: 130-141.
Davidson K, Hallberg A, McCubbin D, Hubbell B. Analysis of PM2. 5 using the environmental
benefits mapping and analysis program (BenMAP). Journal of Toxicology and
Environmental Health, Part A 2007; 70: 332-346.
Davis DA, Akopian G, Walsh JP, Sioutas C, Morgan TE, Finch CE. Urban air pollutants reduce
synaptic function of CA 1 neurons via an NMDA/NȮ pathway in vitro. Journal of
neurochemistry 2013; 127: 509-519.
de Gouw JA, Brock C, Atlas E, Bates T, Fehsenfeld F, Goldan P, et al. Sources of particulate
matter in the northeastern United States in summer: 1. Direct emissions and secondary

80
formation of organic matter in urban plumes. Journal of Geophysical Research:
Atmospheres 2008; 113.
Delfino RJ, Staimer N, Tjoa T, Arhami M, Polidori A, Gillen DL, et al. Associations of primary
and secondary organic aerosols with airway and systemic inflammation in an elderly panel
cohort. Epidemiology (Cambridge, Mass.) 2010; 21.
Demerjian KL. A review of national monitoring networks in North America. Atmospheric
Environment 2000; 34: 1861-1884.
Desert Research Institute. DRI STANDARD OPERATING PROCEDURE DRI Model 2001
Thermal/Optical Carbon Analysis (TOR/TOT) of Aerosol Filter Samples Method
IMPROVE_A. 2005.
Di Biagio C, Formenti P, Cazaunau M, Pangui E, Marchand N, Doussin J-F. Aethalometer
multiple scattering correction Cref for mineral dust aerosols. Atmospheric Measurement
Techniques 2017; 10: 2923-2939.
Drinovec L, Gregorič A, Zotter P, Wolf R, Bruns EA, Prévôt AS, et al. The filter-loading effect
by ambient aerosols in filter absorption photometers depends on the coating of the sampled
particles. Atmospheric Measurement Techniques 2017; 10: 1043-1059.
Drinovec L, Močnik G, Zotter P, Prévôt A, Ruckstuhl C, Coz E, et al. The" dual-spot"
Aethalometer: an improved measurement of aerosol black carbon with real-time loading
compensation. Atmospheric measurement techniques 2015; 8: 1965-1979.
Drinovec L, Sciare J, Stavroulas I, Bezantakos S, Pikridas M, Unga F, et al. A new optical-based
technique for real-time measurements of mineral dust concentration in PM 10 using a
virtual impactor. Atmospheric Measurement Techniques 2020; 13: 3799-3813.
Ealo M, Alastuey A, Ripoll A, Pérez N, Minguillón MC, Querol X, et al. Detection of Saharan
dust and biomass burning events using near-real-time intensive aerosol optical properties
in the north-western Mediterranean. Atmospheric Chemistry and Physics 2016; 16: 12567-
12586.
Ebisu K, Berman JD, Bell ML. Exposure to coarse particulate matter during gestation and birth
weight in the U.S. Environment International 2016; 94: 519-524.
Eck TF, Holben B, Sinyuk A, Pinker R, Goloub P, Chen H, et al. Climatological aspects of the
optical properties of fine/coarse mode aerosol mixtures. Journal of Geophysical Research:
Atmospheres 2010; 115.
Edgerton ES, Casuccio GS, Saylor RD, Lersch TL, Hartsell BE, Jansen JJ, et al. Measurements of
OC and EC in coarse particulate matter in the southeastern United States. Journal of the
Air & Waste Management Association 2009; 59: 78-90.

81
Evan AT, Dunion J, Foley JA, Heidinger AK, Velden CS. New evidence for a relationship between
Atlantic tropical cyclone activity and African dust outbreaks. Geophysical Research
Letters 2006; 33.
Falkovich AH, Schkolnik G, Ganor E, Rudich Y. Adsorption of organic compounds pertinent to
urban environments onto mineral dust particles. Journal of Geophysical Research:
Atmospheres 2004; 109.
Fang T, Guo H, Verma V, Peltier R, Weber R. PM 2.5 water-soluble elements in the southeastern
United States: automated analytical method development, spatiotemporal distributions,
source apportionment, and implications for heath studies. Atmospheric Chemistry and
Physics 2015; 15: 11667-11682.
Fang T, Verma V, Bates JT, Abrams J, Klein M, Strickland MJ, et al. Oxidative potential of
ambient water-soluble PM 2.5 in the southeastern United States: contrasts in sources and
health associations between ascorbic acid (AA) and dithiothreitol (DTT) assays.
Atmospheric Chemistry and Physics 2016; 16: 3865-3879.
Farahani VJ, Soleimanian E, Pirhadi M, Sioutas C. Long-term trends in concentrations and sources
of PM2. 5–bound metals and elements in central Los Angeles. Atmospheric Environment
2021; 253: 118361.
Ferrero L, Mocnik G, Ferrini B, Perrone M, Sangiorgi G, Bolzacchini E. Vertical profiles of
aerosol absorption coefficient from micro-Aethalometer data and Mie calculation over
Milan. Science of the Total Environment 2011; 409: 2824-2837.
Fialho P, Cerqueira M, Pio C, Cardoso J, Nunes T, Custódio D, et al. The application of a multiwavelength Aethalometer to estimate iron dust and black carbon concentrations in the
marine boundary layer of Cape Verde. Atmospheric Environment 2014; 97: 136-143.
Fialho P, Freitas MdC, Barata F, Vieira B, Hansen A, Honrath R. The Aethalometer calibration
and determination of iron concentration in dust aerosols. Journal of aerosol science 2006;
37: 1497-1506.
Fialho P, Hansen A, Honrath R. Absorption coefficients by aerosols in remote areas: a new
approach to decouple dust and black carbon absorption coefficients using sevenwavelength Aethalometer data. Journal of Aerosol Science 2005; 36: 267-282.
Fitzgerald E, Ault AP, Zauscher MD, Mayol-Bracero OL, Prather KA. Comparison of the mixing
state of long-range transported Asian and African mineral dust. Atmospheric Environment
2015; 115: 19-25.
Formenti P, Caquineau S, Desboeufs K, Klaver A, Chevaillier S, Journet E, et al. Mapping the
physico-chemical properties of mineral dust in western Africa: mineralogical composition.
Atmospheric Chemistry and Physics 2014; 14: 10663-10686.

82
Foster ML, Rao DB, Francher T, Traver S, Dorman DC. Olfactory toxicity in rats following
manganese chloride nasal instillation: a pilot study. Neurotoxicology 2018; 64: 284-290.
Gamage LP, Hix EG, Gichuhi WK. Ground-Based Atmospheric Measurements of CO: CO2 Ratios
in Eastern Highland Rim Using a CO Tracer Technique. ACS Earth and Space Chemistry
2020; 4: 558-571.
Garcia GJ, Kimbell JS. Deposition of inhaled nanoparticles in the rat nasal passages: dose to the
olfactory region. Inhalation toxicology 2009; 21: 1165-1175.
Gauderman WJ, Urman R, Avol E, Berhane K, McConnell R, Rappaport E, et al. Association of
improved air quality with lung development in children. N Engl J Med 2015; 372: 905-
913.
Geller MD, Biswas S, Fine PM, Sioutas C. A new compact aerosol concentrator for use in
conjunction with low flow-rate continuous aerosol instrumentation. Journal of Aerosol
Science 2005; 36: 1006-1022.
Giannossa LC, Cesari D, Merico E, Dinoi A, Mangone A, Guascito MR, et al. Inter-annual
variability of source contributions to PM10, PM2. 5, and oxidative potential in an urban
background site in the central mediterranean. Journal of Environmental Management 2022;
319: 115752.
Giles DM, Holben BN, Eck TF, Sinyuk A, Smirnov A, Slutsker I, et al. An analysis of AERONET
aerosol absorption properties and classifications representative of aerosol source regions.
Journal of Geophysical Research: Atmospheres 2012; 117.
Goudie AS. Desert dust and human health disorders. Environment International 2014; 63: 101-
113.
Goudie AS. Dust storms and human health. Extreme Weather Events and Human Health. Springer,
2020, pp. 13-24.
Grahame TJ, Schlesinger RB. Cardiovascular health and particulate vehicular emissions: a critical
evaluation of the evidence. Air Quality, Atmosphere & Health 2010; 3: 3-27.
Greilinger M, Schauer G, Baumann-Stanzer K, Skomorowski P, Schöner W, Kasper-Giebl A.
Contribution of saharan dust to ion deposition loads of high alpine snow packs in Austria
(1987–2017). Frontiers in Earth Science 2018; 6: 126.
Habre R, Girguis M, Urman R, Fruin S, Lurmann F, Shafer M, et al. Contribution of tailpipe and
non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern
California. Journal of the Air & Waste Management Association 2021; 71: 209-230.

