University of Southern California Dissertations and Theses

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

(USC Thesis Other)

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

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content Chemical and Toxicological Characteristics and Historical Trends of Size-fractioned Particulate Matter from Traffic-related Emissions in Los Angeles Doctoral Thesis By: Farimah Shirmohammadi Submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy In Environmental Engineering Department of Civil and Environmental Engineering University of Southern California Faculty of the USC Graduate School: Dr. Constantinos Sioutas (Chairman) Dr. George Ban-Weiss Dr. Caleb Finch August 2018 1 Table of Contents Chapter 1. Introduction ..................................................................................................................... 4 1.1 Background ................................................................................................................................ 4 1.2 Characteristics of airborne particles ........................................................................................... 4 1.3 Health effects associated with PM ............................................................................................. 5 1.4 Rationale for proposed research ............................................................................................…. 6 1.5 Proposal layout………………………………………………………………………………… 7 Chapter 2. Synopsis of the completed work…………………………………………….................. 8 Case study 1. Source apportionment of size-fractioned PM in the Los Angeles basin and the relative importance of sources on PM oxidative potential and its historical trend … …… … … …… ………... 9 1. Introduction…………………………………………………………………………….......... 10 2. Methodology……………………………………………………………………………......... 11 3. Results and discussion……………………………………………………………………….. 15 Case study 2. Oxidative potential of on-road fine particulate matter (PM2.5) measured on major freeways of Los Angeles, CA, and a 10-year comparison with earlier roadside studies…………… 39 1. Introduction…………………………………………………………………………….......... 40 2. Methodology……………………………………………………………………………......... 41 3. Results and discussion……………………………………………………………………….. 43 Case study 3. Emission rates of particle number, mass and black carbon by the Los Angeles International Airport (LAX) and its impact on air quality in Los Angeles.…………...................... 56 1. Introduction…………………………………………………………………………….......... 57 2. Methodology……………………………………………………………………………........ 58 3. Results and discussion……………………………………………………………………….. 61 Case study 4. Chemical composition and redox activity of quasi-ultrafine particles (PM0.25) at Los Angeles International Airport and comparisons to an urban traffic site.…………………………… 72 1. Introduction…………………………………………………………………………….......... 73 2. Methodology……………………………………………………………………………........ 73 3. Results and discussion………………………………………………………………………. 76 References ………………………………………………………………………………………….. 87 2 Chapter 1 Introduction 3 1. Introduction 1.1 Background An aerosol is defined as a solid or liquid suspended in a gas medium. It is a two-phase system and describes various forms of microscopic particles that remain in the air such as re-suspended soil dust, particles generated from vehicular combustion, photochemically formed particles, and sea salt from the ocean. Particulate matter (PM) refers to the particles or liquid droplets in the aerosol and is responsible for environmental effects such as visibility degradation and climate change as well as numerous adverse health effects. In addition, PM is a major component of photochemical smog and influences surface albedo by decreasing the amount of heat reaching the surface (Seinfeld and Pandis, 2006). The composition of PM is highly complex and varies depending on local sources, source strength, and atmospheric processes such regional transport and gas-to-particle partitioning. Therefore, investigating the physico-chemical and toxicological characteristics of PM is crucial in understanding its environmental and health effects for both policymakers and for the general public. Particulate matter is made up of a number of chemical constituents including inorganic ions (nitrate and sulfate), crustal metals and trace elements, elemental carbon (EC), and organic species. The chemical components are derived from both natural and anthropogenic sources. Natural sources include re-suspended crustal elements, sea spray, and windborne biological materials; anthropogenic sources include vehicular emissions, burning of fossil fuels and biomass, and emissions from industrial activity. Particles that are emitted directly into the atmosphere are known as primary pollutants, while particles formed in the atmosphere are known as secondary pollutants, i.e. photochemical reactions with gaseous precursors (i.e. nitrogen oxides). Once emitted, particles may undergo various physical and chemical processes that may alter particle size and chemical composition. 1.2 Characteristics of airborne particles In the context of PM, the most important parameter is particle size, which is usually expressed as aerodynamic diameter or dp. Because particles exist in various shapes, aerodynamic diameter is defined as the diameter of a unit density sphere that has the same settling velocity as the particle. Airborne particles can range from the submicron (<1 µm) mode to tens of microns in size. There are three major PM size fractions: coarse mode (or PM10-2.5) contains particles in the range of 2.5 to 10 µm, accumulation mode contains particles in the range of 100 nm to 2.5 µm, and ultrafine PM are particles less than 100 nm. Fine PM (or PM2.5) refers to particles less than 2.5 µm and PM10 refers to particles less than 10 µm. Particles greater than 10 µm are typically of less interest because these particles are characterized by low atmospheric residence times and respiratory deposition in the nasal region, while PM10 can enter the thoracic region and is of great interest to air pollution studies and for regulatory purposes. The PM size ranges exhibit differences in respiratory deposition, atmospheric formation and deposition mechanisms, particle composition, and optical properties. The coarse fraction is formed mainly from mechanical processes such as grinding, erosion, and wind resuspension, and due to its relatively high settling velocity, its primary deposition mechanism is gravitational settling (Hinds, 1999). The accumulation mode is formed mainly through physical atmospheric aging processes such as coagulation of smaller particles and growth of existing particles by condensation. This mode tends to remain in the atmosphere for longer because its removal mechanism is neither dominated by gravitational settling or diffusion processes. Ultrafine particles are formed through incomplete combustion and gas-to-particle nucleation processes and are primarily removed through coagulation with other particles into a larger size mode. Although they have negligible mass, they dominate in particle number concentration and are efficiently deposited by diffusional mechanisms into all regions of the respiratory tract, 4 including the alveolar region. In addition, its greater surface area per mass compared with larger particles renders ultrafine particles to be more biologically active (Brown et al., 2001; Oberdörster et al., 2005). Currently, PM10 and PM2.5 are regulated in the National Ambient Air Quality Standards (NAAQS) under the Environmental Protection Agency (EPA), which uses mass concentration (µg/m 3 ) as the metric for regulation. The law sets two standards: the primary standard is designed to protect public health (i.e. sensitive populations such as children, elderly, and those with respiratory illnesses) and the secondary standard is designed to protect public welfare (i.e. visibility, damage to buildings and crops). 1.3 Health effects associated with PM Adverse health effects associated with PM remains one of the main motivations for current aerosol research. Numerous studies have found a link between respiratory, pulmonary, cardiovascular effects and long-term exposure to atmospheric PM (III and Dockery, 2006; N. Li et al., 2009; Samet et al., 2000; Schwartz et al., 2002). Recent in-vivo and in-vitro studies have shown that ultrafine particles may trigger a proinflammatory response in the mouse brain that can contribute to neurodegenerative diseases (Campbell et al., 2005; Morgan et al., 2011; Woodward et al., 2017). Although the biological mechanisms responsible for toxicity of PM are still uncertain and questions remain on the underlying drivers of PM toxicity, numerous studies have found a positive correlation with PM toxicity and its chemical components, including organic carbon (OC) and elemental carbon (EC) (Mar et al., 2000; Metzger et al., 2004), trace metals (Saldiva et al., 2002), and quinones and polycyclic aromatic hydrocarbons (PAHs) (Xia et al., 2004). It is also postulated that PM components have oxidative properties and the potential to generate reactive oxygen species (ROS) which contribute to oxidative stress in the human body. While ROS is a natural byproduct of aerobic metabolism, the presence of excess ROS concentrations compared to the antioxidant capacity of the body to neutralize them leads to further oxidation of cellular components which can eventually lead to adverse health outcomes(Delfino et al., 2005a; N. Li et al., 2009). An increase in ROS concentrations has been shown to play a direct role in pulmonary inflammation (Tao et al., 2003), which may lead to decreased lung function and exacerbation of respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). The magnitude of ROS generation has also been hypothesized to be driven by redox reactions of PM constituents such as transition metals (Vishal Verma et al., 2010) and organic compounds (Cho et al., 2005a) as well as the PM size fraction (Hu et al., 2008a). Particle size is another key factor in affecting the toxicity and adverse health effects of PM inhalation. Although current NAAQS standards set by the US-EPA only regulate coarse and fine PM, ultrafine PM are believed to have higher toxicity compared to the larger regulated size ranges (Delfino et al., 2005a; Hughes et al., 1998; Solomon et al., 2012). As described earlier, ultrafine particles typically account for a small fraction of the total PM mass in urban atmosphere, although they dominate the total particle number. Due to their smaller size, ultrafine particles have higher surface area per mass and therefore they can carry significant amounts of toxic agents. Because of the complex chemical nature of PM and the spatial and temporal variation of local PM sources, further research is needed to understand the physical processes and the chemical components that contribute to ambient PM. 1.3.1 Oxidative Potential: A Metric for Toxicity Assessment Although a substantial number of studies have supported the association between particulate matter (PM) and adverse health outcomes (Metzger et al., 2004; Samet et al., 2000), many questions remain on the underlying drivers of PM toxicity. Oxidative stress, an in vivo state of disequilibrium due to an imbalance between antioxidant defense capacity and reactive oxygen species (ROS), has been suggested as a mechanistic explanation for PM toxicity (Nel, 2005; Valavanidis et al., 2008; Xia et al., 2006). Oxidative potential (OP), referred to as the 5 ability of particles to generate ROS by consumption of antioxidants and/or generation of oxidants, has been used as a health-based exposure measure of PM in several recent studies (Akhtar et al., 2010; Bates et al., 2015; Yang et al., 2016). Elevated ROS levels in the cells (and the resulting oxidative stress) can alter the redox status of the cells and consequently trigger a series of acute and chronic responses such as inflammation in pulmonary tract as well as the cardiovascular system (Squadrito et al., 2001) and mitochondrial damage (Li et al., 2003a), that consequently result in a myriad of adverse health outcomes. The current metrics for assessment of the oxidative stress can be divided into two subcategories, Chemical (i.e. acellular) assays and biological (i.e. cellular) assays. A brief description of these two categories is provided below: Chemical (Acellular) Assays: Chemical assays develop procedures that reflect the chemical agents in the atmospheric particles that are known to be responsible for their ability to induce oxidative potential. Some of the prominent chemical assays commonly used for measurement of oxidative potential include PM-catalyzed dithiothreitol (DTT) consumption assay and ascorbic acid depletion assay (Ayres et al., 2008). DTT assay is a cell-free construct in which DTT acts as a surrogate of the biological reducing agent nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH) (Kumagai et al., 2002a). The chemical assay used in the studies that will be discussed in this proposal is based on the DTT assay. DTT assay is a cell-free construct in which DTT acts as a surrogate of the biological reducing agent nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH) (Kumagai et al., 2002a); and The ability of a PM sample to catalyze the transfer of electrons from DTT to oxygen by generating superoxide radical anions is measured in this assay (Verma et al., 2014a). The concentration of redox-active species in any given sample determines the rate of depletion of DTT (nmol/min) under a standardized set of conditions (i.e. 100 mMDTT, temperature 37 ºC and pH 7.4). Ascorbic Acid depletion assay quantifies the oxidative potential by measuring the rate through which atmospheric PM depletes ascorbic acid (a strong antioxidant agent present in the lung lining fluid), rather than by quantification of directly generated oxidant agents(Campian et al., 2002). Although chemical assays can provide insight on the oxidative potential of particles, there remains some gaps between physiological relevance of these assays to the actual in-vivo PM exposure, as these assays do not reflect the biological response caused by the interaction of the particles with the human cells. Simplicity, rapidness and relatively lower analytical costs are, however, the major advantages of chemical assays compared to the biological assays. Biological (cellular) Assays: Biological assays employ an in-vitro exposure module for quantification of the oxidative stress. Intracellular ROS generated from the oxidative stress associated with the lysosomal action of the macrophages (i.e. digestion of the soluble oxidative stressors such as redox active water soluble metals) is considered as the most important biological pathway leading to inflammatory responses (Carroll-Ann W. Goldsmith Amy Imrich, 1998; Imrich et al., 2007). This increased abundance of inflammatory mediators within the cells is thought to be directly associated with the lung malfunction and aggravation of respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) (Delfino et al., 2005a). A series of cellular assays have been developed during the past few years to better simulate these cell-ROS interactions upon inhalation of ambient as well as engineered nanoparticles (Nel et al., 2006). 1.4 Rationale for proposed research In major metropolitan areas, vehicular emissions are the primary source of ambient PM (Schauer et al. 1996), and are of particular importance to populations in the vicinity of trafficked areas or downwind of major freeways. Studies conducted near freeways and major roadways have found that PM levels were substantially elevated relative to areas that are farther from the traffic source (Zhu et al. 2002; Ning et al. 2010). Populations in the proximity of trafficked roadways are most susceptible to PM health effects (Tonne et al. 2007). However, the most sensitive demographics are developing children near roadways (Brunekreef et al. 1997; Dales et al. 2009) and the elderly (Liao et al. 1999; Creason et al. 2001). 6 The specific chemical components and associated pathways through which PM emission/formation sources dominantly drive the PM-induced oxidative potential in cells is not fully understood. The regulatory measures in urban areas have, therefore, been focused on the PM mass as the sole surrogate of the PM-related health effects, with minimal consideration of the chemical and toxicological properties associated with the specific emission sources and their components. In the current regulatory framework, same mass emissions from all different sources are treated as similar, while the relative toxicological potency of various sources can be significantly different. One of the main the main purpose of this dissertation is to fill some of the knowledge gaps between the current clinical knowledge regarding the PM-induced toxicity and the actual sources and chemical components that drive the PM toxicity in the urban atmosphere, with the aim of promoting current regulations toward a more targeted and effective approach with an emphasis on traffic emissions. The other purpose of my research is to evaluate the impact of implemented regulations and emission standards, mostly on heavy duty vehicles, starting 2007 on PM composition, oxidative potential and the important emerging sources/markers that should be considered a concern for public health in light of reduction in vehicle exhaust emissions. 1.5 Proposal layout Chapter 1 (i.e. the current chapter) provides an introduction about this dissertation, including an overview of the basic concepts and the rationale for the proposed research. Chapter 2 highlights the research that has been performed in a form of four case studies. 7 Chapter 2 Synopsis of completed work This chapter is divided into three case studies 8 Case Study 1. Source apportionment of size-fractioned PM in the Los Angeles basin and the relative importance of sources on PM oxidative potential and its historical trend 9 1. Introduction In many urban areas in developed countries, the majority of primary ultrafine and fine PM in ambient air originate from vehicular emissions (Hasheminassab et al., 2013a; Westerdahl et al., 2005) . Over the past decade, several major regulations have been implemented on motor vehicles in the US and California, as exposure to PM from these sources has found to be one of the major drivers of the associated health outcomes (de Kok et al., 2006; Delfino et al., 2005b; Ostro et al., 2011). Years after diesel exhaust had been acknowledged as one of the major sources of pollution by California Air Resource Board (CARB) in 1998, starting in 2007, the United States Environmental Protection Agency (EPA) mandated all 2007 model year (MY) diesel trucks to reduce their PM emission by 90%, and 50% of total sales of diesel trucks to reduce their nitrogen oxides (NOx) emissions by 95%. The NOx regulation was further amended in 2010 when the EPA mandated 100% of the newly-manufactured trucks to reduce their NOx emissions (U.S. EPA Regulatory Announcement, 2000). Following EPA’s 2007 emissions standards, further restrictions on heavy-duty diesel trucks were implemented in the following years and all of the vehicles with 1989-1993 MY engines along with 1994-2003 MY engines were required to be retrofitted. Moreover, in January 2012 CARB’s Truck and Bus regulation required heavy diesel trucks to use diesel particulate filters (DPFs) (California code of regulations, 2008). Despite a 38% increase in regional motor vehicle activity, PM 2.5 and PM10 decreased by 21%, and 15%, respectively, during a 20-year time period in Southern California (Lurmann et al., 2015a). Emission control strategies in California have achieved dramatic reductions in ambient PM2.5 and PM10. However, additional reductions will still be needed to achieve current health-based clean air standards (Lurmann et al., 2015a). Several studies have postulated that organic components may play an important role in PM toxicity (Moller et al., 2014; V. Verma et al., 2010; Wu et al., 2014). Accordingly, intrinsic toxicity appears to be correlated with the organic content of ambient PM, particularly with species such as polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes (Cho et al., 2005b; Hu et al., 2008b; Li et al., 2003b; McDonald et al., 2004). In addition, certain metals such as Fe, Cu and Mn have been shown to be associated with DTT activity as well (Charrier and Anastasio, 2011; Kumagai et al., 2002b; Q. Li et al., 2009; Schoonen et al., 2006). Furthermore, recent studies have shown that PM generated by specific sources such as vehicle tailpipe emissions, food cooking and secondary aerosols formation have different contributions to the overall oxidative potential of ambient PM and are strongly associated with health outcomes (Charrier et al., 2015; Lall et al., 2010; Verma et al., 2014b). Therefore, how to target and apportion the toxicity of PM to specific sources remains a heated topic in current aerosol research. In this study, size-segregated ambient PM was collected at two different locations of the LA Basin (i.e. Central LA and Anaheim) as part of the Cardiovascular Health and Air Pollution Study (CHAPS), a cohort panel study investigating the pathophysiological responses to particle exposures in elderly people. Spatial and temporal variability of the size-fractioned PM constituents (i.e. organic and elemental content of the PM) and its oxidative potential are discussed and the levels are compared to a comprehensive data set obtained over the past decade in Central LA. Major source contributions to ambient PM2.5 and PM0.18-bound organic carbon (OC) were also determined using a novel hybrid Molecular Marker-Chemical Mass Balance (MM-CMB). To provide more comprehensive insight into the dominant sources driving the PM toxicity, both primary and secondary CMB- derived sources contributing to ambient PM2.5 and PM0.18, along with some other sources not included by the CMB model (e.g. vehicular abrasion, crustal material, etc.), were included in a multiple linear regression (MLR) analysis to identify possible predictors of the DTT activity. 10 2. Methodology 2.1 Sampling plan and sites Time-integrated sampling was conducted every week from Monday to Friday, between July 2012 and February 2013 in Central LA, and from Sunday to Thursday, between July 2013 and February 2014 in Anaheim. PM sampling in Anaheim was discontinued in December 2013 and resumed in January 2014. Throughout this manuscript, “warmer months” refer to July to September period, while “colder months” refer to October to February. The Central LA site (referred to as an urban site) was located approximately 150 m to the east and downwind of a major freeway (I-110) at the Particle Instrumentation Unit (PIU) of the University of Southern California, about 3 km south of downtown Los Angeles. The other site in Anaheim (referred to as a suburban site), was situated in a residential area and about 500 m upwind of freeway I-5. Ambient size-fractioned PM were collected using two collocated Micro-Orifice Uniform Deposit Impactors (MOUDIs, Model 110 MSP Corporation, Minneapolis, Minnesota, USA), each operating at 30 L/min collecting particles in three stages: <0.18 µm (ultrafine), 0.18-2.5 µm (accumulation), and 2.5-10 µm (coarse). In this study we focused on ultrafine (PM0.18) and fine (ultrafine + accumulation) PM (PM2.5) only. For the purpose of chemical speciation, one MOUDI was loaded with Teflon filters (Teflon, 47mm , pore size 2 µm, Pall Life Sciences, Ann Arbor, MI, USA) only, while the other one with aluminum-foil substrates in the coarse and accumulation stages and quartz microfiber filters (Whatman International Ltd, Maidstone, England) in the ultrafine stage. 2.2 Gravimetric and chemical analysis Weekly samples were analyzed to quantify the mass concentrations of PM and its chemical constituents. The PM mass concentrations were determined by pre- and post-weighting the Teflon filters, using a microbalance (Model MT5, Mettler Toledo Inc., Columbus, OH, USA; ± 0.001 mg readability), after equilibration under controlled temperature (22–24 °C) and relative humidity within the range of 40-50%. A 1.5 cm 2 punch of the aluminum and quartz filters were analyzed by the National Institute for Occupational Safety and Health (NIOSH) Thermal Optical Transmission (TOT) method in order to measure the elemental carbon (EC) and organic carbon (OC) content of the samples (Birch and Cary, 1996a). Furthermore, by means of gas chromatography mass spectrometry (GC-MS), organic constituents were quantified (Stone et al., 2008a). Total elemental composition of the samples was measured by high resolution inductively coupled plasma sector field mass spectrometry (SF- ICPMS) after microwave-aided solubilization of the PM in mixture of acids (HNO3, HF and HCl). Ion Chromatography (IC) was applied to measure the water soluble inorganic ions (Zhang et al., 2008) as well. 2.3 Toxicological analysis As explained earlier in the previous chapter dithiothreitol (DTT) assay is a commonly used cell-free approach to measure the oxidative potential of PM. The ability of a PM sample to catalyze the transfer of electrons from DTT to oxygen by generating superoxide radical anions is measured in this assay (Charrier et al., 2015; Verma et al., 2014b). The concentration of redox-active species in a given sample determines the rate of depletion of DTT (i.e. nmol/min) under a standardized set of conditions. The methodological details of this chemical assay can be found elsewhere (Cho et al., 2005c). 11 2.4 Source apportionment A novel hybrid approach of molecular marker-based chemical mass balance (MM-CMB) model has been exploited in order to assess the contribution of different sources to OC in two size fractions; PM0.18 and PM2.5. The model was mathematically solved with an effective-variance-least-squares solution (Watson et al., 1984a), using the U.S. Environmental Protection Agency’s CMB software (EPA-CMB 8.2). With the exception of vehicular emissions, source profiles were adopted from Heo et al. (2013) who performed a positive matrix factorization (PMF) analysis on a unique data set of ambient organic molecular markers, measured in Central LA between 2009 and 2010, to identify and quantify sources of ambient PM2.5 OC. Heo et al. (2013) identified five major sources of PM2.5 OC, including mobile emissions, wood smoke, primary biogenic (vegetative detritus, food cooking, and re-suspended soil dust), and two types of secondary organic carbon (SOC- 1 and SOC-2, corresponding to anthropogenic and biogenic origins, respectively). The uncertainties of the PMF- derived source profiles were estimated by PMF2 model performed by Heo et al. (2013) and were directly used along with the source profiles in our hybrid MM-CMB model. Heo et al. (2013) have rigorously quantified the uncertainties of the PMF input data by accounting for instrument analysis uncertainties and field blanks. In addition, Heo et al. (2013) screened the PMF input data for species with weak signal-to-noise ratio and did not find any species in this category to protect against adding noise to the PMF analysis. Moreover, they did control for rotational ambiguity and explored different pseudorandom number of factors and robust mode, as well as FPEAK and FKEY values to reach to the most stable and optimal results from the PMF model. The reasonable uncertainties that were produced by the PMF2 model for the PMF-derived source profiles are consistent with the error structure commonly used for MM-CMB models with uncertainties of around 10-20 percent for key tracers for each profile and much higher relative uncertainties for compounds that are dominated by other sources (Pant et al., 2014; Stone et al., 2008b). The PMF-derived source profiles from Heo et al. (2013), except for mobile sources, were used as input data into our MM-CMB model to apportion PM2.5 and PM0.18 OC. Heo et al. (2013) characterized the PMF-derived SOC-2 source profile by high concentrations of pinonic acid, pinic acid, and methylthreitols, compounds that were not detected in the majority of our samples. SOC-2 was, therefore, excluded from the MM-CMB input source profiles. In addition, Heo et al. (2013) identified only one profile for mobile sources, representing the emissions from all types of vehicles (i.e. diesel, gasoline, smoking vehicles, etc.). The main drawback of using this profile in the current MM-CMB model was the fact that a single source profile from the PMF analysis for mobile sources derived from the 2009-2010 data by Heo et al. (2013) was not considered an appropriate match for the relative emissions from each mobile source group in 2012-2014 of the current study. The single PMF- derived mobile source profile inherently assumes that the relative impact of diesel, gasoline and smokers are constant, while several studies have shown that the ratio of the contributions from different vehicle mixes change with respect to location and size fraction. Moreover, Subramanian et al. (2006) (Subramanian et al., 2006) suggested that individual profiles cannot fully represent the emissions from entire fleet, therefore a combined set of available profiles are to be used to create a more representative fleet-average profile. On the other hand and contrary to vehicular emissions, a reliable source testing for SOC or primary biogenic (vegetative detritus, food cooking, and re-suspended soil dust) emissions cannot be performed. As a result, to estimate the contributions from these two sources in an MM-CMB, PMF-derived source profiles are the best resources available. Therefore, as a major advancement in MM-CMB modeling, a hybrid approach was applied using the PMF- derived source profiles for biomass burning, primary biogenic (vegetative detritus, food cooking, and re- suspended soil dust), and SOC adopted from Heo et al. (2013), along with three source profiles for vehicular 12 emissions (i.e. diesel, gasoline, and smoking vehicles) from a study conducted by (Lough et al., 2007a). In traditional CMB receptor modeling, the source profiles used as input data were obtained by direct source-testing measurements. The primary advantage of the hybrid model in comparison to previous MM-CMB studies is the inclusion of SOC and primary biogenic source profiles as input data in the CMB model to directly apportion their contributions to OC in the LA Basin. Although the average emissions rates have changed considerably since the source profiles of Lough et al. (2007), these profiles are still the best representative of the composition of organic carbon emissions from these vehicles categories since emissions are still dominated by higher emitting vehicles. The study by Lough et al. (2007) was conducted with a relatively large number of vehicles, covering several age groups and weight classes. Although large variations were observed among emissions from different types of vehicles, fleet- average profiles, weighted by mass emission rate, had much lower uncertainty than that associated with intervehicle variation. Along with the aforementioned source profiles, the following species were used as fitting species in the hybrid MM-CMB model: EC, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e)pyrene, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, coronene, 17α(H)-21β(H)-hopane, n-alkanes with odd-carbon between 24 to 36, organic acids with carbon number between 18 and 30 (except 27 & 29), phthalic acid, isophthalic acid, terephthalic acid, methylphthalic acid, succinic acid, glutaric acid, adipic acid, pimelic acid, suberic acid, azelaic acid, sebacic acid and levoglucosan. Moreover, the compounds used as fitting species in the CMB have been extensively used in previous source apportionment studies in this area as well as other parts of the world, and have been demonstrated to be predominantly in the particle phase and also chemically stable during transport from source to receptor (Arhami et al., 2010; Chow et al., 2007; Hasheminassab et al., 2013b; Heo et al., 2013b; Minguillón et al., 2008a; Schauer et al., 1996a; Zheng et al., 2002a). OC source apportionment results from the CMB model were converted to PM2.5 and PM0.18 mass concentrations to evaluate the source contributions to total mass, using the OC-to-PM ratios for the wood smoke and mobile source profiles (Philip M. Fine et al., 2004a; Lough et al., 2007b). For primary biogenic and SOC sources, which were identified by the PMF model on organic species (Heo et al., 2013c), a PM/OC ratio of 2 was used for mass conversion of both sources. The un-apportioned OC (referred to as “other OC”) was converted to “other OM” by multiplying a factor of 1.6 (Turpin and Lim, 2001). In addition to sources quantified by the CMB model, secondary ions (i.e. sum of NO3 - , NH4 + and SO4 -2 ), crustal material, vehicular abrasion and sea salt contributions were also considered in PM2.5 and PM0.18 mass apportionment. Crustal material was calculated by summing the oxides of Al, K, Fe, Ca, Mg, Ti and Si based on the following equation (Hueglin et al., 2005; Marcazzan et al., 2001a): Crustal material = 1.89Al + 1.21K + 1.43Fe + 1.40Ca + 1.66Mg + 1.67Ti + 2.14Si [1] Si was not measured in this study, but was estimated as 3.14×Al (Mason, 1966). Vehicular abrasions, especially particles emitted from brake wear, have been postulated to contribute significantly to the overall ambient PM2.5 particle levels, especially in recent years and as tailpipe emissions have generally decreased (Hasheminassab et al., 2014a; Narváez et al., 2008; Pant and Harrison, 2013). Several studies have also reported the contribution of road dust and vehicular abrasion to ultrafine size fraction as well (Dahl et al., 2006; Gustafsson et al., 2008; Saffari et al., 2013a). These studies have also acknowledged the importance of crustal elements such as Al, Ca, Fe and Ti in the ultrafine particles size range (Dahl et al., 2006; Gustafsson et al., 2008; Saffari et al., 2013a; Sanderson et al., 2014). 