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

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

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Content INVESTIGATING THE ROLE OF URBAN EMISSION SOURCES ON REDOX- ACTIVE PM COMPOUNDS AND THE CHEMICAL ANALYSIS OF THE STANDARDIZED DIESEL EXHAUST PARTICLES by Vahid Jalali Farahani A Dissertation Presented to FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ENVRIONMENTAL ENGINEERING) May 2023 Copyright 2023 Vahid Jalali Farahani ii Acknowledgements First and foremost, I would like to express my gratitude toward my PhD advisor, Professor Constantinos Sioutas, for his continuous council and mentorship, mostly from thousands of kilometers away, which made this work possible. I am eternally indebted to him for all of his support. My sincere thanks to my PhD candidacy and defense committee members, Professor Rima Habre, Professor Kelly Sanders, Professor Amy Childress and Professor Sam Silva for providing me with their constructive comments. Last but not least, I would also like to extend my gratitude to my former and current colleagues and group-mates at USC's Aerosol lab for their sincere help and support in the research projects that I have been involved in during my doctoral studies at USC. Special thanks to Dr. Sina Taghvaee, Dr. Milad Pirhadi, Dr. Abdulmalik Altuwayjiri for being amazing and very supportive mentors and friends. I learned a lot from them and will be eternally grateful to them. I would also like to thank my amazing colleague Ramin Tohidi for making my life so much easier with his amazing work and being a constant source of energy and enthusiasm. My thanks also go to Mohammad Aldekheel and Mohammad Mahdi Badami; it was certainly a pleasure to work with both of them. This work was partly supported by the USC Viterbi School of Engineering’s PhD Fellowship award. I would also like to thank the financial support from the National Institutes of Health (grant numbers: P01AG055367-03, R01AI065617 and R01ES029395). iii Table of Contents Acknowledgements List of Tables List of Figures Abstract Chapter 1 : Introduction 1.1 Background 1.2 List of objectives 1.3 Overview Chapter 2 : The oxidative potential of particulate matter (PM) in different regions around the world and its relation to air pollution sources 2.1 Introduction 2.2 Methodology 2.2.1 Sampling information 2.2.2 Chemical analysis 2.2.3 PM oxidative potential 2.3 Results and discussion 2.3.1 Chemical composition of PM 2.3.2 Comparison of PM oxidative potential among various locales 2.3.3 The impact of emission sources on PM oxidative potential 2.4 Summary and conclusions Chapter 3 : Assessing lifetime cancer risk associated with population exposure to PM- bound PAHs and carcinogenic metals in various metropolitan environments 3.1 Introduction 3.2 Methodology 3.2.1 Sampling information 3.2.2 Chemical analysis 3.2.3 Health risk characterization 3.3 Results and discussion 3.3.1 Concentration of carcinogenic metals and PAHs 3.3.2 Cancer Risk Assessment 3.4 Summary and Conclusions Chapter 4 : Are standardized diesel exhaust particles (DE) representative of ambient particles in air pollution toxicological studies? 4.1 Introduction 4.2 Methodology ii v vi viii 1 1 2 3 6 7 8 8 9 10 11 11 15 16 19 21 22 24 24 24 25 25 27 30 32 34 35 36 iv 4.2.1 DEP type and sampling 4.2.2 Chemical analysis of DEP 4.2.3 Comparisons with real-world PM 4.3 Results and discussion 4.3.1 Chemical profiles of standardized DEP 4.3.2 Comparison between DEP and real-world PM 4.4 Summary and conclusions Chapter 5 : Long-term trends in concentrations and sources of PM 2.5-bound metals and elements in central Los Angeles 5.1 Introduction 5.2 Methodology 5.2.1 Sampling site, period, and collection 5.2.2 Source apportionment analysis 5.3 Results and discussion 5.3.1 Long-term trend in ambient PM 2.5 concentration of metal elements 5.3.2 Source apportionment results 5.4 Summary and conclusions Chapter 6 : Tailpipe and non-tailpipe emission factors and source contributions of PM 10 on major freeways in the Los Angeles basin 6.1 Introduction 6.2 Methodology 6.2.1 Sampling site and period 6.2.2 Instrumentation 6.2.3 Positive Matrix Factorization (PMF) model 6.2.4 Emission factor estimation 6.3 Results and discussion 6.3.1 Overview of mass concentration and chemical composition of PM 10 6.3.2 Source apportionment of ambient particulate matter 6.3.3 Emission factors of PM 10 6.4 Summary and conclusions Chapter 7 : Conclusions and recommendations Bibliography 36 37 37 39 39 41 48 49 50 51 51 53 54 54 61 69 70 71 72 72 73 74 74 76 76 77 81 87 89 91 v List of Tables Table 2.1- Summary of information pertaining to the collected PM2.5 and PM10 (particulate matters with diameters below 2.5 and 10 µm, respectively) across different locations worldwide. Table 2.2 - The mass fraction of PM chemical components in the studied cities. Redox- active metals are highlighted in the table. Table 2.3 - The mass fraction of PAH components in the studied cities. The values below limits of detection (LOD) are shown as <LOD. Table 2.4 – The intrinsic and extrinsic DTT activity for the collected PM batches across the globe. Table 2.5 - The regression analysis between PM constituents and intrinsic DTT activity. Table 3.1 – Summary of information pertaining to the collected particles in Los Angeles, Milan, and Thessaloniki. Table 3.2 – The inhalation unit risk (IUR) values reported by IRIS (https://www.epa.gov/iris) for investigated metals. Table 3.3 – Concentration of particle phase PAHs (ng/m3) in Los Angeles, Thessaloniki and Milan. Table 3.4 – Comparison of total BaPeq values in this study with estimated values in the literature. Table 3.5 – Carcinogenic risks (×10 -6 ) by inhalation of selected PM-bound toxic components for population in Los Angeles, Thessaloniki and Milan. Table 4.1 - Mass fraction of PAHs in DEP sample and PM 2.5 sampling sites (I-110 freeway, I710 freeway, Wilshire/Sunset Blvd., and USC). Table 4.2 - Mass fraction of metals and elements in the DEP sample, PM2.5 sampling sites (I110 freeway, I-710 freeway, Wilshire/Sunset Blvd., and USC), and PM0.25 sampling sites (Long Beach (LB), Los Angeles (LA), Riverside, and Lancaster). Table 5.1- Summary statistics of PM2.5 mass and its metal and trace element mass concentrations for the sampling site in central Los Angeles (all values are in units of µg/m 3 ). Table 6.1 - Distance-driven emission factors of PM10 and its major chemical components on I-110 and I-710 freeways as well as HDV and LDV fleets. Table 6.2 - Speciated PM 10 emission factors of non-tailpipe sources for: a) I-110 and I-710 freeways and: b) LDV and HDV fleet. Table 6.3- Comparison of PM 10 emission factors of non-tailpipe sources to previously reported values in the literature. All values are in units of mg km -1 veh -1 . Table 6.4 - Speciated daily tailpipe and non-tailpipe emission rates of LDVs and HDVs on the entire Los Angeles basin freeways. All values are in units of kg/day. 9 12 14 15 16 25 26 29 30 31 46 47 55 82 84 86 87 vi List of Figures Figure 2.1- The comparison of PAH levels in Milan and Los Angeles I-110 and I-710 freeways. Figure 2.2 - The comparison of PAH mass fractions (ng/µg of PM) in the ambient PM during and after a wildfire in LA to that of Milan. Figure 3.1 – Comparison of ambient concentration of carcinogenic metals in Los Angeles (LA), Thessaloniki and Milan. Figure 4.1- Schematic of the laboratory setup. Figure 4.2 - The mass fraction of a) EC and OC, b) inorganic ions, c) PAHs, and d) metals and elements in the standardized DEP sample. Figure 4.3 - PM mass fraction of EC and OC at cruise and transient urban dynamometer driving schedule (UDDS) cycles. Figure 4.4 - PM mass fraction of EC and OC at Interstate 110 (I-110), Interstate 710 (I-710), Wilshire/Sunset Blvd., and USC sampling site. Figure 4.5 - Ambient PM2.5 chemical composition during year 2019 in a) Houston, b) Los Angeles, c) New York, and d) Pittsburgh obtained from the Chemical Speciation Network (CSN) database provided by the US Environmental Protection Agency (US EPA). Figure 5.1- Location of the monitoring site in central Los Angeles (CELA). Figure 5.2 - Annual box plots (left panel) and median (right panel) of ambient mass concentrations for: (a) Fe; (b) Ni; (c) V; (d) Zn; (e) Ba; (f) Pb; (g) Ti; (h) Al; (i) Ca; (j) K; (k) Mn; and (l) Cu. Figure 5.3 - PMF-resolved factor profiles. Figure 5.4 - Source contributions to total metal elements for the period of 2005 to 2018: (a) relative source contribution; (b) absolute source contribution; and (c) absolute source contribution during cold season (CS) and warm season (WS). Figure 5.5 - The relative contribution of PMF-resolved sources to individual metal elements: (a) Fe; (b) Ni; (c) Zn; (d) V; (e) Pb; (f) Ba; and (g) Ti. Figure 6.1 - Map of the sampling routes in Los Angeles area. Figure 6.2 - Box plots of a) CO 2 concentrations (ppm), and b) PM 10 concentrations (µg/m 3 ) at USC background site, I-110 and I-710 freeways. Figure 6.3 - Mass concentrations of PM 10 and its components at USC background site, I-110 and I- 710 freeways during the sampling period. Figure 6.4 - The PMF-resolved profiles for the five identified factors during the entire sampling campaign. 14 18 28 37 40 42 43 44 52 58 64 66 68 73 76 77 80 vii Figure 6.5 - Relative contribution of identified sources including secondary aerosols (SA), seasalt, vehicle exhaust, resuspended dust and tire and brake wear to the freeway PM 10 concentrations. Figure 6.6 - Comparison of distance-driven PM 10 emission factors in I-110 and I-710 freeways with reported values in the Turkey tunnel (Gaga et al., 2018), Sweden’s Tingstad tunnel and Lundby tunnel (Sternbeck, 2002), Los Angeles I-110 and I-710 freeways (Ning et al., 2008), Pensylvannia tunnels (Grieshop et al., 2006) and Toronto highway (Wang et al., 2021). Figure 6.7 - The comparison of fuel-based PM 10 emission factors for LDV fleets to the estimated values in Wilshire/Sunset boulevards in Los Angeles (Kam et al., 2012). 81 83 84 viii Abstract PM is released from variety of natural and anthropogenic sources, each of which impose unique alteration in ambient particles. As the first step, the impact of urban emission sources on the chemical composition of ambient PM as well as the associated oxidative potential was investigated. The results illustrated that among the major PM components, the WSOC exhibited the highest correlation with the quantified redox activity. The estimated risk values for the content of metal elements and PAHs in the biomass burning samples exceeded the US EPA standards by a considerable margin. As one of the redox-active components, the long-term trend in concentration of PM2.5- bound redox active metals in Los Angeles basin were examined and the main contributing sources and their temporal changes during the last two decade were determined. Mineral dust and re-suspended road dust are the dominant contributors to total metal concentrations, followed by combustion emissions and tire wear. While the emissions from combustion sources significantly reduced between 2005-2018 period due to regulatory efforts in LA, an increase in re-suspended road dust emissions were observed. Considering the importance of non-tailpipe emissions, a methodology was developed in this study to quantify the PM emission factors of various traffic sources characterized by highways and vehicle fleet. According to the results, the collective PM emissions from non-tailpipe sources are almost twice of tailpipe emissions. The estimated emission factors corresponding to non-tailpipe sources largely exceeds the most recent exhaust PM10 emissions standards. 1 Chapter 1 : Introduction 1.1 Background Rapid urbanization and the resulting surge in anthropogenic emissions have made air pollution a major environmental and public health concern. Previous studies have extensively investigated the severe effects of air pollution on the human health such as cardiovascular diseases, lung cancer, neurodegenerative disorders, and premature mortality (Apte et al., 2018; Brook et al., 2010; Delfino et al., 2009). Particulate matter (PM), as a criteria air pollutant, has been the focus of a wide range of in vivo and in vitro health studies (Hazlehurst et al., 2021; Kim et al., 2015; Stanek et al., 2011). Long-term exposure to ambient PM is typically associated with cardiovascular, respiratory and carcinogenic diseases and premature death (Chowdhury et al., 2020; Jo et al., 2017; Lee et al., 2014; Li et al., 2017). The latest study of the global burden of diseases has linked over 4 million annual deaths worldwide to exposure to ambient PM (Cohen et al., 2017). PM can be released as primarily aerosols from variety of natural and anthropogenic sources or formed through secondary chemical processes (Joutsensaari et al., 2018; Sandrini et al., 2016). Each emission source imposes unique alterations in the physio-chemical and toxicological characteristics of ambient PM (e.g., chemical composition, size) which may contribute to potentially different health outcomes (Anenberg et al., 2014; Gerlofs-Nijland et al., 2019; Kelly and Fussell, 2012). Ambient particles are subject to a lot of changes even from similar sources. For instance, the chemical composition of PM originating from vehicle exhaust depends on the age and type of engine, fuel composition, load characteristics, lube oil compounds, and efficiency of after-treatment emission control technologies (Mauderly, 2001; Pakbin et al., 2009; Rosenkranz, 1996; Singh et al., 2004; Thiruvengadam et al., 2014). Therefore, a great deal of uncertainties is still associated with the ambient PM which merits further investigation of the PM characteristics (e.g., size, chemical composition, toxicity) under various scenarios with unique emissions sources and urban conditions. These investigations can further deepen our understanding of the ambient PM and its impacts on the public health, which will be of great importance to the policy makers and air quality officials to prioritize their emission reduction policy to mitigate population exposure to the sources with highest levels of adverse health effect and in turn, tremendously decrease the adverse health consequences and their related health care costs. 2 1.2 List of objectives The first study had the following objectives: - Identifying the chemical composition of ambient PM in various location sites throughout the globe and exploring the impact of urban emission sources on the chemical composition of particles. - Comparison of the PM oxidative potential dominated by various emission sources. - Investigating the impact of particles chemical components on the PM toxicity. The second study had the following objectives: - Determining the lifetime cancer risk values from population exposure to PAHs and metals in different metropolitan environments - Investigation of the impact of LA regulation on the cancer risk values in comparison with other polluted cities. The third study had the following objectives: - Determining the chemical composition of the standardized DEP. - Investigating the extent of dissimilarities in DEP chemical composition to ambient PM and assessing whether DEP is a suitable proxy of real-word conditions in urban environment. The fourth study had the following objectives: - Evaluating the long-term trends in PM2.5-bound ambient concentrations of redoxactive metals and trace elements in Los Angeles. - Identifying the main contributing sources to total metals concentrations throughout the period of 2005-2018. - Investigating the annual changes in source contribution to total metals and identifying the impact of air quality policies in redox-active metals levels across Los Angeles. The last study had the following objectives: - Obtaining the chemical composition of ambient PM on two major Los Angeles freeways with different traffic composition (I-110 and I-710 freeways). - Identifying the main pollutant sources of freeway PM and estimating the contribution of each source. 3 - Quantification of non-tailpipe and tailpipe PM10 emission factors on both freeways and across entire LA county, characterized by low-duty (LDV) and heavy-duty vehicle (HDV) fleets. 1.3 Overview As the first study, we investigated the impact of urban emission sources on the chemical composition of ambient particulate matter (PM) as well as the associated oxidative potential. We collected six sets of PM samples in five urban location sites around the world over long time periods varying from weeks to months, intentionally selected for their PM to be dominated by unique emission sources: 1) PM2.5 produced mainly by traffic emissions in central Los Angeles, United States (US); 2) PM2.5 dominated by biomass burning in Milan, Italy; 3) PM2.5 formed by secondary photochemical reactions thus dominated by secondary aerosols in Athens, Greece; 4) PM10 emitted by refinery and dust resuspension in Riyadh, Saudi Arabia (SA); 5) PM10 generated by dust storms in Riyadh, SA, and 6) PM2.5 produced mainly by industrial and traffic emissions in Beirut, Lebanon. The PM samples were chemically analyzed and their oxidative potential were quantified by employing the dithiothreitol (DTT) assay. We observed that PM toxicity were mostly driven by biomass burning and secondary organic aerosols which release substantial levels of water-soluble organic carbons (WSOC) and OC. The examination of lifetime cancer risk assessment based on inhalation of carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) in Los Angeles, Milan and Thessaloniki also corroborated the substantial health hazards caused by biomass burning emissions. The estimated risk values for the content of metal elements (i.e., Arsenic and Chromium) and PAHs in the biomass burning samples exceeded the US EPA standards by a considerable margin. In contrast, the estimated risks associated with metal and PAHs in Los Angeles were mostly comparable to the guideline value which highlights the impact of local air quality measures in improving the air quality and lowering the cancer risks in Los Angeles compared to the other two cities. In the next study, we investigated the standardized DEPs used in toxicology studies as a proxy for ambient PM and evaluated the extent to which these standardized materials can represent ambient particles of various ambient PM scenarios, as shown in the first chapter. To this end, we analyzed the chemical characteristics of standardized diesel exhaust particles (DEP) and compared them to those of read-world particulate matter (PM) collected in different urban settings. Standard reference material SRM-2975 was obtained from the National Institute 4 of Standards and Technology (NIST) and was chemically analyzed for the content of elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), inorganic ions, and several metals and trace elements. Our results revealed several dissimilarities between the chemical constituents of ambient PM and those of DEP including high levels of EC, lack of high molecular weight carcinogenic PAHs and trace levels of redox-active metals in the DEP sample. For the third study, we focused on metal and trace elements as one of the main redox- active PM components and investigated long-term trends in the ambient concentrations and sources of redox-active metals and trace elements in central Los Angeles over the period of 2005-2018. Mass concentrations of PM2.5-bound metals and crustal elements were obtained from the Chemical Speciation Network (CSN) database provided by the US Environmental Protection Agency (US EPA). The recorded metal concentrations showed considerable variations throughout the study period, but they generally followed a descending trend from 2005 to 2018. In order to further investigate and interpret the observed decreasing trends, this dataset of 2005-2018 was employed in the positive matrix factorization (PMF) model to determine the contribution of different sources to the total metals’ concentrations and their trends over time. Four major sources were identified by the PMF model, including mineral dust, re-suspended road dust, combustion, and tire wear. Mineral dust and re-suspended road dust were the dominant contributors to total metal concentrations, followed by combustion emissions and tire wear. While the PMF results showed generally consistent contributions of mineral dust to total metals concentration throughout the investigation period, the contribution of re-suspended road dust to total metals increased from 2013 to 2018 probably due to the increased road traffic (expressed in the form of vehicle miles traveled, VMT) as well as the growing use of electric vehicles (EVs) (which increases resuspension of road dust particles due to their heavy weight) in the area during the same period. In contrast, the contribution of combustion emissions decreased by almost 88% from 2005 to 2018. The results of this study underscored the impact of traffic emissions and particularly non-tailpipe emissions on toxicity of ambient PM in city of Los Angeles. In the next chapter we estimated the emission factors of PM10 and its chemical constituents from various traffic sources including non-tailpipe and tailpipe emissions were on two interstate freeways in the Los Angeles basin. PM10 samples were collected on the I-110 and I710 freeways as well as at the University of Southern California (USC) campus as the urban background site, while freeway and urban background CO2 levels were measured simultaneously. PM samples were analyzed for their content of chemical species which were 5 used to estimate the emission factors of PM and its constituents on both I-110 and I-710 freeways. The estimated values were employed to determine the emission factors for light (LDV) and heavy-duty vehicles (HDV). The quantified species were also processed by the positive matrix factorization (PMF) model to produce PM10 freeway source profiles and their contribution to PM10 mass concentrations. Using the PMF factor profiles and emission factors on the two freeways, we characterized the emission factors for light-duty and heavy-duty vehicles by each non-tailpipe source. Our findings indicated higher non-tailpipe emission factors of PM10 and metal elements on the I-710 freeway compared to the I-110 freeway, due to the higher fraction of heavy-duty vehicles (HDVs) on the former. Furthermore, the resuspended dust was the prevailing PM10 source across all traffic emission sources. 6 Chapter 2 : The oxidative potential of particulate matter (PM) in different regions around the world and its relation to air pollution sources In this study, we investigated the impact of urban emission sources on the chemical composition of ambient particulate matter (PM) as well as the associated oxidative potential. We collected six sets of PM samples in five urban location sites around the world over long time periods varying from weeks to months, intentionally selected for their PM to be dominated by unique emission sources: 1) PM2.5 produced mainly by traffic emissions in central Los Angeles, United States (US); 2) PM2.5 dominated by biomass burning in Milan, Italy; 3) PM2.5 formed by secondary photochemical reactions thus dominated by secondary aerosols in Athens, Greece; 4) PM10 emitted by refinery and dust resuspension in Riyadh, Saudi Arabia (SA); 5) PM10 generated by dust storms in Riyadh, SA, and 6) PM2.5 produced mainly by industrial and traffic emissions in Beirut, Lebanon. The PM samples were chemically analyzed and their oxidative potential were quantified by employing the dithiothreitol (DTT) assay. Our results revealed that the Milan samples were rich in water soluble organic carbon (WSOC) and PAHs, even exceeding the levels measured on Los Angeles (LA) freeways. The PM in Athens was characterized by high concentrations of inorganic ions, specifically sulfate which was the highest of all PM samples. The ambient PM in LA was impacted by the traffic-emitted primary organic and elemental carbon. Furthermore, the contribution of metals and elements per mass of PM in Riyadh and Beirut samples were more pronounced relative to other sampling areas. The highest intrinsic PM redox activity was observed for PM with the highest WSOC fraction, including Milan (biomass burning) and Athens (secondary organic aerosols, SOA). PM in areas characterized by high metal emissions including dust events, refinery and industry, such as Riyadh and Beirut, had the lowest oxidative potential as assessed by the DTT assay. The results of this study illustrate the impact of major emission sources in urban areas on the redox activity and oxidative potential of ambient PM, providing useful information for developing efficient air pollution control and mitigation policies in polluted areas around the globe. This paper is based on the following publication: Farahani, V.J., Altuwayjiri, A., Pirhadi, M., Verma, V., Ruprecht, A.A., Diapouli, E., Eleftheriadis, K. and Sioutas, C., 2022. The oxidative potential of particulate matter (PM) in different regions around the world and its relation to air pollution sources. Environmental Science: Atmospheres, 2(5), pp.1076-1086. 7 2.1 Introduction The main objective of this study is to investigate the impact of different urban source emissions on the chemical and potential toxicological properties of ambient particulate matter (PM) by comparing the oxidative potential and chemical constituents of PM samples in various distinct locations throughout the globe, each dominated by a specific emission source. Due to the considerable differences in chemical composition of ambient particles formed from different emission sources, the PM mass concentration is probably not an ideal measure for the toxicity of PM. Therefore, researchers have proposed oxidative potential (OP), the capacity of aerosols to induce oxidative damage, as one of the metrics to reflect the acute and chronic health effects of PM exposure (Bates et al., 2019a; Fu et al., 2014; Fushimi et al., 2021; PasztiGere et al., 2012; Song et al., 2021; Weichenthal et al., 2016). The OP is generally measured by means of cellular and acellular (chemical) assays. One of the most commonly employed acellular method is the dithiothreitol (DTT) assay. While DTT may not be a direct indicator for PM toxicity, it is a suitable proxy for redox activity and has been extensively employed in the literature (Cho et al., 2005; Saffari et al., 2014a; Verma et al., 2012; Vreeland et al., 2017). In this method, DTT is oxidized to its disulfide form after its interaction with redox-active chemicals in PM, and the linear decay rate of DTT is used as an index of the oxidative capacity of the PM (Cheng et al., 2013). Numerous epidemiological and toxicological studies have been conducted to identify the PM components inducing oxidative activity (Daher et al., 2014; Kim et al., 2017; Ma et al., 2019; Saffari et al., 2014b; Verma et al., 2009a). A recently published study by Bates et al. (2019) underscored the significant role of water-soluble total carbon (WSTC) in PM10 oxidative properties in the Alpine area. Lovett et al. (2018) observed significant associations between EC and OC (tracers of tailpipe emissions), WSOC (tracer of SOA), and heavy metals such as Ni, Cu, Zn, As, V, Cd, and Pb (tracers of non-tailpipe emissions) with the oxidative potential of ambient particulate matter in Beirut. Saffari et al. (2014a) integrated the results of several studies that were conducted at different locations across the world and linked PM-induced oxidative potential with particle chemical composition. The authors of that study employed a cell-based microphage assay (i.e., 2′,7′dichlorodihydrofluorescein diacetate, DCFH-DA) to estimate the oxidative potential of PM. The results of that study included the PM collected in various areas globally almost a decade ago. However there has been substantial alteration in the emission sources of ambient PM in the studied locations such as Los Angeles, due to promulgation of vehicle and industrial emissions control technologies (Farahani et al., 2021); in Italy, due to elevated domestic 8 biomass burning (Paglione et al., 2020); in Greece, due to reduction in fossil fuel use as well as the growing use of diesel passenger cars following the withdrawal of the ban on these types of vehicles (Eleftheriadis, 2019; German et al., 2016); and in Lebanon, due to change in vehicle fleet composition and enhanced fuel consumption of diesel generators (Baayoun et al., 2019; Marrouch and Mourad, 2019). To the best of our knowledge, no recent studies have evaluated the ambient PM chemical and potential toxicological characteristics in various densely populated cities with unique urban settings and emission sources. A series of comprehensive ambient air sampling campaigns were conducted in the metropolitan areas of Los Angeles (USA), Athens (Greece), Milan (Italy), Beirut (Lebanon), and Riyadh (Saudi Arabia). PM2.5 (particulate matter with aerodynamic diameters below 2.5 µm) and PM10 (particulate matter with aerodynamic diameters below 10 µm) collected on filters were analyzed for their carbonaceous content and chemical components (i.e., watersoluble ions, metals, and trace elements). The DTT in vitro assay was deployed to determine the oxidative potential of these samples. Findings of this work advance our knowledge of complex source emission impacts on the PM oxidative potential and chemical composition in different environments and provide important insights for more targeted and cost-effective air pollution strategies in polluted areas around the globe. 