83
Hamad S, Green D, Heo J. Evaluation of health risk associated with fireworks activity at Central
London. Air Quality, Atmosphere & Health 2016; 9: 735-741.
Harrison RM, Hester RE, Querol X. Airborne Particulate Matter: Sources, Atmospheric Processes
and Health: Royal Society of Chemistry, 2016.
Harrison RM, Jones AM, Gietl J, Yin J, Green DC. Estimation of the contributions of brake dust,
tire wear, and resuspension to nonexhaust traffic particles derived from atmospheric
measurements. Environmental science & technology 2012; 46: 6523-6529.
Hasheminassab S, Daher N, Ostro BD, Sioutas C. Long-term source apportionment of ambient
fine particulate matter (PM2. 5) in the Los Angeles Basin: A focus on emissions reduction
from vehicular sources. Environmental Pollution 2014a; 193: 54-64.
Hasheminassab S, Daher N, Saffari A, Wang D, Ostro B, Sioutas C. Spatial and temporal
variability of sources of ambient fine particulate matter (PM 2.5) in California.
Atmospheric Chemistry and Physics 2014b; 14: 12085-12097.
Hasheminassab S, Daher N, Schauer JJ, Sioutas C. Source apportionment and organic compound
characterization of ambient ultrafine particulate matter (PM) in the Los Angeles Basin.
Atmospheric environment 2013; 79: 529-539.
Hasheminassab S, Pakbin P, Delfino RJ, Schauer JJ, Sioutas C. Diurnal and seasonal trends in the
apparent density of ambient fine and coarse particles in Los Angeles. Environmental
pollution 2014c; 187: 1-9.
Hasheminassab S, Sowlat MH, Pakbin P, Katzenstein A, Low J, Polidori A. High time-resolution
and time-integrated measurements of particulate metals and elements in an environmental
justice community within the Los Angeles Basin: Spatio-temporal trends and source
apportionment. Atmospheric Environment: X 2020; 7: 100089.
Haveman J, Thornberg C. Clean Trucks Program: An Economic Policy Analysis. Beacon
Economics 2008.
Hayes P, Ortega A, Cubison M, Froyd K, Zhao Y, Cliff S, et al. Organic aerosol composition and
sources in Pasadena, California, during the 2010 CalNex campaign. Journal of Geophysical
Research: Atmospheres 2013; 118: 9233-9257.
Health Effects Institute. HEI strategic plan for understanding the health effects of air pollution
2015–2020. Health Effects Institute Boston, MA, 2015.
Healy RM, Sofowote U, Su Y, Debosz J, Noble M, Jeong CH, et al. Ambient measurements and
source apportionment of fossil fuel and biomass burning black carbon in Ontario.
Atmospheric Environment 2017; 161: 34-47.

84
Henneman LR, Shen H, Hogrefe C, Russell AG, Zigler CM. Four Decades of United States Mobile
Source Pollutants: Spatial–Temporal Trends Assessed by Ground-Based Monitors, Air
Quality Models, and Satellites. Environmental Science & Technology 2021; 55: 882-892.
Heo J-B, Hopke P, Yi S-M. Source apportionment of PM 2.5 in Seoul, Korea. Atmospheric
Chemistry and Physics 2009; 9: 4957-4971.
Heo J, de Foy B, Olson MR, Pakbin P, Sioutas C, Schauer JJ. Impact of regional transport on the
anthropogenic and biogenic secondary organic aerosols in the Los Angeles Basin.
Atmospheric Environment 2015; 103: 171-179.
Heo J, Dulger M, Olson MR, McGinnis JE, Shelton BR, Matsunaga A, et al. Source
apportionments of PM2.5 organic carbon using molecular marker Positive Matrix
Factorization and comparison of results from different receptor models. Atmospheric
Environment 2013a; 73: 51-61.
Heo J, Dulger M, Olson MR, McGinnis JE, Shelton BR, Matsunaga A, et al. Source
apportionments of PM2. 5 organic carbon using molecular marker Positive Matrix
Factorization and comparison of results from different receptor models. Atmospheric
Environment 2013b; 73: 51-61.
Herich H, Hueglin C, Buchmann B. A 2.5 year's source apportionment study of black carbon from
wood burning and fossil fuel combustion at urban and rural sites in Switzerland.
Atmospheric Measurement Techniques 2011; 4: 1409-1420.
Herner JD, Hu S, Robertson WH, Huai T, Chang M-CO, Rieger P, et al. Effect of advanced
aftertreatment for PM and NO x reduction on heavy-duty diesel engine ultrafine particle
emissions. Environmental science & technology 2011; 45: 2413-2419.
Hersey S, Craven J, Schilling K, Metcalf A, Sorooshian A, Chan M, et al. The Pasadena Aerosol
Characterization Observatory (PACO): chemical and physical analysis of the Western Los
Angeles basin aerosol. Atmospheric Chemistry and Physics 2011; 11: 7417-7443.
Ho KF, Lee SC, Cao JJ, Chow JC, Watson JG, Chan CK. Seasonal variations and mass closure
analysis of particulate matter in Hong Kong. Science of The Total Environment 2006; 355:
276-287.
Hochgatterer K, Moshammer H, Haluza D. Dust is in the air: effects of occupational exposure to
mineral dust on lung function in a 9-year study. Lung 2013; 191: 257-263.
Honda A, Matsuda Y, Murayama R, Tsuji K, Nishikawa M, Koike E, et al. Effects of Asian sand
dust particles on the respiratory and immune system. Journal of applied toxicology 2014;
34: 250-257.