13 Metals and elements such as V, Cr, Mn, Ni, Cu, Zn, As, Se, Sr, Ba and Pb have been identified as important tracers of vehicular abrasion emissions (Jj et al., 2006; Thorpe and Harrison, 2008; Wåhlin et al., 2006). The contribution of tire wear to trace metal emissions from motor vehicles has been reported to be negligible compared with contributions of other sources (e.g., brake wear) and it has been considered primarily a source of organic compounds (Hildemann et al., 1991; Jj et al., 2006; Kumata et al., 2002; Reddy and Quinn, 1997). In this study, vehicular abrasion was estimated based on a study by Schauer et al.(Jj et al., 2006) in which the sources of metals associated with motor vehicle traffic were determined by a CMB analysis. Intensive sampling was conducted to construct source profiles with a relatively large number of on-road vehicles in a tunnel test in Milwaukee along with brake wear and tire wear dust from Wisconsin vehicles. Schauer et al. (Jj et al., 2006) reported an averaged source profile for different compositions of brake pads. Taking Ba as our basis, with the assumption that all Ba emissions are from brake wear (Sanderson et al., 2014), we estimated the brake wear contribution from the atmospheric concentrations of Ba and using the average mass ratio of this specie from the re-suspended brake composition reported by Schauer et al.(Jj et al., 2006). An average Ba mass fraction of 13.3 ± 0.14 mg/g PM of brake dust in PM2.5 was applied for this conversion and was used for both PM2.5 and PM0.18 vehicular abrasion estimations. As noted above, Schauer et al. (Jj et al., 2006) indicated that tire wear may be a significant contributor to motor vehicle emissions of OC, but its contribution to metals emissions is negligible. Therefore, it should be noted that vehicular abrasion source estimation in this study is mainly associated with contribution from brake wear. Lastly, sea salt was also estimated as the sum of soluble Na + and the sea salt fraction of typical sea water components such as Cl - , Mg 2+ , K + , Ca 2+ and SO4 2- (Murphy et al., 1998) as follows: Sea Salt = [Na + ] + ss [Cl - ] + ss [Mg +2 ] + ss [K + ] + ss [Ca +2 ] + ss [SO4 -2 ] [2] where ss Cl − = 1.8, ss Mg 2+ = 0.12, ss K + = 0.036, ss Ca 2+ = 0.038 and ss SO4 2− = 0.252 (Murphy et al., 1998). 3. Results and discussion 3.1 PM2.5 and PM0.18 chemical composition Table 1 presents the summary of monthly-averaged concentrations of EC, OC, total elements, as well as secondary ions (as sum of NO3 - , NH4 + and SO4 -2 ) for the two PM size ranges in Central LA and Anaheim. In general, PM2.5 EC and OC constituted 4 and 24% of PM mass concentration in Central LA, while these ratios were 3 and 31% for EC and OC in Anaheim, respectively. Percent contribution of both EC and OC in PM0.18 size range increased to 13 and 48% in Central LA, respectively. In Anaheim, OC dominated the PM 0.18 composition with an average contribution of 55% while the contribution of EC to PM 0.18 was 10%. Inorganic elements constituted 8 and 10% of PM mass concentration in PM2.5 and PM0.18 size ranges, respectively, in Central LA. Similarly, the contribution of these species to PM mass was 10% for both size ranges in Anaheim. The contribution of secondary ions to PM2.5 mass concentration was 33 and 31% in Central LA and Anaheim, respectively, while in the PM0.18 size fraction they accounted for 13 and 14% in Central LA and Anaheim, respectively. 14 Table 1 (a-b). Monthly-averaged mass concentrations (µg/m 3 ) of EC, OC, total metals and elements (i.e. sum of 50 species), as well as secondary ions (i.e. sum of NO3 - , NH4 + and SO4 -2 ) at a) Central LA and b) Anaheim. Sampling was not conducted at Anaheim in December 2013. a) Central LA Sampling months EC OC Metals and elements Secondary ions PM 2.5 PM 0.18 PM 2.5 PM 0.18 PM 2.5 PM 0.18 PM 2.5 PM 0.18 Jul 2012 0.40 ± 0.12 0.23 ± 0.07 1.86 ± 0.43 0.75 ± 0.21 1.35 ± 0.01 0.14 ±0.00 4.14 ± 1.40 0.56 ± 0.23 Aug 2012 0.52 ± 0.18 0.31 ± 0.14 2.02 ± 0.42 0.86 ± 0.27 1.53 ± 0.01 0.19 ± 0.00 4.03 ±1 .01 0.38 ± 0.09 Sep 2012 0.59 ± 0.08 0.25 ± 0.03 2.51 ± 0.38 0.95 ± 0.17 1.33 ± 0.01 0.16 ± 0.00 3.58 ± 0.56 0.31 ± 0.06 Oct 2012 0.70 ± 0.30 0.37 ± 0.17 2.93 ± 0.93 1.31 ± 0.37 0.82 ± 0.01 0.25 ± 0.00 3.81 ± 2.60 0.30 ± 0.07 Nov 2012 0.45 ± 0.12 0.25 ± 0.06 3.00 ± 0.62 1.03 ± 0.36 1.00 ± 0.01 0.26 ± 0.00 6.96 ± 2.35 0.26 ± 0.08 Dec 2012 0.43 ± 0.10 0.26 ± 0.06 3.51 ± 1.23 1.22 ± 0.44 0.87 ± 0.01 0.39 ± 0.01 3.38 ± 1.38 0.15 ± 0.05 Jan 2013 0.51 ± 0.05 0.32 ± 0.02 3.97 ± 0.68 1.36 ± 0.11 0.55 ± 0.00 0.25 ± 0.00 1.89 ± 0.46 0.18 ± 0.03 Feb 2013 0.5 4± 0.32 0.30 ± 0.18 3.48 ± 1.86 1.21 ± 0.65 0.78 ± 0.01 0.20 ± 0.00 5.20 ± 1.69 0.25 ± 0.05 b) Anaheim Sampling months EC OC Metals and elements Secondary ions PM 2.5 PM 0.18 PM 2.5 PM 0.18 PM 2.5 PM 0.18 PM 2.5 PM 0.18 Jul 2013 0.13 ± 0.03 0.11 ± 0.02 1.64 ± 0.35 1.01 ± 0.22 1.00 ± 0.01 0.17 ± 0.00 2.25 ± 0.48 0.40 ± 0.19 Aug 2013 0.18 ± 0.06 0.16 ± 0.05 2.43 ± 0.14 1.22 ± 0.14 0.95 ± 0.01 0.20 ± 0.00 3.17 ± 0.43 0.48 ± 0.11 Sep 2013 0.18 ± 0.03 0.15 ± 0.03 1.82 ± 0.15 1.12 ± 0.15 0.99 ± 0.01 0.17 ± 0.00 1.97 ± 0.34 0.44 ± 0.13 Oct 2013 0.31± 0.00 0.26 ± 0.00 2.95 ± 0.00 1.68 ± 0.00 1.12 ± 0.00 0.40 ± 0.00 2.29 ± 1.1 0.40 ± 0.11 Nov 2013 0.53 ± 0.19 0.42 ± 0.20 4.62 ± 1.71 1.85 ± 0.71 1.05 ± 0.00 0.43 ± 0.00 2.68 ± 0.67 0.32 ± 0.08 Dec 2013 Jan 2014 0.46 ± 0.20 0.37 ± 0.17 3.74 ± 1.13 1.57 ± 0.49 0.94 ± 0.01 0.34 ± 0.00 4.46 ± 0.69 0.23 ± 0.07 Feb 2014 0.33 ± 0.18 0.28 ± 0.17 3.15 ± 0.99 1.54 ± 0.03 0.39 ± 0.00 0.16 ± 0.00 3.47 ± 0.22 0.31 ± 0.03 15 Organic compounds Polycyclic Aromatic Hydrocarbons (PAHs) Particle-bound PAHs are common products of incomplete combustion of fossil fuels (Manchester-Neesvig et al., 2003). The concentrations of these compounds, which are known to be toxic and carcinogenic (Boström et al., 2002; Li et al., 2003b), are significantly affected by several factors such as atmospheric conditions, source strength, gas-particle partitioning, and deposition processes (Polidori et al., 2008). In the LA urban area, gasoline- and diesel- fuelled vehicles, as well as biomass burning are the major sources of ambient PAHs (Polidori et al., 2008). Studies have shown that diesel vehicle emissions are enriched in lower molecular weight PAHs, whereas higher molecular weight PAHs are associated with gasoline vehicle emissions (Geller et al., 2006; Rogge et al., 1991). Figure 1 displays the monthly average concentrations of selected PAHs for both size fractions and sampling sites. Generally, concentrations of PAHs in both size fractions are higher in the near-freeway Central LA sampling site compared to Anaheim. On average over all sampling months, concentration of total PM 2.5 PAHs was over 60% higher in Central LA (1.11±0.67 ng/m 3 ) compared to Anaheim (0.68±0.54 ng/m 3 ). A clear seasonality in the cumulative concentration of selected PAHs is observed at both sampling sites, with higher levels in colder months, while lower or below detection limit in warmer months. The elevated concentration of PAHs in the colder months is mainly due to the enhanced atmospheric stability and higher emissions from fossil fuel combustions during this period of the year, in addition to higher gas-to-particle partitioning of the semi-volatile species favored at lower temperatures. Furthermore, a notable source of higher molecular weight PAHs (e.g. benzo(ghi)perylene and indeno(1,2,3-cd) pyrene) in the cold season is the cold-start spark-ignition of gasoline vehicles (Philip M. Fine et al., 2004b; Lough et al., 2007a; Miguel et al., 1998). On the other hand, oxidizing gases such as ozone, nitrogen oxides and hydrogen peroxide can react with PAHs and lower their concentrations (Grosjean et al., 1983) and these reactions are more pronounced during warmer months. Hence, reaction with oxidizing gases in addition to combined volatilization effect can be conducive to lower PAH concentration in warmer months (Arey et al., 1988; Grosjean et al., 1983). Hopanes and Steranes Hopanes and steranes are organic tracers of vehicular emissions (Zheng et al., 2002b) and are mainly emitted from lubrication oil of gasoline- and diesel- fueled vehicles (Schauer et al., 1996b). Hopanes and steranes are reasonably stable compounds during transport from source to receptor, and therefore are reliable tracers of mobile source emissions in this area for source apportionment using receptor models (Heo et al., 2013a). Figure 2 displays the variation of selected hopanes and steranes (including 17α(H)-22,29,30-trisnorhopane, 17α(H)-21β(H)- hopane, 17 α (H)-21β(H)-30-norhopane, 22S-homohopane, 22R-homohopane, ABB-20R-C27-cholestane, ABB- 20R-C29-sitostane, ABB-20S-C29-sitostane) over the sampling months for the two sites and size fractions. On average, cumulative concentrations of the aforementioned compounds were about 1.8 and 1.6 times higher in Central LA compared to Anaheim for PM2.5 and PM0.18 particles, respectively, indicating higher contributions from vehicular emissions in Central LA. The seasonal average concentration of selected hopanes and steranes in the ultrafine size range varies from 0.11±0.02 ng/m 3 in warmer months to 0.23±0.06 ng/m 3 in colder months in Central LA, whereas in Anaheim these compounds have a lower concentration ranging from 0.04±0.004 ng/m 3 in warmer months to 0.17±0.06 ng/m 3 in colder months. These seasonal and spatial variations reflect the combined changes in source strength and atmospheric mixing height. Hopanes and steranes had a higher per mass contribution to the ultrafine mode at both sites compared to PM2.5 size fraction, which is consistent with the 16 findings of (Arhami et al., 2009; Ning et al., 2007a), indicative of the higher abundance of sub-micron fresh primary emissions at both sites. n-Alkanes Figure 3 shows the concentration of individual n-alkanes (namely C19-C38) at both sites and size ranges. Sum of all measured n-alkanes in PM2.5 was 14.7±2.4 ng/m 3 and 15.6±4.8 ng/m 3 in colder months in Central LA and Anaheim, respectively. On the other hand, warmer months’ concentrations were 7.3±1.5 ng/m 3 and 7.9±1.6 ng/m 3 , indicating about 2 fold increase in colder months period in both sites. The lower levels of n-alkanes during warmer months could be due to volatilization of particulate phase into gas phase (Ruehl et al., 2011). Furthermore, the elevated concentration in colder months can be attributed to the lower atmospheric mixing height in the winter that limited dilution of total n-alkanes, in addition to the increased source strengths of these species in the winter. In order to distinguish the biogenic and anthropogenic- derived n-alkanes, Carbon Preference Index (CPI) was calculated at each site (Figure 3). CPI is defined as the sum of concentration of odd-carbon alkanes divided by that of even-carbon alkanes (Simoneit, 1986). CPI values shown in Figure 3 are all between 1 and 2 indicating the pre-dominance of anthropogenic emissions of n-alkanes both in Central LA and Anaheim. Organic acids Organic acids are either directly emitted from various natural and anthropogenic sources (Oliveira et al., 2007) or secondarily formed from oxidation of gas-phase precursors followed by gas/particle partition (Wang et al., 2012). Pyrolysis of vegetative material, vehicular emissions, photo-oxidation of aromatic hydrocarbons, and the oxidative decay of higher carbon number organic acids (Sorooshian et al., 2007) are some major sources of theses acids. Overall, higher concentrations, along with more distinctive temporal variability, are observed at Central LA compared to Anaheim. Sum of organic acids concentrations from C15 to C30 in PM2.5 increased by 131% and 51% in Central LA and Anaheim from warmer to colder months. C16 and C18 are the dominant species in both sampling sites and size fractions. Previous studies have shown that the lower molecular weight n-alkanoic acids (<C20) are mainly found in emissions from petroleum-based sources, such as gasoline and diesel vehicles (Rogge et al., 1993a; Schauer et al., 2002), and fuel oil combustion (Rogge et al., 1997a). (Oliveira et al., 2007) has also argued that the release of organic acids from fossil fuel combustion is an important source of the lower molecular weight n-alkanoic acids, peaking at C16, consistent with our findings. On the other hand, emissions from biogenic sources are the main source of the higher molecular weight (C>20) organic acids (Park et al, 2006). Concentration of C16 in both size fraction is comparatively much higher in Central LA than Anaheim (by a factor of 1.5 and 1.8 in PM0.18 and PM2.5, respectively), reaffirming the significant influence of vehicular emission in the sampling site in Central LA. Levoglucosan This compound, which is generated by pyrolysis of cellulose, is a tracer of biomass burning emissions (Philip M. Fine et al., 2004b; Schauer and Cass, 2000; Simoneit, 1999). Monthly average levels of levoglucosan are depicted for PM0.18 and PM2.5 at both sites in Figure 4. The average concentration of levoglucosan in PM2.5 is 5.7±2.8 ng/m 3 in warmer months and 73.1±101.5 ng/m 3 during colder months in Central LA. For the same size fraction in Anaheim the concentration varies from 8.4±1.07 ng/m 3 in warmer months to 50.2±26.4 ng/m 3 in colder months. This trend clearly reveals the higher wood burning activities, particularly for domestic heating purposes in colder months. Similarly, levoglucosan in ultrafine mode shows a distinct seasonality, with a minimum seasonal average concentration in warmer months (1.6±0.7 ng/m 3 and 2.5±0.6 ng/m 3 ) to a maximum in colder months (72.7±69.5 ng/m 3 and 18.1±10.5 ng/m 3 ) for Central LA and Anaheim, respectively. 17 Figure 1 (a-b). Monthly average concentration of selected polycyclic aromatic hydrocarbons (PAHs) (ng/m 3 ) for PM2.5 and PM0.18 in a) Central Los Angeles and b) Anaheim. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Concentration (ng/m 3 ) PM 2.5 0 0.1 0.2 0.3 0.4 0.5 0.6 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Concentration (ng/m 3 ) PM 0.18 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Concentration (ng/m 3 ) PM 2.5 Benzo(b)fluoranthene Benzo(k)fluoranthene Benzo (e) pyrene Benzo(a)pyrene Indeno(1,2,3-cd)pyrene Benzo(g,h,i)perylene Coronene a) Central LA b) Anaheim 0 0.1 0.2 0.3 0.4 0.5 0.6 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Concentration (ng/m 3 ) PM 0.18 18 Figure 2 (a-b). Monthly average concentration of selected hopanes and steranes (ng/m 3 ) for PM2.5 and PM0.18 in a) Central Los Angeles and b) Anaheim. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Concentration (ng/m 3 ) PM 2.5 17A(H)-22,29,30-Trisnorhopane 17A(H)-21B(H)-30-Norhopane 17A(H)-21B(H)-Hopane 22S-Homohopane 22R-Homohopane ABB-20R-C27-Cholestane ABB-20R-C29-Sitostane ABB-20S-C29-Sitostane 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Concentration (ng/m 3 ) PM 0.18 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Concentration (ng/m 3 ) PM 0.18 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Concentration (ng/m 3 ) PM 2.5 a) Central LA b) Anaheim 19 Figure 3 (a-b). Monthly average concentration of n-alkanes (ng/m 3 ) for PM2.5 and PM0.18 in a) Central Los Angeles and b) Anaheim. Black dots are the Carbon Preference Index (CPI). Error bars correspond to one standard deviation. V 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 5 10 15 20 25 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 CPI Concentration (ng/m 3 ) PM 2.5 n-Nonadecane n-Eicosane n-Heneicosane n-Docosane n-Tricosane n-Tetracosane n-Pentacosane n-Hexacosane n-Heptacosane n-Octacosane Nonacosane Triacontane Hentriacontane Dotriacontane Tritriacontane Tetratriacontane Pentatriacontane Hexatriacontane Heptatriacontane Octatriacontane CPI 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 2 4 6 8 10 12 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 CPI Concentration (ng/m 3 ) PM 0.18 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 5 10 15 20 25 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 CPI Concentration (ng/m 3 ) PM 2.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 2 4 6 8 10 12 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 CPI Concentration (ng/m 3 ) PM 0.18 a) Central LA b) Anaheim 20 Figure 4 (a-b). Monthly average concentration of levoglucosan (ng/m 3 ) for PM2.5 and PM0.18 in a) Central Los Angeles and b) Anaheim. Error bars correspond to one standard deviation. 0 50 100 150 200 250 300 350 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Concentration (ng/m 3 ) PM 2.5 0 20 40 60 80 100 120 140 160 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Concentration (ng/m 3 ) PM 0.18 a) Central LA b) Anaheim 0 50 100 150 200 250 300 350 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Concentration (ng/m 3 ) PM 2.5 0 20 40 60 80 100 120 140 160 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Concentration (ng/m 3 ) PM 0.18 21 3.2 Spatial and temporal variations in DTT activity The spatial and temporal variability of the DTT activity for both size ranges were investigated. The rate of DTT consumption normalized by volume of air sampled (expressed in units of nmol/ min m 3 ) is presented in Figure 5, while the DTT consumption rate normalized by PM mass data (expressed in units of nmol/min mg) are presented in Figure 6. Our measurements showed that the volume-normalized DTT activity, as a metric for comparison of inhalation exposures, at both sites varied across a range of 0.05-0.15 nmol/min m 3 and 0.2-0.6 nmol/min m 3 for PM0.18 and PM2.5, respectively. Mass-normalized DTT activity, indicative of PM intrinsic toxicity, typically ranged from 20-60 nmol/min mg and 20-45 nmol/min mg for PM0.18 and PM2.5, respectively at the two study sites. As evident from Figure 5, within the same size range DTT activity is spatially uniform. Between both sampling sites, a seasonal variability with 40 and 90% increase in volume-normalized DTT activity in colder months in comparison to warmer months was observed for PM2.5 and PM0.18, respectively. This increase was also seen in mass-based DTT activity with 20 and 40% increase in colder months for PM 2.5 and PM0.18, respectively. These trends are in general agreement with the recent study of Saffari et al. (Saffari et al., 2014a), who investigated the seasonal variation of PM0.25 in the LA Basin as well as with the study by Verma et al.(Verma et al., 2014b), which also demonstrated higher levels of ambient PM2.5 DTT activity in the southeastern USA during colder periods. The elevated levels of volume-normalized DTT activity during these months are mainly attributed to the higher atmospheric stability in addition to enhanced gas-to-particle partitioning of redox active semi-volatile organic compounds (Saffari et al., 2014b; Verma et al., 2014b). 22 Figure 5 (a-b). Monthly-averaged volume-normalized dithiothreitol (DTT) activity (nmol/min m 3 ) of ambient PM2.5 and PM0.18 at a) Central LA and b) Anaheim. February 2014 data corresponds to one sample. Sampling was not conducted in December 2013 at Anaheim. Error bars correspond to one standard deviation. a) Central LA b) Anaheim 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 DTT (nmol/ min m 3 ) PM 0.18 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 DTT (nmol/min m 3 ) PM 0.18 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 DTT (nmol/min m 3 ) PM 2.5 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 DTT (nmol/min m 3 ) PM 2.5 23 Figure 6 (a-b). Monthly-averaged mass-normalized dithiothreitol (DTT) activity (nmol/min mg) of ambient PM2.5 and PM0.18 at a) Central LA and b) Anaheim. February 2014 data corresponds to one sample. Sampling was not conducted in December 2013 at Anaheim. Error bars correspond to one standard deviation. a) Central LA b) Anaheim 0 10 20 30 40 50 60 70 80 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 DTT (nmol/min mg) PM 0.18 0 10 20 30 40 50 60 70 80 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 DTT (nmol/min mg) PM 2.5 0 10 20 30 40 50 60 70 80 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 DTT (nmol/min mg) PM 2.5 0 10 20 30 40 50 60 70 80 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 DTT (nmol/min mg) PM 0.18 24 3.3 Source apportionment of organic carbon in PM2.5 and PM0.18 Weekly results of the CMB source contribution estimates were averaged to obtain monthly average source contributions, presented in Figure 7 for PM2.5 and PM0.18 at both sites. Contribution of mobile sources to PM2.5 OC fraction was about 0.65±0.25 µg/m 3 (21% of total OC) in Central LA and 0.32±0.25 µg/m 3 (9.4% of OC) in Anaheim. In the ultrafine size fraction, OC from mobile sources accounts for about 23% and 11% of total OC in Central LA and Anaheim, respectively. Contribution of mobile sources to PM2.5 OC increased by a factor of 1.9 and 4.5 in Central LA and Anaheim, respectively, in the colder period compared to the warmer months. Contribution of primary biogenic sources (including emissions from vegetative detritus, food cooking, and re- suspended soil dust) to total OC concentration was on average, 1.07±0.30 and 1.01±0.36 µg/m 3 for PM2.5 in Central LA and Anaheim, respectively. Primary biogenic source is characterized by high concentrations of odd alkane and n-alkanoic acids. Therefore, there is a possibility that several sources, such as vegetative detritus, meat smoke and possibly soil debris, were included in primary biogenic source 77 . Wood smoke showed a pronounced seasonal pattern, peaking in the colder months, with an average PM2.5 OC contribution of 17.8 and 15.6% in Central LA and Anaheim, respectively. The higher wood smoke contribution in colder months is mainly associated with higher biomass burning and/or wood combustion during colder months. Contribution of anthropogenic SOC to PM2.5 ranged from 0.19-0.70 µg/m 3 and 0.29-0.79 µg/m 3 in Central LA and Anaheim, respectively. For the ultrafine size fraction, SOC contributed to 16% and 13.2% of total OC concentrations in Central LA and Anaheim, respectively. Unidentified OC, denoted as “other OC”, is the residual difference between the measured OC and the sum of all source contribution estimates considered in the MM-CMB model. “Other OC” accounts for primary sources not considered in the model (e.g. natural gas combustion, ship emissions, etc.), along with partial contribution from secondary sources which might not be captured by the SOC profile included in the model. In Central LA, “other OC” accounted for 0.23±0.10 µg/m 3 of PM2.5 and PM0.18 OC contribution was negligible on average over all sampling months, respectively. Contribution of “other OC” was relatively higher in Anaheim than Central LA, with monthly average concentrations of 0.77±0.46 µg/m 3 and 0.42±0.20 µg/m 3 in PM2.5 and PM0.18 size fractions, respectively. These results indicate that in Central LA “other OC” accounted for about 8% and 0.7% of PM2.5 and PM0.18, respectively. Therefore, it can be inferred that measured OC was apportioned to a reasonable extent and there should not be any other major sources of OC in Central LA that were not considered in the model. Moreover, The source profiles which were used in our hybrid CMB model were reported by several previous studies to be major sources of organic carbon in the LA Basin (Arhami et al., 2010; Hasheminassab et al., 2013b; Minguillón et al., 2008a; Pratsinis et al., 1984; Schauer et al., 1996a; Williams et al., 2010; Zhang et al., 2013). In Anaheim, contribution of “other OC” was relatively higher, averaging 29% and 32% for PM2.5 OC and PM0.18 OC, respectively. Elevated contribution of “other OC” in Anaheim could be attributed to the fact that the same PMF-derived source profiles derived at Central LA were applied to Anaheim, while the detailed nature of the SOC and primary biogenic aerosols may not be an exact match for these two sites due to their distinctive locations and PM emission sources. As discussed above, Anaheim is located in the prevalent air trajectory crossing the LA basin from coast to inland, and thus affected by advection of aged and photo-chemically processed PM from upwind regions. To develop improved and more accurate results using this new hybrid model, it is recommended that future studies apply site-specific PMF-derived source profiles as input for their MM-CMB model. 25 Figure 7 (a-b). Monthly average source contributions (µg/m 3 ) to ambient OC for PM2.5 and PM0.18 in a) Central Los Angeles and b) Anaheim. Primary biogenic source accounts for emissions from vegetative detritus, food cooking and re-suspended soil dust. a) Central LA b) Anaheim 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 PM 2.5 OC (µg/m 3 ) PM 2.5 Diesel Gasoline Smoking vehicles Wood smoke Primary biogenic SOC Other OC 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 PM 2.5 OC (µg/m 3 ) PM 2.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Jul 2012 Aug 2012 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 PM 0.18 OC (µg/m 3 ) PM 0.18 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 PM 0.18 OC (µg/m 3 ) PM 0.18 26 Figure 8 (a-b). Monthly average source contributions (µg/m 3 ) to ambient PM2.5 and PM0.18 mass concentrations in a) Central Los Angeles and b) Anaheim. Primary biogenic source accounts for emissions from vegetative detritus, food cooking and re-suspended soil dust. 0 1 2 3 4 5 6 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 PM 0.18 mass concentration (µg/m 3 ) PM 0.18 0 2 4 6 8 10 12 14 16 18 20 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 PM 2.5 mass concentration (µg/m 3 ) PM 2.5 Other Sea salt Secondary ions Vehicular abrasion Crustal material SOM Primary biogenic Wood smoke Vehicle tailpipe emissions 0 1 2 3 4 5 6 Jul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 PM 0.18 mass concentration (µg/m 3 ) PM 0.18 0 2 4 6 8 10 12 14 16 18 20 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 PM 2.5 mass concentration (µg/m 3 ) PM 2.5 a) Central LA b) Anaheim 27 3.4 Regression analysis and source apportionment of DTT 3.4.1 Correlations between DTT activity and PM chemical species To investigate the association of DTT activity with PM2.5 and PM0.18 chemical composition, univariate correlation analysis was performed between the weekly samples of volume-normalized DTT activity and chemical species concentrations at each sampling site separately. Univariate correlation also helped identify important chemical species that should be considered as input species for the subsequent MLR analysis. Table 2 shows the Spearman’s correlation coefficients between the air volume-normalized DTT activity and concentrations of chemical species including EC, OC, PAHs, hopanes, n-alkanes, organic acids, levoglucosan and selected elements for PM2.5 and PM0.18 in Central LA and Anaheim. Most notably, organic compounds showed overall high correlation values with DTT activity in both size ranges (R > 0.60), especially in Central LA. Similarly high correlations between the DTT activity and organic compounds such as OC, hopanes, and PAHs have also been reported in previous studies conducted in the LA Basin (Cheung et al., 2009; Cho et al., 2005a; Saffari et al., 2014a). EC, which is a marker of combustion processes mainly from vehicle tailpipe emissions (Schauer, 2003), was also highly correlated with DTT activity at both sampling sites especially in PM 0.18 size range (R = 0.76 averaged over Central LA and Anaheim). Verma et al. (Verma et al., 2014b) also demonstrated strong correlations between DTT activity and EC in southeastern United States. Overall, high correlations of these specific markers elucidate the underlining toxicity associated with the sources of these tracers. Levoglucosan, a tracer of biomass burning, showed relatively high correlation with DTT activity, with R values of 0.64 and 0.61 for PM2.5 and PM0.18, respectively, averaged over Central LA and Anaheim. This observation is also in line with findings of Verma et al. (Verma et al., 2015, 2014c) who also indicated biomass burning as an important source of reactive oxygen species and observed strong correlations between DTT activity and biomass burning characterized by high concentration of levoglucosan. Some metals such as Ba, Cu, Fe, K, Mn, Pd and Pb were also significantly correlated with DTT activity in both size ranges in Central LA (R > 0.60) and Anaheim (R > 0.70). A number of previous studies have also reported strong correlations of these metals with the DTT activity (Charrier and Anastasio, 2012; Fang et al., 2015; Ntziachristos et al., 2007; Verma et al., 2009a, 2009c). Metals such as Ba, Cu, Fe, Mn, Pb and Sr are primarily associated with vehicular abrasion and re-suspension of soil and road dust (Amato et al., 2011; Pakbin et al., 2011; Thorpe and Harrison, 2008). MLR analysis was conducted between DTT activity and chemical species for both size ranges in Central LA and Anaheim in order to investigate the compounds driving the PM redox activity. The best fitted regression models for both size ranges and sampling sites are presented in Table 3. The optimum model for Central LA in the PM2.5 size range included the sum of phtalic, glutaric and succinic acids as the tracers of SOC (Heo et al., 2013c), as well as Ba, a tracer of vehicular abrasion (Thorpe and Harrison, 2008), leading to an R 2 value of 0.50. EC, an important tracer of vehicular emissions (Schauer, 2003), and Ba for PM0.18 were found to be the best fitted species in Central LA with R 2 value of 0.60. In Anaheim, for the PM2.5 size fraction, Cu and sum of organic acids, indicative of vehicular abrasion (Iijima et al., 2007; Sanders et al., 2003) and SOC sources (Ding et al., 2008; Fisseha et al., 2004), respectively, best predicted DTT activity (R 2 = 0.86). In the PM0.18 size range at that site, EC and Pb (an indicator of vehicular abrasion (Amato et al., 2011; Thorpe and Harrison, 2008)), are the main driving species of the DTT activity (R 2 = 0.71). It should be mentioned that other tracers of the same source were also examined in the MLR models, and they all led to similar results albeit with somewhat lower R 2 values, with either statistically significant (P < 0.05) or approaching significance (P < 0.08) contribution to DTT activity. For instance, in Central LA for PM2.5 size fraction, SOC tracers along with other tracers of vehicular abrasion such as Fe and Zn also had similar results with R 2 of 0.38 and 0.31, respectively. Other combinations of the tracers of vehicle tailpipe emissions and vehicular abrasion in PM0.18 at this site were also examined such as Cu and EC (R 2 28 = 0.37) as well as PAHs and Mn (R 2 = 0.59). Similarly, in Anaheim other tracers of vehicular abrasion such as Zn and Ba with SOC tracers showed association with PM2.5 DTT activity with R 2 of 0.81 and 0.78, respectively. PAHs and Zn as other tracers of vehicle tailpipe emissions and vehicular abrasion, respectively were associated with PM0.18 DTT activity with R 2 of 0.73. 