2.2 Methodology 2.2.1 Sampling information Six batches of PM samples with varied particle size fractions (i.e., PM2.5 or PM10) and sampling periods were the focus of this synthesis study. The PM samples were collected in cities of Los Angeles, Milan, Athens, Riyadh, and Beirut, each corresponding to unique set of PM emission sources. Table 2.1 summarizes the relevant information for each sampling batch. The samples in Riyadh were collected during two periods, one corresponding to dust storm events to measure the impact of dust particles on the oxidative activities and the other corresponding to a non-dust period to investigate the contribution of refinery emissions and dust resuspension to the PM oxidative potential. The samples in Athens were collected during the summertime when the rate of photochemical oxidation is at peak, enhancing the content of secondary organic aerosols (SOA) as the main PM emission source in that region. Furthermore, the high temperature in the summer minimizes the contribution of primary volatile and semivolatile compounds to PM mass. Biomass burning is another major source generating PM 9 redox activity in the metropolitan areas in the colder time period of the year. We investigated the impact of this emission source on the PM oxidative potential by incorporating the samples collected in Milan during the winter. According to our previous investigation, the overall oxidative potential of Milan’s ambient particles during cold periods is largely induced by the biomass burning activities in this urban region (Hakimzadeh et al., 2020). The ambient PM in cities of Los Angeles and Beirut are both heavily impacted by the traffic emissions. However, they are dissimilar in their PM chemical composition. The Beirut PM redox activity is dominated by the transition metals, compared to the LA area, in which organic compounds are the driving components in generating redox activity (Daher et al., 2014). Furthermore, particles in Beirut are also influenced by industrial emissions to a much higher degree than those in Los Angeles. Table 2.1- Summary of information pertaining to the collected PM2.5 and PM10 (particulate matters with diameters below 2.5 and 10 µm, respectively) across different locations worldwide. City Particle size Dominant PM emission source Sampling period(s) Sampling days Study Los Angeles, USA PM 2.5 Traffic emissions August 2018 Dec 2018 – Jan 2019 24 Pirhadi et al. (2020) Milan, Italy PM 2.5 Biomass burning Dec 2018 – Feb 2019 14 Hakimzadeh et al. (2020) Athens, Greece PM 2.5 Secondary organic aerosols (SOA) Aug 2020 20 Current study Beirut, Lebanon PM 2.5 Traffic and industrial emissions March 2020 and May 2020 34 Current study Riyadh, Saudi Arabia PM 10 Dust and refinery emissions December 2019 – August 2020 63 Altuwayjiri et al. (2022) 2.2.2 Chemical analysis The PM samples were chemically analyzed for their content of elemental carbon (EC), organic carbon (OC), water-soluble inorganic ions, polycyclic aromatic hydrocarbons (PAHs) as well as metals and trace elements by the Wisconsin State Lab of Hygiene (WSLH). The EC/OC content in the PM samples were measured via the Thermal Evolution/Optical Transmittance (TOT) analytic method using a model-4-semi-continuous OC/EC field analyzer (Sunset Laboratory Inc, USA) (M. E. Birch and Cary, 1996). A small fraction of the filter was punched and kept in an oven, which was set at a temperature of 820°C to capture the oxidized OC fraction. The oven temperature was decreased and then raised to the temperature of 860°C 10 through the mixture of helium and oxygen gases to measure the oxidized EC fraction. The WSOC fraction of PM samples was quantified through the extraction of PM in ultrapure water and the subsequent filtration (0.22 µm pore size) of the samples using a Sievers 900 total organic carbon (TOC) analyzer (GE Analytical Instruments, Boulder, CO, USA) (Stone et al., 2008). The analysis of PM-bound metals and trace elements was performed by means of inductively coupled plasma-mass spectroscopy (ICPMS) (Drago et al., 2018; Lough et al., 2005). A punch of the filter was digested in a mixture of nitric acid, hydrochloric acid, and hydrofluoric acid (0.6mL 16N HNO3, 0.2mL 12N HCl, 0.1mL 28N HF) using an automated, temperature- and pressure-regulated, trace analysis microwave system (Milestone Ethos+). The PAH concentrations was measured through chromatography/mass spectrometry (GC/MS) analysis as described in Sánchez et al. (2013). The remaining part of the filters were extracted in a mixture of ultrapure water and ethanol by means of ion chromatography using a Dionex Model DX-500 Ion Chromatograph to quantify the PM inorganic ions content including ammonium (NH4 + ), nitrate (NO3 - ), and sulfate (SO4 2− ). 2.2.3 PM oxidative potential Oxidative potential of the PM samples was determined by employing an in vitro DTT assay (Fang et al., 2017). Th DTT is one of the most commonly used acellular assays to quantify PM OP (Cho et al., 2005; Xiong et al., 2017). The OP measured by the DTT assay has shown a stronger association with wide range of adverse health effects compared to PM2.5 mass concertation (Abrams et al., 2017; Bates et al., 2015; Fang et al., 2016; Yang et al., 2016) and therefore is a suitable metric to represent the health effects associated with PM exposure. Furthermore, past studies have shown that the DTT-based OP is sensitive to chemical composition (Bates et al., 2019b; Verma et al., 2009a; Wang et al., 2018), changes seasonally (Patel and Rastogi, 2018; Verma et al., 2014; Yu et al., 2021) and is extremely responsive to episodic events such as wildfires (Verma et al., 2009b), haze (Lyu et al., 2018) and fireworks (Puthussery et al., 2018). The quantification of PM oxidative potential using this assay involves the incubating DTT with PM aqueous extracts, and the DTT depletion rate is measured, which is proportional to the generation rate of PM-driven ROS. The PM samples were stored in a freezer at a temperature of -20°C and extracted into an aqueous media using high purity Milli-Q water. The extracts were incubated in the mixture of potassium phosphate (KPO4) buffer and DTT, and then the linear rate of DTT consumption was quantified. Further details of the DTT methodology are available elsewhere (Verma et al., 2014). The intrinsic and extrinsic DTT 11 values were estimated by normalizing DTT depletion rate to PM mass and sampled air volume, respectively. The intrinsic DTT values are in units of nmoles/min per mg of PM and reflect the oxidative potential of PM per unit mass, while the extrinsic DTT values are in units of nmol/min per m 3 of air and indicate the oxidative potential per unit volume of air of the aerosol component. The consistency of the measured DTT was evaluated by positive control as well as multiple field blanks, which were included in every sample queue. The standard chemical employed for positive control was 9,10-Phenanthraquinone (PQ) based on the reported sensitivities (Cho et al., 2005; Xiong et al., 2017). The concentration of the PQ was 0.2 mM in the reaction vial of DTT while the average rate of positive control was 1.867 ± 0.094 µM/min or 0.037 ± 0.039 pmol/(min.µg) (Yu et al., 2020). 2.3 Results and discussion 2.3.1 Chemical composition of PM The PM chemical constituents were investigated by organic carbon (OC) and the associated water-soluble organic carbon (WSOC), elemental carbon (EC), metal and trace elements as well as inorganic ions including chloride (Cl-), nitrate (NO3-), sulfate (SO42-), sodium (Na+), ammonium (NH4+) and potassium (K+). The total metallic content was calculated as the sum of the crustal and trace elements. The crustal portion of the metal elements was quantified using the following equation which is based on the oxidized form of metal elements (HUEGLIN et al., 2005; Marcazzan et al., 2001): C = 1.89𝐴𝑙 + 1.21𝐾 + 1.43𝐹𝑒 + 1.40𝐶𝑎 + 1.66𝑀𝑔 + 1.70𝑇𝑖 + 2.14𝑆𝑖 (2.1) Silicon (Si) is not determined in the ICP-MS analysis; thus, the concentration of this specie was calculated as 3.41 times of aluminum concentration (Al) (HUEGLIN et al., 2005). The average mass fraction of individual species for PM sample sets at each location is shown in Table 2.2. The PM2.5 sample sets in Milan were dominated by water-soluble inorganic ions (380.71 µg/mg PM) and OC (217.76 µg/mg PM), followed by trace amounts of metals (119.57 µg/mg PM). High levels of water-soluble organic carbon (WSOC) (118.79 µg/mg PM), a tracer of biomass burning during the cold periods in the absence of photochemistry (Sannigrahi et al., 2006; Urban et al., 2012), were observed in Milan as a result of significant biomass burning in that city during the sampling period (Hakimzadeh et al., 2020). Nitrate (NO3-) and ammonium 12 (NH4+) constituted the highest fraction of PM among inorganic ions (224.75 and 91.05 µg/mg PM, respectively), which further corroborates the impact of biomass burning on PM levels. This is also consistent with Daellenbach et al. (2020) findings in Europe, attributing high SOA levels in winter to the oxidation of the anthropogenic precursors, mainly from biomass burning activities. Similar to Milan, the ambient fine particles in Athens displayed high levels of OC (241.64 µg/mg PM) and water-soluble inorganic ions (359.74 µg/mg PM). Markers of secondary organic aerosols (i.e., SO4-2 and NH4+) were among the highest of all PM batches and dominated Athens’s water-soluble inorganic ions, underscoring the impact of SOA on ambient PM loadings in this region. Furthermore, the WSOC per mass value was 147.40 µg/mg PM, a significant increase from that of measured in Beirut (51.98 µg/mg PM) and Riyadh during dust storms (12.86 µg/mg PM) and non-dust periods (44.08 µg/mg PM). The samples in Riyadh exhibited significant loadings of PM-bound metals during both dust (299.63 µg/mg PM) and non-dust (300.78 µg/mg PM) periods. The chemical composition of ambient PM2.5 in Los Angeles was primarily composed of carbonaceous compounds. The quantified EC (23.05 µg/mg PM) and OC (241.12 µg/mg PM) were among the highest of the studied sites in this heavily trafficked region, while WSOC constituted relatively small fraction (68.6 µg/mg PM) of the total organic carbon. Nitrate was the dominant specie (179.15 µg/mg PM) among the water-soluble inorganic ions, followed by sulfate (56.41 µg/mg PM) and ammonium (46.83 µg/mg PM). Moderate loadings of total metals (160.14 µg/mg PM) were also observed in the LA basin which have been mostly associated with the traffic sources including resuspended dust and the vehicle abrasion (Farahani et al., 2021; Oroumiyeh et al., 2022). Table 2.2 - The mass fraction of PM chemical components in the studied cities. Redox- active metals are highlighted in the table. PM constituents (µg/mg PM) Beirut Athens Los Angeles Riyadh (Dust) Riyadh (Non-dust) Milan EC 10.04 13.36 23.05 6.71 23.00 28.05 OC 90.27 241.64 241.12 34.62 76.55 217.76 WSOC 51.98 147.40 68.62 12.86 44.08 118.79 Cl - 0.13 1.09 - 4.56 6.23 9.74 NO 3 - 0.94 3.93 179.15 20.73 40.45 224.75 SO 4 -2 93.41 264.17 56.41 37.37 77.97 40.29 Na 3.26 15.18 - 4.30 7.07 3.03 NH 4 + 31.89 69.57 46.83 2.67 8.33 91.05 K + 2.17 5.79 - 2.07 2.87 11.85 13 Metals (ng/mg PM) Ca 58088.44 44540.28 19023.33 134400.06 160764.66 10754.79 Al 13476.45 18532.70 7729.16 58536.25 40047.85 5424.49 Fe 8540.81 18755.25 7333.21 39446.27 28165.42 5973.15 Mg 17613.01 8073.74 9885.59 28090.01 18773.74 4964.33 Zn 4211.02 1348.21 453.39 221.65 2157.01 618.18 Ba 537.62 451.12 478.53 430.75 538.88 214.18 Cu 801.49 281.73 262.63 84.59 218.36 241.26 Ti 849.11 989.80 481.08 3848.63 2806.80 119.48 Mn 1032.63 387.01 118.95 758.83 562.91 158.37 Pb 210.67 259.08 55.92 46.60 185.52 293.95 Ni 586.03 145.63 47.45 95.74 70.91 37.14 Sn 56.92 134.82 71.62 11.14 33.22 147.62 Cr 199.16 203.04 86.14 118.88 92.63 111.43 V 29.06 132.10 17.78 112.59 97.13 6.60 Li 31.34 8.46 15.91 33.14 20.91 4.17 Cd 5.67 6.57 1.22 1.01 3.11 4.84 Pd 0.65 0.56 2.34 1.09 0.62 4.52 Table 2.3 compares the PAH levels observed in Beirut, Athens, LA, Riyadh and Milan. The PAHs per PM mass concentration in Milan were substantially higher than other location sites across all PAH species. The PAHs are carcinogenic organic compounds which are typically released in particle phase during incomplete combustion from gasoline and diesel- fueled vehicles as well as biomass burning (Alves et al., 2015; Galarneau, 2008; Lima et al., 2005; Miller et al., 2010). The PAH content of the Milan samples, which are mainly driven by biomass burning as noted earlier, were significantly greater compared to the corresponding ambient levels in LA basin where PM is primary released from vehicle emissions. In fact, the observed values in Milan even exceeded our previously measured PAH values inside two major Los Angeles’s freeways (Shirmohammadi et al., 2017b), as shown in Figure 2.1. The cumulative PAH content in Milan were 1.35 ng/µg PM which is greater than values at I-110 (0.16 ng/µg PM) and I-710 (0.15 ng/µg PM) freeways by approximately one order of magnitude. These observations underscore the importance of biomass burning as one of the main driving factors in formation of PAHs. The low PAH levels in Athens during summer are probably the result of higher photo-degradation rate of these species with oxidizing gases (ozone, nitrogen oxides, hydrogen peroxide, etc.) (S. O. Baek et al., 1991; Grosjean et al., 1983), as well as increased partitioning to the gas phase (Saarnio et al., 2008), due to the higher ambient temperatures in that time of year. Similarly, Riyadh and Beirut are both Middle Eastern 14 cities associated with very high temperature events during spring and summer, resulting in minor PAH levels for the reasons noted earlier. Fewer number of PAH species were detected in the coastal city of Beirut due to the frequent daytime on-shore winds at this site, which increases the atmospheric dilution and coupled with the high temperature, it can enhance the volatilization, photodegradation and dispersion of PAHs (Daher et al., 2014). Figure 2.1- The comparison of PAH levels in Milan and Los Angeles I-110 and I-710 freeways. Table 2.3 - The mass fraction of PAH components in the studied cities. The values below limits of detection (LOD) are shown as <LOD. PAH species (ng/µg PM) Beirut Athens LA Riyadh (Dust) Riyadh (Non- dust) Milan Phenanthrene <LOD 0.0076 0.0030 0.0002 0.0042 0.0124 Fluoranthene 0.0010 <LOD 0.0026 0.0004 0.0075 0.0462 Pyrene 0.0008 <LOD 0.0031 0.0002 0.0070 0.0433 Benzo(ghi)fluoranthene <LOD <LOD 0.0005 0.0001 0.0048 0.0945 Benz(a)anthracene <LOD <LOD 0.0042 <LOD <LOD 0.0623 Chrysene <LOD <LOD 0.0002 0.0001 0.0047 0.2253 Benzo(b)fluoranthene <LOD <LOD 0.0109 0.0007 0.0249 0.2402 Benzo(k)fluoranthene <LOD <LOD 0.0016 0.0001 0.0040 0.2214 Benzo(j)fluoranthene <LOD <LOD <LOD <LOD <LOD 0.0080 Benzo(e)pyrene <LOD <LOD 0.0090 0.0006 0.0175 0.1665 Benzo(a)pyrene <LOD <LOD <LOD <LOD <LOD 0.0054 Indeno(1,2,3-cd) pyrene <LOD <LOD <LOD <LOD 0.0144 0.0665 Benzo(g,h,i)perylene 0.0003 <LOD 0.0006 0.0002 0.0087 0.0743 15 Coronene <LOD <LOD <LOD <LOD 0.0072 0.0217 Dibenzo(a,e)pyrene <LOD <LOD <LOD <LOD <LOD 0.0016 2.3.2 Comparison of PM oxidative potential among various locales Table 2.4 shows the oxidative potential measured by the DTT assay normalized by PM mass (intrinsic DTT) as well as extrinsic DTT values. The data illustrate a wide range of oxidative responses measured by the DTT assay, underscoring the impact of location-specific emission sources and the resulting PM chemical composition on the PM oxidative stress. The lowest intrinsic level was measured in Beirut, where intrinsic DTT consumption rate was 8.22 ± 1.27 nmoles/(min.mg). The PM sample sets collected in Athens and Milan exhibited 4-7 times higher DTT consumption rate, which in part can be attributed to the greater WSOC content of PM, originated from biomass burning and secondary aerosols, respectively in these cities. In agreement with this observation, Daellenbach et al. (2020), who have investigated the sources of PM and oxidative potential across Europe, reported significant intrinsic oxidative potential from residential biomass burning activities. We will discuss the impact of different chemical constituents on the PM oxidative potential in detail in the following section. The intrinsic DTT values in Riyadh were somewhat higher for the PM collected during the non- dust period (12.53 ± 1.43 nmoles/(min.mg)) relative to those collected in a dust event period (9.32 ± 0.80 nmoles/(min.mg)). The local traffic emission sources in Los Angeles resulted in DTT activity of 28.10 ± 5.23 nmoles/(min.mg). The extrinsic DTT consumption rates spanned from 0.35 ± 0.04 nmoles/(min.m 3 ) in LA to 5.62 ± 0.19 nmoles/(min.m 3 ) in Athens. The different trends in volume-normalized DTT values compared to the mass-normalized approach can be attributed to the higher PM mass concentration in the respective samples, resulting into greater exposure to redox-active aerosols despite the lower capability of PM chemical constituents in inducing oxidative activity. Table 2.4 – The intrinsic and extrinsic DTT activity for the collected PM batches across the globe. Location PM size Intrinsic DTT (nmoles/(min.mg)) Extrinsic DTT (nmoles/(min.m 3 )) Milan PM 2.5 65.29 ± 5.17 3.38 ± 0.26 Athens PM 2.5 49.20 ± 1.66 5.62 ± 0.19 Beirut PM 2.5 8.22 ± 1.27 3.51 ± 0.54 Los Angeles PM 2.5 28.10 ± 5.23 0.35 ± 0.04 Riyadh – Dust period PM 10 9.32 ± 0.80 1.85 ± 0.15 16 Riyadh – Non-dust period PM 10 12.53 ± 1.43 1.05 ± 0.12 2.3.3 The impact of emission sources on PM oxidative potential We performed a linear regression analysis between measured PM chemical constituents and the DTT activities at different sites to identify the emission sources linked to PM oxidative potential, the results of which are summarized in Table 2.5. In order to perform the regression analysis, we employed the average values for the PM chemical constituents (shown in Table 2.2 and Table 2.3) and intrinsic DTT rates (Table 2.4) for sample sets collected at individual locations. We then estimated the correlation coefficient and p-values based on the regression of the average values of DTT and chemical components at these studied sites. Table 2.5 - The regression analysis between PM constituents and intrinsic DTT activity. The correlation coefficient values above 0.7 and the p-values below 0.1 are highlighted. PM constituents R p EC 0.57 0.31 OC 0.81 0.02 WSOC 0.89 0.09 WIOC 0.71 0.02 NO3- 0.61 0.28 SO4-2 0.29 0.12 K + 0.95 0.06 PAHs 0.74 0.03 Ca -0.50 0.06 Al -0.54 0.02 Fe -0.39 0.01 Zn -0.76 0.02 Ba -0.51 0.09 Cu -0.56 0.06 Ti -0.56 0.04 Mn -0.67 0.02 Pb -0.01 0.01 Ni -0.47 0.16 Cr 0.09 0.00 17 V -0.08 0.24 According to the table, the DTT activity is mostly correlated with the K+ (R = 0.94), a marker of biomass burning reported in the literature (Cheng et al., 2013; Oroumiyeh et al., 2022; Urban et al., 2012). The observed regression is also statistically significant (p = 0.06), corroborating the robust redox activity of this emission source. Strong and significant (p = 0.08) correlation between WSOC mass fraction and DTT activities (R = 0.89) was also observed. This observation agrees with previous studies reporting WSOC as one of the driving factors in the DTT consumption rate (Verma et al., 2009a; Vreeland et al., 2017). The highest WSOC content was measured at Athens with the mass fraction of 147 µg/mg PM, followed by Milan (~118 µg/mg PM), Beirut (~52 µg/mg PM) and Riyadh during non-dust (~44 µg/mg PM) and dust periods (~13 µg/mg PM). WSOC is mainly formed by biomass burning and secondary photochemical reactions (Hasheminassab et al., 2013; Weber et al., 2007; Zhang et al., 2012), however the formation of WSOC via photochemical reactions in winter-time is limited by the low temperatures. Thus, the large fraction of WSOC in Milan, which exhibited highest DTT activity among all locations, is most likely associated with the domestic biomass burning activities in this region. The water-insoluble organic carbon (WIOC) content, calculated as the difference between OC and WSOC, was also significantly correlated with the DTT activity (R = 0.71, p = 0.02). Since the DTT analysis was performed on the extracted filters, driven by water-soluble compounds, the high correlation of insoluble fraction of organic compounds with DTT values can be attributed to the fact that WIOC is likely emitted from similar sources as water-soluble DTT-active compounds. The PAHs have also clearly contributed to the overall redox activity of PM as corroborated by the regression values (R = 0.74, p = 0.03). PAH exposure can activate aryl hydrocarbon receptor (AhR) which causes the inflammation through inducing pro-inflammatory molecules and upregulation of proinflammatory genes (Gutiérrez-Vázquez and Quintana, 2018; Podechard et al., 2008). Biomass burning is one of the major sources of PAHs, as discussed in previous section. The significant PM loadings of total PAH in Milan were consistent with the high DTT consumption rate at this site. To illustrate the importance of different biomass burning types on PAH emissions, in Figure 2.2 we compared the PAH levels and DTT values in Milan to the corresponding values in LA during a wildfire event (Verma et al., 2009b). The PAH levels in Milan (79.5 ng/mg PM) were higher than that of LA (0.08 ng/µg PM) by almost two orders of magnitude. The substantial difference in PAH levels can be attributed to the different biomass 18 burning sources in these two cities, where Milan’s emissions are mostly from domestic heating whereas the LA’s biomass burning emissions were dominated by a wildfire event. Furthermore, the content of the winter-time PAH of the PM samples in Milan was impacted by local meteorological conditions (average temperature of 4 ℃ and relative humidity of 73 %) and atmospheric stagnation, leading to higher PAH concentrations relative to corresponding values in LA, which were associated with higher temperature (26 ℃) and lower relative humidity (31%) prevailing during the wildfire episodes. The higher PAH content in Milan was also consistent with the city’s higher DTT activity (65.29 ± 5.17 nmoles/(min.mg)) compared to that of LA during wildfire (24.10 ± 8.12 nmoles/(min.mg)). This observation demonstrates that PM emitted by different biomass burning activities (i.e., residential heating and wildfires) do not necessarily comprise the same toxicological properties. Figure 2.2 - The comparison of PAH mass fractions (ng/µg of PM) in the ambient PM during and after a wildfire in LA to that of Milan. The WSOC level in Athens was primarily driven by SOA formation as the samples at this location were collected during summer, where peak photochemical activities are observed in this region (Taghvaee et al., 2019b). This is further supported by the substantially higher mass fraction of SO4-2, which was also elevated similar to the DTT values, in Athens (~264 µg/mg PM) compared to Beirut (~93 µg/mg PM), Milan (~40 µg/mg PM), LA (~39 µg/mg PM), Riyadh during dust event (~37 µg/mg pm) and non-dust period (~78 µg/mg PM). Sulfate correlation with DTT was significant (p = 0.12) which is probably due to its photochemical origin and correlation with WSOC, since sulfate is not generally regarded as a redox active PM 19 component (Cho et al., 2005; Fang et al., 2017). Thus, the PM’s high DTT activity in Athens, can be associated with the enhanced photochemical reactions in this city. The heavy-trafficked city of LA showed moderate redox activity (28.10 ± 5.23 nmoles/(min.mg)) which is consistent with the moderate correlation of EC, a marker of vehicle exhaust emission, with observed DTT levels (R = 0.57), as well as with the high and significant correlation between DTT and WIOC (R=0.89), with the latter species being a marker of primary OC emissions mainly from traffic (Hasheminassab et al., 2013). Metals, which are typically emitted from vehicular abrasion and combustion sources (e.g., industrial and traffic emissions) (Bates et al., 2015; Farahani et al., 2021; Shirmohammadi et al., 2017b), are reported as one of the contributors to the PM oxidative potential (Gao et al., 2020; Lyu et al., 2018). Specifically, transition metals have been shown to have a large oxidative capacity (Bates et al., 2019b; Daellenbach et al., 2020). However, the DTT consumption rates across the sites chosen in our study, were not correlated with the corresponding content of metals, as evident by negative correlation coefficient for nearly all metal species. This observation could be due to two prime reasons. First, the greater potency of WSOC and OC species on a per PM mass basis in generating redox activity compared to PM elemental content may have led to negative R values between DTT and the mass fraction of metal components. PM samples with high metals and low WSOC and OC contents had the lowest DTT responses. For example, the total metallic mass fraction in Riyadh was the highest among the studied cities during both dust (~299 µg/mg PM) and non-dust periods (~300 µg/mg PM), while the WSOC per mass content was the lowest, resulting in lowest intrinsic DTT values during dust (9.32 ± 0.80 nmoles/(min.mg)) and non-dust periods (12.53 ± 1.43 nmoles/(min.mg)) in this Middle Eastern city. Secondly, the water-solubility and oxidation state of the PM components play a major role in their oxidation activity. The consumption of the DTT assay used on the filters is primarily driven by water-soluble compounds. Therefore, the lack of correlation between DTT consumption rate and the PM total metal content, can also be attributed to the water-insoluble fraction of some metals that decreases their correlation with water-soluble DTT values. 2.4 Summary and conclusions In summary, six sets of PM samples, each dominated by unique emission sources in various locations around the globe were collected and analyzed for their content of chemical constituents and the associated oxidative potential. The DTT acellular assay was employed to quantify the redox activity of PM samples. The dominant component of collected samples in 20 cities of Milan, Athens, Los Angeles were NO3- (224.75 µg/mg PM), SO4-2 (264.17 µg/mg PM) and OC (241.12 µg/mg PM), respectively while samples in Beirut, Riyadh during dust and non-dust periods were dominated by metals (139.87, 299.63 and 300.78 µg/mg PM, respectively). The Milan and Athens samples also exhibited significant fraction of WSOC (118.79 and 147.40 µg/mg PM), indicating the impact of biomass burning and secondary photochemical reactions at the respective location sites. The PAH content of the ambient PM in Milan were significantly enhanced compared to the corresponding ambient levels in other cities, even exceeding our previously measured PAH values in LA during a wildfire as well as the measured values on LA’s I-110 and I-710 freeways. According to our results, the highest intrinsic DTT activity were observed in Milan (65.29 nmoles/(min.mg)), Athens (49.20 nmoles/(min.mg)) which were rich in OC, WSOC and PAHs, indicating that PM emitted by biomass burning activities as well as secondary organic aerosols formed by photochemical reactions can induce higher toxicity in comparison with other PM emission sources. 