85
Huang R-J, Zhang Y, Bozzetti C, Ho K-F, Cao J-J, Han Y, et al. High secondary aerosol
contribution to particulate pollution during haze events in China. Nature 2014; 514: 218-
222.
Huang X-F, Yu JZ. Size distributions of elemental carbon in the atmosphere of a coastal urban
area in South China: characteristics, evolution processes, and implications for the mixing
state. Atmospheric chemistry and physics 2008; 8: 5843-5853.
Hueglin C, Gehrig R, Baltensperger U, Gysel M, Monn C, Vonmont H. Chemical characterisation
of PM2. 5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland.
Atmospheric Environment 2005; 39: 637-651.
Hughes LS, Allen JO, Bhave P, Kleeman MJ, Cass GR, Liu D-Y, et al. Evolution of atmospheric
particles along trajectories crossing the Los Angeles basin. Environmental science &
technology 2000; 34: 3058-3068.
Hwang R, Doniger D. Comments on the proposed adoption of regulations by the California Air
Resources Board (CARB) to control greenhouse gas emissions from motor vehicles.
NRDC, California, 2004.
Jacob DJ. Introduction to atmospheric chemistry: Princeton University Press, 1999.
Jaén C, Villasclaras P, Fernández P, Grimalt JO, Udina M, Bedia C, et al. Source apportionment
and toxicity of PM in urban, sub-urban, and rural air quality network stations in Catalonia.
Atmosphere 2021; 12: 744.
Jalali Farahani V, Altuwayjiri A, Taghvaee S, Sioutas C. Tailpipe and Nontailpipe Emission
Factors and Source Contributions of PM10 on Major Freeways in the Los Angeles Basin.
Environmental Science & Technology 2022.
Jeong C-H, Wang JM, Hilker N, Debosz J, Sofowote U, Su Y, et al. Temporal and spatial
variability of traffic-related PM2.5 sources: Comparison of exhaust and non-exhaust
emissions. Atmospheric Environment 2019; 198: 55-69.
Jimenez JL, Canagaratna M, Donahue N, Prevot A, Zhang Q, Kroll JH, et al. Evolution of organic
aerosols in the atmosphere. Science 2009; 326: 1525-1529.
Jung J, Lee S, Kim H, Kim D, Lee H, Oh S. Quantitative determination of the biomass-burning
contribution to atmospheric carbonaceous aerosols in Daejeon, Korea, during the riceharvest period. Atmospheric Environment 2014; 89: 642-650.
Kapustin NO, Grushevenko DA. Long-term electric vehicles outlook and their potential impact on
electric grid. Energy Policy 2020; 137: 111103.

86
Karanasiou A, Diapouli E, Cavalli F, Eleftheriadis K, Viana M, Alastuey A, et al. On the
quantification of atmospheric carbonate carbon by thermal/optical analysis protocols.
Atmospheric Measurement Techniques 2011; 4: 2409-2419.
Kim E, Hopke PK. Source apportionment of fine particles in Washington, DC, utilizing
temperature-resolved carbon fractions. Journal of the Air & Waste Management
Association 2004; 54: 773-785.
Kim JJ, Smorodinsky S, Lipsett M, Singer BC, Hodgson AT, Ostro B. Traffic-related air pollution
near busy roads: the East Bay Children's Respiratory Health Study. American journal of
respiratory and critical care medicine 2004; 170: 520-526.
Kim S, Jaques PA, Chang M, Froines JR, Sioutas C. 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 I: Development and laboratory
characterization. Journal of Aerosol Science 2001; 32: 1281-1297.
Kirchstetter TW, Novakov T, Hobbs PV. Evidence that the spectral dependence of light absorption
by aerosols is affected by organic carbon. Journal of Geophysical Research: Atmospheres
2004; 109.
Kirchstetter TW, Singer BC, Harley RA, Kendall GR, Hesson JM. Impact of California
reformulated gasoline on motor vehicle emissions. 2. Volatile organic compound
speciation and reactivity. Environmental Science & Technology 1999; 33: 329-336.
Klaver A, Formenti P, Caquineau S, Chevaillier S, Ausset P, Calzolai G, et al. Physico‐chemical
and optical properties of Sahelian and Saharan mineral dust: In situ measurements during
the GERBILS campaign. Quarterly Journal of the Royal Meteorological Society 2011; 137:
1193-1210.
Krasowsky TS, McMeeking GR, Wang D, Sioutas C, Ban-Weiss GA. Measurements of the impact
of atmospheric aging on physical and optical properties of ambient black carbon particles
in Los Angeles. Atmospheric Environment 2016; 142: 496-504.
Krudysz MA, Froines JR, Fine PM, Sioutas C. Intra-community spatial variation of sizefractionated PM mass, OC, EC, and trace elements in the Long Beach, CA area.
Atmospheric Environment 2008; 42: 5374-5389.
Kuang Y, He Y, Xu W, Yuan B, Zhang G, Ma Z, et al. Photochemical aqueous-phase reactions
induce rapid daytime formation of oxygenated organic aerosol on the North China Plain.
Environmental Science & Technology 2020; 54: 3849-3860.
Künzi L, Krapf M, Daher N, Dommen J, Jeannet N, Schneider S, et al. Toxicity of aged gasoline
exhaust particles to normal and diseased airway epithelia. Scientific reports 2015; 5: 1-10.

87
Laidlaw MA, Filippelli GM. Resuspension of urban soils as a persistent source of lead poisoning
in children: a review and new directions. Applied Geochemistry 2008; 23: 2021-2039.
Lanz V, Alfarra M, Baltensperger U, Buchmann B, Hueglin C, Prévôt A. Source apportionment
of submicron organic aerosols at an urban site by factor analytical modelling of aerosol
mass spectra. Atmospheric Chemistry and Physics 2007; 7: 1503-1522.
Lázaro FJ, Gutiérrez L, Barrón V, Gelado MD. The speciation of iron in desert dust collected in
Gran Canaria (Canary Islands): Combined chemical, magnetic and optical analysis.
Atmospheric Environment 2008; 42: 8987-8996.
Lee G, Ritchie SG, Saphores J-D, Jayakrishnan R, Ogunseitan O. Assessing air quality and health
benefits of the Clean Truck Program in the Alameda corridor, CA. Transportation Research
Part A: Policy and Practice 2012; 46: 1177-1193.
Lee J-S. The environmental costs of Asian dust damages in Korea: applying a choice experiment.
Environmental Economics and Policy Studies 2018; 20: 641-654.
Lee S, Russell AG, Baumann K. Source apportionment of fine particulate matter in the
southeastern United States. Journal of the Air & Waste Management Association 2007; 57:
1123-1135.
Li H, Wang J, Wang Qg, Tian C, Qian X, Leng Xz. Magnetic properties as a proxy for predicting
fine-particle-bound heavy metals in a support vector machine approach. Environmental
science & technology 2017; 51: 6927-6935.
Li HZ, Dallmann TR, Li X, Gu P, Presto AA. Urban organic aerosol exposure: spatial variations
in composition and source impacts. Environmental science & technology 2018a; 52: 415-
426.
Li J, Liu C, Yin Y, Kumar KR. Numerical investigation on the Ångström exponent of black carbon
aerosol. Journal of Geophysical Research: Atmospheres 2016; 121: 3506-3518.
Li W, Li L, Chen C-l, Kacarab M, Peng W, Price D, et al. Potential of select intermediate-volatility
organic compounds and consumer products for secondary organic aerosol and ozone
formation under relevant urban conditions. Atmospheric Environment 2018b; 178: 109-
117.
Lim H-J, Turpin BJ. Origins of Primary and Secondary Organic Aerosol in Atlanta: Results of
Time-Resolved Measurements during the Atlanta Supersite Experiment. Environmental
Science & Technology 2002; 36: 4489-4496.
Lin X, Zhu B, An J, Yang H. Potential contribution of secondary organic aerosols and ozone of
VOCs in the northern suburb of Nanjing. China Environmental Science 2015; 35: 976-986.