3.4.2 Correlations between DTT activity and sources Univariate analysis was performed to assess how individual sources (i.e., the CMB-derived sources along with secondary ions, crustal material, vehicular abrasion and sea salt) correlate with the DTT activity (Table 4). Wood smoke showed positive significant correlations with DTT activity with R values of 0.56 and 0.61 in Central LA for PM2.5 and PM0.18, respectively, and similarly, in Anaheim with R values of 0.55 and 0.57 for PM2.5 and PM0.18, respectively. Vehicle tailpipe emissions also exhibited positive and significant correlations with DTT activity with R values of 0.69 and 0.72 for PM2.5 in Central LA and Anaheim, respectively. For PM0.18, stronger correlations between DTT activity and vehicle tailpipe emissions were observed in comparison to PM 2.5 in both Central LA (R = 0.77) and Anaheim (R = 0.88), underscoring the greater impact of vehicle tailpipe emissions on the DTT activity in this size range. Primary biogenic sources, which include emissions from vegetative detritus, food cooking and re-suspended soil dust (Heo et al., 2013c) significantly correlated with DTT activity in both size ranges in Central LA with an average R value of 0.71. SOC also showed positive association with the DTT activity in PM2.5 size range with higher correlation in Anaheim (R = 0.58). Secondary ions showed low or negative correlations except for PM2.5 in Anaheim. Crustal materials in PM0.18 (R= 0.58) and vehicular abrasion emissions in both size ranges (R = 0.73) also showed statistically significant correlations in both size ranges in Central LA. In Anaheim, remarkably higher R values of 0.77 and 0.82 for crustal materials and vehicular abrasion, respectively, were observed for both size fractions on average. Sea salt, on the other hand, showed low or negative correlations with DTT activity in both size ranges at both sampling sites. Furthermore, MLR analysis was also performed on DTT activity and the PM sources mentioned above to identify the best and statistically significant (P < 0.05) predictors of the DTT activity. The details of the output models are presented in Table 5. In Central LA, primary biogenic, SOC and vehicular abrasion sources contributed significantly to PM2.5 DTT activity with R 2 value of 0.71, while in PM0.18 size range, vehicle tailpipe emissions as well as vehicular abrasion were the main drivers of the DTT activity (R 2 = 0.72). Similarly, in Anaheim, SOC and vehicular abrasion in PM2.5 size range (R 2 = 0.81) and vehicle tailpipe emissions as well as vehicular abrasion in PM0.18 (R 2 = 0.75) were identified to be the significant contributors of DTT activity. 29 Table 2. Spearman’s correlation coefficients (R) between the dithiothreitol (DTT) activity (nmol/min m 3 ) and selected PM2.5 and PM0.18 components at Central LA and Anaheim. Bold numbers indicate values with R > 0.50 and P < 0.05. * indicates values with R < 0.5 and P < 0.05. Central LA Anaheim Species PM2.5 DTT PM0.18 DTT PM2.5 DTT PM0.18 DTT EC 0.54 0.67 0.72 0.86 OC 0.79 0.89 0.81 0.87 PAHs 0.63 0.69 0.60 0.78 Hopanes 0.62 0.68 0.84 0.81 n-Alkanes 0.74 0.57 0.57 0.85 Organic acids 0.66 0.69 0.38 0.13 Levoglucosan 0.56 0.64 0.72 0.58 NO 3 - 0.32 0.35 0.60 0.40 SO 4 -2 -0.44 -0.11 -0.59 -0.73 NH 4 + -.019 -.299 .431 .205 Al 0.21 0.65 0.69 0.76 P 0.53 0.72 0.74 0.84 S -0.37 0.21 -0.49 0.15 K 0.55 0.83 0.73 0.86 Ca 0.36 0.70 0.62 0.83 Ti 0.48 * 0.64 0.66 0.75 V -0.20 0.06 -0.39 0.29 Cr 0.29 0.34 0.64 0.46 * Mn 0.46 * 0.70 0.84 0.82 Fe 0.54 0.75 0.81 0.83 Co 0.26 0.45 * 0.59 0.69 Ni -0.11 0.04 0.34 0.39 Cu 0.51 0.58 0.81 0.88 Zn 0.54 0.51 0.83 0.88 Rb 0.64 0.79 0.70 0.78 Sr 0.53 0.72 0.48 * 0.62 Pd 0.57 0.78 0.72 0.77 Cd 0.61 0.78 0.66 0.81 Sn 0.12 0.22 0.76 0.82 Sb 0.60 0.75 0.66 0.84 Ba 0.58 0.80 0.79 0.85 Pb 0.58 0.64 0.68 0.78 30 Table 3. Output of multiple linear regression (MLR) analysis between the dithiothreitol (DTT) activity (nmol/ min m 3 ) and chemical species for PM2.5 and PM0.18 size ranges at Central LA and Anaheim. DTT activity Species Unstandardized Coefficient Units Standard error Partial R P value R 2 Central LA PM 2.5 (Constant) 0.12 nmol min -1 m -3 0.057 - 0.65 SOC tracers * 0.01 nmol min -1 ng -1 0.004 0.432 0.022 Ba 0.012 nmol min -1 ng -1 0.003 0.685 0.000 Central LA PM 0.18 (Constant) 0.011 nmol min -1 m -3 0.021 - 0.70 EC 0.155 nmol min -1 ng -1 0.076 0.37 0.053 Ba 0.014 nmol min -1 ng -1 0.003 0.658 0.000 Anaheim PM 2.5 (Constant) 0.121 nmol min -1 m -3 0.051 - 0.83 Organic acids 0.002 nmol min -1 ng -1 0.001 0.490 0.033 Cu 0.023 nmol min -1 ng -1 0.003 0.891 0.000 Anaheim PM 0.18 (Constant) 0.032 nmol min -1 m -3 0.009 - 0.84 EC 0.136 nmol min -1 ng -1 0.065 0.453 0.051 Pb 0.056 nmol min -1 ng -1 0.022 0.533 0.019 * Sum of phtalic acid, glutaric acid and succinic acid 77 . 31 Table 4. Spearman’s correlation coefficients (R) between the dithiothreitol (DTT) activity (nmol/min m 3 ) and sources of PM2.5 and PM0.18 at Central LA and Anaheim. Bold numbers indicate values with R > 0.50 and P < 0.1 and * indicates values with P < 0.05. Central LA Anaheim Sources PM2.5 DTT PM0.18 DTT PM2.5 DTT PM0.18 DTT Wood smoke 0.56 * 0.61 * 0.55 * 0.57 * Primary biogenic 0.68 * 0.76 * 0.32 0.21 SOC 0.28 -0.14 0.58 * -0.08 Secondary Ions -0.07 -0.05 0.49 * 0.02 Crustal material 0.33 0.72 * 0.73 * 0.81 * Vehicular abrasion 0.58 * 0.80 * 0.79 * 0.85 * Sea salt -0.46 -0.00 -0.32 0.42 Vehicle tailpipe emissions 0.69 * 0.77 * 0.72 * 0.88 * Table 5. Output of multiple linear regression analysis between the dithiothreitol (DTT) activity (nmol/min m 3 ) and sources of PM2.5 and PM0.18 size ranges at Central LA and Anaheim. DTT activity Sources Unstandardized Coefficient Units Standard error Partial R P value R 2 Central LA PM 2.5 (Constant) -0.015 nmol min -1 m -3 0.056 - 0.71 Primary biogenic 0.081 nmol min -1 µg -1 0.025 0.582 0.004 SOC 0.159 nmol min -1 µg -1 0.036 0.697 0.000 Vehicular abrasion 0.069 nmol min -1 µg -1 0.032 0.425 0.043 Central LA PM 0.18 (Constant) 0.012 nmol min -1 m -3 0.013 - 0.72 Vehicle tailpipe emission 0.134 nmol min -1 µg -1 0.037 0.630 0.002 Vehicular abrasion 0.093 nmol min -1 µg -1 0.032 0.539 0.010 Anaheim PM 2.5 (Constant) 0.175 nmol min -1 m -3 0.031 - 0.81 SOC 0.081 nmol min -1 µg -1 0.034 0.487 0.029 Vehicular abrasion 0.181 nmol min -1 µg -1 0.032 0.798 0.000 Anaheim PM 0.18 (Constant) 0.046 nmol min -1 m -3 0.008 - 0.75 Vehicle tailpipe emission 0.068 nmol min -1 µg -1 0.030 0.474 0.040 Vehicular abrasion 0.105 nmol min -1 µg -1 0.029 0.658 0.002 32 3.5 Comparison with previous studies in Los Angeles Starting in 2007, major modifications were implemented on federal, state, and local regulations on vehicular emissions. In the LA Basin, Hasheminassab et al. (2014a) showed a reduction of 20-25% in PM2.5 originating from vehicular emissions, as the result of stringent regulations implemented after 2007. Whereas historical trends in PM mass concentration levels imply an overall reduction in total PM emissions, examination of specific organic tracers could provide additional insight on the extent to which these decreasing PM trends are ensued specifically from vehicular emissions and may assist regulatory agencies in the design and implementation of more effective strategies to protect public health. In order to assess the impact of regulations on vehicular emissions, the concentrations of carbonaceous species and organic compounds in PM2.5 and PM0.18 size fractions were examined, using the PM data acquired in earlier studies conducted over the past decade in our sampling site in Central LA. Table 6 summarizes the sampling period and instruments used to collect PM 2.5 and PM0.18 in each study. For the year-long studies (i.e. Heo et al., (2013) and Sardar et al. (2005) average concentrations between July and February are reported to be consistent with the sampling period of this study. The values presented in Table 6 reveal an overall decrease in the concentration of carbonaceous species (i.e. EC and OC) in Central LA over the past decade. The average PM 2.5 OC concentration obtained in this study is 2.91±0.74 µg/m 3 , which is 56% and 38% lower than the values reported by Sardar et al. (in 2002-2003) and Heo et al. (in 2009-2010), respectively. Comparison with Minguillón et al. (2008b) shows a reduction of 16% in PM2.5 OC concentration in the corresponding sampling months in their study. Likewise, PM 2.5 EC concentrations decreased from an average value of 1.11 ± 0.53 µg/m 3 in 2002-2003 (Sardar et al., 2005) to 0.52 ± 0.10 µg/m 3 in this study (i.e. near 75% reduction). In PM0.18, comparison of the results between this study and those reported by Sardar et al. (2005) and Ning et al. (2007b)indicates 8-30% and 42-76% reductions in the concentrations of OC and EC, respectively. T-tests showed that all of the aforementioned reductions were statistically significant at 95% confidence level (i.e. p < 0.05). Hopanes and steranes, well-established organic tracers of vehicular emissions, showed dramatic reductions in Central LA. In 2009-2010, Heo et al. (2013) reported an average value of 1.19±0.44 ng/m 3 for the sum of hopanes and steranes for PM2.5 OC. About 3 years later, in the current study, this average value decreased by nearly 48% to 0.61±0.34 ng/m 3 . In PM0.18, Ning et al. (2007) reported an average value of 2.52±0.52 ng/m 3 for the sum of hopanes and steranes during June and July, while in this study the average cumulative concentration of these compounds in PM0.18 is 0.12±0.01 ng/m 3 in July. Overall, these trends are in line with the findings of Hasheminassab et al. (2014), who showed significant reductions in the contribution of vehicular emissions to ambient PM2.5 in Central LA after 2007, following the implementation of major federal, state, and local regulations on vehicular emissions. PAHs followed similar trends to hopanes and steranes with lowest concentrations observed in this study in both size fractions. For PM2.5, Heo et al. (2013) reported an average concentration of 1.91±1.26ng/m 3 for total PAHs in July 2009 to February 2010, while in the current study this average value reached 1.11±0.66 ng/m 3 (i.e. near 41% reduction) in the corresponding months. For PM0.18, average concentration of PAHs in this study in July was about 6 times lower than the average value reported by Ning et al. (2007). Moreover the reduction trend in PAHs level is also evident in comparison with the studies of Verma et al. (2009b) and Minguillón et al. (2008b), who reported average concentrations of 1.77±1.40 and 0.30±0.08 µg/m 3 for PM2.5 –bound PAHs in Jun- Aug 2008 and Jul-Sep 2007, respectively. In another study conducted by Fine et al. (2004) during 2002-2003 in the same location, the diurnal variation of individual organic compounds in two separate months (August and January, representing the typical warm and 33 cold seasons in LA basin, respectively) was investigated. In Table 6, the average concentrations of benzo(ghi)perylene (BgP), total hopanes, and levoglucosan in both size fractions from study of Fine et al. (2004b) are reported. BgP is a PAH with a high molecular weight, emitted mostly from gasoline vehicles (Miguel et al., 1998). In the current study the average BgP concentrations in PM 0.18 in the months of August and January are 0.07±0.03 and 0.15±0.02 ng/m 3 , respectively, indicating a roughly 80-90% reduction compared to the values reported by Fine et al. (2004b) about a decade ago. The concentration of BgP in PM2.5 in months of August and January also showed a significant reduction of 49-72%. Average of total hopanes concentration in the months of August and January decreased 63% and 33% in PM0.18 and PM2.5, respectively. These findings again corroborate the major reduction of the tracers of vehicular sources in Central LA in the past decade. Source contribution estimates for PM2.5 OC from previous studies in Central LA were pooled together and compared to the findings of this study. Minguillón et al. (2008b) reported an average value of 2.46±0.61µg/m 3 for mobile source contribution (gasoline and diesel) between July and September 2007, while in this study mobile source contribution was evaluated as 0.40±0.15 µg/m 3 for PM2.5 OC in the corresponding months indicating an 83% reduction in vehicular emissions. Similarly, comparison to study of Heo et al. (2013) also revealed a 57% reduction in mobile source contribution estimates. The reduction trend in vehicular emissions underscores the impact of implementing major regulations and improvement in emission control techniques. A recent study by Posner and Pandis (Posner and Pandis, n.d.) in Eastern US reported that gasoline accounts for majority of number concentration of ultrafine particles with diameter greater than 3nm and contributed almost equally with industrial and diesel emission for ultrafine particles with diameter between 10-100nm. In the current study, on the other hand, contribution of gasoline and diesel vehicles to PM0.18 mass was quite similar, accounting for 3% and 4% of total mass, respectively. Lastly, it is noteworthy that Hasheminassab et al. (2014a) showed that the levels of important parameters of meteorological conditions such as temperature and relative humidity were quite consistent from 2002 to 2013 over the LA Basin which underscores the fact that reduction in organic compounds and mobile source contributions were not due to changes in meteorological conditions, but rather due to major regulations implemented on vehicular emissions. Table 7 (a-b) presents a comparison of DTT activity measured in the current study with previous studies conducted in Central LA, all at the same sampling site, over the past decade for fine and ultrafine particles. Overall a small increase in the per PM mass-normalized DTT activity of ambient PM2.5 can be seen from 2002 to 2012. The DTT activity normalized per m 3 of air volume has been more stable over time in Central LA, which may be attributed to an increase in intrinsic DTT activity, combined with the documented decrease in PM2.5 mass concentration over time in Central LA, as discussed in following sections. Unlike PM2.5, in the ultrafine size range, the cut point was not consistent among different studies. Nonetheless, both air volume and PM mass-based DTT activity have generally decreased over the past decade in Central LA. Verma et al. (2009a) reported PM0.18 DTT activity of 0.07±0.03 nmol/min µg in June-August 2009, while three years later, this value approximately decreased by 66% to 0.024±0.001 nmol/min µg in July-August in Central LA. Even though Saffari et al.(2014b) reported PM0.25 DTT activity levels, still a 42% decrease can be seen in comparison to PM0.18 DTT levels measured at this study after four years. Several studies have shown a major reduction in ambient PM2.5 levels and its sources over the past decade in the Los Angeles Basin and demonstrated that stringent regulations on mobile sources, in particular starting 2007, have resulted in major reductions in tailpipe emissions (Bishop et al., 2013; Hasheminassab et al., 2014a; Lurmann et al., 2015b; McDonald et al., 2015; Preble et al., 2015). However, the small but consistent increase in PM2.5 mass-based DTT activity level reveals that other factors besides tailpipe emissions may affect the PM 2.5 toxicity 34 as well. The results discussed in the previous section revealed that vehicular abrasion, an important component of non-tailpipe emissions, together with secondary OC, were two major sources significantly contributing to PM2.5 DTT activity, in addition to tailpipe emissions. The above discussions, along with the comparison of ultrafine PM redox activity with previous studies, corroborate the effectiveness of stringent regulations on vehicular tailpipe emissions in reducing the overall ambient ultrafine PM toxicity. However, tailpipe emissions are not the only contributor of PM toxicity in the fine PM, as indicated and discussed above. Harrison et al. (2012) showed that tracers of vehicular abrasion, which are generally more prevalent in the coarse (PM 2.5-10) size fraction, also partition into the upper size range of PM2.5. Similar findings have been reported in other studies as well (Geller et al., 2004; , Majestic et al., 2008). Recently, Shirmohammadi et al. (2015) discussed the increase in metals and trace element concentrations in the coarse PM over a 3-year period in Central LA, especially those emitted from vehicular abrasion and road dust. This was attributed to the increase in the vehicle’s speed and number of trucks (by 6 and 15%, respectively, which was also statistically significant (Mann-Whitney Rank Sum Test, P < 0.001) as the turbulence caused by the passing traffic highly contributes to substantial amount of coarse particles emissions from re-suspension of soil and road dust (Harrison et al., 2001), in Central LA. Moreover, comparison with several previous studies conducted in Central LA also revealed a similar trend, with an increase in the contribution of vehicular abrasion tracers in PM2.5 mass over time unlike the PM2.5 mass concentration decrease after implemented regulations on vehicle tailpipe emissions (Figure 9 a-b). For instance, as can be inferred from Figure 9 b, compared to a study conducted by Minguillón et al. (2008b) during a 13-week period between March- September 2007 (except June) in the same sampling site in Central LA, PM 2.5 mass concentration decreased by 24% in this study, while the contribution of the sum of VA tracers (i.e. Ba, Cu, Mn, Pb and Sr) to PM2.5 mass increased by a factor of near 2.2. In line with this comparison, the annual trends of individual VA tracers from 2008 to 2012, obtained from the Speciation Trends Network (STN) data at the North Main street in downtown Los Angeles, showed that, although the per air volume concentrations of these species were relatively stable from 2008 to 2012, an increasing trend in the contribution of these tracers to PM2.5 mass concentration was evident from their year-to-year per mass concentrations levels. The median value of the sum of per PM mass concentrations (µg/µg PM) of the VA tracers (i.e. Ba, Cu, Mn, Pb and Sr) increased statistically significantly by 11% from 2008 to 2012 (Mann-Whitney Rank Sum Test, P = 0.05). This increase over these years can also be seen for individual species. For example, the per mass concentration of Ba (which was also used as the basis of our VA contribution’s estimation in previous section) increased statistically significantly by 66% from 2008 to 2012 (Mann-Whitney Rank Sum Test, P < 0.01). Therefore, it can be hypothesized that the per PM mass increase in redox active metals and other trace elements from non-tailpipe emissions may counteract the reduction in vehicular tailpipe emissions in PM2.5, leading to a slight increase in the overall DTT activity in this size fraction of PM with time. This is in contrast to the ultrafine PM, the toxicity of which seems to be largely dominated by tailpipe emissions, and appears to be decreasing with time, as shown in Table 7b. 35 Table 6 (a-b). Reported concentrations of carbonaceous species and organic compounds in Central Los Angeles for a) PM2.5 and b) PM0.18 size fractions. a) PM2.5 Study Sardar et al. 2005 Fine et al. 2004b Minguillón et al. 2008 Verma et al. 2009 Heo et al. 2013 Current study Instrument MOUDI High-vol sampler PCIS 1 High-vol sampler URG-3000B medium volume sampler MOUDI Size fraction PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 Sampling year 2002-2003 2002-2003 2007 2008 2009-2010 2012-2013 Season Aug-Feb Aug Jan Jul-Sep Jun-Aug Jul-Feb Jul-Feb Carbonaceous/ organic species OC (µg/m 3 ) 6.98 ± 2.23 - - 3.98 ± 1.03 - 4.67 ± 0.92 2.91 ± 0.74 EC (µg/m 3 ) 1.11 ± 0.53 - - 0.72 ± 0.15 - 0.99 ± 0.39 0.52 ± 0.10 PAHs (ng/m 3 ) - 0.25 ± 0.15 2 1.04 ± 0.57 2 0.30 ± 0.08 1.77 ± 1.40 1.91 ± 1.26 1.11 ± 0.66 Hopanes+Steranes (ng/m 3 ) - 0.89 ± 0.26 3 1.03 ± 0.27 3 1.03 ± 0.23 1.90 ± 1.51 1.19 ± 0.44 0.61 ± 0.34 n-Alkanes (ng/m 3 ) - - - 20.03 ± 21.4 45.83 ± 21.04 25.03 ± 7.89 11.76 ± 4.26 Organic acids (ng/m 3 ) - - - 79.11 ± 15.23 105.66 ± 8.71 204.58 ± 71.6 99.81 ± 39.75 Levoglucosan (ng/m 3 ) - 6.13 ± 3.11 73.5 ± 31.62 0.75 ± 1.0 BDL 72.81 ± 67.82 47.81 ± 57.34 b) PM0.18 Study Sardar et al. 2005 Fine et al. 2004b Ning et al. 2007 Current study Instrument MOUDI High-vol sampler High-vol sampler/MOUDI MOUDI Size fraction PM0.18 PM0.18 PM0.18 PM0.18 Sampling year 2002-2003 2002-2003 2006 2012-2013 Season Aug-Feb Aug Jan Jun-Jul Jul-Feb Carbonaceous/ organic species OC (µg/m 3 ) 2.13 ± 0.48 - - 1.55 ± 0.21 1.09 ± 0.22 EC (µg/m 3 ) 0.41 ± 0.13 - - 1.2 ± 0.71 0.29 ± 0.05 PAHs (ng/m 3 ) - 0.19 ± 0.14 2 0.81 ± 0.55 2 1.21 ± 1.02 0.44 ± 0.26 Hopanes+Steranes (ng/m 3 ) - 0.75 ± 0.25 3 0.7 ± 0.24 3 2.52 ± 0.52 0.22 ± 0.13 n-Alkanes (ng/m 3 ) - - - 20.95 ± 13.08 5.02 ± 2.24 Organic acids (ng/m 3 ) - - - 277.05 ± 134.42 47.26 ± 16.17 Levoglucosan (ng/m 3 ) - 1.25 ± 0.35 29.25 ± 20.55 - 21.59 ± 37.75 1 Sioutas Personal Cascade Impactor Samplers (Sioutas™ PCIS, SKC Inc., Eighty Four, PA, USA) 2 Reported values correspond to the concentration of Benzo(ghi)perylene only 3 Reported values correspond to total hopanes only 36 Table 7 (a-b). Comparison of dithiothreitol (DTT) activity levels (± standard deviation) with previous studies conducted at Central Los Angeles for: a) PM2.5 and b) PM0.18 size ranges. a) Study Size fraction Sampling period DTT activity (nmol/min µg) DTT activity (nmol/min m 3 ) Li et al. 2003 PM 2.5 Mar 2002 0.013 0.28 Hu et al. 2008 PM 2.5 Mar- May 2007 0.022 0.33 Verma et al. 2009b PM 2.5 Nov 2007 0.007 ± 0.003 0.35 ± 0.30 Current study PM 2.5 2012-2013 0.028 ± 0.005 0.35 ± 0.04 b) Study Size fraction Sampling period DTT activity (nmol/min µg) DTT activity (nmol/min m 3 ) Li et al. 2003 PM 0.15 Mar 2002 0.091 0.35 Verma et al. 2009a PM 0.18 Aug 2009 0.07 ± 0.03 0.33 ± 0.25 Saffari et al. 2014 PM 0.25 2008-2009 0.078 ± 0.007 0.82 ± 0.12 Current study PM 0.18 2012-2013 0.045 ± 0.008 0.11 ± 0.03 37 Figure 9 (a-b). Comparison of the sum of vehicular abrasion (VA) tracers’ concentrations (i.e. sum of Ba, Cu, Mn, Pb and Sr) a) per volume of air (µg/m 3 ) and b) per mass collected (µg/µg PM) in PM2.5 size range with previous studies conducted at Central LA. Error bars correspond to one standard deviation. The dates in the parentheses refer to the pertinent sampling dates. a) b) 38 Case Study 2. Oxidative potential of on-road fine particulate matter (PM2.5) measured on major freeways of Los Angeles, CA, and a 10-year comparison with earlier roadside studies 39 1. Introduction High-traffic roadways, where elevated concentrations of PM are observed, play an important role in the overall exposure to ambient air pollutants of both commuters and residents in close proximity to these roadways (Pant and Harrison, 2013). Various methodologies have been implemented to measure and characterize vehicular emissions, including on-road, roadside, tunnel, and dynamometer measurements (Kleeman et al., 2000; Kristensson et al., 2004; Ning et al., 2007; Ban-Weiss et al., 2008; Kam et al., 2012a; Franco et al., 2013). Chassis dynamometer studies capture direct tailpipe emissions under controlled experimental conditions. However, these measurements do not take into account non-tailpipe emissions, which may contribute significantly to overall traffic-associated PM concentrations (Thorpe and Harrison, 2008), particularly in light of observed reductions in primary exhaust emissions (Bishop et al., 2013; Hasheminassab et al., 2014). Very importantly, dynamometer studies do not consider various urban atmospheric processes, such as atmospheric dilution and photochemical transformation, which may substantially change the physicochemical and oxidative characteristics of the emitted particles (Charron and Harrison, 2003; Canagaratna et al., 2004). In addition, dynamometer studies cannot replicate the entire in-use mixed vehicle fleet, as they are conducted only on a sub-set of vehicles. While tunnel studies account for a larger fraction of on-road vehicles and capture both tailpipe and non-tailpipe emissions, results from these studies may not be applicable to open roadways, where meteorological conditions and atmospheric dilution are remarkably different from tunnel environments (Ketzel and Berkowicz, 2004). Roadside studies provide the opportunity to measure a more representative, larger vehicle fleet under actual ambient conditions. However, since the measurements are restricted to fixed sampling sites, they may not take into account emissions in the whole target roadway. More realistic estimates of on-road vehicular emissions under actual atmospheric and driving conditions can be obtained through on-road mobile platform measurements (Jamriska and Morawska, 2001; Abu-Allaban et al., 2003; Franco et al., 2013). This methodology is arguably more appropriate to estimate traffic emissions, as it closely captures the emissions from exhaust, abrasion, and re- suspension sources. It should be clarified that on-road PM sampling followed in this paper is different from in- cabin measurements, where the effect of vehicle characteristics, ventilation settings, driving conditions, and air exchange rates may play a significant role on in-vehicle levels of pollutants and how they compare to the roadway levels (Hudda et al., 2011; Zhu et al., 2007). In this study, time-integrated samples of on-road PM were collected in three distinct arterial routes in Los Angeles: a major freeway which is dominated by light-duty-vehicles (LDVs) (I-110), another major freeway with a higher fraction of heavy-duty-vehicles (HDVs) (I-710) and two major surface street roadways (the Wilshire/Sunset boulevards). Furthermore, sampling was carried out at an urban stationary reference site at the main campus of the University of Southern California (USC) for comparison of PM chemical components and its oxidative potential at urban ambient levels. The current study is unique in that we have characterized the oxidative potential of PM collected on-road across these major roadways. To the best of our knowledge, this is the first study investigating the PM oxidative potential on-road and across major roadways of Los Angeles, the results of which provide valuable insights into commuters’ exposure to high concentrations of toxic PM. The unique sampling set-up employing light-weight, battery operated cascade impactors made it possible to conduct on-road characterization of PM chemistry and toxicity in different roadway environments. Each micro-environment was sampled for an extended time period (ranging from 55 to 95 hours total) to ensure the collection of adequate PM mass loadings for the chemical and oxidative potential measurements of the PM2.5, as discussed in the following section. Oxidative potential of PM2.5 was quantified using the dithiothreitol (DTT) assay and its association with different chemical species was investigated. 40 2. Methodology 2.1 Sampling campaign Sampling was conducted in three distinct roadway environments in Los Angeles within non-rush hour periods (e.g. 10:00 am to 3:00 pm) from October 2014 to January 2015, and again from November 2015 until January 2016. The sampling periods were intentionally selected during the colder months of the year to minimize, to the degree possible, the contribution of secondary organic aerosols (SOA) to PM mass. SOA is a regional aerosol (thus impacting all sampling environments to more or less the same degree) and has also been associated with the generation of excess ROS (Delfino et al., 2010a; McWhinney et al., 2013; Verma et al., 2012). Recent studies in Los Angeles indicated that the contribution of SOA to PM concentration is lower by about 50-60 % in colder seasons (i.e. from October to February) compared to warmer seasons (i.e. from March to September) (Heo et al., 2015, 2013; Hasheminassab et al., 2013). The sampling routes are presented in Figure 10. I-110 is a heavily trafficked freeway, which connects the Port of Los Angeles in the south to Pasadena in the north in a 51-km route. A 13-km segment of the I-110 freeway, starting from its junction with US-101 freeway all the way up north to Pasadena, is open only to LDVs, while the rest of the freeway is open to both LDVs and HDVs. The other freeway sampling environment, I-710, is a 43- km-running freeway and serves as the main corridor to and from the Ports of Los Angeles and Long Beach, with a higher fraction of HDVs in comparison to I-110. The route on Wilshire and Sunset boulevards covers 48-km on two major surface streets, connecting the coastal areas of Los Angeles to the city center, with some of the heaviest traffic in this megacity. Hourly-averaged number of vehicles were obtained from vehicle detection stations (VDS) on each freeway from the freeway performance measurement system (PeMS), operated by the California Department of Transportation (CalTrans). The average vehicle flow (both LDV and HDV) in I-110 and I-710 were 4583±1325 and 3905±1896 vehicles/hr during the sampling periods and HDVs constituted about 3.6±2.8% and 11.8±6.0% of the traffic composition in the aforementioned freeways, respectively. Lastly, sampling was also carried out at the Particle Instrumentation Unit at the main campus of the University of Southern California (USC) as the urban fixed reference site, referred to as “USC”, which is located about 4 km southwest of downtown Los Angeles and 150 m downwind of the I-110 freeway. It should be noted that since only one mobile sampling platform was available, sample collections at different micro-environments were not performed concurrently. However, the meteorological parameters (i.e. temperature, relative humidity and wind speed) during the sampling campaign were relatively stable and comparable within and between the studied micro-environments. Meteorological data were obtained from nearby air quality monitoring sites operated by the South Coast Air Quality Management District (SCAQMD) and are presented in Table 8. In general, ambient temperature spanned a narrow range of 18.8 to 24.0 ºC in all the sampled micro- environments. Relative humidity had a maximum of 56.3% and a minimum of 26.7% with an average value of 40.9±8.3% over all sampling micro-environments during the study period. Average wind speed at the SCAQMD’s stations closest to the studied roadways was 4.7±0.4 mph, while it was relatively lower at USC (2.6±0.1 mph). The current study focuses on investigation of the PM characteristics and in order to facilitate this, PM constituents and toxicity as well as their association have been analyzed based on the per PM mass data. The PM mass-based analysis also provides direct insight on intrinsic properties of PM chemical composition and relative toxicity. 