21 Chapter 3 : Assessing lifetime cancer risk associated with population exposure to PM-bound PAHs and carcinogenic metals in various metropolitan environments Lifetime cancer risk characterization of ambient PM-bound carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) were examined in the cities of Los Angeles, Thessaloniki and Milan, which share similar Mediterranean climates but were different in their urban emission sources and governing air quality regulations. The samples in Milan and Thessaloniki were mostly dominated by biomass burning activities whereas the particles collected in Los Angeles were primary impacted by traffic emissions. We analyzed the ambient PM2.5 mass concentration of Cadmium (Cd), Hexavalent Chromium (Cr(VI)), Nickel (Ni), Lead (Pb), as well as 13 PAH compounds in the PM samples, collected during both cold and warm periods at each location site. The Pb exhibited the highest annual-average concentration in all 3 cities, followed by Ni, As, Cr(VI), Cd and PAHs, respectively. The PAHs were converted into a benzo(α)pyrene (BaP) factor at each location site to calculate cancer risk. The cancer risk assessment based on outdoor pollutants was then performed based on three different scenarios, with each scenario corresponding to a different level of infiltration of outdoor pollutants into the indoor environment. Thessaloniki exhibited rather high risk value associated with lifetime inhalation of As, Cr(VI) and PAHs. The highest cancer risk values were calculated in Milan where the lifetime risk of the exposure to As, Cr(VI) and PAHs exceeded the US EPA standard across all scenarios by a considerable margin. The risk associated with Cr(VI) in Milan, which was the most significant among the investigated elements, ranged from (6.08±0.36)×10 -6 to (9.82±0.58) ×10 -6 . In contrast, the estimated risks associated with metal and PAHs in Los Angeles were mostly comparable to the guideline value, even when the infiltration factor was assumed to be at peak. This observation highlights the impact of local air quality measures in improving the air quality and lowering the cancer risks in Los Angeles compared to the other two cities. 22 3.1 Introduction Previous studies have shown that both short-term (i.e., acute) and long-term (i.e., chronic) exposure to fine particulate matter (PM2.5) can lead to increased cardiopulmonary mortality and morbidity in humans (Brook et al., 2010; Turner et al., 2011). According to the international agency for research in cancer (IARC), an agency of the world health organization (WHO), the second most common cancer disease for all ages and both sexes is lung cancer with approximately 2.21 million reported cases worldwide in 2020 (IARC, 2020). It was estimated that prolonged exposure to ambient PM2.5 could lead to approximately 5% of bronchus, trachea, and lung cancer mortality in urban areas around the world (Cohen et al., 2005). Various studies have attributed lung cancer to the inhalation of carcinogenic species of ambient PM, including polycyclic aromatic hydrocarbons (PAHs) and redox-active metals (e.g., arsenic, cadmium) (Kalagbor et al., 2019; Park et al., 2008; Taghvaee et al., 2018a; Zhang et al., 2009). PAHs are a broad group of chemical compounds composed of multiple fused aromatic rings of carbon and hydrogen atoms that can be arranged in a linear, angular, or clustered configuration with varying complexity and lipophilic properties (Boström et al., 2002; Patel et al., 2020). Particulate-phase PAHs are low-volatility toxic organic compounds that have the potential to travel long distances, thus developing genotoxic effects when inhaled by humans (Sarigiannis et al., 2015; Wang et al., 2014). They can originate from a wide range of sources, including road traffic (i.e., automobile engines), incomplete combustion of fuels in industrial activities, cooking and biomass burning (Alves et al., 2015; Boström et al., 2002; Guo, 2003). The United States Environmental Protection Agency (USEPA) has identified numerous PAH species as priority pollutants due to their potential to cause mutagenesis and carcinogenesis (Delgado-Saborit et al., 2011; USEPA, 2003). In particular, benzo(α)pyrene (BaP) has been widely employed in cancer risk assessment studies as a surrogate for all PAHs due to its established and potent carcinogenic properties (Callén et al., 2014; Collins et al., 1991). This approach involves converting the concentrations of all targeted PAHs to BaP-equivalent concentrations using potency equivalent factors (PEFs) (Lemieux et al., 2015). Moreover, several toxic metal species present in ambient PM, when inhaled, can cause serious health deterioration and carcinogenic effects in humans, including nose, liver, kidney, and lung cancers (Kalagbor et al., 2019; Panne et al., 2001). According to IARC, chromium VI (Cr-VI), arsenic (As), cadmium (Cd), and metallic nickel (Ni) have all been classified as Group 1 carcinogens, indicating sufficient and strong evidence of their ability to cause cancer in humans 23 (IARC, 2012). Research has found that vehicular and industrial emissions are the primary contributors to high levels of heavy toxic metals in ambient PM in various developed and developing countries (Fenger, 2009; Suvarapu and Baek, 2017). The three cities investigated in this study, namely Los Angeles, Milan, and Thessaloniki, are densely populated urban centers, and vulnerable to various carcinogenic pollutants emitted from a multitude of sources, posing a significant risk to the health of their inhabitants (Farahani et al., 2022). The south coast of California, including Los Angeles, has been classified as a non-attainment area since 2004, meaning it does not meet air quality standards for ambient PM2.5 (Kim et al., 2010). As a result, policymakers and air quality agencies implemented strict plans to regulate emission sources in California, especially motor vehicles, and attain PM2.5 standards (Hasheminassab et al., 2014). The California Air Resources Board (CARB) has implemented stringent low-emission vehicle (LEV) regulations in order to reduce tailpipe emissions, including criteria pollutants and greenhouse gases from medium and light-duty vehicles (Lurmann et al., 2015). Milan is located in the Po valley of northern Italy and has been impacted by various urban emission sources, including traffic and secondary organic aerosol (SOA) in the summer season and intense biomass burning in the winter period (Daher et al., 2012; Hakimzadeh et al., 2020). During the wintertime in the metropolitan area of Milan, previous studies found that ambient PM caused premature cell division and DNA damage, which was linked to increased concentrations of PAHs and transition metals (Gualtieri et al., 2011). Furthermore, Thessaloniki, the second most populous city in Greece, has a population of more than a million people and is widely regarded as one of the most polluted cities in Europe in terms of air quality (Moussiopoulos et al., 2009). Ambient PM pollution in Thessaloniki is mainly caused by vehicular emissions and residential heating, primarily when wood burning is used for heating during the cold months (Manoli et al., 2016a; Saffari et al., 2013a). Exposure to PM emissions related to wood burning has been linked to various adverse health impacts (e.g., blood pressure, increased biomarkers of inflammation) due to the presence of redox-active species, including PAHs (Argyropoulos et al., 2016a; Barregard et al., 2006; McCracken et al., 2007). This study aimed to assess the lifetime potential cancer risk of PM-associated carcinogenic species in these three major metropolitan areas (e.g., Los Angeles, Milan, and Thessaloniki) with similar Mediterranean climates but different emission sources and air quality regulations. Given the great variability of indoor concentrations and sources of pollutants, our focus is primarily on health risks associated with exposure to outdoor pollutants. This work provides a comprehensive evaluation of the potential cancer risk posed by inhalation of airborne toxic 24 particles to enable researchers, medical professionals, and government agencies to make more informed decisions regarding safeguarding human health. 3.2 Methodology 3.2.1 Sampling information Three sets of PM2.5 samples, each collected at a unique location site including Los Angeles, Milan and Thessaloniki, were employed in this study. A summary of sampling information for each of these PM batches is shown in Table 3.1. PM2.5 (particles with aerodynamic diameter ≤ 2.5 µm) samples in Los Angeles were collected during two periods of summertime (August 2018) and wintertime (December 2018 to January 2019) at a location site situated in close proximity of a major freeway (i.e., I-110). This location has been often used in prior studies as it offers a combination of various urban pollutant sources, releasing PM in a variety of sizes and chemical constitution. The samples in this region were mostly impacted by the traffic- emitted particles transported from the freeway by the dominant southwesterly winds (Pirhadi et al., 2020). The field campaign in Milan was conducted from December 2018 to February 2019 and from May 2019 to July 2019 to include meteorological conditions of both cold and warm periods. PM2.5 samples were collected in the municipality of Bareggio, which is a suburb located upwind of Milan and recipient of significant vehicular and residential emissions (Hakimzadeh et al., 2020). The samples investigated for Thessaloniki consisted of two main campaigns: 1) PM2.5 sampling from February 2012 to March 2012 and from January 2013 to February 2013 at a residential area in the northern part of Thessaloniki, where samples were mostly dominated by biomass burning emissions due to elevated residential heating activities; 2) PM0.49 sampling from January 2013 to March 2013 and from May 2013 to June 2013 in a commercial city center located near one of the heavily trafficked roadways in downtown Thessaloniki. 25 Table 3.1 – Summary of information pertaining to the collected particles in Los Angeles, Milan, and Thessaloniki. City Particle size Sampling period(s) Study Los Angeles, USA PM 2.5 August 2018 December 2018 – January 2019 Pirhadi et al. (2020) Milan, Italy PM 2.5 December 2018 – February 2019 Hakimzadeh et al., (2020) Thessaloniki, Greece PM 2.5 and PM 0.49 February - March 2012, January - February 2013 January - March 2013, May 2013 - June 2013 Saffari et al. (2013a) Argyropoulos et al. (2016b) 3.2.2 Chemical analysis The elemental characterization of the PM samples was performed by means of inductively coupled plasma-mass spectroscopy (ICP-MS). A section of the filters was initially solubilized in an acid mixture containing nitric acid, hydrochloric acid, and hydrofluoric acid (0.6mL 16N HNO3, 0.2mL 12N HCl, 0.1mL 28N HF) by employing an automated microwave-assisted digestion system (Milestone ETHOS+). The solutions were then analyzed for the content of metal and trace elements using a high-resolution ICP-MS (Lough et al., 2005). To quantify the mass concentration of particle-phase PAHs, a punch of filter was digested in a mixture of dichloromethane/n-hexane. The PAH mass content was then measured by chromatography/mass spectrometry (GC/MS) analysis. Further details regarding this analysis are described in Sánchez et al. (2013). 3.2.3 Health risk characterization 3.2.3.1 Carcinogenic metals The cancer risk associated with inhalation exposure to metal elements was quantified by employing inhalation unit risks (IUR) for each element. The IUR is defined as the upper-bound excess lifetime cancer risk that may be incurred from continuous exposure per 1 µg/m 3 of a component’s ambient concentration (US EPA, 2009). This approach has been extensively employed to assess exposure risk via inhalation (Guo and Kannan, 2011; Hu et al., 2018; Vega et al., 2021). In this method, the concentration of individual metals is multiplied by their corresponding IUR values to obtain the lifetime cancer risk values. However, it is important to incorporate the different indoor and outdoor exposure times as well as the difference in indoor and outdoor pollutant levels in the calculation of the lifetime cancer risk. By assuming that 26 people will spend 80% of their time indoors and therefore, 20% outdoors (Taghvaee et al., 2018b), the lifetime individual cancer risk can be estimated using the following equation: LICR = IUR × (0.2 × C i outdoor + 0.8 × C i indoor ) (3.1) where LICR is a dimensionless unit indicating the lifetime cancer risk through inhalation of carcinogenic metals; IUR shows the inhalation unit risk (per μg/m 3 ) reported by the Integrated Risk Information System (IRIS) based on the epidemiological lifetime exposure (Table 3.2); C i outdoor and C i indoor are the concentration of the metal i in outdoor and indoor environments, respectively (μg/m 3 ). The outdoor concentration values correspond to the average values extracted from the PM samples. Since the indoor emission sources and their relative contribution is subject to extreme variations depending on the buildings’ conditions and residents’ activities, including a variable corresponding to indoor pollutant sources will impose great uncertainty in our calculation. Thus, the risk assessment in this study was solely focused on exposure to outdoor emission sources, making infiltration of outdoor pollutants the sole indoor source in our investigation. The indoor concentration of a PM component (C i indoor ) is therefore obtained by multiplying the outdoor concentration (C i outdoor ) by the infiltration factor (F inf ), which represents the portion of ambient PM that penetrates the indoor space and remains suspended in the air. Table 3.2 – The inhalation unit risk (IUR) values reported by IRIS (https://www.epa.gov/iris) for investigated metals. Metal As Cd Cr(VI) Ni Pb IUR value (per μg/m 3 ) 0.0043 0.0018 0.012 0.00024 0.000012 3.2.3.2 Polycyclic aromatic hydrocarbons (PAHs) The lifetime lung cancer risk associated with exposure to PM-bound PAHs was estimated using the BaP equivalent method (BaPeq). This approach has been adopted in several studies to assess the carcinogenic risks of airborne PAHs(Delistraty, 1997; Hoseini et al., 2016; Taghvaee et al., 2018b; Vega et al., 2021). Benzo(a)pyrene (BaP), which is considered the most potent PAH compound, is used as a proxy for the PAH fraction of complex mixtures. The carcinogenic potency of other PAH compounds is determined relative to that of BaP. The concentration of BaPeq for each PAH component is quantified by multiplying the concentration of individual 27 PAHs by their corresponding potency equivalent factor (PEF). The total BaPeq is then estimated by adding the individual BaPeq values, as shown in the following equation: BaP eq = ∑ C i . PEF i i=1 (3.2) where C i is the concentration of the PAH compound i and PEF i is the corresponding potency equivalent factor (PEF). The office of environmental health hazard assessment (OEHHA) reported a PEF value of 1 for Benzo(a)pyrene and Dibenzo(a)pyrene; 0.1 for Benzo(b)fluoranthene, Benzo(k)fluoranthene, Benzo(j)fluoranthene Benz(a)anthracene and Indeno(1,2,3-cd)pyrene; 0.01 for Anthracene, Chrysene, Benzo(g,h,i)perylene; and 0.01 for Phenanthrene, Fluoranthene, Acephenanthrylene and Pyrene (OEHHA, 2015). The lifetime lung cancer risk from exposure to PAHs is then calculated using Equation (1) and substituting C i with total BaP eq values. The IUR value corresponding to BaP is 0.0006 per µg/m 3 . 3.3 Results and discussion 3.3.1 Concentration of carcinogenic metals and PAHs Figure 3.1 shows the annually averaged concentration of carcinogenic metals extracted from samples in Los Angeles, Milan and Thessaloniki. The redox-active metals investigated in this study for their carcinogenic impact on humans were Arsenic (As), Cadmium (Cd), Hexavalent Chromium (Cr(VI)), Nickel (Ni) and Lead (Pb), which all have been identified as potentially carcinogenic through inhalation pathway (IARC, 2012, 2006). Chromium is present in the atmosphere in mainly two valence states of non-carcinogenic Cr(III) and carcinogenic Cr(VI). The concentration of Cr(VI) was estimated according to the reported ratio of carcinogenic Cr(VI) to total Cr concentration in the literature (i.e., 1/7) (Fakhri et al., 2022; Hao et al., 2020; Ramírez et al., 2020; Vega et al., 2021). Pb and Ni demonstrated the highest loadings on the collected samples, followed by trace levels of Cr(VI), As and Cd, with concentrations below 1 ng/m 3 . The results revealed a strong variation in levels of carcinogenic metals across the studied metropolitan areas. The concentrations of As and Cd in the samples collected in Los Angeles differed significantly from those of Milan and Thessaloniki, as did the concentrations of Pb and Ni. One of the primary sources of As, Cd and Ni is the industrial processes such as metallurgical activities (Schwela et al., 2002; Thomaidis et al., 2003), while As and Ni are also emitted from oil combustion. Additionally, Pb is one of the tracers of 28 combustion processes in vehicles and industries in metropolitan areas (Fernández-Camacho et al., 2012; Lough et al., 2005; Tian et al., 2012). The significant difference in the concentration of heavy metals in Los Angeles can be attributed to the impact of aftertreatment and local air quality policies in reducing the contribution of combustion sources. According to a study examining the long-term trend of air pollutant sources in Los Angeles, the contribution of combustion emissions to ambient concentration of PM2.5-bound metals decreased by nearly 88% during 2005-2018 period due to the abovementioned emissions control policies(Farahani et al., 2021). Figure 3.1 – Comparison of ambient concentration of carcinogenic metals in Los Angeles (LA), Thessaloniki and Milan. Table 3.3 summarizes the average concentration of PAH components measured at the investigated location sites. PAHs are mostly emitted from incomplete combustion processes in vehicle exhaust and biomass burning activities. In general, the proportions of outdoor 3-ring PAHs were the lowest among all location sites compared to heavy molecular weight PAHs (i.e., compounds of four or more aromatic rings). Among the three studied urban areas, the highest proportions of PM-bound PAHs with three rings were found in Thessaloniki samples. The total concentration of PAH components in this region was 11.30 ± 9.43 ng/m 3, with benzo(g,h,i)perylene and benzo(e)pyrene showing the highest levels. The examination of PAH emission sources in the literature indicates that vehicle emissions and oil combustion are the most common sources of the particle-phase PAHs (Alves et al., 2015; Galarneau, 2008; Lima et al., 2005). However, the enhanced PAH levels during the 2013 measurements were mostly driven by wood combustion for residential heating(Saffari et al., 2013a). This observation is 29 corroborated by high levels of benzo(a)pyrene, chrysene and benz(a)anthracene, which are tracers of biomass burning (Manoli et al., 2016b). Furthermore, Milan exhibited the highest levels of particle-bound PAHs, with a total concentration of 31.83 ± 40.58 ng/m 3 . Ambient levels of chrysene and several high molecular weight PAHs (e.g., benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e)pyrene) were considerably elevated during the sampling periods in this urban site. This is largely the result of significantly higher PAH emissions during wintertime due to increased biomass combustion, raising the annual average values (Hakimzadeh et al., 2020). In fact, the total PAH concentration in Milan was not only higher than our measurements in Thessaloniki and Los Angeles basin, but also exceeded the measurements in most cities, including Florence (Martellini et al., 2012), Zaragoza and Monzon (Callén et al., 2011). On the contrary, we observed low levels of PAHs in the LA basin, with multiple components registering values below the detection limit. The total concentration of PAHs in Los Angeles was 0.88 ± 0.50 ng/m 3 . Table 3.3 – Concentration of particle phase PAHs (ng/m3) in Los Angeles, Thessaloniki and Milan.The values below detection limit are indicated as BDL. PAH species Los Angeles Thessaloniki Milan Phenanthrene 0.04 ± 0.02 0.425 ± 0.33 0.307 ± 0.381 Retene BDL BDL 0.383 ± 0.581 Anthracene BDL 0.165 ± 0.121 BDL Pyrene 0.032 ± 0.012 0.794 ± 0.546 1.035 ± 1.276 Chrysene 0.023 ± 0.026 1.212 ± 0.932 5.228 ± 6.798 Benz(a)anthracene 0.014 ± 0.017 1.025 ± 0.847 1.458 ± 1.867 Acephenanthrylene BDL 0.136 ± 0.051 0.096 ± 0.228 Fluoranthene 0.082 ± 0.031 0.774 ± 0.566 1.108 ± 1.377 Benzo(ghi)fluoranthene 0.012 ± 0.017 BDL 2.2 ± 2.801 Benzo(b)fluoranthene 0.071 ± 0.048 0.586 ± 0.394 5.613 ± 7.027 Benzo(k)fluoranthene 0.108 ± 0.042 0.951 ± 0.911 5.159 ± 6.558 Benzo(e)pyrene 0.081 ± 0.044 1.316 ± 0.957 3.896 ± 4.921 Benzo(a)pyrene 0.071 ± 0.083 1.128 ± 0.754 0.155 ± 0.26 Benzo(g,h,i)perylene 0.201 ± 0.099 1.476 ± 1.139 1.791 ± 2.171 1-Methylchrysene BDL BDL 0.497 ± 0.636 Perylene BDL BDL BDL Benzo(j)fluoranthene BDL BDL 0.192 ± 0.264 Dibenz(a,h)anthracene BDL 0.188 ± 0.141 0.37 ± 0.477 Picene BDL BDL 0.233 ± 0.292 30 Cyclopenta(cd)pyrene BDL BDL BDL Indeno(1,2,3-cd)pyrene 0.104 ± 0.044 1.128 ± 0.754 1.579 ± 1.915 Dibenzo(a,e)pyrene BDL BDL 0.033 ± 0.123 Coronene 0.045 ± 0.036 BDL 0.513 ± 0.64 3.3.2 Cancer Risk Assessment Table 3.4 compares the total BaPeq in the investigated location sites as well as the reported values in the literature. Our results indicated that Milan’s enhanced PAH concentration translated into the highest total BaPeq value among the urban sites examined in this study. The total BaPeq in Milan was 1.42 times higher than that of Thessaloniki, which is impacted by similar biogenic and anthropogenic sources. However, the estimated BaPeq in LA was approximately 25 and 35 times lower than the corresponding values in Milan and Thessaloniki, respectively. As discussed in the previous section, the stringent air quality regulations in LA have impacted the PAH levels in the region, as evidenced by the significantly higher BaPeq values (i.e., by one order of magnitude) in LA freeways and surface streets in nearly six years prior. It is also important to highlight the impact of biomass burning emissions in both European cities (i.e., Milan and Thessaloniki), which significantly increased the PAH levels and consequently, the total BaPeq values. In fact, the BaPeq value in Milan was higher compared to observed values in China and other European location sites. Table 3.4 – Comparison of total BaPeq values in this study with estimated values in the literature. Study Location Total BaPeq (ng/m 3 ) Current study Los Angeles, US 0.1 ± 0.1 Current study Thessaloniki, Greece 2.5 ± 1.78 Current study Milan, Italy 3.5 ± 4.6 Wang et al. (2020) Wuhan, China 2.9 ± 1.4 Masiol et al. (2012) Venice, Italy 1.9 ± 2.6 Martellini et al. (2012) Florence, Italy 0.8 Kam et al. (2013) I-110 freeway in Los Angeles, US 12.7 ± 2.1 Kam et al. (2013) I-710 freeway in Los Angeles, US 23.3 ± 4.4 Kam et al. (2013) Surface streets in Los Angeles, US 8.6 ± 1.5 31 Table 3.5 shows the lifetime cancer risk values according to the concentration of metal elements and BaPeq values obtained in Los Angeles, Thessaloniki and Milan. The table presents three scenarios for each location site: 1) Worst-case Scenario (WS), where the infiltration of particles into the indoor environment is assumed to be at peak due to opened windows and doors and poor indoor ventilation; 2) Best-case Scenario (BS), where the infiltration is limited as the result of closed windows constraining the entrance of outdoor pollutants. 3) Mixed Scenario (MS): which is a mixture of two previous scenarios and is closer to real-world conditions. We assumed the infiltration factor value of 0.8 and 0.4 for BS and WS, respectively, which is within the range of the infiltration factors reported during the opened window and closed window conditions in the literature (Allen et al., 2003; Chen and Zhao, 2011; Hassanvand et al., 2015; Kearney et al., 2014; Rivas et al., 2015). The infiltration factor associated with the mixed scenario was assumed to be 0.6, which is the average of BS and WS infiltration factors. Table 3.5 – Carcinogenic risks (×10 -6 ) by inhalation of selected PM-bound toxic components for population in Los Angeles, Thessaloniki and Milan.WS, BS and MS indicate the Worst- case, Best-case and Mixed scenarios, respectively. The bolded values represent the carcinogenic risk exceeding the US EPA standard. Los Angeles Thessaloniki Milan Specie WS BS MS WS BS MS WS BS MS As 0.33±0.10 0.21±0.07 0.27±0.09 1.57±0.78 0.97±0.48 1.27±0.63 2.08±0.44 1.29±0.27 1.69±0.35 Cd 0.04±0.01 0.03±0.01 0.03±0.01 0.34±0.19 0.21±0.12 0.28±0.16 0.46±0.03 0.29±0.02 0.38±0.03 Cr(VI) 2.69±0.19 1.66±0.12 2.17±0.16 2.91±1.01 1.80±0.63 2.36±0.82 9.82±0.58 6.08±0.36 7.95±0.47 Ni 0.22±0.03 0.14±0.02 0.18±0.02 0.38±0.23 0.23±0.15 0.31±0.19 0.6±0.05 0.38±0.03 0.49±0.04 Pb 0.02±0.01 0.01±0.01 0.01±0.01 0.06±0.03 0.04±0.02 0.05±0.02 0.19±0.02 0.12±0.01 0.16±0.02 BaPeq 0.06±0.05 0.04±0.04 0.05±0.05 1.25±0.9 0.77±0.56 1.01±0.73 1.77±2.33 1.1±1.45 1.44±1.89 Total 3.36±0.39 2.07±0.27 2.71±0.34 6.51±3.14 4.02±1.96 5.28±2.55 14.92±3.45 9.26±2.14 12.11±2.80 According to the table, Cr(VI) poses the highest lifetime critical risk among the studied PM components, with LICR values exceeding the minimal acceptable risk level by a considerable margin among all location sites and scenarios. The margin was most pronounced in Milan, where the lifetime cancer risk was 5-10 times higher compared to the US EPA standard value (10 -6 )(US EPA, 2011). The cancer risk values of Ni, Pb, and Cd in all studied cities and across different scenarios were considerably lower than EPA’s standard value, with LICR values in the range of 0.01 - 0.46. Moreover, Arsenic registered LICR values higher than the US EPA standard in both Milan and Thessaloniki. In general, we observed a similar pattern in both cities, where the risk values corresponding to As, PAHs and Cr(VI) were higher 32 than the standard level in most scenarios. However, the cancer risk values in Los Angeles basin were noticeably lower compared to the other location sites. Most remarkably, the risk from PAH exposure did not exceed the acceptable value even in the worst-case scenario (i.e., highest infiltration rate) in this region. As mentioned earlier, our sampling site in LA is located in the heart of the urban section of the city and near a major vehicle roadway, denoting traffic and residential heating emissions as the potential drivers of the concertation of PAHs in this city. However, the possible impact of residential/biomass burning emissions is curtailed by the short cold periods, turning traffic emissions into the prime source of PAH components at this sampling site. Therefore, the low cancer risk associated with PAH levels highlights the impact of air quality policies targeting combustion emissions in reducing the release of carcinogenic components in the LA basin. This is further supported by the cancer risk estimation of other PM components which were mostly below the EPA standard. It is important, however, to note that the collective cancer risk values from all carcinogenic components and PAHs exceeded the EPA standard across all location sites. For instance, the corresponding value even in Los Angeles was above 10 -6 even for the scenario with the lowest infiltration of outdoor pollutants into indoor environment (i.e., 2.07×10 -6 ). As expected, Milan showed the highest total cancer risk, reaching almost 15 times of the EPA standard. 