88
Linke C, Möhler O, Veres A, Mohácsi Á, Bozóki Z, Szabó G, et al. Optical properties and
mineralogical composition of different Saharan mineral dust samples: a laboratory study.
Atmospheric Chemistry and Physics 2006; 6: 3315-3323.
Liu F, Saavedra MG, Champion JA, Griendling KK, Ng NL. Prominent Contribution of Hydrogen
Peroxide to Intracellular Reactive Oxygen Species Generated upon Exposure to
Naphthalene Secondary Organic Aerosols. Environmental Science & Technology Letters
2020a; 7: 171-177.
Liu F, Saavedra MG, Champion JA, Griendling KK, Ng NL. Prominent Contribution of Hydrogen
Peroxide to Intracellular Reactive Oxygen Species Generated upon Exposure to
Naphthalene Secondary Organic Aerosols. Environmental Science and Technology Letters
2020b; 7: 171-177.
Ljubimova JY, Braubach O, Patil R, Chiechi A, Tang J, Galstyan A, et al. Coarse particulate matter
(PM2. 5–10) in Los Angeles Basin air induces expression of inflammation and cancer
biomarkers in rat brains. Scientific reports 2018; 8: 1-11.
Lough GC, Schauer JJ, Park J-S, Shafer MM, DeMinter JT, Weinstein JP. Emissions of metals
associated with motor vehicle roadways. Environmental science & technology 2005; 39:
826-836.
Lu Z, Streets DG, Winijkul E, Yan F, Chen Y, Bond TC, et al. Light absorption properties and
radiative effects of primary organic aerosol emissions. Environmental science &
technology 2015; 49: 4868-4877.
Lurmann F, Avol E, Gilliland F. Emissions reduction policies and recent trends in Southern
California’s ambient air quality. Journal of the Air & Waste Management Association
2015; 65: 324-335.
Ma Q, Liu Y, Liu C, He H. Heterogeneous reaction of acetic acid on MgO, α-Al 2 O 3, and CaCO
3 and the effect on the hygroscopic behaviour of these particles. Physical Chemistry
Chemical Physics 2012; 14: 8403-8409.
Mabahwi NAB, Leh OLH, Omar D. Human health and wellbeing: Human health effect of air
pollution. Procedia-Social and Behavioral Sciences 2014; 153: 221-229.
Maeda Y, Aoki K, Munemori M. Chemiluminescence method for the determination of nitrogen
dioxide. Analytical Chemistry 1980; 52: 307-311.
Manousakas M, Diapouli E, Papaefthymiou H, Migliori A, Karydas A, Padilla-Alvarez R, et al.
Source apportionment by PMF on elemental concentrations obtained by PIXE analysis of
PM10 samples collected at the vicinity of lignite power plants and mines in Megalopolis,
Greece. Nuclear Instruments and Methods in Physics Research Section B: Beam
Interactions with Materials and Atoms 2015; 349: 114-124.

89
Mansha M, Ghauri B, Rahman S, Amman A. Characterization and source apportionment of
ambient air particulate matter (PM2. 5) in Karachi. Science of the total environment 2012;
425: 176-183.
Marr LC, Harley RA. Spectral analysis of weekday–weekend differences in ambient ozone,
nitrogen oxide, and non-methane hydrocarbon time series in California. Atmospheric
Environment 2002; 36: 2327-2335.
Masiol M, Hopke P, Felton H, Frank B, Rattigan O, Wurth M, et al. Analysis of major air pollutants
and submicron particles in New York City and Long Island. Atmospheric Environment
2017; 148: 203-214.
Massabò D, Caponi L, Bove M, Prati P. Brown carbon and thermal–optical analysis: A correction
based on optical multi-wavelength apportionment of atmospheric aerosols. Atmospheric
Environment 2016; 125: 119-125.
Mauderly JL, Chow JC. Health effects of organic aerosols. Inhalation toxicology 2008; 20: 257-
288.
McDonald BC, De Gouw JA, Gilman JB, Jathar SH, Akherati A, Cappa CD, et al. Volatile
chemical products emerging as largest petrochemical source of urban organic emissions.
Science 2018; 359: 760-764.
Middleton N, Yiallouros P, Kleanthous S, Kolokotroni O, Schwartz J, Dockery DW, et al. A 10-
year time-series analysis of respiratory and cardiovascular morbidity in Nicosia, Cyprus:
the effect of short-term changes in air pollution and dust storms. Environmental health
2008; 7: 1-16.
Middleton NJ. Desert dust hazards: A global review. Aeolian Research 2017; 24: 53-63.
Miguel AH, Eiguren-Fernandez A, Jaques PA, Froines JR, Grant BL, Mayo PR, et al. Seasonal
variation of the particle size distribution of polycyclic aromatic hydrocarbons and of major
aerosol species in Claremont, California. Atmospheric Environment 2004; 38: 3241-3251.
Minguillón M, Querol X, Baltensperger U, Prévôt A. Fine and coarse PM composition and sources
in rural and urban sites in Switzerland: local or regional pollution? Science of the Total
Environment 2012; 427: 191-202.
Minguillón MC, Arhami M, Schauer JJ, Sioutas C. Seasonal and spatial variations of sources of
fine and quasi-ultrafine particulate matter in neighborhoods near the Los Angeles–Long
Beach harbor. Atmospheric Environment 2008; 42: 7317-7328.
Mohr C, DeCarlo PF, Heringa MF, Chirico R, Richter R, Crippa M, et al. Spatial variation of
aerosol chemical composition and organic components identified by positive matrix

90
factorization in the Barcelona region. Environmental science & technology 2015; 49:
10421-10430.
Moody JD, Freeman JP, Doerge DR, Cerniglia CE. Degradation of phenanthrene and anthracene
by cell suspensions of Mycobacterium sp. strain PYR-1. Applied and environmental
microbiology 2001; 67: 1476-1483.
Mooibroek D, Schaap M, Weijers E, Hoogerbrugge R. Source apportionment and spatial
variability of PM2. 5 using measurements at five sites in the Netherlands. Atmospheric
Environment 2011; 45: 4180-4191.
Moore KF, Verma V, Minguillón MC, Sioutas C. Inter-and intra-community variability in
continuous coarse particulate matter (PM10-2.5) concentrations in the Los Angeles area.
Aerosol Science and Technology 2010; 44: 526-540.
Morrison RD, Murphy BL. Environmental forensics: contaminant specific guide: Elsevier, 2010.
Mousavi A, Sowlat MH, Hasheminassab S, Polidori A, Sioutas C. Spatio-temporal trends and
source apportionment of fossil fuel and biomass burning black carbon (BC) in the Los
Angeles Basin. Science of the Total Environment 2018a; 640: 1231-1240.
Mousavi A, Sowlat MH, Lovett C, Rauber M, Szidat S, Boffi R, et al. Source apportionment of
black carbon (BC) from fossil fuel and biomass burning in metropolitan Milan, Italy.
Atmospheric Environment 2019; 203: 252-261.
Mousavi A, Sowlat MH, Sioutas C. Diurnal and seasonal trends and source apportionment of
redox-active metals in Los Angeles using a novel online metal monitor and Positive Matrix
Factorization (PMF). Atmospheric Environment 2018b; 174: 15-24.
Myhre G, Shindell D, Pongratz J. Anthropogenic and natural radiative forcing. 2014.
Ng N, Kroll J, Chan A, Chhabra P, Flagan R, Seinfeld J. Secondary organic aerosol formation
from m-xylene, toluene, and benzene. Atmospheric Chemistry and Physics 2007; 7: 3909-
3922.
Ning Z, Polidori A, Schauer JJ, Sioutas C. Emission factors of PM species based on freeway
measurements and comparison with tunnel and dynamometer studies. Atmospheric
Environment 2008; 42: 3099-3114.
Norris G, Duvall R, Brown S, Bai S. Epa positive matrix factorization (pmf) 5.0 fundamentals and
user guide prepared for the us environmental protection agency office of research and
development, washington, dc. Washington, DC 2014.
Norris G, Vedantham R, Wade K, Zahn P, Brown S, Paatero P, et al. Guidance document for PMF
applications with the Multilinear Engine. Prepared for the US Environmental Protection

