University of Southern California Dissertations and Theses

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

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

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Content Chemical and Toxicological Characteristics of Particulate Matter in Urban Environments with a
Focus on its Sources, Associated Health Impacts and Mitigation Policies
by
Abdulmalik Altuwayjiri
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ENVIRONMENTAL ENGINEERING)
May 2022
Copyright 2022 Abdulmalik Altuwayjiri
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Dedication

To my parents, brothers, and sister, for their endless love, support, and encouragement throughout
my life.

iii
Acknowledgements

These studies were financially supported by National Institute of Health (grant numbers:
P01-AG055367; 1P01AG055367-01A1; 1RF1AG051521-01; 5R01AI065617-18; and
5R01ES029395-02) and Department of Defense (US-Army Medical Research Acquisition
Activity (grant number W81XWH-17-1-0535). I am also grateful to the financial support from
Majmaah University scholarship award.

I would like to express my deepest appreciation to my supervisor, Professor Constantinos
Sioutas. It was impossible to perform and finish each of these studies without his thoughtful
mentorship and unconditional support during the way. It has been an honor for me to work under
his supervision.

My appreciation also goes to the following people and groups that significantly helped us
to conduct the sampling campaigns and chemical analysis.

King Abdulaziz City for Science and Technology (KACST):
Dr. Badr Alharbi

Royal Commission for Riyadh City (RCRC):
Eng. Abdelaziz Elmegbl
Eng. Abdullah Albakri

Qassim University (QU):
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Dr. Abdulrahman Altwaijri

Saudi Envirozone :
Mohammed Kalafy

International Society of Doctors for the Environment (ISDE), Italy:
Ario A. Ruprecht
Alessandro Borginic
Cinzia De Marcoc

Environment and Territory Agency, Milan, Italy:
Silvia Moroni
Paolo Palomba

My gratitude also goes to my former and current colleagues and groupmates at Aerosol lab
of the University of Southern California due to their productive collaboration and support during
the research projects:
Dr. Amirhosein Mousavi
Dr. Sina Taghvaee
Dr. Milad Pirhadi
Ehsan Soleimanian
Vahid Jalali Farahani
Ramin Tohidi
v

And lastly, I am grateful to the committee members of my qualifying and defense exams:
Dr. Constantinos Sioutas (Chair)
Dr. George Ban-Weiss (peace be upon his kind soul)
Dr. Kelly Sanders
Dr. Rima Habre
Dr. Amy Childress

vi
Table of Contents
Dedication
Acknowledgements
List of Tables
List of Figures
Abstract

Chapter 1: Introduction

1.1. Background
1.2. Overview
1.3. List of objectives

Chapter 2: Association of systemic inflammation and coagulation biomarkers with source-specific
PM 2.5 mass concentrations among elderly and young subjects in central Tehran

2.1. Introduction
2.2. Methodology
2.2.1. Study design
2.2.2. Blood biomarker measurements
2.2.3. PM 2.5 measurement and source apportionment
2.2.4. Statistical analysis
2.3. Results
2.3.1. Characteristics of PMF-resolved PM 2.5 sources in central Tehran
2.3.2. Association of blood biomarkers with PM 2.5 sources
2.3.2.1. White blood cells (WBC)
2.3.2.2. Von Willebrand factor (vWF)
2.3.2.3. High sensitive C-reactive protein (hsCRP)
2.3.2.4. Tumor necrosis factor-soluble receptor-II (sTNF-RII)
2.3.2.5. Interleukin-6 (IL-6)
2.4. Discussion
2.5. Limitations
2.6. Summary and conclusions

Chapter 3: 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

3.1. Introduction
3.2. Methods
3.2.1. Sampling locations and period
3.2.2. PM 2.5 collection, instrumentation, and analysis
3.2.3. Source apportionment analysis
3.2.3.1. Positive Matrix Factorization (PMF) model
3.2.3.2. PMF input data screening and preparation
3.3. Results and discussion
3.3.1. Data Overview
3.3.2. PMF source apportionment results
3.3.2.1. Number of factors
3.3.2.2. Factor identification
3.3.2.2.1. Factor 1: Tailpipe emissions
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3.3.2.2.2. Factor 2: Non-tailpipe emissions
3.3.2.2.3. Factor 3: Secondary Organic Aerosols (SOA)
3.3.2.2.4. Factor 4: Biomass burning
3.3.2.2.5. Factor 5: Local industrial activities
3.3.3. Comparison with previous studies in the area
3.3.4. Timeline of regulations and their association with the PMF results
3.4. Summary and conclusions

Chapter 4: The impact of stay-home policies during Coronavirus-19 pandemic on the chemical and
toxicological characteristics of ambient PM 2.5 in the metropolitan area of Milan, Italy

4.1. Introduction
4.2. Methodology
4.2.1. Sampling site, instrumentation and collection period
4.2.2. Analysis
4.2.3. PM 2.5 oxidative potential measurement
4.3. Results and Discussion
4.3.1. Impact of COVID-19 stay-home strategies on ambient levels of atmospheric
pollutants
4.3.2. Investigation of domestic biomass burning emissions during the lockdown
period
4.3.3. Impact of COVID-19 restrictions on PM 2.5 components
4.3.3.1. Carbonaceous aerosols
4.3.3.2. Individual organic species
4.3.3.3. Redox-active metals
4.3.4. PM 2.5 oxidative potential
4.4. Summary and conclusions

Chapter 5: Impact of different sources on the oxidative potential of ambient particulate matter
PM 10 in Riyadh, Saudi Arabia: A focus on dust emissions

5.1. Introduction
5.2. Methodology
5.2.1. Sampling location and collection period
5.2.2. Gravimetric and chemical analysis
5.2.3. Oxidative potential of PM 10
5.2.4. Source apportionment of the PM 10 oxidative potential
5.3. Results and Discussion
5.3.1. PM 10 mass concentration and chemical composition
5.3.1.1. PM 10 mass and carbonaceous species
5.3.1.2. Inorganic ions
5.3.1.3. Metals and trace elements
5.3.2. Oxidative potential of PM 10
5.3.3. Source apportionment of ambient PM 10 and its associated oxidative potential
5.3.3.1. Source apportionment of PM 10 mass concentration using the PCA
Approach
5.3.3.2. Source apportionment of PM 10 oxidative potential using MLR approach
5.4. Summary and conclusions

Chapter 6: Conclusions

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List of Tables

Table 2.1. Description of participants’ demographic and blood biomarkers.

Table 2.2. The average source-specific PM 2.5 mass concentration and relative contribution of
the PMF-resolved sources to ambient PM 2.5 concentrations at the retirement home and school
dormitory, along with the chemical species used as PM source tracers.

Table 2.3. Spearman rank order correlation analyses between PM 2.5 sources in a) Tohid
retirement home; and b) School dormitory.

Table 2.4. Descriptive statistics of PM 2.5 (µg/m3) concentrations in young and elderly panels.

Table 3.1. Summary of the meteorological parameters at CELA and Riverside.

Table 3.2. Spearman rank order correlation analysis between the PMF-resolved sources and
non-tailpipe tracer species in a) CELA; and b) Riverside.

Table 3.3. Statistical summary of the measured species employed as the input to the PMF
model for: a) CELA; and b) Riverside sites.

Table 4.1. Detailed description of the adopted COVID-19 lockdown strategies across the
Milan metropolitan area in 2020 (PP: pre-pandemic; PL1: first partial-lockdown; FL: full-
lockdown; PL2: second partial-lockdown; FR: full-relaxation with some limitations).

Table 4.2. Monthly average values of meteorological parameters at Milano - Piazzale Zavattari
station (nearest to Bareggio) during the spring/early-summer of 2019 and 2020.

Table 5.1. Seasonal averages (± standard deviation) of meteorological parameters during the
warm and cool period.

Table 5.2. Seasonal and dust event averages (± standard deviation) of PM 10 mass and chemical
component concentrations and associated oxidative potential (OP).

Table 5.3. Pearson correlation coefficients (R) between dithiothreitol (DTT) activity data
(nmol/min/m3 air) and mass concentration (μg/m3) of chemical species (OC, EC, inorganic
ions and water-soluble metals) at the sampling location.

Table 5.4. Loadings of chemical species in the factors resolved by the principal component
analysis (PCA). Loadings > 0.7 are bolded.

Table 5.5. Results of the multiple linear regression (MLR) analysis between PM 10 oxidative
potential (as the dependent variable) and PCA resolved factor scores (as independent
variables).

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List of Figures

Figure 2.1. Associations between white blood cells (WBC) and source-specific PM 2.5
concentrations in the panel of: (a) the elderly (n = 44) and (b) healthy young adults (n = 40).

Figure 2.2. Associations between von Willebrand factor (vWF) and source-specific PM 2.5
concentrations in the panel of (a) the elderly (n = 44) and (b) healthy young adults (n = 40).

Figure 2.3. Associations between high sensitive C-reactive protein (hsCRP) and source-
specific PM 2.5 concentrations in the panel of the elderly (n = 44).

Figure 2.4. Associations between tumor necrosis factor-soluble receptor-II (sTNF-RII) and
source-specific PM 2.5 concentrations in the panel of (a) the elderly (n = 44) and (b) healthy
young adults (n = 40).

Figure 2.5. Associations between interleukin-6 (IL-6) and source-specific PM 2.5
concentrations in the panel of (a) the elderly (n = 44) and (b) healthy young adults (n = 40).

Figure 3.1. Map of the two study locations in the Los Angeles basin.

Figure 3.2. Seasonal OC concentration trends over the 2005-2015 period for a) CELA; and
b) Riverside.
Figure 3.3. Seasonal EC concentration trends over the 2005-2015 period for a) CELA; and
b) Riverside.

Figure 3.4. PMF-resolved factor profiles in a) CELA; and b) Riverside for 2005.

Figure 3.5. PMF-resolved factor profiles in a) CELA; and b) Riverside for 2010.
Figure 3.6. PMF-resolved factor profiles in a) CELA; and b) Riverside for 2015.
Figure 3.7. The relative (fractional) contribution of PMF-resolved sources to ambient OC in
CELA and Riverside over the years of 2005, 2010, and 2015.

Figure 3.8. Absolute source contributions to ambient OC mass concentrations during the years
of 2005, 2010 and 2015 in CELA and Riverside.

Figure 3.9. Seasonal trends in the absolute contributions of PMF-resolved sources to ambient
OC levels in CELA and Riverside during the years of 2005, 2010 and 2015.

Figure 3.10. PM 2.5 inventory data for on road vehicles emission between the years of 2005
and 2015 for a) CELA, and b) Riverside.
Figure 4.1. Location of the Bareggio sampling site and Milano-via Pascal air monitoring
station in the Milan metropolitan area.

Figure 4.2. Average wind rose during the investigation periods of (a) 2019; and (b) 2020. The
plots have been depicted using WRPLOT View version 7.0 based on hourly wind speeds and
wind directions.

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Figure 4.3. Temporal trends in the concentrations of (a) PM 2.5; (b) BC; (c) NO2; and (d) C6H6
from January 2020 to early-June 2020. Each box plot corresponds to the period of one week
during pre-pandemic (PP), full-lockdown (FL), partial-lockdowns (PL1 and PL2), and full-
relaxation (FR).

Figure 4.4. Temporal trends in the concentrations of (a) PM 2.5; (b) BC; (c) NO2; and (d) C6H6
during lockdown phase (i.e., PL1, FL, and PL2) of 2020 and the corresponding period in 2019.

Figure 4.5. Temporal variations in the BC/NO2 ratio during the investigation period for (a)
2019; and (b) 2020.

Figure 4.6. Correlation analysis between the EC and NO2 mass concentrations at central Los
Angeles for the year of 2019.

Figure 4.7. Weekly box plots of estimated (a) BCbb; and (b) BCnb mass concentrations during
January to early-June of 2020.

Figure 4.8. Temporal variations in mass concentrations of (a) BCbb; and (b) BCnb during the
lockdown phase (i.e., PL1, FL, and PL2) of 2020 and the corresponding period in 2019.

Figure 4.9. The elemental carbon (EC), organic carbon (OC), and water-soluble organic
carbon (WSOC) fractions of PM 2.5 during full-lockdown (FL), second partial-lockdown
(PL2), and full-relaxation (FR) periods: (a) normalized by the air volume; and (b) normalized
by PM 2.5 mass.

Figure 4.10. Temporal trends in levoglucosan and total PAHs concentrations during COVID-
19 period normalized by (a) air volume; and (b) PM 2.5 mass content. FL, PL2, and FR refer to
full-lockdown, second partial-lockdown, and full-relaxation periods, respectively.

Figure 4.11. PM 2.5-bound redox-active metals concentrations measured during full-lockdown
(FL), second partial-lockdown (PL2), and full-relaxation (FR) periods: (a) normalized by the
air volume; and (b) normalized by PM 2.5 mass.

Figure 4.12. Air volume-based (extrinsic) and mass-based (intrinsic) oxidative potential of
ambient PM 2.5 during the investigation period measured by the means of (a) DCFH
macrophage; and (b) DTT assay (FL: full-lockdown; PL2: second partial-lockdown; FR: full-
relaxation).

Figure 5.1. Map of the study location in the Riyadh metropolitan area.

Figure 5.2. The seasonal and dust event average concentrations of: a) PM 10; b) EC; and c) OC.

Figure 5.3. The seasonal and dust event average concentrations of selected inorganic ions: a)
ammonium; b) sulfate; and c) nitrate.

Figure 5.4. Average concentrations of metals and trace elements during the investigated
periods.

Figure 5.5. PM 10 oxidative potential for cool and warm periods and dust events: a) volume-
based, or extrinsic oxidative potential (per m3 of air); b) mass-normalized, or intrinsic
oxidative potential (per PM mass).

Figure 5.6. Relative source contributions to PM 10 oxidative potential

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Abstract
Increased numbers of motor vehicles, rapid urbanization, and industrialization have
triggered significant air pollution challenges in many urban environments worldwide. Among
various air pollutants, airborne particulate matter (PM) is of notable importance mostly due to its
complex physiochemical and toxicological characteristics and well-established adverse health
consequences (e.g., respiratory and cardiovascular diseases). The majority of previous studies have
used total PM mass concentration as the key parameter to investigate PM health effects. However,
some PM constituents and sources are more toxic than others. Therefore, it is vital from a
regulatory perspective to elaborately examine the sources and components of ambient PM as well
as its associated toxicity in order to adopt effective PM emission reduction policies. The core
objective of the presented dissertation is to evaluate the impact of primary and secondary source
emissions on chemical and toxicological characteristics of ambient particulate matter in different
urban environments. To this end, a series of comprehensive investigations were conducted in Los
Angeles, Tehran, Milan, and Riyadh metropolitan areas using statistical and source apportionment
techniques such as positive matrix factorization (PMF), principal component analysis (PCA) and
regression modeling. The findings of these studies advance our knowledge of complex source
emission impacts on the PM toxicity and chemical composition in different cities and provide
valuable insights for more effective and targeted air quality regulations in polluted areas around
the globe.

1
Chapter 1: Introduction
1.1. Background
Exposure to ambient particulate matter (PM) has been identified as a serious public health
concern worldwide (World Health Organization, 2016). Numerous epidemiological and
toxicological studies have reported a strong association between ambient PM and severe health
consequences, such as lung cancer, cardiovascular diseases, neurodegeneration, and daily
mortality and premature death (Delfino et al., 2005; Dockery and Stone, 2007; Miller et al., 2007;
Pope et al., 2004; Pui, 2014). Recent studies over a wide range of geographical areas reported an
increase in the daily mortality rates (approximately 1–5%) with every 10 μg/m
3
rise in the ambient
PM2.5 mass concentrations (Balakrishnan et al., 2019; Burnett et al., 2018; Lelieveld et al., 2019;
Shkirkova et al., 2020), underscoring the necessity to investigate the sources and toxicity
associated with the ambient PM.
A large portion of ambient PM mass is carbonaceous aerosols (i.e., elemental carbon (EC),
organic carbon (OC), and carbonate carbon (CC) (Karanasiou et al., 2011; Li et al., 2013; Schwarz
et al., 2008; Snyder et al., 2010)), which significantly contributes to the toxicity of PM in urban
environments (Bates et al., 2019; Biswas et al., 2009). Ambient OC also originates from a
multitude of sources, such as vehicular emissions (Chirizzi et al., 2017a; Hasheminassab et al.,
2013; Heo et al., 2013; Soleimanian et al., 2019a; Warneke et al., 2012), biomass burning (Schauer
and Cass, 2000; Skiles et al., 2018), and secondary organic aerosols (SOAs) (Heo et al., 2013;
Jimenez et al., 2009; Ke et al., 2007). Thus, these specific characteristics of ambient PM-bound
OC highlight the need to further investigate its sources in urban areas.
2
There have been large numbers of studies investigating the impact of usual emission scenarios
on air quality (AQ). However, when major disruptions to these ‘normal’ emissions occur, it is very
important to know how these might influence AQ. The outbreak of the Coronavirus (COVID-19)
on March 2020, has been declared as a worldwide health emergency, rapidly spreading throughout
different parts of the globe (Jain and Sharma, 2020; Le et al., 2020; Wu et al., 2020; Zangari et al.,
2020). In an effort to restrain the rapid spread of this infectious pathogen, governments have
adopted various prevention and control strategies such as social distancing, businesses shutdown,
and city-wide lockdowns (Anjum, 2020; Collivignarelli et al., 2020). The implementation of these
strategies provided a unique opportunity to investigate the chemical and toxicological
characteristics of ambient PM2.5 in the absence of mobile source emissions.
1.2. Overview
In the first study, we investigated the association between short-term exposure to different
sources of fine particulate matter (PM2.5) and biomarkers of coagulation and inflammation in two
different panels of elderly and healthy young individuals in central Tehran. Five biomarkers,
including white blood cells (WBC), high sensitive C-reactive protein (hsCRP), tumor necrosis
factor-soluble receptor-II (sTNF-RII), interleukin-6 (IL-6), and von Willebrand factor (vWF) were
analyzed in the blood samples drawn every 8 weeks from the subjects between May 2012 and May
2013. The studied populations consisted of 44 elderly individuals at a retirement home as well as
40 young adults residing at a school dormitory. PMF-resolved source-specific PM2.5 mass
concentrations were used as the input to the linear mixed-effects regression model to evaluate the
impact of exposure to previously identified PM sources on the biomarker levels of subjects at
retirement home and school dormitory in two time lag configurations: lag 1-3 (1-3 days before the
blood sampling), and lag 4-6 (4-6 days before the blood sampling). Our analysis of the elderly
3
revealed positive associations of all biomarkers (except hsCRP) with particles of secondary origin
in both time lags, further corroborating the toxicity of secondary aerosols formed by
photochemical processing in central Tehran. Moreover, industrial emissions, and road dust
particles were positively associated with WBC, sTNF-RII and IL-6 among seniors, while vehicular
emissions exhibited positive associations with all biomarkers in either first- or second-time lag. In
contrast, most of the PM2.5 sources showed insignificant associations with biomarkers of
inflammation in the panel of healthy young subjects. Therefore, findings from this study indicated
that various PM2.5 sources increase the levels of inflammation and coagulation biomarkers,
although the strength and significance of these associations vary depending on the type of PM
sources, demographic characteristics, and differ across the different time lags.
Organic carbon (OC), a major constituent of the ambient fine particulate matter (PM 2.5),
originates from various sources including anthropogenic and biogenic emissions and has also been
extensively associated with adverse effects on human health. The motivation of the second study
is to investigate the long-term temporal variation in the sources contributing to OC in the central
Los Angeles (CELA) over the 2005-2015 years, a time period during which stringent PM mass-
based regulations were implemented to reduce the tailpipe emissions in the region. The measured
concentrations of OC, OC volatility fractions (i.e., OC1, OC2, and OC3), elemental carbon (EC),
ozone (O3), sulfate, the ratio of potassium ion to potassium (K
+
/K ), and selected metal elements
were used as the input to the positive matrix factorization (PMF) model. PMF resolved tailpipe
emissions, non-tailpipe emissions, secondary organic aerosol (SOA), biomass burning, and local
industrial activities as the main sources contributing to ambient OC at both sampling sites.
Vehicular exhaust emissions, non-tailpipe emissions, and SOA were dominant sources of OC
across our sampling sites, accounting cumulatively for more than 80% of total OC mass throughout
4
the study period. Our findings showed a significant reduction in the absolute and relative
contribution of tailpipe emissions to the ambient OC levels in CELA and Riverside over the time
period of 2005-2015. The contribution of exhaust emissions to total OC in CELA decreased from
3.5 μg/m
3
(49%) in 2005 to 1.5 μg/m
3
(34%) in 2015, while similar trends were observed at
Riverside during this period. These reductions are mainly attributed to the implementation of
several federal, state, and local air quality regulations targeting tailpipe emissions in the area. The
implementation of these regulations furthermore reduced the emissions of primary organic
precursors of secondary aerosols, resulting in an overall decrease (although not statistically
significant, P values ranging from 0.4 to 0.6) in SOA mass concentration in both locations over
the study period. In contrast to the tailpipe emissions, we observed an increasing trend (by ~ 4 to
14%) in the relative contribution of non-tailpipe emissions to OC over this time period at both
sites. Our results demonstrated the effectiveness of air quality regulations in reducing direct
tailpipe emissions in the area, but also underpinned the need to develop equally effective mitigation
policies targeting non-tailpipe PM emissions.
In the third study, we characterized changes in components and toxicological properties of
PM2.5 during the nationwide 2019-Coronavirus (COVID-19) lockdown restrictions in Milan, Italy.
Time-integrated PM2.5 filters were collected at a residential site in Milan metropolitan area from
April 11
th
to June 3
rd
at 2020, encompassing full-lockdown (FL), the followed partial-lockdown
(PL2), and full-relaxation (FR) periods of COVID-19 restrictions. The collected filters were
analyzed for elemental and organic carbon (EC/OC), water-soluble organic carbon (WSOC),
individual organic species (e.g., polycyclic aromatic hydrocarbons (PAHs), and levoglucosan),
and metals. According to online data, nitrogen dioxide (NO2) and benzene (C6H6) levels
significantly decreased during the entire COVID-19 period compared to the same time span in
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2019, mainly due to the government-backed shutdowns and curtailed road traffic. Similarly, with
a few exceptions, surrogates of tailpipe emissions (e.g., traffic-associated PAHs) as well as re-
suspended road dust (e.g., Fe, Mn, Cu, Cr, and Ti) were relatively lower during FL and PL2 periods
in comparison with year 2019, whereas an increasing trend in mass concentration of mentioned
species was observed from FL to PL2 and FR phases due to the gradual lifting of lockdown
restrictions. In contrast, comparable concentrations of ambient PM2.5 and black carbon (BC)
between lockdown period and the same time span in 2019 were attributed to the interplay between
decreased road traffic and elevated domestic biomass burning as a result of adopted stay-home
strategies. Finally, the curtailed road traffic during FL and PL2 periods led to ~25% drop in the
PM2.5 oxidative potential (measured via 2’,7’-dichlorodihydrofluorescein (DCFH) and
dithiothreitol (DTT) assays) with respect to the FR period as well as the same time span in 2019.
The results of this study provide insights into the changes in components and oxidative potential
of PM2.5 in the absence of road traffic during COVID-19 restrictions.
Finally, the last study was defined to investigate the chemical and toxicological
characteristics and sources of ambient PM collected at central Riyadh, Saudi Arabia. The collected
samples were chemically analyzed for their organic and metal contents. In addition, the oxidative
potential of the PM samples was quantified by mean of the dithiothreitol (DTT) assay. The
principal component analysis (PCA) in combination with multiple linear regression (MLR) was
then employed to link sources of ambient PM10 to the measured oxidative potential. Our findings
revealed that the oxidative potential of the collected ambient PM10 samples was higher during dust
episodes compared to non-dust events, mainly due to higher concentrations of metals during these
events. The results of the MLR analyses indicated that the major pollution sources contributing to
the oxidative potential of ambient PM10 were soil and resuspended dust emissions (identified by
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Al, K, Fe and Li) (31%), followed by secondary organic aerosol (SOA) formation (traced by SO4
-
2
and NH
+
4) (20%), industrial activities (identified by Se and La) (19%), and traffic emissions
(characterized by EC, Zn and Cu) (17%). Results from this study underscore the impact of dust
emissions on the oxidative potential of ambient PM10 in Riyadh and can be helpful in adopting
appropriate public health policies regarding detrimental outcomes of exposure to PM10.

1.3. List of objectives
The first study had the following objectives:
- Investigating the association between the source-specific PM2.5 mass concentrations and
systemic inflammation and coagulation biomarkers.
- Examining the possible heterogeneity between the effects of short exposure to different
PM2.5 sources among the young and the elderly panel.
The second study had the following objectives:
- Investigating the long-term (i.e., 2005 to 2015) trends of ambient PM2.5 organic carbon
(OC) concentration and source contributions in Los Angeles megacity.
- Identifying and quantifying the seasonal and temporal contributions of PM2.5 sources to
total OC in two different sites across the Los Angeles basin.
- Investigating the implemented PM related emission regulations during the same
investigated period in the Los Angeles basin.

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The third study had the following objectives:
- Investigating the chemical and toxicological characteristics of ambient PM2.5, considering
a major reduction in urban activity source emissions due to COVID-19 pandemic (i.e.,
March 2020) .
- Quantifying the oxidative potential and chemical compositions of PM2.5 in the city of
Milan.
- Evaluating the biomass and non-biomass burning contribution to PM- bound black carbon
concentrations in Milan area.

Finally, in the fourth study, our objectives can be listed as follows:
- Investigating and quantifying the sources contributing to the toxicity of PM10 in central
Riyadh, Saudi Arabia during cool (December 2019–March 2020) and warm (May 2020–
August 2020) seasons.
- Assessing the impact of dust storm on the toxicity of PM10 in the Riyadh area.