3.4 Summary and Conclusions This study investigated the lifetime cancer risk from population exposure to carcinogenic PM-bound components in three urban environments including Los Angeles, Milan and Thessaloniki which shared similar Mediterranean climate but were different in their emission source and governing air quality policies. According to our results, Milan exhibited the highest lifetime cancer risk values compared to the corresponding values in Los Angeles and Thessaloniki, with values ranging from (0.19±0.02)×10 -6 to (9.82±0.58)×10 -6 . The population exposure to As, Cr(VI) and PAHs in Milan exceeded the US EPA standard across all scenarios, highlighting the magnitude of the air pollution in this metropolitan area. We also observed lower concentrations of heavy metals and PAHs in Los Angeles. The PAH levels were significantly reduced compared to our measurements in this basin, six years prior. The lower mass concentration of particle-bound components in Los Angeles, translated into the lowest cancer risk estimations among the examined sites. Excluding Cr(VI), the cancer risk values associated with inhalation of individual metals and PAHs were well-below the guideline level in this region. These observations further underscore the impact of stringent air quality 33 measures in the LA basin during the last two decade which has substantially improved the air quality and lowered the cancer risks in a region which was once considered as one of the most polluted megacities in the world. However, the collective cancer risk value corresponding to exposure to carcinogenic metal and PAHs were still above the standard in Los Angeles, indicating that additional steps are still required to improve the air quality of this region. 34 Chapter 4 : Are standardized diesel exhaust particles (DE) representative of ambient particles in air pollution toxicological studies? In this study, we investigated the chemical characteristics of standardized diesel exhaust particles (DEP) and compared them to those of read-world particulate matter (PM) collected in different urban settings to evaluate the extent to which standardized DEPs can represent ambient particles for use in toxicological studies. Standard reference material SRM-2975 was obtained from the National Institute of Standards and Technology (NIST) and was chemically analyzed for the content of elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), inorganic ions, and several metals and trace elements. The analysis on the filter-collected DEP sample revealed very high levels of EC (i.e., ~397 ng/µg PM) which were comparable to the OC content (~405 ng/µg PM). This is in contrast with the carbonaceous content in the emitted particles from typical filter-equipped diesel-powered vehicles, in which low levels of EC emissions were observed. Furthermore, the EC mass fraction of the DEP sample did not match the observed levels in the ambient PM of multiple US urban areas, including Los Angeles (8%), Houston (~14%), Pittsburgh (~12%), and New York (~17%). Our results illustrated the lack of several high molecular weight carcinogenic PAHs in the DEP samples, unlike our measurements in major freeways of Los Angeles. Negligible levels of inorganic ions were observed in the sample and the DEP did not contain toxic secondary organic aerosols (SOAs) formed through synchronized reactions in the atmosphere. Lastly, the analysis of redox-active metals and trace elements demonstrated that the levels of many species including vehicle emission tracers (e.g., Ba, Ti, Mn, Fe) on Los Angeles roadways were almost 20 times greater than those in the DEP sample. Based on the abovementioned inconsistencies between the chemical composition of the DEP sample and those of real-world PM measured and recorded in different conditions, we conclude that the standardized DEPs are not suitable representatives of traffic emissions nor typical ambient PM to be used in toxicological studies. This chapter is based on the following publication: Farahani, V.J., Pirhadi, M. and Sioutas, C., 2021. Are standardized diesel exhaust particles (DEP) representative of ambient particles in air pollution toxicological studies?. Science of The Total Environment, 788, p.147854. 35 4.1 Introduction The complex nature of physiochemical properties of PM makes it difficult to reproduce in the laboratory (Bladt et al., 2012; Filep et al., 2016; Keskinen and Rönkkö, 2010). Therefore, it is vital to employ real-world PM in controlled toxicological studies in order to improve our understanding of the identified associations between PM and adverse effects. However, direct use of ambient PM in the lab is not always a viable alternative, because ambient PM concentrations are generally not sufficiently high to induce acute adverse health effects in toxicological studies (Jung et al., 2010; Liu et al., 2008; Pirhadi et al., 2019). Recently, the use of standardized DEPs collected under controlled conditions has been extended in toxicological studies. Diesel exhaust particles (DEP) is a mixture of various chemical components including elemental carbon (EC) and organic carbon (OC) which constitute the carbon core, metals and trace elements, and organic compounds such as n-alkanes, branched alkanes, alkylcycloalkanes, alkyl-benzenes, polycyclic aromatic hydrocarbons (PAHs) and various cyclic aromatics. The protocol for generation and collection of DEPs has been developed by the National Institute of Standards and Technology (NIST); DEPs are available as standard reference material (SRM) used in analytical methods to evaluate complex diesel mixtures. For instance, standard reference material SRM-2975 (NIST, US) is a commonly used standardized DEP sample and has been employed in multiple health effect studies of air pollution (Mundandhara et al., 2006; Szewczyńska et al., 2017). For example, Van den Brule et al. (2021) used DEP SRM-2975 (NIST) to investigate the effect of diesel particles on the profile and function of the gut microbiota in the mice. Robinson et al. (2018) conducted a health study to identify the mechanisms involved in activation of airway sensory afferents by DEPs, in which SRM-2975 (NIST) was considered a representative of the real-world environmental exposure. Similarly, human bronchial epithelial cells were exposed to SRM-2975 (NIST) by Smyth et al. (2020) and changes in barrier function were monitored to determine the effect of diesel exhaust exposure on airway epithelial barrier function. Lawal et al. (2015) used human microvascular endothelial cells to examine the toxic effects of DEPs on endothelial cells and their role in inducing heme oxygenase-1 (HO-1). Enhancement in ROS production, cellular toxicity/inflammatory, and cellular adhesion were linked to SRM 2975 (NIST) transfection. Rychlik et al. (2019) explored the effects of in utero UFP exposure on young pulmonary immune response in mice, using SRM 2975 (obtained from NIST). Considering the widespread use of DEPs in the field of air pollution and its health effects, it is essential for the air pollution 36 research communities and policymakers to evaluate the extent to which the standardized DEP represents typical ambient PM as well as PM originated from traffic emissions. In this study, we conducted a comprehensive investigation of the representativeness of DEP samples by analyzing the chemical composition of DEP SRM-2975 (NIST) and comparing it with that of real-world PM collected in different urban settings. 4.2 Methodology 4.2.1 DEP type and sampling We used SRM-2975 (NIST) as a reference for the chemical and toxicological constitutes of the standardized DEPs. The DEP sample is generated by a diesel-powered industrial forklift and collected by a particular filtering system designed for diesel forklifts under “hot” conditions, without a dilution tunnel (Singh et al., 2004; Wright et al., 1991). The DEP sample employed in our study was purchased from NIST and processed for chemical analysis in a multi-stage procedure discussed in detail below. Figure 4.1 illustrates the schematic of our laboratory setup. First, DEP suspension was prepared by dissolving 20 mg of the sample in 100 ml of ultrapure Milli-Q water (Millipore A10, EMD Millipore, Billerica, MA, US). The solution was then sonicated for 30 min by means of an ultrasonic bath (3510R-MT, Branson Ultrasonics Corp., US) to achieve a homogenous slurry with a concentration of approximately 200 µg/ml. The DEP slurry was re-aerosolized using the aerosol generation system elaborately discussed in Taghvaee et al. (2019a). In summary, the prepared slurry is atomized into small aerosol droplets, using a HOPE nebulizer (Model 11310, B&B Medical Technologies, US). The re-aerosolized stream is then mixed with the HEPA-filtered clean air, followed by passing through a diffusion dryer (Model 3620, TSI Inc., US) filled with silica gel to remove excess water from the airborne particles. Subsequently, the aerosol stream passes through a column filled with Po-210 neutralizers (Model 2U500, NRD Inc., US) to remove electrical charges from the particles. The re- aerosolized PM was collected simultaneously on a 37-mm PTFE filter (2-μm pore, Pall Corp., Life Sciences, US) and a pre-baked quartz filer (Pall Corp., Life Sciences, US) to compare their chemical composition to the corresponding values of the real-world particles collected in different urban environments. 37 Figure 4.1- Schematic of the laboratory setup. 4.2.2 Chemical analysis of DEP The DEP sample was chemically analyzed for the content of EC/OC, PAHs, inorganic ions, metals and trace elements at the Wisconsin State Laboratory of Hygiene (WSLH). The EC and OC concentration of the samples were determined by following the Thermal Optical Transmission (TOT) protocol of the National Institute for Occupational Safety and Health (NIOSH) (Birch and Cary, 1996). A punch of the quartz filter was directly extracted and then analyzed by the semi-continues EC/OC field analyzer (Sunset Laboratory Inc., US). The mass content of anions (sulfate, nitrate, chloride and phosphate) and cations (sodium, ammonium, potassium) was determined by applying ion chromatography (IC) on the filtered water extract of a part of the PTFE filter. The metals and trace element constituents of DEP were quantified by the inductively coupled plasma mass spectrometer (ICP-MS), using a microwave-aided mixed-acid (Lough et al., 2005). A punch of the PTFE filter was digested with a mixture of ultrahigh-purity nitric acid and ultrahigh-purity hydrochloric acid. Lastly, to determine the content of PAHs, a section of the PTFE filter was extracted and analyzed by means of the Gas Chromatography/Mass Spectrometry (GC/MS) (Schauer et al., 2001). 4.2.3 Comparisons with real-world PM The quantified chemical components of the DEP SRM-2975 (NIST), including EC/OC, PAHs, inorganic ions, and metals and trace elements were compared to the corresponding ambient values in major US metropolitan areas. The annual average of ambient PM2.5 chemical composition at four major urban areas of the US, including Los Angeles, Houston, Pittsburgh, and New York city, were retrieved through the Air Quality System (AQS) from chemical speciation network (CNS) database recorded by the US Environmental Protection Agency (US EPA). Further details regarding the CSN database have been elaborated elsewhere (Solomon 38 et al., 2014). The data on the mass concentration of OC, EC, sulfate, nitrate, ammonium, and total metals and elements were extracted for year 2019, which was the most recent year for which the annual data were available. Moreover, these four areas were selected to cover a wide range of geographical locations in the US. We evaluated the EC and OC profile of the DEP sample by comparing it to the corresponding values in the heavy-duty diesel vehicles (HDDV) exhaust tests under different aftertreatment configurations, conducted earlier by our group (Biswas et al., 2009). The recorded values included measurements on HDDVs with six various configurations of vehicles with advanced emission control technologies: 1) a 1998 Kenworth truck HDDV without aftertreatment as the baseline. 2) the same HDDV with a continuously regenerating technology [CRT], 3) the HDDV with a CRT in combination with a vanadium-based selective catalytic reduction system [V-SCRT], 4) the HDVV with a CRT in combination with a zeolite-based selective catalytic reduction system [Z-SCRT], 5) a Caltrans truck with an Engelhard DPX filter, and 6) a school bus equipped with a Cleaire electric particle filter [Horizon]. We compared the mass fraction of individual metals and elements as well as PAHs in the DEP sample to the real-world ambient data measured on major freeways in Los Angeles to assess if the standardized DEP can capture the complex nature of the ambient particles originated from traffic emissions. The measurements were conducted on the main campus of the University of Southern California (USC), which was used as a reference urban background site, as well as three roadways in the Los Angeles basin including the I-110 freeway, I-710 freeway, and the Wilshire/Sunset boulevard (Shirmohammadi et al., 2017c). A 2014 Toyota Prius C hybrid car equipped with six battery-operated Leland Legacy pumps (SKC Inc., Eighty-Four, PA) was used to perform the on-road sampling. Each pump was connected to a Sioutas Personal Cascade Impactor Sampler (PCIS) (SKC Inc., Eighty-Four, PA) and the flow rate for each PCIS was set to be 9 lpm (Misra et al., 2002). The PAH species analyzed included: phenanthrene, fluoranthene, acephenanthrylene, pyrene benzo(ghi)fluoranthene, benzo(a) anthracene, Chrysene, benzo(b)fluoranthene, benzo(k)fluo-ranthene, benzo(j)fluoranthene, benzo(e)pyrene, benzo(a)pyrene, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, and coronene. The investigated metals and elements were Mg, Al, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Sb, Ba, and Pb. Particles generated from traffic emissions, especially tailpipe emissions, are mostly in the quasi-ultrafine range (i.e., PM0.25, particles less than 0.25 µm in diameter) (Habre et al., 2021; Hasheminassab et al., 2013). To further investigate the relevance of the standardized DEP sample, its chemical composition was compared to that of the ambient PM0.25 concentrations measured earlier by our group in the Los Angeles basin (Saffari et al., 39 2013b). The ambient PM0.25 were collected at ten distinct locations across the Los Angeles South Coast air basin, using two parallel Sioutas PCIS which were operating simultaneously with a flow rate of 9 lpm. The total metallic and elemental composition of the samples were analyzed using a highresolution magnetic sector inductively coupled plasma sector field mass spectrometry (ICPSFMS, Thermo-Finnigan Element 2) (Herner et al., 2006). 4.3 Results and discussion 4.3.1 Chemical profiles of standardized DEP Figure 4.2 shows the PM mass fraction of the measured chemical compounds in the standardized DEP sample. According to Figure 4.2(a), the EC concentration (~397 ng/µg PM) is high and almost comparable to OC (~405 ng/µg PM). This observation is not consistent with the measured EC and OC values in many studies conducted on the real-world PM, which observed significantly low EC emissions. This inconsistency will be further elaborated in Section 4.3.2.1. As shown Figure 4.2(b), the content of inorganic ions (e.g., ammonium, sulfate and nitrate) in the DEP sample is negligible. The quantified mass fractions for ammonium, sulfate and nitrate are 2.77, 7.97 and 1.5 ng/µg PM, respectively, which is almost 40 times smaller than those of OC and EC. The inorganic content of the sample will be discussed in detail in Section 4.3.2.2. The PAHs mass fraction profiles measured in the DEP sample are illustrated in Figure 4.2(c). While the DEP sample was missing some low molecular weight PAHs (e.g., anthracene, acephenanthrylene, cyclopenta(cd)pyrene), it was almost entirely devoid of several high molecular weight PAHs (e.g., benzo(a)pyrene, benzo(j)fluoranthene, benzo(g,h,i)perylene, dibenz(a,h)anthracene, dibenzo(a,e)pyrene, perylene, and picene). Thus, DEP contained mostly low molecular weight PAHs (e.g., phenanthrene, fluoranthene, pyrene, benzo(ghi)fluoranthene, benz(a)anthracene, chrysene) with low-to-moderate mass fractions. 40 Figure 4.2 - The mass fraction of a) EC and OC, b) inorganic ions, c) PAHs, and d) metals and elements in the standardized DEP sample. (a) (b) (c) (d) 41 This observation is in contrast with the PM chemical profile in the typical ambient conditions, where the combined contribution of medium and high molecular weight PAHs was 52.2% (Pakbin et al., 2009). Additionally, other studies have reported high levels of three and four-ring PAHs (anthracene, phenanthrene, and pyrene) in HDDV exhaust which is not consistent with the recorded values in the SRM-2975 (NIST) powder (Liu et al., 2008; Marr et al., 1999; Riddle et al., 2007). This inconsistency will be elaborated further in Section 4.3.2.3. Figure 4.2(d) shows the PM mass fraction of metals and trace elements in the standardized DEP particles. The metal and trace elements levels range from 0.0003 ng/µg PM (Cd) to 11.17 ng/µg PM (Ca). The sample’s metallic content will be further investigated and compared to the corresponding ambient values in Section 4.3.2.4. 4.3.2 Comparison between DEP and real-world PM 4.3.2.1 EC/OC Carbonaceous compounds constitute a large portion of vehicle exhaust emissions, and EC has been extensively used as surrogate of diesel particulate matter (Schauer, 2003). As discussed earlier, the chemical analysis of carbonaceous compounds in the standardized DEP shows high levels of EC (of almost 40% by mass), resulting in the EC/OC ratio of almost 1. Investigation of EC emissions from diesel-powered vehicles in earlier studies conducted mostly in 1990s and early 2000s show comparable or even higher levels of EC contribution compared to OC (Lowenthal et al., 1994), similar to those found in the DEP sample. However, it is imperative to consider the impact of the emission control regulations implemented in various countries in recent years, leading to significant alterations in the EC and OC content of tailpipe emissions. Starting in 2007, a number of stringent regulations were implemented in state of California to mitigate the tailpipe emissions including: (i) Low emission vehicles II (LEV II) program (2007), requiring vehicles to meet the newly-developed emission standards on non-methane organic gases (NMOG), PM, NOx, and CO defined for various vehicle groups; (ii) Low carbon fuel standards (LCFS) program (2013), which encouraged California’s car manufactures to use low-carbon fuel to decrease the petroleum-related emissions; and (iii) Cleaner port truck (CPT) program (2007), which was implemented to limit the diesel emissions from drayage company’s trucks working at the ports of Los Angeles and Long Beach. Comparing the EC/OC ratios pre- and post- 2007 clearly shows the impact of the abovementioned regulations on carbonaceous content of diesel particulate matter. For instance, in a study on gasoline and diesel vehicle engine emissions in San Francisco Bay area, BanWeiss 42 et al. (2008) have reported the EC/OC ratio of near 0.7 and 2 for light duty and dieselpowered trucks, respectively. Similar studies in the past have measured the EC/OC ratios in the 0.8-1.8 range, depending on the vehicle engine (Watson et al., 1990). The adopted regulations and standards resulted in the development of novel after-treatment emission control technologies which have altered the chemical composition in vehicle exhaust PM. In a study conducted earlier by our group (Biswas et al., 2009), the EC/OC profiles of PM emitted from a diesel exhaust with a wide range of retrofitted diesel vehicle configurations (i.e., CRT, VSCRT, Z- SCRT, DPX, Horizon) and a diesel exhaust without after-treatment (baseline) were investigated over steady state cruise (50 mph) and transient EPA urban dynamometer driving schedule (UDDS) cycles, the results of which are shown in Figure 4.3. The EC emissions in the baseline are significantly reduced after employing different common retrofitted configurations in cruise and transient cycles. According to the figure, the mass fraction of EC is less than 5% of OC in most retrofitted configurations. This observation is not consistent with the EC and OC content of the standardized DEP sample (Figure 4.2(a)). The difference in the EC content of diesel exhaust emissions and that of the DEP sample suggest that the high EC content of standardized DEP particles cannot be considered representative of the emissions from most of the recent diesel-powered vehicles. Figure 4.3 - PM mass fraction of EC and OC at cruise and transient urban dynamometer driving schedule (UDDS) cycles. Figure 4.4 shows the measured mass fraction of EC and OC in three major roadways of Los Angeles County, including I-110, I-710, and Wilshire/Sunset Blvd. as well as the University of Southern California (USC) sampling site. The three selected roadways are among the most trafficked routes in Los Angeles, representing an urban area heavily impacted by vehicle engine emissions. The main campus of USC served as the urban reference site. According to the figure, the EC fraction of PM did not exceed levels of 0.13 ± 0.04 µg/µg PM, 43 with the average of almost 0.1 µg/µg PM in all sampling sites. The higher EC levels on freeways have been attributed to the significant number of HDVs, as one of the major EC emission sources, on these routes (Schauer, 2003; Shirmohammadi et al., 2017c). According to Figure 4.5, which demonstrates the annual chemical composition of ambient PM2.5 in multiple cities across the US, the EC fraction of ambient PM2.5 in Los Angeles, Houston, Pittsburgh and New York was around 8%, 14%, 12% and 17%, respectively. This observation further illustrates that not only standardized DEP particles do not have the same EC and OC profile of the particles emitted from typical vehicles exhaust, but they also are not representatives of real-world ambient PM to which most of the people are exposed in typical urban areas. Figure 4.4 - PM mass fraction of EC and OC at Interstate 110 (I-110), Interstate 710 (I-710), Wilshire/Sunset Blvd., and USC sampling site. 44 Figure 4.5 - Ambient PM2.5 chemical composition during year 2019 in a) Houston, b) Los Angeles, c) New York, and d) Pittsburgh obtained from the Chemical Speciation Network (CSN) database provided by the US Environmental Protection Agency (US EPA). (a) (b) © (d) 4.3.2.2 Inorganic ions and SOAs As shown in Figure 4.2(b), nitrate, sulfate and ammonium constitute a small fraction of the DEP mass concentration. However, according to Figure 4.5, inorganic ions have a large contribution to ambient PM2.5 concentrations. Combined contribution of nitrate, sulfate and ammonium to ambient PM2.5 mass in Los Angeles, New York, Houston and Pittsburgh were 59.3%, 41.5%, 40.7% and 50.1%, respectively, which is not consistent with the observed values in the DEP sample. Secondary organic aerosols (SOAs) are formed from photochemical reactions of species emitted by combustion sources in the atmosphere (Huang et al., 2014), and have been extensively linked to adverse health effects (Chowdhury et al., 2018; Kramer et al., 45 2016; Lodovici and Bigagli, 2011; Lund et al., 2013; McDonald et al., 2010). Recent studies have corroborated the significant role of SOAs in underlying mechanisms leading to PM oxidative potential and toxicity (Chu et al., 2014; Daumit et al., 2016; Tuet et al., 2017). SOAs constitute a considerable fraction of ambient PM mass in different urban environments. The SOA in Los Angeles ambient air has been reported to be about 40-45% of total OC (Altuwayjiri et al., 2021; Polidori et al., 2007). Consistent with this observation, the ambient SOA values in Atlanta, Claremont, CA and Pittsburgh have been estimated to be around 44%, 40% and 38% of total OC, respectively (Cabada et al., 2004; Lim and Turpin, 2002; Turpin and Huntzicker, 1995). The DEP sample is devoid of secondary aerosols since DEP samples are collected from freshly emitted particles immediately after the exhaust and do not have sufficient time to undergo different atmospheric processes. The lack of SOA components in the DEP sample further weakens its relevance in PM toxicological studies. 4.3.2.3 Polycyclic aromatic hydrocarbons (PAHs) As discussed in previous chapter, PAHs are organic compounds that are products of incomplete fossil fuel combustion in industries, vehicle emissions and biomass burning (Alves et al., 2015; Galarneau, 2008; Lima et al., 2005; Miller et al., 2010). Table 4.1 compares the concentrations of multiple PAHs per PM mass in the DEP sample and their corresponding values measured in the different sampling sites of Los Angeles as mentioned earlier (i.e., I110, I-710, Wilshire/Sunset, and USC). Combined concentrations of PAHs on I-110 (0.16 ± 0.01 ng/µg PM) and I-710 (0.15 ± 0.01 ng/µg PM) were 3 and 3.3-fold greater than that of Wilshire/Sunset Blvd. and USC, respectively (Shirmohammadi et al., 2017c). The cumulative mass fraction of PAHs in the DEP sample was 0.068 ± 0.005 ng/µg PM which is significantly lower than the levels exhibited in Los Angeles roadways. According to the table, this difference largely stems from the lack of several high molecular weight PAHs (i.e., PAHs having four or more rings) in the standardized DEP particles, including benzo(j)fluoranthene, benzo(a)pyrene, benzo(g,h,i)perylene and benzo(k)fluoranthene. High molecular weight PAHs are characterized by having four or more rings and are mostly found in particulate phase due to their low vapor pressure (S.O. Baek et al., 1991; Park et al., 2002; Taghvaee et al., 2018b). While PAHs can be formed from the combustion of both diesel fuel and lubrication oil, higher levels of high molecular weight PAHs have been reported in the used lubricating oil (Pakbin et al., 2009). Due to their mutagenic characteristics, the higher molecular weight PAHs are often more carcinogenic according to the International Agency for Research on Cancer 46 (IARC). Thus, the absence of several heavy PAHs in the chemical compounds of the standardized DEP questions the relevance of its application in toxicological studies. Table 4.1 - Mass fraction of PAHs in DEP sample and PM2.5 sampling sites (I-110 freeway, I710 freeway, Wilshire/Sunset Blvd., and USC). All values are in units of ng/µg PM. Species DEP I-710 I-110 Wilshire/Sunset USC Phenanthrene 0.0070 0.0164 0.0038 0.0017 0.0058 Fluoranthene 0.0167 0.0246 0.0007 0.0034 0.0020 Pyrene 0.0018 0.0246 0.0033 0.0016 0.0044 Benzo(ghi)fluoranthene 0.0072 0.0038 0.0011 0.0014 0.0048 Benz(a)anthracene 0.0025 0.0113 0.0079 0.0014 0.0010 Chrysene 0.0027 0.0038 0.0061 0.0034 0.0055 Benzo(b)fluoranthene 0.0181 0.0318 0.0307 0.0089 0.0044 Benzo(e)pyrene 0.0037 0.0068 0.0154 0.0020 0.0034 Indeno(1,2,3-cd)pyrene 0.0069 0.0010 0.0102 0.0027 0.0014 Coronene 0.0016 0.0017 0.0020 0.0058 0.0027 Acephenanthrylene -- 0.0010 0.0311 0.0044 0.0003 Benzo(j)fluoranthene -- 0.0010 0.0003 0.0007 0.0020 Benzo(a)pyrene -- 0.0003 0.0116 0.0007 0.0007 Benzo(g,h,i)perylene 0.0000 0.0102 0.0225 0.0085 0.0048 Benzo(k)fluoranthene 0.0001 0.0061 0.0099 0.0034 0.0020 4.3.2.4 Metals and trace elements Table 4.2 shows the mass fraction of metals and trace elements in the DEP sample in comparison with the ambient measured values in various sampling sites in Los Angeles. The content of several redox-active species (e.g., Ti, V, Mn, Fe, As, Cd, Sb, Ba) in the DEP sample are significantly lower those of ambient PM2.5 in different locations. Ba, Ti, and Mn are documented tracers of vehicle emission (Farahani et al., 2021; Jain et al., 2018; Lim et al., 2010). While Fe has been treated as a marker of mineral dust in previous studies (Almeida et al., 2005), this species has been also attributed to vehicular emissions in the PM2.5 range as well in Los Angeles (Mousavi et al., 2018). The sum of vehicle emission tracers (e.g., Ba, Ti, Mn, and Fe) found in the DEP sample was 1.47 ± 0.58 ng/mg PM. In contrast, the cumulative mass fractions of the mentioned redox-active metals measured on I-110, I-710, and Wilshere/Sunset 47 were 49.82 ± 20.38, 46.37 ± 19.27, and 11.82 ± 4.97 ng/mg PM, respectively. On average, the concentrations of vehicle emission tracers on roadways were 21-fold greater than those in the DEP sample. These observations are further corroborated by the annual average PM0.25 mass fraction of metals and trace elements in several urban areas spread across the Los Angeles basin: Long Beach, Los Angeles, Riverside and Lancaster, as shown in Table 4.2. The content of several elemental and metallic constituents in the DEP sample are considerably smaller than the respective values measured in ambient quasi-ultrafine