91
Agency, Research Triangle Park, NC, by the National Exposure Research Laboratory,
Research Triangle Park, NC 2009.
Okoshi R, Rasheed A, Reddy GC, McCrowey CJ, Curtis DB. Size and mass distributions of
ground-level sub-micrometer biomass burning aerosol from small wildfires. Atmospheric
Environment 2014; 89: 392-402.
Oroumiyeh F, Jerrett M, Del Rosario I, Lipsitt J, Liu J, Paulson SE, et al. Elemental composition
of fine and coarse particles across the greater Los Angeles area: Spatial variation and
contributing sources. Environmental Pollution 2022; 292: 118356.
Paatero P, Eberly S, Brown S, Norris G. Methods for estimating uncertainty in factor analytic
solutions. Atmospheric Measurement Techniques 2014; 7: 781-797.
Paatero P, Hopke PK, Song X-H, Ramadan Z. Understanding and controlling rotations in factor
analytic models. Chemometrics and intelligent laboratory systems 2002; 60: 253-264.
Paatero P, Tapper U. Positive matrix factorization: A non‐negative factor model with optimal
utilization of error estimates of data values. Environmetrics 1994; 5: 111-126.
Pakbin P, Hudda N, Cheung KL, Moore KF, Sioutas C. Spatial and temporal variability of coarse
(PM10− 2.5) particulate matter concentrations in the Los Angeles area. Aerosol Science
and Technology 2010; 44: 514-525.
Pakbin P, Ning Z, Schauer JJ, Sioutas C. Characterization of particle bound organic carbon from
diesel vehicles equipped with advanced emission control technologies. Environmental
science & technology 2009; 43: 4679-4686.
Pakbin P, Ning Z, Shafer MM, Schauer JJ, Sioutas C. Seasonal and spatial coarse particle
elemental concentrations in the Los Angeles area. Aerosol Science and Technology 2011;
45: 949-963.
Pan X, Uno I, Wang Z, Nishizawa T, Sugimoto N, Yamamoto S, et al. Real-time observational
evidence of changing Asian dust morphology with the mixing of heavy anthropogenic
pollution. Scientific reports 2017; 7: 1-8.
Peltier RE, Cromar KR, Ma Y, Fan Z-HT, Lippmann M. Spatial and seasonal distribution of
aerosol chemical components in New York City:(2) Road dust and other tracers of trafficgenerated air pollution. Journal of exposure science & environmental epidemiology 2011;
21: 484-494.
Pepe N, Pirovano G, Balzarini A, Toppetti A, Riva GM, Amato F, et al. Enhanced CAMx source
apportionment analysis at an urban receptor in Milan based on source categories and
emission regions. Atmospheric Environment: X 2019; 2: 100020.

92
Perrino C, Catrambone M, Dalla Torre S, Rantica E, Sargolini T, Canepari S. Seasonal variations
in the chemical composition of particulate matter: a case study in the Po Valley. Part I:
macro-components and mass closure. Environmental Science and Pollution Research
2014; 21: 3999-4009.
Phairuang W, Inerb M, Furuuchi M, Hata M, Tekasakul S, Tekasakul P. Size-fractionated
carbonaceous aerosols down to PM0. 1 in southern Thailand: Local and long-range
transport effects. Environmental Pollution 2020; 260: 114031.
Phillips-Smith C, Jeong C-H, Healy RM, Dabek-Zlotorzynska E, Celo V, Brook JR, et al. Sources
of particulate matter components in the Athabasca oil sands region: investigation through
a comparison of trace element measurement methodologies. Atmospheric Chemistry and
Physics 2017; 17: 9435-9449.
Pirhadi M, Mousavi A, Sioutas C. Evaluation of a high flow rate electrostatic precipitator (ESP)
as a particulate matter (PM) collector for toxicity studies. Science of the Total Environment
2020a; 739: 140060.
Pirhadi M, Mousavi A, Taghvaee S, Shafer MM, Sioutas C. Semi-volatile components of PM2.5
in an urban environment: Volatility profiles and associated oxidative potential.
Atmospheric Environment 2020b; 223: 117197.
Pollack I, Ryerson T, Trainer M, Parrish D, Andrews A, Atlas EL, et al. Airborne and ground‐
based observations of a weekend effect in ozone, precursors, and oxidation products in the
California South Coast Air Basin. Journal of Geophysical Research: Atmospheres 2012;
117.
Pope CA, Hansen ML, Long RW, Nielsen KR, Eatough NL, Wilson WE, et al. Ambient particulate
air pollution, heart rate variability, and blood markers of inflammation in a panel of elderly
subjects. Environmental health perspectives 2004; 112: 339-345.
Propper R, Wong P, Bui S, Austin J, Vance W, Alvarado A, et al. Ambient and emission trends of
toxic air contaminants in California. Environmental Science & Technology 2015; 49:
11329-11339.
Qiu H, Yu IT-s, Tian L, Wang X, Tse LA, Tam W, et al. Effects of coarse particulate matter on
emergency hospital admissions for respiratory diseases: a time-series analysis in Hong
Kong. Environmental health perspectives 2012; 120: 572-576.
Ramanathan V, Crutzen PJ, Kiehl J, Rosenfeld D. Aerosols, climate, and the hydrological cycle.
science 2001; 294: 2119-2124.
Ran L, Deng Z, Wang P, Xia X. Black carbon and wavelength-dependent aerosol absorption in
the North China Plain based on two-year aethalometer measurements. Atmospheric
Environment 2016; 142: 132-144.

93
Reff A, Eberly SI, Bhave PV. Receptor modeling of ambient particulate matter data using positive
matrix factorization: review of existing methods. Journal of the Air & Waste Management
Association 2007; 57: 146-154.
Ren Y, Ichinose T, He M, Arashidani K, Yoshida Y, Yoshida S, et al. Aggravation of ovalbumininduced murine asthma by co-exposure to desert-dust and organic chemicals: an animal
model study. Environmental Health 2014; 13: 1-11.
Russell P, Bergstrom R, Shinozuka Y, Clarke A, DeCarlo P, Jimenez J, et al. Absorption Angstrom
Exponent in AERONET and related data as an indicator of aerosol composition.
Atmospheric Chemistry and Physics 2010; 10: 1155-1169.
Saffari A, Hasheminassab S, Wang D, Shafer MM, Schauer JJ, Sioutas C. Impact of primary and
secondary organic sources on the oxidative potential of quasi-ultrafine particles (PM0. 25)
at three contrasting locations in the Los Angeles Basin. Atmospheric Environment 2015;
120: 286-296.
San Tam WW, Wong TW, Wong AH. Association between air pollution and daily mortality and
hospital admission due to ischaemic heart diseases in Hong Kong. Atmospheric
Environment 2015; 120: 360-368.
Sardar SB, Fine PM, Sioutas C. Seasonal and spatial variability of the size‐resolved chemical
composition of particulate matter (PM10) in the Los Angeles Basin. Journal of Geophysical
Research: Atmospheres 2005; 110.
Saylor R, Myles L, Sibble D, Caldwell J, Xing J. Recent trends in gas-phase ammonia and PM2.
5 ammonium in the Southeast United States. Journal of the Air & Waste Management
Association 2015; 65: 347-357.
SCAQMD. Monitoring and laboratory analysis protocol (MATES-IV) Final Report: Appendix III,
September 2014. 2014.
SCAQMD. Multiple Air Toxics Exposure Study in the South Coast Air Basin (MATESIV) Final
Report, May 2015. 2015.
Scarnato B, China S, Nielsen K, Mazzoleni C. Perturbations of the optical properties of mineral
dust particles by mixing with black carbon: a numerical simulation study. Atmospheric
Chemistry and Physics 2015; 15: 6913-6928.
Scarnato B, Vahidinia S, Richard D, Kirchstetter T. Effects of internal mixing and aggregate
morphology on optical properties of black carbon using a discrete dipole approximation
model. Atmospheric Chemistry and Physics 2013; 13: 5089-5101.
Schauer JJ. Evaluation of elemental carbon as a marker for diesel particulate matter. Journal of
Exposure Science & Environmental Epidemiology 2003; 13: 443-453.