41 Figure 10. Map of sampling routes: I-110 (green), I-710 (blue) and Wilshire/Sunset boulevards (red). The USC urban reference fixed site is also denote by a star. 42 3.6 Sampling instrumentation The on-road sampling was performed with a 2014 Toyota Prius C hybrid car equipped with six battery-operated Leland Legacy pumps (SKC Inc., Eighty-Four, PA). In this set up, 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 9 lpm (Misra et al., 2002). Roadway air was drawn into the PCIS units through a curved stainless steel inlet with an inner diameter of 0.95 cm with a total flow rate of 54 lpm (6 PCIS units each operating at 9 lpm). The inlet was designed with a 90º-bend resulting in a 50% cut point of about 10 µm (Peters and Leith, 2004), directing roadway air into the vehicle window to a manifold with six branches each connected to one PCIS. Because each pump operated at relatively moderate flow rate of 9 lpm, prolonged sampling periods were necessary in each micro- environment to reach sufficient PM mass loading for the chemical speciation and toxicity analyses. Over the entire campaign, five sets of surface street (Wilshire/Sunset) samples were collected representing a total of 60 hours of sampling. Three sets of samples were collected on each freeway, with total sampling periods of 95 and 55 hours for I-110 and I-710, respectively. Two sets of samples were collected at the USC site for 55 hours and sampling took place during the times that corresponded to the freeways and surface streets sampling periods. Each PCIS unit was equipped with one impaction stage with 50% cut-point of 2.5 µm to collect coarse PM, followed by an after-filter stage to collect PM2.5. For the purpose of chemical speciation three PCIS units were loaded with 37-mm PTFE (Teflon) filters (Pall Life Sciences, 2-μm pore, Ann Arbor, MI) as the after-filter and 25-mm Zefluor-supported PTFE filters (Pall Life Sciences, 0.5-μm pore, Ann Arbor, MI) as the impaction substrates. The remaining three PCIS units were loaded with 25-mm and 37-mm quartz filters (Whatman International Ltd, Maidstone, England) as impaction substrates and after-filter, respectively. The quartz filters were prebaked at 550 ºC for 12 hour and stored in baked aluminum foil prior to sampling. 3. Results and discussion 3.1 PM2.5 mass concentrations and chemical composition Table 8 presents the average PM2.5 mass concentrations for all the sampled sets in each micro-environment. Average PM2.5 mass concentrations on freeways were 15.9±8.7 and 25.5±8.8 µg/m 3 in I-110 and I-710, respectively. The Wilshire/Sunset boulevards, one of the highly trafficked major surface streets of Los Angeles, exhibited the highest PM2.5 concentration of the sampled roadways with an average concentration of 40.4±12.6 µg/m 3 , while the average concentration at the USC reference site was 19.4±3.2 µg/m 3 . Table 8. Summary of the average PM2.5 mass concentration (µg/m 3 ) and meteorological data (± standard deviation) in the four sampled environments. The meteorological data were obtained from the nearby air quality monitoring sites operated by the South Coast Air Quality Management District (SCAQMD). Environment PM 2.5 concentration (µg/m 3 ) Temperature (ºC) RH (%) Wind speed (mph) Wilshire/Sunset 20.4 ± 12.6 24.0 ± 0.2 49.2 ± 0.7 4.5 ± 0.2 I-110 15.9 ± 8.7 18.8 ± 0.6 29.4 ± 0.5 5.1 ± 0.5 I-710 25.5 ± 8.8 21.7 ± 0.4 41.8 ± 3.2 4.4 ± 0.1 USC 19.4 ± 3.2 21.9 ± 0.4 43.3 ± 0.9 2.6 ± 0.1 43 3.2 Elemental carbon (EC) and organic carbon (OC) Figure 11 shows the average mass fractions (µg/µg PM) of elemental carbon (EC) and organic carbon (OC) in each micro-environment. Overall, I-110 exhibited the highest mass fractions of OC and EC (0.37±0.07 and 0.13±0.04 µg/µg PM, respectively), while Wilshire/Sunset and USC roadways had the lowest mass fractions of OC and EC. The mass fractions of OC in Wilshire/Sunset and USC were comparable and approximately 40% lower than the level observed in I-110. Overall, mass fraction of OC on both freeways was on average 1.5 times greater than values obtained at Wilshire/Sunset and USC. The volumetric OC concentrations spanned a range of 2.22–5.89 µg/m 3 between all four environments and showed the highest concentration in I-710, followed by I- 110, Wilshire/Sunset and USC site. For EC, an indicator of vehicular emissions especially from diesel trucks (Schauer, 2003), the I-710 and I-110 mass fractions were comparable (0.12±0.02 and 0.13±0.04 µg/µg PM, respectively) and on average was 3.6 and 2.1 times higher than the Wilshire/Sunset and USC, respectively. Although Wilshire/Sunset boulevards are highly trafficked surface streets, their low levels of EC can be explained by their negligible HDV volume. Higher EC levels on freeways are indicative of significant contribution of vehicular emissions (with higher fraction of HDVs) in these roadways. Similarly to OC, EC concentration also showed highest levels in the I-710 (2.42±1.17 µg/m 3 ) followed by I-110 (1.48±0.51 µg/ m 3 ). Figure 11. Mass fraction (µg/ µg PM) of EC and OC in PM2.5 size range in the sampling environments. Error bars correspond to standard deviation. 44 3.3 Polycyclic aromatic hydrocarbons (PAHs) The cumulative mass fractions of measured PAHs were substantially higher on the freeways than in other environments (Figure 12). The average cumulative mass fraction of PAHs on both freeways were 3 and 3.3-fold greater than the Wilshire/Sunset and USC levels, respectively. The I-110 and I-710 exhibited levels of 0.16±0.01 and 0.15±0.01 ng/µg PM, respectively. The higher mass fraction of PAHs on freeways indicates the significant role of vehicular emissions in these environments. Several studies which have apportioned PAHs emission factors (mg of pollutant per kg of fuel burned) for HDVs and LDVs through tunnel studies (Phuleria et al., 2006) and dynamometer studies (Schauer et al., 2002, 1999) have found that HDVs can emit up to 50 times higher levels of PAHs than LDVs. These studies also demonstrated that HDVs have greater contributions to low molecular weight (MW≤228) PAHs while high molecular weight PAHs (MW≥276) can be emitted by both LDVs and HDVs (i.e. benzo(a)pyrene, benzo(ghi)perylene and indeno (1, 2, 3-cd) pyrene) with substantially higher amounts from LDVs. The sum of mass fractions of the aforementioned PAHs (i.e. benzo(a)pyrene, benzo(ghi)perylene and indeno(1, 2, 3-cd) pyrene) was about 3.8 times higher in I-110 than I-710. Similar trend was also observed in the volumetric PAHs concentrations in the sampled micro-environments with highest levels on freeways (3.51±0.22 and 2.35±0.13 ng/m 3 in I-710 and I-110, respectively) and cumulative levels ranging from 0.98 to 3.51 ng/m 3 . Figure 12. Mass fraction (ng/µg PM) of polycyclic aromatic hydrocarbon (PAHs) in PM2.5 size range in the sampling environments. 45 3.4 Metals and trace elements Figure 13a illustrates the PM2.5 mass fraction of metals and elements measured in the four roadway environments. S, Al, Ca, Fe, and Na were the dominant species in all the sampled environments. The concentrations of certain metals, including As, Cd, Cu, Ni, V and Zn, were higher in the freeways than in the Wilshire/Sunset boulevards and the USC site. These metals originate primarily from fuel combustion and lube oil emissions (Geller et al., 2006; Schauer et al., 2006; Lough et al., 2005; Sternbeck et al., 2002). Copper is also reported to be present in brake wear emissions while tire wear has been reported to be a significant source of Zn as well (Thorpe and Harrison, 2008; Pant and Harrison, 2013). Higher levels of these metals in the roadway environment, especially in the I-110 and I-710 freeways are indicative of traffic as their primary source of emission. The elements shown in Figure 13a were further segregated by their common sources and grouped as tracers of re-suspension of soil and road dust (i.e. sum of Al, Ca, Mg and Ti) and vehicular abrasion (i.e. sum of Ba, Cr, Cu, Mn, Ni, Pb, Sb and Zn) (Thorpe and Harrison, 2008), as depicted in Figure 13b. Road dust, of which crustal dust is a key component, derive from different sources including traffic and mineral dust (Kupiainen et al., 2005; Tanner et al., 2008). Brake wear, including abrasion of brake lining material and brake discs is known to contribute to the airborne PM trace metal concentration as well (Wåhlin et al., 2006; Gietl et al., 2010; Pant and Harrison, 2013). The sum of vehicular abrasion tracers was on average 3.8±0.8 and 2.9±0.6 times higher on both freeways than Wilshire/Sunset and USC, respectively. In contrast, the sum of Al, Ca, Mg and Ti as common tracers of re-suspended soil and road dust showed higher mass fractions on the two freeways by a factor of 1.8±0.6 than surface streets, while it had comparable levels to USC site. These metals can be re-suspended and contribute to aerosol PM in roadways due to the turbulence caused by the passing traffic (Lough et al., 2005; Thorpe and Harrison, 2008). Thorpe et al. (2007) also reported a strong association between heavy-duty traffic and re- suspension of soil and road dust in the UK. The volumetric concentrations of metals and elements were overall higher in roadways in comparison to USC reference site, particularly those that are associated with vehicular abrasion. 3.5 DTT activity of PM2.5 in various microenvironments The PM2.5 DTT activity of the four sampled micro-environments is presented in Figure 14. Similar to the chemical data, the rate of DTT consumption (activity) is normalized by PM mass (expressed in units of nmol/min mg PM). As evident from Figure 14, the intrinsic DTT activity of PM varied substantially among the sampled environments, and in line with the discussions of the previous sections, the DTT activity was highest at the I-710 (32.35±13.26 nmol/min mg PM), followed by I-110, Wilshire/Sunset and USC site, with DTT activity levels of 27.90±8.51, 16.07±3.17 and 14.33±1.72 nmol/min mg PM, respectively. The DTT activity on freeways (i.e. with an average of 30.13±3.15 nmol/min mg PM) was roughly 1.9 and 2.1 times greater than the values obtained at Wilshire/Sunset and USC, respectively. The strong influence of freeways emissions is evident in the pronounced difference between freeways DTT activity and surface streets as well as USC site. Despite the higher airborne PM mass concentrations at Wilshire/Sunset boulevards in comparison to the two freeways, a 1.7-2 fold increase in the per PM mass-normalized DTT activity levels on freeways indicated higher levels of toxic content per unit mass of PM on freeways. The per m 3 of air DTT activity spanned a range of 0.02-0.04 nmol/min m 3 in the sampling micro-environments, with highest and lowest levels observed in Wilshire/Sunset boulevards and USC site, respectively. 46 Figure 13 (a, b). a) Mass fraction (ng/ µg PM) of metals and trace elements, and: b) sum of metals and elements in groups in the sampled environments in PM2.5 size range. Sum of soil/road dust resuspension tracers: ∑ Al, Ca, Mg and Ti and sum of vehicular abrasion tracers: ∑Ba, Cr, Cu, Mn, Ni, Pb, Sb and Zn. Error bars correspond to standard deviation. Cr at USC site was below detection limit. 47 3.6 Correlation analysis between PM chemical species and DTT activity To investigate the association of DTT activity with PM2.5 chemical composition, univariate correlation analysis was performed between the mass-normalized DTT activity and the mass fractions of the chemical species in the all sampled environments. Table 9 presents the Spearman correlation coefficients (R) for the univariate correlation analysis. The species with high correlation (R > 0.60) are highlighted in bold, and statistically significant values (P < 0.05) are denoted by an asterisk. The PM2.5 DTT activity was correlated with several elements which are commonly associated with tailpipe and non-tailpipe vehicular sources, including As, Ba, Cu, Sb, V and Zn. Arsenic, Cu, V and Zn are emitted as a result of fuel combustion and lube oil emissions while Ba, Cr, Cu, Mn, Ni, Sb as well as Zn can also be emitted from vehicular abrasion processes especially from brake and tire wear abrasion (Lough et al., 2005; Schauer et al., 2006; Pant and Harrison, 2013). Moreover, the sum of vehicular abrasion tracers (i.e. Ba, Cr, Cu, Mn, Ni, Pb, Sb and Zn) was highly correlated with DTT activity as well (R = 0.61). Calcium and Ti which are mainly associated with re-suspension of soil and road dust were moderately, yet statistically significantly, correlated (R > 0.50) with DTT activity. The correlation of these metals and elements with DTT activity confirms findings from previous studies (Fang et al., 2015a; Verma et al., 2015, 2009). EC and OC were also strongly correlated (R ≥ 0.70) with DTT activity. The correlation of OC with DTT activity underlies the contribution of organic compounds to the oxidative potential of ambient PM. Moreover, correlation of EC with DTT activity also suggests vehicular tailpipe emissions as one of the significant contributors to PM-induced oxidative potential. The sum of mass fraction of all measured PAHs, discussed in previous sections, was also correlated with DTT activity (R = 0.66). The association between DTT activity and organic compounds in this study is also consistent with previous studies (Cho et al., 2005; Verma et al., 2012). We acknowledge that despite its lower wintertime levels, SOA may still have some contribution to the total OC and thus the PM mass levels even in colder months, therefore its contribution to overall PM toxicity measured in our study’s environments cannot be completely disregarded. Other possible sources, such as ship fuel oil combustion may also contribute to certain chemical species discussed above (e.g. V and some PAHs) that were correlated with DTT activity. However, with the aid of mobile sampling platform and choice of sampling periods, we attempted to minimize, to the degree possible, such impacts and mainly focus on PM freshly emitted from vehicles in the roadway environments in this study. Figure 14. Mass-normalized PM2.5 DTT activity (nmol/min mg PM) of the sampled environments. Error bars correspond to standard deviation. 48 Table 9. Spearman correlation coefficients (R) between dithiothreitol (DTT) activity (nmol/min mg PM) and mass fraction of chemical species in PM2.5. Bold numbers indicate R > 0.60 and * denotes values with P < 0.05. Sum of vehicular abrasion tracers include: Ba, Cr, Cu, Mn, Ni, Pb, Sb and Zn. Species PM2.5 DTT activity Na 0.05 Mg 0.26 Al 0.08 S 0.30 K 0.15 Ca 0.51 * Ti 0.54 * V 0.73 * Cr 0.05 Mn 0.53 * Fe 0.63 * Co 0.50 * Ni 0.22 Cu 0.65 * Zn 0.57 * As 0.52 * Sb 0.56 * Cd 0.70 * Ba 0.69 * Pb 0.59 * OC 0.70 ** EC 0.76 ** Sum of PAHs 0.66 * Sum of vehicular abrasion tracers 0.61 * 49 3.7 Comparison with previous on-road and roadside studies To put our results in context with previous studies, the measured mass fractions of carbonaceous species, PAHs, metals and trace elements in the PM2.5 size fraction were compared to previous on-road and roadside studies in the Los Angeles Basin. A summary of these results is presented in Figures 15-17. Of the published studies, only Kam et al. (2012a) conducted on-road measurements (in 2011) and the remainder of the cited studies performed stationary sampling on the edge of the freeways (distance range: 2.5-10 m away from the edge of the freeway) before 2007, when major regulations on vehicular emissions, particularly on diesel trucks, came into effect. Although the studies considered in this meta-analysis were conducted intermittently in different years, comparison of PM mass-normalized levels among different studies reduces the potential differences in meteorological conditions and atmospheric dilution. Compared to Kam et al. (2012a), where identical sampling methodology at the same roadway environments was applied in 2011, similar levels were observed in this study on I-710 for EC (0.10±0.01 vs 0.12±0.02 µg/µg PM), OC (0.30±0.06 vs 0.30±0.07 µg/µg PM) and PAHs (0.15±0.03 vs 0.15±0.04 ng/µg PM). On I-110, however, mass fractions of EC, OC and PAHs increased by factors of about 2.6, 1.5 and 1.6, respectively, in this study compared to the levels reported by Kam et al. (2012a). Moreover, mass fractions of important groups of metals (e.g. vehicular abrasion) showed comparable levels on both freeways between these two on-road studies (Figure 17 (a, b)). Comparison of the current on-road study results with previous roadside measurements near I-710 (Ning et al., 2007; Phuleria et al., 2007; Ntziachristos et al., 2007b), which has one the highest HDV fraction of about 12% in the United States, revealed that the mass fraction of EC, OC and PAHs (important tracers of exhaust emissions) have overall decreased by 58±16%, 43±12%, and 47±10%, respectively. On I-110 freeway (which has a much smaller HDV fraction)), while mass fraction of OC decreased by 32±21% compared to previous roadside studies conducted between 2004 and 2005 (Kuhn et al., 2007; Ning et al., 2007; Phuleria et al., 2007), the levels of EC and PAHs did not change significantly. The reduction trends on I-710 are also in concert with another on-road study on the same freeway conducted by Kozawa et al. (2014), which reported a nearly 70% reduction in the fuel- based emission factors of black carbon (BC) between 2009 and 2011. Although these findings are based on rather limited number of samples in each study, the reduction trends observed especially at the I-710 indicate an overall effectiveness of major policies implemented on diesel exhaust emissions over the past decade. These reductions have also been verified by other studies with somewhat more robust datasets and various methodologies (Bishop et al., 2013; Hasheminassab et al., 2014; McDonald et al., 2015; Su et al., 2016). In contrast to the bulk and speciated organic outcomes, as it can be seen from Figure 16 (a, b), the mass fractions of certain metals and elements such as Mn, Ni, Cu, Ba and Pb (important tracers of vehicular abrasion) have increased by factors of 3.3±2.1 and 2.7±1.4 on I-110 and I-710, respectively, in the current on-road study compared to previous roadside measurements conducted in 2004-2006. Unlike the other elements, we observed a decreasing trend in the vanadium mass fraction (47±20% reduction) over the years in both freeways, which may be due to the use of cleaner diesel fuels in recent years (Shafer et al., 2012; Liu et al., 2015). Metals were also further classified into soil/road dust and vehicular abrasion groups, as discussed in previous sections (Figure 17 a-b). As can be seen, the mass-normalized concentrations of non-tailpipe PM sources increased in this study compared to previous roadside measurements by factors of 6.0±3.4 and 3.4±0.8 on I-110 and I-710, respectively. These results, altogether, indicate that while regulations have been effective in reducing tailpipe emissions, non- tailpipe emissions remain an important traffic-related source of PM in the Los Angeles Basin with an increasing contribution to total PM mass over the past decade (Shirmohammadi et al., 2015b). 50 Historic data on PM oxidative potential is much more limited; only Ntziachristos et al. (2007a) reported PM oxidative potential (using the DTT assay) in the vicinity of I-110 freeway. There are no other studies reporting on-road PM toxicity in Los Angeles, and as noted before, investigating the oxidative potential of on-road PM in Los Angeles is a major novelty of this study. Ntziachristos et al. (2007a) reported PM2.5 DTT activity values of 25.0 nmol/min mg PM (based on a single measurement), 2.5m away from the edge of I-110 freeway. This value is generally consistent with the mass-normalized DTT activity of 27.90 (±8.51) nmol/min mg PM measured at the I-110 in the current study. Figure 15 (a, b). Comparison of reported mass fractions of PM2.5 EC, OC (µg/ µg PM) and PAHs (ng/µg PM) with previous studies conducted on or near: a) I-110 and b) I-710 freeways in Los Angeles. Kam et al. (2012a) conducted on-road measurements and the rest performed stationary sampling on the edge of freeways. Error bars correspond to one standard deviation. 1: Kuhn et al., 2007, 2: Ning et al., 2007, 3: Phuleria et al., 2007, 4: Ntziachristos et al., 2007b and 5: Kam et al., 2012a. 51 Figure 16 (a, b). Comparison of reported mass fractions of PM2.5 metals and trace elements (ng/ µg PM) with previous studies conducted on or near a) I-110, b) I-710. Kam et al., (2012a) conducted on-road measurements and the rest performed stationary sampling on the edge of freeways. Error bars correspond to one standard deviation. 1: Kuhn et al., 2007, 2: Ning et al., 2007, 3: Ntziachristos et al., 2007c and 4: Kam et al., 2012a. 52 Figure 17 (a, b). Comparison of the sum of soil/road dust (SRD) resuspension tracers and vehicular abrasion (VA) tracers (SRD: ∑ Al, Ca, Mg and Ti; VA: ∑Ba, Cr, Cu, Mn, Ni, Pb, Sb and Zn) with previous studies conducted inside or near on a) I-110 and b) I-710 freeways. Kam et al. (2012a) conducted on-road measurements and the rest performed stationary sampling on the edge of freeways. Error bars correspond to one standard deviation. 1: Kuhn et al., 2007, 2: Ning et al., 2007, 3: Ntziachristos et al., 2007c and 4: Kam et al., 2012a. 3.8 Comparison of measured DTT activity levels with previous dynamometer studies Previous dynamometer studies (Biswas et al., 2009; Cheung et al., 2009; Geller et al., 2006) have investigated the associations between PM components and redox activity based on the DTT assay for different types of vehicles (i.e. gasoline/ diesel, light duty/heavy duty). Cheung et al. (2009) and Geller et al. (2006) have investigated tailpipe emissions from different types of gasoline fueled LDVs, while Biswas et al. (2009) studied exhaust emissions from HDVs. To compare the DTT activity values obtained in this study from on-road measurements to what would have been estimated based on dynamometer measurements, the DTT activity of I- 110 and I-710 freeways were reconstructed based on the aforementioned dynamometer studies as follows. The traffic composition (i.e. the fraction of LDVs and HDVs) and total number of vehicles were obtained from PeMS using the hourly-averaged number of vehicles for each freeway during the sampling period. On average, HDVs constituted 3.6±2.8% and 11.8±6.0% of the total traffic flow in I-110 and I-710 freeways, respectively. Therefore, in order to re-construct the DTT activity based on previous dynamometer studies on HDVs, it was assumed that all HDVs on these freeways were equipped with retrofitted technologies. Per- PM mass DTT activity values for different types of vehicles with implemented control technologies, were obtained from the studies of Geller et al. (2006), Biswas et al. (2009), and Cheung et al. (2009). Gasoline powered LDVs had generally lower DTT activity, ranging from 12.0±1.0 (Cheung et al., 2009) to 25.0±30.0 nmol/min mg PM (Geller et al., 2006), in comparison to HDVs (30.5±26.3 nmol/min mg PM (Biswas et al., 2009)). In order to estimate the DTT activity of PM based on the dynamometer studies in I-110 and I-710 freeways, the following equations were used: 53 DTT activity (nmol/min mg PM) in I-110 = (LDV DTT activity (nmol/min mg PM) × 0.964) + (HDV DTT activity (nmol/min mg PM) × 0.036 [1] DTT activity (nmol/min mg PM) in I-710 = (LDV DTT activity (nmol/min mg PM) × 0.882) + (HDV DTT activity (nmol/min mg PM) × 0.118 [2] To obtain the errors of the estimated DTT activity, a Monte Carlo simulation was carried out. A range of 12.0- 25.0 (nmol/min mg PM) (Cheung et al., 2009; Geller et al., 2006) was used for DTT activity of gasoline powered LDVs, while the HDV DTT activity (30.5±26.3 nmol/min mg PM) was obtained from Biswas et al. (2009). Figure 18 illustrates the comparison of the re-constructed DTT activity of PM for I-110 and I-710 with those obtained from this study. The DTT activity of the on-road PM2.5 measurements reported in this study was higher than that estimated from dynamometer studies by factors of 1.4 and 1.5 for I-710 and I-110 freeways, respectively. Mann- Whitney Rank Sum test indicated that the difference between the DTT activity obtained by the two methods approached significance (P = 0.1) despite the relatively small sample size (N = 6). This quite pronounced difference is likely due to the additional contribution of non-tailpipe emissions to the overall PM2.5 DTT activity in this study’s sampled environments compared to dynamometer studies. The role of non-tailpipe emissions on the oxidative potential of PM2.5 is also reflected by the high correlation of DTT activity with several elemental tracers of re-suspended road dust and vehicular abrasion (Table 9) as previously discussed. Figure 18. Comparison between the measured DTT activity (nmol/min mg PM) on the two studied freeways with reconstructed DTT activity based on previous dynamometer studies. Error bars correspond to standard deviation. 54 Case Study 3. Emission rates of particle number, mass and black carbon by the Los Angeles International Airport (LAX) and its impact on air quality in Los Angeles 55 1. Introduction Exposure to airborne particulate matter (PM) in urban areas has been a major concern for public health. Among the various combustion sources of PM in urban areas, considerable attention has been paid to airport-related emissions, as accurate assessment of these emissions and how they compare to other predominant PM sources such as traffic emissions is essential in understanding the impact of airports on air quality, climate and human health. Although efforts have been made to substantially reduce aircraft emissions over the past two decades, these may be offset by growth in the aviation industry and increases in airport traffic (ICAO 2011). It is now well known that air quality degradation in proximity of airports is a real public health hazard, with a number of recent reports specifically linking aviation activities with morbidity and premature mortality as well as adverse lung effects in asthmatics (Ashok et al., 2013; Rima Habre et al., 2016; Yim et al., 2013). Aviation PM emissions are characterized by particles with aerodynamic diameters usually smaller than 100-200 nm (Lobo et al., 2007; Mazaheri et al., 2009; Petzold et al., 2003; Zhu et al., 2011), also known as ultrafine particles (UFPs). The Los Angeles International Airport (LAX) is the fifth busiest passenger airport in the world and the third largest in the United States (LAWA, 2014). In addition, a large population lives in communities immediately downwind of the airport with population density of roughly 14000 ± 3500 people per square mile according to U.S. Census 2000 (i.e. roughly 1 million people live in the impact zone). Several studies have characterized aircraft emissions from the LAX airport. Zhu et al. (2011) reported significantly elevated levels of PN, PM mass, and two carbonyl compounds (formaldehyde and acrolein) at the airport compared to a background reference site. In a more recent study, Hudda et al. (2014) measured PN concentrations in the impact zone of the LAX. Results from that study indicated a 2 to 5-fold increase in PN concentrations in areas 8-16 km downwind of the airport. However, no study has so far evaluated systematically the relative impact of aircraft emissions from LAX and their comparison to vehicular emissions from the nearby freeways on the air quality of the area. In this study we measured PN, BC and PM2.5 mass emission rates of overall airport operations, mainly from takeoffs and landings with smaller contributions also from complex ground operations, in a location immediately downwind of one of the major airport runways. These ground operations include aircraft taxiing, diesel-powered ground support vehicles and a mixture of gasoline, compressed natural gas, and diesel-powered vehicles on the terminal roadway and surface streets near the airport. We also performed sampling for the same parameters in three adjacent freeways using a mobile platform in order to compare vehicular emissions from freeways to the overall contribution of LAX airport emissions to PN, BC and PM2.5 mass both at a local (limited to an area directly impacted by the airport) and regional (in the entire Los Angeles County) scales. Results reported in the present study broaden the insight on airport-related PN and BC emissions in a major urban area and provide guidelines for future epidemiological studies to evaluate the possible health impacts associated with proximity to a major source of air pollution in Los Angeles. 2. Methodology 2.1 Description of the sampling sites LAX is the third busiest passenger airport in the US and ranks fifth in terms of air cargo capacity. LAX runways and facilities are located at the western border of the South Coast Air Basin near the Pacific Ocean. Flights in and out of LAX typically proceed from east to west. LAX has a total of four runways, two of which are located to the 56 north and the other two are located to the south of the central terminal complex; the runways are also aligned with the west-to-east prevailing winds (Figure 19 a). The sampling site, denoted with a star in Figure 19 b, was located about 150 m downwind of the south runways. This location was selected to ideally capture emissions both from takeoff and landing thrusts, given the dominant westerly wind direction. Concomitant mobile monitoring was also performed using a mobile monitoring platform on transects of three major freeways passing adjacent to the airport facility, namely, I-110, I-105, and I-405, with a total sampling route of about 30 km. The area surrounded by the three freeways was chosen as the impact zone of the LAX airport, as discussed earlier by (Hudda et al., 2014). The authors of that study demonstrated that the impact zone, extending 16 km downwind of the airport, is significantly impacted by the airport emissions, with at least a 2-fold increase in PN concentrations over the background levels in this region. While aircraft emissions may contribute to the pollutant levels sampled inside freeways, we assume that contributions are negligible since they are significantly more diluted compared to vehicular sources. Figure 19. a) Wind rose and b) map of the monitoring location at the Los Angeles International Airport (denoted with a black star). Wind data were obtained from the Air Quality Management District (AQMD) weather station at the airport during the sampling period. The segments of I-110, I-105 and I-405 freeways sampled in the mobile monitoring are highlighted in green. The area between the studied freeways is considered as the LAX zone of impact. 2.2 Sampling methods and instruments Sampling was conducted on six randomly chosen weekdays between May 2016 and July 2016 all within the same season with comparable meteorological conditions. Measurements were conducted within the time period after 10 am and before 4 pm to minimize the impact of morning and afternoon traffic rush hours on the measurements. In addition to a more stable traffic flow during these hours of the day, Los Angeles weather conditions in terms of temperature and relative humidity also have small variability during this time period relative to early morning and late afternoon. Table 10 presents the sampling dates and the averaged values for selected meteorological parameters during the sampling period at the LAX airport for each day. Both the airport monitoring and on-road freeway data collection were conducted using portable battery-powered instruments. PN and mean particle size were measured using a diffusion size classifier (DiSCmini, Matter Aerosol, Switzerland) that was calibrated to measure particles with diameters ranging from 7 to 500 nm. CO 2 was measured using a 57 non-dispersive infrared analyzer (Licor, Lincoln, NE, model LI-840 CO2/H2O analyzer). BC was monitored using a MicroAethalometer (AethLabs, San Francisco, CA, model AE51). A DustTrack Aerosol Monitor (TSI Inc., Shoreview, Model 8520, MN, USA) was also deployed in the field to collect continuous PM 2.5 mass concentrations. All of these instruments were operated at a time resolution of 1 second, making it possible to capture instantaneous variations in pollutant concentrations. Instrument clock times were synchronized prior to each sampling time to allow for capturing of simultaneous peaks in the time-series for different parameters measured. In case of delayed response from an instrument, the measurements were temporally shifted to match the reference time. Takeoff and landing times were manually recorded on a log sheet during the sampling period to help later identify plumes attributable to aircrafts. A total of 175 plumes were successfully detected, of which 95 plumes were for takeoffs and 80 plumes were for landings. Aircraft emission analysis was performed on plumes selected based on CO2 concentration increase of at least 25 ppm (relative to the background levels) with concomitant peaks in the particle concentration time profiles that were matched with aircraft takeoffs/landings. It should be noted here that since the main goal of this study was to characterize emission rates of PN, BC, and PM2.5 mass from the LAX airport and its facilities as an overall source of pollution, plume analysis for individual aircraft activities constituted only part of our study’s scope. Therefore, background-subtracted data obtained during each sampling day at the airport were considered for the emission rate calculations, as discussed in the following section. Table 10. Averaged meteorological parameters (± standard error) during the sampling period at the Los Angeles International airport for each day. Data are obtained from the Air Quality Management District (AQMD) monitoring station at Los Angeles International Airport. Wind direction is shown in Figure 19 a. Date Temperature (°C) Relative humidity (%) Wind speed (m/s) 5/9/2016 18.1 ± 0.1 61.5 ± 0.7 5.9 ± 0.1 5/19/2016 17.4 ± 0.2 79.3 ± 1.4 6.6 ± 0.2 6/6/2016 19.6 ± 0.1 65.2 ± 0.5 5.1 ± 0.2 6/14/2016 18.9 ± 0.2 70.2 ± 1.4 4.1 ± 0 6/27/2016 24.8 ± 0.1 66.4 ± 1.2 4.1 ± 0.2 7/6/2016 23.0 ± 0.02 57.3 ± 0.3 5.0 ± 0.28 2.3 Data analysis 2.3.1 Freeways data High-emitting vehicles significantly impact the emission rate measurements performed near or inside freeways. It has been therefore suggested to remove the instantaneous fingerprints of these vehicles from the data set prior 58 to roadway emission rate calculations (Choi et al., 2012). In this work, potential outliers in the data set were detected by defining the interquartile range (IQR) as the difference between 75 th (Q3) and 25 th percentiles (Q1), and removing the observations that fall below Q1 - 1.5 * (IQR), or above Q3 +1.5 * (IQR) from the subsequent analyses (Laurikkala et al., 2000). The freeway’s fuel-based emission factor for each measured pollutant was defined as the mass or number of pollutant emitted per mass of fuel burned (W. Kirchstetter et al., 1999). Under normal driving conditions, carbon mass in the vehicle's exhaust is mostly in the form of CO2, and the contribution of CO and other carbon-containing compounds to the total emitted carbon mass is negligible (Yli-Tuomi et al., 2005). Thus, the emission factor of a pollutant can be calculated as: EFp = ( [𝑃 ] 𝑓𝑤 −[𝑃 ] 𝑏𝑔 [𝐶𝑂 2 ] 𝑓𝑤 −[𝐶𝑂 2 ] 𝑏𝑔 ) 𝑤 𝑐 × 𝛼 Eq. 1 where, P is the concentration of the pollutant; [CO2] is the concentration of CO2 (μg of carbon/m 3 ) measured during the sampling period; wc is the weight fraction of carbon in the relevant fuel, reported as 0.85 for gasoline and 0.87 for diesel (Graham et al., 2008; W. Kirchstetter et al., 1999). The subscripts “fw” and “bg” denote freeway and background values, respectively. Urban background concentrations during each sampling day were estimated as the 5 th percentile of the data collected on each freeway (Riley et al., 2016). α is the unit conversion factor applied for each pollutant: For BC and PM 2.5, [P] has units of µg/m 3 and α = 10 3 , with resulting EFBC and EF PM 2.5 reported in g/kg fuel burned. For PN, [P] has unit of particles/cm 3 and α = 10 15 . Resulting units for EPN are particles/kg fuel burned. In order to compare the magnitude of emissions from LAX to those from freeways, emission rates of freeways were also calculated as: Average daily emission rate of pollutant P = EFp ∗ ρ ∗ FC ∗ VMT Eq. 2 where, ρ is the fuel density (kg/L); FC is the average vehicle fuel consumption (L/km); and VMT is the vehicle- miles traveled per day. Fuel densities were estimated as 0.74 kg/L for gasoline and 0.84 kg/L for diesel (Ban- Weiss et al., 2008). In addition, average fuel consumption rates of 0.12 L/km and 0.47 L/km were considered for light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs), respectively (Kirchstetter et al., 1999). Average traffic count and composition (i.e., the fraction of LDVs and HDVs) as well as the vehicle-miles traveled (pertinent to the sampling period) were obtained from the Performance Measuring System (PeMS) website, operated by the California Department of Transportation (CalTrans). Subsequently, the freeway-specific values in Equation 2 were obtained by adding the proportional contribution of LDVs (f LDV) and HDVs (fHDV) (e.g., ρI- 110 = (0.84 kg/L) * (fHDV) + (0.74 kg/L) * (1− fHDV)). Overall, the average traffic count (± standard error) for both LDVs and HDVs were 6243±92, 4694±71, and 6393±229 vehicles/hr on I-110, I-105, and I-405, respectively. Moreover, HDVs constituted about 3.7±0.2%, 1.8±0.3%, and 4.6±0.6% of the traffic composition on the aforementioned freeways, respectively. The average vehicle-miles traveled were 75433±1150, 30019±268 and 62380±1622 for I-110, I-105, and I-405, respectively. 2.3.2 Airport data In order to investigate the impact of aircraft takeoffs and landings on pollutant levels, plumes attributable to aircraft activities were identified by CO2 concentration increases of >25 ppm (as suggested by (Krasowsky et al., 59 2015)) with a concomitant peak in number concentration time series, matched with the exact time of the arrival and departure events from our records by manual inspection. The emission factor (EF), defined as the pollutant emission per unit mass of fuel burned, was subsequently calculated for PN, BC, and PM2.5 according to the following equation (Herndon et al., 2005; Zhu et al., 2011): EFp = (∆x/∆CO2) ∗ EI (CO2) ∗ Mair/MCO2 ∗ (1/ρair) × α Eq. 3 where, ∆x is the incremental concentration increase compared to the background, ∆CO2 is the incremental CO2 concentration increase compared to the background (ppm). Urban background concentrations were estimated as the 5 th percentile of the data collected during each sampling day (Riley et al., 2016). After the data were background-subtracted, the time interval over which the plume was integrated was chosen by manual inspection of the plume data. EI (CO2) is the emission index of CO2 which is 3160 g CO2/kg fuel burned from aircrafts, Mair and MCO2 is the molar mass of air (29 g/mol) and CO2 (44 g/mol), respectively, ρair is the density of air (1.2 g/L), and α is the unit conversion factor: For BC and PM2.5, ∆x has units of µg/m 3 and α = 10 −3 , and EFBC and EF PM 2.5 would be in units of g/kg fuel burned. For PN, ∆x has units of particles/cm 3 and α = 10 9 and EPN is expressed in particles/kg fuel burned. The emission rates of target pollutants for the LAX airport were calculated as: LAX Average daily emission rate = (∆x/∆CO2) ∗ EI (CO2) ∗ Mair/MCO2 ∗ (1/ρair) ∗ α ∗ FC ∗ 24 ∗ 3600 Eq. 4 The pollutant’s emission factor (as the mass or number of the pollutant emitted per mass of the fuel burned) for the airport as an overall source of pollution rather than for individual plumes was estimated by background subtraction of the average pollutant’s concentration during each sampling day. FC is the aircraft fuel consumption rate (kg/s). The mean aircraft fuel consumption rates of 1.09 kg/s and 0.31 kg/s during takeoff and landing, were used in our calculations, respectively (EASA, 2010). During the sampling period, 51.2±1.7% and 48.8±1.7% of the recorded aircraft activities were attributed to takeoffs and landings, respectively. As mentioned above, emission rates of PN, BC and PM2.5 mass of the LAX airport and freeways were compared at both local and regional scales. At the local scale, only the freeway segments immediately adjacent to the LAX airport (a total length of 30 km) were included in our calculations and emissions from these segments were compared to those from the LAX airport. This was done to evaluate the impact of LAX against local freeways on the air quality of the area within the impact zone of the LAX airport. At the regional scale, however, the magnitude of LAX airport emissions was compared to that of all freeways across the Los Angeles County (a total length of roughly 1500 km) (California Department of Transportation). Emission rates from all the freeways in the Los Angeles County were estimated based on measurements on three studied freeways, assuming that measured vehicle emission factors on transects of I-110, I-105 and I-405 are representative of vehicles for the entire Los Angeles basin and contributions from overhead aircraft to measured vehicle emissions are negligible. 60 3. Results and discussion 3.1 Pollutant concentrations and characteristics 3.1.1 Particle number concentrations and mean size Figure 20 (a-c) illustrates box plots of the measurements during the study period for PN, BC, and PM 2.5 concentrations, and how the pollutant levels at the LAX site varied in comparison to the freeways. As shown in the figures, substantial temporal variability was observed for all of the measured pollutants. PN also showed sporadic peaks, simultaneous with the recorded takeoff and landing incidents. The background PN concentration (estimated as the 5 th percentile of the data collected during each sampling day (Riley et al., 2016)) measured during the sampling period at the LAX ranged between 6.61 × 10 3 and 1.24 × 10 5 particles/cm 3 . The average increase in PN concentration over background during the entire sampling period was 1.08 × 10 5 ± 1.89 × 10 4 particles/cm 3 . A maximum peak of 2.00 × 10 6 particles/cm 3 was recorded for particle counts, reflecting the impact of emissions of individual aircraft takeoffs and landings. (Hudda et al., 2014) indicated that in an area 8- 10 km downwind (and beneath the incoming flight path) of the airport, PN concentrations were elevated 4- to 5- fold over the background concentration. Our data revealed that freeway PN concentration levels measured using the mobile sampling platform were 4.1±1.2 -fold lower than the PN concentrations downwind of the LAX, as indicated in Figure 20 a. Overall, PN concentrations inside I-110, I-105, and I-405 freeways spanned within 3.04 × 10 3 − 2.24 × 10 5 particles/cm 3 . Figure 21 (a-b) indicates an example of the spatial pattern of particle number concentration and mean diameter inside of the freeways. The higher number of particles observed at the LAX site represents the contribution of the overall airport activities, including takeoffs, landings, and complex ground operations. These ground operations include aircraft taxiing, diesel-powered ground support vehicles, and a mixture of gasoline, compressed natural gas, and diesel-powered vehicles on the terminal roadway and surface streets near the airport. In comparison to previous studies conducted at the LAX airport, (Westerdahl et al., 2008) observed PN concentrations in the range of 2.0 × 10 4 - 5.8 × 10 5 particles/cm 3 in the size range of 7-350 nm at the same sampling location in April 2003, while PN concentrations in our study (measured in the size range of 7-500 nm) varied in the range of 6.0 × 10 3 - 1.54 × 10 5 particles/cm 3 . Fanning et al. (2007) also reported PN concentration ranging between 1.4 × 10 5 -1.4 × 10 6 particles/cm 3 for particles smaller than 15 nm at the blast fence located south runway at the LAX airport during 2005-2006. In addition, (Hsu et al., 2013) demonstrated that LTO activity contributed to a median PN concentration of 1.5 × 10 5 particles/cm 3 measured in the size range of 6-100 nm, (compared to 9.70 × 10 4 particles/cm 3 in the current study, which was statistically significantly lower (p < 0.001)) at the south runway of LAX, in summer 2008. The physical properties of UFPs were also different at LAX compared to freeways. The median UFP sizes measured downwind of the airport were distinctly smaller than those measured on freeways. Moreover, the median particle size at LAX varied within a much narrower range compared to that of the freeways, as inferred by the smaller interquartile range of the LAX particle mean diameter box plots compared to the freeways, shown in Figure 20 b. Particles measured at LAX had an average diameter of about 20 nm, while on-road freeway measurements on I-110, I-105, and I-405 indicated an average particle diameter of >35 nm, a particle size range that is more typical of vehicle traffic and not aircraft emissions in urban areas (Riley et al., 2016). These results are also in concert with those reported in previous studies, which compared particle diameters in transects under the runways of LAX to the surrounding freeways (Hudda and Fruin, 2016; Riley et al., 2016). The inverse correlation of PN concentration and particle size for the six sampling days of our study is illustrated in Figure 61 21c. The day-by-day variation in PN concentrations may be due to the interaction of several factors including differences in meteorological conditions, atmospheric stability, as well as differences in flight activities. Furthermore, the smaller mean particle diameter observed at LAX suggests that these particles are fresh products of fuel combustion and have a different primary source origin compared to vehicle traffic. As discussed by (Riley et al., 2016), particle size could serve as a physical tracer to detect UFPs from aircraft emissions. Larger variability in the size of particles in the LAX zone of impact could be attributed to the effect of aging and abrupt dilution on the freshly emitted combustion particles (Hudda and Fruin, 2016). Another factor which may influence the increased particle mean diameter in freeways is that on-road freeway measurements capture a mixture of emissions from both tailpipe and non-tailpipe related sources (e.g. abrasion of tire and brake wear and re- suspension of soil and road dust), with the latter containing particles that are dominantly partitioned in larger size ranges; however, they can also contribute to the Aitken size range (<50nm) of ultrafine particles as demonstrated by previous studies (Saffari et al., 2013b; Shirmohammadi et al., 2016). 3.1.2 Black carbon concentrations Aircraft BC emissions strongly contribute to climate forcing (Lee et al., 2009). Although modern aircrafts have less visible plumes, BC is still the primary form of non-volatile PM emitted by jet engines (Timko et al., 2010). BC concentrations in our study varied across the study environments, with an average (± standard error) of 2.87±0.03, 4.07±0.07, 2.20±0.03, and 2.40±0.03 µg/m 3 at LAX, I-110, I-105, and I-405, respectively. Although BC concentration increases were not large compared to PN, an elevation of 2.59±0.03 μg/m 3 was observed over the background level during the sampling period at the LAX site. Compared to previous studies, (Westerdahl et al., 2008) reported BC concentration of 3.8 µg/m 3 downwind of LAX close to our sampling location. Fanning et al. (2007), however, reported much higher BC concentrations of 13.9 ± 14.3 µg/m 3 and 14.0 ± 10.2 µg/m 3 for 131 continuous hours in September 2005, and 46 hours in May 2006, respectively, behind the blast fence of the south runway where aircrafts initiate to take off at the LAX airport. 3.1.3 PM2.5 mass concentrations Average PM2.5 mass concentration (± standard error) during the sampling period was 33.03±0.15 µg/m 3 at the LAX site, while the average PM2.5 concentrations on I-110, I-105, and I-405 freeways were 48.78±0.48, 51.60±0.33, and 36.12±0.35 µg/m 3 , respectively. PM2.5 concentrations were generally lower at LAX compared to the freeways, which is probably due to the fact that in addition to primary emissions, PM2.5 concentrations contain a large fraction of secondary aerosols, which are formed on a regional scale as the air parcels move inland in the Los Angeles Basin (Gelencsér et al., 2007; Marcazzan et al., 2001b; Saffari et al., 2015a). Aircraft takeoffs and landings mainly affect PN concentration as opposed to PM2.5 mass, as the aircraft-emitted particles are mostly in the ultrafine range, having limited effect on the mass concentration. Moreover, a recent PN source apportionment study by (Sowlat et al., 2016) indicated that PN concentrations were dominated by factors with smaller mode diameters, such as traffic and nucleation; whereas, PM mass concentrations were mostly affected by sources with larger mode diameters, including secondary aerosols and soil/road dust in Los Angeles, further corroborating the relatively comparable PM2.5 mass in LAX and freeways observed in the present study. 3.2 Aircraft emission factors Takeoff and landing plumes were monitored and recorded using the approach explained in section 2.3. A concomitant rise in CO2 concentration and a peak in number concentration (with the corresponding CO2 increased by >25 ppm (relative to the background) were observed for individual plumes when compared against the background air concentrations. Every peak associated with aircraft takeoff or landing events at the runways was 62 identified from the time-series plots. The identified landing/takeoff peaks had similar shapes, as shown in Figure 22, which increased rapidly and decayed to background levels within a 30-90 seconds timescale. It is also likely that elevations in PN and BC concentrations were superimposed by contributions from several aircrafts, as dispersion of ground level aircraft emissions to background level took longer than the time interval of the next aircraft landing/takeoff event. It should also be noted that the mass concentration of PM 2.5 exhibited some fluctuations, but its value was relatively stable overall, as sharp increases in PN have minimal impact on PM mass. Figure 23 (a-c) illustrates the overall emission factors for PN, BC, and PM2.5 mass measured during the entire sampling period for takeoff and landing events. A total of 175 plumes were detected, of which 95 plumes were for takeoffs and 80 plumes were for landings. 3.2.1 PN emission factors Generally, the PN EF values ranged between 1.65 × 10 13 − 8.66 × 10 16 particles/kg fuel burned during takeoffs and varied over 3.20 × 10 13 − 4.10 × 10 16 particles/kg fuel burned during landings. Noteworthy is that the average PN EF (± standard error) values for aircraft takeoffs and landings were comparable, with values of 8.69 × 10 15 particles/kg fuel and 8.16 × 10 15 particles/kg fuel, respectively. This observation is in agreement with findings of (Ren et al., 2016) at Tianjin International Airport in China who also reported similar PN emission factors for takeoffs and landings with mean values of 7.5 × 10 15 and 7.6 × 10 15 particles/kg fuel, respectively. (Zhu et al., 2011) conducted their sampling at the blast fence location during summer 2005, as well as winter and spring 2006, approximately 140 m away from where aircrafts initiate takeoff on the south runway at LAX airport. They reported an average PN emission factor of 3.4 × 10 16 particles/kg fuel during takeoffs for the size range of 7-320 nm. Comparison of their measurements with the current study revealed a nearly 4-fold and statistically significant (p < 0.001) reduction in PN emission factor during takeoffs. This reduction may be due to engine modifications, fuel reformulations, and implemented regulations in the recent years which has resulted in significant improvements in engine and technical progress to reduce emissions (Masiol and Harrison, 2014). Among different alternative fuels, Fischer-Tropsch fuel has received considerable attention due to its low sulfur and aromatic content leading to the reduction of emissions of particulate matter, soot, and other precursors from aircraft engines (Beyersdorf et al., 2014; Corporan et al., 2007). (Lobo et al., 2011) also reported a nearly 67% reduction in PN emissions by using 50% FT/50% Jet-A1 blend instead of Jet-A1. In general, as also presented in Table 11, the PN emission factors obtained in this study fall well within the ranges reported in the literature. 3.2.2 Black carbon emission factor The arithmetic mean (± standard error) emission factor for BC during takeoffs and landings were 0.12±0.02 and 0.11±0.01 g/kg, respectively (Figure 23 b). Similar to the PN emission factor, BC also had comparable emission factors during takeoffs and landings. The similar BC emission factors for takeoffs and landings in our study could be in part due to additional BC emissions from other activities, including aircraft taxiing and vehicular ground operations that might convolute a direct distinction between takeoff and landing emissions. 3.2.3 PM2.5 emission factor The mean PM2.5 EF (± standard error) for takeoffs and landings were comparable with values of 0.38±0.04 and 0.40±0.05 g/kg fuel burned, respectively (Figure 23 c). Previous studies have discussed the evolution of PM characteristics in the advected engine exhaust plumes over time. Condensation of initially gas-phase semi-volatile compounds of the exhaust emissions, due to the significant expansion and cooling of the plume, results in a bi- modal distribution with a nucleation mode consisting of freshly nucleated particles and an accumulation mode consisting of non-volatile PM with a coating of semi-volatile material as a secondary PM (Robinson et al., 2010). This phenomenon generally enhances the PM number-based emissions and to a lower extent the mass-based 63 emissions, given that the nucleation mode particles have very minor contribution to PM mass concentration. (Mazaheri et al., 2009)ranges of 0.1-0.7 and 0.2-0.5 g/kg fuel, respectively. 3.3 Airport versus freeway emission rates in two different spatial scales The overall emission rates of the LAX airport, representing primarily the aircraft takeoff and landing events and also smaller contributions from the complex ground operations at the airport, were calculated based on Equations 3 and 4 as discussed earlier for each day of sampling, with ∆x defined as average pollutant concentration over the background. Similarly, freeway-based emission rates were also calculated using Equations 1 and 2. In this study, the results indicated that LAX airport’s contribution to the total particle number emissions (± standard error) was 2.82 × 10 20 ± 2.67 × 10 19 particles/day. The substantial PN emissions (which is mostly in the ultrafine size range) can impact the air quality of the neighborhoods in the vicinity of the airport. (Riley et al., 2016) argued that UFPs observed downwind of the airport pathways were distinct as they had smaller diameter than particles measured in other neighborhoods and freeways in Los Angeles. The mobile on-road monitoring of I-110, I-105, and I-405 freeways indicated that the studied transects of these three freeways together contributed to 2.58 × 10 19 ± 2.41 × 10 18 particles/day, an emission rate about 11 times smaller than that of the LAX airport. Comparison for BC levels also revealed a 2.5-fold larger contribution in daily emissions from LAX airport (6.09±0.41 kg/day) in comparison to the total daily BC emissions from the three freeways (2.39±0.08 kg/day). PM2.5 emission rate from the LAX airport (21.64±2.25 kg/day) was also somewhat higher than those of the three freeways together (15.44±1.34 kg/day). Therefore, the LAX airport can be considered as a significant source of PN emissions in the area. The relative contribution of LAX airport and these three freeways on the overall PN, BC and PM2.5 emissions in the vicinity of the airport facility is shown in Figure 24 a. To put into further perspective the extent of contribution of LAX airport to emissions of PN, BC and PM 2.5 versus freeways, the freeway-based emission rates of the aforementioned species from all the freeways in the Los Angeles County were estimated by taking into account an estimate of total freeway length and the traffic flow. Given that traffic is the major contributor to emissions of BC and PN in the Los Angeles basin (Schauer, 2003; Sowlat et al., 2016), comparisons of the estimated contributions of these species provide insight on the relative contribution of LAX airport in the region. It should also be noted that this analysis mostly accounts for primary PM2.5 emissions from vehicular emissions, as the data are based on PM2.5 measurements on three segments of freeways. CalTrans has reported that Los Angeles County has about 1500 km of freeways and highways. Average vehicle- miles traveled in the Los Angeles County for each sampling day were estimated based on the average of vehicle- miles traveled for the three studied freeways per their length multiplied by the total freeway length in the Los Angeles County. Based on these estimations and the emission factors derived for segments of I-110, I-105, and I-405, the total daily emission rates of freeways and highways in Los Angeles County were estimated. This analysis relies on the assumptions that measured vehicle emission factors are representative of vehicles for the entire Los Angeles basin and contributions from overhead aircraft to measured vehicle emissions are negligible. The results indicated that PN emission rate (± standard error) from all the freeways would be 2.53 × 10 21 ± 4.48 × 10 20 particles/day. Although this value is greater than the PN emission rates from the LAX airport (2.82 × 10 20 ± 2.67 × 10 19 particles/day) by a factor of 9, it indicates that the LAX airport alone contributes to about 10% of PN concentration in the Los Angeles County compared to mobile traffic sources. BC and PM 2.5 emission rates were estimated to be 1.43 × 10 2 ± 7.74 kg/day and 5.97 × 10 2 ± 6.14 × 10 kg/day, respectively. 64 These estimated emission rates are substantially larger for BC and PM 2.5 emissions from the LAX airport (23- and 28-fold difference, respectively). The results are illustrated in Figure 24 b. The above results revealed that at the regional scale (i.e. across the county of Los Angeles), freeways can be considered as a much more important source of PN, BC, and PM2.5 compared to the LAX airport. However, in a local scale within the vicinity of the airport, LAX was a more significant contributor to PN, a proxy for UFPs, with about 11 times higher daily emissions in comparison to the adjacent freeways. The above estimation indicates that at the regional scale (i.e. across the county of Los Angeles), freeways are much more important source of PN, BC, and PM2.5 compared to the LAX airport. However, in a local scale within the vicinity of the airport, as defined in our previous sections, LAX is a major contributor to PN, a proxy for UFPs, with about 11 times higher daily emissions in comparison to the adjacent freeways. 65 Table 11. Summary of the results reported by previous studies for pollutants’ concentrations and emission factors (EF) at different airports. Study Airport Take- off/Landing Particle size range (nm) Particle number (particles/cm 3 ) BC (µg/m 3 ) PM2.5 (µg/m 3 ) EF Number (particles/kg fuel) EF BC (g/kg fuel) EF PM2.5 (g/kg fuel) Herndon et al., 2005 John F. Kennedy International Airport, New York, USA Takeoff 7-2500 - - - (1.0 ± 0.7) × 10 14 - - Herndon et al., 2005 Logan International Airport, Boston, USA Takeoff 7-2500 - - - (8.8 ± 7.6) × 10 15 - - Westerdahl et al., 2008 Los Angeles International Airport, USA Takeoff/Landing 7-350 2 × 10 4 − 5.8 × 10 5 1.8 - 3.8 - - - - Fanning et al., 2007 Los Angeles International Airport, USA Takeoff 10-100 1.4 × 10 5 − 1.4 × 10 6 13.9 ± 14.3 & 14.0 ± 10.2 32 - 42 - - - Herndon et al., 2008 Hartsfield Jackson Atlanta International Airport, USA Takeoff 7-2500 - - - 1.8 × 10 15 − 5.6 × 10 15 0.2 - 1.5 - Hu et al., 2009 Santa Monica Airport, CA, USA Takeoff 5.6-560 1 × 10 4 − 3 × 10 5 0.7 - 2.7 - 5 × 10 16 - - Mazaheri et al., 2009 Brisbane Airport, Australia Takeoff 4-710 - - - 2.1 × 10 16 − 5.4 × 10 16 - 0.2 - 0.3 Landing 7.7 × 10 15 − 4.3 × 10 16 - 0.3 - 0.5 Zhu et al., 2011 Los Angeles International Airport, USA Takeoff 7-320 0.4 × 10 4 − 8.4 × 10 4 0.1 - 3.6 37.1 ± 15.4 3.4 × 10 16 - - Klapmeyer & Marr 2012 Roanoke Regional Airport in western Virginia, USA Takeoff - 1.5 × 10 3 − 1.7 × 10 5 - - 1.4 × 10 16 − 7.1 × 10 16 0.2 - 0.5 - Lobo et al., 2012 Oakland International Airport, CA, USA Takeoff 5-1000 2 × 10 5 − 1.3 × 10 6 - - 4 × 10 15 − 2 × 10 17 - 0.1 - 0.7 Hudda et al., 2014 Los Angeles International Airport, USA Takeoff/Landing 10-1000 4 × 10 4 − 6 × 10 4 1.4 - 1.6 - - - - Lobo et al., 2015 Hartsfield-Jackson Atlanta International Airport Takeoff 5-1000 - - - 6 × 10 17 − 2 × 10 18 - 0.1 - 0.6 Ren et al., 2016 Tianjin International Airport, China Takeoff 10-1000 4 × 10 4 − 4.4 × 10 5 - - 2 × 10 15 − 3.2 × 10 16 - - Landing 6 × 10 4 − 4.5 × 10 5 - - 2.5 × 10 15 − 3.3 × 10 16 - - Current study Los Angeles International Airport, USA Takeoff 7-500 𝟏 . 𝟓𝟑 × 𝟏 𝟎 𝟓 ± 𝟑 . 𝟏𝟏 × 𝟏 𝟎 𝟒 2.87 ± 0.0.3 33 ± 0.15 (𝟖 . 𝟔𝟗 ± 𝟏 . 𝟐𝟎 ) × 𝟏 𝟎 𝟏𝟓 0.12 ± 0.02 0.38 ± 0.04 Landing (𝟖 . 𝟏𝟔 ± 𝟏 . 𝟎𝟎 ) × 𝟏 𝟎 𝟏𝟓 0.11 ± 0.01 0.40 ± 0.05 66 Figure 20. Box plots of a) particle number concentration (particles/cm 3 ), b) mean particle diameter (nm), c) black carbon concentration (BC) (µg/m 3 ), d) PM2.5 concentration (µg/m 3 ), and e) CO2 concentration (ppm) at LAX, I-110, I-105, and I-405 freeways. Dotted lines represent the arithmetic mean. Black dots correspond to the 5 th and 95 th percentiles. 67 Figure 21. Spatial pattern of a) particle number concentration (particles/cm 3 ) and b) mean particle diameter (nm) measured inside of freeways on June 6, 2016 between 12:00-3:00 PM. c) Mean particle diameter versus particle number concentration during the sampling at the Los Angeles International Airport (LAX) with the average ambient temperature (ºC) during the sampling period obtained from the Air Quality Management District (AQMD) monitoring station at LAX. The data shown here are raw data. 68 Figure 22. Time series of a portion of the observations at the sampling site downwind of the Los Angeles International Airport south runway on 06/27/2016 for particle number (particles/cm 3 ) and CO2 (ppm) concentrations. Data shown in this figures are raw data. 69 Figure 23. Box plots of calculated emission factors during aircraft takeoffs and landings for a) particle number, b) black carbon (BC) and c) PM2.5. Dotted lines represent the arithmetic mean. Black dots correspond to the 5 th and 95 th percentiles. 70 Figure 24 (a-b). Pie charts of the contribution of Los Angeles International Airport (LAX) to particle number (PN), black carbon (BC) and PM2.5 daily emissions versus a) the segments of the three adjacent freeways (i.e. I-110, I-105 and I-405) and b) total freeways in the Los Angeles County. 71 Case Study 4. Chemical composition and redox activity of quasi-ultrafine particles (PM0.25) at Los Angeles International Airport and comparisons to an urban traffic site 72 1. Introduction There is an increasing concern regarding human exposure to airport-related pollutants due to growth in the aviation industry and concomitant increases in airport traffic. Aircraft engines, auxiliary power units, and ground support diesel and gasoline-powered vehicles on the terminal roadways and surface streets near the airport are the primary sources of particulate matter (PM) emissions at airports. Airport operations have been shown to significantly contribute to increased PM levels in surrounding communities 1–4 . Recently, Hudda et al. 2 indicated a 4- to 5-fold increase in particle number (PN) concentration in an area of about 60 km 2 located 8- 10 km downwind of the Los Angeles International Airport (LAX), as compared to unaffected background PN concentrations further afield. In a companion paper, Shirmohammadi et al. 5 measured particle number and black carbon (BC) concentrations near the LAX Airport (roughly 150 m downwind of LAX's south runways), as well as performing on-road measurements of the aforementioned pollutants using a mobile platform on three major freeways (i.e., I-110, I-105, and I-405). The measured PN concentration immediately downwind of LAX was 4.1 ± 1.2 times greater than PN concentrations measured on the I-110, I-105, and I-405 freeways. A comparison of BC levels revealed a 2.5-fold larger contribution in daily emissions from LAX airport in comparison to the total daily BC emissions from the three freeways combined. Additional studies, in which sampling was conducted downwind on the edge of freeways, have demonstrated that the impact of ground-level line sources (e.g., freeways) are localized, and that submicron PM emissions can diminish exponentially to urban background levels with increasing downwind distances up to 300 m of the source 6,7 . Several studies have characterized PM, black carbon (BC), sulfur oxides (SOx), carbon monoxide (CO), nitrogen oxides (NO x), and volatile organic compounds (VOCs) as combustion products of jet fuel 8–10 . However, the chemical composition and toxicological properties of combustion-generated ultrafine particles (UFPs) in the vicinity of airports have not been fully investigated from a population health perspective. There is growing evidence linking PM to adverse health effects including cardiovascular, respiratory and neurodevelopmental disorders 11–14 . Many of the toxic effects of PM are thought to be triggered through PM- induced oxidative stress due to the cellular generation of reactive oxygen species (ROS). ROS generation is induced by the interaction of PM with epithelial cells and macrophages, as part of the overall cellular inflammatory response 15,16 . Quasi-ultrafine particles (quasi-UFPs), defined as particles with aerodynamic diameters smaller than 0.25 µm (PM0.25) 17 , have been shown to exhibit substantially higher toxicity and induce more serious adverse health effects compared to larger PM size fractions 18,19 . Quasi-UFPs may originate from primary emissions including, but not limited to, vehicle exhaust, fuel oil combustion, biomass burning and various industrial sources. There is an extensive body of literature documenting that primary PM emissions generated through combustion processes significantly contribute to the toxicity and redox activity of airborne particles, primarily due to the presence of transition metals as well as organic oxidizing agents, such as quinones, in these emissions 20–22 . As most large airports are located near heavily populated urban settlements, they may have a potentially significant impact on the environment and health of people living in their vicinity. Therefore, assessing these emissions and how they compare to a predominant PM source, such as traffic emissions, in a metropolitan city is essential from the standpoint of population exposure. The primary objective of this study was to quantify and compare the toxicological properties and chemical composition of quasi-UFPs predominantly emitted from the Los Angeles International Airport (LAX) versus those emitted from a major freeway in Los Angeles. Airborne quasi-UFPs were collected at 2 selected locations, one near the LAX airport and the other in central Los Angeles, downwind of the I-110 freeway. The oxidative potential of PM0.25 at these sites was quantified using a cell-based macrophage ROS assay. Additionally, comprehensive chemical analyses were conducted on the collected samples, followed by a source apportionment analysis using the molecular marker-based chemical mass balance (MM-CMB) model. The relative impact of dominant markers in this model on the PM0.25 oxidative potential provided further evidence of the underlying toxicity associated with the sources of these tracers. 2. Methodology 2.1 Sampling location and meteorology 73 Size-segregated PM samples were collected at two locations in the Los Angeles area. One site was in “central Los Angeles” at the Particle Instrumentation Unit (PIU) of the University of Southern California, located about 3 km south of downtown Los Angeles. This site is situated approximately 150 m to the east and downwind of a major freeway (I-110), and samples collected here represent urban mixed particles, emitted mostly from vehicular sources. The other site was located near a residential area of Playa del Rey, north of LAX and about 600 m from the upper runway, at a South Coast Air Quality Management District (SCAQMD) monitoring station. This site also experiences a prevailing onshore sea breeze, with no major PM sources upwind of the site other than airport emissions. There are two high schools nearby, which may introduce some bus emissions to the area. However, in general, traffic is very light in this area. Table S1 presents the average of selected meteorological parameters at both sites during the sampling period. Average temperature was 17.5 ± 4.1 ºC and 18.4 ± 5.3 ºC at the LAX and central Los Angeles sites, respectively. Relative humidity was also fairly comparable with values of 58.6 ± 24.3 % and 48.9 ± 23.3 % at the LAX and central Los Angeles sites, respectively. Overall, average wind speed was similar at both sites (2.4 ± 2.0 and 2.1 ± 0.9 m/s at LAX and central Los Angeles, respectively) from predominantly the westerly direction. Figure S1 presents the sampling locations and wind rose plots applicable during the sampling period. 2.2 Sampling schedule and method Ambient quasi-UFPs were collected on filters concurrently at both sampling sites on a weekly basis including weekends (i.e., each weekly sample corresponded to 7 days of sampling), continuously from October to December 2016. Sampling was intentionally conducted during the colder months of the year to minimize, to the degree possible, the contribution of secondary organic aerosols (SOA) to PM0.25 mass. SOA is a regional aerosol (thus impacting all sampling environments to more or less the same degree) that is formed during periods of prolonged sunlight and has been associated with the generation of excess ROS 23,24 . Four parallel Sioutas personal cascade impactor samplers (PCIS) (SKC, Inc., Eighty Four, PA, USA) operating at a flow rate of 9 lpm were used to collect the samples at each site 25 . Particles were collected in three different size ranges as follows: <0.25 μm (quasi-UFP), 0.25–2.5 μm (accumulation) and 2.5–10 μm (coarse PM). The focus of this article is on quasi-UFP. For the purpose of chemical speciation, three PCISs were loaded with 37-mm PTFE (Teflon) filters (Pall Life Sciences, 3-µm pore, Ann Arbor, MI) and the other PCIS with 37- mm quartz filters (Whatman International Ltd., Maidstone, England). The quartz filters were prebaked at 550 °C for 12 hours and stored in baked aluminum foil prior to sampling. 2.3 Gravimetric and chemical analyses Weekly samples were analyzed to quantify PM0.25 mass concentrations as well as the chemical PM constituents. Gravimetric mass concentrations were determined by pre- and post-weighing the Teflon and quartz filters, using a high precision (±0.001 mg) microbalance (MT5, Mettler Toledo Inc., Columbus, OH), after equilibration under controlled temperature (22-24 °C) and relative humidity (40-50%). Elemental and organic carbon (EC and OC) content were quantified by analyzing a 1 cm 2 punch of the weekly quartz filters using the National Institute for Occupational Safety and Health (NIOSH) Thermal Optical Transmission (TOT) method 26 . The remaining sections of the quartz filters were composited in two and three-week sets in order to supply sufficient mass for the organic speciation analyses. Water-soluble organic carbon (WSOC) content was quantified using a Sievers 900 Total Organic Carbon Analyzer 27 . Moreover, the composited samples were analyzed by Gas Chromatography/Mass Spectrometry (GC/MS) for organic species including Polycyclic Aromatic Hydrocarbons (PAHs), n-alkanes, hopanes, steranes and levoglucosan. To measure the total elemental composition of the PM, a section of each Teflon filter was digested in a mixture of 1.5 mL of 16 M nitric acid, 0.5 mL of 12 M hydrochloric acid and 0.2 mL of hydrofluoric acid using a microwave-aided (Milestone Ethos+) sealed Teflon bomb solubilization protocol. The digests were then analyzed for 49 elements using a high resolution (magnetic sector) inductively coupled plasma mass spectrometer (SF-ICPMS, Thermo-Finnigan Element 2). Chemical analyses were carried out on the bi-weekly and tri-weekly composites for GC/MS and WSOC, while ICPMS and EC/OC analyses were done on individual weekly filters. 74 2.4 Toxicological analyses Oxidative potential, referred to as the ability of particles to generate reactive oxygen species (ROS) by consumption of antioxidants and/or generation of oxidants, has been used as a health-based exposure measure of PM in several recent studies 15,28–30 . Oxidative potential can be measured using biological 31,32 and chemical 33,34 assays involving culturing and exposing cells to PM or simulating PM-catalyzed electron transfer from cellular antioxidants, respectively. In this study, oxidative potential of the PM samples was quantified using an alveolar macrophage-based ROS assay. To conduct the ROS cell-based assay, Teflon filters were extracted using 1.00 ml sterilized Milli-Q water, and then subjected to 16 hours of agitation at room temperature in the dark followed by 30 min of sonication. The ROS assays were then performed by exposing the rat alveolar cell line (NR8383, American Type Culture Collection) to the extracted aqueous PM suspensions. 2´, 7´- dichlorodihydrofluorescein diacetate (DCFH-DA), a membrane permeable compound, was used as a fluorescent probe. Upon entering a cell, DCFH-DA is de-acetylated, yielding 2´, 7´-dichlorodihydrofluorescein (DCFH). ROS species produced within the cell cytoplasm convert non-fluorescent DCFH into the highly fluorescent 2, 7- dichlorofluorescein (DCH), which was monitored using a microplate reader. Final ROS responses were reported in units of Zymosan equivalents 35 . 2.5 Source apportionment 2.5.1 Principal component analysis (PCA) Chemical species emitted from the same or similar sources are assumed to be intrinsically correlated. Groups of correlated chemicals representing major source factors of PM0.25 were identified by principal component analysis (PCA). Data from both sampling sites were pooled together to increase the statistical power of the analysis while assuming that PM source profiles were consistent across sites 36,37 . PCA was then conducted using SPSS statistical software (SPSS Inc., version 22.0). A VARIMAX rotation was employed for interpretation of the principal components (PCs) and factors with eigenvalues greater than unity were retained in the analysis 38 . Furthermore, to determine whether the data were suitable for PCA, a Kaiser-Meyer-Olkin (KMO) value, as the measure of sampling adequacy, of greater than 0.5 was required 39 . The PCA results provide further insight in determining potential chemical source markers. 2.5.2 Chemical mass balance (CMB) model A molecular-marker based source apportionment model (MM-CMB) was used to determine sources of PM0.25 organic carbon (OC) 40 . The model was implemented using CMB software (EPA CMB v8.2) available from the US Environmental Protection Agency, which applies an effective-variance least squares algorithm to the linear combination of the product of the source contribution and its concentration 41 . Molecular marker compounds that are chemically stable during transport from source to receptor, and that were detected in the samples, were selected as fitting species. These included EC, nonacosane, hentriacontane, tritriacontane, levoglucosan, 17α(H)-21β(H)-hopane, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e)pyrene, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, vanadium (V), aluminum (Al), titanium (Ti) and calcium (Ca). The model input source profiles, which are based on observed primary tracers, considered in the model include: diesel and gasoline motor vehicles 42,43 , biomass burning 44,45 , vegetative detritus 46 , ship emissions 47,48 and re-suspended dust 49 . Vehicular emissions source profiles were based on recent on-road studies conducted on the I-110 and I-710 freeways in Los Angeles and mobile source contributions were determined as the sum of both light-duty and heavy-duty vehicle (LDV and HDV) source contributions 50,51 . A source profile for jet engine exhaust emissions that includes chemically stable molecular markers from source to receptor was not found within the source apportionment literature, and thus could not be used in our CMB model. Therefore, source contributions from aircraft emissions are determined indirectly from the “un- apportioned OC fraction”, defined as the residual difference between the measured OC and the sum of all identified primary source contribution estimates. Moreover, EC is not the sole surrogate of vehicle exhaust emissions at the LAX site. Agrawal et al. 52 demonstrated that the PM in aircraft exhaust emissions shifts from 75 OC-rich, while the engine is in idle mode, to EC-rich with increasing power. In a companion paper, Shirmohammadi et al. 5 indicated a 2.5-fold larger contribution to daily emissions of EC from LAX airport (6.09 ± 0.41 kg/day) in comparison to the total daily EC emissions from the three adjacent freeways (2.39 ± 0.08 kg/day). As the gasoline and diesel input source profiles were also highly characterized by EC emissions, the EC concentration at LAX was modified using the aforementioned ratio in order to separate the mobile source factor from aircraft emissions in the CMB model. Therefore, EC from traffic emissions was estimated by dividing the measured EC concentration at LAX site by 3.5. In areas influenced by anthropogenic sources, primary water soluble organic carbon (WSOC) is mainly emitted from biomass burning sources, whereas secondary WSOC is formed through photochemical reactions (i.e., secondary organic carbon (SOC) formation) 24 . Since the tracers of secondary sources were not included in the CMB model, non-biomass burning water soluble organic carbon (WSOC nb) was calculated to estimate the extent to which SOC contributes to the un-apportioned OC fraction in the CMB model 53 . WSOCnb is quantified as the difference between total measured WSOC and water soluble portion of OC attributed to biomass burning (WSOCbb) 53 . It has been determined by Sannigrahi et al. 54 that about 71% of OC emitted from biomass burning is water soluble. Therefore, for each site, the measured concentration of levoglucosan, an indicator of wood combustion 55 , was divided by the levoglucosan-to-OC ratio (0.143) obtained from the wood smoke source profile 44,45 in order to estimate the OC emitted from biomass burning. WSOCbb was determined as 71% of calculated OC from wood smoke and WSOCnb was then determined by subtraction of WSOC bb from measured total WSOC. 2.6 Statistical analyses Univariate analysis was performed on weekly data sets (i.e. EC, OC, metals and elements) to investigate the correlation of ROS activity with chemical species. As a preliminary step in determining which source factors were redox active, as represented by their chemical markers, we selected species that were known to contribute to one of the source profiles, using factor loadings from the factor analysis model and known source profiles. A multiple linear regression (MLR) model further determined which chemical source markers were significantly associated with ROS activity using a sequential regression entry method in the model. The model was then developed based on statistically significant (p < 0.05) species that were not co-linear with each other (if variance inflation factor < 2.5), while achieving the highest R 2 value and improving the prediction of ROS activity. Based on the MLR model, changes in redox activity per interquartile range (IQR) increases in chemical source marker concentrations were presented as the means to compare the effect of different source markers on ROS activity. 3 Results and Discussion 3.1 Chemical composition 3.1.1 PM0.25 mass concentration and carbonaceous species Average PM0.25 mass concentrations, as well as the average concentrations of carbonaceous species (OC and EC) are shown in Figure 1. The average PM0.25 mass concentration was comparable at the two study locations with values of 5.05 ± 1.59 µg/m 3 and 5.84 ± 2.46 µg/m 3 at LAX and central Los Angeles, respectively. Average OC and EC levels were slightly higher at central Los Angeles, which had values of 1.77 ± 0.65 µg/m 3 and 0.64 ± 0.0.24 µg/m 3 , respectively, compared to the levels at LAX, which were 1.51 ± 0.41 µg/m 3 and 0.59 ± 0.22 µg/m 3 , respectively. EC, which is an indicator of combustion emissions from diesel trucks 56 as well as jet engines, especially at high power conditions 52 , contributed to a small and comparable fraction of quasi-ultrafine mass (11.6 ± 5.7% and 11.0 ± 6.2% at LAX and central Los Angeles, respectively). The slightly higher concentrations of EC and OC at the central Los Angeles site can be attributed to the fact that this site is impacted by several sources, the most significant of which is traffic emissions. Water soluble organic carbon was 0.37 ± 0.10 µg/m 3 and 0.34 ± 0.13 µg/m 3 at LAX and central Los Angeles, respectively. To distinguish the contribution of biomass burning and secondary processes to WSOC, its concentrations were further separated into biomass burning water soluble organic carbon (WSOCbb), and non-biomass burning organic carbon 76 (WSOCnb) as described earlier. WSOCnb was used as a metric to estimate the relative contribution of SOC to total mass 53 . The analysis indicated that the overall contribution of SOC to OC was 12.5 ± 2.0% and 10.1 ± 1.0% at LAX and central Los Angeles, respectively, underscoring the minimal effect of secondary OC formation on total OC, consistent with the intentional choice of the winter months for our sampling period. Figure 1. Mass concentration (µg/m 3 ) of quasi-ultrafine PM (PM0.25) as well as carbonaceous species (Organic Carbon (OC), and Elemental Carbon (EC)) concentrations (µg/m 3 ) at the two study locations. Error bars represent standard deviation. 3.1.2 Organic species Concentrations of individual organic species (clustered into their corresponding organic groups, namely Polycyclic Aromatic Hydrocarbons (PAHs), hopanes and steranes and n-alkanes) at the two study locations are presented in Figure 2 (a-d) and Table S2. PAHs are common products of incomplete combustion of fossil fuels 57 . The concentrations of these toxic and carcinogenic compounds 58,59 are significantly affected by several factors such as atmospheric conditions, source strength, gas-particle partitioning, and deposition processes. Studies have shown that diesel vehicle emissions are enriched in lower molecular weight PAHs (MW≤228), whereas higher molecular weight PAHs (MW≥276) are associated with gasoline vehicle emissions 60 . Furthermore, a notable source of higher molecular weight PAHs (e.g. benzo(ghi) perylene and indeno (1,2,3-cd) pyrene) in the cold season is the cold-start spark-ignition of gasoline vehicles 51,61 . Cumulative concentrations of all measured PAHs separated by their molecular weights are presented in Figure 2b. Kinsey et al. 62 , as part of the three test campaigns of the Aircraft Particle Emissions eXperiment (APEX), have reported similar PAH emissions of aircraft engines at idle and high power conditions and attributed their observations to the operating conditions inside the combustor, especially with respect to pressure. Agrawal et al. 52 have reported increased aircraft emissions of naphthalene as power increased from idle mode, then falling off as the engine operated at the highest mode for all engines tested. In this study, the cumulative concentration of PAHs was higher at the central Los Angeles site (0.67 ± 0.30 ng/m 3 ) in comparison to the LAX site (0.45 ± 0.16 ng/m 3 ), highlighting the stronger PAHs emission source from the nearby freeway compared to the LAX site. 77 Hopanes and steranes are organic tracers of vehicular emissions and are mainly emitted from lubrication oil in gasoline- and diesel-fueled vehicles 40,63 . Similar to PAHs, hopanes and steranes were more abundant at the central Los Angeles site (0.12 ± 0.04 ng/m 3 ) than at LAX (0.04 ± 0.02 ng/m 3 ). The cumulative concentration of n-alkanes (C21-C34) was overall comparable at the central Los Angeles (7.02 ± 2.00 ng/m 3 ) and LAX (6.01 ± 0.63 ng/m 3 ) sties. To investigate the origin of n-alkanes, the carbon preference index (CPI), defined as the concentration ratio of their odd-to-even numbered homologues, was estimated. A CPI about 1 indicates a dominance of anthropogenic sources, whereas a CPI greater than 2 indicates a prevalence of biogenic sources 64 . The estimated CPI was in the range of 1.2 to 1.4 at both sites suggesting the predominance of anthropogenic sources, such as fossil fuel combustion in both vehicles and jets, as well as wood-smoke 65,66 . Furthermore, as discussed in Kinsey et al. 62 , unburnt fuel or lubrication oil from aircraft engines are also potential sources of n-alkane emissions in the vicinity of airports, and were found to be highly dependent on the engine type. Levoglucosan, a tracer for biomass burning 67 , was comparable at both sites with concentrations of 38.05 ± 17.66 and 34.70 ± 28.15 ng/m 3 at LAX and central Los Angeles, respectively. 78 Figure 2. Concentration of organic species (ng/m 3 ) at the two study locations: (a) selected Polycyclic Aromatic Hydrocarbons (PAHs), (b) all PAHs separated by their molecular weights (MW), (c) Hopanes and Steranes, and (d) n-Alkanes. 79 3.1.3 PM0.25 trace element and metal content Average total concentrations of major and trace elements at both sampling sites are presented in Figure 3 (the error bars correspond to standard deviations). Metals and trace elements in the sub-micron size range can arise from abrasion of brakes, tire wear, and re-suspension of road dust, as well as from lubricating oil additives and engine wear debris accumulated in the oil 68,69 . The emission of unburned or partially combusted lubricating oil greatly increases the mass emission of particulate matter, which is likely accompanied by an increase in metal emissions 69 . In previous laboratory studies of diesel particulate matter emissions, lubricating oil dominated as a source of increased emission rates of many metals 69 . Other studies have also reported formation of ultrafine particles from abrasion and resuspension of road dust due to the tire-pavement interaction, with a peak in number size distribution at 40 nm and the mean particle number in the diameter range of 15–50 nm 68,70 . In a year-long study at 10 distinct locations in the Los Angeles basin, Saffari et al. 71 also identified road dust (influenced by vehicular emissions as well as re-suspended soil), vehicular abrasion and residual oil combustion as major sources of trace elements and metals in PM0.25 size range. Overall, S, K, Al, Fe, Na and Ca were the most abundant elements at both sampling sites. Among the indicated elements only Na, S, V and As had higher concentrations at LAX in comparison to central Los Angeles, whereas elements associated with abrasion of brake and tire wear (e.g. Ba, Cu, Fe, Mn, Pb and Zn 72 ) as well as re-suspended road dust (e.g. Al, Ca, K and Ti 73 ) were higher at the central Los Angeles site. Statistically significantly higher (p < 0.001) PM0.25 concentrations of sulfur at LAX (by about 50%) can be mainly attributed to the aircraft emissions based on previous studies indicating that S is the most abundant element in aircraft exhaust emissions 52,62,76 . Secondary sulfate is an aerosol component formed in the atmosphere through the oxidation of sulfur dioxide 74 , and is the most predominant form of S found in urban areas. This species, however, occurs mostly in the accumulation mode 19 rather than in the ultrafine range, with a very low spatial variability 17,77 , and thus, is expected to be found in similar concentrations at both sampling sites. In contrast, sulfate in the quasi-UF range primarily originates from combustion of sulfur-containing fuels 19,52 . Previous studies have also reported the highest emission index for sulfur, among other elements, in aircraft exhaust emissions and argued that emissions of metals do not show any significant trends with changing power conditions, implying that the metals distribution in aircraft emissions is dependent on engine type 52,62 . As LAX is located next to the ocean with a dominant westerly wind direction, higher concentrations of Na and V can be attributed to the sea salt in the ocean breeze and ship emissions, respectively. Arsenic (As), which is also emitted from both aircraft engine exhaust and motor vehicle sources 52,69 , showed statistically higher contributions to PM0.25 mass at LAX in comparison to central Los Angeles (p < 0.05). Figure 3. Total concentrations (ng/m 3 ) of PM0.25-bound elements and metals at the two study locations. Error bars represent standard deviation. 0.01 0.1 1 10 100 1000 S Fe Na Al K Ca Mg Ba Zn Cu Ti Pb Sb Mn Cr V Ni As Cd La Co Concentration (ng/m 3 ) LAX Central Los Angeles 80 3.2 Reactive oxygen species (ROS) activity The oxidative potential of quasi-UFPs, quantified by a macrophage reactive oxygen species (ROS) assay, is illustrated in Figure 4 (a-b) for both study locations. In Fig. 4a, the ROS activity (expressed as units of Zymosan equivalents) is normalized by the total quasi-UFP mass and therefore represents the intrinsic toxicity of the particles. Mass-normalized ROS activity was slightly higher at LAX (4600.93 ± 1516.98 µg Zymosan/mg PM) than central Los Angeles (4391.22 ± 1902.54 µg Zymosan/mg PM), however this difference was not statistically significant (p = 0.42). While the mass-normalized expression (Figure 4a) provides insight regarding the intrinsic toxicities of the particles, the volume-normalized ROS activity (Figure 4b) is a metric for comparison of inhalation exposures, indicating the severity of exposure to the redox-active components in PM0.25. Due to a slightly higher PM0.25 mass concentration in central Los Angeles, the overall volume- normalized ROS activity levels showed little spatial variability with no statistically significant difference between the averages observed at LAX (24.75 ± 14.01 µg Zymosan/m 3 ) and central Los Angeles (27.77 ± 20.32 µg Zymosan/m 3 ). Figure 4. Oxidative potential of PM0.25 at the two study locations: (a) normalized by the total PM mass (µg Zymosan/mg PM) and (b) normalized by the air volume (µg Zymosan/m 3 ). Error bars represent standard deviation. 81 3.3 Principal component analysis (PCA) results Two major principal components were identified for the considered dataset as presented in Table 1. The first principal component (PC1) showed significant loadings (greater than 0.75) for EC attributed to traffic emissions only (as described in previous sections) and metals including Ba, Cu, Fe and Pb, which are more abundant in brake and tire wear emissions 72,78 suggesting that the PC1 source factor is likely attributable to traffic exhaust as well as non-exhaust emissions (i.e. abrasion and re-suspended road dust, as wear emissions may deposit on the road surface and be subsequently re-suspended). As explained earlier in section 2.5.2, the EC concentration at LAX was modified based on the recent paper by Shirmohammadi et al. 5 to account for EC attributed to traffic emissions, since at that site EC is not the sole surrogate of vehicle exhaust emissions. Furthermore, several earlier studies have demonstrated that emissions associated with brake and tire wear, as well as re- suspended road dust due to tire-pavement interaction, can be found in the ultrafine size range 68,70,79 . PC1 accounts for about 64% of total variance in the dataset. The second principal component (PC2) showed high loadings (greater than 0.9) of S and V, suggesting that this component is likely influenced by aircraft emissions. This source factor might partially be impacted by marine vessel emissions, as vanadium may reflect the influence of fuel oil/residual oil combustion products in ship emissions and emissions generated by other port- related industrial activities 75,80 . These two source factors together explain about 90% of the total variance in the dataset. Using factor loadings, known source profiles and chemical compositions of exhaust emissions, we identified EC and S as the most appropriate representative species of traffic emissions and aircraft emissions, respectively. Table 1. Principal component loadings (VARIMAX normalized) of selected chemical species in PM0.25 for the two sampling sites combined. High loadings > 0.750 are in shown in bold. 82 3.4 Chemical mass balance model (CMB) results PM0.25 OC source apportionment results are shown in Figure 5. The model summary (e.g. R 2 , CHI square and % mass) and the individual source contribution estimates along with their uncertainties are also reported in the supplementary information (Table S3). The CMB model output was robust for all sites, with an average R 2 of 0.95 and 0.97 for LAX and central Los Angeles, respectively. At the central Los Angeles site, which is heavily impacted by the nearby freeway emissions, the mobile primary emissions (from light duty (LDV) and heavy duty vehicles (HDV)) were the dominant source, contributing to about 82% of the total PM0.25 OC (1.55 ± 0.12 µg/m 3 ). As discussed earlier in the methodology section, EC is not a unique tracer of vehicle exhaust emissions at LAX. Therefore, the EC concentration at LAX was modified using the ratio suggested by Shirmohammadi et al. 5 to eliminate, to the degree possible, the effect of aircraft emissions on the mobile source factor. The estimated mobile source contribution to PM0.25 OC at the LAX site was 28% (0.44 ± 0.06 µg/m 3 ). The difference in mobile source contribution estimates between the two sites is further illustrated by comparing the ratio of concentrations of hopanes and steranes (as exclusive tracers of vehicular emissions in Los Angeles Basin 63 ) to EC. This ratio was 0.06 ± 0.02 ng/µg EC at LAX, while it was substantially higher at central Los Angeles, with a value of 0.17 ± 0.10 ng/µg EC, indicating the higher contribution of mobile sources at the central Los Angeles site in comparison to the LAX site. Comparisons of mass fractions of selected PAHs, hopanes and steranes as normalized to total carbon (the sum of EC and OC) at both study locations, as well as that of LDV and HDV source profiles, presented in Figure S2 also indicates higher levels at the central Los Angeles compared to the LAX site, and a stronger similarity with mobile source profiles in central Los Angeles. Biomass burning was the second largest contributor to measured OC, with about 18% (0.28 ± 0.04 µg/m 3 ) and 14% (0.26 ± 0.05 µg/m 3 ) at LAX and central Los Angeles, respectively. The PM0.25 re-suspended road dust contribution to OC was estimated as 0.07 ± 0.01µg/m 3 and 0.08 ± 0.01 µg/m 3 at LAX and central Los Angeles, respectively. Ship emissions, detectable only at LAX, comprised a small portion (0.0001 ± 0.00001 µg/m 3 ) of PM0.25 OC. The contribution of “other primary OC,” which is the residual difference between the measured OC and the sum of all source contribution estimates considered in the model, represents the primary contributions from sources not accounted for in the model. As described earlier in section 2.5.2, since a reliable CMB source profile for jet engine exhaust emissions was not available, the aircraft emissions are reflected in the “other primary OC” fraction. The CMB model explained 105.1 ± 5.4 % of the PM0.25 OC at central Los Angeles, but only 50.9 ± 11.5 % at the LAX site. The SOC contribution was shown to be minimal, as discussed in Section 3.1.1 (12.5 ± 2.0% at LAX), and there are no other major sources at the LAX site; thus, the un-apportioned OC fraction can be largely attributed to the aircraft exhaust emissions, roughly contributing 0.55 ± 0.09 µg/m 3 to PM0.25 OC. Aircraft emissions followed by mobile source emissions were the two largest contributors to PM0.25 OC, at 36% and 28%, respectively, at the LAX site. Figure 5. Average source contributions (µg/m 3 ) to PM0.25 OC derived from the CMB model. 83 3.5 Association of species with PM0.25 oxidative potential To investigate the association of ROS activity with PM0.25 chemical composition, bivariate correlation analysis was performed based on the volume-normalized data of both locations. Due to the limited number of data points, the sampling sites were combined, with the assumption that although the contribution of different sources may vary between sites, the relative composition of the sources remains quite consistent 36,81,82 . Table 2 presents the Spearman correlation coefficients (R); the species with high correlations (R ≥ 0.70) are highlighted in bold, and statistically significant p-values (p < 0.05) are denoted by an asterisk. EC was strongly correlated with ROS activity (R = 0.79) while OC showed moderate correlation (R = 0.62). The association between EC and PM oxidative potential has been observed and linked to its source origin (vehicular exhaust) in several previous publications 19,33,60,83 . Metals are another toxicologically important fraction of quasi-UFP, the redox-activity of which has been documented in numerous past studies 15,84–86 . Metals associated with both exhaust and non-exhaust emissions from abrasion of brake pads and/or re-suspended road dust, including Ba, Cu, Fe, Mn, Pb, and Zn 72 , showed moderate to strong correlations with ROS activity, indicating the effect of these sources on PM0.25 oxidative potential at both sites. On the other hand, S and V, which were more abundant at LAX and are the dominant elemental components of aircraft emissions, were also strongly correlated with ROS activity (R > 0.70). The correlations observed between elements and ROS activity in our study suggests the important contribution of their sources to the PM0.25 oxidative potential. To evaluate the relative impact of the two major identified sources on ROS activity, (as represented by their relevant species), MLR analysis was also performed between chemical source marker concentrations (independent variables), as identified from the factor analysis, and volume-normalized ROS activity using the pooled data from the two sampling sites. As was done in the CMB model for LAX, the EC concentration attributed to the traffic emissions was utilized in the MLR model. The traffic EC was determined by considering the ratio of the LAX airport contribution to the total emission rate of EC to that from the three adjacent freeways, as suggested by Shirmohammadi et al. 5 . The EC concentration at the central Los Angeles site was assumed to be largely due to traffic emissions, given the proximity of the site to a nearby freeway (I-110). This estimated EC, denoted as “EC (traffic),” therefore largely accounts for vehicle exhaust emissions. In the MLR model, the source markers for both traffic and aircraft emissions remained strongly associated with redox activity as presented in Table 3 (a-b). Positive associations between ROS activity and traffic-EC and S (a marker of aircraft emissions) were observed in our regression model: IQR-increases in traffic EC and sulfur were associated with 11.6% (95% CI: 8.4, 14.8) and 11.3% (95% CI: 9.2, 13.3) increases in cellular redox activity (μg Zymosan/m 3 ), respectively (Table 3b). The source markers for the two sources were not correlated (all VIFs < 2.5), indicating a lack of collinearity in the regression model (Table 3a). The model is associated with a relatively high coefficient of determination (R 2 = 0.69) as displayed in Table 3a, suggesting that 69% of the variance in the ROS activity can be explained by these two markers alone. The predicted ROS activity by the model for LAX was 24.51 ± 15.16 µg Zymosan/m 3 , which was consistent with the actual measured ROS activity (24.75 ± 14.01 µg Zymosan/m 3 ). Similarly, the model also predicted the ROS activity (28.44 ± 12.84 µg Zymosan/m 3 ) at the central Los Angeles site, which was again well within the range of measured ROS activity (27.77 ± 20.32 µg Zymosan/m 3 ) values. Figure 6 illustrates measured ROS activity versus modeled ROS activity. It should be noted that sulfur is a source marker used as a representative of aircraft emissions rather than a redox active species itself in the regression model. Water-soluble sulfur is more abundant as sulfate in the Los Angeles basin 50,75 . Previous studies have indicated that the observed associations of sulfate with ROS activity is mainly due to the co-linearity of this species with important redox active species, including WSOC, and not because of the toxicity of this component per se 33,87 . Similar observations regarding the association of S (used as a source marker of coal combustion in two rural cities in China), and ROS were made in a recent study 36 . Finally, several studies have also indicated a strong association between EC from traffic emissions and ROS activity 23,88 . 84 Table 2. Spearman's correlation coefficients (R) between the ROS activity (µg Zymosan/m 3 ) and chemical species in PM0.25 at the two study locations. Bold numbers indicate R ≥ 0.70 and * denotes values with p < 0.05. Table 3 (a-b). (a): Output of multiple linear regression (MLR) analysis between the ROS activity (µg Zymosan/m 3 ) as dependent variable and chemical species at the two study locations. (b): Expected increase (%) in ROS activity per interquartile range (IQR) increase in chemical source marker for the two major sources determined by factor analysis: traffic emissions and aircraft emissions. The effect estimates for a given source marker is controlled for the source marker from the other source. 85 Figure 6. Linear regression between measured ROS activity and modeled ROS activity (µg Zymosan/m 3 ) with EC from traffic and sulfur as independent variables. 4 Summary and conclusions In this study, quasi-ultrafine particles (PM0.25) were collected at a site near the Los Angeles International Airport (LAX) as well as at a central Los Angeles site that is heavily impacted by nearby freeway emissions. The chemical analyses results indicated the cumulative concentration of PAHs was higher at the central Los Angeles site (0.67 ± 0.30 ng/m 3 ) in comparison to the LAX site (0.45 ± 0.16 ng/m 3 ). Hopanes and steranes showed a similar trend with higher levels at the central Los Angeles site (0.12 ± 0.04 ng/m 3 ) than at the LAX site (0.04 ± 0.02 ng/m 3 ). Among various metals and trace elements, only Na, S, V and As were more abundant at the LAX site, indicating the significant contribution of aircraft emissions along with other possible nearby sources, including airborne sea salt and ship emissions. Although mass-normalized ROS activity was slightly higher at LAX (4600.93 ± 1516.98 µg Zymosan/mg PM) than at the central Los Angeles site (4391.22 ± 1902.54 µg Zymosan/mg PM), there was no statistically significant difference between the volume-normalized averages observed at LAX (24.75±14.01 µg Zymosan/m 3 ) and central Los Angeles (27.77 ±20.32 µg Zymosan/m 3 ). These results indicated similar toxic PM exposure levels in the vicinity of the LAX airport as compared to the vicinity of freeways. MM-CMB results revealed that mobile sources were the major contributor to PM0.25 OC at the central Los Angeles (82%) site, followed by biomass burning (14%). Since the aircraft emissions source profile was not included in the model, the contribution of aircraft emissions to PM 0.25 OC was estimated from the un-apportioned primary OC fraction. Mobile source and aircraft emissions contributed to 28% and 36% of OC, respectively, at the LAX site. Multiple linear regression analysis revealed that EC from traffic (“traffic-EC”) and elemental S were the best predictors of ROS activity at both sites, underscoring the contribution of vehicle and aircraft exhaust emissions, respectively, to PM0.25 oxidative potential. The contribution of other possible sources, such as non-exhaust emissions (e.g. abrasion of brake and tire wear), was also reflected in strong correlation of their elemental tracers with ROS activity. 86 References: Agrawal, H., Malloy, Q.G.J., Welch, W.A., Wayne Miller, J., Cocker III, D.R., 2008a. In-use gaseous and particulate matter emissions from a modern ocean going container vessel. Atmos. Environ. 42, 5504–5510. doi:10.1016/j.atmosenv.2008.02.053 Agrawal, H., Sawant, A.A., Jansen, K., Wayne Miller, J., Cocker III, D.R., 2008b. Characterization of chemical and particulate emissions from aircraft engines. Atmos. Environ. 42, 4380–4392. doi:10.1016/j.atmosenv.2008.01.069 Akhtar, U.S., McWhinney, R.D., Rastogi, N., Abbatt, J.P.D., Evans, G.J., Scott, J.A., 2010. Cytotoxic and proinflammatory effects of ambient and source-related particulate matter (PM) in relation to the production of reactive oxygen species (ROS) and cytokine adsorption by particles. Inhal. Toxicol. 22, 37–47. doi:10.3109/08958378.2010.518377 Amato, F., Pandolfi, M., Moreno, T., Furger, M., Pey, J., Alastuey, A., Bukowiecki, N., Prevot, A.S.H., Baltensperger, U., Querol, X., 2011. Sources and variability of inhalable road dust particles in three European cities. Atmos. Environ. 45, 6777–6787. doi:10.1016/j.atmosenv.2011.06.003 Anderson, B.E., Branham, H.-S., Hudgins, C.H., Plant, J.V., Ballenthin, J.O., Miller, T.M., Viggiano, A.A., Blake, D.R., Boudries, H., Canagaratna, M., 2005. Experiment to Characterize Aircraft Volatile Aerosol and Trace-Species Emissions (EXCAVATE). Araujo, J.A., Barajas, B., Kleinman, M., Wang, X., Bennett, B.J., Gong, K.W., Navab, M., Harkema, J., Sioutas, C., Lusis, A.J., Nel, A.E., 2008. Ambient Particulate Pollutants in the Ultrafine Range Promote Early Atherosclerosis and Systemic Oxidative Stress. Circ. Res. 102, 589–596. doi:10.1161/CIRCRESAHA.107.164970 Arey, J., Zielinska, B., Atkinson, R., Winer, A.M., 1988. Formation of nitroarenes during ambient high-volume sampling. Environ. Sci. Technol. 22, 457–462. doi:10.1021/es00169a015 Arhami, M., Minguillón, M.C., Polidori, A., Schauer, J.J., Delfino, R.J., Sioutas, C., 2010. Organic compound characterization and source apportionment of indoor and outdoor quasi-ultrafine particulate matter in retirement homes of the Los Angeles Basin. Indoor Air 20, 17–30. doi:10.1111/j.1600-0668.2009.00620.x Arhami, M., Sillanpää, M., Hu, S., Olson, M.R., Schauer, J.J., Sioutas, C., 2009. Size-Segregated Inorganic and Organic Components of PM in the Communities of the Los Angeles Harbor. Aerosol Sci. Technol. 43, 145–160. doi:10.1080/02786820802534757 Ashok, A., Lee, I.H., Arunachalam, S., Waitz, I.A., Yim, S.H.L., Barrett, S.R.H., 2013. Development of a response surface model of aviation’s air quality impacts in the United States. Atmos. Environ. 77, 445–452. doi:10.1016/j.atmosenv.2013.05.023 Ayres, J.G., Borm, P., Cassee, F.R., Castranova, V., Donaldson, K., Ghio, A., Harrison, R.M., Hider, R., Kelly, F., Kooter, I.M., Marano, F., Maynard, R.L., Mudway, I., Nel, A., Sioutas, C., Smith, S., Baeza-Squiban, A., Cho, A., Duggan, S., Froines, J., 2008. Evaluating the Toxicity of Airborne Particulate Matter and Nanoparticles by Measuring Oxidative Stress Potential—A Workshop Report and Consensus Statement. Inhal. Toxicol. 20, 75–99. doi:10.1080/08958370701665517 Bahreini, R., Middlebrook, A.M., de Gouw, J.A., Warneke, C., Trainer, M., Brock, C.A., Stark, H., Brown, S.S., Dube, W.P., Gilman, J.B., Hall, K., Holloway, J.S., Kuster, W.C., Perring, A.E., Prevot, A.S.H., Schwarz, J.P., Spackman, J.R., Szidat, S., Wagner, N.L., Weber, R.J., Zotter, P., Parrish, D.D., 2012. Gasoline emissions dominate over diesel in formation of secondary organic aerosol mass. Geophys. Res. Lett. 39, L06805. doi:10.1029/2011GL050718 Ban-Weiss, G.A., McLaughlin, J.P., Harley, R.A., Lunden, M.M., Kirchstetter, T.W., Kean, A.J., Strawa, A.W., Stevenson, E.D., Kendall, G.R., 2008. Long-term changes in emissions of nitrogen oxides and particulate matter from on-road gasoline and diesel vehicles. Atmos. Environ. 42, 220–232. doi:10.1016/j.atmosenv.2007.09.049 Bates, J.T., Weber, R.J., Abrams, J., Verma, V., Fang, T., Klein, M., Strickland, M.J., Sarnat, S.E., Chang, H.H., Mulholland, J.A., Tolbert, P.E., Russell, A.G., 2015. Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects. Environ. Sci. Technol. 49, 13605– 13612. doi:10.1021/acs.est.5b02967 Beyersdorf, A.J., Timko, M.T., Ziemba, L.D., Bulzan, D., Corporan, E., Herndon, S.C., Howard, R., Miake-Lye, R., Thornhill, K.L., Winstead, E., Wey, C., Yu, Z., Anderson, B.E., 2014. Reductions in aircraft particulate emissions due to the use of Fischer–Tropsch fuels. Atmos Chem Phys 14, 11–23. doi:10.5194/acp- 14-11-2014 Birch, M.E., Cary, R.A., 1996. Elemental Carbon-Based Method for Monitoring Occupational Exposures to Particulate Diesel Exhaust. Aerosol Sci. Technol. 25, 221– 241. doi:10.1080/02786829608965393 Bishop, G.A., Schuchmann, B.G., Stedman, D.H., 2013. Heavy-Duty Truck Emissions in the South Coast Air Basin of California. Environ. Sci. Technol. 47, 9523– 9529. doi:10.1021/es401487b Boström, C.-E., Gerde, P., Hanberg, A., Jernström, B., Johansson, C., Kyrklund, T., Rannug, A., Törnqvist, M., Victorin, K., Westerholm, R., 2002. Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambient air. Environ. Health Perspect. 110, 451–488. Brown, D.M., Wilson, M.R., MacNee, W., Stone, V., Donaldson, K., 2001. Size-Dependent Proinflammatory Effects of Ultrafine Polystyrene Particles: A Role for Surface Area and Oxidative Stress in the Enhanced Activity of Ultrafines. Toxicol. Appl. Pharmacol. 175, 191–199. doi:10.1006/taap.2001.9240 Brunekreef, B., N. A. H. Janssen, et al. (1997). Air pollution from truck traffic and lung function in children living near motorways. Epidemiology 8(3): 298-303. Campbell, A., Oldham, M., Becaria, A., Bondy, S.C., Meacher, D., Sioutas, C., Misra, C., Mendez, L.B., Kleinman, M., 2005. Particulate matter in polluted air may increase biomarkers of inflammation in mouse brain. Neurotoxicology 26, 133–140. doi:10.1016/j.neuro.2004.08.003 Campian, E.C., Cai, J., Benz, F.W., 2002. Acrylonitrile irreversibly inactivates glyceraldehyde-3-phosphate dehydrogenase by alkylating the catalytically active cysteine 149. Chem. Biol. Interact. 140, 279–291. doi:10.1016/S0009-2797(02)00046-7 Carroll-Ann W. Goldsmith Amy Imrich, H.D.Y.N., 1998. Analysis of Air Pollution Particulate-Mediated Oxidant Stress in Alveolar Macrophages. J. Toxicol. Environ. Health A 54, 529–545. doi:10.1080/009841098158683 Charrier, J.G., Anastasio, C., 2012. On dithiothreitol (DTT) as a measure of oxidative potential for ambient particles: evidence for the importance of soluble transition metals. Atmospheric Chem. Phys. Print 12, 11317–11350. doi:10.5194/acpd-12-11317-2012 Charrier, J.G., Anastasio, C., 2011. Impacts of antioxidants on hydroxyl radical production from individual and mixed transition metals in a surrogate lung fluid. Atmos. Environ., Air Pollution and Health: Bridging the Gap from Sources-to-Health Outcomes 45, 7555–7562. doi:10.1016/j.atmosenv.2010.12.021 Charrier, J.G., Richards-Henderson, N.K., Bein, K.J., McFall, A.S., Wexler, A.S., Anastasio, C., 2015. Oxidant production from source-oriented particulate matter – Part 1: Oxidative potential using the dithiothreitol (DTT) assay. Atmos Chem Phys 15, 2327–2340. doi:10.5194/acp-15-2327-2015 Cheng, H., Saffari, A., Sioutas, C., Forman, H.J., Morgan, T.E., Finch, C.E., 2016. Nano-Scale Particulate Matter from Urban Traffic Rapidly Induces Oxidative Stress and Inflammation in Olfactory Epithelium with Concomitant Effects on Brain. Environ. Health Perspect. doi:10.1289/EHP134 Cheung, K.L., Polidori, A., Ntziachristos, L., Tzamkiozis, T., Samaras, Z., Cassee, F.R., Gerlofs, M., Sioutas, C., 2009. Chemical Characteristics and Oxidative Potential of Particulate Matter Emissions from Gasoline, Diesel, and Biodiesel Cars. Environ. Sci. Technol. 43, 6334–6340. doi:10.1021/es900819t Cho, A.K., Sioutas, C., Miguel, A.H., Kumagai, Y., Schmitz, D.A., Singh, M., Eiguren-Fernandez, A., Froines, J.R., 2005. Redox activity of airborne particulate matter at different sites in the Los Angeles Basin. Environ. Res. 99, 40–47. doi:10.1016/j.envres.2005.01.003 Choi, W., He, M., Barbesant, V., Kozawa, K.H., Mara, S., Winer, A.M., Paulson, S.E., 2012. Prevalence of wide area impacts downwind of freeways under pre-sunrise stable atmospheric conditions. Atmos. Environ. 62, 318–327. doi:10.1016/j.atmosenv.2012.07.084 Chow, J.C., Watson, J.G., Lowenthal, D.H., Chen, L.W.A., Zielinska, B., Mazzoleni, L.R., Magliano, K.L., 2007. Evaluation of organic markers for chemical mass balance source apportionment at the Fresno Supersite. Atmos Chem Phys 7, 1741–1754. doi:10.5194/acp-7-1741-2007 Corporan, E., DeWitt, M.J., Belovich, V., Pawlik, R., Lynch, A.C., Gord, J.R., Meyer, T.R., 2007. Emissions Characteristics of a Turbine Engine and Research Combustor Burning a Fischer−Tropsch Jet Fuel. Energy Fuels 21, 2615–2626. doi:10.1021/ef070015j Creason, J., L. Neas, et al. (001. Particulate matter and heart rate variability among elderly retirees: the Baltimore 1998 PM study. Journal of Exposure Analysis and Environmental Epidemiology 11 (2): 116-122 Dahl, A., Gharibi, A., Swietlicki, E., Gudmundsson, A., Bohgard, M., Ljungman, A., Blomqvist, G., Gustafsson, M., 2006. Traffic-generated emissions of ultrafine particles from pavement–tire interface. Atmos. Environ. 40, 1314–1323. doi:10.1016/j.atmosenv.2005.10.029 87 Dales, R., A. J. Wheeler, et al. 2009. The Influence of Neighborhood Roadways on Respiratory Symptoms Among Elementary Schoolchildren. Journal of Occupational and Environmental Medicine 51(6): 654-660 de Kok, T.M.C.M., Driece, H.A.L., Hogervorst, J.G.F., Briedé, J.J., 2006. Toxicological assessment of ambient and traffic-related particulate matter: A review of recent studies. Mutat. Res. Mutat. Res. 613, 103–122. doi:10.1016/j.mrrev.2006.07.001 Delfino, R.J., Gillen, D.L., Tjoa, T., Staimer, N., Polidori, A., Arhami, M., Sioutas, C., Longhurst, J., 2011. Electrocardiographic ST-segment depression and exposure to traffic-related aerosols in elderly subjects with coronary artery disease. Environ. Health Perspect. 119, 196–202. doi:10.1289/ehp.1002372 Delfino, R.J., Sioutas, C., Malik, S., 2005. Potential Role of Ultrafine Particles in Associations between Airborne Particle Mass and Cardiovascular Health. Environ. Health Perspect. 113, 934–946. Delfino, R.J., Staimer, N., Tjoa, T., Arhami, M., Polidori, A., Gillen, D.L., Kleinman, M.T., Schauer, J.J., Sioutas, C., 2010. Association of biomarkers of systemic inflammation with organic components and source tracers in quasi-ultrafine particles. Environ. Health Perspect. 118, 756–762. doi:10.1289/ehp.0901407 Ding, X., Zheng, M., Yu, L., Zhang, X., Weber, R.J., Yan, B., Russell, A.G., Edgerton, E.S., Wang, X., 2008. Spatial and Seasonal Trends in Biogenic Secondary Organic Aerosol Tracers and Water-Soluble Organic Carbon in the Southeastern United States. Environ. Sci. Technol. 42, 5171–5176. doi:10.1021/es7032636 Docherty, K.S., Stone, E.A., Ulbrich, I.M., DeCarlo, P.F., Snyder, D.C., Schauer, J.J., Peltier, R.E., Weber, R.J., Murphy, S.M., Seinfeld, J.H., Grover, B.D., Eatough, D.J., Jimenez, J.L., 2008. Apportionment of Primary and Secondary Organic Aerosols in Southern California during the 2005 Study of Organic Aerosols in Riverside (SOAR-1). Environ. Sci. Technol. 42, 7655–7662. doi:10.1021/es8008166 Dodson, R.E., Andres Houseman, E., Morin, B., Levy, J.I., 2009. An analysis of continuous black carbon concentrations in proximity to an airport and major roadways. Atmos. Environ. 43, 3764–3773. doi:10.1016/j.atmosenv.2009.04.014 Ensberg, J.J., Hayes, P.L., Jimenez, J.L., Gilman, J.B., Kuster, W.C., de Gouw, J.A., Holloway, J.S., Gordon, T.D., Jathar, S., Robinson, A.L., Seinfeld, J.H., 2014. Emission factor ratios, SOA mass yields, and the impact of vehicular emissions on SOA formation. Atmos Chem Phys 14, 2383–2397. doi:10.5194/acp-14- 2383-2014 Fang, T., Guo, H., Verma, V., Peltier, R.E., Weber, R.J., 2015. PM2.5 water-soluble elements in the southeastern United States: automated analytical method development, spatiotemporal distributions, source apportionment, and implications for heath studies. Atmos Chem Phys Discuss 15, 17189–17227. doi:10.5194/acpd-15-17189-2015 Fine, P.M., Cass, G.R., Simoneit, B.R.T., 2004a. Chemical Characterization of Fine Particle Emissions from the Fireplace Combustion of Wood Types Grown in the Midwestern and Western United States. Environ. Eng. Sci. 21, 387–409. doi:10.1089/109287504323067021 Fine, P.M., Chakrabarti, B., Krudysz, M., Schauer, J.J., Sioutas, C., 2004. Diurnal Variations of Individual Organic Compound Constituents of Ultrafine and Accumulation Mode Particulate Matter in the Los Angeles Basin. Environ. Sci. Technol. 38, 1296–1304. doi:10.1021/es0348389 Fisseha, R., Dommen, J., Sax, M., Paulsen, D., Kalberer, M., Maurer, R., Höfler, F., Weingartner, E., Baltensperger, U., 2004. Identification of Organic Acids in Secondary Organic Aerosol and the Corresponding Gas Phase from Chamber Experiments. Anal. Chem. 76, 6535–6540. doi:10.1021/ac048975f Gelencsér, A., May, B., Simpson, D., Sánchez-Ochoa, A., Kasper-Giebl, A., Puxbaum, H., Caseiro, A., Pio, C., Legrand, M., 2007. Source apportionment of PM2.5 organic aerosol over Europe: Primary/secondary, natural/anthropogenic, and fossil/biogenic origin. J. Geophys. Res. Atmospheres 112, D23S04. doi:10.1029/2006JD008094 Geller, M.D., Fine, P.M., Sioutas, C., 2004. The Relationship between Real-Time and Time-Integrated Coarse (2.5–10 μm), Intermodal (1–2.5 μm), and Fine (<2.5 μm) Particulate Matter in the Los Angeles Basin. J. Air Waste Manag. Assoc. 54, 1029–1039. doi:10.1080/10473289.2004.10470980 Geller, M.D., Ntziachristos, L., Mamakos, A., Samaras, Z., Schmitz, D.A., Froines, J.R., Sioutas, C., 2006. Physicochemical and redox characteristics of particulate matter (PM) emitted from gasoline and diesel passenger cars. Atmos. Environ. 40, 6988–7004. doi:10.1016/j.atmosenv.2006.06.018 Graham, L.A., Belisle, S.L., Baas, C.-L., 2008. Emissions from light duty gasoline vehicles operating on low blend ethanol gasoline and E85. Atmos. Environ. 42, 4498–4516. doi:10.1016/j.atmosenv.2008.01.061 Grosjean, D., Fung, K., Harrison, J., 1983. Interactions of polycyclic aromatic hydrocarbons with atmospheric pollutants. Environ. Sci. Technol. 17, 673–679. doi:10.1021/es00117a010 Gustafsson, M., Blomqvist, G., Gudmundsson, A., Dahl, A., Swietlicki, E., Bohgard, M., Lindbom, J., Ljungman, A., 2008. Properties and toxicological effects of particles from the interaction between tyres, road pavement and winter traction material. Sci. Total Environ. 393, 226–240. doi:10.1016/j.scitotenv.2007.12.030 Harrison, R.M., Jones, A.M., Gietl, J., Yin, J., Green, D.C., 2012. Estimation of the Contributions of Brake Dust, Tire Wear, and Resuspension to Nonexhaust Traffic Particles Derived from Atmospheric Measurements. Environ. Sci. Technol. 46, 6523–6529. doi:10.1021/es300894r Harrison, R.M., Yin, J., Mark, D., Stedman, J., Appleby, R.S., Booker, J., Moorcroft, S., 2001. Studies of the coarse particle (2.5–10 μm) component in UK urban atmospheres. Atmos. Environ. 35, 3667–3679. doi:10.1016/S1352-2310(00)00526-4 Hasheminassab, S., Daher, N., Ostro, B.D., Sioutas, C., 2014a. Long-term source apportionment of ambient fine particulate matter (PM2.5) in the Los Angeles Basin: A focus on emissions reduction from vehicular sources. Environ. Pollut. 193, 54–64. doi:10.1016/j.envpol.2014.06.012 Hasheminassab, S., Daher, N., Schauer, J.J., Sioutas, C., 2013a. Source apportionment and organic compound characterization of ambient ultrafine particulate matter (PM) in the Los Angeles Basin. Atmos. Environ. 79, 529–539. doi:10.1016/j.atmosenv.2013.07.040 Hasheminassab, S., Daher, N., Shafer, M.M., Schauer, J.J., Delfino, R.J., Sioutas, C., 2014d. Chemical characterization and source apportionment of indoor and outdoor fine particulate matter (PM2.5) in retirement communities of the Los Angeles Basin. Sci. Total Environ. 490, 528–537. doi:10.1016/j.scitotenv.2014.05.044 Hayes, P.L., Ortega, A.M., Cubison, M.J., Froyd, K.D., Zhao, Y., Cliff, S.S., Hu, W.W., Toohey, D.W., Flynn, J.H., Lefer, B.L., Grossberg, N., Alvarez, S., Rappenglück, B., Taylor, J.W., Allan, J.D., Holloway, J.S., Gilman, J.B., Kuster, W.C., de Gouw, J.A., Massoli, P., Zhang, X., Liu, J., Weber, R.J., Corrigan, A.L., Russell, L.M., Isaacman, G., Worton, D.R., Kreisberg, N.M., Goldstein, A.H., Thalman, R., Waxman, E.M., Volkamer, R., Lin, Y.H., Surratt, J.D., Kleindienst, T.E., Offenberg, J.H., Dusanter, S., Griffith, S., Stevens, P.S., Brioude, J., Angevine, W.M., Jimenez, J.L., 2013. Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign. J. Geophys. Res. Atmospheres 118, 9233–9257. doi:10.1002/jgrd.50530 Heo, J., Dulger, M., Olson, M.R., McGinnis, J.E., Shelton, B.R., Matsunaga, A., Sioutas, C., Schauer, J.J., 2013a. Source apportionments of PM2.5 organic carbon using molecular marker Positive Matrix Factorization and comparison of results from different receptor models. Atmos. Environ. 73, 51–61. doi:10.1016/j.atmosenv.2013.03.004 Hildemann, L.M., Markowski, G.R., Cass, G.R., 1991. Chemical composition of emissions from urban sources of fine organic aerosol. Environ. Sci. Technol. 25, 744– 759. doi:10.1021/es00016a021 Hinds, W.C., 1999. Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, 2nd Edition - William C. Hinds [WWW Document]. URL http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471194107.html (accessed 4.4.17). Hsu, H.-H., Adamkiewicz, G., Houseman, E.A., Zarubiak, D., Spengler, J.D., Levy, J.I., 2013. Contributions of aircraft arrivals and departures to ultrafine particle counts near Los Angeles International Airport. Sci. Total Environ. 444, 347–355. doi:10.1016/j.scitotenv.2012.12.010 Hu, S., Fruin, S., Kozawa, K., Mara, S., Winer, A.M., Paulson, S.E., 2009. Aircraft emission impacts in a neighborhood adjacent to a general aviation airport in southern California. Environ. Sci. Technol. 43, 8039–8045. doi:10.1021/es900975f Hu, S., Polidori, A., Arhami, M., Shafer, M.M., Schauer, J.J., Cho, A., Sioutas, C., 2008a. Redox activity and chemical speciation of size fractioned PM in the communities of the Los Angeles-Long Beach harbor. Atmos Chem Phys 8, 6439–6451. doi:10.5194/acp-8-6439-2008 Hudda, N., Fruin, S.A., 2016. International Airport Impacts to Air Quality: Size and Related Properties of Large Increases in Ultrafine Particle Number Concentrations. Environ. Sci. Technol. 50, 3362–3370. doi:10.1021/acs.est.5b05313 Hudda, N., Gould, T., Hartin, K., Larson, T.V., Fruin, S.A., 2014. Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind. Environ. Sci. Technol. 48, 6628–6635. doi:10.1021/es5001566 88 Hudda, N., Kostenidou, E., Sioutas, C., Delfino, R.J., Fruin, S.A., 2011. Vehicle and Driving Characteristics That Influence In-Cabin Particle Number Concentrations. Environ. Sci. Technol. 45, 8691–8697. doi:10.1021/es202025m Hudda, N., Simon, M.C., Zamore, W., Brugge, D., Durant, J.L., 2016. Aviation Emissions Impact Ambient Ultrafine Particle Concentrations in the Greater Boston Area. Environ. Sci. Technol. 50, 8514–8521. doi:10.1021/acs.est.6b01815 Hueglin, C., Gehrig, R., Baltensperger, U., Gysel, M., Monn, C., Vonmont, H., 2005. Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland. Atmos. Environ. 39, 637–651. doi:10.1016/j.atmosenv.2004.10.027 Hughes, L.S., Cass, G.R., Gone, J., Ames, M., Olmez, I., 1998. Physical and Chemical Characterization of Atmospheric Ultrafine Particles in the Los Angeles Area. Environ. Sci. Technol. 32, 1153–1161. doi:10.1021/es970280r Pope III, C.A., Dockery, D.W., 2006. Health Effects of Fine Particulate Air Pollution: Lines that Connect. J. Air Waste Manag. Assoc. 56, 709–742. doi:10.1080/10473289.2006.10464485 Iijima, A., Sato, K., Yano, K., Tago, H., Kato, M., Kimura, H., Furuta, N., 2007. Particle size and composition distribution analysis of automotive brake abrasion dusts for the evaluation of antimony sources of airborne particulate matter. Atmos. Environ. 41, 4908–4919. doi:10.1016/j.atmosenv.2007.02.005 Imrich, A., Ning, Y., Lawrence, J., Coull, B., Gitin, E., Knutson, M., Kobzik, L., 2007. Alveolar macrophage cytokine response to air pollution particles: Oxidant mechanisms. Toxicol. Appl. Pharmacol. 218, 256–264. doi:10.1016/j.taap.2006.11.033 Kam, W., Liacos, J.W., Schauer, J.J., Delfino, R.J., Sioutas, C., 2012. Size-segregated composition of particulate matter (PM) in major roadways and surface streets. Atmos. Environ. 55, 90–97. doi:10.1016/j.atmosenv.2012.03.028 Karner, A.A., Eisinger, D.S., Niemeier, D.A., 2010. Near-roadway air quality: synthesizing the findings from real-world data. Environ. Sci. Technol. 44, 5334–5344. doi:10.1021/es100008x Kinsey, J.S., Hays, M.D., Dong, Y., Williams, D.C., Logan, R., 2011. Chemical characterization of the fine particle emissions from commercial aircraft engines during the Aircraft Particle Emissions eXperiment (APEX) 1 to 3. Environ. Sci. Technol. 45, 3415–3421. doi:10.1021/es103880d Krasowsky, T., Daher, N., Sioutas, C., Ban-Weiss, G., 2015. Measurement of particulate matter emissions from in-use locomotives. Atmos. Environ. 113, 187–196. doi:10.1016/j.atmosenv.2015.04.046 Kumagai, Y., Koide, S., Taguchi, K., Endo, A., Nakai, Y., Yoshikawa, T., Shimojo, N., 2002a. Oxidation of Proximal Protein Sulfhydryls by Phenanthraquinone, a Component of Diesel Exhaust Particles. Chem. Res. Toxicol. 15, 483–489. doi:10.1021/tx0100993 Kumata, H., Yamada, J., Masuda, K., Takada, H., Sato, Y., Sakurai, T., Fujiwara, K., 2002. Benzothiazolamines as Tire-Derived Molecular Markers: Sorptive Behavior in Street Runoff and Application to Source Apportioning. Environ. Sci. Technol. 36, 702–708. doi:10.1021/es0155229 Liao, D. P., J. Creason, et al. 1999. Daily variation of particulate air pollution and poor cardiac autonomic control in the elderly. Environmental Health Perspectives 107(7): 521-525. Lall, R., Ito, K., Thurston, G.D., 2010. Distributed Lag Analyses of Daily Hospital Admissions and Source-Apportioned Fine Particle Air Pollution. Environ. Health Perspect. 119, 455–460. doi:10.1289/ehp.1002638 Landreman, A.P., Shafer, M.M., Hemming, J.C., Hannigan, M.P., Schauer, J.J., 2008. A Macrophage-Based Method for the Assessment of the Reactive Oxygen Species (ROS) Activity of Atmospheric Particulate Matter (PM) and Application to Routine (Daily-24 h) Aerosol Monitoring Studies. Aerosol Sci. Technol. 42, 946–957. doi:10.1080/02786820802363819 Laurikkala, J., Juhola, M., Kentala, E., 2000. Informal Identification of Outliers in Medical Data. Lee, D.S., Fahey, D.W., Forster, P.M., Newton, P.J., Wit, R.C.N., Lim, L.L., Owen, B., Sausen, R., 2009. Aviation and global climate change in the 21st century. Atmos. Environ. 43, 3520–3537. doi:10.1016/j.atmosenv.2009.04.024 Li, N., Sioutas, C., Cho, A., Schmitz, D., Misra, C., Sempf, J., Wang, M., Oberley, T., Froines, J., Nel, A., 2003a. Ultrafine particulate pollutants induce oxidative stress and mitochondrial damage. Environ. Health Perspect. 111, 455–460. Li, N., Wang, M., Bramble, L.A., Schmitz, D.A., Schauer, J.J., Sioutas, C., Harkema, J.R., Nel, A.E., 2009. The Adjuvant Effect of Ambient Particulate Matter Is Closely Reflected by the Particulate Oxidant Potential. Environ. Health Perspect. 