8
Chapter 2: Association of systemic inflammation and coagulation biomarkers with source-
specific PM2.5 mass concentrations among elderly and young subjects in central Tehran

2.1. Introduction
Many metropolitan areas around the world are impacted by severe air pollution due to the rapid
rate of urbanization and industrialization. According to the World Health Organization (WHO)
report, ambient air pollution contributed to 7.6% of all deaths (approximately 4.2 million
individuals) around the globe in 2016 (WHO, 2018). Among various air pollutants, fine particulate
matter (PM, particles with aerodynamic diameter less than 2.5 µm) are of notable importance due
to their chemical composition, higher oxidative capacity, higher surface area to particle mass ratio,
and their ability to reach the lung alveoli (H. J. Kim et al., 2017), resulting in several adverse health
consequences, including neurodegenerative disorders, respiratory, cardiovascular, and
cardiopulmonary disease (Brook et al., 2010; Gauderman et al., 2015; Ng et al., 2017; Pope et al.,
2015; Rich et al., 2013; Sun et al., 2016). Despite the complexity of biological pathways leading
to the abovementioned detrimental health outcomes, numerous epidemiological and toxicological
studies have confirmed the major role of PM2.5 in inducing oxidative stress response and
inflammation within macrophages and epithelial cells (Ayres et al., 2008; Bates et al., 2019;
Lionetto et al., 2019; Ostro et al., 2014; Pope et al., 2016). Exposure to PM2.5 has also been
associated with enhanced levels of several blood circulating and inflammation biomarkers,
including white blood cell (WBC), von Willebrand factor (vWF), high sensitive C-reactive protein
(hsCRP), tumor necrosis factor-soluble receptor-II (sTNF-RII) and Interleukin-6 (IL-6) (Chen et
al., 2015; Croft et al., 2017; C. Liu et al., 2017).
9
Complementary studies have indicated the significant linkage between the above listed blood
biomarkers and several adverse health endpoints (Brook et al., 2010; Velde et al., 2014). For
instance, Velde et al. (2014) showed an association between elevated hsCRP blood biomarker with
heart disease and mortality. In addition, WBC counts were associated with enhanced coronary
heart disease risk as well as cardiovascular illnesses (J. H. Kim et al., 2017). vWF, a blood
glycoprotein involved in hemostasis and stored in endothelial cell and platelet granules (Croft et
al., 2017) was significantly associated with myocardial infarction, strokes, and cardiovascular
disorders (Green et al., 2017). Multiple sclerosis (Ribeiro et al., 2019), mortality in elderly
(Bruunsgaard et al., 2003), diabetic kidney, and heart disease (Carlsson et al., 2016) were also
found to be associated with increased levels of sTNF-RII. Finally, IL-6, a pleiotropic inflammatory
cytokine, was shown to be associated with cardiovascular diseases and type 2 diabetes (Heikkilä
et al., 2008; Lowe et al., 2014) as well as mortality in seniors (J. K. Lee et al., 2012).
As per the well-documented toxicological characteristics of PM2.5, some PM constituents are
more toxic than others, indicating the inadequacy of implementing total PM mass concentration as
the sole metric in health exposure assessments (Lippmann, 2010). In fact, a number of studies have
confirmed significant associations of some PM compounds such as redox active metals (Charrier
and Anastasio, 2012; Steenhof et al., 2014), elemental carbon (EC) (Delfino et al., 2009; Samara,
2017; Su et al., 2017), organic carbon (OC) (Chirizzi et al., 2017a), water soluble organic carbon
(WSOC) (Delfino et al., 2010b; Verma et al., 2012; Vreeland et al., 2017a), and polycyclic
aromatic hydrocarbons (PAHs) (Jeng et al., 2011; Lundstedt et al., 2007) with the increased levels
of inflammation biomarkers and PM-induced toxicity. Moreover, according to the literature,
ambient PM2.5 can originate from multiple sources with distinct bulk chemical composition,
including traffic emissions, industrial activities, soil, road dust, secondary aerosols, biomass
10
burning (wood smoke), and coal combustion (Cohen et al., 2009; Crilley et al., 2017;
Hasheminassab et al., 2014a; B. Liu et al., 2017). Consequently, it is vital from a regulatory
perspective to investigate the association of chemical species, and in turn, PM sources with
biomarkers of inflammation as indicators of various adverse health effects. Findings from such
studies are very important to air quality officials in determining the most toxic sources of PM,
particularly in polluted metropolitan areas, in order to adopt effective emission reduction policies
for sources with levels of PM toxicity.
Tehran, the most populated city of Iran with more than 9 million residents, suffers from severe
air pollution episodes, due mainly to vehicular emissions, industrial activities, frequent dust
storms, and residential heating (Amini et al., 2014; Arhami et al., 2017; Hosseini and Shahbazi,
2016; Shahbazi et al., 2016b; Soleimanian et al., 2019d; Taghvaee et al., 2018a). In addition, the
particular topography of Tehran limits the dispersion of air pollutants from the northern and eastern
neighborhoods of the city, due to the presence of the Alborz Mountains with an elevation of 2,700
– 3,000 meters (Axen et al., 2001). Therefore, the transported anthropogenic emissions via the
prevailing westerly and southerly winds are trapped in the central zones of the city, further
deteriorating the air quality in central Tehran (Atash, 2007), and increasing its potential to cause
severe cardiopulmonary diseases (Bayat et al., 2019; Faridi et al., 2019; Hosseinpoor et al., 2005;
Naddafi et al., 2012). Due to the significance of PM2.5 as a criteria pollutant in Tehran, a number
of studies in the literature have reported potential associations between exposure to PM2.5 and
harmful health outcomes in this mega city (Akbarzadeh et al., 2018; Amini et al., 2019;
Hassanvand et al., 2017; MohseniBandpi et al., 2017). For instance, Amini et al. (2019) observed
significant short-term associations between total PM2.5 and non-accidental daily mortality in
Tehran. Moreover, Hassanvand et al. (2017) investigated the short-term impacts of exposure to
11
different PM size ranges on inflammation biomarkers among young and elderly population in
central Tehran. However, to the best of our knowledge, no studies have ever been performed in
the area to evaluate the association of individual sources of PM2.5 with blood biomarkers with.
The main aim of this study was to investigate the association between various blood
biomarkers (WBC, vWF, hsCRP, IL-6, and sTNF-RII) and the source-specific PM2.5 mass
concentrations in central Tehran in two panels of young and elderly subjects. The second objective
was to examine the possible heterogeneity between the effects of exposure to different PM2.5
sources among the young and the elderly panel. Therefore, the abovementioned coagulation and
systemic inflammation biomarkers were measured in the blood samples drawn from each
participant every 8 weeks (a total of 6 blood draws for each participant) from May 2012 to May
2013. In addition, the source-specific PM2.5 levels were previously determined in both sampling
locations by the means of the United States Environmental Protection Agency Positive Matrix
Factorization (USEPA PMF version 5.0) model. These data were used as inputs to the single-
pollutant linear mixed-effects regression model in two different lag configurations; lag 1-3 (1 to 3
days prior to blood sampling), and lag 4-6 (4-6 days prior to blood sampling) to further investigate
the delayed and progressive effects of exposure to PM2.5 sources among elderly and young panel.

2.2. Methodology
2.2.1. Study design
In this study, blood samples were drawn from elderly and young subjects in two parallel
panels. Sixty elderly volunteers (>65 years old) living in a retirement home as well as forty-five
12
young male adult volunteers (15-18 years old) living in a school dormitory were selected for blood
draws. Air sampling took place at these two sites that were located at a close distance (1.1 km)
relative to each other in central Tehran. Both sampling locations were heavily impacted by
vehicular emissions, due to their vicinity to a major freeway (i.e., Chamran freeway) as well as
congested local streets nearby (Hassanvand et al., 2015, 2014). In addition, researchers in the field
have documented the presence of traffic-related, residential and commercial activities in the central
zones of Tehran (Atash, 2007; Shahbazi et al., 2016a, 2016b), leading to high levels of air pollution
in the densely populated city center (Shahbazi et al., 2016a), which makes it an ideal location for
exposure assessment studies. Further information regarding the study sites can be found in
Hassanvand et al. (2015, 2014)
16 out of the 60 elderly volunteers and 5 out of the 45 students were excluded from our
analyses due to the lack of sufficient biomarker data, caused by frequent infections and death.
From May 2012 to May 2013, six blood draw, 8 weeks apart, were scheduled for each participant
on a fixed time of the week (i.e., Wednesday afternoon from 1pm -3 pm), resulting in a total of
240 and 264 samples at the school dormitory and the retirement home, respectively. PM 2.5
concentrations showed distinct seasonal patterns (Hassanvand et al., 2015, 2014), thus, longer time
periods between the measurements were chosen to increase the variability in the fine PM
concentrations. Prior to blood sampling, a physician also visited participants and collected data on
their health status, medication use, and disease to control the confounding factors at each step of
blood sampling. Moreover, no blood sampling was conducted if acute infectious illnesses were
observed in a particular volunteer and only participants with full six blood samples were included
in the analyses. All participants signed a written informed consent to attend in this study which
was approved by the Research Ethics Boards of Tehran University of Medical Sciences (Research
13
Number 90-03-46-15705). More details about the demographic and clinical characteristics of the
participants can be found in Hassanvand et al. (2017), and Table 2.1.

Table 2.1. Description of participants’ demographic and blood biomarkers.

Elderly (n=44) Healthy young adults (n=40)
Variable N (%) or mean ± SD N (%) or mean ± SD
Sex
Male 19 (43.2) 40 (100)
Female 25 (56.8) 0 (0)
Age (years) 75.4 ± 5.8 16.2 ±0.5
Blood Samples 264 240
Blood markers
hsCRP (ng/ml) 16405±956715 6842.73±3106.86
WBC (k/µl) 6.43±1.61 6.38±1.45
IL-6 (pg/ml) 18.95±30.28 15.61±26.32
sTNF-RII (pg/ml) 4828±2664 1979.33±846.51
vWF (ng/ml) 833±351 1220.01±451.21
Abbreviations: SD, standard deviation; n, number; MI, myocardial infarction; hsCRP, high sensitivity C-reactive protein; WBC, white blood cells; IL-6,Interleukin-6; sTNF-RII,
tumor necrosis factor-soluble receptor-II; vWF, von Willebrand factor

2.2.2. Blood biomarker measurements
Utilizing enzyme-linked immunosorbent assay (Quantikine, R&D Systems), we analyzed
three biomarkers including vWF, sTNF-RII, and IL-6 at the Immunology, Asthma and Allergy
Research Institute, Tehran University of Medical Sciences. Moreover, an immunoturbidimetric
method (Sentinel CRP Vario List No. 6K26-02) was used for hsCRP analysis, whereas an
automatic hematological analyzer (CellDyn 4000, Abbott) were employed for WBC counting in
blood samples. All specimens were analytically replicated to ensure the biomarker data
reproducibility.

14
2.2.3 PM2.5 measurement and source apportionment
Detailed description of the PM2.5 measurement and chemical analyses can be found
elsewhere (Hassanvand et al., 2015, 2014). In summary, a low-volume air sampler (FRM
OMNU™ air Sampler, multi-cut inlet; BGI, USA) was used to collect 24-hour time integrated
ambient PM2.5 samples on quartz (47 μm diameter, Whatman Inc.) and PTFE (47 μm diameter,
SKC Inc.) filters in both locations from May 2012 to June 2013. The average PM2.5 mass
concentrations were 30.9 and 33.2 μg/m
3
in the retirement home and school dormitory,
respectively. To achieve the required mass loading for further chemical analyses, the daily
collected samples were composited every three (or four) days. The composited PTFE samples were
chemically analyzed for their trace elements and metallic contents (i.e., Al, Ti, Ba, Fe, Ni, Cd, Se,
Li, Sr, Zn, Sn, Cu, Mn, Cr, and Si), using an Inductively Coupled Plasma Optical Emission
Spectrometer (ICP-OES) instrument. Concentrations of water-soluble ions (i.e., K
+
, NH4
+
, Mg
+2
,
Ca
+2
, NO3
-
, and SO4
-2
) in the samples were also determined by the means of an ion chromatography
(IC) instrument. Moreover, following the NIOSH 5040 protocol, elemental carbon (EC) and
organic carbon (OC) were quantified for limited number (i.e., 12) of quartz filters by thermo-
optical transmittance (TOT) analysis.
Overall, thirty-nine ambient PM2.5 samples were chemically analyzed at each sampling site
and were used as the input concentration matrix to the USEPA PMF version 5.0 for identifying
sources and quantifying their relative contribution to the ambient PM2.5 at the school dormitory
and the retirement home (Norris et al., 2014; Paatero, 1997; Paatero and Tapper, 1994).
Furthermore, the PMF model was run in the robust mode and the uncertainties associated with the
PMF-resolved source specific mass concentrations were investigated at both sampling sites by the
means of various PMF error estimation tools including displacement (DISP) method, bootstrap
15
(BS) analyses, and a combination of DISP and BS (BS-DISP). According to the output of PMF
built-in error estimation tools, our PMF solution in both sampling location was valid in terms of
BS analyses, since >80% of resolved factors were mapped. In school dormitory, the percentage of
mapping was within the range of 88% (for biomass burning) to 96% (for the vehicular emissions).
Similarly in retirement home, the percentage of mapping for resolved BS factors to each base
factor varied from 83% (for soil factor) to 90% (for vehicular emissions). Moreover, quite small
BS errors were associated with the chemical markers of PMF-resolved sources, indicating
negligible impact of random errors and (to some extent) rotational ambiguity on the final PMF
outputs. Moreover, results of the PMF runs were acceptable in terms of DISP analyses due to
negligible decrease (i.e., dQ= 0.001% in school dormitory, and dQ=0.01% in Tohid retirement
home) in PMF objective function (Q) as well as absence of any factor swaps for dQmax=4. In
addition, there were significantly negligible DISP errors associated with the tracers of PM2.5
sources, further underscoring the insignificancy of rotational ambiguity in PMF-resolved source
specific mass concentration. Finally, BS-DISP (frequently used as the hybrid uncertainty
estimation approach) corroborated the robustness of our PMF analysis, judging by the less than
0.5% decrement of Q, and negligible uncertainties associated with the source-specific tracers of
PMF solution (Norris et al., 2014; Paatero et al., 2014; Reff et al., 2007). Comprehensive
explanations regarding the PM2.5 source apportionment via the PMF model can be found in
Taghvaee et al. (2018).

16
2.2.4. Statistical analysis
The association between the blood biomarkers (i.e., WBC, vWF, hsCRP, IL-6, and sTNF-
RII) and the PM2.5 source contributions was evaluated by single-pollutant linear mixed-effects
regression model using Stata software 2013 (Stata Statistical Software: Release 13. College
Station, TX: StataCorp LP) in the healthy young adults and elderly subjects, separately. As
mentioned earlier, repeated blood sampling was scheduled for each individual every 8 weeks to
ensure independent examination of each subject over time. We included the random intercept term
in our statistical model. Since there was no concern for controlling the effect of time-invariant
subject characteristics by the study design, adjustment was done for the effect of meteorological
variables (i.e., temperature and relative humidity) by setting these parameters as fixed in the model.
The delayed and progressive impacts of exposure to PM sources were investigated by considering
the concentration of source-specific particulate matter and meteorological parameters in two-time
lags: lag 1-3 (1 to 3 days prior to blood sampling), and lag 4-6 (4-6 days prior to blood sampling).
In addition, the logarithms of IL-6 and sTNF-RII were used in the analysis due to the skewness
observed in their residuals. Eventually, the percent change in blood biomarker levels per an
interquartile range (IQR) increase in exposure to PM sources (with 95% confidence interval (CI))
was calculated using following equation:
𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝑐 ℎ𝑎𝑛𝑔𝑒 =
𝑌 ́
− 𝑌 𝑌 ∗ 100
Where 𝑌 ́
and Y represent the new and old biomarker levels, respectively.

17
2.3. Results
2.3.1. Characteristics of PMF-resolved PM2.5 sources in central Tehran
Table 2.2 indicates the relative contributions of the PMF-resolved sources to ambient PM2.5
concentrations as well as source-specific PM2.5 mass concentration at our sampling sites.
According to the table, the PMF model identified vehicular emissions, secondary aerosols,
industrial emissions, biomass burning, soil, and road dust as PM2.5 sources at both retirement home
and school dormitory. Vehicular emissions and secondary aerosol were the two major sources,
contributing to approximately 50%, and 25% of PM2.5 mass concentrations in central Tehran,
respectively. Specifically, vehicular emissions contributed to 15.1 μg/m
3
and 16.4 μg/m
3
of PM2.5
mass concentrations in the retirement home and school dormitory, respectively. Moreover,
secondary aerosol particles contributed to nearly 8 μg/m
3
of the total PM2.5 mass concentration at
both sites. While the industrial activities exhibited minimal contribution to PM2.5 at the school
dormitory, their corresponding contribution was around 18% at the retirement home, probably
because of the closer proximity of the retirement home to the industrial activities located in the
western zones of the city. Detailed description of the resolved PM2.5 source contributions in the
selected sampling sites can be found at Taghvaee et al. (2018).

18
Table 2.2. The average source-specific PM2.5 mass concentration and relative contribution of
the PMF-resolved sources to ambient PM2.5 concentrations at the retirement home and
school dormitory, along with the chemical species used as PM source tracers.

Moreover, Table 2.3(a-b) shows the results of Spearman rank order correlation analyses
between the PMF-resolved PM2.5 sources in both school dormitory and retirement home.
According to the table, no meaningful associations were observed between the source-specific
PM2.5 mass concentrations in both school dormitory and retirement home, indicating that the
reported associations of blood biomarkers with a particular PM2.5 source are independent of other
PM2.5 sources. Also, vehicular emissions was the only PM2.5 source that exhibited strong
correlations (R
2
=0.7 in retirement home and R
2
=0.9 in dormitory) with total ambient PM2.5. This
observation is in agreement with the findings of Taghvaee et al. (2018), in which vehicular
activities contributed to approximately half of total PM2.5 mass concentrations at both selected
sampling sites. Nonetheless, the observed strong correlation of vehicular PM2.5 with total ambient
PM2.5 indicates that there is less confidence in the degree to which the reported associations of
Sources
Source-specific
tracers
Average mass
concentrations (μg/m
3
)
Contribution (%)
Retirement
home
School
dormitory
Retirement
home
School
dormitory
Vehicular emissions
Fe, Cu, Zn, Ba,
Cd, Ni, and Mn
15.1 16.4 48.8 49.3
Secondary aerosol
NO 3
−
, NH 4
+
,
SO 4
2−

8.7 8.0 28.1 24.0
Biomass burning K
+
0.9 5.3 3.0 16.0
Industrial emissions
Cu, Cd, Sn, Se,
Ni and Cr

5.4 0.6 17.4 1.8
Soil
Al, Ti, Li, Si and
Fe
0.9 2.7 2.8 8.2
Road dust
Al, Ti, Ba, Fe,
Zn, Mn, Si, Cu,
Cr, and Cd
<0.3 <0.3 <1.0 <1.0
19
various blood biomarkers with vehicular emissions are independent of their associations with total
PM2.5.
Table 2.3. Spearman rank order correlation analyses between PM2.5 sources in a) Tohid
retirement home; and b) School dormitory.
a)

Vehicular
emissions
Secondary
aerosol
Biomass
burning
Industrial
emissions
Soil
Road
dust
Measured
PM2.5
Vehicular
emissions
1.000 -.303 -.393 .045 .054 .429 .687
Secondary
aerosol
-.303 1.000 -.152 -.333 .087 .159 .006
Biomass
burning
-.393 -.152 1.000 .053 -.317 -.418 -.349
Industrial
emissions
.045 -.333 .053 1.000 -.158 .017 -.237
Soil .054 .087 -.317 -.158 1.000 .087 .021
Road dust .429 .159 -.418 .017 .087 1.000 .319
Measured
PM2.5
.687 .006 -.349 -.237 .021 .319 1.000

20
b)

Vehicular
emissions
Secondary
aerosol
Biomass
burning
Industrial
emissions
Soil
Road
dust
Measured
PM2.5
Vehicular
emissions
1.000 -.132 -.095 .058 -.168 -.338 .929
Secondary
aerosol
-.132 1.000 -.078 .007 -.197 -.176 .119
Biomass
burning
-.095 -.078 1.000 -.029 .254 .252 -.046
Industrial
emissions
.058 .007 -.029 1.000 .004 .035 -.047
Soil -.168 -.197 .254 .004 1.000 -.031 .030
Road dust -.338 -.176 .252 .035 -.031 1.000 -.412
**

Measured
PM2.5
.929 .119 -.046 -.047 .030 -.412
**
1.000

2.3.2. Association of blood biomarkers with PM2.5 sources
2.3.2.1. White blood cells (WBC)
The associations of WBC versus various PM2.5 sources are illustrated in Figure 2.1 for both
elderly and young populations. In the elderly panel (Figure 2.1(a)), the WBC levels were positively
associated with all PM2.5 sources in both time lags, except for biomass burning emissions. In detail,
secondary aerosol, industrial emissions, road dust, and total PM2.5 showed statistically significant
associations (Pvalue < 0.05) with WBC in the lag 1-3 and lag 4-6. The percentage changes in the
WBC was 24% (95% CI: 18%, 32%) per an IQR increase in the secondary originated PM2.5 mass
concentrations in the first lag. It is also worth mentioning that traffic-related PM2.5 were associated
with the increase of WBC in the elderly for both time lags, although the associations were not
statistically significant (Pvalue = 0.230 and 0.167, for lag 1-3 and lag 4-6, respectively). In contrast,
21
as shown in Figure 2.1(b), no positive and meaningful associations were observed between this
health biomarker and most of the PM2.5 sources in young people, with the exception of vehicular
PM2.5 that exhibited significantly positive associations (Pvalue < 0.05) with WBC in the first-time
lag for young people. The maximum increase in the percentage of WBC (6.6%, 95% Cl: 2.7%,
10.6) among the young panel was related to vehicular PM2.5 in the first lag. Our findings revealed
negative associations of biomass burning with leukocyte counts in both subjects for all time lags.

Figure 2.1. Associations between white blood cells (WBC) and source-specific PM2.5
concentrations in the panel of: (a) the elderly (n = 44) and (b) healthy young adults (n = 40).
Estimated percent changes in the blood marker (adjusted coefficient and 95% CI)
corresponds to an IQR increase in total PM2.5 and source-specific PM2.5 concentrations (see
IQR in Table 2.4), adjusted for temperature and relative humidity.

22

Table 2.4. Descriptive statistics of PM2.5 (µg/m
3
) concentrations in young and elderly panels.

Averaging
time
PM2.5 Sources
Elderly Subjects Healthy Young Subjects
Mean
(SD)
IQR Min Max Mean (SD) IQR Min Max
1-3-day-ave.
(lag1-3)
Vehicular emissions
14.5
(12.4)
15.1 0.0 38.0 13.4 (8.1) 13.7 0.0 23.9
Secondary aerosol 7.9 (6.1) 14.4 0.0 14.6 10.7 (8.8) 19.9 0.9 22.8
Biomass burning 5.3 (1.2) 2.5 3.5 6.6 3.4 (3.5) 7.5 0.0 8.6
Industrial emissions 0.9 (0.5) 0.8 0.0 1.6 0.8 (0.4) 0.6 0.4 1.3
Soil dust 1.3 (2.7) 0.6 0.0 7.4 3.6 (5.6) 0.8 0.0 16.2
Road dust 0.1 (0.1) 0.1 0.0 0.1 0.2 (0.2) 0.1 0.1 0.7
Total PM 2.5
29.8
(10.5)
7.8 18.8 51.5 32.1 (7.5) 13.0 22.9 44.3
4-6-day-ave.
(lag4-6)
Vehicular emissions 12.9 (7.8) 14.8 0.0 22.3 19.7 (18.8) 18.2 0.0 57.4
Secondary aerosol 7.6 (7.1) 10.3 0.0 21.0 6.3 (6.7) 13.2 0.0 16.0
Biomass burning 5.8 (1.1) 1.8 3.8 7.3 3.1 (3.1) 5.7 0.0 8.5
Industrial emissions 1 (0.7) 1.6 0.0 1.9 0.8 (0.2) 0.3 0.6 1.1
Soil dust 0.5 (0.5) 0.9 0.0 1.4 1.8 (3.3) 0.6 0.0 9.1
Road dust 0.1 (0.1) 0.1 0.0 0.1 0.2 (0.1) 0.1 0.0 0.3
Total PM 2.5 27.8 (6.2) 12.8 18.8 36.0 31.8 (17.2) 30.6 16.9 60.6

23
2.3.2.2. Von Willebrand factor (vWF)
The associations between vWF and PM sources are shown for elderly and young subjects
in Figure 2.2. In the elderly panel (Figure (2.2(a)), secondary source and road dust were positively
associated (Pvalue < 0.05) with vWF (i.e., a marker of endothelial dysfunction) in both time lags. In
contrast, no meaningful associations were observed between this blood biomarker and soil as well
as industrial emissions in either of time lags. Vehicle-derived PM2.5 had significantly positive
association (Pvalue < 0.05) with this blood biomarker in the second time lag in the elderly panel.
The highest increase in the vWF levels (28.9%, 95% CI: 2.5%, 55.3%) were related to an IQR
increase of the traffic emissions in time lag 4-6. Furthermore, biomass burning activity was
negatively associated with vWF in both time lags among elderly (Figure 2.2(a)). Similar to the
results presented for WBC, we did not find any significant associations between PM sources and
vWF in young subjects (Figure 2.2(b)).