94
Schauer JJ, Kleeman MJ, Cass GR, Simoneit BR. Measurement of emissions from air pollution
sources. 1. C1 through C29 organic compounds from meat charbroiling. Environmental
science & technology 1999; 33: 1566-1577.
Schauer JJ, Rogge WF, Hildemann LM, Mazurek MA, Cass GR, Simoneit BR. Source
apportionment of airborne particulate matter using organic compounds as tracers.
Atmospheric Environment 1996; 30: 3837-3855.
Schuster GL, Dubovik O, Holben BN. Angstrom exponent and bimodal aerosol size distributions.
Journal of Geophysical Research: Atmospheres 2006; 111.
Sciare J, Oikonomou K, Cachier H, Mihalopoulos N, Andreae M, Maenhaut W, et al. Aerosol
mass closure and reconstruction of the light scattering coefficient over the Eastern
Mediterranean Sea during the MINOS campaign. Atmospheric Chemistry and Physics
2005; 5: 2253-2265.
Seinfeld JH, Carmichael GR, Arimoto R, Conant WC, Brechtel FJ, Bates TS, et al. ACE-ASIA:
Regional climatic and atmospheric chemical effects of Asian dust and pollution. Bulletin
of the American Meteorological Society 2004; 85: 367-380.
Seinfeld JH, Pandis, S.N. Atmospheric chemistry and physics: from air pollution to climate
change. John Wiley & Sons, Inc, New York. 2006.
Shah RU, Coggon MM, Gkatzelis GI, McDonald BC, Tasoglou A, Huber H, et al. Urban oxidation
flow reactor measurements reveal significant secondary organic aerosol contributions from
volatile emissions of emerging importance. Environmental science & technology 2019; 54:
714-725.
Shah RU, Robinson ES, Gu P, Robinson AL, Apte JS, Presto AA. High-spatial-resolution mapping
and source apportionment of aerosol composition in Oakland, California, using mobile
aerosol mass spectrometry. Atmospheric Chemistry and Physics 2018; 18: 16325-16344.
Shin S-H, Jo W-K. Longitudinal variations in indoor VOC concentrations after moving into new
apartments and indoor source characterization. Environmental Science and Pollution
Research 2013; 20: 3696-3707.
Shirmohammadi F, Hasheminassab S, Saffari A, Schauer JJ, Delfino RJ, Sioutas C. Fine and
ultrafine particulate organic carbon in the Los Angeles basin: trends in sources and
composition. Science of The Total Environment 2016; 541: 1083-1096.
Shirmohammadi F, Sowlat MH, Hasheminassab S, Saffari A, Ban-Weiss G, Sioutas C. Emission
rates of particle number, mass and black carbon by the Los Angeles International Airport
(LAX) and its impact on air quality in Los Angeles. Atmospheric Environment 2017; 151:
82-93.

95
Shotyk W, Bicalho B, Cuss CW, Duke MJM, Noernberg T, Pelletier R, et al. Dust is the dominant
source of “heavy metals” to peat moss (Sphagnum fuscum) in the bogs of the Athabasca
Bituminous Sands region of northern Alberta. Environment International 2016; 92-93:
494-506.
Simoneit BR. Biomass burning—a review of organic tracers for smoke from incomplete
combustion. Applied Geochemistry 2002; 17: 129-162.
Sofowote UM, Di Federico LM, Healy RM, Debosz J, Su Y, Wang J, et al. Heavy metals in the
near-road environment: Results of semi-continuous monitoring of ambient particulate
matter in the greater Toronto and Hamilton area. Atmospheric Environment: X 2019; 1:
100005.
Soleimanian E, Mousavi A, Taghvaee S, Sowlat MH, Hasheminassab S, Polidori A, et al. Spatial
trends and sources of PM2. 5 organic carbon volatility fractions (OCx) across the Los
Angeles Basin. Atmospheric environment 2019a; 209: 201-211.
Soleimanian E, Taghvaee S, Mousavi A, Sowlat MH, Hassanvand MS, Yunesian M, et al. Sources
and temporal variations of coarse particulate matter (PM) in Central Tehran, Iran.
Atmosphere 2019b; 10: 291.
Solomon PA, Crumpler D, Flanagan JB, Jayanty R, Rickman EE, McDade CE. US national PM2.
5 chemical speciation monitoring networks—CSN and IMPROVE: description of
networks. Journal of the Air & Waste Management Association 2014; 64: 1410-1438.
Song Y, Tang X, Xie S, Zhang Y, Wei Y, Zhang M, et al. Source apportionment of PM2. 5 in
Beijing in 2004. Journal of hazardous materials 2007; 146: 124-130.
Song Y, Zhang Y, Xie S, Zeng L, Zheng M, Salmon LG, et al. Source apportionment of PM2. 5
in Beijing by positive matrix factorization. Atmospheric Environment 2006; 40: 1526-
1537.
Sowlat MH, Hasheminassab S, Sioutas C. Source apportionment of ambient particle number
concentrations in central Los Angeles using positive matrix factorization (PMF).
Atmospheric Chemistry and Physics 2016; 16: 4849-4866.
Sricharoenvech P, Lai A, Oo TN, Oo MM, Schauer JJ, Oo KL, et al. Source apportionment of
coarse particulate matter (PM10) in Yangon, Myanmar. International Journal of
Environmental Research and Public Health 2020; 17: 4145.
Stone EA, Snyder DC, Sheesley RJ, Sullivan A, Weber R, Schauer J. Source apportionment of
fine organic aerosol in Mexico City during the MILAGRO experiment 2006. Atmospheric
Chemistry and Physics 2008; 8: 1249-1259.

96
Stone EA, Yoon S-C, Schauer JJ. Chemical characterization of fine and coarse particles in Gosan,
Korea during springtime dust events. Aerosol and Air Quality Research 2011; 11: 31-43.
Sturtz TM, Adar SD, Gould T, Larson TV. Constrained source apportionment of coarse particulate
matter and selected trace elements in three cities from the Multi-Ethnic Study of
Atherosclerosis. Atmospheric Environment 2014; 84: 65-77.
Subramanian R, Kok G, Baumgardner D, Clarke A, Shinozuka Y, Campos T, et al. Black carbon
over Mexico: the effect of atmospheric transport on mixing state, mass absorption crosssection, and BC/CO ratios. Atmospheric Chemistry and Physics 2010; 10: 219-237.
Sun Y-L, Zhang Q, Schwab J, Demerjian K, Chen W-N, Bae M-S, et al. Characterization of the
sources and processes of organic and inorganic aerosols in New York city with a highresolution time-of-flight aerosol mass apectrometer. Atmospheric Chemistry and Physics
2011; 11: 1581-1602.
Taghvaee S, Sowlat MH, Hassanvand MS, Yunesian M, Naddafi K, Sioutas C. Source-specific
lung cancer risk assessment of ambient PM2. 5-bound polycyclic aromatic hydrocarbons
(PAHs) in central Tehran. Environment international 2018a; 120: 321-332.
Taghvaee S, Sowlat MH, Mousavi A, Hassanvand MS, Yunesian M, Naddafi K, et al. Source
apportionment of ambient PM2. 5 in two locations in central Tehran using the Positive
Matrix Factorization (PMF) model. Science of the total environment 2018b; 628: 672-686.
Takegawa N, Miyakawa T, Kondo Y, Jimenez J, Zhang Q, Worsnop D, et al. Seasonal and diurnal
variations of submicron organic aerosol in Tokyo observed using the Aerodyne aerosol
mass spectrometer. Journal of Geophysical Research: Atmospheres 2006; 111.
Tang M, Huang X, Lu K, Ge M, Li Y, Cheng P, et al. Heterogeneous reactions of mineral dust
aerosol: implications for tropospheric oxidation capacity. Atmospheric Chemistry and
Physics 2017; 17: 11727-11777.
Targino AC, Machado BLF, Krecl P. Concentrations and personal exposure to black carbon
particles at airports and on commercial flights. Transportation Research Part D: Transport
and Environment 2017; 52: 128-138.
Terzi E, Argyropoulos G, Bougatioti A, Mihalopoulos N, Nikolaou K, Samara C. Chemical
composition and mass closure of ambient PM10 at urban sites. Atmospheric Environment
2010; 44: 2231-2239.
Tham J, Sarkar S, Jia S, Reid JS, Mishra S, Sudiana I, et al. Impacts of peat-forest smoke on urban
PM2. 5 in the Maritime Continent during 2012–2015: Carbonaceous profiles and
indicators. Environmental Pollution 2019; 248: 496-505.