particles. Compared to the DEP, the average content of V, Fe, Cd, Sb in ambient PM0.25 in all monitoring stations is higher by 217, 11.2, 16.4, 25.1-fold, respectively, even though vehicle emissions are dominant emission sources in the PM0.25 range (Habre et al., 2021). This observation further suggests that standardized DEP particles are not representative of traffic emissions nor of typical ambient PM in major metropolitan areas like Los Angeles. Table 4.2 - Mass fraction of metals and elements in the DEP sample, PM2.5 sampling sites (I110 freeway, I-710 freeway, Wilshire/Sunset Blvd., and USC), and PM0.25 sampling sites (Long Beach (LB), Los Angeles (LA), Riverside, and Lancaster). All values are in units of ng/µg PM. Species PM 2.5 PM 0.25 DEP I-710 I-110 Wilshire USC LB LA Riverside Lancaster Mg 5.33 2.37 1.65 1.15 1.87 1.89 1.29 2.77 2.93 Al 2.78 7.03 3.97 3.85 6.42 7.84 5.08 9.64 11.10 S 5.62 12.48 10.10 7.47 9.80 32.87 31.69 23.60 21.18 K 4.25 4.75 4.47 2.04 3.85 5.32 4.67 8.01 8.72 Ca 11.17 10.41 8.95 4.09 8.18 7.29 4.79 9.91 7.56 Ti 0.09 2.10 2.60 0.67 1.38 0.97 0.74 0.96 1.34 V 0.00 0.08 0.09 0.03 0.05 0.74 0.41 0.21 0.08 Cr 0.12 0.22 0.11 0.16 0.09 0.12 0.13 0.13 0.08 Mn 0.05 0.51 0.53 0.15 0.26 0.27 0.19 0.34 0.34 Fe 1.25 40.46 42.97 10.41 13.25 12.87 12.13 14.23 16.83 Co 0.03 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Ni 0.09 0.23 0.21 0.16 0.04 0.21 0.15 0.11 0.04 Cu 0.78 2.04 3.31 0.58 0.65 0.51 0.99 0.80 0.45 Zn 1.04 1.38 1.60 0.45 0.85 1.48 0.88 0.90 0.66 As 0.00 0.03 0.02 0.01 0.01 0.02 0.02 0.02 0.01 Cd 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 Sb 0.00 0.43 0.65 0.09 0.14 0.08 0.13 0.10 0.06 48 Ba 0.08 3.31 3.73 0.59 0.88 0.56 0.68 0.49 0.55 4.4 Summary and conclusions In this study, we analyzed the chemical constituents of a standardized DEP sample and compared the content of EC, OC, PAHs, inorganic ions, metals, and trace elements to those of real-world PM measured and recorded in different conditions. The ratio of EC/OC in the DEP sample was almost 1. While earlier studies have recorded EC/OC ratios between 0.8-1.5 in the emissions from diesel powered vehicles, the recent filter-equipped diesel vehicles have significantly lower levels of EC emissions. Similarly, the EC mass fraction in the typical ambient PM of selected cities (e.g., Houston, Los Angeles, Pittsburgh and New York) across the US did not exceed ~17.4%. The analysis of PAH compounds in the standardized DEP demonstrated lack of high molecular weight PAHs observed in many ambient environments. The content of inorganic ions in the sample was not comparable to the ambient values. While SOAs, one of the notable drivers of ambient PM toxicity, constitute a significant fraction of ambient PM mass, DEPs do not contain SOAs since they are collected from freshly emitted particles immediately after the exhaust. Moreover, the concentrations of many redox-active metals and elements in the standardized DEP were significantly lower than those of ambient PM. The cumulative levels of vehicle emission tracers (e.g., Ba, Ti, Mn, Fe) in the sample was 1.47 ± 0.58 ng/µg PM, which was considerably lower than the measured values on major Los Angeles roadways, including I-110 (49.82 ± 20.38 ng/µg PM), I-710 (49.82 ± 20.38 ng/µg PM) and Wilshere/Sunset (11.82 ± 4.97 ng/µg PM). The abovementioned discrepancies between the chemical content of the standardized DEP and the measured ambient values lead us to the conclusion that standardized DEP samples are not accurate representatives of neither traffic emissions nor typical ambient PM. 49 Chapter 5 : Long-term trends in concentrations and sources of PM 2.5 -bound metals and elements in central Los Angeles In this study, we investigated long-term trends in the ambient concentrations and sources of redox-active metals and trace elements in central Los Angeles over the period of 2005-2018. Mass concentrations of PM2.5-bound metals and crustal elements were obtained from the Chemical Speciation Network (CSN) database provided by the US Environmental Protection Agency (US EPA). The recorded metal concentrations showed considerable variations throughout the study period, but they generally followed a descending trend from 2005 to 2018. The decline in annual average concentration of metals was more pronounced for V and Ni. In order to further investigate and interpret the observed decreasing trends, this dataset of 2005- 2018 was employed in the positive matrix factorization (PMF) model to determine the contribution of different sources to the total metals’ concentrations and their trends over time. Four major sources were identified by the PMF model, including mineral dust, re-suspended road dust, combustion, and tire wear. Mineral dust (50±8%) and re-suspended road dust (38±13%) were the dominant contributors to total metal concentrations, followed by combustion emissions (9±8%) and tire wear (2±1%). While the PMF results showed generally consistent contributions of mineral dust to total metals concentration throughout the investigation period, the contribution of re-suspended road dust to total metals increased from 2013 to 2018 probably due to the increased road traffic (expressed in the form of vehicle miles traveled, VMT) as well as the growing use of electric vehicles (EVs) (which increases resuspension of road dust particles due to their heavy weight) in the area during the same period. In contrast, the contribution of combustion emissions decreased by almost 88% from 2005 to 2018. Using the PMF-resolved factor profiles, we investigated the contribution of the four identified sources to selected individual metals (i.e., Ba, Br, Fe, Ni, Pb, Ti, V, and Zn). The observed decrease in the mass concentration of V and Ni over time was attributed to the reduction in combustion sources and particularly industrial emissions, further corroborating the effectiveness of aftertreatment and air quality policies in reducing the levels of redox-active metals across Los Angeles. This chapter is based on the following publication: Farahani, V.J., Soleimanian, E., Pirhadi, M. and Sioutas, C., 2021. Long-term trends in concentrations and sources of PM2. 5–bound metals and elements in central Los Angeles. Atmospheric Environment, 253, p.118361. 50 5.1 Introduction As discussed previously, atmospheric particulate matter (PM) has been extensively associated with adverse health effects such as cardiovascular diseases, lung cancer, neurodegenerative disorders, and premature mortality (Apte et al., 2018; Brook et al., 2010; Delfino et al., 2009). Of particular note, ambient PM2.5 (particles with aerodynamic diameter less than 2.5 µm) has been identified as an etiological factor for various types of cancers and the growth of brain tumors (Andersen et al., 2018). Consequently, PM2.5 has been the subject of numerous local and national regulations in the US over the recent years, mainly due to its adverse health effects (Lurmann et al., 2015). As shown in the second chapter, various components of PM2.5 (e.g., organic carbon (OC), elemental carbon (EC), redox active metals and trace elements) are associated with significantly different levels of the toxicity and oxidative potential. Among the PM2.5 components, although metals and trace elements constitute a negligible fraction of the ambient PM2.5 mass in typical urban environments (Reff et al., 2009; J. Wang et al., 2013). Recent studies have documented a strong connection between ambient metal elements (in their soluble and insoluble forms) including Aluminum (Al), Barium (Ba), Iron (Fe), Vanadium (V), Calcium (Ca), Potassium (K), Cobalt (Co), Nickel (Ni), Cupper (Cu), Zinc (Zn), Titanium (Ti) and Manganese (Mn) and the production of the reactive oxygen species (ROS) (Cho et al., 2012; Liu et al., 2018). The air quality in the Los Angeles basin has been affected mainly by the local/regional anthropogenic emissions, making Los Angeles as one of the most polluted metropolitan areas in the US (Saffari et al., 2015; Soleimanian et al., 2019). Road traffic, biomass burning, emissions from LAX airport and the ports of Long Beach and Los Angeles along with industrial activities (e.g., metallurgical processes, refineries) have been identified as the major anthropogenic sources of ambient PM2.5-bound metals in the Los Angeles Basin (Hasheminassab et al., 2020; Heo et al., 2013; Shirmohammadi et al., 2017a; Ying and Kleeman, 2006). However, significant improvements have been made in air quality during the last two decades thanks to strict regulations imposing restrictions on PM emission sources (Lurmann et al., 2015). California experienced one of the most significant reductions in PM2.5 concentrations, primarily due to regulations such as Low Emission Vehicle II (LEV II) which was phased in between 2004 and 2010 targeting vehicular emissions (Lurmann et al., 2015; McDonald et al., 2013; US EPA, 2011). A number of studies in the literature have investigated long-term trends in the concentration of PM2.5 and some of its specific components dominating the PM mass such as 51 organic carbon (OC) and inorganic ions (e.g., nitrate, sulfate, and ammonium) in different areas of the US (Altuwayjiri et al., 2021; Malm et al., 2017; Ridley et al., 2018). However, significantly less attention has been given to metal elements, mostly due to their small contribution to the total PM2.5 mass. In a recent study, Hennigan et al. (2019) investigated long- term trends (2001-2016) in the concentration of PM2.5 transition metals in different urban regions across the US including Los Angeles. They observed that the concentration of most PM2.5 metals generally decreased over the study period in most sites. Hasheminassab et al. (2020) have also examined the sources of particulate hexavalent chromium (Cr (VI)) and other toxic metals in Los Angeles, during a shorter time period (i.e., 2016-2019). Nevertheless, without a long-term investigation of the sources contributing to the metal and trace element emissions, it would be difficult to understand and interpret the long-term trends in the concentration of these species in Los Angeles. In this study, we investigated the dominant sources of PM2.5-bound metals and trace elements in central Los Angeles over the years of 2005-2018. The Chemical Speciation Network (CSN) database provided by the US Environmental Protection Agency (US EPA) used as input to the positive matrix factorization (PMF) model to identify the major emission sources and determine their long-term trends in their contributions to total metal concentrations. 5.2 Methodology 5.2.1 Sampling site, period, and collection Figure 5.1 shows the location of the monitoring site in central Los Angeles (CELA) (34°03′59.7″ N, 118°13′36.8″ W). This site is located in proximity of a major interstate freeway (i.e., I-110) as well as multiple industrial and commercial centers, representing a typical urban area impacted by mobile emission sources and industrial activities (Altuwayjiri et al., 2021; Sina Hasheminassab et al., 2014). The mass concentration data of species employed in our study was extracted from the Air Quality System (AQS) through chemical speciation network (CSN) reported by the US EPA (US EPA, 2019). We obtained data on PM2.5 metals and crustal elements, including Al, Ba, Br, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, Pb, Se, Sn, V and Zn, for our monitoring site. The extracted raw data were analyzed, after excluding outlier data points from the dataset according to the interquartile range (IQR). IQR was defined as the difference between 75th (Q3) and 25th percentiles (Q1), and the observations that fall below 52 Q1-1.5 * (IQR), or above Q3 + 1.5 * (IQR) were removed from the dataset (Shirmohammadi et al., 2017a). Additionally, the values below the detection limit were excluded from our subsequent analyses. Figure 5.1- Location of the monitoring site in central Los Angeles (CELA). The air pollutants data utilized as input to our source apportionment model include the entire period between 2005 and 2018, enabling us to evaluate the historical trends in contribution of different emission sources to total metal mass concentrations observed in our sampling location. Twenty-four-hour time-integrated PM2.5 samples were collected every sixth days throughout the study period. Met One Speciation Air Sampling Systems (SASS™, Met One Instruments, Inc., OR, USA) was used to collect fine particles on polytetrafluoroethylene (PTFE) filters with an operational flow rate of 6.7 lpm. The metal content of PM2.5 samples was quantified employing energy dispersive X-ray fluorescence (EDXRF) by means of the Inorganic Compendium Method IO-3.3 (US EPA, 1999). Metal and trace elements concentrations were measured by irradiating samples with paralleled X-ray beam emanating from a Kevex EDX-771 energy dispersive spectrometer with a 200-watt rhodium target tube as an excitation source (US EPA, 1999). This method is a non-destructive and rapid tool in quantifying elemental content of ambient PM and has been extensively used in the literature (Carvalho et al., 1998; Koksal et al., 2019; Yeung et al., 2003). Quality assurance and quality control of the collected samples, as an essential part of the source apportionment models 53 (Hopke et al., 2020), was conducted by the US EPA implementing the highest sampling standards (Solomon et al., 2014; US EPA, 2014). Further information regarding the field and laboratory audits performed by the EPA can be found in Solomon et al. (2014). 5.2.2 Source apportionment analysis 5.2.2.1 Positive Matrix Factorization (PMF) model PMF is a multivariate receptor model that has widely been employed to identify/quantify the contributing sources to total PM2.5 as well as its particular constituents such as OC, polycyclic aromatic hydrocarbons (PAHs), and metal elements (Callén et al., 2014; Paatero and Tapper, 1994; Wang et al., 2019). The following chemical mass balance equation is mathematically solved by the PMF: X ij = ∑ g ik f kj p k=1 +e ij (5.1) Where 𝑋𝑖𝑗 refers to the concentration of the 𝑗𝑡 ℎ species in the 𝑖𝑡 ℎ sample; 𝑝 is the number of factors; 𝑔𝑖𝑘 shows the contribution of airborne mass concentration from the 𝑘𝑡 ℎ factor to the 𝑖𝑡 ℎ sample; 𝑓𝑘𝑗 represents the factor profile of each source for the 𝑗𝑡 ℎ species; and 𝑒𝑖𝑗 is the residual error for the 𝑗𝑡 ℎ specie in the 𝑖𝑡 ℎ sample. PMF aims to find out the most proper factor profiles and factor contributions by minimization of the objective function, Q, according to the following equation: Q= ∑ ∑ ( e ij u ij ) 2 m j=1 n i=1 (5.2) Where 𝑛 and 𝑚 are the number of samples and species, respectively; and 𝑢𝑖𝑗 is the uncertainty factor for the concentration of the 𝑗𝑡 ℎ species in the 𝑖𝑡 ℎ sample. The abovementioned minimization is conducted by assigning non-negative values to the factor profiles and contributions as the constraints of the optimization process (Norris et al., 2014). The following equation was utilized to determine the uncertainties of the input species to our PMF model (Paatero et al., 2014): σ ij =(0.05×X ij )+DL j (5.3) Where 𝜎𝑖𝑗 is the uncertainty associated with the 𝑗𝑡 ℎ species in the 𝑖𝑡 ℎ sample; 𝑋𝑖𝑗 represents the concentration of the 𝑗𝑡 ℎ species in the 𝑖𝑡 ℎ sample; and 𝐷𝐿𝑗 refers to the limit detection attributed to the 𝑗𝑡 ℎ species. 54 The mass concentration of the species as well as the above-mentioned user-defined uncertainty were employed as the input to the PMF model version 5.0. The PMF runs were conducted using the robust mode in which the impact of samples with high uncertainties are minimized. We implemented 20% extra modeling uncertainty to reduce the impact of unaccounted errors and uncertainties. The missing values in the concentration dataset were interpolated and the corresponding uncertainty values were tripled to decrease their contribution to the final results (Norris et al., 2014; Reff et al., 2007). To further validate the PMF results, we performed different uncertainty analyses including the Bootstrap (BS), Displacement (DISP), and BS-DISP (Bootstrap + Displacement) tests. The BS analysis verified our PMF outputs, since around 90% of the resolved factor profiles were re-mapped. Regarding the DISP analysis, our PMF solutions were considered reliable without any rotational ambiguity due to the <1% drop in the Q value and absence of any factor swap for dQmax=4. 5.2.2.2 PMF input In order to identify the sources contributing to total metal mass concentrations in CELA, we examined numerous combinations of the chemical components, different number of factors, and extra modeling uncertainties as the input to our PMF model according to the following criteria: 1) Strong correlation coefficient (i.e., R2 value above 0.9) between predicted versus measured total metal mass concentrations; 2) Physically interpretable PMF-resolved source profiles; 3) Investigation of the temporal trends in the PMF-resolved factor contributions; and 4) Evaluation of the uncertainty analyses (i.e., BS, DISP, and BS-DISP). Based on the aforementioned criteria, the optimum solution in our study (i.e., the most physically interpretable and statistically robust outcome) included Fe, Ni, Ti, Pb, Zn, Ba, V, and Br. 5.3 Results and discussion 5.3.1 Long-term trend in ambient PM2.5 concentration of metal elements Table 5.1 shows the method detection limit, average mass concentration and standard deviation (SD) for PM2.5-bound metal elements used as input to our source apportionment model based on the criteria elaborated earlier throughout the 2005-2018 period. Figure 5.2 represents annual box plots (left panel) and annual median concentrations (right panel) of redox-active metal and element concentrations including Fe, Ni, V, Zn, Ba, Pb, Ti, Al, Ca, K, Mn and Cu. Starting with Fe, according to Figure 5.2(a), although there is considerable variation in Fe concentrations for each year, the ambient levels remain overall stable during 55 the 2005-2018 time period. There might be a small decrease in overall Fe levels from 2005 to 2018 (based on the negative slope of regression (right panel)), but the observed trend is not statistically significant (p-value = 0.27). Fe is a tracer of mainly mineral dust (Almeida et al., 2005; Perrino et al., 2014), although minimal contributions of metallurgical processes have also been attributed to this element in urban environments (Marris et al., 2013). Since previous studies have indicated that soil particles are not expected to vary significantly over time in CELA (Hennigan et al., 2019), we attribute the observed slight decrease in Fe levels to the reduction of combustion-generated emissions, although we need to stress again that this small reduction is not statistically significant. Table 5.1- Summary statistics of PM2.5 mass and its metal and trace element mass concentrations for the sampling site in central Los Angeles (all values are in units of µg/m 3 ). Species Min Max Average SD Method Detection Limit PM 2.5 1.30 78.30 13.75 7.81 -- Total PM 2.5-bound metals 0.11 2.14 0.51 0.24 -- Al <0.02 0.41 0.06 0.05 0.01088 Ba <0.06 0.15 2E-6 0.03 0.05876 Br <0.01 0.02 5E-3 3E-3 0.00199 Ca <0.01 0.32 0.07 0.04 0.00347 Cd <0.02 0.04 6E-3 5E-3 0.01050 Co <0.01 4E-3 1E-3 7E-4 0.00141 Cr <0.01 0.08 3E-3 8E-3 0.00159 Cu <0.01 0.13 0.01 0.01 0.00135 Fe <0.01 1.17 0.19 0.13 0.00196 K <0.01 0.57 0.06 0.05 0.00341 Mg <0.02 0.35 0.05 0.04 0.01841 Mn <0.01 0.24 0.012 0.02 0.00231 Ni <0.01 0.25 3E-3 0.01 0.00125 Pb <0.01 0.05 5E-3 7E-4 0.00549 Se <0.01 5E-3 1E-3 8E-4 0.00212 Sn <0.02 0.05 0.01 9E-3 0.01787 Ti <0.01 0.05 9E-3 7E-3 0.00208 V <0.01 0.02 1E-3 2E-3 0.00150 The concentrations of Ni and V decreased substantially from 2005 to 2018 (Figure 5.2). As shown in the right panel, the observed decreasing trend in ambient levels of Ni and V was statistically significant (p-value <0.01). The concentrations of Ni showed very high variations 56 within the study period. Previous studies have indicated that Ni can be emitted from industries such as cement, glass, stainless steel, and brick production (Sina Hasheminassab et al., 2014; Tian et al., 2012). In addition, Ni is a tracer of vehicles engines and ship emissions (de Foy et al., 2012; Morawska and (Jim) Zhang, 2002) as well as particles generated by industrial and agricultural activities accumulated on the soil surface (Cempel and Nikel, 2006). V is mainly originated from heavy oil combustion in ships (Lin et al., 2018; Zhang et al., 2014). Therefore, the decreasing trends in Ni and V concentrations can most probably be attributed to reductions of these metals from heavy oil combustion (Hennigan et al., 2019). While ambient Zn levels were within the variation of data during 2005 to 2018, neglecting the average concentration of Zn in 2005 due to high variability of data in this year, a decreasing but not statistically significant trend (p-value = 0.26) can be observed in Zn concentration from 2006 (~0.17 µg/m 3 ) to 2017-2018 (~0.12 µg/m 3 ). According to the literature, while Zn is a tracer of tire wear abrasion (Harrison et al., 2012a), it is also used in several industrial sectors including automotive, aerospace, construction, electricity, energy, and electronics mainly due to its resistance to corrosion and non-magnetic properties (Gerdol et al., 2000; Uski et al., 2015). In addition, this metal element has been linked to motor oil and grease combustion (Hayakawa et al., 2015), as well as motor vehicles sources (Nyangababo and Hamya, 1986). Since emissions from tire wear abrasion are expected to remain rather constant over time (and possibly even increase due to the increases in road traffic, as we will discuss later), slight reductions in Zn levels can be ascribed to the combustion sources in both transportation and industrial sectors (Hennigan et al., 2019). According to Figure 5.2(e), no explicit trend was observed in Ba concentrations from 2005 to 2013. However, an increasing trend can be seen in the Ba concentrations from 2013 to 2018. Ba is mainly a product of non-tailpipe emissions, particularly in the form of brake wear abrasion (Gietl et al., 2010; Harrison et al., 2012b). Thus, the observed increasing trend from 2013 to 2018 can be ascribed to increased road traffic activities in the area during the same time period (as will be further elaborated in Section 5.3.2.2). It should be noted that the spike in Ba levels for the years of 2009 and 2010 does not necessarily show higher concentration of Ba during these years due to the high variation of the data. According to Figure 5.2(f), ambient Pb concentrations decreased slightly from 2005 to 2013. While this redox-active metal is a documented chemical marker of re-suspended road dust emissions (Deocampo et al., 2012; Faiz et al., 2009), combustion sources in motor vehicles and industries can also contribute to Pb levels in urban environments (Fernández-Camacho et al., 2012; Lough et al., 2005; Tian et al., 2012). Thus, the slight decrease in Pb levels during 57 the 2005-2013 period may be attributed to the reduced emissions from combustion sources. Similar to Ba, the concentrations of Pb increased gradually from 2013 to 2018. Both Ba and Pb are tracers of resuspended road dust (Hasheminassab et al., 2020; Hennigan et al., 2019), and the observed rise in their annual concentrations may be the attributed to the elevated traffic activities. According to Figure 5.2(g), while Ti concentrations decreased from 2005 to 2011 (p-value = 0.03), comparable levels of this metal were observed within the period of 2011 to 2018. Ti has been documented as a tracer of mineral dust (Fitzgerald et al., 2015; Xuan, 2005) as well as re-suspended road dust emissions (Song et al., 2006). In addition, this element is also used in chemical and petrochemical industries primarily for corrosion resistance as well as surface coating (Fan et al., 2008). While mineral emissions are expected to be consistent during 2005 to 2018, the small decrease in Ti levels can most probably be attributed to the industrial emissions. Figure 5.2(h) and Figure 5.2(i) illustrate the temporal trend in Al and Ca concentrations, respectively. Due to high variability of data between 2007 and 2009, the Al and Ca values during this period were neglected in the temporal analysis. Ambient levels of Al and Ca were quite consistent during the study period. Similarly, Figure 5.2(j) exhibits no explicit trend in K concentrations during the study period. Al, Ca and K are documented tracers of soil and mineral dust (S. Hasheminassab et al., 2014; Heo et al., 2009; Sowlat et al., 2012; Thurston et al., 2011; Zong et al., 2016). K emissions have also been linked to the biomass burning activities (Jain et al., 2018; Thurston et al., 2011). According to Hennigan et al. (2019), the soil particles are not subject to significant changes in CELA which is in agreement with the observed trends for Ca, K and Al in our study throughout the 2005-2018 period. According to the literature, since Al can also be found in re-suspended dust particles (Taghvaee et al., 2018b), the slightly elevated Al concentrations within 2015-2018 can be attributed to the increase in vehicular activities in the area. According to Figure 5.2(k), the ambient Mn levels followed a statistically significant decreasing trend (p-value = 0.005), from ~0.036 µg/m 3 in 2005 to ~0.003 µg/m 3 in 2018. The figure shows massive drop in Mn concentrations post-2007. Mn particles are emitted from manganese alloy industry (Hernández-Pellón and Fernández-Olmo, 2019; Marris et al., 2012; Mbengue et al., 2015) and it is also widely used in manufacturing steel, acting as the alloy constituent (Pekney et al., 2006). In addition, Mn is a tracer of vehicular emissions (Lim et al., 2010; Taghvaee et al., 2018b) in which Mn-containing fuel additives are used to mitigate smoke and catalyst combustion (Jain et al., 2018). The observed decrease in Mn concentrations 58 in 2007 can be attributed to the reduction of this metal from vehicular emissions due to the tailpipe and combustion sources emission control regulations (e.g., U.S. EPA 2007 emission standards, and CARB regulations) which were implemented in 2007 (Altuwayjiri et al., 2021). Excluding the year 2007 due to high variability of the data, a decreasing trend in Cu concentrations is observed from 2005 (~0.02 µg/m 3 ) to 2018 (~0.009 µg/m 3 ) which is statistically significant (p-value = 0.002). While Cu can be a tracer of brake wear abrasion (Schauer et al., 2006), it has also been associated with direct vehicular emissions (Sharma et al., 2016; Taghvaee et al., 2018b) as well as small-to-medium scale industries such as metal processing industries, industrial effluents and coal fired thermal power plants (Jain et al., 2020; Reff et al., 2009). Thus, the decreasing trend in Cu levels as shown in Figure 5.2(l) can be attributed to curtailed emissions from combustion sources due to the emission control policies. Figure 5.2 - Annual box plots (left panel) and median (right panel) of ambient mass concentrations for: (a) Fe; (b) Ni; (c) V; (d) Zn; (e) Ba; (f) Pb; (g) Ti; (h) Al; (i) Ca; (j) K; (k) Mn; and (l) Cu. (a) (b) (c) 59 (d) (e) (f) (g) 60 (h) (i) (j) (k) 61 (l) We have also investigated the ambient levels of the aforementioned metals during the same period (i.e., 2005-2018) at Riverside, CA (https://ars.els-cdn.com/content/image/1- s2.0S1352231021001795-mmc1.docx). This monitoring site is located approximately 90 km to the east of CELA, and is the receptor of primary PM emissions from the Los Angeles area, as the prevailing westerly and south-westerly wind transport these emissions from Los Angeles to the eastern parts of the basin (Altuwayjiri et al., 2021; Heo et al., 2013; Saffari et al., 2015). Despite the 90 km distance between these two sites, the temporal trends in concentrations of most metals and elements such as Fe, Ca, Mn, V, Pb, Ba, and Zn are almost identical between Riverside and CELA. 5.3.2 Source apportionment results 5.3.2.1 Number of factors As discussed earlier, the PMF model was run with different configuration sets following a trial-and-error approach. While various number of factors and extra modeling uncertainties were investigated, the optimum result with 4 factors was selected based on the criteria elaborated in Section 5.2.2.1. The coefficient of statistical determination, R 2 , value for the linear regression between the predicted and measured total metals mass concentration was 0.92, corroborating the ability of our PMF model to successfully apportion the sources of ambient 62 metal concentrations in the area. Figure 5.3 represents the PMF-resolved factor profiles including mineral dust, re-suspended road dust, tire wear, and combustion sources as the four prime factors contributing to the total PM2.5-bound metals in CELA. Figure 5.5 illustrates the relative and absolute contribution of the identified sources to total metal concentrations between 2005 and 2018, while Figure 5.4 shows the relative contribution of sources to the concentrations of individual metals and trace elements. 5.3.2.2 Factor identification and source contributions to total metals According to Figure 5.3, the first factor accounted for 84.3% and 56.2% of Fe, Ti concentrations, respectively. Previous studies have documented Fe and Ti as major chemical markers of soil particles (Almeida et al., 2005; Perrino et al., 2014; Xuan, 2005), suggesting mineral dust as the dominant source of this factor profile. Based on the results in Figure 5.3(a), mineral dust was the dominant contributor to total metal elements throughout the study period (with the average contribution of 50 ± 8%). In concert with our results, a recent study conducted in the city of Paramount (an urban area located approximately 18 km to the southeast of CELA) has attributed 48.8% of total metals concentrations to the soil particles (Hasheminassab et al., 2020). Furthermore, as shown in Figure 5.3(b), the contribution of mineral dust to metals concentrations in the period of 2005 to 2018 was quite consistent with absolute contribution of 0.26 ± 0.05 µg/m 3 (SD/mean = 19%). This finding is also in agreement with previous studies reporting negligible variations in mineral dust emissions over time in CELA (Hennigan et al., 2019). The second factor was associated with high loadings of Ba (100%) and Pb (66.1%). Ba is a well-known tracer of brake wear resulting from grinding of brake pad materials (Chan and Stachowiak, 2004). In addition, Pb has widely been associated with road dust emissions (Deocampo et al., 2012; Faiz et al., 2009). Therefore, this factor was labeled as “re-suspended road dust” with average relative contribution of 38 ± 13% to total metal concentrations. Figure 5.3 shows no discernible trends in the contribution of re-suspended road dust to total metals concentrations between 2005 and 2013. However, the absolute contribution of re-suspended road dust increased by 70.3% from 2013 (0.17 µg/m 3 ) to 2018 (0.29 µg/m 3 ). This observed trend can be attributed to the increased traffic activities in the Los Angeles basin, starting in 2013. Figure 5.6(a) and (b) exhibit the total vehicle miles traveled (VMT), respectively, on I110 as the closest major roadway to our monitoring site and the entire Los Angeles County routes. According to the Figure 5.6(a), while the monthly mean VMT remained relatively constant between 2005 and 2013 on I-110, a notable increase was observed from 164 million 63 miles in 2013 to 192 miles in 2017-2018, which is in agreement with the increased contribution of re-suspended road dust emissions to total metals within the 2013-2018 period. Similar trends can be observed in Figure 5.6(b) for the total Los Angeles County routes. Additionally, recent studies have highlighted the role of electric vehicles (EV) in the increased re-suspended road dust emissions. EV fleets are considerably heavier than internal combustion engine (ICE) vehicles, resulting in higher friction force between vehicles and both the tire and road surface, leading to increased resuspension of road dust particles (Timmers and Achten, 2016). This is in agreement with the results of studies reporting higher PM2.5 emission factors for EVs (by almost ~5%) in comparison with euro-6 diesel and petrol equivalent (Beddows and Harrison, 2021). Thus, part of the increased re-suspended road dust emissions can be attributed to the increasing use of EVs in the Los Angeles area (Kapustin and Grushevenko, 2020). The third factor is almost exclusively represented by very high loadings of Zn concentrations (74.6%), with negligible (<5%) loadings of other metal and elements. Zn has been documented as a surrogate of tire wear abrasion (Adachi and Tainosho, 2004; Harrison et al., 2012a). Although tire wear emissions can be classified as a sub-category of road dust particles, Apeagyei et al. (2011) reported significantly higher (by a factor of 15) levels of Zn in tire wear compared to other non-tailpipe particles. Therefore, we labeled this factor as “tire wear”, to distinguish it from “re-suspended road dust”. According to Figure 5.3, this factor exhibited a minimal relative contribution to total metals concentrations (2 ± 1%) with consistent absolute contribution of 0.010±0.003 µg/m 3 throughout the study period. Finally, the last factor demonstrated high loadings of Ni and V (59% and 100%, respectively). Both of these species have generally been associated with the combustion of heavy fuel oil in ships and industrial sectors (de Foy et al., 2012; Lin et al., 2018; Zhang et al., 2014). Thus, the last factor can be identified as “combustion sources” which include emissions from ships, vehicles, and industries. It is noteworthy that moderate loading of 22% was observed for Pb in this factor profile, which can be attributed to the vehicular emissions as well as smelting process in local industries (Harrison and Laxen, 1981; Lim et al., 2010). According to Figure 5.3, the absolute contribution of combustion emissions to total metals decreased from ~0.11 µg/m 3 in 2005-2006 to ~0.02 µg/m 3 in 2017-2018, demonstrating the effects of enforcement actions and rule developments in the area, including stringent regulations on vehicular emissions such as LEV II, reducing marine fuel-S content, and usage of S-reformed heavy oil in industrial sectors (Sina Hasheminassab et al., 2014; Hennigan et al., 2019; Lurmann et al., 2015; Spada et al., 2018). The relative contribution of this factor to total metal and element concentrations decreased by almost 88% in 2005-2018 time period. In comparison 64 to our study, higher contribution of industrial sources to metal elements were reported by (Hasheminassab et al., 2020) in the city of Paramount (i.e., 10-14%), presumably due to the high impact of emissions from several industrial sectors and forging facilities in that area. It should be noted that although absolute contributions of mineral/road dust to total metals were consistent from 2005 to 2013 as discussed earlier, the decreasing trend in contribution of combustion emissions has led to increased relative contribution of other PMF-resolved factors (i.e., mineral dust, re-suspended road dust, and tire wear) to total metals concentrations during this time span. Figure 5.3 - PMF-resolved factor profiles. (a) (b) (c) 65 (d) The seasonal variations in contribution of the PMF-resolved sources to total metal concentrations is shown in Figure 5.4(c). We divided each year into two six-month periods, including cold season (October to March) and warm season (April to September). The total metals and trace elements concentrations were higher (p-value <0.001) during the cold season (~0.56 µg/m 3 ) in comparison with the warm phase (~0.45 µg/m 3 ). This observation is in agreement with the results of previous studies in the Los Angeles area, reporting higher levels of redox-active metals during winter mainly due to the higher atmospheric stability and lower mixing height during the colder months of the year which limit the vertical and horizontal dispersion of emitted particles (Mousavi et al., 2018; Pakbin et al., 2011). According to Figure 5.4(c), while higher contribution of mineral dust to total metals was observed in the cold season (as a result of higher atmospheric stability), re-suspended road dust and tire wear demonstrated rather constant contribution to metal elements throughout the year. This can be justified by the lower relative humidity and higher wind speed during the warm season, facilitating the resuspension of particles due to traffic movement, and overcoming the impact of higher mixing height during the summer season (Laidlaw and Filippelli, 2008; Sowlat et al., 2016). 66 Figure 5.4 - Source contributions to total metal elements for the period of 2005 to 2018: (a) relative source contribution; (b) absolute source contribution; and (c) absolute source contribution during cold season (CS) and warm season (WS). (a) (b) (c) 5.3.2.3 Quantification of source contributions to redox-active metal species Figure 5.5 demonstrates the relative contribution of PMF-resolved sources to individual metal species. According to Figure 5.5(a), mineral dust was the dominant contributor to Fe (88 ± 5%) followed by combustion emissions (7 ± 6%) and minimal contributions of resuspended road dust (4 ± 2%) and tire wear abrasion (1.0 ± 0.4%). In addition, the relative contribution of combustion sources to Fe decreased from 2005-2006 (~19%) to 2017-2018 (~2%), probably as a result of the implementation of emission control policies such as vehicle petroleum standards, marine fuel regulations as well as use of reformed fuel in metallurgy sectors. In concert with our observation, Hennigan et al. (2019) also ascribed the reduction in Fe levels to more effective control of metallurgical processes and industrial emissions. Similarly, 67 contribution of combustion emissions to Ni overall decreased during the investigation period (Figure 5.5(b)), from ~83% in 2005-2006 to ~29% in 2017-2018. Therefore, similar to Fe, we conclude that the observed decrease in the Ni concentrations is associated with the reduced emission from combustion-related sources. As shown in Figure 5.5(c), Zn concentrations were dominated by tire wear emissions (74 ± 8%) followed by minimal contributions of mineral dust (19 ± 4%) and combustion sources (6 ± 5%). In addition, excluding year of 2005 (due to high variation in data), the relative contribution of combustion sources decreased from 2006 to 2018, justifying the observed reductions in Zn ambient concentration during this time period. Consistent with our findings, Hennigan et al. (2019) attributed the decreasing trend in Zn levels to the reduction in oil combustion rather than substantial changes in paved and unpaved road dust, agricultural soil, and construction dust. According to Figure 5.5(d), the ambient V concentration decreased from 2005 to 2018. As illustrated in Figure 5.5(d), combustion emissions were the sole contributors to the ambient V levels for this period. Thus, the observed decreasing trend is associated with the reductions in emissions from transportation and industrial sectors. Similarly, the PMF model attributed 100% of Ba concentration to the re-suspended road dust emissions (Figure 5.5(f)). According to Figure 5.5(e), re-suspended road dust (66 ± 22%) and combustion emissions (21 ± 21%) were the major contributors to Pb, followed by minimal contributions of soil (10 ± 3%) and tire wear (4.0 ± 0.8%). While the relative contribution of combustion emissions to Pb decreased from ~64% in 2006 to ~6% in 2018, the contribution of re-suspended road dust to Pb levels increased from ~21% in 2006 to ~85% in 2018. The increasing trend is probably associated with the synchronized impacts of reductions in industry sectors (i.e., combustion emissions) combined with the higher non-tailpipe traffic emissions. Finally, as shown in Figure 5.5(g), mineral dust (56 ± 8%) and re-suspended road dust (34 ± 11%) were the major sources of Ti followed by a minimal contribution of combustion sources (8 ± 7%). Similar to Pb, we observed an increasing trend in the contribution of the resuspended road dust factor to Ti concentration during 2013-2018, which we attribute to the increase in road traffic activities (i.e., VMT) in the area as discussed earlier. 68 Figure 5.5 - The relative contribution of PMF-resolved sources to individual metal elements: (a) Fe; (b) Ni; (c) Zn; (d) V; (e) Pb; (f) Ba; and (g) Ti. (a) (b) (c) (d) (e) (f) (g) 69 5.4 Summary and conclusions In this study, we investigated the long-term trends in redox-active metals concentrations in central Los Angeles, and analyzed the contribution of various emission sources to total metal concentrations during 2005 to 2018, using the PMF model. Mineral dust and re-suspended road dust shared the largest contributions to metals concentrations (50 ± 8% and 38 ± 13%, respectively) followed by marginal contribution from combustion emissions (9 ± 8%) and tire wear particles (2 ± 1%). The temporal trends of the PMF-resolved factors revealed that combustion emissions decreased significantly over the study period while the absolute contributions of mineral dust and tire wear to total metals concentrations remained rather consistent. We also observed a rise in re-suspended road dust contribution from 2013 to 2018 due to elevated VMT in the area during this time period. We further examined the contribution of identified sources to individual metals concentrations. The reduction in concentrations of Ni and V was attributed to the curtailed emissions from combustion sources. The primary emission sources for Ba, Fe and Zn (i.e., re-suspended road dust, mineral dust and tire wear, respectively) remained almost constant between 2005 and 2013. However, we observed an increasing trend in the contributions of re-suspended road dust and brake wear to the concentration of trace elements, starting in 2013, which was attributed to the increase in traffic activities along with higher percentage of EVs in this time period. While the observed reduction in combustion emissions from 2005 to 2018 denotes the effectiveness of the implemented aftertreatment and emission control regulations, the increase in re-suspended road dust emissions during the past 5-6 years signifies the challenges in curtailing non-tailpipe emissions and the need to develop effective strategies for their reduction in the future. 70 Chapter 6 : Tailpipe and non-tailpipe emission factors and source contributions of PM 10 on major freeways in the Los Angeles basin In this study, the emission factors of PM10 and its chemical constituents from various contributing sources including non-tailpipe and tailpipe emissions were estimated on two interstate freeways in the Los Angeles basin. PM10 samples were collected on the I-110 and I- 710 freeways as well as at the University of Southern California (USC) campus as the urban background site, while freeway and urban background CO2 levels were measured simultaneously. PM10 samples were analyzed for their content of chemical species which were used to estimate the emission factors of PM10 and its constituents on both I-110 and I-710 freeways. The estimated values were employed to determine the emission factors for light (LDV) and heavy-duty vehicles (HDV). The quantified species were also processed by the positive matrix factorization (PMF) model to produce PM10 freeway source profiles and their contribution to PM10 mass concentrations. Using the PMF factor profiles and emission factors on the two freeways, we characterized the emission factors for light-duty and heavy-duty vehicles by each non-tailpipe source. Our findings indicated higher non-tailpipe emission factors of PM10 and metal elements on the I-710 freeway compared to the I-110 freeway, due to the higher fraction of heavy-duty vehicles (HDVs) on that freeway. Furthermore, the generation of non-tailpipe PM10 from resuspension of road dust was twice of tire and brake wear. The results of this study provide significant insights into PM10 freeway emissions and particularly the overall contribution of non-tailpipe and tailpipe sources in Los Angeles, which can be helpful to modelers and air quality officials in assessing the importance of individual traffic-related emissions on the overall population exposure. This chapter is based on the following publication: Jalali Farahani, V., Altuwayjiri, A., Taghvaee, S. and Sioutas, C., 2022. Tailpipe and nontailpipe emission factors and source contributions of PM10 on major freeways in the Los Angeles basin. Environmental Science & Technology, 56(11), pp.7029-7039. 71 6.1 Introduction Traffic emissions are one of the prime sources of ambient PM in metropolitan areas (Pant and Harrison, 2013) and they fall into two major categories: 1) Tailpipe emissions, which are generated from the oxidation of the fuel and lubricant volatilization during the combustion process; and 2) Non-tailpipe emissions, which involve road dust resuspension, tire wear, brake wear, clutch wear and road surface wear (Piscitello et al., 2021) The distinction of these two emission sources is not limited to the formation process. The emitted particles are also different in size distribution and chemical composition (Pernigotti et al., 2016). PM from tailpipe emissions is mostly in PM2.5 or PM0.1 size range (particles with aerodynamic diameter less than 0.1 µm) and mainly contain hydrocarbons (Agudelo-Castañeda et al., 2017), while non-tailpipe emitted particles are largely released in coarse size range and are rich in redox active and toxic metals which are also of great importance due to their high oxidative potential (Bates et al., 2019b; Kasprzak, 2002; Prahalad et al., 1999). Tailpipe and non-tailpipe emissions have revealed different trends during the past two decades. While the tailpipe emissions have substantially decreased due to the promulgation of after-treatment emission control technologies (Squizzato et al., 2018), recent studies have reported an increase in the contribution of non-tailpipe emissions (Farahani et al., 2021; Grigoratos and Martini, 2015; Habre et al., 2021; Shirmohammadi et al., 2017b). The increase in non-tailpipe emissions is the result of the synchronized effect of reductions in tailpipe emissions due to strict emission control policies in the US, combined with the increase in nontailpipe emissions due to higher traffic activities and possibly the popularity of electric vehicles which are known to emit higher road dust emissions. Non-tailpipe emissions have been recently identified as the dominant source of pollution associated with traffic sources (Amato et al., 2016; Singh et al., 2020; Timmers and Achten, 2016). Despite various control measures (e.g., dust suppressants, street cleaning), the mitigation of non-tailpipe emissions is more challenging compared to tailpipe emissions, due to their complex nature that depends on the abrasion of different vehicle components (i.e., tires, brakes, metal compartments), the general condition of road surfaces and local meteorology (Piscitello et al., 2021; Wang et al., 2021). Emission factors have been used to quantify the release of PM into the environment. Previous studies have estimated non-tailpipe emission factors from the vehicle fleet mixtures (Gaga et al., 2018; Ning et al., 2008; Sternbeck, 2002) or individual light-duty vehicles (LDV) fleet and heavy-duty vehicles (HDV) fleet (Bukowiecki et al., 2010; Luhana et al., 2004), using 72 various approaches including direct laboratory or on-site monitoring (Alves et al., 2020; Panko et al., 2013), elemental composition and mass balance analysis (Bukowiecki et al., 2010, 2009; Luhana et al., 2004). Other studies have quantified non-tailpipe emissions by comparing PM and CO2 or NOx concentrations at a traffic and background site (Amato et al., 2016; Bukowiecki et al., 2010; Gehrig et al., 2004a). In this study, the emission factors were estimated on two interstate freeways of I-110 and I-710 which traverse the Los Angeles (LA) basin, a metropolitan area of roughly 18 million inhabitants heavily impacted by traffic emissions. The positive matrix factorization (PMF) model was used to apportion major PM10 sources and identify factor profiles for tailpipe and non-tailpipe sources which facilitated us in estimating the emission factors of light-duty and heavy-duty vehicles characterized by each non-tailpipe emission source. We focused on the PM10 size fraction that includes coarse particles released by the resuspension and vehicle friction as well as fine and ultrafine particles emitted by vehicle exhaust. We believe this study provides significant insights to the impact of emission sources in Southern California and improves our understanding of significant contributors to vehicular emissions of PM10 in the largest metropolitan area of the US. 6.2 Methodology 6.2.1 Sampling site and period On-road sampling was conducted on two of Los Angeles major freeways, I-110 and I-710 (Figure 6.1). The I-110 freeway is a 51-km high-traffic route connecting the Port of Los Angeles to Downtown Los Angeles and Pasadena. The vehicle profile on this route comprises mostly of light-duty vehicles (LDVs). The I-710 freeway covers 43-km, from Long Beach to Valley Boulevard and includes a large number of heavy-duty vehicles (HDVs) traveling to and from the Ports of Los Angeles and Long Beach (Shirmohammadi et al., 2017a). The average HDV fraction of entire vehicle fleet on I-110 and I-710 during the sampling period was 1.33 ± 0.09% and 12.71 ± 0.95%, respectively. Particles at the two freeways were sampled via a stainless steel and curved PM10 inlet at the height of 1.50 m above the ground. The samplings were conducted in multiple lanes of I-110 and I-710 freeways with variable distance from the curb. The urban background concentrations were retrieved from concurrent sampling at the rooftop of the chemical engineering building at the University of Southern California (USC). 73 Figure 6.1 - Map of the sampling routes in Los Angeles area. A total of 54 samples were collected during the three phases of sampling campaigns. The summer samples were collected in two periods of June 17th to June 21st and August 5th to August 8th in 2019. The intermediate campaign was between November 18th and November 22nd of 2019 as well as December 2nd to December 5th of 2019. The winter sampling was conducted during December 23rd to December 27th of 2019 and January 6th to January 9th in 2020. The daily samples were collected between 10:00 AM and 05:00 PM. 6.2.2 Instrumentation Two sets of PM10 samples were collected on quartz filters during morning and afternoon of each sampling day, using four Sioutas personal cascade impactors (Sioutas™ PCIS, SKC Inc., Eighty Four, PA, USA) at sampling flowrate of 10 lpm. The PM10 collected samples were analyzed for the content of elemental carbon (EC), organic carbon (OC), water-soluble ions, metals and trace elements at the Wisconsin State Laboratory of Hygiene (WSLH). The EC and OC constituents were determined by the means of model-4-semi-continuous OC/EC field analyzer (Sunset Laboratory Inc, USA), using the Thermal Optical Transmission (TOT) method (M E Birch and Cary, 1996). The content of water-soluble ions was quantified by employing ion chromatography (IC, Dionex ICS 2100, and ICS 1100) on the 0.45 µm filtered water extract (2-h with constant agitation) of a punch of the PM accumulated filter. The elemental analysis was performed on a punch of the filter by means of inductively coupled 74 plasma-mass spectroscopy (ICPMS) which involved placing the filter in a mixture of nitric acid, hydrochloric acid, and hydrofluoric acid (0.6 mL 16N HNO3, 0.2 mL 12N HCl, 0.1 mL 28N HF). The samples were then heated with an automated, temperature and pressureregulated, trace analysis microwave system (Milestone Ethos+), followed by an hour of cooling (Lough et al., 2005). Further details of our chemical analysis procedures are available in the SI file. Concurrent concentrations of CO2 were also measured at the freeways and urban background site via CO2 monitors (Q-Trak IAQ Monitor 8551, TSI Inc., USA). 6.2.3 Positive Matrix Factorization (PMF) model We used the U.S. Environmental Protection Agency (US EPA) PMF model 5.0 to obtain the factor profiles of traffic sources at the two freeway sites. The mass concentrations of PM10 species on I-110 and I-710 freeways during summer, intermediate and winter period were combined into one dataset and processed in the PMF model. We performed numerous runs with various combination of PM chemical constituents and number of factors to identify the contribution of major sources to the freeway PM10 concentrations. An extra modeling uncertainty of 10% was also implemented to account for the unconsidered errors in our dataset. The PM chemical constitutes in the selected solution included organic and elemental carbon (OC, EC), sodium (Na + ), ammonium (NH4 + ), sulfate (SO4 2- ) ions, aluminum (Al), calcium (Ca), titanium (Ti), iron (Fe), copper (Cu), zinc (Zn) and lead (Pb). The optimum solution was selected in which the following criteria were satisfied: i) High correlation coefficient (i.e., R2 value above 0.9) between predicted and measured PM10 values; ii) Physically interpretable PMF-resolved source profiles; and iii) Evaluation of the PMF uncertainty estimation analyses (i.e., BS, DISP, and BS-DISP). 6.2.4 Emission factor estimation Under typical driving conditions, the combustion efficiency for diesel and gas fuel is over 90% (Yli-Tuomi et al., 2005) which makes carbon dioxide a reliable proxy for the amount of fuel consumed. Thus, the concentrations of a traffic-related pollutant relative to the CO2 concentrations represent the pollutant’s emission per quantity of fuel burned. The fuel-based emission factor for each pollutant was calculated based on the following equation (Kam et al., 2012; D. Wang et al., 2013): E ij = [P] fw −[P] bg [CO 2 ] fw −[CO 2 ] bg × w× 10 3 (6.1) 75 Where E ij is the emission factor pollutant i on freeway j (mg/kg fuel); [P] is the concentration of the pollutant (µg/m 3 ) and [CO2] is the concentration of the CO2 (µg of carbon/m 3 ); subscripts fw and bg represent measured concentrations on freeway and at the background, respectively; w is the carbon weight fraction of the fuel, which was considered 0.85 and 0.87 for diesel fuels and gasoline fuels, respectively (Kirchstetter et al., 1999). We should note that this equation is valid when the concentrations of both pollutant and CO2 are dominated by traffic-related emissions. The fuel-based emission factors were normalized into vehicle distance traveled (E d,ij ) in units of mg km -1 veh -1 , using the following equation: E d,ij = E ij × ρ × U (6.2) Where ρ shows the density of the fuel (kg/L), reported 0.74 kg/L and 0.84 kg/L for gasoline and diesel, respectively (Ban-Weiss et al., 2008); and U is the average fuel consumption of the vehicles (L/km). The average fuel consumption rates considered for diesel and gasoline were 0.107 L/km and 0.424 L/km, respectively (Kozawa et al., 2014). The emission factor of pollutants on each freeway is the collected value of light-duty and heavy-duty vehicles. We determined the emission factor for each vehicle fleet by reconstructing the emission factors on the I-110 and 1-710 freeways as a function of LDV (E i,LDV ) and HDV (E i,HDV ) fleets: E ij = φ j,HDV E i,HDV + (1 − φ j,HDV )E i,LDV (6.3) where φ j,HDV represents the fraction of carbon emitted by the HDV fleet on freeway j and is determined as the following: φ HDV = f D U D ρ D w D (f D U D ρ D w D )+((1−f D )U G ρ G w G ) (6.4) where f is the HDV fraction of the traffic. The subscripts D denote diesel and G denote gasoline. The distance-driven LDV and HDV emission factors were employed to calculate the daily emission rates of LDV and HDV fleets, ED (kg/day), on the entire LA basin: ED i,HDV = E i,HDV × VMT HDV × 10 −6 (6.5) ED i,LDV = E i,LDV × VMT LDV × 10 −6 (6.6) where E i,HDV and E i,LDV is the distance driven emission factors of pollutant i, attributed to LDVs and HDVs, respectively (mg km -1 veh -1 ); and VMT is the average vehicles-miles traveled of each vehicle fleet on the entire LA freeways per day which were 4,328,835 miles (6,966,585 km) and 92,234,186 miles (148,436,534 km) for HDVs and LDVs, respectively. 76 The traffic data (i.e., VMT and HDV fraction) for I-110 and I-710 freeways were obtained from the California Department of Transportation (CalTrans) data archiving website, Performance Measuring System (PeMS). 6.3 Results and discussion 6.3.1 Overview of mass concentration and chemical composition of PM10 The average PM10 mass concentration at USC (background), I-110 and I-710 freeways were 34.4, 63.5 and 96.2 µg/m 3 , respectively, while the corresponding values for CO2 were 449.5, 595.7 and 603.8 ppm, respectively (Figure 6.2). The average freeway concentrations of EC (3.0 µg/m 3 ) and OC (25.7 µg/m 3 ) were greater than the measured levels at the background site by factors of 3.5 and 3.2, respectively, as shown in Figure 6.3. The EC and OC freeway over background ratio was higher on I-710 freeway. Furthermore, PM10 composition on this route was also higher in concentrations of road dust, and vehicle abrasion elements (i.e., Ba, Pb, Fe, Zn, Ca, Cu, Al). These observations are attributed to greater fraction of HDVs on this freeway as previous studies have reported higher emission of carbonaceous content as well as road dust, brake and tire wear elements from diesel vehicles (Amato et al., 2012; Bukowiecki et al., 2010; Cheung et al., 2009; Luhana et al., 2004). Consistent with the literature, Ba, Cu, Ca and Fe showed the greatest freeway/background ratio (6.2, 4.9, 4.4 and 4.2, respectively) of all metal and trace elements (Harrison et al., 2012a; Hicks et al., 2021; Jeong et al., 2019). Excluding sulfate, water-soluble ions in PM10 had higher freeway concentrations compared to the background site. Figure 6.2 - Box plots of a) CO2 concentrations (ppm), and b) PM10 concentrations (µg/m 3 ) at USC background site, I-110 and I-710 freeways. (a) (b) 77 Figure 6.3 - Mass concentrations of PM10 and its components at USC background site, I-110 and I-710 freeways during the sampling period. The shown values are the average mass concentrations of the measurements in summer, intermediate and winter sampling periods. 6.3.2 Source apportionment of ambient particulate matter The optimum solution in our PMF model identified five emission sources which resulted in high correlation coefficient (R 2 ) of 0.91 between measured and modeled PM10 mass concentrations. This observation along with the <1% drop for the Q value and the absence of factor swap for dQmax=4 in the PMF statistical analysis, confirms our model’s ability to resolve adequately emission sources for the PM10 concentrations on the two freeways. The identified emission sources were tire and brake wear, vehicle exhaust, resuspended dust, seasalt and secondary aerosols (SA). The PMF-resolved factor profiles are shown in the Figure 6.4 and the relative contributions of the five factors to the freeway PM10 concentrations are presented in Figure 6.5. The first factor is associated with high loadings of NH4 + (80.6%) and sulfate SO4 2- (48.7%) both of which have been used as markers of secondary aerosols (SA) in the literature (Altuwayjiri et al., 2021; Huang et al., 2014; Taghvaee et al., 2018b). SA are formed through photochemical reactions from combustion sources and typically account for a considerable fraction of ambient PM 53-55. As shown in the Figure 6.5, the SA contributed to 14.2 ± 2.3% of PM10 mass concentration throughout the study period, which is consistent with the resolved contribution from the previous studies in the Los Angeles basin (Cheung et al., 2011; Mousavi et al., 2018). 78 The second factor is characterized by high levels of Na + (87.8%) and accounted for 11.2 ± 2.9% of freeway PM10 concentrations. Na + is an indicator of fresh and aged sea-salt (S. Hasheminassab et al., 2014). The contribution of sea-salt can be attributed to the proximity of a segment of I-110 and I-710 freeways to the ports of Los Angeles and Long Beach. This assessment is corroborated by the findings of previous studies which have reported substantial sea-salt contribution to fine and coarse PM size fractions in the vicinity of Long Beach and Los Angeles harbors (Arhami et al., 2009; Habre et al., 2021) and USC site (Wang et al., 2021). The third factor is represented by high loadings of EC (67.2%), which is a tracer of vehicle exhaust (Sina Hasheminassab et al., 2014; Schauer, 2003; Taghvaee et al., 2019b). This is the second largest factor contributing to the freeway PM10 levels, with average contribution of 26.1 ± 3.2%. Harrison et al. (2011) have resolved similar contribution (22.4%) from tailpipe sources to PM levels of a major highway in central London. Habre et al. (2021) have also identified elevated contribution of vehicle exhaust to the ambient fine and ultrafine particle concentrations in the LA basin. Significant loadings of OC were observed in the vehicle exhaust factor as well as other resolved sources. OC is not a unique marker of a specific emission source since is emitted from a variety of combustion sources (i.e., industrial emissions, airport, traffic activities) and is also formed by secondary reactions (Arhami et al., 2018; Paraskevopoulou et al., 2014; Tohidi et al., 2022). The high mass contribution of OC in all emission sources suggests that the PMF model was not able to accurately resolve this PM compound among the identified factors and it is likely that mixing of factors have occurred to some degree. The fourth factor had significant loadings of metals and elements including Ti (69.9%), Al (62.2%), Fe (48.4%) and Ca (47.3%). High concentration of iron and calcium have been measured in road wear-originated PM and Ti is a typical tracer of road dust in the literature (Harrison et al., 2012a; Kotchenruther, 2016; Taghvaee et al., 2018b). The abovementioned elements have been associated with soil dust particles but they also have been employed as chemical markers of the resuspended dust (Altuwayjiri et al., 2022; Amato et al., 2016; Jeong et al., 2016). While elements of soil dust are present in this factor, we believe that the majority of PM10 is emitted from non-tailpipe sources such as resuspended particles, road surface wear and road dust. Furthermore, the resolved factor profile is in agreement with the road dust profile in London Street canyons in which values of 0.040, 0.009, 0.023 and 0.572 were reported for Ti/Fe, Cu/Fe, Ti/Ca and Fe/Ca ratios, respectively (Jeong et al., 2019). The corresponding values of these elements in the PMF-resolved factor of our study were 0.060, 0.008, 0.056 and 0.943, respectively, corroborating the road-dust origin of this source. Thus, we labeled this 79 factor as “resuspended dust” which was the highest contributor to the on-road PM10 mass with an average contribution of 32.1 ± 4.4% throughout the sampling campaign. The fifth factor was identified based on high levels of tire and brake wear elements including, Zn (64.5%), Cu (56.8%) and Pb (51.0%). Cu has been used as the tracer for brake wear in the literature (Amato et al., 2011; Duong and Lee, 2011; Oroumiyeh et al., 2022; Song and Gao, 2011a). Zn is added during the manufacturing of tires as an activator for the vulcanization processes and is a surrogate of tire abrasion (Adachi and Tainosho, 2004; Habre et al., 2021; Harrison et al., 2012a). Furthermore, Pb has been employed as a tracer of brake and tire wear in the literature (Almeida et al., 2020; Song and Gao, 2011b). The tire and brake wear factor contributed 16.4 ± 2.6% to the freeway PM10, increasing the collective contribution of non-tailpipe sources (i.e., resuspended dust and tire and brake wear) to 48.5%. Non-tailpipe emissions accounted for approximately 65% of total traffic emissions which is in agreement with previous studies, reporting similar share of traffic emissions from non-tailpipe sources (Amato et al., 2016; Bukowiecki et al., 2010). 80 Figure 6.4 - The PMF-resolved profiles for the five identified factors during the entire sampling campaign. The blue bars represent the apportioned concentration of each species to the factor. The yellow dots represent the apportioned percentage of each species to the factor. The yellow dots represent the apportioned percentage of each species to the factor. 81 Figure 6.5 - Relative contribution of identified sources including secondary aerosols (SA), seasalt, vehicle exhaust, resuspended dust and tire and brake wear to the freeway PM10 concentrations. 6.3.3 Emission factors of PM10 6.3.3.1 Total emission factors Table 6.1 demonstrates the speciated PM10 emission factors, which contain both tailpipe and non-tailpipe emissions, based on the distance driven (i.e. mg of species per km driven). The PM species with background values close to their respective on-road concentrations were omitted from the table. Based on the data in these tables, the emission factors on the I-710 were considerably higher than that of the I-110 freeway with EC showing the most notable difference. The greatest emission factors among the metal and trace elements were observed for Fe, Ca and Al which are all trace elements and metals of resuspended dust. The average emission factor of Fe (2.63 mg km -1 veh -1 ) and Ca (2.04 mg km -1 veh -1 ) on both freeways are comparable to EC (2.3 mg km -1 veh -1 ) which further reflects the major impact of non-tailpipe emissions on ambient air quality. The total PM10 mass emission factor attributed to LDV (64.81 mg km -1 veh -1 ) and HDV fleet (183.68 mg km -1 veh -1 ) are reasonably in line with (Bukowiecki et al., 2010) study, reporting values of 50 mg km -1 veh -1 for LDVs and 288 mg km -1 veh -1 for HDVs at an interurban freeway site in Switzerland. 82 Table 6.1 - Distance-driven emission factors of PM10 and its major chemical components on I-110 and I-710 freeways as well as HDV and LDV fleets. Species I-110 I-710 LDV HDV (mg km -1 veh -1 ) PM10 69.07 ± 20.63 125.63 ± 24.4 64.81 ± 21.08 183.68 ± 21.11 OC 11.68 ± 0.15 26.55 ± 1.06 10.28 ± 0.36 45.84 ± 4.42 EC 0.76 ± 0.18 3.84 ± 0.46 0.4 ± 0.15 8.85 ± 1.21 NO3 0.75 ± 0.33 1.81 ± 0.54 0.65 ± 0.32 3.18 ± 0.73 Cl - 0.37 ± 0.16 1.07 ± 0.23 0.3 ± 0.16 2.08 ± 0.45 Mg 0.46 ± 0.24 0.88 ± 0.4 0.43 ± 0.24 1.33 ± 0.63 Al 0.65 ± 0.22 1.26 ± 0.25 0.6 ± 0.23 1.92 ± 0.48 K 0.29 ± 0.16 0.61 ± 0.21 0.26 ± 0.16 0.99 ± 0.26 Ca 1.45 ± 0.67 2.64 ± 0.91 1.37 ± 0.68 3.79 ± 1.28 Ti 0.05 ± 0.02 0.1 ± 0.01 0.05 ± 0.02 0.16 ± 0.03 Cr 0.04 ± 0.01 0.08 ± 0.01 0.04 ± 0.01 0.12 ± 0.02 Fe 1.73 ± 0.33 3.52 ± 0.4 1.58 ± 0.33 5.62 ± 0.58 Cu 0.07 ± 0.01 0.14 ± 0.05 0.06 ± 0.01 0.24 ± 0.11 Zn 0.05 ± 0.02 0.13 ± 0.06 0.04 ± 0.02 0.25 ± 0.13 Ba 0.14 ± 0.02 0.35 ± 0.02 0.12 ± 0.02 0.64 ± 0.04 (µg km -1 veh -1 ) Mn 17.68 ± 4.94 42.68 ± 4.97 15.26 ± 5 75.91 ± 4.19 Ni 6.42 ± 3.66 16.29 ± 0.63 5.49 ± 4.19 29.08 ± 7.15 Pb 4.09 ± 1.6 14.68 ± 5.09 2.89 ± 1.18 31.36 ± 11.83 Figure 6.6 compares previously reported emission factors in the literature, displaying a wide range of values for PM components. The high variability in emission factors, especially for metallic elements is likely the result of differences in traffic variables and meteorological parameters as well as measurement uncertainties (Lee et al., 2012; Ning et al., 2008). Nevertheless, the estimated values for I-110 and I-710 freeways are within the range of emission factors on roadways throughout the US and Europe. Specifically, Ning et al. (2008) estimated the emission factors for PM2.5 components on the same interstate freeways 13 years earlier. Our lower EC emission rate (0.76 and 3.84 mg km-1 veh-1 on I-110 and I-710, respectively) in comparison with Ning et al. (2008) values (1.8 and 16.1 mg km-1 veh-1 on I110 and I-710, respectively) is likely the result of the implementation of after-treatment control technologies especially on HDV, which were mandated by the 2007 emission control policies in the LA basin (Lurmann et al., 2015; McDonald et al., 2013). However, the emission factors for PM10 metal elements in our study are higher than those of Ning et. al (2008). This is expected as non-tailpipe metals are dominantly released in coarse size range which were not 83 investigated in that study. Additionally, the increased traffic in the basin(Farahani et al., 2021) has probably contributed to higher emission of non-tailpipe particles. Figure 6.7 illustrates the comparison of LDV emission factors in this study and the reported values for Wilshire and Sunset Boulevards, which are two major Los Angeles surface streets, 26 and 35 km long, respectively, with the traffic composition consisting of only LDVs (Kam et al., 2012). The figure displays lower PM10 emission factor in Wilshire/Sunset Boulevards which in part could be attributed to more frequent street cleaning activities in these surface streets compared to freeways, probably decreasing the dust resuspension and, in turn, the PM10 emission factors. This is supported by the higher Al and Ca emission factors in the freeway, which as previously discussed, are two major tracers of road dust. The quantified emission factors for most PM species are in close agreement with that of Wilshere/Sunset Boulevards. The Fe, Cu and Ba emission factors on surface streets slightly exceeded the values in this study which is possibly due to the higher braking frequency on surface streets compared to the freeway. Figure 6.6 - Comparison of distance-driven PM10 emission factors in I-110 and I-710 freeways with reported values in the Turkey tunnel (Gaga et al., 2018), Sweden’s Tingstad tunnel and Lundby tunnel (Sternbeck, 2002), Los Angeles I-110 and I-710 freeways (Ning et al., 2008), Pensylvannia tunnels (Grieshop et al., 2006) and Toronto highway (Wang et al., 2021). 84 Figure 6.7 - The comparison of fuel-based PM10 emission factors for LDV fleets to the estimated values in Wilshire/Sunset boulevards in Los Angeles (Kam et al., 2012). 6.3.3.2 Non-tailpipe emission factors The non-tailpipe emission factors for total PM10 mass and the associated metals and trace elements were calculated by applying the PMF-resolved factor profiles of “resuspended dust” and “tire and brake wear” to the total emission factors. A summary of results by individual non-tailpipe source is presented in Table 6.2. Total non-tailpipe emission factors are the collective value of the two identified non-tailpipe sources. Table 6.2 - Speciated PM10 emission factors of non-tailpipe sources for: a) I-110 and I-710 freeways and: b) LDV and HDV fleet. (a) Total non-tailpipe Resuspended dust Tire and brake wear Species I-110 I-710 I-110 I-710 I-110 I-710 (mg km -1 veh -1 ) PM10 33.49 ± 10.01 60.92 ± 11.83 22.17 ± 6.62 40.32 ± 7.83 11.33 ± 3.38 20.6 ± 4 OC 3.3 ± 0.04 7.51 ± 0.3 2.31 ± 0.03 5.25 ± 0.21 0.99 ± 0.01 2.25 ± 0.09 EC 0.17 ± 0.04 0.86 ± 0.1 0.06 ± 0.01 0.3 ± 0.04 0.11 ± 0.03 0.56 ± 0.07 Al 0.4 ± 0.14 0.79 ± 0.16 0.4 ± 0.14 0.79 ± 0.16 0 0 Ca 0.89 ± 0.42 1.63 ± 0.56 0.69 ± 0.32 1.25 ± 0.43 0.21 ± 0.1 0.38 ± 0.13 Fe 1.12 ± 0.22 2.28 ± 0.26 0.84 ± 0.16 1.7 ± 0.19 0.28 ± 0.05 0.58 ± 0.07 (µg km -1 veh -1 ) Ti 36.83 ± 12.51 73.3 ± 6.99 36.83 ± 12.51 73.3 ± 6.99 0 0 Cu 43.24 ± 8.51 92.16 ± 30.2 4.96 ± 0.98 10.58 ± 3.47 38.28 ± 7.53 81.58 ± 26.73 Zn 33.16 ± 13.58 85 ± 37.78 0 0 33.16 ± 13.58 85 ± 37.78 Pb 2.36 ± 0.92 8.46 ± 2.94 0.27 ± 0.11 0.97 ± 0.34 2.09 ± 0.81 7.49 ± 2.6 85 (b) Total non-tailpipe Resuspended dust Tire and brake wear Species HDV LDV HDV LDV HDV LDV (mg km -1 veh -1 ) PM10 89.06 ± 10.23 31.42 ± 10.22 58.94 ± 6.77 20.8 ± 6.76 30.12 ± 3.46 10.63 ± 3.46 OC 12.96 ± 1.25 2.91 ± 0.1 9.07 ± 0.88 2.03 ± 0.07 3.89 ± 0.38 0.87 ± 0.03 EC 1.98 ± 0.27 0.09 ± 0.03 0.69 ± 0.09 0.03 ± 0.01 1.29 ± 0.18 0.06 ± 0.02 Al 1.19 ± 0.3 0.37 ± 0.15 1.19 ± 0.3 0.37 ± 0.15 0 0 Ca 2.34 ± 0.79 0.84 ± 0.42 1.79 ± 0.61 0.65 ± 0.32 0.55 ± 0.18 0.2 ± 0.1 Fe 3.64 ± 0.38 1.02 ± 0.21 2.72 ± 0.28 0.76 ± 0.16 0.92 ± 0.1 0.26 ± 0.05 (µg km -1 veh -1 ) Ti 113.85 ± 19.51 33.86 ± 13.8 113.85 ± 19.51 33.86 ± 13.8 0 0 Cu 155.58 ± 0.07 38.67 ± 0.01 17.86 ± 7.96 4.44 ± 0.65 137.72 ± 61.35 34.23 ± 5.05 Zn 159.18 ± 80.64 27.87 ± 10.92 0 0 159.18 ± 80.64 27.87 ± 10.92 Pb 18.08 ± 6.82 1.67 ± 0.68 2.08 ± 0.79 0.19 ± 0.08 16 ± 6.04 1.47 ± 0.6 The EC values in the table are the derivative of the non-tailpipe factors profile in which minor levels of EC were resolved in both the resuspended dust and tire and brake wear sources by the PMF model. The higher heavy-duty traffic on I-710 enhanced the emission factors on this freeway as heavier vehicles exert greater fictional force on tires, brakes and road surface (Oroumiyeh and Zhu, 2021; Simons, 2016). The non-tailpipe emission factors on I-710 freeway were 1.82 times higher than the I-110 freeway. The PM emission from resuspended dust source were considerably greater than the collective brake and tire wear values, which is consistent with previous studies, identifying the resuspension of road dust as the dominant nontailpipe source (Piscitello et al., 2021). OC showed the greatest non-tailpipe emission factor while Fe, Ca and Al had the highest value among the metals and trace elements. The total non-tailpipe emission factor for LDVs (31.42 ± 10.22 mg km -1 veh -1 ) and HDVs (89.06 ± 10.22 mg km -1 veh -1 ) were within the range of the estimated values by (Gehrig et al., 2004b) (LDV: 47 mg km -1 veh -1 , HDV: 74 mg km -1 veh -1 ). Table 6.3 shows a comparison of PM10 emission factors of non-tailpipe sources to previously reported values in the literature. Broad ranges of values have been reported in the literature for the “resuspended dust” source. Hicks et al. (2021) estimated a value of 8 mg km -1 veh -1 for the resuspension on London streets, while Bukowiecki et al. (2010) obtained emission factor of 48 mg km -1 veh -1 on a freeway in Switzerland. Similarly, the vehicle-specific emission factors for resuspended dust showed high variability in the literature, spanning from LDV and HDV values of 1.1 ± 0.6 mg km -1 veh -1 and 172 ± 7 mg km -1 veh -1 , respectively (Thorpe et al., 2007) to 47 ± 39 mg km -1 veh -1 for LDVs and 260 ± 220 mg km -1 veh -1 for HDV (Amato et al., 2012). This observation is the result of environmental and roadway-related factors affecting the resuspended dust source, 86 including the vehicle weight (Casotti Rienda and Alves, 2021), traffic speed (Amato et al., 2017), roadway conditions (Wang et al., 2021), street cleaning frequency (Amato et al., 2010), wind speed and wind direction (Bukowiecki et al., 2010). Overall, the average resuspension emission factor (30.15 mg km -1 veh -1 ) derived from the previously mentioned studies agrees with our average freeway emission factor (31.20 ± 7.22 mg km -1 veh -1 ). Table 6.3- Comparison of PM10 emission factors of non-tailpipe sources to previously reported values in the literature. All values are in units of mg km -1 veh -1 . Total non-tailpipe Resuspended Dust Brake Wear Tire Wear Current study 47.2 31.2 16.0* 16.0* Alves et al. (2018) - 33.0 - - Alves et al. (2020) - - - 2.0 Amato et al. (2010) 97.0 - - - Amato et al. (2016) - 17.0 - - Amato et al. (2017) - 44.9 - - Bukowiecki et al. (2009) - - 8.0 - Bukowiecki et al. (2010) 51.0 48.0 3.0 - Hicks et al. (2021) 34.5 8.0 12.9 7.8 Lawrence et al. (2016) 19.3 - - - Panko et al. (2013) - - - 7.0 NAEI (2018) - - 7.0 7.0 US EPA (2014) - - 18.5 6.1 * This value is the collective emission factor of tire and brake wear The combined tire and brake wear emission factor for average vehicle fleet in this study was 16.00 ± 3.69 mg km -1 veh -1 which is in agreement with the Lamoree and Turner (1999) estimation (20 mg km -1 veh -1 ). Furthermore, the PM10 emission factors of tire and brake wear for LDVs (10.63 ± 3.46 mg km -1 veh -1 ) and HDVs (30.12 ± 3.46 mg km -1 veh -1 ) are generally consistent with the corresponding values in Luhana et al. (2004) study, estimating 6.9 mg km1 veh-1 and 49.7 mg km -1 veh -1 for LDV and HDV fleet, respectively. In addition to the greater friction force, the higher HDV emission factors are probably due to the low-metallic pads in heavy duty trucks and buses which emit higher levels of trace metals during abrasion compared to LDVs (Geller et al., 2006; Sanders et al., 2003; Wang et al., 2021). Various studies have reported separate PM10 emission factors for tire and brake wear. The estimated emission values for brake wear range from 3 mg km-1 veh-1 (Sternbeck, 2002) to 18.5 mg km -1 veh -1 (Piscitello et al., 2021) with an average value of 10 mg km -1 veh -1 . The emission factors for tire wear are from 2 mg km -1 veh -1 (Alves et al., 2020) to 7.8 mg km -1 veh -1 (Hicks et al., 2021) with an average value of 6 mg km -1 veh -1 . The sum of brake wear and tire abrasion emission values in 87 the literature are in reasonable agreement with our collective emission factor for tire and brake wear source. The emission factor for road surface wear was not estimated in this study as we were limited by the PMF-resolved factors. However, the contribution of road surface wear to on-road PM10 concentrations is not as significant as other non-tailpipe sources based on previous studies. For example, Amato et al. (2014) attributed 20% of road dust emissions to the road surface wear loadings. Using the tailpipe and non-tailpipe emission factors attributed to LDVs and HDVs and the corresponding PMF-resolved factor profiles, we calculated the daily traffic emission rates of light-duty and heavy-duty vehicle fleets on the entire freeways in Los Angeles basin, the result of which is displayed in Table 6.4. While LDVs exhibited lower distance-driven emissions compared to HDVs, the larger number of LDVs on LA freeways resulted into significantly higher daily emission rates for these vehicle fleets on the LA basin. The daily non-tailpipe and tailpipe PM10 particles emitted from total LDV fleet in the basin was 7.5 times higher compared to that of HDV fleet. Additionally, the LDV-emitted metal and trace elements were on average 5.4 times higher than that of HDV. Table 6.4 - Speciated daily tailpipe and non-tailpipe emission rates of LDVs and HDVs on the entire Los Angeles basin freeways. All values are in units of kg/day. Total non-tailpipe Resuspended dust Tire and brake wear Tailpipe Species HDV LDV HDV LDV HDV LDV HDV LDV PM 10 620.46 4664.57 410.64 3087.20 209.81 1577.37 333.57 2507.75 OC 90.31 431.33 63.20 301.85 27.11 129.49 82.35 393.33 EC 13.80 13.13 4.82 4.58 8.98 8.54 41.44 39.43 Al 8.30 55.53 8.30 55.53 0 0 1.71 11.47 Ca 16.31 125.39 12.49 96.05 3.82 29.33 4.65 35.73 Ti 0.79 5.03 0.79 5.03 0 0 0 0 Fe 25.36 151.93 18.94 113.45 6.42 38.48 4.69 28.10 Cu 1.08 5.74 0.12 0.66 0.96 5.08 0 0 Zn 1.11 4.14 0 0 1.11 4.14 0.34 1.27 Pb 0.13 0.25 0.01 0.03 0.11 0.22 0.35 1.32 6.4 Summary and conclusions We collected the ambient coarse particles on two major Los Angeles freeway and measured the concurrent CO2 concentrations during three seasonal sampling campaigns to estimate the contribution of main PM sources on LA freeway and quantify the emission factors of nontailpipe and tailpipe sources on these freeways. As the first step, we quantified the total 88 PM10 emission factors on the I-110 and I-710 freeways. The results showed higher values on the I710 freeways compared to the I-110, mainly due to the higher fraction of heavy vehicles driving on the former. According to the source-apportionment analysis Sea-salt, SA, tire and brake wear, resuspended dust and vehicle exhaust constituted the five main sources of the measured PM10 levels. The non-tailpipe sources (i.e., resuspended dust and tire and brake wear) were responsible for nearly half of the emitted PM on the freeway, followed by vehicle exhaust (26.1%), SA (14.2%) and sea-salt (11.2%). This observation underscores the significant role of non-tailpipe sources on releasing coarse particle into the ambient air, which merits action needed to reduce the emissions. As the next step, we estimated the PM10 emission factors characterized by each traffic emission source and the vehicle type (i.e., LDV and HDV). Our results revealed that the resuspension of road dust generates almost twice the amount of PM10 compared to tire and brake wear and HDVs emit nearly three times more particulate matter from non-tailpipe sources compared to those of LDVs. However, the higher number of LDVs in the LA county translates into significantly higher PM10 emission from total LDV fleet driving in LA in comparison with total HDV fleet. 89 Chapter 7 : Conclusions and recommendations In the first study, we integrated the results of our recent measurements in Athens and Beirut as well as our previous studies conducted in cities of Riyadh, Los Angeles and Milan to highlight the relation of PM emission sources and the corresponding chemical composition and PM-induced oxidative potential. It was illustrated that highest PM intrinsic redox activity were induced in samples with the highest WSOC fraction including Milan (biomass burning activities) and Athens (SOA). The significant impact of biomass burning emissions in reducing the air quality was further corroborated in the next chapter where the lifetime cancer risk values exceeded the US EPA standard by a considerable margin, while the corresponding values in Los Angeles were significantly lower, possibly due to the regulatory measures in this region during the past decade. Findings of this work will further advance our knowledge of complex source emission impacts on the PM oxidative potential and chemical composition in different environments and provide important insights for more targeted and cost-effective air pollution strategies in polluted areas around the globe. We then investigated the chemical composition of standardized diesel exhaust particles (DEP) which is often used as a proxy for ambient air in toxicology studies. According to our findings, there are major dissimilarities in DEP composition and ambient PM to which populations are exposed. Nearly half of the mass fraction DEP is composed of EC which is significantly higher than the EC content of PM inside the freeways. Additionally, the DEP lacks several high molecular weight PAHs and SOA which further corroborates the lack of resemblance of this chemical powder to the real-word ambient conditions. In the fourth chapter, we conducted an investigation of temporal changes in concentration of PM2.5-bound redox active metals and the contributing sources during the 2005-2018 period which can assists the policymakers in understanding and interpreting the long-term trends in the concentration of these species in Los Angeles. The decrease of combustion emitted metals during the investigated period underscores the impact of LA emission control policies. However, the increase in re-suspended road dust emissions during the past 5–6 years which corresponds with the growing popularity of EVs, undermines the use of EVs as a measure to reduce PM emissions in urban environment. It is therefore imperative to reconsider the current state EVs as “zero impact vehicles”, and hence, as the definitive solution to urban PM pollution. Our results signify the challenges in curtailing non- tailpipe emissions and the need to develop effective strategies for their reduction in the future. 90 In the last chapter, we took a closer look at the non-tailpipe and tailpipe emissions and quantified the emission factors of various traffic sources characterized by highways and vehicle fleet. The non-tailpipe emission is a field with few high-quality field studies and a shortage of field-collected data and the results of our study makes a useful addition to the literature, particularly as it compares two freeways with differing traffic characteristics. According to our results, the non-tailpipe emissions from the HDV fleet were substantially greater than the LDVs, due to the greater weight combined with the size of tires as well as the material used in the brakes. However, the LDVs were collectively the dominant PM traffic source in the freeways of entire LA county, due to the greater number of low-duty vehicles driving on LA roads. We showed that the resuspended dust is the prevailing non-tailpipe emission source, with the emission factor values of 58.9 and 20.8 mg km -1 veh -1 for HDV and LDV, respectively. The respective value for tire and brake wear were 30.1 and 10.6 mg km -1 veh -1 . These values largely exceed the most recent exhaust PM10 emissions standards (i.e., 5 mg km -1 veh -1 set by EURO 6), and consequently emphasize the need for future research on particulate emissions from traffic to give more prominence to non-tailpipe emissions, rather than exhaust emissions. 91 Bibliography Abrams, J.Y., Weber, R.J., Klein, M., Samat, S.E., Chang, H.H., Strickland, M.J., Verma, V., Fang, T., Bates, J.T., Mulholland, J.A., Russell, A.G., Tolbert, P.E., 2017. 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Abstract (if available)

Abstract Airborne particulate matter (PM) is one of the most toxic air pollutants and has been consistently the subject of governmental regulation. PM is released from variety of natural and anthropogenic sources, each of which impose unique alteration in the physio-chemical and toxicological characteristics of ambient particles. Therefore, there are still a great deal of uncertainties associated with the PM characteristics (e.g., size, chemical composition, toxicity) under various scenarios with unique emissions sources and urban conditions. As the first step, the impact of urban emission sources on the chemical composition of ambient particulate matter (PM) as well as the associated oxidative potential was investigated in this study. The results illustrated that among the major PM components, the water-soluble organic carbon (WSOC) exhibited the highest correlation with the quantified redox activity, underscoring the substantial potency of this component in inducing PM toxicity. Furthermore, the highest PM intrinsic redox activity were observed in samples dominated by the biomass burning activities, even exceeding those of dominated by vehicle emissions. The lifetime cancer risk assessment based on inhalation of carcinogenic metals and polycyclic aromatic hydrocarbons (PAHs) also corroborated the substantial health hazards caused by biomass burning emissions. The estimated risk values for the content of metal elements (i.e., Arsenic and Chromium) and PAHs in the biomass burning samples exceeded the US EPA standards by a considerable margin. As one of the redox-active components, the long-term trend in concentration of PM 2.5 -bound redox active metals in Los Angeles basin were examined and the main contributing sources and their temporal changes during the last two decade were determined. Mineral dust (50±8%) and resuspended road dust (38±13%) are the dominant contributors to total metal concentrations, followed by combustion emissions (9±8%) and tire wear (2±1%). While the emissions from combustion sources significantly reduced between 2005-2018 period due to regulatory efforts in LA, an increase in re-suspended road dust emissions were observed. This observation indicates the challenges in curtailing non-tailpipe emissions and the need to develop effective strategies for their reduction in the future. Considering the importance of non-tailpipe emissions, a methodology was developed in this study to quantify the PM emission factors of various traffic sources characterized by highways and vehicle fleet. According to the results, the collective PM emissions from non-tailpipe sources are almost twice of tailpipe emissions. The estimated emission factors corresponding to non-tailpipe sources largely exceeds the most recent exhaust PM 10 emissions standards (i.e., 5 mg km -1 veh -1 ) set by EURO 6, and consequently emphasizes the need for future research on particulate emissions from traffic to give more prominence to non-tailpipe emissions, rather than tailpipe emissions.

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Creator Jalali Farahani, Vahid (author)

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

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Engineering (Environmental Engineering)

Degree Conferral Date 2023-05

Publication Date 03/31/2023

Defense Date 02/24/2023

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

Tag Air pollution,health risk assessment,non-tailpipe emissions,OAI-PMH Harvest,particulate matter,tailpipe emissions

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

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

Creator Email jalalifa@usc.edu,vahid.jalali.farahani@gmail.com

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Format theses (aat)

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