24
Figure 2.2. Associations between von Willebrand factor (vWF) and source-specific PM2.5
concentrations in the panel of (a) the elderly (n = 44) and (b) healthy young adults (n = 40).
Estimated percent changes in the blood marker (adjusted coefficient and 95% CI)
corresponds to an IQR increase in total PM2.5 and source-specific PM2.5 concentrations (see
IQR in Table 2.4), adjusted for temperature and relative humidity.

25
2.3.2.3. High sensitive C-reactive protein (hsCRP)
Figure 2.3 illustrates the relationship between hsCRP and PM sources for the senior
populations during our sampling campaign. Based on Figure 2.3, industry-originated and total
PM2.5 were positively and significantly (Pvalue < 0.05) associated with hsCRP in lag 1-3 among
elderly. Indeed, industrial activities were the major contributing source to the elevated hsCRP
levels (15.8%, 95% CI: 3.2%, 23.3%) per an IQR increase in their corresponding emissions in the
first-time lag. Moreover, vehicular emissions and secondary aerosols demonstrated positive
association with this biomarker among seniors in time lag 1-3, although their associations were
not significant (Pvalue = 0.253 and 0.127, for vehicular emissions and secondary aerosols,
respectively). On the other hand, no significant associations were observed between source-
specific PM concentrations and hsCRP in the second time lag. However, secondary aerosols,
industrial emissions, soil and total PM2.5 were positively associated with this indicator of
cardiovascular diseases for the elderly in time lag 4-6. In addition, similar to the case of other
biomarkers, we found unexpected and significant (Pvalue < 0.05) inverse associations of biomass
burning with hsCRP in retirement home, during the whole sampling campaign. Moreover, our
statistical analysis revealed insignificant association of hsCRP with all PM2.5 sources in the school
dormitory within all-time lags.

26
Figure 2.3. Associations between high sensitive C-reactive protein (hsCRP) and source-
specific PM2.5 concentrations in the panel of the elderly (n = 44). Estimated percent changes
in the blood marker (adjusted coefficient and 95% CI) corresponds to an IQR increase in
total PM2.5 and source-specific PM2.5 concentrations (see IQR in Table 2.4), adjusted for
temperature and relative humidity.

2.3.2.4. Tumor necrosis factor-soluble receptor-II (sTNF-RII)
The association between sTNF-RII versus PM2.5 sources are presented for both elderly and
young subjects in Figure 2.4. As shown in Figure 2.4(a), sTNF-RII was positively associated
(Pvalue<0.05) with all PMF-resolved sources among seniors, except for biomass burning (in all time
lags), and vehicular emissions (in lag 4-6). In addition, generally stronger associations were
observed between this biomarker and PM sources in the second time lag. For instance, the industry-
related increase in the sTNF-RII levels was higher in the second lag (24.3%, 95% CI: 13.5%,
36.1%) compared to that of first lag (20.3%, 95% CI: 12.4%, 28.7%) in elderly panel. Similarly to
other biomarkers, we found negative associations between biomass burning and sTNF-RII for all-
time lags. In contrary, we observed no meaningful associations between sTNF-RII and most of
identified PM sources in the blood samples of the young panel during the sampling time lags
27
(Figure 2.4(b)). However, industrial emissions and biomass burning exhibited positive
associations with this blood biomarker in the first-time lag, although their association were
marginally significant (Pvalue = 0.121 and 0.130, for industrial emissions and biomass burning,
respectively).
Figure 2.4. Associations between tumor necrosis factor-soluble receptor-II (sTNF-RII) and
source-specific PM2.5 concentrations in the panel of (a) the elderly (n = 44) and (b) healthy
young adults (n = 40). Estimated percent changes in the blood marker (adjusted coefficient
and 95% CI) corresponds to an IQR increase in total PM2.5 and source-specific
PM2.5concentrations (see IQR in Table 2.4), adjusted for temperature and relative humidity.

28
2.3.2.5. Interleukin-6 (IL-6)
Figure 2.5 presents the associations between IL-6 and source-specific PM2.5 levels in elderly
and young panels. Our statistical analysis of elderly subjects revealed positive and significant
associations of IL-6 with total ambient PM2.5 levels as well as various PM sources including
secondary aerosol, industry, soil and road dust in both time lags. In addition, exposure of the
elderly population to vehicular PM2.5 caused significant (Pvalue<0.05) increases in IL-6 levels in
the second time lag (Figure 2.5(a)). For both studied time lags, the maximum percentage increase
in IL-6 was due to secondary aerosols, indicating 402.9% (95% CI: 251.7%, 619.2%), and 239.1%
(95% CI: 166.4%, 331.8%) elevations in this pro-inflammatory cytokine per an IQR increase of
secondary PM concentrations. IL-6 was inversely associated (Pvalue<0.05) with biomass burning
particles among elderly group for both time lags. Finally, no significant associations were observed
between IL-6 and most of the source-specific PM concentrations in the healthy young adults.
Among all identified PM sources, IL-6 had significantly negative associations (Pvalue<0.05) with
only one PM source (i.e., particles formed by secondary photochemical processes) in the first-time
lag, while vehicular emissions and secondary particles were the only sources indicating significant
positive associations (Pvalue<0.05) with IL-6 in the lag 1-3 and lag 4-6, respectively (Figure 2.5(b)).

29
Figure 2.5. Associations between interleukin-6 (IL-6) and source-specific PM2.5
concentrations in the panel of (a) the elderly (n = 44) and (b) healthy young adults (n = 40).
Estimated percent changes in the blood marker (adjusted coefficient and 95% CI)
corresponds to an IQR increase in total PM2.5 and source-specific PM2.5 concentrations (see
IQR in Table 2.4), adjusted for temperature and relative humidity.

30
2.4. Discussion
WBC count is an inflammatory biomarker which is used to detect vascular inflammation
and cardiovascular illnesses (Chen and Schwartz, 2008). Previous studies have indicated
significantly positive associations of WBC counts with transition metals (i.e., Cu, Ni, and V)
(Steenhof et al., 2014). According to the literature, Cu is a chemical tracer of road dust (Karanasiou
et al., 2009; Lim et al., 2010), while Ni, and V are chemical markers of various industrial emissions
including cement, stainless steel, and brick production (Banerjee et al., 2015; Song et al., 2006),
heavy oil combustion (Cesari et al., 2014), and non-tail pipe emissions (Duong and Lee, 2011;
Wang et al., 2005). Therefore, significant and positive associations were observed between WBC
and industry-related PM2.5 as well as road dust in the elderly subjects. Among the seniors, the
observed 12% road dust-related increases in the WBC levels were also comparable with the
findings of Steenhof et al. (2014), in which the authors reported around 10% increase in the average
WBC counts as a result of exposure to Cu, and Ni as tracers of road dust PM. Furthermore, the
presented positive association of traffic-originated PM and leucocyte counts among elderly (in
both time lags) and young panels (in the first time lag) can also be attributed to the previously
documented association of EC (i.e., a well-known tracer of vehicular emissions (R. Zhang et al.,
2013; Zong et al., 2016)), and NOx with this blood biomarker (Steenhof et al., 2014; Su et al.,
2017). Zhang et al. (2018) reported lower WBC counts with higher physical activities, further
corroborating the observed insignificant associations of most source-specific PM concentrations
with leukocyte counts in the more active young populations. Lastly, the unexpected negative
associations of biomass burning emissions with WBC might be due to the inverse association of
this PM source with secondary aerosols. Specifically, while higher biomass burning contributions
were observed during the colder period of the year in our sampling locations, particles originating
31
from secondary formation peak at summer time due to the higher photochemical oxidation during
that period (Taghvaee et al., 2018a). Since our statistical analyses showed strong and positive
associations of various blood biomarkers with secondary particles in different time lags, the
abovementioned hypothesis probably explains the inverse association of WBC (as well as other
blood biomarkers such as vWF, hsCRP, with sTNF-RII, and IL-6) with PM originating from
biomass combustion. Similarly to our findings, Ng et al. (2017) attributed the negative association
of biomass burning PM2.5 with term low birth weight (TLBW) to the anti-association of this PM
source with the secondary ammonium sulfate as the major contributing source to the adverse birth
outcomes in California. In another study, Al Hanai et al. (2019) also observed the negative
association of levoglucosan (i.e., a well-known tracer of biomass burning (Simoneit et al., 1999))
with TNF-α as a biomarker of coagulation in Tehran, Iran. In addition, while biomass burning
emissions tend to peak at colder seasons, individuals spend less time outdoors during that period
(which decreases their overall exposure to ambient PM2.5), probably justifying the negative
association between particles originating from biomass burning with various blood biomarkers
among both young and elderly.
vWF is a prognostic marker that can be utilized to predict systemic inflammation and
cardiovascular illnesses (Gragnano et al., 2017). Several studies have indicated positive
associations of aluminum (i.e., tracer of road dust (Keuken et al., 2013)), and precursors of
secondary PM (e.g., SOx, and NOx) with vWF, a biomarker of coagulation (Wu et al., 2012; J.
Zhang et al., 2013); in concert with our observations in terms of positive associations between
abovementioned sources and vWF in the retirement home. Among the elderly group, our study
revealed a secondary PM-related increase of 15%-16% in the vWF levels, which is comparable
with the results of Zhang et al. (2013), reporting 8% increase in vWF associated with IQR increase
32
in NO2, and SO2 concentrations at various time lags. In addition, our statistical findings were in
line with the results of Bell et al. (2014), in which the authors reported significantly positive
association of PM2.5 road dust with the cardiovascular hospital admissions. Furthermore, results
from chamber and atmospheric measurement studies have indicated that the toxicity of primary
combustion products increases as they undergo photo-chemical processing due to the increased
oxidative potential of the reaction products, i.e. SOA (Jiang et al., 2016; Tuet et al., 2017)
compared to the precursors. Additionally, the positive association (Pvalue = 0.032) of vWF with
vehicular emissions among seniors (in the second lag) is consistent with the results of Yuan et al.
(2013) and Yue et al. (2007), exhibiting the impact of traffic emissions on increased vWF plasma
levels. However, our reported traffic-related increase (around 20%) in the vWF levels was higher
than the results of Yuan et al. (2013), in which vehicular emissions contributed to 11.3% (95% CI:
5,17.6%) increase in this biomarker level during the 0-4 days of exposure. Nevertheless, other
studies have observed insignificant association of vWF with vehicular sources (Jacobs et al., 2011;
Wu et al., 2014). This inconsistency regarding the association of vWF with vehicular emissions
was also reflected in Figure 2.2(a), in which insignificant associations were observed for vehicular
sources versus vWF in the first-time lag, despite the significantly positive associations observed
in the second lag.
hsCRP is a biomarker widely used for predicting the risk of cardiovascular diseases
(Kamath et al., 2015). In our study, hsCRP had positive associations (Pvalue<0.05) with industry-
related (12%-15% increase in hsCRP levels) and total PM2.5 (5%-9% increase in hsCRP levels) in
the elderly group, which is consistent with the results of Hennig et al. (2014), in which the authors
observed 7.96% (95% CI: 3.45, 12.67%), and 4.53% (95% CI: 2.76, 6.33%) changes in hsCRP
levels per 1 µg/m
3
increase in ambient PM2.5 and industry-related PM2.5 concentrations,
33
respectively. In addition, the reported positive association (in the first time lag) of this biomarker
with traffic-related emissions among elderly was in agreement with a number of earlier studies in
literature (Delfino et al., 2009; Jacobs et al., 2010; Pilz et al., 2018; Rückerl et al., 2014; Siponen
et al., 2015). For instance, Delfino et al., (2009) observed positive association of vehicular tracers
(i.e., EC, primary organic carbon (POC),NOx, and CO) with this biomarker of systematic
inflammation. Previous studies have also reported positive association of hsCRP with sulfate (Li
et al., 2017) and ozone (Arjomandi et al., 2015), corroborating the observed association of
secondary sources with this biomarker in the elderly panel. Nonetheless, the observed secondary-
related increase (i.e., 12%-16%) in hsCRP among the elderly was higher than that of Li et al.
(2017), reporting 4%-5% increase in hsCRP levels per 2µg/m
3
increase in sulfate concentrations.
The fast response nature of hsCRP (Siponen et al., 2015) probably explains the slightly higher
associations of this biomarker and various PM sources in the first lag compared to the second lag
in the retirement home. Additionally, the observed insignificant associations of PM sources with
hsCRP (as well as other biomarkers including sTNF-RII and IL-6 ) in young people may be due
to the demographic characteristics (e.g., age and stress factors) which influence the susceptibility
of subjects during exposure campaigns (Clougherty, 2010; Laskin et al., 2010).
sTNF-RII, i.e., a soluble form of the TNF receptor, is an indicator of coronary heart
failure (Carlsson et al., 2016; Hassanvand et al., 2017; Heaney and Golde, 1998). In the elderly
panel, the increased levels of this blood biomarker as a result of exposure to secondary, industry,
and road dust PM are in agreement with previous studies in the literature, indicating positive
association of sTNF-RII with O3 (tracer of secondary organic aerosol (Soleimanian et al., 2019a))
(Li et al., 2017), WSOC (chemical marker of secondary aerosol (Snyder et al., 2009)) (Delfino et
al., 2010a), secondary organic carbon (i.e., tracer of secondary PM2.5 (Wu et al., 2014)), and zinc
34
(tracer of both industrial and road dust emissions (Keuken et al., 2013)) (Wu et al., 2012). For
instance, Wu et al. (2012) reported a 12.50% (95% CI: 5.88, 19.54) increase in this blood
biomarker with an IQR increase in Zn concentrations (as a tracer of road dust), further
corroborating our findings showing that road dust PM caused an approximate 10% increase in
sTNF-RII level in both time lags among the elderly population. In addition, vehicular emissions
were positively associated with sTNF-RII (Delfino et al., 2010a, 2009, 2008; Jiang et al., 2019),
further supporting our observations among the senior group. In contrast, Bräuner et al. (2008)
concluded that healthy young responses to the changes in tumor necrosis factor α levels is rarely
influenced by ambient air pollution, which supports our own results showing insignificant
associations of this blood biomarker with different PM2.5 sources in young subjects.
IL-6 is an important indicator of coronary diseases by exciting immune cells within
human body (Hassanvand et al., 2017; Kritchevsky et al., 2005). According to our statistical
model, we observed positive associations (Pvalue < 0.05) of IL-6 with secondary, industrial, soil
and road dust PM in the elderly group. This observation is in line with previously documented
positive associations of secondary organic aerosol (Jiang et al., 2019; Ma et al., 2019; Niu et al.,
2017; Sheng and Lu, 2017), industry-related (Eze et al., 2016; Quay et al., 1998), and soil/road
dust derived PM (Veranth et al., 2006) with proinflammatory cytokines IL-6. In addition, our
statistical analyses in the elderly panel revealed significant (Pvalue<0.05) elevations of IL-6 per an
IQR increase in traffic-related PM2.5 in the second time lag, which is in agreement with the results
of previous biomarker studies (Delfino et al., 2010b, 2009, 2008; Jiang et al., 2019), attributing
the IL-6 secretion to the traffic-related PM2.5. Moreover, the strengthening in the associations of
most PM sources with IL-6 from lag 1-3 to lag 4-6 in the retirement home can be probably
attributed to the relatively slower response rate of this biomarker (Hassanvand et al., 2017).
35
2.5. Limitations
Although the results of this study provide insights regarding the association of various
inflammation and coagulation blood biomarkers with source-specific PM2.5 mass concentration
among young and elderly group in Tehran, we acknowledge that some limitations are associated
with the implemented methodology and analyses. First, we acknowledge that seasonal variations
in the source specific PM2.5 mass concentrations might create seasonal trends in the PM2.5 exposure
among both young and elderly people. However, the limited number of PM2.5 samples prevented
us from conducting seasonal correlation analyses between various biomarkers and PM 2.5 sources
in the multi linear regression model. Secondly, seasonal differences in PM2.5 exposure might vary
across the young and elderly individuals due to the different patterns of physical activity, and
residential penetration of PM indoors. However, as mentioned in Table 2.2 and according to
Taghvaee et al. (2018), the ambient source-specific PM2.5 mass concentration levels were
comparable in both dormitory and retirement homes during cold and warm seasons. In addition,
Hassanvand et al. (2015, 2014) reported similar ratios of PMindoor/PMoutdoor (i.e., around 0.56) in
both locations. Consequently, both young and senior populations are being exposed to comparable
levels of source-specific PM mass concentrations during cold and warm seasons. We also
acknowledge that young people (in comparison to the elderly) spend more time outside of their
residential building; thus, they are likely to be exposed to higher levels of PM mass concentrations.
Particularly during the cold season, young people are even more exposed to the outdoor PM, since
the senior people tend to spend more of their time in indoor environments during that period.
However, despite the presumed higher exposure of young people to PM throughout the year, our
results revealed that young people were less vulnerable to source-specific PM2.5 emissions. As
another limitation of this study, no gaseous air pollutants (e.g., CO, NO2, O3, and SO2) mass
36
concentrations were monitored during our sampling campaign. Thus, we were not able to examine
the correlation between PM2.5 and criteria gaseous air pollutants to ensure that our reported
associations of blood biomarkers with PM2.5 were independent of those gaseous pollutants. These
caveats notwithstanding, no meaningful associations were observed between the fine and coarse
fractions of particulate matter in the school dormitory (R
2
=0.01), and the Tohid retirement home
(R
2
=0.01), further corroborating that the reported associations between blood biomarkers and total
PM2.5 are independent of the larger size fraction of PM. Another possible explanation for the
observed different health impacts of various PM2.5 sources is the variation in the degree to which
emissions from particular sources contribute to the population exposures in our selected sampling
sites. While the complexity of physical and chemical pollutant transport processes make it difficult
to quantify the exact amount of individual exposure to emissions from various sources, PM2.5 mass
concentrations in both dormitory and retirement home were dominated by particles emitted by
vehicular sources and secondary reactions, contributing to nearly 50%, and 25% of total PM2.5
mass concentration, respectively. Therefore, it is a reasonable assumption that both elderly and
young groups are exposed to higher degree of PM2.5 emissions from vehicular activities and
secondary aerosols in comparison to other source of PM2.5. The higher rate of population exposure
to vehicular and secondary PM2.5 is in line with the results of our single pollutant multi-linear
regression model among seniors, indicating positive associations of most biomarkers with particles
of secondary origin, and vehicular emissions. Lastly, although we implemented some of the
presented methods in Mostofsky et al. (2012) for the highly correlated PM2.5 sources (e.g.,
vehicular emissions) with biomarkers among the young and elderly group, no meaningful changes
were observed in the association results. Thus, considering that the interpretation and
37
comparability of the abovementioned models was not straightforward, we did not use such models
in our analysis.

2.6. Summary and conclusions
The current study examined the association of inflammation blood biomarkers (i.e., hsCRP,
WBC, vWF, sTNF-RII and IL-6) with various PM2.5 sources among young and elderly populations
in central Tehran. In the elderly panel, secondary aerosol was positively associated with all blood
biomarkers (except hsCRP) within all-time lags, whereas vehicular emissions exhibited the same
trend in either first- or second-time lag. Furthermore, we observed positive associations of
industry-generated and road dust PM2.5 with WBC, sTNF-RII and IL-6 among the senior group.
In contrast, the majority of PM2.5 sources were not meaningfully associated with increased levels
of biomarkers in the school dormitory, probably because young healthy individuals are less
vulnerable to pollution resulting from PM2.5 exposure. Nonetheless, traffic-emitted particles had
significant associations with the WBC and IL-6 levels that are indicators of cardiovascular
disorders in the young panel. Our results indicate that elderly individuals seem to be more
susceptible by exposure to source-specific PM2.5 in comparison to young subjects. Further research
is required to elucidate the impact of other environmental factors on the increased levels of health
biomarkers in highly polluted urban environments.

38
Chapter 3: Long-term trends in the contribution of PM2.5 sources to organic carbon (OC)
in the Los Angeles basin and the effect of PM emission regulations

3.1. Introduction
Increased numbers of motor vehicles, rapid urbanization, and industrialization have triggered
serious air pollution challenges in many urban environments around the globe during the last
decades. Among various air pollutants, fine particulate matter (i.e., PM2.5; particles with
aerodynamic diameter < 2.5 µm) is of particular concern and the subject of the extensive research
due to its complex physico-chemical properties, harmful environmental impacts (e.g., visibility
impairment) (Khanna et al., 2018; Tao et al., 2009), and adverse health effects including
respiratory inflammation, cardiovascular diseases, and nurodegenrative disorders (Apte et al.,
2018; Crouse et al., 2012; Gauderman et al., 2015; Li et al., 2019; Qiu et al., 2012; Tam et al.,
2015). studies have also illustrated that long-term exposure to PM2.5 may represent a new
etiological factor for different cancer types and the development of brain tumors (Andersen et al.,
2018; Poulsen et al., 2016; Raaschou-Nielsen et al., 2011).
Accordingly, PM2.5 has been nationally and locally regulated in the United States over the last
three decades (Cheung et al., 2012b; Suh et al., 2000). The National Ambient Air Quality Standard
(NAAQS) for PM2.5 was first introduced in 1987 by the U.S. Environmental Protection Agency
(EPA) aiming the reduction in ambient concentrations of this criteria pollutant (Cheung et al.,
2012b; Dominici et al., 2007). Additionally, stricter laws were imposed by the state of California
targeting tailpipe emissions, including California Low Emission Vehicles I, II, and III (CA LEV-
I, CA LEV-II, CA LEV-III) (CARB, 2012, 2000; Lurmann et al., 2015; Warneke et al., 2012). For
39
example, CA LEV-II urged emission reduction of various air pollutants (e.g., non-methane organic
gases, carbon monoxide (CO), nitrogen oxides (NOx) and particulates) from vehicles up to 8500
lbs., and was fully implemented by 2007 for all vehicles with 2004 and subsequent model years
(CARB, 2000; Friedman; et al., 1998; Hwang and Doniger, 2004). More details regarding these
regulations are discussed in Section 4.2.
Due to the implementation of abovementioned regulations, several studies have reported an
improvement in air quality (Hasheminassab et al., 2014a; Lurmann et al., 2015) and public health
(Gauderman et al., 2015; Rice et al., 2016) during the last decades in Southern California. For
example, Hasheminassab et al. (2014) showed that the contribution of traffic activities to PM2.5
mass concentration decreased substantially (i.e., by 21 to 24%) in 2008-2012 period compared to
the period of 2002-2006 in two different sampling locations across Los Angeles basin (i.e., central
Los Angeles (CELA) and Rubidoux), as a result of the adopted stringent regulations and standards
on vehicular emissions assigned by the California Air Resource Board (CARB). Despite these
drastic reductions in exhaust emissions during the last decade, an increasing trend in the relative
contribution of non-tailpipe emissions to PM2.5 mass has been observed in the Los Angeles basin,
probably due to the lack of regulations controlling theses emissions in the region (Shirmohammadi
et al., 2017). Non-tailpipe sources are generally categorized as road dust resuspension, tire dust,
and brake wear particles, containing several redox active metals such as Ba, Cu, Cr, Mn, Ti, Ni,
Fe, and Zn (Roy M Harrison et al., 2012; Kotchenruther, 2016; Pant and Harrison, 2013). Previous
studies in the literature have demonstrated associations between the toxicity induced by PM and
the above-mentioned redox active elements (Umme S Akhtar et al., 2010; Charrier and Anastasio,
2012; Janssen et al., 2014; Liu et al., 2019).
40
A significant fraction of ambient PM2.5 mass consists of carbonaceous aerosols, including
elemental carbon (EC), organic carbon (OC), and carbonate carbon (CC) (Karanasiou et al., 2011;
Li et al., 2013; Schwarz et al., 2008; Snyder et al., 2010), which have also been linked to the
oxidative potential and associated toxicity of ambient PM2.5 (Bates et al., 2019; Pirhadi et al.,
2020c; Samara, 2017). Ambient OC originates from primary sources such as vehicular emissions
(Chirizzi et al., 2017a; Hasheminassab et al., 2013; Heo et al., 2013; Soleimanian et al., 2019a;
Warneke et al., 2012), and biomass burning (Schauer and Cass, 2000; Skiles et al., 2018) as well
as atmospheric photochemical processes forming secondary organic aerosols (SOA) (Heo et al.,
2013; Jimenez et al., 2009; Ke et al., 2007). Earlier studies have apportioned the ambient OC mass
concentration to its contributing sources in the Los Angeles basin (Hasheminassab et al., 2013;
Heo et al., 2013; Shirmohammadi et al., 2016; Soleimanian et al., 2019a; Zhang et al., 2009). For
example, Heo et al. (2013) implemented the Positive Matrix Factorziation (PMF) model to identify
the sources and quantify their relative contributions to the ambient PM2.5-bound OC at two
different sampling site (i,e, CELA, and Riverside) across the basin. According to their findings,
while traffic activities were the major sources of OC mass concentration in CELA, OC was mainly
dominated by SOA in Riverside.
Previously conducted OC source apportionment studies in the area were limited at best to a
single year. The main objective of this study was to investigate long-term temporal trends in the
contribution of primary and secondary sources to PM2.5-bound OC at two locations (i.e., CELA,
and Riverside) across the Los Angeles basin over the time period of 2005-2015, during which
stringent PM mass-based regulations were implemented to reduce the tailpipe emissions in the
region. Results from this study provide insights on the effectiveness of implemented PM2.5
41
emission regulations in controlling PM-bound OC levels in Los Angeles, one of the air pollution
hotspots in the United States.