97
Thorpe A, Harrison RM. Sources and properties of non-exhaust particulate matter from road
traffic: a review. Science of the total environment 2008; 400: 270-282.
Thurston GD, Ito K, Lall R. A source apportionment of US fine particulate matter air pollution.
Atmospheric environment 2011; 45: 3924-3936.
Tian P, Zhang L, Ma J, Tang K, Xu L, Wang Y, et al. Radiative absorption enhancement of dust
mixed with anthropogenic pollution over East Asia. Atmospheric Chemistry and Physics
2018; 18: 7815-7825.
Timmers VR, Achten PA. Non-exhaust PM emissions from electric vehicles. Atmospheric
Environment 2016; 134: 10-17.
Tohidi R, Altuwayjiri A, Pirhadi M, Sioutas C. Quantifying ambient concentrations of primary
and secondary organic aerosol in central Los Angeles using an integrated approach
coupling source apportionment with regression analysis. Atmospheric Environment 2021:
118807.
Tohidi R, Farahani VJ, Sioutas C. Real-time measurements of mineral dust concentration in coarse
particulate matter (PM10–2.5) by employing a novel optical-based technique in Los
Angeles. Science of The Total Environment 2022: 156215.
Tohidi R, Sajadi B, Ahmadi G. The effect of nasal airway obstruction on the dispersion and
deposition of inhaled volatile droplets in the human nasal cavity: A numerical study.
Journal of Aerosol Science 2020; 150: 105650.
Tositti L, Brattich E, Masiol M, Baldacci D, Ceccato D, Parmeggiani S, et al. Source
apportionment of particulate matter in a large city of southeastern Po Valley (Bologna,
Italy). Environmental Science and Pollution Research 2014; 21: 872-890.
Turnbull JC, Miller J, Lehman S, Tans P, Sparks R, Southon J. Comparison of 14CO2, CO, and
SF6 as tracers for recently added fossil fuel CO2 in the atmosphere and implications for
biological CO2 exchange. Geophysical research letters 2006; 33.
Turpin BJ, Huntzicker JJ. Identification of secondary organic aerosol episodes and quantitation of
primary and secondary organic aerosol concentrations during SCAQS. Atmospheric
Environment 1995; 29: 3527-3544.
Turpin BJ, Huntzicker JJ, Larson SM, Cass GR. Los Angeles summer midday particulate carbon:
primary and secondary aerosol. Environmental science & technology 1991; 25: 1788-1793.
US EPA. List of designated reference and equivalent methods. United State Environmental
Protection Agency-methods-list.pdf., 2019.

98
Venkatachari P, Hopke PK, Grover BD, Eatough DJ. Measurement of particle-bound reactive
oxygen species in rubidoux aerosols. Journal of Atmospheric Chemistry 2005; 50: 49-58.
Visser S, Slowik JG, Furger M, Zotter P, Bukowiecki N, Canonaco F, et al. Advanced source
apportionment of size-resolved trace elements at multiple sites in London during winter.
Atmospheric Chemistry and Physics 2015; 15: 11291-11309.
Wang D, Kam W, Cheung K, Pakbin P, Sioutas C. Development of a two-stage virtual impactor
system for high concentration enrichment of ultrafine, PM2. 5, and coarse particulate
matter. Aerosol Science and Technology 2013; 47: 231-238.
Wang G, Kawamura K, Xie M, Hu S, Gao S, Cao J, et al. Size-distributions of n-alkanes, PAHs
and hopanes and their sources in the urban, mountain and marine atmospheres over East
Asia. Atmospheric Chemistry and Physics 2009; 9: 8869-8882.
Wang Q, Fang J, Shi W, Dong X. Distribution characteristics and policy-related improvements of
PM2. 5 and its components in six Chinese cities. Environmental Pollution 2020; 266:
115299.
Wang Q, He X, Huang XH, Griffith SM, Feng Y, Zhang T, et al. Impact of secondary organic
aerosol tracers on tracer-based source apportionment of organic carbon and PM2. 5: A case
study in the Pearl River Delta, China. ACS Earth and Space Chemistry 2017; 1: 562-571.
Wang S, Hu G, Yan Y, Wang S, Yu R, Cui J. Source apportionment of metal elements in PM2. 5
in a coastal city in Southeast China: Combined Pb-Sr-Nd isotopes with PMF method.
Atmospheric Environment 2019; 198: 302-312.
Warneke C, De Gouw JA, Edwards PM, Holloway JS, Gilman JB, Kuster WC, et al.
Photochemical aging of volatile organic compounds in the Los Angeles basin: Weekday‐
weekend effect. Journal of Geophysical Research: Atmospheres 2013; 118: 5018-5028.
Warneke C, De Gouw JA, Holloway JS, Peischl J, Ryerson TB, Atlas E, et al. Multiyear trends in
volatile organic compounds in Los Angeles, California: Five decades of decreasing
emissions. Journal of Geophysical Research: Atmospheres 2012; 117.
Warneke C, McKeen S, De Gouw J, Goldan P, Kuster W, Holloway J, et al. Determination of
urban volatile organic compound emission ratios and comparison with an emissions
database. Journal of Geophysical Research: Atmospheres 2007; 112.
Watson JG, Chow JC, Chen L-WA. Summary of organic and elemental carbon/black carbon
analysis methods and intercomparisons. Aerosol and Air Quality Research 2005; 5: 65-
102.

99
Weingartner E, Saathoff H, Schnaiter M, Streit N, Bitnar B, Baltensperger U. Absorption of light
by soot particles: determination of the absorption coefficient by means of aethalometers.
Journal of Aerosol Science 2003; 34: 1445-1463.
White WH. Chemical markers for sea salt in IMPROVE aerosol data. Atmospheric Environment
2008; 42: 261-274.
WHO. Workshop: Evaluating the short-term health effects of desert and anthropogenic dust, 29
October 2018, Geneva. , 2018.
Williams TA, Chigoy B, Borowiec JD, Glover B. Methodologies used to estimate and forecast
Vehicle Miles Traveled (VMT). 2016.
Wong YK, Liu KM, Yeung C, Leung KK, Yu JZ. Measurement report: Characterization and
source apportionment of coarse particulate matter in Hong Kong: insights into the
constituents of unidentified mass and source origins in a coastal city in southern China.
Atmospheric Chemistry and Physics 2022; 22: 5017-5031.
Woodward NC, Levine MC, Haghani A, Shirmohammadi F, Saffari A, Sioutas C, et al. Toll-like
receptor 4 in glial inflammatory responses to air pollution in vitro and in vivo. Journal of
neuroinflammation 2017; 14: 1-15.
Worton DR, Isaacman G, Gentner DR, Dallmann TR, Chan AW, Ruehl C, et al. Lubricating oil
dominates primary organic aerosol emissions from motor vehicles. Environmental science
& technology 2014; 48: 3698-3706.
Wu C, Yu JZ. Determination of primary combustion source organic carbon-to-elemental carbon
(OC/EC) ratio using ambient OC and EC measurements: secondary OC-EC correlation
minimization method. Atmospheric Chemistry and Physics 2016; 16: 5453-5465.
Wu W, Zhao B, Wang S, Hao J. Ozone and secondary organic aerosol formation potential from
anthropogenic volatile organic compounds emissions in China. Journal of Environmental
Sciences 2017; 53: 224-237.
Wu X, Liu J, Wu Y, Wang X, Yu X, Shi J, et al. Aerosol optical absorption coefficients at a rural
site in Northwest China: The great contribution of dust particles. Atmospheric
Environment 2018; 189: 145-152.
Xia W, Wang Y, Chen S, Huang J, Wang B, Zhang GJ, et al. Double Trouble of Air Pollution by
Anthropogenic Dust. Environmental Science & Technology 2021.
Xu J, Hu W, Liang D, Gao P. Photochemical impacts on the toxicity of PM2. 5. Critical Reviews
in Environmental Science and Technology 2020a: 1-27.