117, 1116–1123. doi:10.1289/ehp.0800319 Li, Q., Wyatt, A., Kamens, R.M., 2009. Oxidant generation and toxicity enhancement of aged-diesel exhaust. Atmos. Environ., Air Pollution Related to Transport 43, 1037–1042. doi:10.1016/j.atmosenv.2008.11.018 Liacos, J.W., Kam, W., Delfino, R.J., Schauer, J.J., Sioutas, C., 2012. Characterization of organic, metal and trace element PM2.5 species and derivation of freeway- based emission rates in Los Angeles, CA. Sci. Total Environ. 435–436, 159–166. doi:10.1016/j.scitotenv.2012.06.106 Lobo, P., Hagen, D.E., Whitefield, P.D., 2011. Comparison of PM Emissions from a Commercial Jet Engine Burning Conventional, Biomass, and Fischer–Tropsch Fuels. Environ. Sci. Technol. 45, 10744–10749. doi:10.1021/es201902e Lobo, P., Hagen, D.E., Whitefield, P.D., Alofs, D.J., 2007. Physical Characterization or Aerosol Emissions from a Commercial Gas Turbine Engine. J. Propuls. Power 23, 919–929. doi:10.2514/1.26772 Lough, G.C., Christensen, C.G., Schauer, J.J., Tortorelli, J., Mani, E., Lawson, D.R., Clark, N.N., Gabele, P.A., 2007a. Development of Molecular Marker Source Profiles for Emissions from On-Road Gasoline and Diesel Vehicle Fleets. J. Air Waste Manag. Assoc. 57, 1190–1199. doi:10.3155/1047-3289.57.10.1190 Lurmann, F., Avol, E., Gilliland, F., 2015a. Emissions reduction policies and recent trends in Southern California’s ambient air quality. J. Air Waste Manag. Assoc. 65, 324–335. doi:10.1080/10962247.2014.991856 Lurmann, F., Avol, E., Gilliland, F., 2015b. Emissions reduction policies and recent trends in Southern California’s ambient air quality. J. Air Waste Manag. Assoc. 65, 324–335. doi:10.1080/10962247.2014.991856 Majestic, B.J., Schauer, J.J., Shafer, M.M., Fine, P.M., Singh, M., Sioutas, C., 2008. Trace metal analysis of atmospheric particulate matter: A comparison of personal and ambient samplersThe research described herein has not been subjected to the agency’s required peer and policy review and therefore does not necessarily reflect the views of the agency, and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use. J. Environ. Eng. Sci. 7, 289–298. doi:10.1139/S08-005 Manchester-Neesvig, J.B., Schauer, J.J., Cass, G.R., 2003. The Distribution of Particle-Phase Organic Compounds in the Atmosphere and Their Use for Source Apportionment during the Southern California Children’s Health Study. J. Air Waste Manag. Assoc. 53, 1065–1079. doi:10.1080/10473289.2003.10466265 Mar, T.F., Norris, G.A., Koenig, J.Q., Larson, T.V., 2000. Associations between air pollution and mortality in Phoenix, 1995-1997. Environ. Health Perspect. 108, 347– 353. Marcazzan, G.M., Vaccaro, S., Valli, G., Vecchi, R., 2001a. Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy). Atmos. Environ. 35, 4639–4650. doi:10.1016/S1352-2310(01)00124-8 Masiol, M., Harrison, R.M., 2014. Aircraft engine exhaust emissions and other airport-related contributions to ambient air pollution: A review. Atmos. Environ. 95, 409–455. doi:10.1016/j.atmosenv.2014.05.070 Mason, B., 1966. Principles of Geochemistry, Third. ed. New York, Wiley. Mazaheri, M., Johnson, G.R., Morawska, L., 2009. Particle and gaseous emissions from commercial aircraft at each stage of the landing and takeoff cycle. Environ. Sci. Technol. 43, 441–446. McDonald, B.C., Goldstein, A.H., Harley, R.A., 2015. Long-Term Trends in California Mobile Source Emissions and Ambient Concentrations of Black Carbon and Organic Aerosol. Environ. Sci. Technol. 49, 5178–5188. doi:10.1021/es505912b McDonald, J.D., Eide, I., Seagrave, J., Zielinska, B., Whitney, K., Lawson, D.R., Mauderly, J.L., 2004. Relationship between Composition and Toxicity of Motor Vehicle Emission Samples. Environ. Health Perspect. 112, 1527–1538. Metzger, K.B., Tolbert, P.E., Klein, M., Peel, J.L., Flanders, W.D., Todd, K., Mulholland, J.A., Ryan, P.B., Frumkin, H., 2004. Ambient air pollution and cardiovascular emergency department visits. Epidemiol. Camb. Mass 15, 46–56. doi:10.1097/01.EDE.0000101748.28283.97 Miguel, A.H., Kirchstetter, T.W., Harley, R.A., Hering, S.V., 1998. On-Road Emissions of Particulate Polycyclic Aromatic Hydrocarbons and Black Carbon from Gasoline and Diesel Vehicles. Environ. Sci. Technol. 32, 450–455. doi:10.1021/es970566w 89 Minguillón, M.C., Arhami, M., Schauer, J.J., Sioutas, C., 2008a. Seasonal and spatial variations of sources of fine and quasi-ultrafine particulate matter in neighborhoods near the Los Angeles–Long Beach harbor. Atmos. Environ. 42, 7317–7328. doi:10.1016/j.atmosenv.2008.07.036 Misra, C., Singh, M., Shen, S., Sioutas, C., Hall, P.M., 2002. Development and evaluation of a personal cascade impactor sampler (PCIS). J. Aerosol Sci. 33, 1027– 1047. doi:10.1016/S0021-8502(02)00055-1 Moller, P., Danielsen, P.H., Karottki, D.G., Jantzen, K., Roursgaard, M., Klingberg, H., Jensen, D.M., Christophersen, D.V., Hemmingsen, J.G., Cao, Y., Loft, S., 2014. Oxidative stress and inflammation generated DNA damage by exposure to air pollution particles. Mutat. Res.-Rev. Mutat. Res. 762, 133–166. doi:10.1016/j.mrrev.2014.09.001 Morgan, T.E., Davis, D.A., Iwata, N., Tanner, J.A., Snyder, D., Ning, Z., Kam, W., Hsu, Y.-T., Winkler, J.W., Chen, J.-C., Petasis, N.A., Baudry, M., Sioutas, C., Finch, C.E., 2011. Glutamatergic neurons in rodent models respond to nanoscale particulate urban air pollutants in vivo and in vitro. Environ. Health Perspect. 119, 1003–1009. doi:10.1289/ehp.1002973 Murphy, D.M., Anderson, J.R., Quinn, P.K., McInnes, L.M., Brechtel, F.J., Kreidenweis, S.M., Middlebrook, A.M., Pósfai, M., Thomson, D.S., Buseck, P.R., 1998. Influence of sea-salt on aerosol radiative properties in the Southern Ocean marine boundary layer. Nature 392, 62–65. doi:10.1038/32138 Narváez, R.F., Hoepner, L., Chillrud, S.N., Yan, B., Garfinkel, R., Whyatt, R., Camann, D., Perera, F.P., Kinney, P.L., Miller, R.L., 2008. Spatial and Temporal Trends of Polycyclic Aromatic Hydrocarbons and Other Traffic-Related Airborne Pollutants in New York City. Environ. Sci. Technol. 42, 7330–7335. doi:10.1021/es801273h Nel, A., 2005. Air Pollution-Related Illness: Effects of Particles. Science 308, 804–806. doi:10.1126/science.1108752 Nel, A., Xia, T., Mädler, L., Li, N., 2006. Toxic Potential of Materials at the Nanolevel. Science 311, 622–627. doi:10.1126/science.1114397 Ning, Z., Geller, M.D., Moore, K.F., Sheesley, R., Schauer, J.J., Sioutas, C., 2007a. Daily Variation in Chemical Characteristics of Urban Ultrafine Aerosols and Inference of Their Sources. Environ. Sci. Technol. 41, 6000–6006. doi:10.1021/es070653g Ning, Z., N. Hudda, et al. 2010. Impact of roadside noise barriers on particle size distributions and pollutants concentrations near freeways. Atmospheric Environment 44(26): 3118-3127 Ntziachristos, L., Froines, J.R., Cho, A.K., Sioutas, C., 2007. Relationship between redox activity and chemical speciation of size-fractionated particulate matter. Part. Fibre Toxicol. 4, 5. doi:10.1186/1743-8977-4-5 Oberdörster, G., Oberdörster, E., Oberdörster, J., 2005. Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environ. Health Perspect. 113, 823–839. Oberdörster, G., Sharp, Z., Atudorei, V., Elder, A., Gelein, R., Kreyling, W., Cox, C., 2004. Translocation of Inhaled Ultrafine Particles to the Brain. Inhal. Toxicol. 16, 437–445. doi:10.1080/08958370490439597 Oliveira, C., Pio, C., Alves, C., Evtyugina, M., Santos, P., Gonçalves, V., Nunes, T., Silvestre, A.J.D., Palmgren, F., Wåhlin, P., Harrad, S., 2007. Seasonal distribution of polar organic compounds in the urban atmosphere of two large cities from the North and South of Europe. Atmos. Environ. 41, 5555–5570. doi:10.1016/j.atmosenv.2007.03.001 Ostro, B., Tobias, A., Querol, X., Alastuey, A., Amato, F., Pey, J., Perez, N., Sunyer, J., 2011. The Effects of Particulate Matter Sources on Daily Mortality: A Case- Crossover Study of Barcelona, Spain. Environ. Health Perspect. 119, 1781–1787. Pakbin, P., Ning, Z., Shafer, M.M., Schauer, J.J., Sioutas, C., 2011. Seasonal and Spatial Coarse Particle Elemental Concentrations in the Los Angeles Area. Aerosol Sci. Technol. 45, 949–963. doi:10.1080/02786826.2011.571309 Pant, P., Harrison, R.M., 2013. Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: A review. Atmos. Environ. 77, 78–97. doi:10.1016/j.atmosenv.2013.04.028 Pant, P., Yin, J., Harrison, R.M., 2014. Sensitivity of a Chemical Mass Balance model to different molecular marker traffic source profiles. Atmos. Environ. 82, 238– 249. doi:10.1016/j.atmosenv.2013.10.005 Petzold, A., Stein, C., Nyeki, S., Gysel, M., Weingartner, E., Baltensperger, U., Giebl, H., Hitzenberger, R., Döpelheuer, A., Vrchoticky, S., Puxbaum, H., Johnson, M., Hurley, C.D., Marsh, R., Wilson, C.W., 2003. Properties of jet engine combustion particles during the PartEmis experiment: Microphysics and Chemistry. Geophys. Res. Lett. 30, 1719. doi:10.1029/2003GL017283 Polidori, A., Hu, S., Biswas, S., Delfino, R.J., Sioutas, C., 2008. Real-time characterization of particle-bound polycyclic aromatic hydrocarbons in ambient aerosols and from motor-vehicle exhaust. Atmos Chem Phys 8, 1277–1291. doi:10.5194/acp-8-1277-2008 Posner, L.N., Pandis, S.N., n.d. Sources of ultrafine particles in the Eastern United States. Atmos. Environ. doi:10.1016/j.atmosenv.2015.03.033 Pratsinis, S., Ellis, E.C., Novakov, T., Friedlander, S.K., 1984. The Carbon Containing Component of the Los Angeles Aerosol: Source Apportionment and Contributions to the Visibility Budget. J. Air Pollut. Control Assoc. 34, 643–650. doi:10.1080/00022470.1984.10465792 Preble, C.V., Dallmann, T.R., Kreisberg, N.M., Hering, S.V., Harley, R.A., Kirchstetter, T.W., 2015. Effects of Particle Filters and Selective Catalytic Reduction on Heavy-Duty Diesel Drayage Truck Emissions at the Port of Oakland. Environ. Sci. Technol. 49, 8864–8871. doi:10.1021/acs.est.5b01117 Reddy, C.M., Quinn, J.G., 1997. Environmental Chemistry of Benzothiazoles Derived from Rubber. Environ. Sci. Technol. 31, 2847–2853. doi:10.1021/es970078o Ren, J., Liu, J., Li, F., Cao, X., Ren, S., Xu, B., Zhu, Y., 2016. A study of ambient fine particles at Tianjin International Airport, China. Sci. Total Environ. 556, 126– 135. doi:10.1016/j.scitotenv.2016.02.186 Riley, E.A., Gould, T., Hartin, K., Fruin, S.A., Simpson, C.D., Yost, M.G., Larson, T., 2016. Ultrafine particle size as a tracer for aircraft turbine emissions. Atmos. Environ. 139, 20–29. doi:10.1016/j.atmosenv.2016.05.016 Rima Habre, Sandrah P. Eckel, Scott Fruin, Temuulen Enebish, Edward Rappaport, Frank Gilliland, 2016. Short-Term Effects of Airport-Associated Ultrafine Particle Exposure on Lung Function and Inflammation, in: B48. ASTHMA: INSIGHTS FROM THE BENCH, GENETICS, AND EPIDEMIOLOGY, American Thoracic Society International Conference Abstracts. American Thoracic Society, pp. A3699–A3699. Robinson, A.L., Grieshop, A.P., Donahue, N.M., Hunt, S.W., 2010. Updating the Conceptual Model for Fine Particle Mass Emissions from Combustion Systems Allen L. Robinson. J. Air Waste Manag. Assoc. 60, 1204–1222. doi:10.3155/1047-3289.60.10.1204 Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1997a. Sources of Fine Organic Aerosol. 8. Boilers Burning No. 2 Distillate Fuel Oil. Environ. Sci. Technol. 31, 2731–2737. doi:10.1021/es9609563 Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1993a. Sources of fine organic aerosol. 5. Natural gas home appliances. Environ. Sci. Technol. 27, 2736–2744. doi:10.1021/es00049a012 Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1993b. Sources of fine organic aerosol. 4. Particulate abrasion products from leaf surfaces of urban plants. Environ. Sci. Technol. 27, 2700–2711. doi:10.1021/es00049a008 Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1991. Sources of fine organic aerosol. 1. Charbroilers and meat cooking operations. Environ. Sci. Technol. 25, 1112–1125. doi:10.1021/es00018a015 Ruehl, C.R., Ham, W.A., Kleeman, M.J., 2011. Temperature-induced volatility of molecular markers in ambient airborne particulate matter. Atmos Chem Phys 11, 67– 76. doi:10.5194/acp-11-67-2011 Saffari, A., Daher, N., Shafer, M.M., Schauer, J.J., Sioutas, C., 2014a. Seasonal and spatial variation in dithiothreitol (DTT) activity of quasi-ultrafine particles in the Los Angeles Basin and its association with chemical species. J. Environ. Sci. Health Part A 49, 441–451. doi:10.1080/10934529.2014.854677 Saffari, A., Daher, N., Shafer, M.M., Schauer, J.J., Sioutas, C., 2013a. Seasonal and spatial variation of trace elements and metals in quasi-ultrafine (PM 0.25) particles in the Los Angeles metropolitan area and characterization of their sources. Environ. Pollut. 181, 14–23. Saffari, A., Hasheminassab, S., Wang, D., Shafer, M.M., Schauer, J.J., Sioutas, C., 2015a. Impact of primary and secondary organic sources on the oxidative potential of quasi-ultrafine particles (PM0.25) at three contrasting locations in the Los Angeles Basin. Atmos. Environ. 120, 286–296. doi:10.1016/j.atmosenv.2015.09.022 Saldiva, P.H.N., Clarke, R.W., Coull, B.A., Stearns, R.C., Lawrence, J., Murthy, G.G.K., Diaz, E., Koutrakis, P., Suh, H., Tsuda, A., Godleski, J.J., 2002. Lung Inflammation Induced by Concentrated Ambient Air Particles Is Related to Particle Composition. Am. J. Respir. Crit. Care Med. 165, 1610–1617. doi:10.1164/rccm.2106102 90 Samet, J.M., Dominici, F., Curriero, F.C., Coursac, I., Zeger, S.L., 2000. Fine Particulate Air Pollution and Mortality in 20 U.S. Cities, 1987–1994. N. Engl. J. Med. 343, 1742–1749. doi:10.1056/NEJM200012143432401 Sanders, P.G., Xu, N., Dalka, T.M., Maricq, M.M., 2003. Airborne Brake Wear Debris: Size Distributions, Composition, and a Comparison of Dynamometer and Vehicle Tests. Environ. Sci. Technol. 37, 4060–4069. doi:10.1021/es034145s Sanderson, P., Delgado-Saborit, J.M., Harrison, R.M., 2014. A review of chemical and physical characterisation of atmospheric metallic nanoparticles. Atmos. Environ. 94, 353–365. doi:10.1016/j.atmosenv.2014.05.023 Sardar, S.B., Fine, P.M., Mayo, P.R., Sioutas, C., 2005. Size-Fractionated Measurements of Ambient Ultrafine Particle Chemical Composition in Los Angeles Using the NanoMOUDI. Environ. Sci. Technol. 39, 932–944. doi:10.1021/es049478j Schauer, J.J., 2003. Evaluation of elemental carbon as a marker for diesel particulate matter. J. Expo. Sci. Environ. Epidemiol. 13, 443–453. doi:10.1038/sj.jea.7500298 Schauer, J.J., 1998. Source contributions to atmospheric organic compound concentrations : emissions measurements and model predictions (phd). California Institute of Technology. Schauer, J.J., S., Gc, L., Mm, S., Wf, C., Mf, A., Jt, D., Js, P., 2006. Characterization of metals emitted from motor vehicles. Res. Rep. Health Eff. Inst. 1-76-88. Schauer, J.J., Cass, G.R., 2000. Source Apportionment of Wintertime Gas-Phase and Particle-Phase Air Pollutants Using Organic Compounds as Tracers. Environ. Sci. Technol. 34, 1821–1832. doi:10.1021/es981312t Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 2002. Measurement of Emissions from Air Pollution Sources. 5. C1−C32 Organic Compounds from Gasoline-Powered Motor Vehicles. Environ. Sci. Technol. 36, 1169–1180. doi:10.1021/es0108077 Schauer, J.J., Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1996a. Source apportionment of airborne particulate matter using organic compounds as tracers. Atmos. Environ. 30, 3837–3855. doi:10.1016/1352-2310(96)00085-4 Schoonen, M.A.A., Cohn, C.A., Roemer, E., Laffers, R., Simon, S.R., O’Riordan, T., 2006. Mineral-Induced Formation of Reactive Oxygen Species. Rev. Mineral. Geochem. 64, 179–221. doi:10.2138/rmg.2006.64.7 Schwartz, J., Laden, F., Zanobetti, A., 2002. The concentration-response relation between PM(2.5) and daily deaths. Environ. Health Perspect. 110, 1025–1029. Seinfeld, J.H., Pandis, S.N., 2006. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd Edition - John H. Seinfeld, Spyros N. Pandis [WWW Document]. URL http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118947401.html (accessed 4.4.17). Sheesley, R.J., Schauer, J.J., Zheng, M., Wang, B., 2007. Sensitivity of molecular marker-based CMB models to biomass burning source profiles. Atmos. Environ. 41, 9050–9063. doi:10.1016/j.atmosenv.2007.08.011 Shirmohammadi, F., Hasheminassab, S., Wang, D., Saffari, A., Schauer, J.J., Shafer, M.M., Delfino, R.J., Sioutas, C., 2015a. Oxidative potential of coarse particulate matter (PM10–2.5) and its relation to water solubility and sources of trace elements and metals in the Los Angeles Basin. Environ. Sci. Process. Impacts. doi:10.1039/C5EM00364D Shirmohammadi, F., Hasheminassab, S., Wang, D., Schauer, J.J., Shafer, M.M., Delfino, R.J., Sioutas, C., 2016. The relative importance of tailpipe and non-tailpipe emissions on the oxidative potential of ambient particles in Los Angeles, CA. Faraday Discuss. 189, 361–380. doi:10.1039/C5FD00166H Shirmohammadi, F., Sowlat, M.H., Hasheminassab, S., Saffari, A., Ban-Weiss, G., Sioutas, C., 2017. Emission rates of particle number, mass and black carbon by the Los Angeles International Airport (LAX) and its impact on air quality in Los Angeles. Atmos. Environ. 151, 82–93. doi:10.1016/j.atmosenv.2016.12.005 Simoneit, B.R.T., 1999. A review of biomarker compounds as source indicators and tracers for air pollution. Environ. Sci. Pollut. Res. 6, 159–169. doi:10.1007/BF02987621 Simoneit, B.R.T., 1986. Characterization of Organic Constituents in Aerosols in Relation to Their rigin and Transport: A Review. Int. J. Environ. Anal. Chem. 23, 207– 237. doi:10.1080/03067318608076446 Solomon, P.A., Costantini, M., Grahame, T.J., Gerlofs-Nijland, M.E., Cassee, F.R., Russell, A.G., Brook, J.R., Hopke, P.K., Hidy, G., Phalen, R.F., Saldiva, P., Sarnat, S.E., Balmes, J.R., Tager, I.B., Özkaynak, H., Vedal, S., Wierman, S.S.G., Costa, D.L., 2012. Air pollution and health: bridging the gap from sources to health outcomes: conference summary. Air Qual. Atmosphere Health 5, 9–62. doi:10.1007/s11869-011-0161-4 Sorooshian, A., Ng, N.L., Chan, A.W.H., Feingold, G., Flagan, R.C., Seinfeld, J.H., 2007. Particulate organic acids and overall water-soluble aerosol composition measurements from the 2006 Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS): ORGANIC ACID AEROSOL DURING GOMACCS 2006. J. Geophys. Res. Atmospheres 112, n/a-n/a. doi:10.1029/2007JD008537 Sowlat, M.H., Hasheminassab, S., Sioutas, C., 2016. Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF). Atmos Chem Phys 16, 4849–4866. doi:10.5194/acp-16-4849-2016 Squadrito, G.L., Cueto, R., Dellinger, B., Pryor, W.A., 2001. Quinoid redox cycling as a mechanism for sustained free radical generation by inhaled airborne particulate matter. Free Radic. Biol. Med. 31, 1132–1138. doi:10.1016/S0891-5849(01)00703-1 Stettler, M.E.J., Boies, A.M., Petzold, A., Barrett, S.R.H., 2013. Global civil aviation black carbon emissions. Environ. Sci. Technol. 47, 10397–10404. doi:10.1021/es401356v Stone, E.A., Snyder, D.C., Sheesley, R.J., Sullivan, A.P., Weber, R.J., Schauer, J.J., 2008a. Source apportionment of fine organic aerosol in Mexico City during the MILAGRO experiment 2006. Atmos Chem Phys 8, 1249–1259. doi:10.5194/acp-8-1249-2008 Stone, E.A., Snyder, D.C., Sheesley, R.J., Sullivan, A.P., Weber, R.J., Schauer, J.J., 2008b. Source apportionment of fine organic aerosol in Mexico City during the MILAGRO experiment 2006. Atmos Chem Phys 8, 1249–1259. doi:10.5194/acp-8-1249-2008 Stone, E.A., Snyder, D.C., Sheesley, R.J., Sullivan, A.P., Weber, R.J., Schauer, J.J., 2008c. Source apportionment of fine organic aerosol in Mexico City during the MILAGRO experiment 2006. Atmos Chem Phys 8, 1249–1259. doi:10.5194/acp-8-1249-2008 Subramanian, R., Donahue, N.M., Bernardo-Bricker, A., Rogge, W.F., Robinson, A.L., 2006. Contribution of motor vehicle emissions to organic carbon and fine particle mass in Pittsburgh, Pennsylvania: Effects of varying source profiles and seasonal trends in ambient marker concentrations. Atmos. Environ. 40, 8002–8019. doi:10.1016/j.atmosenv.2006.06.055 Tao, F., Gonzalez-Flecha, B., Kobzik, L., 2003. Reactive oxygen species in pulmonary inflammation by ambient particulates. Free Radic. Biol. Med. 35, 327–340. doi:10.1016/S0891-5849(03)00280-6 Thorpe, A., Harrison, R.M., 2008. Sources and properties of non-exhaust particulate matter from road traffic: A review. Sci. Total Environ. 400, 270–282. doi:10.1016/j.scitotenv.2008.06.007 Timko, M.T., Onasch, T.B., Northway, M.J., Jayne, J.T., Canagaratna, M.R., Herndon, S.C., Wood, E.C., Miake-Lye, R.C., Knighton, W.B., 2010. Gas Turbine Engine Emissions—Part II: Chemical Properties of Particulate Matter. J. Eng. Gas Turbines Power 132, 061505–061505. doi:10.1115/1.4000132 Tonne, C., S. Melly, et al. 2007. A case-control analysis of exposure to traffic and acute myocardial infarction. Environmental Health Perspectives 115(1): 53-57. Turpin, B.J., Lim, H.-J., 2001. Species Contributions to PM2.5 Mass Concentrations: Revisiting Common Assumptions for Estimating Organic Mass. Aerosol Sci. Technol. 35, 602–610. doi:10.1080/02786820119445 Valavanidis, A., FIOTAKIS, K., VLACHOGIANNI, T., 2008. Airborne Particulate Matter and Human Health: Toxicological Assessment and Importance of Size and Composition of Particles for Oxidative Damage and Carcinogenic Mechanisms. J. Environ. Sci. Health Part C 26, 339–362. doi:10.1080/10590500802494538 Verma, V., Fang, T., Guo, H., King, L., Bates, J.T., Peltier, R.E., Edgerton, E., Russell, A.G., Weber, R.J., 2014a. Reactive oxygen species associated with water- soluble PM2.5 in the southeastern United States: spatiotemporal trends and source apportionment. Atmos Chem Phys 14, 12915–12930. doi:10.5194/acp-14- 12915-2014 Verma, V., Fang, T., Xu, L., Peltier, R.E., Russell, A.G., Ng, N.L., Weber, R.J., 2015. Organic Aerosols Associated with the Generation of Reactive Oxygen Species (ROS) by Water-Soluble PM2.5. Environ. Sci. Technol. 49, 4646–4656. doi:10.1021/es505577w Verma, V., Ning, Z., Cho, A.K., Schauer, J.J., Shafer, M.M., Sioutas, C., 2009a. Redox activity of urban quasi-ultrafine particles from primary and secondary sources. Atmos. Environ. 43, 6360–6368. doi:10.1016/j.atmosenv.2009.09.019 Verma, V., Pakbin, P., Cheung, K.L., Cho, A.K., Schauer, J.J., Shafer, M.M., Kleinman, M.T., Sioutas, C., 2010. Physicochemical and Toxicological Characteristics of Semi-volatile Components of Atmospheric Aerosols in an Urban Environment. AGU Fall Meet. Abstr. 1, 1. 91 Verma, V., Polidori, A., Schauer, J.J., Shafer, M.M., Cassee, F.R., Sioutas, C., 2009c. Physicochemical and Toxicological Profiles of Particulate Matter in Los Angeles during the October 2007 Southern California Wildfires. Environ. Sci. Technol. 43, 954–960. doi:10.1021/es8021667 Verma, V., Shafer, M.M., Schauer, J.J., Sioutas, C., 2010. Contribution of transition metals in the reactive oxygen species activity of PM emissions from retrofitted heavy-duty vehicles. Atmos. Environ. 44, 5165–5173. doi:10.1016/j.atmosenv.2010.08.052 Verma, V., Weber, R.J.J., Fang, T., Xu, L., Ng, N.L., Russell, A.G., 2014c. Biomass burning as an important source of reactive oxygen species associated with the atmospheric aerosols in Southeastern United States - Implications for health effects of ambient particulate matter. AGU Fall Meet. Abstr. 53, 3204. Wåhlin, P., Berkowicz, R., Palmgren, F., 2006. Characterisation of traffic-generated particulate matter in Copenhagen. Atmos. Environ. 40, 2151–2159. doi:10.1016/j.atmosenv.2005.11.049 W. Kirchstetter, T., Harley, R.A., Kreisberg, N.M., Stolzenburg, M.R., Hering, S.V., 1999. On-road measurement of fine particle and nitrogen oxide emissions from light- and heavy-duty motor vehicles. Atmos. Environ. 33, 2955–2968. doi:10.1016/S1352-2310(99)00089-8 Wang, G., Kawamura, K., Cheng, C., Li, J., Cao, J., Zhang, R., Zhang, T., Liu, S., Zhao, Z., 2012. Molecular Distribution and Stable Carbon Isotopic Composition of Dicarboxylic Acids, Ketocarboxylic Acids, and α-Dicarbonyls in Size-Resolved Atmospheric Particles From Xi’an City, China. Environ. Sci. Technol. 46, 4783–4791. doi:10.1021/es204322c Watson, J.G., Cooper, J.A., Huntzicker, J.J., 1984a. The effective variance weighting for least squares calculations applied to the mass balance receptor model. Atmospheric Environ. 1967 18, 1347–1355. doi:10.1016/0004-6981(84)90043-X Watson, J.G., Cooper, J.A., Huntzicker, J.J., 1984b. The effective variance weighting for least squares calculations applied to the mass balance receptor model. Atmospheric Environ. 1967 18, 1347–1355. doi:10.1016/0004-6981(84)90043-X Weber, R.J., Sullivan, A.P., Peltier, R.E., Russell, A., Yan, B., Zheng, M., de Gouw, J., Warneke, C., Brock, C., Holloway, J.S., Atlas, E.L., Edgerton, E., 2007. A study of secondary organic aerosol formation in the anthropogenic-influenced southeastern United States. J. Geophys. Res. Atmospheres 112, D13302. doi:10.1029/2007JD008408 Westerdahl, D., Fruin, S.A., Fine, P.L., Sioutas, C., 2008. The Los Angeles International Airport as a source of ultrafine particles and other pollutants to nearby communities. Atmos. Environ. 42, 3143–3155. doi:10.1016/j.atmosenv.2007.09.006 Westerdahl, D., Fruin, S., Sax, T., Fine, P.M., Sioutas, C., 2005. Mobile platform measurements of ultrafine particles and associated pollutant concentrations on freeways and residential streets in Los Angeles. Atmos. Environ. 39, 3597–3610. doi:10.1016/j.atmosenv.2005.02.034 Wey, C.C., Anderson, B.A., Wey, C., Miake-Lye, R.C., Whitefield, P., Howard, R., 2007. Overview on the Aircraft Particle Emissions Experiment (APEX). J. Propuls. Power 23, 898–905. doi:10.2514/1.26406 Williams, B.J., Goldstein, A.H., Kreisberg, N.M., Hering, S.V., Worsnop, D.R., Ulbrich, I.M., Docherty, K.S., Jimenez, J.L., 2010. Major components of atmospheric organic aerosol in southern California as determined by hourly measurements of source marker compounds. Atmos Chem Phys 10, 11577–11603. doi:10.5194/acp-10-11577-2010 Woodward, N.C., Pakbin, P., Saffari, A., Shirmohammadi, F., Haghani, A., Sioutas, C., Cacciottolo, M., Morgan, T.E., Finch, C.E., 2017. Traffic-related air pollution impact on mouse brain accelerates myelin and neuritic aging changes with specificity for CA1 neurons. Neurobiol. Aging 53, 48–58. doi:10.1016/j.neurobiolaging.2017.01.007 Woody, M., Haeng Baek, B., Adelman, Z., Omary, M., Fat Lam, Y., Jason West, J., Arunachalam, S., 2011. An assessment of Aviation’s contribution to current and future fine particulate matter in the United States. Atmos. Environ. 45, 3424–3433. doi:10.1016/j.atmosenv.2011.03.041 Wu, W., Muller, R., Berhane, K., Fruin, S., Liu, F., Jaspers, I., Diaz-Sanchez, D., Peden, D.B., McConnell, R., 2014. Inflammatory Response of Monocytes to Ambient Particles Varies by Highway Proximity. Am. J. Respir. Cell Mol. Biol. 51, 802–809. doi:10.1165/rcmb.2013-0265OC Xia, T., Korge, P., Weiss, J.N., Li, N., Venkatesen, M.I., Sioutas, C., Nel, A., 2004. Quinones and Aromatic Chemical Compounds in Particulate Matter Induce Mitochondrial Dysfunction: Implications for Ultrafine Particle Toxicity. Environ. Health Perspect. 112, 1347–1358. Xia, T., Kovochich, M., Brant, J., Hotze, M., Sempf, J., Oberley, T., Sioutas, C., Yeh, J.I., Wiesner, M.R., Nel, A.E., 2006. Comparison of the Abilities of Ambient and Manufactured Nanoparticles To Induce Cellular Toxicity According to an Oxidative Stress Paradigm. Nano Lett. 6, 1794–1807. doi:10.1021/nl061025k Yang, A., Janssen, N.A.H., Brunekreef, B., Cassee, F.R., Hoek, G., Gehring, U., 2016. Children’s respiratory health and oxidative potential of PM2.5: the PIAMA birth cohort study. Occup Env. Med oemed-2015-103175. doi:10.1136/oemed-2015-103175 Yim, S.H.L., Stettler, M.E.J., Barrett, S.R.H., 2013. Air quality and public health impacts of UK airports. Part II: Impacts and policy assessment. Atmos. Environ. 67, 184–192. doi:10.1016/j.atmosenv.2012.10.017 Yli-Tuomi, T., Aarnio, P., Pirjola, L., Mäkelä, T., Hillamo, R., Jantunen, M., 2005. Emissions of fine particles, NOx, and CO from on-road vehicles in Finland. Atmos. Environ. 39, 6696–6706. doi:10.1016/j.atmosenv.2005.07.049 Zhang, X., Lin, Y.-H., Surratt, J.D., Weber, R.J., 2013. Sources, Composition and Absorption Ångström Exponent of Light-absorbing Organic Components in Aerosol Extracts from the Los Angeles Basin. Environ. Sci. Technol. 47, 3685–3693. doi:10.1021/es305047b Zhang, Y., Schauer, J.J., Shafer, M.M., Hannigan, M.P., Dutton, S.J., 2008. Source Apportionment of in Vitro Reactive Oxygen Species Bioassay Activity from Atmospheric Particulate Matter. Environ. Sci. Technol. 42, 7502–7509. doi:10.1021/es800126y Zheng, M., Cass, G.R., Schauer, J.J., Edgerton, E.S., 2002a. Source Apportionment of PM2.5 in the Southeastern United States Using Solvent-Extractable Organic Compounds as Tracers. Environ. Sci. Technol. 36, 2361–2371. doi:10.1021/es011275x Zhu, Y., Eiguren-Fernandez, A., Hinds, W.C., Miguel, A.H., 2007. In-Cabin Commuter Exposure to Ultrafine Particles on Los Angeles Freeways. Environ. Sci. Technol. 41, 2138–2145. doi:10.1021/es0618797 Zhu, Y., Fanning, E., Yu, R.C., Zhang, Q., Froines, J.R., 2011. Aircraft emissions and local air quality impacts from takeoff activities at a large International Airport. Atmos. Environ. 45, 6526–6533. doi:10.1016/j.atmosenv.2011.08.062 Zhu, Y., Hinds, W.C., Kim, S., Sioutas, C., 2002. Concentration and Size Distribution of Ultrafine Particles Near a Major Highway. J. Air Waste Manag. Assoc. 52, 1032–1042. doi:10.1080/10473289.2002.10470842 92

Abstract (if available)

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

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

PDF

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

PDF

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

PDF

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

PDF

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

PDF

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

PDF

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

PDF

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

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

PDF

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

PDF

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

PDF

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

PDF

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

PDF

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

PDF

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

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

PDF

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

PDF

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

PDF

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

PDF

Mechanics of abrasion emissions of particulate matter and microplastics

PDF

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

PDF

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

Asset Metadata

Creator Shirmohammadi, Farimah (author)

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

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Engineering (Environmental Engineering)

Publication Date 08/15/2018

Defense Date 05/08/2018

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

Tag air quality,chemical composition,historical trends,Los Angeles,OAI-PMH Harvest,particulate matter,toxicological properties,traffic emissions

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sioutas, Constantinos (committee chair), Ban-Weiss, George (committee member), Finch, Caleb (committee member)

Creator Email farimah.shirmohammadi@gmail.com,shirmoha@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-70826

Unique identifier UC11668928

Identifier etd-Shirmohamm-6756.pdf (filename),usctheses-c89-70826 (legacy record id)

Legacy Identifier etd-Shirmohamm-6756.pdf

Dmrecord 70826

Document Type Dissertation

Format application/pdf (imt)

Rights Shirmohammadi, Farimah

Type texts

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

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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