3.2. Methods
3.2.1. Sampling locations and period
The air pollutant data used in our study were obtained from the regulatory monitoring
conducted by the US Environmental Protection Agency (US EPA) as part of the Air Quality
System (AQS) and PM2.5 chemical speciation network (CSN) database (US EPA, 2019a). Figure
3.1 shows the locations of central Los Angeles (CELA), and Rubidoux as our selected monitoring
sites in the Los Angeles basin. The urban site in CELA (34°03′59.7″N,118°13′36.8″W) represents
a “source” site impacted mostly by freshly emitted PM originating from urban activities including
traffic-related emissions (Hasheminassab et al., 2014a; Mousavi et al., 2018a; Pirhadi et al.,
2020a). On the other hand, the Rubidoux site in Riverside county (33°59′58.5″N,117°24′57.6″W),
located approximately 80 km to the east of the CELA, is a semi-urban sampling location with
lower levels of vehicular emissions. The prevailing westerly and south westerly wind directions
transport primary PM emissions from central Los Angeles to the eastern parts of the basin;
Riverside is therefore a “receptor” site affected by the long-range transported and photochemically
aged secondary aerosols formed during their advection to that area (Hasheminassab et al., 2013;
Pakbin et al., 2010; Soleimanian et al., 2019a).

42

Figure 3.1. Map of the two study locations in the Los Angeles basin.

The ambient measurements used in our source apportionment analysis corresponded to the
calendar years of 2005, 2010, and 2015 to investigate the long-term trends in the contribution of
various emission sources to the total OC mass concentration at our selected locations. In addition,
each year was divided into a warm (spanning from May to September), and a cold period (from
January to April and October to December). Table 3.1 shows the average seasonal meteorological
parameters (i.e., temperature and relative humidity (RH)), obtained from the California Air
Resource Board (CARB) website for the whole sampling period.

43
Table 3.1. Summary of the meteorological parameters at CELA and Riverside.

Site Type Year
Temperature (℃) Relative humidity (%)
Warm Cold Warm Cold
CELA Urban
2005 28.0±2.1 22.4±2.3 60.1±29.3 93.1±1.4
2010 27.2±2.2 22.1±1.5 78.3±1.8 77.3±4.7
2015 27.0±3.3 23.5±2.7 80.3±3.0 79.5±4.4
Riverside Semi-rural
2005 31.3±3.2 22.1±2.8 70.0±16.7 88.5±5.5
2010 31.3±3.0 21.6±2.0 76.2±3.9 78.2±5.8
2015 31.0±4.7 23.9±3.3 70.3±7.8 69.8±7.1

3.2.2. PM2.5 collection, instrumentation, and analysis
24-hour time-integrated PM2.5 samples were simultaneously collected every three days on
different types of filters (i.e., quartz, polytetrafluoroethylene (PTFE), and nylon) using low volume
Met One Speciation Air Sampling Systems (SASS™, Met One Instruments, Inc.). The quartz
filters were analyzed for their EC, OC, and OC volatility fractions (OCx) by means of the Desert
Research Institute (DRI) thermal/optical Carbon Analyzer (model 2001) following the US EPA
approved Interagency Monitoring of Protected Visual Environments (IMPROVE_A) thermal
protocol (DRI, 2005). In this method, the OC fractions (i.e., OC1, OC2, OC3, OC4) are categorized
based on their volatility at different temperature stages, ranging from 140 °C to 580 °C, where the
OC1, and OC4 represent the lowest, and highest volatile fraction of OC, respectively. More details
regarding the IMPROVE_A temperature protocol can be found at Chow et al. (2007; 2012). In
addition, the trace element and metal content of PM2.5 was determined by analyzing Teflon filters
by means of energy dispersive X-ray fluorescence (EDXRF), using Inorganic Compendium
Method IO-3.3 (US EPA, 1999a). The PM2.5 samples collected on nylon filters were extracted in
deionized distilled water and analyzed for inorganic ions via ion chromatography (IC). Further
information regarding the extraction and analysis procedures for the IC and IO-3.3 method can be
found elsewhere (US EPA, 1999b, 1999a). In addition to the abovementioned PM2.5 chemical
44
components, the hourly mass concentrations of specific gaseous pollutants (e.g., Ozone (O3)) were
continuously recorded using the Federal Equivalent Method (FEM) ultraviolet (UV) continuous
monitor (i.e., THERMO ELECTRON 49 (US EPA, 2019b)). Although quality assurance and
control of the samples is a serious issue in source apportionment studies as elaborated by Hopke
et al. (2020), we used the data provided by the EPA in our study with highest quality standards
(Solomon et al., 2014; US EPA, 2014). Detailed information about the field audits as well as
laboratory audits conducted by the EPA on the CSN network data can be found in Solomon et al.
(2014).

3.2.3. Source apportionment analysis
3.2.3.1. Positive Matrix Factorization (PMF) model
PMF is a multivariate model that has been extensively utilized for identifying the contributing
sources and quantifying their relative contributions to a specific constituent of air pollutants,
including PM2.5 and OC (Heo et al., 2014, 2013; Paatero, 1997; Paatero et al., 2014; Paatero and
Tapper, 1994). This receptor model is mathematically solving the following chemical mass
balance equation (1):
𝑋 𝑖𝑗
= ∑ 𝑔 𝑖𝑘
𝑓 𝑘𝑗
+ 𝑒 𝑖𝑗
𝑝 𝑘 =1
(1)
Where Xij stands for the mass concentration of the ith sample and the jth

species, P refers to the
number of resolved factors by the PMF model, gik indicates the relative contribution of kth factor
to ith sample, fkj represents the resolved factor profile for each source of the jth species, and eij
demonstrates the PMF residual error associated with the ith sample and the jth species.
45
In order to achieve the most suitable factor profile and contribution, PMF minimizes the objective
function (Q) which is defined as below:
Q = ∑ ∑ (
𝑒 𝑖𝑗
𝑢 𝑖𝑗
)
2
𝑚 𝑗 =1
𝑛 𝑖 =1
(2)
where n represents the number of samples, m stands for the number of species; and uij refers to the
uncertainty associated with the measured mass concentration of the ith sample and the jth species.
Non-negative values are being assigned to the PMF-resolved source profiles and factor
contributions as the only constraint during optimization (Norris et al., 2014).
The uncertainty of the measured species was evaluated using the following equation,
widely used by previous PMF studies (Ito et al., 2004; Park et al., 2011; Reff et al., 2007;
Soleimanian et al., 2019d):
σ
𝑖𝑗
= (0.05 × 𝑋 𝑖𝑗
) + 𝐷𝐿
𝑗 (3)
where, σij refers to the estimated uncertainty of the ith sample and the jth species, Xij is the mass
concentration of sample i and species j, and Dij stands for the detection limit associated with the
quantification of the ith

sample and the jth species.
The above user-provided uncertainty values were used along with the measured mass
concentration of species as the inputs to the USEPA PMF version 5.0 for identifying the sources
and quantifying their contributions to the total OC at CELA and Riverside for the years of 2005,
2010, and 2015. The model was run in the robust mode to significantly reduce the impact of
samples with high levels of uncertainty on the final PMF outputs. In addition, the PMF runs were
separately executed for each site and the total OC concentration was set as the total variable. A
46
10% extra modeling uncertainty was chosen in this study to achieve the optimum solution in terms
of physical interpretations and statistical considerations.
The uncertainties associated with the implementation of PMF model were also estimated
using bootstrap (BS), displacement (DISP), and BS-DISP (bootstrap+ displacement) methods
(Norris et al., 2014). According to the BS analysis results, the PMF outputs were considered valid
because more than 80-90% of the resolved factors were mapped for each of the PMF solutions.
For DISP analysis, our solutions were deemed reliable due to the less than 1% drop in the Q value
as well as no factor swap for the smallest dQ max (i.e., dQmax=4). Finally, the drop in the Q value
was less than 0.5% in the BS-DISP runs, further corroborating the validity of our PMF analysis in
terms of uncertainty considerations (Brown et al., 2015; Norris et al., 2014; Reff et al., 2007).

3.2.3.2. PMF input data screening and preparation
To identify the contributing sources to total OC mass concentration in our study locations,
different combinations of PM2.5 chemical components and gaseous pollutants were tried as the
input concentration matrix to the PMF model. Our optimum solution (i.e., the most statistically
robust and physically interpretable result based on the criteria discussed in section 3.2.1) included
EC, OC, OCx (i.e., OC1,OC2, and OC3), sulfate (SO4
2-
), O3, potassium ion (K+), and metal
elements such as potassium (K), titanium (Ti), copper (Cu) and chromium (Cr) in the PMF
analysis. Numerous studies have documented EC, OC1, OC2, and OC3 as indicators of gasoline
and diesel exhaust emissions (Cao et al., 2005; Li et al., 2018; Schauer, 2003; Zong et al., 2016),
O3 and SO4 as chemical markers of the photochemical reactions (Heo et al., 2014; Jacob, 1999;
Taghvaee et al., 2018a), K
+
/K ratio as a frequently used tracer of biomass burning (Lee et al.,
47
2007; Zhu et al., 2017), Cu and Ti as surrogates of road dust and brake abrasion particles (Adamiec
et al., 2016; Roy M Harrison et al., 2012) and Cr as a marker of industrial activities (Mousavi et
al., 2018b; Propper et al., 2015).
As noted earlier, while non-tailpipe particles are typically characterized by high loadings of
several metal elements such as Zn, Ti, Cu, Fe, and Al (Cohen et al., 2009; Dall’Osto et al., 2008a;
Roy M Harrison et al., 2012; Pant and Harrison, 2013), the optimum solution in this study included
only Ti and Cu as the non-tailpipe tracers. To further evaluate the validity of selecting Ti and Cu
among other tracers of non-tailpipe emissions, we performed Spearman rank order correlation
analysis between the PMF-resolved factor contributions and other chemical markers of non-
tailpipe emissions, the results of which are shown in Table 3.2. It should be noted that the PMF
source contributions were combined in each study site for the whole study period to increase the
statistical significance of our analysis. Moreover, SOA and biomass burning contributions to OC
were excluded from Table 3.2, since we would not expect any potential contribution of these
sources to the surrogates of non-tailpipe activities. According to the results shown in this table, the
PMF-resolved non-tailpipe factor (characterized by high loadings of Ti and Cu) exhibited equally
strong associations with other markers of non-tailpipe emissions, indicating that the selected
species (i.e., Cu, and Ti) can be adequately used as indicators of non-tailpipe emissions.

48
Table 3.2. Spearman rank order correlation analysis between the PMF-resolved
sources and non-tailpipe tracer species in a) CELA; and b) Riverside.
a)
PMF-resolved sources Fe Cu Al Ti Zn
Local industrial activities -0.03 0.29 0.20 0.30 0.31
Non-tailpipe emissions 0.70 0.65 0.60 0.61 0.69
Tailpipe emissions 0.23 0.02 -0.01 0.13 0.25

b)
PMF-resolved sources Fe Cu Al Ti Zn
Local industrial activities -0.04 0.09 -0.27 -0.16 -0.17
Non-tailpipe emissions 0.80 0.60 0.61 0.90 0.53
Tailpipe emissions 0.33 0.17 0.20 0.13 0.44

3.3. Results and discussion
3.3.1. Data Overview
Table 3.3 shows the statistical summary of the measured species in CELA and Riverside
sampling sites throughout the study period. These statistical properties include mean, minimum
(min), maximum (max), standard error (SE) and signal to noise (S/N) ratio. Previous studies have
reported S/N ratio as an important statistical criterion in conducting uncertainty analysis. Species
with S/N >1 are generally considered as strong variables with acceptable signals (Reff et al., 2007).
According to Table 3.3, we also observed a significant reduction (Pvalue < 0.05) in the annual mass
concentration of EC and OC in both sampling sites within the period of 2005-2015. The OC
concentrations in CELA decreased significantly (Pvalue< 0.01) from 7.1±0.29 μg/m
3
in 2005 to
5.8±0.24 μg/m
3
and 4.4±0.22 μg/m
3
in 2010, and 2015, respectively. The ambient OC levels in
49
Riverside followed a similar significant decreasing trend (Pvalue < 0.01) with mass concentrations
of 7.4± 0.35 μg/m
3
, 6.3±0.28 μg/m
3
, 4.0±0.26 μg/m
3
during 2005, 2010, and 2015, respectively.
Furthermore, the ambient EC mass concentrations in CELA also decreased significantly ((P value<
0.04) from 1.9±0.1 μg/m
3
in 2005 to 1.6±0.1 μg/m
3
, and 1.0 ±0.1 μg/m
3
during 2010 and 2015,
respectively. Similarly, the ambient EC levels in Riverside decreased from around 1.6±0.1 μg/m
3

to 1.5±0.1 μg/m
3
and 1.0±0.1 μg/m
3
during the same time period.
Table 3.3. Statistical summary of the measured species employed as the input to the PMF
model for: a) CELA; and b) Riverside sites.
a)

Species
2005 2010 2015
Mean Min Max SE S/N Mean Min Max SE S/N Mean Min Max SE S/N
PM2.5 20.7 4.0 132.6 0.7 6.4 20.3 5.6 58.1 0.4 6.3 20.6 7.2 86.6 0.5 6.3
Cr (ng/m
3
) 3.0 0.0 14.0 0.4 1.8 2.1 0.0 5.1 0.2 1.8 2.8 0.0 44.0 0.9 1.7
Cu (ng/m
3
) 22.5 6.0 56.0 1.4 8.4 12.0 3.4 30.3 0.8 8.4 8.3 0.3 20.4 0.9 6.1
Ti (ng/m
3
) 18.8 5.3 50.3 1.1 1.4 8.0 0.5 22.9 0.7 1.4 5.7 0.0 28.6 0.9 1.3
EC (μg/m
3
) 2.0 0.5 4.5 0.1 9.6 1.7 0.1 4.5 0.1 9.6 1.0 0.1 4.1 0.1 8.6
(K
+
/K) ratio 1.1 0.0 6.3 0.1 6.7 0.8 0.0 3.5 0.1 5.8 0.8 0.2 4.2 0.1 6.0
OC (μg/m
3
) 7.2 2.8 13.7 0.3 7.2 5.8 2.8 11.5 0.2 7.2 4.4 1.2 8.9 0.2 5.9
OC1(μg/m
3
) 2.0 0.7 4.6 0.1 1.7 0.3 0.0 1.2 0.0 1.1 0.1 0.0 0.5 0.0 1.2
OC2 (μg/m
3
) 1.8 0.6 3 .4 0.1 4.2 1.4 0.7 2.5 0.1 4.0 1.1 0.3 2.1 0.1 3.2
OC3 (μg/m
3
) 1.2 0.4 2.3 0.1 4.9 1.7 0.8 3.6 0.1 3.5 1.6 0.6 2.9 0.1 4.5
Sulfate
(μg/m
3
)
4.0 0.6 11.4 0.3 8.0 1.5 0.4 3.6 0.1 9.9 1.3 0.2 4.1 0.1 9.9
Ozone (ppb) 18.4 3.0 50.0 1.3 2.2 19.3 5.3 40.0 1.1 2.2 26.9 11.8 42.5 1.1 3.2

50

b)

Species
2005 2010 2015
Mean Min Max SE S/N Mean Min Max SE S/N Mean Min Max SE S/N
PM2.5 22.3 3.3 112.6 0.57 7.2 22.2 5.1 93.4 0.6 6.5 19.3 4.5 61.1 0.5 7.0
Cr (ng/m
3
) 6.0 0.0 113.0 2.1 2.9 2.3 0.1 31.3 0.7 1.5 0.9 0.0 13.3 0.3 1.1
Cu (ng/m
3
) 16.0 4.1 31.1 0.9 9.3 8.0 1.3 19.0 0.6 7.0 3.3 0.1 12.0 0.4 3.2
Ti (ng/m
3
) 17.4 2.0 46.8 1.2 4.6 6.6 0.3 16.4 0.6 1.7 3.7 0.1 16.6 0.4 1.1
EC (μg/m
3
) 2.0 0.2 5.7 0.2 9.8 1.7 0.2 6.1 0.2 9.7 0.8 0.0 3.3 0.1 7.5
(K
+
/K) ratio 1.0 0.4 3.5 0.1 9.9 1.0 0.0 2.8 0.1 9.8 1.1 0.0 4.7 0.1 9.9
OC (μg/m
3
) 7.4 2.5 13.2 0.4 8.1 6.3 3.2 11.0 0.3 7.6 4.0 0.4 8.8 0.3 5.4
OC1(μg/m
3
) 1.9 0.4 3.9 0.1 5.2 0.4 0.0 1.5 0.1 1.1 0.0 0.0 0.2 0.0 1.4
OC2 (μg/m
3
) 2.0 0.8 4.0 0.1 5.5 2.1 1.1 3.2 0.1 5.8 1.2 0.1 2.8 0.1 3.4
OC3 (μg/m
3
) 1.2 0.3 2.5 0.1 3.6 2.3 0.9 5.5 0.1 6.2 1.8 0.3 4.1 0.1 5.0
Sulfate
(μg/m
3
)
3.0 0.4 7.3 0.3 9.0 1.7 0.2 4.2 0.2 9.9 1.0 0.1 3.4 0.1 9.5
Ozone (ppb) 28.4 4.5 69.2 1.9 3.2 33.0 6.4 52.2 1.8 3.8 31.5 9.8 59.6 1.6 3.7

The long-term seasonal trends in the mass concentration of OC and EC are also presented in
Figure 3.2, and 3.3, respectively. According to Figure 3.2, higher winter-time OC levels were
observed in CELA compared to the warm period, due mainly to the prevailing atmospheric
stability and lower mixing height during cold season, as well as the lower ambient temperatures
favoring the gas-to-particle phase conversion of semi volatile organic compounds (Hasheminassab
et al., 2013; Perrino et al., 2008). The measured OC mass concentrations in CELA were 6.2±0.3,
5.2±0.2 and 3.9±0.2 μg/m
3
during the warm phases of 2005, 2010, and 2015, respectively, whereas
the corresponding winter-time values were 7.6±0.4, 6.1±0.3, and 4.7±0.3 μg/m
3
within the same
time period. In contrast, the OC mass concentrations in the warm season were higher by about 0.8
51
μg/m
3
on average compared to the cold season at Riverside. This observation is in agreement with
the enhanced SOA formation during the summertime combined with the increased advection of
OC to this sampling site originating from the upwind source areas of central Los Angeles (Saffari
et al., 2015). Lastly, as depicted in Figure 3.3, significantly (Pvalue < 0.05) higher EC mass
concentrations were reported in both sites during the cold season compared to the warm period
which, as discussed earlier, might be attributed to the more stable metrological conditions during
the winter. For example, the observed EC concentrations in CELA increased from 1.6±0.1, 1.2±0.1
and 0.7±0.1 μg/m
3
in the warm season to 2.3±0.2, 2.0±0.2, and 1.3±0.1 μg/m
3
in the cold seasons
of 2005, 2010, and 2015, respectively.

52
Figure 3.2. Seasonal OC concentration trends over the 2005-2015 period for a) CELA; and
b) Riverside.

53
Figure 3.3. Seasonal EC concentration trends over the 2005-2015 period for a) CELA; and
b) Riverside.

54
3.3.2. PMF source apportionment results
3.3.2.1. Number of factors
We followed a trial and error approach to determine the contributing emission sources to the
total OC mass concentrations according to several criteria, including: (i) strong correlation (i.e.,
high R
2
value) of predicted versus measured OC mass concentrations, (ii) physically interpretable
PMF-resolved source profiles, (iii) investigating the temporal and spatial trends for PMF-resolved
factors in both sampling sites, and (iv) evaluating the PMF built-in uncertainty analyses (BS, DISP,
and BS-DISP). After testing various number of factors and extra modeling uncertainties, a five-
factor solution was identified as the most physically and statistically interpretable solution in our
selected sampling locations. The PMF-resolved factors were tailpipe emissions, non-tailpipe
emissions, biomass burning, secondary organic aerosol (SOA), and local industrial activities.
Figure 3.4 as well as Figures 3.5 and 3.6 indicate the PMF-resolved factor profiles for the years of
2005, 2010 and 2015 in CELA and Riverside sites. Figures 3.7 and 3.8 also depict the relative and
absolute contribution of the identified sources to the total OC mass concentrations for the study
locations in 2005, 2010, and 2015, respectively. Finally, the seasonal source-specific OC mass
concentrations are also exhibited in Figure 3.9 for both study sites during 2005, 2010 and 2015.

55

Figure 3.4. PMF-resolved factor profiles in a) CELA; and b) Riverside for 2005.

56
Figure 3.5. PMF-resolved factor profiles in a) CELA; and b) Riverside for 2010.

57
Figure 3.6 PMF-resolved factor profiles in a) CELA; and b) Riverside for 2015.

58
Figure 3.7. The relative (fractional) contribution of PMF-resolved sources to ambient OC
in CELA and Riverside over the years of 2005, 2010, and 2015.
2005 2010 2015

CELA

Riverside

32%
28%
11%
21%
8%
34%
28%
10%
19%
9%
23%
22%
6%
3%
46%
33%
27%
10%
2%
28%
29%
26%
13%
1%
31%
49%
14%
7%
16%
14%
59
Figure 3.8. Absolute source contributions to ambient OC mass concentrations during the
years of 2005, 2010 and 2015 in CELA and Riverside.

Figure 3.9. Seasonal trends in the absolute contributions of PMF-resolved sources to ambient OC
levels in CELA and Riverside during the years of 2005, 2010 and 2015.

60
3.3.2.2. Factor identification
3.3.2.2.1. Factor 1: Tailpipe emissions
This factor is characterized by high loadings of EC and OC1 as well as moderate loadings of
OC2, OC3, and metallic elements (e.g., Cu, and Ti) (Figures 3.4, 3.5, and 3.6). It should be noted
that OC1 data were excluded from the PMF input concentration matrix in some years, since their
values were below the detection limit (i.e., zero). This observation is consistent with the higher
vapor pressure of OC1 compared to other OC fractions, which favors the partitioning of this species
in the gas phase, resulting often in PM phase concentrations below the detection limit of the
IMPROVE_A method. Several previous studies have documented EC (Mooibroek et al., 2011;
Schauer, 2003; Zong et al., 2016) and OC1 (Cao et al., 2006; Zhu et al., 2010) as tracers of
vehicular emissions. In addition, the presence of other OCx (i.e., OC2, OC3), Cu, and Ti in the
profile of this factor also corroborates its vehicular origins (Cao et al., 2005; Li et al., 2018; Lim
et al., 2010). The seasonal trend for this factor demonstrated significantly higher (Pvalue < 0.05)
winter-time contributions in both CELA and Riverside sites, due mainly to the stable
meteorological conditions and depressed mixing heights during cold weather which restrict the
atmospheric dispersion and dilution of these emissions (Pirhadi et al., 2020b; Sowlat et al., 2016)
(Figure 3.9). The aforementioned discussion suggests that “tailpipe emissions” is an appropriate
title for this factor.
Our findings also revealed major contributions of exhaust emissions to the total OC mass
concentrations in both sampling locations during the whole study period. As shown in Figure 3.7,
this factor accounted for 49±3.1%, 32±3.2%, and 34±3.0% of ambient CELA OC levels in 2005,
2010 and 2015, respectively, while the corresponding fractional contribution in Riverside was
46±3.0%, 28±3.1%, and 31±3.0% during the same time period. Furthermore, the absolute
61
contribution of this factor to total OC (Figure 3.8) decreased significantly (Pvalue < 0.05) in both
sites over the 2005-2015 period, likely due to the implementation of several tailpipe emission
regulations (e.g., U.S. EPA 2007 emission standards (Hasheminassab et al., 2014a), and CARB
regulations (California Code Of Regulations, 2008)) during this time period. For example, we
observed a 58% decrease (from 3.5±0.3 μg/m
3
to 1.5±0.2 μg/m
3
) in the vehicular OC mass
concentrations over the 2005-2015 period at CELA site, whereas the corresponding reduction at
the Riverside site was nearly 62% (from 3.3±0.4 μg/m
3
to 1.2±0.2 μg/m
3
).

3.3.2.2.2. Factor 2: Non-tailpipe emissions
Factor 2 has high loadings of Cu, and Ti in both locations throughout the study period (Figures
3.4, 3.5, and 3.6). Cu and Ti have been previously used as tracers of non-tailpipe emissions,
including road dust, brake wear, and engine abrasion particles (Adamiec et al., 2016; Roy M
Harrison et al., 2012; Peltier et al., 2011; Thorpe and Harrison, 2008). Similar to tailpipe emissions,
we observed significantly higher (Pvalue < 0.05) contributions of this factor to total ambient OC in
CELA during the cold period compared to the warm season. This factor also exhibited higher
winter-time contributions to OC mass in Riverside, although the seasonal differences were not
statistically significant (Pvalue=0.5) (Figure 3.9). The higher contribution of this factor to ambient
OC during the colder period might be justified by the limited atmospheric dilution during the cold
seasons, caused by the stable meteorological conditions (i.e., lower wind speed and mixing height).
This observation is in agreement with the findings of previous studies in the Los Angeles basin
(Hasheminassab et al., 2014a; Mousavi et al., 2018b), which also reported higher mass
concentrations of PM2.5 non-tailpipe particles during the winter period.
62
Moreover, as discussed in section 3.3.2.2.1, the significant reduction in direct tailpipe
emissions decreased also their fractional contributions to total OC mass concentration in both study
locations. As a result, the relative contribution of non-tailpipe emissions to total OC increased in
both locations over the 2005-2015 period (Figure 3.7). As shown in Figure 3.7, the relative
contribution of non-tailpipe particles to total OC in CELA increased from 14±1.3% in 2005 to
28±2.1% in 2010 and to 28±2.5% in 2015. We should point out, however, that the absolute
contribution of non-tailpipe emissions to total OC mass concentrations were comparable
throughout the study period in both sites (Pvalues of 0.6 and 0.4 in CELA and Riverside,
respectively) (Figure 3.8).
While previous source apportionment studies conducted in CELA and elsewhere have
attributed a considerable portion of OC to their road dust factor (Hasheminassab et al., 2014b;
Mousavi et al., 2018b; Zheng et al., 2005) consistent with our findings, Pant et al. (2015) showed
that the content of OC in road dust is not noticeable. This inconsistency might be attributed to the
fact that these particles most likely emit as a result of resuspension of settling soil and traffic-
induced non-tailpipe emissions (Dall’Osto et al., 2014; Pant and Harrison, 2013), and their re-
suspension is highly dependent on the metrological conditions, such as wind speed and
precipitation (Harrison et al., 2001; Hasheminassab et al., 2014b). Therefore, we do expect to
observe considerable regional variations in the loading profiles of non-tailpipe emissions (Roy M
Harrison et al., 2012; Hasheminassab et al., 2014b).

63
3.3.2.2.3. Factor 3: Secondary Organic Aerosols (SOA)
The third factor is represented by high loadings of sulfate (SO4
2-
), and O3 and contributes to a
major portion of total ambient OC mass concentration across the Los Angeles basin (Figures 3.4,
3.5, and 3.6). According to previous studies in the literature, sulfate (mainly in the form of
ammonium sulfate), O3 and SOA are species formed through synchronized photochemical
reactions in the atmosphere (Budisulistiorini et al., 2015; Carlton et al., 2009; Jacob, 1999; Yuan
et al., 2006); therefore, sulfate and O3 are considered surrogates of SOA formation. (Heo et al.,
2014; Taghvaee et al., 2018a). Analyzing the seasonal trends also revealed significantly higher
(Pvalue < 0.05) absolute contribution of this factor to the total OC mass concentration in the warm
season throughout the investigated years and sampling locations (Figure 3.9). This observation is
in agreement with the results of previous studies in the same area (Hasheminassab et al., 2013;
Heo et al., 2013; Soleimanian et al., 2019a; Zhang, 2012), reporting higher contributions of SOA
to total OC levels in the summer, as a result of the enhanced photochemical oxidation processes
during the warm period. Based on the above discussion, we label this factor as “SOA”.
As discussed earlier in section 3.3.1, primary OC (POC) emissions are further transported from
source sites (i.e., CELA in our study) to receptor areas (i.e., Riverside in our case) by means of
dominant southerly and west southerly wind patterns during which SOA are formed through
photochemical oxidation of the advected pollutants from central Los Angeles (Saffari et al., 2015).
We observed significantly higher (Pvalue <0.05) mass concentrations of SOA in Riverside compared
to CELA throughout the study period. This factor contributed to 1.62±0.2 μg/m
3
(23±2.5%),
2.02±0.2 μg/m
3
(33±2.8%) and 1.16±0.1 μg/m
3
(29±2.37%) of total OC in Riverside in 2005, 2010
and 2015, respectively; whereas the corresponding SOA levels in CELA were 1.16±0.1 μg/m
3

(16±2.6%), 1.20±0.1 μg/m
3
(21±2.2%) and 0.77±0.1 μg/m
3
(19±2.5%). The decrease in the
64
relative contribution of tailpipe emissions might explain the higher fractional contributions of SOA
to total OC concentrations in both sites from 2005 to 2015 (Figure 3.7). Nonetheless, the
implementation of various air quality regulations during the study period limited the emissions of
primary organic precursors of secondary aerosols, leading to an overall decrease (although not
statistically significant, with Pvalue in the range of 0.4-0.6) in the annual SOA concentrations in
both sampling locations, as shown in Figure 3.8.

3.3.2.2.4. Factor 4: Biomass burning
This factor is identified by high loadings of K
+
/K ratio, and accounts for nearly 9.5±2.5%
(0.5±0.1 μg/m
3
) of total OC mass concentrations in both study locations throughout the study
period (Figures 3.4, 3.5, and 3.6). Several previous studies have used the ratio of K
+
/K as an
indicator of biomass burning emissions in metropolitan environments (Jung et al., 2014; Lee et al.,
2007; Yu et al., 2018). Although we were expecting to observe higher contributions of this factor
to OC during the winter period, further evaluation of the seasonal trends (Figure 3.9) indicated
comparable contributions during the warm and cold periods in CELA (Pvalue = 0.3), and Riverside
(Pvalue = 0.5) site. This observation might be justified by the summer-time wildfire incidents in the
region (Okoshi et al., 2014; Warneke et al., 2012), counter balancing the impact of increased wood
burning activities during the winter time (Heo et al., 2013; Lee et al., 2007; Mousavi et al., 2018a;
Sheppard et al., 1999; Szidat et al., 2009).

65
3.3.2.2.5. Factor 5: Local industrial activities
The source profiles of the fifth PMF-resolved factor were associated with high loadings of Cr
in both sampling locations. Several studies in the literature have reported Cr as an indicator of
industrial activities, including electroplating, metallurgy, and metal casting industries (Mansha et
al., 2012; Morrison and Murphy, 2010; Mousavi et al., 2018b; Papp, J.F. and Lipin, 2006; Propper
et al., 2015; Tiwari et al., 2013; Tositti et al., 2014). Analyzing the temporal pattern of this factor
also indicates comparable seasonal concentration of industry-related OC in CELA (Pvalue = 0.23),
and Riverside (Pvalue = 0.58) during the entire study period (Figure 3.9). While the industrial
sources in CELA accounted for 14±1.5%, 8±0.9%, and 9±1.9% of total OC levels during 2005,
2010, and 2015, respectively, the corresponding contribution of this factor to total OC was
negligible in Riverside, consistently with the lower industrial activities in eastern parts of Los
Angeles basin compared to the central and western regions (Kim et al., 2002; Singh et al., 2002).

3.3.3. Comparison with previous studies in the area
Our PM2.5-bound OC source apportionment results are in overall agreement with earlier studies
for the same sampling locations in the Los Angeles basin (Heo et al., 2013; Soleimanian et al.,
2019a). Heo et al. (2013) reported higher absolute contribution of the “secondary organic carbon
(SOC)” factor to the total OC concentrations compared to the “mobile” sources in the Riverside
area during the warm season of 2009, whereas the contribution of mobile sources to ambient OC
peaked during the cold period. These findings are in agreement with our results, showing lower
average contribution of “tailpipe emissions” than SOA to total OC during the warm season of our
study period, while reverse trends were observed for the cold season of the investigated years. In
66
another study, Soleimanian et al. (2019) apportioned total ambient OC in CELA to its contributing
sources during the period of 2012-2013 and reported that 57% of the OC in CELA originated from
traffic sources, followed by SOA (~35%) and biomass burning emissions (~8%). These findings
are also in agreement with our results, in which the vehicular emissions (encompassing both
tailpipe and non-tailpipe emissions) were the dominant sources of OC in CELA contributing to
~60% of total OC, while we attributed 20% and 11% of OC levels to SOA and biomass burning
activities, respectively. In addition, consistently with our observation, Soleimanian et al. (2019)
reported higher contributions of SOA to the total OC in Riverside as opposed to CELA.
We extracted the PM2.5 “on road vehicles emission” data for the three investigated years (i.e.,
2005, 2010 and 2015) in Los Angeles and Riverside using the annual-average inventory database
developed by California Air Resources Board (2019). Although these data refer to PM2.5 rather
than ambient OC, they provide insights on the long-term trends of tailpipe and non-tailpipe
emissions in the area. Figure 3.10 shows the annual average PM2.5 emissions (in units of tons per
day) by on road vehicles between years of 2005 and 2015. In the figure, “tailpipe” is solely
corresponding to emissions from vehicle exhaust, for all type of fuels (e.g., gasoline and diesel).
Moreover, the “non-tailpipe” category encompasses only the non-exhaust emissions, such as break
and tire wear. The figure shows that total on-road and tailpipe emissions have drastically decreased
in both the sampling sites (i.e., Los Angeles and Riverside) over time. On the other hand, a modest
increase in non-tailpipe emissions during the same period is observed in both the sampling
locations. During the 2005-2015 period, for example, the daily emissions of tailpipe category in
Los Angeles County dropped by 70% (6.1 tons/day), whereas non-tailpipe emissions increased
modestly by 4% (0.2 tons/day). Similarly, the vehicle exhaust emissions in Riverside area in 2015
released significantly lower PM2.5 mass (approximately 2.6 tons/day) compared to those in 2005,
67
while the non-exhaust emissions increased by 30% (about 0.51 tons/day) during the same time
period. These results for PM2.5 mass are in agreement with the findings of this study in which a
significant decreasing trend in the tailpipe and an increase in non-tailpipe contributions to the total
OC mass concentrations were observed from 2005 to 2015.

Figure 3.10. PM2.5 inventory data for on road vehicles emission between the years of 2005
and 2015 for a) CELA, and b) Riverside.
a) Los Angeles

68
b) Riverside

3.3.4. Timeline of regulations and their association with the PMF results
Extensive regulatory policies and programs have been implemented in California during
the last three decades, a major part of which was during the time period of this study (CARB,
2012, 2000; Lurmann et al., 2015; Warneke et al., 2012). In 1990, the nation’s first tailpipe
emissions control program (i.e., low emission vehicles (LEV I)) was introduced by California Air
Resources Board (CARB), which was later amended to new updated programs including LEV II
(1998), LEV III (2012), and Zero-Emission Vehicle (ZEV) program (2018) (CARB, 2012, 2000;
Walsh, 2000). These programs identified vehicle emissions levels targeting major pollutants (i.e.,
non-methane organic gases (NMOG), carbon monoxide (CO), nitrogen oxides (NOx) and
particulate matter (PM)) generated by vehicles in addition to a zero emissions category which
required auto-manufacturers to comply with for sale in the state (Lurmann et al., 2015; “The
California Low-Emission Vehicle Regulations,” 2019).
69
During the time period of this study, the most important and widespread regulation
affecting our findings is the LEV II which was gradually implemented between 2004 and 2010 for
vehicles with model year 2004 and above (Lurmann et al., 2015; “The California Low-Emission
Vehicle Regulations,” 2019). Under this program, auto manufacturers were required to develop
advanced emission control technologies to meet the fleet average NMOG standards as well as
other emission standards (e.g., CO and PM) that were defined for different vehicle categories: low
emission vehicles (LEV), ultra-low emission vehicles (ULEV), super-ultra-low emission vehicles
(SULEV), and partial-zero emission vehicle (PZEV). While each vehicle had to be certified to one
of the above acronyms to enter the market, passenger cars (PC) as well as light duty vehicles with
less than 8500 Ibs gross weight were required to achieve a fleet average NMOG standard of 0.035
g/mi in 2010. These regulations were reported to cause significant reductions in the exhaust
emissions effectively. For example, McDonald et al. (2013) reported around 50% reduction in non-
methane hydrocarbons (NMHC) and CO emissions in 2010 as opposed to 2005 year in Los
Angeles. Similarly, we observed that contributions of tailpipe emissions to total OC significantly
decreased (i.e., relative contribution of 48% and absolute contribution of 1.17 µg/m
3
) in 2010
compared to 2005 in CELA, consistently with the findings of McDonald et al. (2013) as discussed
earlier.
Another important regulation during this period is the low carbon fuel standards (LCFS)
implemented in 2013 encouraging the use and production of low-carbon fuel in California to
reduce petroleum dependency and GHG emissions 10% by 2020 (Andress et al., 2010). The LCFS
standards are measured in terms of the carbon intensity (CI) of the used conventional fuel and their
substitutes, then compared to a declining CI benchmark that is preset by CARB for each year (“The
California Low-Emission Vehicle Regulations,” 2019). In addition to the abovementioned
70
regulations, other programs ( e.g., financial incentives for replacement (1998-2012) and cleaner
port (drayage) trucks (CPT) program (2007)) have also been enacted to alleviate the emission of
air pollutants linked to high polluting vehicles and trucks (Goodchild and Mohan, 2008; Haveman
and Thornberg, 2008; G. Lee et al., 2012). As the trucks providing services to the ports of Los
Angeles and Long Beach were found responsible for roughly 60% of the particulate matter
resulting from port activities (Haveman and Thornberg, 2008), the CPT program was implemented
to reduce the diesel emissions associated with these trucks (Haveman and Thornberg, 2008; G.
Lee et al., 2012) and enforce drayage companies to comply with the port standards (Haveman and
Thornberg, 2008; G. Lee et al., 2012). Thus, the decreasing trend of tailpipe emissions contribution
to total OC from 2010 to 2015 observed in our study in both the sampling locations (i.e., CELA
and Riverside) is most likely a result of these regulations discussed in the preceding paragraphs.

3.4. Summary and conclusions
In this study, we implemented the PMF model to conduct a long-term source apportionment
of PM2.5 OC mass concentrations in CELA and Riverside over the time period of 2005-2015. A 5-
factor solution including tailpipe emissions, non-tailpipe emissions, secondary organic aerosol
(SOA), biomass burning, and local industrial activities was resolved by the PMF model as the
optimum solution in both sampling sites during the study period. Our results also revealed
significant reductions in relative and absolute contribution of exhaust emissions to total OC in both
sampling sites between the years of 2005 and 2015, due mainly to the implementation of major
federal, state and local regulations during this time period. The decrease in the emissions of
primary vehicular pollutants that are precursors of secondary aerosols has also resulted in a general
71
reduction (although not statistically significant) in the SOA mass concentrations during the 2005-
2015 period. The significant reductions in the contribution of tailpipe emissions have led to an
increase in the fractional contribution of non-tailpipe emissions to total OC, underscoring the need
to develop effective mitigation strategies for these emissions in order to reduce the overall PM 2.5
OC levels in the Los Angeles basin.

72
Chapter 4: The impact of stay-home policies during Coronavirus-19 pandemic on the
chemical and toxicological characteristics of ambient PM2.5 in the metropolitan area of
Milan, Italy

4.1. Introduction
The outbreak of novel Coronavirus (COVID-19), caused by severe acute respiratory
syndrome Coronavirus 2 (SARS-CoV-2), has been declared as a worldwide health emergency,
rapidly spreading throughout different parts of the globe (Jain and Sharma, 2020; Le et al., 2020;
Wu et al., 2020; Zangari et al., 2020). In addition to transmission by macro-droplets (>5 microns)
and fomites (Allen and Marr, 2020; Chia et al., 2020; Ong et al., 2020), the pathogenic and
contagious characteristics of SARS-CoV-2 along with its relatively high residence time in the
atmosphere (with a lifetime of 1–3 hours in aerosols (Doremalen et al., 2020; Setti et al., 2020a))
facilitate efficient transmission of the virus amongst humans, resulting in respiratory disorders,
and in severe cases, mortality (Grasselli et al., 2020; Wu et al., 2020; Yang et al., 2020). The
unprecedented pace of COVID-19 transmission from China to Europe, United States of America,
and other parts of the world has led to globally more than 1 million deaths as of 9
th
October 2020
(WHO, 2020). In an effort to restrain the rapid spread of this infectious pathogen, governments
have adopted various prevention and control strategies such as social distancing, businesses
shutdown, and city-wide lockdowns (Anjum, 2020; Collivignarelli et al., 2020). Due to the
implementation of abovementioned strategies, several studies have reported improvements in
urban air quality, mainly attributed to the significant decreases in road traffic and (to some extent)
industrial emissions (Bao and Zhang, 2020; Raffaelli et al., 2020; Tobías et al., 2020). Among
different criteria air pollutants, ambient fine particulate matter (PM2.5, particle with aerodynamic
73
diameter <2.5 µm) is of great importance due to its distinct physio-chemical characteristics and
well-established adverse health consequences (e.g., respiratory and cardiovascular disease) (Brook
et al., 2010; Gauderman et al., 2002; Pope et al., 2015). Therefore, the COVID-19 shutdowns
around the world have provided a unique opportunity for researchers to investigate the changes in
the chemical/toxicological characteristics of ambient PM2.5, in light of a reduction in major urban
activity source emissions.
Many studies have reported a significant reduction in PM2.5 and gaseous pollutant levels
(e.g., carbon monoxide (CO), nitrogen oxides (NOx), and benzene (C6H6)) during COVID-19
lockdown period across polluted cities (e.g., New York, Milan, and Beijing), due to the drastic
decline in anthropogenic emissions (Chauhan and Singh, 2020; Collivignarelli et al., 2020; Dutheil
et al., 2020; Jain and Sharma, 2020; Setti et al., 2020b; Xu et al., 2020). For instance, Chauhan and
Singh (2020) observed significant reductions in ambient PM2.5 levels in New York, Beijing, and
Delhi (by almost 32%, 50%, and 35%, respectively), during the COVID-19 period in comparison
with the same time span in 2019. In another study, Collivignarelli et al. (2020) reported
respectively ~47% and ~71% reductions in PM2.5 and black carbon (BC) mass concentrations at
Milan area during the lockdown period (i.e., March 23
rd
, 2020 to April 5
th
, 2020) compared to the
normal conditions (i.e., February 7
th
, 2020 to February 20
th
, 2020). In contrast to the global
reductions in PM2.5 levels, a few studies have reported unchanged/increased ambient PM2.5
concentrations during COVID-19 lockdown (Huang et al., 2020; Le et al., 2020; Lv et al., 2020).
For example, Le et al. (2020) attributed the enhanced concentration of PM2.5 in northern China
during COVID-19 period to the impact of meteorological factors (particularly high relative
humidity) on the formation of secondary aerosols. Additionally, it was documented that decreased
traffic emissions in eastern China cannot offset the severe haze pollution due to the elevated levels
74
of atmospheric oxidants, and in turn, higher rate of secondary aerosols formation (Huang et al.,
2020; Lv et al., 2020). Some researchers have hypothesized probable association between
enhanced rates of Coronavirus-related mortality and high background concentration of air
pollutants in different urban environments (e.g., China, Italy) (Dutheil et al., 2020; Frontera et al.,
2020; Maria A Zoran et al., 2020; Maria A. Zoran et al., 2020). Conticini et al. (2020) and Wu et
al. (2020) postulated that long-term exposure to PM2.5 provokes the vulnerability of individuals to
COVID-19 infectious pathogens, although this hypothesis requires further investigation to reveal
the direct impact of ambient PM on the biological and physio-chemical mechanisms of virus
transmission. To the best of our knowledge, neither of the published studies have provided any
insights on the changes in either the chemical composition or the toxicity of PM, resulting from
the adopted lockdown restrictions during the COVID-19 period.
The Po Valley, located in the Lombardy region of northern Italy, has suffered from
persistent air pollution challenges during the last decades (Conticini et al., 2020; Lonati et al.,
2008; Maria A Zoran et al., 2020; Maria A. Zoran et al., 2020). Previous studies have identified
vehicular emissions, domestic biomass burning, industrial emissions, and formation of secondary
organic aerosols (SOA) as well as secondary inorganic aerosols (SIA) as the major sources of
ambient PM2.5 in Po Valley region (Daher et al., 2012; Decesari et al., 2017; Gilardoni et al., 2011;
Pecorari et al., 2014; Perrone et al., 2012; Squizzato et al., 2013), which, combined with the
particular topographical and meteorological conditions (facilitating stagnation and fog formation
especially during fall and winter), have led to the deterioration of the air quality in the area. The
high mountains of the western and central-eastern Alps along with the wintertime stagnant
atmospheric conditions limit the horizontal and vertical dispersion of air pollutants emitted in the
valley (Caserini et al., 2017; Cermak et al., 2009; Decesari et al., 2017, 2001; Finardi et al., 2014;
75
Frontera et al., 2020; Squizzato et al., 2016; Tositti et al., 2014). In addition to the severe air
pollution episodes in the Lombardy region, this area has been the epicenter of COVID-19
pandemic in Europe, with unmatched rates of confirmed infectious individuals and lethality
(Contini and Costabile, 2020; Frontera et al., 2020; Ogen, 2020). Although the strict lockdown
policies and in turn curtailed traffic decreased the ambient PM2.5 levels in this area with respect to
the pre-pandemic period (Collivignarelli et al., 2020), the contribution of domestic biomass
burning emissions to total PM2.5 may have surged due to the stay-home strategies (Sicard et al.,
2020). Previous studies have documented the extensive domestic biomass burning (for residential
heating purposes) as a major source of pollution, elevating the PM2.5 mass concentration in the
area during the colder periods of the year (Paglione et al., 2020; Ricciardelli et al., 2017; Tositti et
al., 2014). In particular, the published study by Hakimzadeh et al. (2020) attributed a dominant
fraction of PM2.5 oxidative potential in the Lombardy area to the domestic biomass burning
emissions. It is therefore important to evaluate the ambient PM2.5 components and toxicological
characteristics during the COVID-19 lockdown period to investigate the impact of the decreased
traffic as well as the potentially increased domestic biomass burning emissions in this time period
on the overall air quality in the area.
Accordingly, this study sought to characterize ambient PM2.5 components and oxidative
potential in an urban background site in the Milan metropolitan area due to the major COVID-19
related stay-home policies. Ambient PM2.5 samples were collected within three periods, including
major COVID-19 national lockdown (i.e., full-lockdown), the followed partial-lockdown, and full-
relaxation (i.e., post-lockdown with limited restrictions) phases. The collected filters were
analyzed, and the dithiothreitol (DTT) as well as 2’,7’-dichlorodihydrofluorescein (DCFH) in vitro
assays were deployed to determine the oxidative potential of these samples. While several efforts
76
have investigated the ambient concentrations of pollutants during COVID-19 restrictions in
comparison with the pre-pandemic period in 2020 (similar to the study by Collivignarelli et al.
(2020) in Milan area), our analysis considered the trends in components and toxicological
characteristics of PM2.5 with respect to the same time span in 2019. This approach enabled us to
investigate the impact of COVID-19 stay-home policies on atmospheric PM2.5 regardless of
variations in meteorological factors from winter (i.e., pre-pandemic phase) to spring and summer
(i.e., full-lockdown and full-relaxation periods).

4.2. Methodology
4.2.1. Sampling site, instrumentation and collection period
Sampling was carried out at two locations across Milan, Italy: (I) an institutional network’s
air quality station (i.e., Milano – via Pascal) for continuous air pollution monitoring, and (II) a
residential area located at the suburban site of Bareggio for time-integrated PM2.5 filter sampling.
The town of Bareggio, a suburban area located 14 km to the north-west of the Milan’s metropolitan
center (see Figure 4.1), has been previously elected as an urban background district to determine
public exposure to the baseline levels of ambient PM2.5 in the Lombardy region (Hakimzadeh et
al., 2020; Mousavi et al., 2019).

77
Figure 4.1. Location of the Bareggio sampling site and Milano-via Pascal air monitoring
station in the Milan metropolitan area.

Daily average mass concentrations of PM2.5, nitrogen dioxide (NO2), C6H6, and BC were
obtained within the period of January to early-June at 2019 and 2020 from the Environmental
Protection Agency of Lombardy (Agenzia Regionale per la Protezione Ambientale (ARPA))
website for Milano – via Pascal air quality station (45°28'42"N 9°13'54"E) as the closest sampling
unit to the Bareggio area (Figure 4.1). It should be noted that the Milano – via Pascal station is
located in a small urban playground/little garden inside the court of University of Milan area called
“Citta Studi”, therefore not directly impacted by vehicular emissions and it is also officially
classified “urban background” station, as part of the national air quality network and used for PM2.5
exposure assessment within the National Evaluation Programme. The investigation period in this
study has encompassed the pre-pandemic conditions prior to the COVID-19 restrictions (PP), the
78
first partial-lockdown period (PL1), full-lockdown (FL), the second partial-lockdown (PL2), and
full-relaxation phase with limited restrictions (FR). Further details regarding the start and end date
of each phase as well as the corresponding adopted restrictions are provided in Table 4.1.

Table 4.1. Detailed description of the adopted COVID-19 lockdown strategies across the
Milan metropolitan area in 2020 (PP: pre-pandemic; PL1: first partial-lockdown; FL: full-
lockdown; PL2: second partial-lockdown; FR: full-relaxation with some limitations).

Given the unprecedented pandemic circumstances along with the extensive wintertime
domestic biomass burning in the Milan metropolitan area which undoubtedly influences PM 2.5
levels/toxicity (as discussed earlier in Hakimzadeh et al. (2020) and Daher et al. (2012)), filter
sampling campaign was conducted from April 11
th
at 2020 to investigate the impacts of COVID-
Period Start date End date Restrictions description
PP January 1
st
February 25
th
- Normal conditions
PL1 February 26
th
March 24
th

- People across Lombardy area were in quarantine.
- Schools and universities were closed.
- All public events were cancelled.
- All religious services were cancelled.
- Train services to the most affected areas were
suspended.
- No movement into and out of the Lombardy areas were
allowed unless for emergency issues.
- All gyms, swimming pools, and theaters were closed.
FL March 25
th
May 4
th

- Tighter regulations were imposed on free movement.
- Open-air sports and running were banned.
- Parks and playgrounds were closed.
- All “unnecessary” activities were canceled.
- People were prohibited from travelling by
private/public transport unless for proven work.
PL2 May 5
th
May 18
th

- Public parks and take-away service for catering
activities were opened.
- Free movement between regions was allowed.
FR May 19
th
June 3
rd

- Indoor and outdoor places for recreational activities
were re-opened.
- The regions were allowed to loosen or restrict the
measures based on the epidemiological situation in the
territories.
79
19 stay-home policies (with a special focus on traffic restrictions) on the PM 2.5 components and
toxicological characteristics. Thus, ambient PM2.5 samples were collected within three distinct
periods: (I) FL spanning from April 11
th
to May 4
th
, (II) PL2 encompassing May 5
th
to May 18
th
,
and (III) FR with limited restrictions from May 19
th
to June 3
rd
. By excluding PM2.5 filter sampling
during the colder months of COVID-19 pandemic (i.e., February – March), we sought to minimize
the substantial impact of extensive wintertime domestic biomass burning emissions on our
sampling and analysis, and evaluate solely the impact of COVID-19 traffic restrictions on the
PM2.5 oxidative potential.
Bi-weekly time-integrated PM2.5 samples were collected on prebaked quartz filters (37mm,
Pall Life Sciences, 2-μm pore size, Ann Arbor, MI) by means of the Sioutas Personal Cascade
Impactor Samplers
TM
(PCISs, SKC Inc., Eighty-Four, PA, USA) (Misra et al., 2002; Singh et al.,
2003). Although the PCIS has 4 different impaction stages (A through D), only the first stage of
the PCIS (i.e., stage A) was used to remove particles > 2.5μm from the air stream, followed by an
“after-filter” stage collecting PM2.5 on prebaked 37-mm quartz filters at an operational flowrate of
9 liters per minute (LPM). The surface of stage “A” was covered with a thin layer of grease (Super
Lude, NY, USA) to prevent the particles bouncing off the impaction surface. A microbalance
(MT5, Mettler Toledo Inc., Columbus, OH) with a precision of 0.001 mg was used to determine
the PM2.5 collected mass on the quartz filters as the difference between the pre-sampling and post-
sampling weight of filters, after equilibration under temperature of 22-24℃, and relative humidity
of 40-50%, following US EPA’s protocols for PM gravimetric analysis (US EPA, 2016, 2008).
Finally, the meteorological parameters (i.e., temperature, relative humidity (RH), precipitation,
wind speed, and wind direction) were obtained from the Milano – Piazzale Zavattari weather
station, located ~12 km to the west of our time-integrated sampling site. The monthly average
80
values of abovementioned meteorological parameters and the corresponding wind rose patterns
during the study period are reported in Table 4.2 and Figure 4.2, respectively. Further discussion
regarding the meteorological characteristics of our sampling area can be found in section 4.3.1.

Table 4.2. Monthly average values of meteorological parameters at Milano - Piazzale
Zavattari station (nearest to Bareggio) during the spring/early-summer of 2019 and 2020.

Figure 4.2. Average wind rose during the investigation periods of (a) 2019; and (b) 2020. The
plots have been depicted using WRPLOT View version 7.0 based on hourly wind speeds and
wind directions.
Year Month Temperature (℃) Relative humidity (%) Precipitation (mm) Wind speed (m/s)
2019
Jan 3.7 ± 3.7 70.6 ± 23.6 0.0 ± 0.1 1.2 ± 1.0
Feb 7.3 ± 4.7 69.3 ± 22.7 0.1 ± 0.2 1.0 ± 0.8
Mar 11.8 ± 4.4 53.4 ± 22.6 0.0 ± 0.3 1.4 ± 1.1
Apr 13.8 ± 3.8 63.8 ± 21.3 0.1 ± 0.8 1.5 ± 0.9
May 15.4 ± 3.8 67.4 ± 18.4 0.1 ± 0.6 1.3 ± 0.7
2020
Jan 5.3 ± 3.4 85.2 ± 15.8 0.0 ± 0.4 0.8 ± 0.3
Feb 9.5 ± 3.6 66.6 ± 26.8 0.0 ± 0.1 1.2 ± 0.9
Mar 10.6 ± 4.3 67.8 ± 20.5 0.1 ± 0.5 1.2 ± 0.7
Apr 16.0 ± 4.8 53.9 ± 23.0 0.0 ± 0.4 1.1 ± 0.6
May 20.3 ± 3.9 62.3 ± 22.3 0.3 ± 2.6 1.3 ± 0.5
(a) (b)

81
4.2.2. Analysis
The collected PM2.5 samples were analyzed for elemental carbon (EC), organic carbon
(OC), water-soluble organic carbon (WSOC), individual organic species, metals, and trace
elements by the Wisconsin State Lab of Hygiene (WSLH). In summary, 1 cm
2
punch from each
filter was used in thermo-optical transmittance (TOT) analysis by a model-4-semi-continuous
OC/EC field analyzer (Sunset Laboratory Inc, USA) to measure the OC/EC content of the PM 2.5
samples (Birch and Cary, 1996). After extracting a quarter section of the collected filters in
ultrapure water and filtering (0.22 μm pore size) the aqueous suspension, the WSOC fraction of
samples was quantified by the means of a Sievers 900 Total Organic Carbon Analyzer (Stone et
al., 2008; Sullivan et al., 2004). In addition, inductively coupled plasma mass spectroscopy (ICP-
MS) analysis was employed on another ¼ section of each filter to measure the metal and trace
element components of PM2.5 samples (Herner et al., 2006). Finally, speciated organic compounds
including levoglucosan and polycyclic aromatic hydrocarbons (PAHs) were measured by means
of gas chromatography/mass spectrometry (GC/MS) method (Schauer et al., 1999).

4.2.3. PM2.5 oxidative potential measurement
The fluorogenic 2’,7’-dichlorodihydrofluorescein (DCFH) and dithiothreitol (DTT) assays
were employed as two of the well-established methods in the literature to measure the oxidative
potential of PM2.5 samples (Fuller et al., 2014; King and Weber, 2013; Landreman et al., 2008;
Sauvain et al., 2013; Verma et al., 2015; Vreeland et al., 2017b; Wang et al., 2019). For the case
of DCFH alveolar macrophage assay, the collected PM2.5 samples were initially extracted in 1.00
ml of sterilized Milli-Q water at room temperature and in dark environment, followed by agitation
82
(for 16 hours) and 30 minutes of sonication. Cultures of rat alveolar macrophage cells (NR8383,
American Type Culture Collection) were exposed to the aqueous PM2.5 suspensions (derived from
the filter extraction procedure) along with 2′,7′‑dichlorodihydrofluorescein diacetate (DCFH-DA)
probe as a fluorescent detector to quantify the rate of reactive oxygen species (ROS) generation,
and thus, PM oxidative activity. Upon reacting with cellular esterases, the fluorescent DCFH-DA
is converted to non-fluorescent DCFH, followed by oxidization to the highly fluorescent 2′,7′-
dichlorofluorescein (DCF) due to the generation of ROS within the cellular structure. The DCF
production rate (i.e., a reliable proxy of PM oxidative stress) is determined via a microplate reader
in fluorescence units per PM mass (FU/μg PM). Zymosan, a fungal–glucan, was used for
macrophage activation to trigger a robust immuno-chemical cellular response. Recorded
fluorescence data were control-corrected to the Zymosan response, and reported as intrinsic
oxidative potential in normalized units of μg Zymosan/mg PM (Landreman et al., 2008). To
account for the effect of atmospheric dilution, the extrinsic oxidative potential of PM 2.5 was
derived by normalizing the measured Zymosan response to the volume of sampled air (Fang et al.,
2015; Tuet et al., 2016). In contrast, the DTT assay measures the linear consumption rate of
dithiothreitol as a metric of the oxidative potential of PM. In this method, the PM 2.5 liquid
suspensions (derived from the aqueous extraction of quartz filters) were reacted with a mixture of
potassium phosphate (KPO4) buffers and DTT. Based on designated time intervals (i.e., 0, 15, 30,
45, and 60 minutes), trichloroacetic acid was added to vials of the incubation mixture for
quenching the reaction, followed by recording the absorbance via a plate reader. The DTT rate of
depletion was determined by converting the recorded absorbance to the remained DTT in units of
nmol.min
-1
. The reported oxidative potential values in this study (for both DCFH and DTT assays)
have been blank corrected to account for the potential uncertainties associated with the mentioned
83
procedures (e.g., filter extraction, sonication). More details regarding the methodology of DCFH
and DTT assays can be found in Shafer et al. (2016) and Cho et al. (2005), respectively.

4.3. Results and discussion
4.3.1. Impact of COVID-19 stay-home strategies on ambient levels of atmospheric pollutants
Figure 4.3(a-d) indicates the weekly box plots of PM2.5, BC, NO2, and C6H6
mass concentrations from January to early-June of 2020 at the Milano – via Pascal
urban background station. According to the figure, a significant (Pvalue<0.0001)
decreasing trend was observed in the ambient levels of abovementioned air pollutants
during the investigation period. For example, the average concentrations of PM2.5, and
BC decreased as much as ~78% and ~90% from January to May, respectively. All of
the investigated pollutants decreased in concentration significantly (Pvalue<0.0001),
moving from the PP to the lockdown period (including PL1, FL, and PL2 phases). For
example, the average NO2 concentrations decreased by ~58% from the PP period
(51.94±8.00 µg/m
3
) to the lockdown phase (21.44±12.05 µg/m
3
). Although the
significant decline in the ambient levels of pollutants from PP to the lockdown period
could be attributed to the national-wide lockdown and COVID-19 related traffic
restrictions, it is necessary to decouple the impacts of changes in PM emission rates
(e.g., road traffic, domestic biomass burning) from the temporal variation of
meteorological factors (e.g., boundary layer dilution properties) on the ambient levels
of pollutants (Daher et al., 2012; Hakimzadeh et al., 2020; Marcazzan et al., 2001).
Atmospheric mixing height is associated with meteorological parameters including
84
vertical profiles of potential temperature, relative humidity, and wind speed (Ferrero et
al., 2011). In summer, higher wind speed develops a broader mixing layer which
facilitates the dispersion of pollutants in the atmosphere. Conversely, winter is
characterized by stable weather conditions and weak atmospheric mixing due to the
persistent thermal inversions along with fog situations when considerable amounts of
air pollutants trap in the lower layers of the atmosphere (Marcazzan et al., 2001;
Perrino et al., 2014).

85
Figure 4.3. Temporal trends in the concentrations of (a) PM2.5; (b) BC; (c) NO2; and (d) C6H6
from January 2020 to early-June 2020. Each box plot corresponds to the period of one week
during pre-pandemic (PP), full-lockdown (FL), partial-lockdowns (PL1 and PL2), and full-
relaxation (FR).
(a)
(b)
(c)
(d)
86
To further evaluate the exclusive impact of the adopted lockdown strategies on
the local air quality, the ambient concentrations of PM2.5, BC, NO2, and C6H6 are
depicted during the lockdown period (i.e., early-March to mid-May 2020) and
contrasted to the same time span in year 2019 (Figure 4.4). It should be noted that the
meteorological parameters (i.e., temperature, relative humidity, precipitation, and wind
speed) were comparable (Pvalue in the range of 0.15-0.72) between the years of 2019
and 2020, thus precluding any significant influence of meteorological factors on
atmospheric dilution and air pollutant levels between these years (see Table 4.2 and
Figure 4.2). Ambient temperature and relative humidity are not expected to induce
significant variations in the rate of secondary aerosols formation from 2019 to 2020
based on the comparable temperature and relative humidity during the investigation
periods of these consecutive years (Fang et al., 2019; Pun et al., 2006). As shown in
Figure 4.4, the levels of PM2.5 and BC were comparable (Pvalue in the range of 0.10-
0.75) between the lockdown period (i.e., PL1, FL, and PL2) and the same time span in
year 2019. Our observations are not in accordance with the results of studies across the
Po Valley area, attributing the decreased levels of atmospheric air pollutants during the
COVID-19 solely to lockdown restrictions (Collivignarelli et al., 2020; Sicard et al.,
2020; Maria A Zoran et al., 2020; Maria A. Zoran et al., 2020). The observed trends in
our study might be attributed to the impact of enhanced domestic heating and biomass
burning counterbalancing the effect of curtailed road traffic during the COVID-19
restrictions (as suggested by Sicard et al. (2020)). Both PM2.5, and BC originate from
vehicular and biomass burning emissions (Al Madhoun et al., 2011; Briggs and Long,
2016; Masiol et al., 2019; Mousavi et al., 2019; Zhang et al., 2015), and the relative
87
contributions of these distinct pollution sources determine the overall concentration of
each species during the lockdown period.
Figure 4.4. Temporal trends in the concentrations of (a) PM2.5; (b) BC; (c) NO2; and (d) C6H6
during lockdown phase (i.e., PL1, FL, and PL2) of 2020 and the corresponding period in
2019.

(a)
(b)
(c)
(d)

88
In contrast to the case of PM2.5 and BC, the NO2 and C6H6 concentrations
decreased significantly (Pvalue<0.05) during the lockdown period with respect to the
same time span in 2019. For instance, the average NO2 levels decreased by ~35%
during the lockdown period (21.44±12.05 µg/m
3
) in comparison with the same time
span in 2019 (33.17±14.49 µg/m
3
). Previous studies have documented road traffic
(particularly diesel engines) and fossil fuel combustion as major sources of NO2 (Afzal
et al., 2012; Pepe et al., 2019; Y. Zhang et al., 2018). The same sources but focusing on
gasoline engines are associated with C6H6 (Keenan et al., 2010; Whaley et al., 2020).
Consequently, in line with the finding of studies (Anjum, 2020; Bauwens et al., 2020;
Collivignarelli et al., 2020; Dutheil et al., 2020; Le et al., 2020; Otmani et al., 2020;
Sicard et al., 2020), the significant decrease in NO2 and C6H6 levels during the
lockdown phase with respect to the same period in 2019 can be attributed to the
marked reduction of emissions from road traffic.

4.3.2. Investigation of domestic biomass burning emissions during the lockdown period
In order to evaluate the rate of increase in biomass burning emissions during the COVID-
19 shutdown phase, we have estimated the mass concentration of BC originating from domestic
biomass burning during the study period. Figure 4.5 shows the temporal trends in the weekly box
plots of BC to NO2 ratio (both in units of ug/m
3
) during the investigation period for the years of
2019 and 2020. According to the figure, the average BC/NO2 values follow a decreasing trend,
starting from ~0.12 in early-January 2020 and approaching asymptotically a value of
approximately 0.040 in the warmer weeks of the campaign, when residential heating emissions
89
(i.e., domestic biomass burning) are assumed to be negligible. Similar observations were also made
in the year 2019, indicating an average BC to NO2 ratio of ~0.040 in the warmer season. Therefore,
we selected 0.040 as an approximate upper ratio of non-biomass burning BC (BCnb) to NO2 in the
metropolitan area of Milan. In concert with our observation, Kim et al. (2004) reported 0.034 as
the BC/NO2 ratio during their experimental measurements in the vicinity of busy roadways in the
San Francisco metropolitan area. In addition, as demonstrated in Figure 4.6, the BC/NO2 ratio was
~0.032 in central Los Angeles (i.e., an urban environment heavily affected by vehicular emissions
(Kozawa et al., 2009; Soleimanian et al., 2019b)) in 2019. Finally, according to the European
Monitoring and Evaluation Program (EMEP) emission inventory database for Italy, the average
ratio of BC/NO2 was found to be ~0.031 within the 2015-2017 period (EMEP, 2019). The slightly
higher ratio of BCnb/NO2 (i.e., 0.040) in our sampling site compared with those of urban
environments impacted by vehicular emissions can be attributed to the higher fraction of diesel
cars in Milan’s fleet along with the impact of non-traffic related (excluding biomass burning)
combustion sources (e.g., cooking, local industries, and off-road equipment), emitting BC without
major contributions to the NO2 emissions in the Po Valley region.

90
Figure 4.5. Temporal variations in the BC/NO2 ratio during the investigation period for (a)
2019; and (b) 2020.
(a)

(b)

91
Figure 4.6. Correlation analysis between the EC and NO2 mass concentrations at central Los
Angeles for the year of 2019*.

*Data for EC and NO 2 levels were obtained from Chemical Speciation Network (CSN) and California Air Resources
Board (CARB) websites, respectively.

Considering the BCnb /NO2 ratio of 0.040 in our sampling location, the non-biomass
burning fraction of BC (BCnb) was estimated as 4.0% of NO2 concentrations. Pepe et al. (2019)
reported that the ambient NO2 in the Milan area is predominantly originated from vehicular
emissions and fossil fuel combustion along with negligible contribution (~1%) of biomass burning.
In addition, since the NO2 fluctuations are mostly explained by non-biomass burning combustion
sources, the excess increase in the BC/NO2 ratio during the colder periods of the year can be
attributed to the impact of increased domestic biomass burning. Subsequently, the biomass burning
originated BC (BCbb) was derived by subtracting BCnb from total BC concentration. Figure 4.7
shows the weekly box plots of BCbb and BCnb mass concentrations during January to early-June
of 2020. As shown in the figure, BCbb mass concentrations were significantly higher (Pvalue
<0.0001) during the colder months of the year (i.e., January and February) compared to the warmer
92
period. In addition, while domestic biomass burning emissions contributed to the majority (i.e.,
61±14%) of total BC mass concentrations in January, the contribution of non-biomass burning
emissions to the ambient BC levels increased from ~40% in January to ~82% in May. These
observations are in very good agreement with the findings of a previous study in the same area,
attributing nearly 65% and 20% of total BC concentration to the domestic biomass burning during
the winter and summer seasons, respectively (Mousavi et al., 2019). Although Figure 4.7(b)
represents BCnb mass concentrations derived from ambient NO2 levels shown in Figure 4.3(c),
there are a few differences between the two graphs (e.g., the 21
st
and 22
nd
weeks). For these few
instances (caused by the NO2 and BC measurement uncertainties), estimated BCnb was marginally
higher than total measured BC, and thus, we assumed equal concentrations of BC nb and total BC
with a negligible contribution of BCbb.

93
Figure 4.7. Weekly box plots of estimated (a) BCbb; and (b) BCnb mass concentrations during
January to early-June of 2020.
(a)

(b)

94
Figure 4.8 shows the concentrations of BCbb and BCnb during the lockdown period (i.e.,
PL1, FL, and PL2) with respect to the same time span in year 2019. According to Figure 4.8 (a),
while the BCbb concentrations were negligible within the early-March to mid-May period in 2019,
relatively higher concentrations of this pollutant were observed during the same period (i.e.,
lockdown period) in 2020, further demonstrating the impact of domestic biomass burning during
the lockdown period, when stay-home strategies have presumably increased the residential heating
emissions. Moreover, as shown in Figure 4.8 (b), BCnb showed a statistically significant
(Pvalue<0.05) decrease from 1.26±0.79 μg/m
3
in 2019 to 0.84±0.48 μg/m
3
during the lockdown
period (i.e., PL1, FL, and PL2) in 2020, underscoring the effect of COVID-19 restrictions on
decreased road traffic. Therefore, as discussed in section 4.3.1, the comparable concentrations of
PM2.5 and BC during the lockdown phase with respect to the similar period in 2019 were probably
related to the increased domestic biomass burning emissions despite the lower road traffic in the
Po Valley area during the COVID-19 restrictions (Sicard et al., 2020).

95
Figure 4.8. Temporal variations in mass concentrations of (a) BCbb; and (b) BCnb during the
lockdown phase (i.e., PL1, FL, and PL2) of 2020 and the corresponding period in 2019.
(a)

(b)

4.3.3. Impact of COVID-19 restrictions on PM2.5 components
4.3.3.1. Carbonaceous aerosols
Figure 4.9 illustrates the mass concentration and content of carbonaceous fractions,
including EC, OC, and WSOC in our sampling site during FL, PL2, and FR periods. As shown in
the figure, PM2.5-bound EC (as a marker of traffic and biomass burning emissions (Saffari et al.,
2013; Schauer, 2003)) remained almost constant during the investigation period (in the range of
96
0.24-0.29 µg/m
3
). The comparable levels of EC during FL, PL2, and FR periods are in agreement
with the observed trends in BC mass concentration (as discussed in section 4.3.2) and demonstrate
the interplay between relative increase in domestic biomass burning and curtailed road traffic
emissions in total EC concentration within lockdown phase. In addition, the measured EC levels
during FL and PL2 periods were within the range of previously reported values at the same
sampling site and time span in 2019 (i.e., ~0.35 µg/m
3
) as well as several locations across Po
Valley (in the range of 0.20-0.55 µg/m
3
)(Daher et al., 2012; Hakimzadeh et al., 2020; Ricciardelli
et al., 2017), further underscoring that lockdown restrictions have not led to significant reductions
in ambient EC concentrations.

Figure 4.9. The elemental carbon (EC), organic carbon (OC), and water-soluble organic
carbon (WSOC) fractions of PM2.5 during full-lockdown (FL), second partial-lockdown
(PL2), and full-relaxation (FR) periods: (a) normalized by the air volume; and (b)
normalized by PM2.5 mass.

(a)

97
(b)

According to Figure 4.9(a), PM2.5-bound OC mass concentration increased from 2.35±0.19
µg/m
3
during FL and PL2 to 2.79±0.14 µg/m
3
in FR phase. Likewise, ambient WSOC
concentration increased by almost 40% from FL and PL2 (1.37±0.40 µg/m
3
) to FR period
(1.92±0.16 µg/m
3
). To further investigate the observed trends in OC and WSOC levels, the
biomass burning fraction of WSOC (WSOCbb) was quantified following the suggested approach
by Fine et al. (2004). In this method, biomass burning originated OC (OCbb) was estimated based
on the levoglucosan/OCbb ratio of 0.135 derived from wood smoke source profile (Fine et al.,
2004). OCbb was multiplied by a factor of 0.71 to estimate WSOCbb, whereas the difference in
mass concentrations of total WSOC and WSOC bb is ascribed to the non-biomass-associated
WSOC (WSOCnb) (Sannigrahi et al., 2006; Stone et al., 2008). As shown in Figure 4.9(a), WSOCbb
decreased by a factor of 2.6 from ~0.29 µg/m
3
in FL to ~0.11 µg/m
3
during FR period. The
comparison between measured WSOCbb levels and the previously reported value of ~0.08 µg/m
3

at the same sampling site in 2019 (Hakimzadeh et al., 2020) validates our hypothesis regarding
relatively higher residential heating emissions during stay-home strategies with respect to 2019.
Further discussion about enhanced domestic biomass burning within lockdown period is provided
98
in section 4.3.3.1 of the manuscript. In contrast, the PM2.5-bound WSOCnb, which is mainly
associated with SOA (Sun et al., 2011; Weber et al., 2007), increased from FL and PL2 (~1.11
µg/m
3
) to FR period (~1.79 µg/m
3
). It has been shown that modest formation of secondary aerosols
during COVID-19 lockdown may be attributed to the reduced emissions of their precursors from
primary sources (Lv et al., 2020). Thus, the observed trend in WSOCnb can be justified by the
enhanced levels of anthropogenic SOA precursors emitted during FR period along with higher
photochemistry within the warmer months of the year (Daher et al., 2012; Prévôt et al., 2004;
Ricciardelli et al., 2017; Unger, 2012). On the other hand, water-insoluble organic carbon (WIOC),
which is dominantly originated from combustion sources (Saffari et al., 2015; Snyder et al., 2009),
exhibited consistent concentrations during the investigation period (in the range of 0.87-1.01
µg/m
3
). Although we expected increased levels of WIOC within the FR period (compared to FL
and PL2), higher ambient temperature during FR can lead to evaporation of insoluble semi-volatile
organic species emitted from road traffic (Shirmohammadi et al., 2016a), reducing the impact of
increased traffic on WIOC concentrations. Therefore, based on the abovementioned discussion,
the enhanced concentration of PM2.5-bound OC during FR period (as opposed to FL and PL2)
resulted from the higher formation of SOA due to the elevated photo-oxidation of SOA precursors.
Finally, it is noteworthy that OC concentration during FL and PL2 (~2.35 µg/m
3
) was slightly
lower than previously reported values across Po Valley within spring/summer season (in the range
of 2.5-3.5 µg/m
3
), underscoring the decreased OC levels during lockdown restrictions
(Hakimzadeh et al., 2020; Perrino et al., 2014; Ricciardelli et al., 2017).

99
4.3.3.2. Individual organic species
Figure 4.10 shows the total PM2.5-bound PAHs and levoglucosan mass
concentrations/fractions during COVID-19 period in our sampling site. According to the literature,
levoglucosan has been identified as a tracer of biomass burning emissions (Simoneit, 2002),
whereas PAHs are dominantly semi-volatile species originating from incomplete combustion of
fossil fuel as well as wood burning emissions (Alves et al., 2015; Delgado-Saborit et al., 2011;
Galarneau, 2008). As shown in Figure 4.10 (a), levoglucosan mass concentration decreased by a
factor of almost 2.5 from ~56 ng/m
3
in FL to ~22 ng/m
3
during FR phase. The ambient
levoglucosan concentration measured during FL and PL2 periods was higher than the previously
reported values within identical time span at the same sampling location in 2019 (i.e., ~16 ng/m
3
)
as well as Milan city center in 2010 (i.e., ~18 ng/m
3
) (Daher et al., 2012; Hakimzadeh et al., 2020).
Moreover, none of the mentioned studies identified an explicit decreasing/increasing trend in
levoglucosan mass concentration from April to June. Therefore, the elevated levels of
levoglucosan during COVID-19 lockdown period further corroborate the results of our analyses
in section 4.3.2, suggesting relative increase in domestic biomass burning due to the adopted stay-
home strategies.

100
Figure 4.10. Temporal trends in levoglucosan and total PAHs concentrations during
COVID-19 period normalized by (a) air volume; and (b) PM2.5 mass content. FL, PL2, and
FR refer to full-lockdown, second partial-lockdown, and full-relaxation periods,
respectively.

(a)
(b)

According to Figure 4.10(a), total PM2.5-bound PAHs concentrations were comparable (in
the range of 1.41-1.61 ng/m
3
) during FL, PL2, and FR periods at our sampling site. The relatively
consistent levels of total PAHs during the investigation period can be attributed to the cumulative
impact of domestic biomass burning and traffic sources to these organic species. For instance, the
ambient mass concentration of indeno[123-cd]pyrene, a known surrogate of vehicle exhaust
emission (Marr et al., 2006), exhibited an approximate 3-fold increase from 0.22 ng/m
3
in FL to
101
0.73 ng/m
3
during FR phase. Conversely, benzo(b)fluoranthene as a chemical marker of biomass
burning combustion, decreased by 37% from ~0.46 ng/m
3
in FL to ~0.29 ng/m
3
within FR phase.
Therefore, the impact of curtailed traffic on total PAHs mass concentrations within lockdown
phase has to some extent been counterbalanced by the elevated residential biomass burning during
stay-home strategies, leading to consistent levels of total PAHs from FL to PL2 and FR periods.
In addition to the mentioned interplay between the contribution of vehicular and domestic biomass
burning emissions to total PAHs mass concentrations, higher ambient temperature during FR phase
might have precluded the conversion of semi-volatile PAHs into the particle phase (Pehnec et al.,
2016; Soleimanian et al., 2020b), thus attenuating the impact of increased emissions from mobile
sources on total PAHs levels.
4.3.3.3. Redox-active metals
Figure 4.11 shows the concentration of selected metals and trace elements, including
titanium (Ti), chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), and cupper (Cu) during the
investigation period. These metals, characterized as redox-active ones responsible for PM
oxidative potential (Decesari et al., 2017; Lai et al., 2016; Ntziachristos et al., 2007; Yuan et al.,
2001), are well-known tracers of re-suspended road dust particles originating from soil, tire and
brake wear, and road abrasion (Adamiec et al., 2016; Al-Shidi et al., 2020; Dall’Osto et al., 2008b;
Godri et al., 2011; Roy M Harrison et al., 2012; Soleimanian et al., 2019d). In addition, Ni can be
emitted as a result of fuel combustion in industrial sectors, power plants, vehicles engines, and to
some extent residential heating (Masiol et al., 2020; Moreno et al., 2010; Zhang et al., 2014).
According to Figure 4.11 (a), the ambient concentrations of Ti, Cr, Mn, Fe, and Cu increased
considerably from FL to PL2 and FR period. Because of the association between these redox-
active metals and road traffic in Milan area (Hakimzadeh et al., 2020; Vecchi et al., 2004), the
102
observed trends are attributed to the increase in road traffic from FL to FR due to the gradual lifting
of COVID-19 restrictions. Additionally, higher ambient temperature and lower relative humidity
during FR phase (as opposed to FL) facilitate the resuspension rate of open surface mineral/road
dust particles through vehicle movement (Branis and Safranek, 2011; Charron and Harrison,
2005), further elevating concentrations of these species during FL period. In contrast to the
observed trends in ambient concentrations of metal elements, Ni levels were almost consistent
between FL, PL2, and FR periods (in the range of 0.70-0.84 ng/m
3
), which can be attributed to the
fact that, unlike road traffic and domestic biomass burning, industrial sectors/power plants
operations have not been significantly affected by COVID-19 restrictions, so the variations in Ni
concentrations from FL to PL2 and FR are rather minimal.

Figure 4.11. PM2.5-bound redox-active metals concentrations measured during full-
lockdown (FL), second partial-lockdown (PL2), and full-relaxation (FR) periods: (a)
normalized by the air volume; and (b) normalized by PM2.5 mass.

(a)

103
(b)

Finally, the concentration of selected metal elements during FL and PL2 periods was lower
than previously reported values in Milan metropolitan area during spring/summer season
(Hakimzadeh et al., 2020; Vecchi et al., 2004). For instance, comparing our findings with those of
Hakimzadeh et al. (2020) at the same sampling site in 2019, we noted consistent levels of Mn, Fe,
Ti and Cu between FR phase and identical time span in 2019, while significant reductions (by on
average 77±33%) in concentrations of mentioned metals were observed during the FL period (with
respect to FR phase). Similarly, Fe and Ti decreased by almost 2.5 times during the FL period in
comparison with the reported values for the Milan area during summer season (Vecchi et al., 2004).

4.3.4. PM2.5 oxidative potential
Earlier studies across the Po Valley have shown that PM oxidative potential is influenced
by the concentrations of metals (e.g., Cr, Mn, Fe, Ni, Cu) and carbonaceous species (e.g., OC, EC)
(Hakimzadeh et al., 2020; Longhin et al., 2020; Pietrogrande et al., 2019; Visentin et al., 2016).
These species are mainly associated with road traffic, domestic biomass burning, oil combustion,
and SOA across Italy (Larsen et al., 2012; Pietrogrande et al., 2019); however, it has been shown
104
that the oxidative potential of PM in the Po Valley region, and in particular in the Milan
metropolitan area, is elevated due to the high density of anthropogenic emission sources in
conjunction with specific stable atmospheric conditions, favoring the accumulation and aging of
particles in the atmosphere (Perrone et al., 2016, 2010; Pietrogrande et al., 2019). Figure 4.12
shows PM2.5 oxidative potential during COVID-19 restrictions measured by means of DCFH in
vitro and DTT assays. For the case of DCFH (Figure 4.12 (a)), the extrinsic PM 2.5 oxidative
potential increased from 55.2±6.0 µg Zymosan/m
3
in FL period to 62.3±6.5 µg Zymosan/m
3

during PL2 and to 74.1±6.3 µg Zymosan/m
3
within FR phase. Similarly, the mass-based PM2.5
oxidative potential increased from FL and PL2 (5056±762 µg Zymosan/mg PM) to FR phase
(6241±536 µg Zymosan/mg PM). The intrinsic PM2.5 toxicity during FR was in very good
agreement with previously reported values (i.e., 6037±549 µg Zymosan/mg PM) within the same
time period in 2019 for Milan metropolitan area (Hakimzadeh et al., 2020). Comparing the mass-
based PM2.5 oxidative potential with reported values in the literature, the measured intrinsic
toxicity of collected samples during FL and PL2 periods was almost half of the detected PM 2.5-
induced ROS across the traffic impacted areas of Milan city center (i.e., ~11000 µg Zymosan/mg
PM) as well as Athens, Greece (i.e., ~13000 µg Zymosan/mg PM) (Daher et al., 2012; Taghvaee
et al., 2019a). However, the recorded PM2.5 oxidative potential at our sampling site was drastically
higher than several cities across the world, such as Bologna (~1800 μg Zymosan/mg PM), Denver
(~2000 μg Zymosan/mg PM), Thessaloniki, Greece (~750 μg Zymosan/mg PM), and even Los
Angeles freeways (~3500 μg Zymosan/mg PM) (Decesari et al., 2017; Saffari et al., 2014;
Shirmohammadi et al., 2017). This observation revealed that PM2.5 toxicity in the urban
background site of Milan metropolitan area was still higher than several crowded cities across the
world, despite the significant drop in road traffic during COVID-19 restrictions. Similar to the
105
results of DCFH assay, curtailed PM2.5 oxidative potential during city-wide shutdown strategies
was also verified by the DTT assay, in which the recorded values increased from 0.70±0.15
nmol/min.m
3
in FL and PL2 to 0.99±0.20 nmol/min.m
3
during FR period. It is noteworthy that
measured PM2.5 oxidative potential via DTT assay during the FR phase is consistent with the
previously reported DTT consumption rate during summer 2019 at the same sampling site (i.e.,
0.85±0.10 nmol/min.m
3
) (Hakimzadeh et al., 2020), but significantly higher than annual average
DTT reported for Rome (i.e., ~0.23 nmol/min.m
3
)(Jedynska et al., 2017).

106
Figure 4.12. Air volume-based (extrinsic) and mass-based (intrinsic) oxidative potential of
ambient PM2.5 during the investigation period measured by the means of (a) DCFH
macrophage; and (b) DTT assay (FL: full-lockdown; PL2: second partial-lockdown; FR:
full-relaxation).

(a)

(b)

The observed trends in PM2.5 oxidative potential measured by both DCFH and DTT assays
clearly demonstrated a reduction in the toxicity of PM2.5 due to the COVID-19 restrictions.
Although total PM2.5 mass concentration was not significantly impacted following lockdown
strategies (as discussed in section 4.3.1), curtailed road traffic during FL and PL2 periods have led
to noticeable reductions in PM2.5-bound OC levels as well as redox-active metals concentrations,
leading to lower PM2.5 toxicity during lockdown restrictions. Finally, as shown in Figure 4.12,
107
while DCFH macrophage indicated a slight increase (by ~14%) in PM2.5 oxidative potential from
FL to PL2 phase, the DTT assay showed comparable PM2.5 toxicity between the mentioned
periods. This observation can be ascribed to the distinct characteristics of DCFH-DA and DTT
probes, which demonstrate different levels of sensitivity to toxic PM species (Jovanovic et al.,
2019; Sauvain et al., 2013). Therefore, the mentioned discrepancies are within the experimental
uncertainties in quantifying PM oxidative potential via various assays.

4. Summary and conclusions
In this study, we investigated the chemical properties and oxidative potential of ambient
PM2.5 during the COVID-19 lockdown period in the metropolitan area of Milan. Significant
reductions (Pvalue<0.05) were observed in the ambient concentrations of NO2 and C6H6 (as
surrogates of vehicular emissions) during the lockdown phase in comparison with the same time
period in 2019, due mainly to the restricted road traffic. Similarly, COVID-19 traffic restrictions
have led to reduced levels of OC, and traffic-originated PAHs (produced from engine combustion)
as well as redox-active metal elements (stemmed from re-suspended road dust) during the
lockdown period. The concentration of selected PM2.5-bound metals increased (by 40% to 180%)
from FL to PL2 and FR periods, coinciding with the gradual lifting of COVID-19 restrictions. On
the other hand, PM2.5 and BC levels exhibited comparable (Pvalue= 0.10-0.75) concentrations
between the lockdown phase and the same period in the year 2019. This observation can be
attributed to the increased domestic residential heating during stay-home strategies which to some
extent counterbalanced the reduction in traffic-induced emissions at our sampling area. Of
particular note, the PM2.5 oxidative potential (measured by means of DCFH and DTT assays)
108
decreased by ~25% during FL and PL2 periods as opposed to the year 2019 (as well as FR phase),
due mainly to the curtailed road traffic within lockdown restrictions. The results of this study
provide insights into the changes in composition and oxidative potential of ambient PM2.5 in the
absence of primary emissions from road traffic during COVID-19 restrictions in the metropolitan
area of Milan.

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Chapter 5: Impact of different sources on the oxidative potential of ambient particulate
matter PM10 in Riyadh, Saudi Arabia: A focus on dust emissions

5.1. Introduction
A growing body of epidemiological and toxicological evidence indicates strong
associations between exposure to ambient particulate matter (PM) and adverse effects on human
health, including chronic obstructive pulmonary diseases, endothelial dysfunction, cardiovascular
illnesses, adverse birth outcomes, and lung cancer (Consonni et al., 2018; Delfino et al., 2005; Du
et al., 2016; Fasola et al., 2020; Hyun et al., 2021; Orellano et al., 2020; Sapkota et al., 2012;
Vaduganathan et al., 2016). One of the underlying mechanisms for the toxicity of PM is the
excessive cellular production of reactive oxygen species (ROS), leading to oxidative stress,
inflammation, and consequently produce adverse health outcomes (Delfino et al., 2013; Ghio et
al., 2012; Lodovici and Bigagli, 2011). As a result, several studies have attempted to develop
chemical and biological assays to quantify the airborne particle oxidative potential (Umme S.
Akhtar et al., 2010; Bates et al., 2019). A well-established and widely used assay is the
dithiothreitol (DTT) assay, which measures the PM capability to catalyze the transfer of electrons
from DTT to oxygen by creating superoxide radicals that can directly be linked to the PM oxidative
potential (Borlaza et al., 2018; Chow et al., 2015; Fang et al., 2016).
Ambient PM is a complex mixture of different chemical species that have been associated
with distinct health impacts (Amato et al., 2018; Sardar et al., 2005; Watson et al., 1994). Specific
PM components including redox active metals (e.g., V, Mn, Ni, and Cu), carbonaceous species
(e.g., elemental carbon (EC) and organic carbon (OC)), and polycyclic aromatic hydrocarbons
(PAHs) have been consistently linked with the PM oxidative potential (Cheung et al., 2012a;
110
Chiara et al., 2018b, 2018a; Daher et al., 2014; Kleinman et al., 2005). These PM species originate
from a variety of sources including soil and road dust emissions (Hsu et al., 2016; Jain et al., 2018),
road traffic (Kavouras et al., 2001; Ryou et al., 2018; Sardar et al., 2005), biomass burning (Saggu
and Mittal, 2020; Stracquadanio et al., 2019; Vicente et al., 2021), and atmospheric photochemical
processes forming secondary organic aerosols (SOA) (Jain et al., 2018; Rezaei et al., 2018; Ryou
et al., 2018). Therefore, it is essential for policy makers to identify these sources and more
importantly their contribution to the PM oxidative potential to target the PM emissions with greater
toxicity more effectively.
Few studies have investigated the air quality deterioration and negative health endpoints
associated with ambient PM in Riyadh, the capital of Saudi Arabia and one of the Middle East’s
largest metropolitan areas, with approximately 7.3 million residents (Alangari et al., 2015; Nasser
et al., 2015; Wahabi et al., 2017). Previous studies in Riyadh have indicated that the ambient PM
in the city was heavily impacted by regional natural dust as well as local activities (e.g., traffic and
industrial emissions) (Alangari et al., 2015; Alharbi et al., 2015a; El-mubarak et al., 2012;
Modaihsh et al., 2015). Alharbi et al. (2015) reported that the average ambient PM 10 (PM with
aerodynamic diameter < 10 μm) in the metropolitan area of Riyadh exceeded the national standard
as well as other recorded PM10 values in the region, such as in Tehran, Beirut, Abu Dhabi, and
Kuwait. They attributed this increase to the natural dust activities in the area during the summer
and vehicular emissions and construction activities during the winter. To the best of our
knowledge, no study has provided any insights on the contribution of the PM sources to the
toxicological characteristics (i.e., oxidative potential) of PM in the city of Riyadh.
The receptor model is a useful tool that is widely used for identification of sources and
quantifications of their contributions to a target variable. Among several receptor models are
111
multivariate factor analysis models, including Principal Component Analysis/ Multiple Linear
Regression (PCA/MLR), UNMIX, and Positive Matrix Factorization (PMF)(Deng et al., 2018;
Hopke et al., 2006; Shi et al., 2014; Wang et al., 2012). Researchers often combine the results of
PCA factor analysis with the MLR method to identify the contribution of different resolved factors
by PCA to a dependent variable (Chakraborty and Gupta, 2010; Harrison et al., 1996; Shi et al.,
2009; Soleimanian et al., 2020a; Srivastava et al., 2008; Taghvaee et al., 2019b; Verma et al., 2012;
Yu et al., 2010). As this model does not require information on source profiles, the source
categories and their contributions can be identified according to the PM ambient dataset (Shi et
al., 2014; Taghvaee et al., 2019b; Zhang et al., 2019). For example, Taghvaee et al., (2019a) using
PCA/MLR model determined the most significant species contributing to the oxidative potential
of PM2.5 in Athens, Greece.
This study investigates the chemical and toxicological characterization of ambient PM 10
during dust and non-dust events in a typical urban area of Riyadh. Ambient PM10 samples were
collected during a cool period (December–March) and a warm period (May–August), covering
dust and non-dust events in the area. Collected PM samples were analyzed for their chemical
components, and the PM oxidative potential was determined using the DTT in vitro assay. The
Principal Component Analysis (PCA) in combination with Multiple Linear Regression (MLR)
were also used to link sources of ambient PM10 to the measured oxidative potential.

5.2. Methodology
5.2.1. Sampling location and collection period
Weekly time-integrated PM10 samples were collected at a residential city park in central
Riyadh, Saudi Arabia (24°38’55”N, 46°43’16”E) between December 2019 and August 2020, using
112
a medium volume sampler (model URG3000ABC, URG Corp, Chapel Hill, NC, USA) operating
at a flow rate of 8 L/min. To achieve the required mass loading for further chemical analyses, the
daily collected samples were composited every three (or four) days. Our selected site is located in
a densely populated residential area in the city center of Riyadh, about 2 km away from a major
highway (i.e., King Fahad Highway), and in a close proximity (500 m away) of some regular
business and auto shops. The site also is about 4 km from the old industrial city. Previous studies
in the area have indicated that central Riyadh has a poor air quality as a result of abundant
vehicular, household, commercial and industrial activities (Alharbi et al., 2015b, 2014; Bian et al.,
2016; El-Mubarak et al., 2014). Figure 5.1 shows the map of the investigated area with the location
of our sampling site. Additionally, considering that a large number of Riyadh population lives
in/near the city center (Alharbi et al., 2015a; Saudi General Authority for Statistics, 2016), it can
be argued that our sampling site properly represent the population exposure to major sources of
air pollution in the Riyadh metropolitan area. The meteorological parameters (i.e., temperature and
relative humidity (RH)) were also obtained from the Royal Riyadh Development Authority station
at the same location of our sampling site during the investigated period. The seasonal average
values of the abovementioned meteorological parameters during the study period are reported in
Table 5.1. We should note that the sampling campaign coincided with seven dust events, and the
filters collected during the time period of these events were investigated separately as will be
discussed in Results and Discussion section. The dust storm events were forecasted using the Saudi
National Center for Meteorology Radar. Samples with average daily concentrations exceeding the
90th percentile were classified as dust samples (Achilleos et al., 2014; Rezaei et al., 2018).

113

Figure 5.1. Map of the study location in the Riyadh metropolitan area.

Table 5.1. Seasonal averages (± standard deviation) of meteorological parameters during the
warm and cool period.

Temperature (°C) Relative Humidity (%)
Warm period 36 ± 3.0 9.8 ± 2.6
Cool period 18.6 ± 3.3 30.2 ± 8.8

5.2.2 Gravimetric and chemical analysis
PM10 samples on 47 mm quartz (Whatman company, 2.5-µm pore, Marlborough, MA) and
teflon (Tisch Scientific, 1-µm pore, North Bend, OH) filters were collected (in parallel) during our
sampling campaign. The mass concentration of PM samples was calculated by dividing the
114
collected mass, measured by a microbalance (MT5, Mettler Toledo Inc., Columbus, OH), on filters
to the volume of sampled air. The collected PM10 mass was determined as the difference between
the pre-sampling and post-sampling weight of filters after equilibration under stable laboratory
conditions (i.e., temperature of 22–24 C and relative humidity of 40–50%).
In addition, the collected PM10 samples were evaluated for their content of EC, OC, metals
and trace elements, and inorganic ions by the Wisconsin State Lab of Hygiene (WSLH). In
summary, the thermo-optical transmittance (TOT) analysis by a model-4- semi-continuous OC/EC
field analyzer (Sunset Laboratory Inc., USA) was used to measure the OC/EC content of the PM10
samples (Birch and Cary, 2007). Moreover, inductively coupled plasma mass spectroscopy (ICP-
MS) analysis and ion chromatography (IC) were employed to measure the metal and trace element
components and inorganic ions of PM10 samples, respectively (Herner et al., 2006; Karthikeyan
and Balasubramanian, 2006).

5.2.3 Oxidative potential of PM10
The dithiothreitol (DTT) assay was employed to assess the oxidative potential of the
collected PM10, as a well-established method in the literature to measure the oxidative potential of
PM samples (Calas et al., 2018; Chirizzi et al., 2017a; Hu et al., 2008; Molina et al., 2020). For
this assay, the linear decay rate of dithiothreitol is used as an index of the oxidative potential of
PM. Briefly, the filter-collected PM were stored frozen at −20 °C and then extracted with high-
purity water (8.0 mL) with continuous shaking, in the dark, over a 16-hr period. PM10 extracts
were then directly incubated in potassium phosphate (KPO4) buffer and DTT. The trichloroacetic
acid was gradually added to vials of the incubation mixture for stopping the reaction, followed by
recording the absorbance at 412 nm (optical density of 2-nitro-5-thiobenzoic acid) and 650 nm
115
(reference wavelength) on an M5e plate reader (Molecular Devices, Sunnydale, CA). The DTT
rate of depletion (per units of time) was then determined by converting the recorded absorbance to
the remained DTT. Further information regarding the DTT methodology is available in (Shafer et
al. (2016) and Cho et al. (2005).

5.2.4. Source apportionment of the PM10 oxidative potential
In this study, the PCA analysis using the Statistical Package for Social Sciences (SPSS)
version 25 was applied on the volumetric (i.e., per m
3
of air) mass concentrations of OC, EC,
sulfate, ammonium, and individual metals (e.g., Cu, Zn, Al, Ti and Fe) to identify and estimate the
possible source factors that contribute to the PM10 mass concentration. In this method, the chemical
data was first transformed into a dimensionless standardized form using the following equation:
Z ij=
𝐶 𝑖𝑗
−𝐶 ̅ 𝑗 𝜎 𝑗 (1)
where Zij stands for the dimensionless standardized form of the i
th
sample and the j
th
species, Cij is
the mass concentration of species j in the i
th
sample, and 𝐶 ̅ 𝑗 and σj refer to the mean mass
concentration and the standard deviation for species j, respectively. The receptor model then
mathematically solves the following chemical mass balance equation:
𝑍 𝑖𝑗
= ∑ 𝑔 𝑖𝑘
ℎ
𝑘𝑗
𝑝 𝑘 =1
(2)
where P refers to the resolved factors by the PCA model and gik and hkj indicate the factor loading
and the factor score, respectively. It should be noted that a varimax orthogonal rotation was
performed on the resolved factors in order to facilitate the interpretation (Abdi, 2003; Dallarosa et
al., 2005). The resolved factors with high eigenvalues compared to the unity were considered to
be a significant contributor. Additionally, the Kaiser-Meyer-Olkin (KMO) value was set to 0.5 and
above to ensure the PCA procedure's suitability (Thomas et al., 2014). The multi-linear regression
116
(MLR) was then employed between the PCA resolved factor scores (as independent variables) and
the extrinsic DTT values (in units of nmol/min. m
3
) as dependent variable (Baker, 2003; Zuo et
al., 2007). The relative source contribution to the PM 10 oxidative potential was determined based
on the standardized regression coefficients (Beta) and derived R
2
value. In details, the relative
source contributions to PM10 oxidative potential were calculated by normalizing the derived Beta
values.

5.3. Results and discussion
5.3.1. PM10 mass concentration and chemical composition
5.3.1.1. PM10 mass and carbonaceous species
Table 5.2 and Figure 5.2(a) and show the average PM10 mass concentrations of collected
samples during the warm and cool periods as well as during the dust events. According to the
figure, higher concentrations (P-value = 0.01) were observed for PM10 in summer (98.7±3.7μg/m
3
)
compared to winter season (80.0±6.0 μg/m
3
), most likely due to the increase in particle
concentration from soil and dust sources during the warm period. The dry atmospheric conditions
(i.e., low relative humidity and high temperature (Table 5.1)) during the warm phase facilitate
particle resuspension from the soil and desert areas in/around the city of Riyadh (Alharbi et al.,
2015a; Alharbi, 2009; El-Mubarak et al., 2014). It is worth noting that the PM10 mass
concentrations dramatically increased (up to 218.2±34.8 μg/m
3
) during dust events, considerably
exceeding the recommended PM10 standard (50 μg/m
3
) by World Health Organization (WHO)
(Lodge, 1988).
The seasonal and dust event average concentrations of carbonaceous compounds including
elemental (EC) and organic carbon (OC) are illustrated in Figures 5.2(b-c). Based on Figure 5.2(b),
117
increased EC mass concentrations were observed in winter season (1.7±0.4) compared to summer
season (1.3±0.2). The stable meteorological conditions (i.e., low mixing height) during the cool
period limit the horizontal and vertical dispersion of air pollutants, including EC, increasing their
concentrations to levels higher than those observed in the warmer season (Kim et al., 2015;
Schwartz et al., 2018; Taghvaee et al., 2019b). The EC levels significantly decreased (p
value<0.05) during dust events compared to normal days, which can be explained by considerably
lower traffic activities, as the major source of EC, during these events in the area.
Examining the seasonal trend of OC concentrations revealed comparable concentrations
(P-values = 0.30) in cool (5.6±0.8μg/m
3
) and warm (4.9±0.6μg/m
3
) period samples, with
wintertime values being slightly higher. OC can be originated from both primary and secondary
sources (Gianini et al., 2013; Soleimanian et al., 2019c; Von Schneidemesser et al., 2010). The
significant contribution of secondary organic aerosols (SOAs) to OC mass concentrations during
warm period most likely counterbalances the higher contributions of primary sources (i.e., traffic
and industrial activities) due to higher gas-to-particulate partitioning during cool period, leading
to comparable OC levels in both periods of the sampling.

118
Table 5.2. Seasonal and dust event averages (± standard deviation) of PM10 mass and
chemical component concentrations and associated oxidative potential (OP).

Species
Cold period
(n=7)
Warm period
(n=7)
Dust event
(n=7)
Limit of detection
LOD (ng/m
3
)
PM10(µg/m
3
) 80.0±6.0 98.7±3.7 218.2±34.8 2.8E+01
EC (µg/m
3
) 1.7±0.4 1.35±0.2 1.0±0.1 5.0E+02
OC (µg/m
3
) 5.6±0.8 4.9±0.6 5.2±0.4 5.0E+02
SO4
-2
(µg/m
3
) 4.7±0.6 5.6±1.2 6.6±1.3 6.9E+00
NH4
+
(µg/m
3
) 0.5±0.1 0.5±0.1 0.6±0.1 1.1E+01
NO3
-
(µg/m
3
) 2.8±0.3 2.6±0.4 2.7±0.4 1.1E+01
Mg (µg/m
3
) 1.1±0.5 2.6±1.3 3.4±2.4 1.0E+01
Al (µg/m
3
) 2.4±1.4 5.4±2.5 7.3±0.5 9.3E+01
K (µg/m
3
) 0.6 ±0.3 1.3±0.6 1.7±1.2 9.9E+00
Ca (µg/m
3
) 11.2±0.4 14.6±5.0 17.3±7.7 4.4E+01
Ti (µg/m
3
) 0.2±0.0 3.6±0.2 0.4±0.3 2.5E+00
V (ng/m
3
) 6.0±2.2 11.5±4.5 14.3±9.1 1.8E-03
Cr (ng/m
3
) 5.9±2.2 11.7±5.9 14.5±10.2 5.6E-02
Mn (ng/m
3
) 36.3±17.3 74.1±36.2 92.6±69.8 2.1E-01
Fe (µg/m
3
) 1.7±0.8 3.7±1.8 4.8 ±3.5 1.8E+00
Ni (ng/m
3
) 4.2±1.8 9.8±5.8 11.5±8.3 9.4E-02
Cu (ng/m
3
) 17.4±10.6 11.0±3.3 14.1±4.0 8.8E-01
Zn (ng/m
3
) 221.5±197.0 63.5±37.1 61.9±60.9 7.9E+00
Cd (ng/m
3
) 0.3±0.2 0.1±0.1 0.2±0.1 1.3E-03
Li (ng/m
3
) 1.2±0.0 3.0±0.0 4.0±0.0 2.1E-02
Co (ng/m
3
) 0.8±0.0 1.9±0.0 2.5±0.0 3.5E-03
Ba (ng/m
3
) 39.2±0.0 45.3±0.0 58.3±0.0 3.0E-01
La (ng/m
3
) 1.0±0.0 2.7±0.0 3.9±0.0 9.8E-04
Ce (ng/m
3
) 2.2±0.0 5.7±0.0 8.2±0.0 2.0E-03
Pr (ng/m
3
) 0.2±0.0 0.7±0.0 0.9±0.0 2.1E-04
Nd (ng/m
3
) 1.0±0.0 2.5±0.0 3.60±0.0 1.0E-03
Lu (ng/m
3
) 0.01±0.0 0.02±0.0 0.04±0.0 6.5E-05
Hg (ng/m
3
) 0.1±0.0 0.4±0.0 0.30±0.0 2.6E-02
Pb (ng/m
3
) 17.1±0.0 8.6±0.0 7.90±0.0 7.9E-02
Se (ng/m
3
) 1.2±0.0 1.1±0.0 1.10±0.0 1.6E-02
Intrinsic PM OP
(nmol/min/mgPM)
12.4±0.7 11.7±1.0 9.30±0.91 1.0E-02
*

Extrinsic PM OP
(nmol/min-m
3
)
1.1±0.1 1.2±0.1 1.50±0.21 1.2E-04
**

*The limit of detection unit is nmol/min/mgPM.
**The limit of detection unit is nmol/min/m
3
.

119
Figure 5.2. The seasonal and dust event average concentrations of: a) PM10; b) EC; and c)
OC.

120
5.3.1.2. Inorganic ions
The average concentrations of sulfate, ammonium, and nitrate, by season, as well as during
the dust period, are illustrated in Figure 5.3. As shown in the figure, the mass concentration of
sulfate was higher in the summer season compared to the cooler period, while the ammonium and
nitrate levels were comparable (p value=0.50 and 0.40, respectively) during these periods. Sulfate
is formed as a result of the photochemical oxidation (through gas-phase reactions with the
hydroxyl radical (OH)) of sulfur dioxide, emitted by combustion sources with sulfur in the fuel
(Fine et al., 2008; Xue et al., 2016). With the higher temperatures during warm season, the degree
of solar radiation is enhanced, causing the photochemical reactions to peak and increase the
formation rate of sulfate (Na et al., 2004; Seinfeld and Pandis, 2006). Moreover, the inorganic ions
(i.e., sulfate, ammonium, and nitrate) levels slightly increased during the dust episodes. This
observation is consistent with the results from the previous study by Alharbi et al. (2015) at the
same area, in which the authors reported higher levels of sulfate and ammonium during dust period
compared with the normal periods. Increased levels of inorganic ions of secondary origin, such as
ammonium nitrate and sulfate, were also observed in previous studies on dust episodes around the
globe (Ghosh et al., 2014; Hassan and Khoder, 2017; Javed and Guo, 2021; Naimabadi et al., 2016;
Saliba et al., 2014; Stone et al., 2011). For example, a study by Javed and Guo (2021) investigated
the impact of dust episodes on the chemical characterization of fine and coarse PM in Doha, Qatar,
and reported a significant increase in the concentrations of PM chemical components, including
inorganic ions, during dust episodes. Additionally, Stone et al. (2011) reported high sulfate
enrichment (by a factor of ~ 2.5) in PM dust samples collected in Gosan, Korea, as opposed to
non-dust samples.

121
Figure 5.3. The seasonal and dust event average concentrations of selected inorganic ions: a)
ammonium; b) sulfate; and c) nitrate.

122
5.3.1.3. Metals and trace elements
Figure 5.4 illustrates the concentrations of selected metals (i.e., Al, Ti, Ba, Li, Pb, Fe, Zn,
Cu, Ca, Ni, Cr, and K) in PM10 samples collected in warm, cool and dust periods. Previous studies
have indicated that these metals can be originated from various sources such as soil and road dust,
tire and brake wear, and industrial emissions (Almeida et al., 2006, 2005; Roy M. Harrison et al.,
2012; Tian et al., 2016). Overall, higher mass concentrations of redox-active metals, including Al,
K, Ti, and Fe as chemical markers of soil and resuspended dust emissions (Almeida et al., 2005;
Cardoso et al., 2018), were observed during warm period as well as during dust events compared
to the cooler period. This is due mainly to the drier atmospheric conditions (i.e., the lower relative
humidity prevailing during these periods) that facilitate the resuspension of soil and desert dust
particles (Laidlaw and Filippelli, 2008; Taghvaee et al., 2019b). For example, the average levels
of Al in dust samples were 7334.8±2214 ng/m
3
which are higher than the observed levels in
summer (5404±2500 ng/m
3
) and winter (2363±1416 ng/m
3
) seasons. Querol et al. (2019) reported
that the emissions from large industries (including petrochemical, petroleum, and power plants)
located nearby several desert areas globally interact with transported dust particles in the affected
region and result in notable increases in the PM metal and element concentrations during dust
episodes. Additionally, lower atmospheric boundary layers typically prevailing during these events
enhance the accumulation of anthropogenic pollutants, including redox-active metals and
elements, during dust storm episodes (Pandol et al., 2014; Querol et al., 2019). Furthermore, lower
concentrations were observed for Cu, Zn and Pb, which are tracers of non-tailpipe emissions (e.g.,
asphalt, brake abrasion and tire wear emissions) (Farahani et al., 2021; Roy M. Harrison et al.,
2012; Soleimanian et al., 2019d; Tecer et al., 2012) during the warm phase of the sampling
campaign. The seasonal trends for non-tailpipe tracers in our study are in agreement with the
123
literature (Alharbi et al., 2015a; Galindo et al., 2018; Pekey et al., 2010). Alharbi et al. (2015)
investigated the chemical characteristics of PM10 in Riyadh and reported similarly higher mass
concentrations of non-tailpipe tracers, including Cu and Mo, during the cool period compared to
the warm period.

Figure 5.4. Average concentrations of metals and trace elements during the investigated
periods.

5.3.2. Oxidative potential of PM10
Figure 5.5 shows the extrinsic (per m
3
of air volume) and intrinsic (per PM mass) levels of
PM10 oxidative potential during the investigated periods. The detailed values during warm, cool,
and dust events are also presented in Table 5.2. Our measurements revealed comparable volumetric
oxidative potential (P-value = 0.30) during the warm (1.2 ± 0.10 nmol/min-m
3
) and cool (1.1 ± 0.1
nmol/min-m
3
) periods (Figure 4(a)). The measured summer and winter-time DTT consumption
rate is almost within the range of previously reported values in Tehran (1.35 ± 0.37 nmol/min-m
3
)
(Rezaei et al., 2018), and considerably higher than those reported in Los Angeles (0.35±0.04
nmol/min-m
3
) (Shirmohammadi et al., 2016c), Atlanta (0.30± 0.10 nmol/min-m
3
) (Verma et al.,
124
2014) and Athens (0.33 ± 0.20 nmol/min-m
3
(Paraskevopoulou et al., 2019). It is worth noticing
that these DTT values are somewhat lower but statistically significant (P-value<0.05) in
comparison to values recorded during dust episodes (1.5±0.2 nmol/min-m
3
). In line with our
results, Lovett et al. (2018) evaluated the oxidative potential of PM in Beirut during Saharan and
Arabian dust events, and revealed an increase in the coarse PM oxidative potential (in units of μg
Zymosan/m
3
of air) in dust samples compared to the samples collected during the non-dust events.
Interestingly, the investigation of the mass-based oxidative potential of the collected ambient PM10
samples revealed that the intrinsic levels of PM10 oxidative potential were higher in the cool
(12.4±0.7 nmol/min/mgPM) period compared to warm (11.7±1.0 nmol/min/mgPM) and dust
(9.3±0.9 nmol/min/mgPM) periods (P-values=0.27 and 0.01, respectively). Consistent with our
results, Chirizzi et al. (2017) examined the influence of Saharan dust outbreaks on the oxidative
potential of water-soluble fractions of PM10 and reported that dust transported from Africa to Lecce
has a lower mass normalized DTT in comparison to the average values observed in regular
samples. This suggests that the observed higher extrinsic PM 10 oxidative potential during dust
storm events is due to the much higher overall PM mass concentrations, however the predominant
PM components during these events may not be as redox-active as species during a regular period.
A regression analysis was conducted to distinguish the association of chemical species, such as
OC, EC, inorganic ions and water-soluble metals, with the DTT activity (as presented in Table
5.3). According to our regression analysis results, most notable correlations (R > 0.70) were
observed between DTT activity and redox active metals in the area. A number of previous studies
have also reported correlations between transition metals and DTT activity (Ntziachristos et al.,
2007; Verma et al., 2009a, 2009b). These redox active transition metals (e.g., Al, Ti, Cr, Cu and
La) in the PM10 size range originate from various sources in the area including resuspended dust
125
and soil, vehicular emissions, and local industrial activities. More discussion related to the sources
contributing to the PM10 toxicity is presented in section 3.3 of the manuscript.

Figure 5.5. PM10 oxidative potential for cool and warm periods and dust events: a) volume-
based, or extrinsic oxidative potential (per m
3
of air); b) mass-normalized, or intrinsic
oxidative potential (per PM mass).

126

Table 5.3. Pearson correlation coefficients (R) between dithiothreitol (DTT) activity data
(nmol/min/m
3
air) and mass concentration (μg/m
3
) of chemical species (OC, EC, inorganic
ions and water-soluble metals) at the sampling location.

Species
Pearson correlation coefficients (R)
OC 0.58
EC 0.60
Ammonium 0.41
Sulfate 0.69
Al 0.84
Ca 0.80
Ti 0.86
Cu 0.71
V 0.87
Cr 0.85
Mn 0.86
Fe 0.86
Ni 0.84
Ba 0.85
La 0.84

5.3.3. Source apportionment of ambient PM10 and its associated oxidative potential
5.3.3.1. Source apportionment of PM10 mass concentration using the PCA approach
Table 5.4 presents the outputs of PCA analysis performed on the weekly time-integrated
EC, OC, as well as trace elements and metals concentrations for the whole study period, which
resulted in the identification of four factors explaining approximately 91% of total variance in the
data. The first factor was identified as soil and resuspended dust emissions due to significant
loadings of Fe, Al, K, Li, and Ti as crustal elements. Previous studies have documented that Fe,
Al, K, Li and Ti are all well-established chemical markers of soil and resuspended dust emissions
in different areas around the globe(Almeida et al., 2005; Karanasiou et al., 2012; Tian et al., 2016).
This factor has a significant contribution to total PM10 concentrations, accounting for about 40%
127
of total PM10 in the city. Although high traffic activities in the area could lead to increase in
concentrations of the redox-active metals (e.g., Ti, Al and K), other mechanisms including the dust
resuspension can majorly contribute to these emissions in the urban areas. Riyadh located close to
Ad-Dhna and Rub’al Khali deserts experiences dry and hot climate along with high wind speeds
that facilitate the transport and resuspension of the dust metals to the area (Alharbi et al., 2013;
Badarinath et al., 2010; Farahat, 2016; Maghrabi et al., 2011; Modaihsh et al., 2017; Smirnov et
al., 2002). Modaihsh et al.(2017) reported that the average annual dust deposition in Riyadh was
about 454 tons/km
2
and is significantly higher than the surrounding regional and worldwide areas.
Previous studies by Farahani et al. (2020) and Givehchi et al. (2013) also highlighted the
significant contribution of dust emissions to PM10 levels in the Middle Eastern region.
The second factor was characterized by high loadings of EC, Cu and Zn. EC is
predominantly emitted from vehicular exhausts and undergoes very limited chemical
transformations(Jain et al., 2018). Numerous studies have documented EC as the major tracer of
tailpipe emissions (Díaz-Robles et al., 2008; Jain et al., 2018; Yin et al., 2010). Additionally,
loading of Cu and Zn in this factor can be attributed to asphalt, brake abrasion and tire wear
emissions (Cao et al., 2006; Querol et al., 2008; Srimuruganandam and Shiva Nagendra, 2012).
Similar to our study, Jain et al. (2018) employed EC, Cu and Zn as the chemical tracers to resolve
the vehicle emissions factor in Delhi, India. Therefore, we labeled this factor as “Traffic
emissions”. This factor has a moderate contribution (~20%) to total PM10 concentrations in the
metropolitan area of Riyadh, which is in good agreement with the findings of previous studies in
the region (Javed and Guo, 2021; Khodeir et al., 2012; Soleimani and Amini, 2014).
The third factor showed very high levels of sulfate (SO4
2−
), and ammonium (NH4
+
) and
contributed to approximately 17% of the total PM10 concentrations in Riyadh. SO4
2−
and NH4
+
are
128
the constituents of ammonium sulfate and ammonium nitrate produced by gas phase reactions of
acidic gaseous precursors (i.e., HNO3 and H2SO4) with ammonia (NH3). Numerous studies used
these species (i.e., SO4
2-
and NH4
+
) as indicators of secondary aerosol formations (Jain et al., 2020;
Sricharoenvech et al., 2020). Consequently, we selected “secondary aerosol (SA)” as the most
suitable title for this factor.
The fourth factor indicated high loadings of lanthanoid (La), and selenium (Se) and
contributed to about 14% of total PM10 concentrations (Table 1). Previous studies reported Se as
a heavy metal used in the electronics, plastic, glass, and paints industry (Risher et al., 1999;
Taghvaee et al., 2018b). Moreover, loadings of La in this factor can be attributed to local oil-
industry emissions (Kulkarni et al., 2006; Moreno et al., 2008). Moreno et al. (2008) used La as a
marker to identify the contribution of oil refinery emissions to PM in Puertollano, Spain.
Therefore, we believe the most suitable label for this factor is “local industrial activities and
petroleum refineries”. In line with our results, Bian et al. (2016) showed low to medium
contributions of industrial activities including refineries to the average concentration of PM10
species (i.e., PAHs) near our sampling site.

129

Table 5.4. Loadings of chemical species in the factors resolved by the principal component
analysis (PCA). Loadings > 0.7 are bolded.

Species
Soil and
resuspended dust
emissions
Traffic
emissions
Secondary
Aerosols (SA)
local industrial
activities and
petroleum
refineries
Ti 0.979 -0.019 0.152 0.109
Fe 0.972 -0.022 0.154 0.145
Al 0.971 -0.111 0.131 0.129
K 0.965 -0.030 0.179 0.163
Li 0.965 -0.071 0.188 0.126
Cu -0.220 0.877 0.004 -0.095
Zn -0.342 0.841 -0.031 -0.121
EC 0.328 0.743 0.256 0.257
OC 0.240 0.636 0.419 0.364
Sulfate 0.096 0.190 0.911 0.247
Ammonium 0.310 0.053 0.903 0.076
Se 0.105 0.058 0.109 0.941
La 0.405 -0.059 0.464 0.715
% of Variance 40.919 19.230 17.281 13.676
Cumulative % 40.919 60.149 77.430 91.106

5.3.3.2. Source apportionment of PM10 oxidative potential using MLR approach
The MLR analysis was performed based on the PCA resolved factor scores to identify the
most significant sources accountable for the PM-induced toxicity (Table 5.5). As can be seen in
the table, “soil and resuspended dust emissions” was the most important source contributing to PM
oxidative potential (Beta=0.65), accounting for 31% of the oxidative potential (Figure 5.6). In
addition, “SA” and “local industrial activities and petroleum refineries” contributed to 20% (Beta
= 0.42) and 19% (Beta= 0.40) of the PM oxidative potential, respectively. Romano et al (2020)
130
reported significant correlations (Pvalue <0.001) between the oxidative potential of PM10 and tracers
of SA (e.g., ammonium), further corroborating the findings of this study. Traffic emissions are
also no less important in the area as they contribute to about 17% (Beta = 0.35) of PM-induced
toxicity. Previous studies also underscored the role of this factor to the overall toxicity of PM in
various urban areas (Hu et al., 2008; Pant et al., 2015a; Shirmohammadi et al., 2016c, 2015; Wang
et al., 2020; Weber et al., 2021). For example, Weber et al. (2021) indicated road traffic as a major
source contributing to the extrinsic PM10 oxidative potential (median 0.36 nmol/min-m
3
) across
different cities in France.

Table 5.5. Results of the multiple linear regression (MLR) analysis between PM10 oxidative
potential (as the dependent variable) and PCA resolved factor scores (as independent
variables).

Factors
Unstandardized
Coefficients (±Std. Error)
Standardized
Coefficients (Beta)
P value R
2

Constant 1.22±0.03

0.000
0.88
Soil and resuspended dust
emissions
0.29±0.04 0.65 0.000
Traffic emissions 0.15±0.04 0.35 0.002
Secondary Aerosols (SA) 0.18±0.04 0.42 0.000
Local industrial activities
and petroleum refineries
0.18±0.04 0.40 0.000

Figure 5.6. Relative source contributions to PM10 oxidative potential

131
5.4. Summary and conclusions
The main goal of this study was to determine and evaluate the sources of PM10 toxicity in
the metropolitan area of Riyadh, which is one of the most populous arid areas in the world. Our
findings revealed higher PM10 mass concentrations in the warm season (98.7±3.7μg/m
3
) compared
to the cooler season (80.0±6.0 μg/m
3
), with enormous PM10 concentrations (as much as 218.2±34.8
μg/m
3
) during dust outbreaks. Moreover, most of the redox active metals (e.g., Fe, Al, K, Li and
Ti) and inorganic ions (sulfate and ammonium) increased from the cooler to the warm period. We
also observed an increase in the DTT levels from the cool period (1.00±0.10 nmol/min-m
3
) to
warm period (1.20±0.10 nmol/min-m
3
) and dust episodes (1.50 ±0.20 nmol/min-m3). Our
statistical analysis (PCA coupled with MLR) indicated soil and resuspended dust emissions,
secondary aerosols, local industrial activities and petroleum refineries, and traffic emissions were
the four sources of ambient PM-induced toxicity in Riyadh, with corresponding contributions of
31%, 20%, 19%, and 17%, respectively. Our results underscore the significant role of soil and
resuspended dust emissions to PM10 toxicity in the Riyadh metropolitan area. Therefore, we
recommend the application of mitigation strategies, including water sprinkling, tree planting, street
cleaning, and dust suppressants, which can effectively reduce the resuspension of dust from loose
soil open surfaces in the city of Riyadh and the surrounding regions.

132
Chapter 6: Conclusions
In the first study, we examined the impact of exposure to fine particulate matter (PM2.5) on
coagulation and systemic inflammation biomarkers (i.e., hsCRP, WBC, IL-6, sTNF-RII, vWF)
among young and elderly populations in central Tehran. Results from this study confirm the effects
of exposure to PM2.5 sources on the increased levels of inflammation and coagulation blood
markers, although the significance of association was more pronounced in the elderly panel and
varied depending on the type of source-specific PM2.5 mass concentration.
In the next study, the positive matrix factorization (PMF) source apportionment model was
employed to investigate the long-term temporal variations in the sources contributing to OC in the
central Los Angeles (CELA) and Riverside (i.e., receptor site) over the 2005-2015 years, a time
during which tailpipe emissions stringently were regulated. Five different factors including tailpipe
emissions, non-tailpipe emissions, biomass burning, secondary organic aerosol (SOA), and local
industrial activities were identified for each of the three years. Our results show a decreasing trend
in the absolute contribution of tailpipe emissions (i.e., main contributor) as well as local industrial
activities to the total OC concentrations in the 2005-2015 period at both sites. On the other hand,
an overall increase trend in the relative contributions of non-tailpipe emissions to OC were
observed in CELA and in Riverside during the investigated period. This finding highlights the
need for further investigation and potential regulations for the increasing role of the non-tailpipe
emissions in urban air quality, given the developed mitigation strategies for tailpipe emissions in
Los Angeles basin over the recent years.
In the third study, we investigated the chemical characterization and oxidative potential of
ambient PM2.5 samples during 2019-Coronavirus (COVID-19) outbreak in Milan, Italy. PM2.5 filter
133
sampling was conducted at the suburban site of Bareggio during the national COVID-19 restriction
periods: full-lockdown (FL), the followed partial-lockdown (PL), and full-relaxation (FR). The
PM2.5 samples were analyzed for their components including elemental and organic carbon
(EC/OC), metals, water-soluble organic carbon (WSOC), and individual organic species.
Furthermore, the 2’,7’-dichlorodihydrofluorescein (DCFH) and dithiothreitol (DTT) assays were
performed to measure the oxidative potential of the PM2.5 samples. Our findings revealed that the
ambient levels of pollutants emitted from vehicles engines (e.g., traffic related polycyclic aromatic
hydrocarbons) and road dust markers (e.g., Fe, Mn, Cu, Cr, and Ti) were reduced during FL and
PL compared to those measured in year 2019. Additionally, mass concentration of the mentioned
species along with PM2.5 oxidative potential were lower during FL in comparison with PL and FR,
mainly attributed to the reduction in traffic emissions. The imposed COVID-19 restrictions also
led to a decrease in the levels of nitrogen dioxide (NO2) and benzene (C6H6) during the entire
COVID-19 period compared to year 2019. Nonetheless, ambient concentrations of PM2.5 and black
carbon (BC) during lockdown phase were comparable (Pvalue= 0.10-0.75) with those measured at
the same period in 2019, due to the enhanced domestic biomass burning emissions as result of
adopted stay-home strategies. Results of this study document the impact of traffic restrictions on
the oxidative potential of PM2.5 in Milan area, and can be helpful in adopting adequate public
health policies regarding adverse outcomes of exposure to PM2.5.
Finally, the last study (chapter 5) was defined to investigate the toxicological
characteristics of ambient PM collected at central Riyadh, Saudi Arabia. The collected samples
were chemically analyzed for their organic and metal contents. In addition, the oxidative potential
of the PM samples was quantified by means of the dithiothreitol (DTT) assay. Finally, the principal
component analysis (PCA) in combination with multiple linear regression (MLR) was employed
134
to link sources of ambient PM10 to the measured oxidative potential. The results of the MLR
analyses indicated that the major pollution sources contributing to the oxidative potential of
ambient PM10 in Riyadh were soil and resuspended dust emissions (identified by Al, K, Fe and Li)
(31%), followed by secondary organic aerosol (SOA) formation (traced by SO4-2 and NH+4)
(20%), and industrial activities (identified by Se and La) (19%), and traffic emissions
(characterized by EC, Zn and Cu) (17%). This study underscores the impact of transported dust
emissions on the oxidative potential of ambient PM10 and can be helpful in adopting appropriate
public health policies regarding detrimental outcomes of exposure to PM10.

135
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Abstract (if available)

Abstract Increased numbers of motor vehicles, rapid urbanization, and industrialization have triggered significant air pollution challenges in many urban environments worldwide. Among various air pollutants, airborne particulate matter (PM) is of notable importance mostly due to its complex physiochemical and toxicological characteristics and well-established adverse health consequences (e.g., respiratory and cardiovascular diseases). The majority of previous studies have used total PM mass concentration as the key parameter to investigate PM health effects. However, some PM constituents and sources are more toxic than others. Therefore, it is vital from a regulatory perspective to elaborately examine the sources and components of ambient PM as well as its associated toxicity in order to adopt effective PM emission reduction policies. The core objective of the presented dissertation is to evaluate the impact of primary and secondary source emissions on chemical and toxicological characteristics of ambient particulate matter in different urban environments. To this end, a series of comprehensive investigations were conducted in Los Angeles, Tehran, Milan, and Riyadh metropolitan areas using statistical and source apportionment techniques such as positive matrix factorization (PMF), principal component analysis (PCA) and regression modeling. The findings of these studies advance our knowledge of complex source emission impacts on the PM toxicity and chemical composition in different cities and provide valuable insights for more effective and targeted air quality regulations in polluted areas around the globe.

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Asset Metadata

Creator Altuwayjiri, Abdulmalik Hamoud (author)

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

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Environmental Engineering

Publication Date 03/21/2022

Defense Date 02/28/2022

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

Tag ambient PM,COVID-19,dust emissions,inflammation and coagulation biomarkers,OAI-PMH Harvest,organic carbon,PM characterization,PM emission regulations,PM oxidative potential,source apportionment

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

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

Creator Email altuwayj@usc.edu,eng.abdulmalik667@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110816695

Unique identifier UC110816695

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Rights Altuwayjiri, Abdulmalik Hamoud

Type texts

Source 20220323-usctheses-batch-917 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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