100
Xu W, Kuang Y, Liang L, He Y, Cheng H, Bian Y, et al. Dust-dominated coarse particles as a
medium for rapid secondary organic and inorganic aerosol formation in highly polluted air.
Environmental Science & Technology 2020b; 54: 15710-15721.
Yu J, Yan C, Liu Y, Li X, Zhou T, Zheng M. Potassium: a tracer for biomass burning in Beijing?
Aerosol and Air Quality Research 2018; 18: 2447-2459.
Yu S, Dennis RL, Bhave PV, Eder BK. Primary and secondary organic aerosols over the United
States: estimates on the basis of observed organic carbon (OC) and elemental carbon (EC),
and air quality modeled primary OC/EC ratios. Atmospheric Environment 2004; 38: 5257-
5268.
Yuan J, Modini RL, Zanatta M, Herber AB, Müller T, Wehner B, et al. Variability in the mass
absorption cross section of black carbon (BC) aerosols is driven by BC internal mixing
state at a central European background site (Melpitz, Germany) in winter. Atmospheric
Chemistry and Physics 2021; 21: 635-655.
Yuan Z, Yu J, Lau AK-H, Louie PKK, Fung J. Application of positive matrix factorization in
estimating aerosol secondary organic carbon in Hong Kong and its relationship with
secondary sulfate. Atmospheric Chemistry and Physics 2006; 6: 25-34.
Zhang F, Chen Y, Su P, Cui M, Han Y, Matthias V, et al. Variations and characteristics of
carbonaceous substances emitted from a heavy fuel oil ship engine under different
operating loads. Environmental Pollution 2021; 284: 117388.
Zhang F, Guo H, Chen Y, Matthias V, Zhang Y, Yang X, et al. Size-segregated characteristics of
organic carbon (OC), elemental carbon (EC) and organic matter in particulate matter (PM)
emitted from different types of ships in China. Atmospheric Chemistry and Physics 2020;
20: 1549-1564.
Zhang X, Wang Y, Zhang X, Guo W, Niu T, Gong S, et al. Aerosol monitoring at multiple
locations in China: contributions of EC and dust to aerosol light absorption. Tellus B:
Chemical and Physical Meteorology 2008a; 60: 647-656.
Zhang Y, Quraishi T, Schauer JJ. Daily variations in sources of carbonaceous aerosol in Lahore,
Pakistan during a high pollution spring episode. Aerosol and Air Quality Research 2008b;
8: 130-146.
Zhang Y, Sheesley RJ, Bae M-S, Schauer JJ. Sensitivity of a molecular marker based positive
matrix factorization model to the number of receptor observations. Atmospheric
Environment 2009; 43: 4951-4958.
Zhang Y, Zeng W, Li Y. Hydrothermal synthesis and controlled growth of hierarchical 3D flowerlike MoS2 nanospheres assisted with CTAB and their NO2 gas sensing properties. Applied
Surface Science 2018; 455: 276-282.

101
Zhao X, Zhao P, Xu J, Meng W, Pu W, Dong F, et al. Analysis of a winter regional haze event and
its formation mechanism in the North China Plain. Atmospheric Chemistry and Physics
2013; 13: 5685-5696.
Zhu C-S, Cao J-J, Tsai C-J, Zhang Z-S, Tao J. Biomass burning tracers in rural and urban ultrafine
particles in Xi'an, China. Atmospheric Pollution Research 2017; 8: 614-618.
Zhu C-S, Chen C-C, Cao J-J, Tsai C-J, Chou CC-K, Liu S-C, et al. Characterization of carbon
fractions for atmospheric fine particles and nanoparticles in a highway tunnel. Atmospheric
Environment 2010; 44: 2668-2673.
Zong Z, Wang X, Tian C, Chen Y, Qu L, Ji L, et al. Source apportionment of PM 2.5 at a regional
background site in North China using PMF linked with radiocarbon analysis: insight into
the contribution of biomass burning. Atmospheric Chemistry and Physics 2016; 16: 11249-
11265.
Zotter P, Herich H, Gysel M, El-Haddad I, Zhang Y, Močnik G, et al. Evaluation of the absorption
Ångström exponents for traffic and wood burning in the Aethalometer-based source
apportionment using radiocarbon measurements of ambient aerosol. Atmospheric
Chemistry and Physics 2017; 17: 4229-4249.

Abstract (if available)

Abstract Particulate matter (PM) is linked to a range of adverse health issues, including neurodegenerative, cardiovascular diseases, and respiratory inflammation. PM is regulated primarily based on PM2.5 and PM10 standards. However, research has shown that ambient PM comprises various chemical compositions, size ranges, and physical characteristics, emitted from pollution sources. Measurement of coarse PM concentrations poses a significant challenge due to the particles’ residence time in ambient air. Addressing this issue, this study proposes a novel, multi-faceted approach, initiating with the creation of an innovative optical technique for the real-time measurement of urban dust, with a focus on coarse PM. This technique offers a deeper understanding of urban dust dynamics and its impact. Among different species of ambient PM, organic carbons (OC) are identified as having higher toxicity. This thesis progresses to quantify the sources of PM2.5-bound OC in central Los Angeles. Through a blend of source apportionment and regression analysis, the study reveals detailed insights into the primary and secondary OC sources in Los Angeles and offers a straightforward way to estimate their concentration based on criteria gaseous pollutants. The latter part of this research examines temporal and seasonal fluctuations of coarse OC, pinpointing major sources such as resuspended road dust, vehicular emissions, and secondary aerosols. These findings illuminate the intricate nature of urban air pollution and emphasize the necessity for more nuanced air quality regulations. This research not only advances methodologies in environmental PM analysis but also lays a solid groundwork for future endeavors in urban air quality management.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

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

PDF

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

PDF

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

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

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

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

PDF

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

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...

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

PDF

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

PDF

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

PDF

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

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

PDF

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

PDF

Characterization of black carbon: from source to evolution of physical and optical properties in the atmosphere

PDF

Toxicity of urban particulate matter: long-term health risks, influences of surrounding geography, and diurnal variation in chemical composition and the cellular oxidative stress response

PDF

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

PDF

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

PDF

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

PDF

Mechanics of abrasion emissions of particulate matter and microplastics

PDF

Evaluating the role of energy system decarbonization and land cover properties on urban air quality in southern California

PDF

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

Asset Metadata

Creator Tohidi, Ramin (author)

Core Title 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

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Engineering (Environmental Engineering)

Degree Conferral Date 2024-05

Publication Date 02/22/2024

Defense Date 02/16/2024

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

Tag dust particles,OAI-PMH Harvest,organic carbon,particulate matter,secondary organic carbon,source apportionment

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sioutas, Constantinos (committee chair), Habre, Rima (committee member), Sanders, Kelly (committee member)

Creator Email ramin.ntcp7@gmail.com,tohidi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113839151

Unique identifier UC113839151

Identifier etd-TohidiRami-12667.pdf (filename)

Legacy Identifier etd-TohidiRami-12667

Document Type Dissertation

Format theses (aat)

Rights Tohidi, Ramin

Internet Media Type application/pdf

Type texts

Source 20240226-usctheses-batch-1126 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu