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

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Content ASSESSING INDOOR AND OUTDOOR AIR QUALITY BY CHARACTERIZING THE PHYSICOCHEMICAL AND TOXICOLOGICAL PROPERTIES OF PARTICULATE MATTER USING WELL-DEVELOPED SAMPLING TECHNOLOGIES AND MULTIVARIATE STATISTICAL ANALYSIS by Mohammad Aldekheel A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ENVIRONMENTAL ENGINEERING) August 2024 Copyright 2024 Mohammad Aldekheel ii Dedication To my beloved parents, brothers, and sisters, for the unwavering support and boundless love you have all generously provided. Your financial and emotional backing, coupled with your steadfast encouragement, has added resilience to my PhD journey. To my dear wife, whose presence has been a constant reminder of the beauty and depth of our shared dreams. Your love, patience, understanding, and companionship have been the light guiding me through this academic voyage. Your unwavering support and sacrifices have not gone unnoticed and have profoundly impacted my journey. And to my precious 10-month-old baby Khaled, though you are too young to grasp the significance of this moment, you have been my greatest inspiration and motivation. Your innocent smiles and joyful laughter serve as a constant reminder of the beauty and simplicity in life, guiding me through the most challenging moments. iii Acknowledgments These studies were financially supported by National Institute of Health (grants: R01ES029395 and R01ES032806) and the dean’s office at USC Viterbi School of Engineering. We are grateful to the financial support from Kuwait University and University of Southern California Ph.D. fellowship awards. I would like to express my deepest appreciation to my supervisor, Professor Constantinos Sioutas. It was impossible to perform and finish each of these studies without his thoughtful mentorship and unconditional support during the way. It has been an honor for me to work under his supervision. My deepest appreciation also goes to Eng. Fahad Alkudari for his continuous efforts in conducting the sampling campaign in Kuwait and to Dr. Ali Al-Hemoud who offered me the location for conducting our in-field sampling at Kuwait Institute of Scientific Research. My gratitude also goes to my former and current colleagues and groupmates at Aerosol lab of the University of Southern California due to their productive collaboration and support during the research projects: Dr. Abdulmalik Altuwayjiri Dr. Vahid Jalali Farahani Dr. Ramin Tohidi Mehdi Badami Yashar Aghaei Lastly, I would like to express my gratitude to my Ph.D. candidacy and defense committee members for their insightful feedback and guidance: Professor Constantinos Sioutas (Chair) Professor Fokion Egolfopoulos Professor Daniel McCurry Professor Lucio Soibelman Professor William Mack iv Table of Contents Dedication....................................................................................................................................... ii Acknowledgments..........................................................................................................................iii List of tables................................................................................................................................... vi List of figures................................................................................................................................ vii Abstract........................................................................................................................................... x Chapter 1 - Introduction.................................................................................................................. 1 1.1. Background ...................................................................................................................... 1 1.2. Overview .......................................................................................................................... 3 1.3. List of objectives.............................................................................................................. 6 Chapter 2 - The role of portable air purifiers and effective ventilation in improving indoor air quality in university classrooms ................................................................................................ 8 2.1. Introduction ...................................................................................................................... 8 2.2. Methods.......................................................................................................................... 10 2.2.1. Measurement sites and protocol.............................................................................. 10 2.2.2. Instrumentation ....................................................................................................... 14 2.2.3. Additional calculations ........................................................................................... 14 2.3. Results and discussion.................................................................................................... 19 2.3.1. Indoor monitoring of PM, PN, and CO2 concentrations in the classrooms............ 19 2.3.2. Indoor monitoring of PM, PN, and CO2 concentrations in classroom 3 in the presence of indoor particle pollution source......................................................................... 25 2.4. Summary and conclusion ............................................................................................... 31 Chapter 3 - Development and performance evaluation of a two-stage cascade impactor equipped with gelatin filter substrates for the collection of multi-sized particulate matter.......... 32 3.1. Introduction .................................................................................................................... 32 3.2. Methods.......................................................................................................................... 34 3.2.1. Gelatin cascade impactor design............................................................................. 34 3.2.2. Impaction theory ..................................................................................................... 37 3.2.3. Laboratory characterization of the first and second impaction stages of the gelatin cascade impactor....................................................................................................... 38 v 3.2.4. Analysis of blank gelatin filters.............................................................................. 41 3.3. Results and discussion.................................................................................................... 44 3.3.1. Laboratory experiments using artificially generated test aerosols.......................... 44 3.3.2. Filter blank analysis................................................................................................ 52 3.3.3. Field comparison between the oxidative potential of particles............................... 59 3.4. Summary and conclusion ............................................................................................... 62 Chapter 4 - Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait. ................................................................. 64 4.1. Introduction .................................................................................................................... 64 4.2. Methods.......................................................................................................................... 66 4.2.1. Description of sampling location ............................................................................ 66 4.2.2. Sampling campaign and meteorological conditions............................................... 67 4.2.3. Instrumentation ....................................................................................................... 69 4.2.4. Chemical and toxicological analysis....................................................................... 70 4.2.5. Principal component analysis and multi-linear regression approach...................... 71 4.3. Results and discussion.................................................................................................... 73 4.3.1. Seasonal variations in PM2.5 mass concentrations and chemical composition ....... 73 4.3.2. PM2.5 oxidative potential......................................................................................... 81 4.3.3. PM2.5 emission sources and their contribution to the oxidative potential. .............. 84 4.4. Summary and conclusion ............................................................................................... 90 Chapter 5 - Conclusion ................................................................................................................. 92 References..................................................................................................................................... 95 Appendix..................................................................................................................................... 119 vi List of Tables Table 2.1 Characteristics of the investigated classrooms. ............................................................ 12 Table 2.2 Indoor, ambient, and indoor-to-outdoor (I/O) ratios of PM and PN in the three measurement phases. LOD refers to the limit of detection of the employed instrument.............. 23 Table 2.3 Theoretical versus experimental decay rates for particle mass (PM) with and without the use of purifier at different settings in classroom 3..................................................... 28 Table 3.1 The physical characteristics and design parameters of the gelatin cascade impactor .. 35 Table 4.1 Spearman bivariate correlation between the extrinsic DTT activity (nmoles/min/m3 ) and the concentrations of selected chemical species. ....................................... 83 Table 4.2 Principal components and loadings of PM chemical species. Loadings > 0.6 are in bold. .............................................................................................................................................. 87 Table 4.3 Output of multi-linear regression (MLR) analysis between PCA factor scores (independent variables) and extrinsic DTT activity (dependent variable).................................... 89 vii List of Figures Figure 2.1 Spatial variability in classrooms 3 and 6 based on (a) PM, (b) PN, and (c) CO2. The error bars indicate standard deviations of values measured in a single day. ......................... 12 Figure 2.2 Air exchange rate (AER) values for the tested classrooms. The error bars indicate standard deviations of the values measured on three different days............................................. 20 Figure 2.3 Average outdoor and indoor CO2 levels during the three phases in the studied classrooms. The error bars indicate standard deviations of values measured in a single day. ..... 22 Figure 2.4 Particle indoor penetration based on: (a) PM2.5 mass concentration and (b) particle number concentration. The error bars indicate standard deviations of the values measured on three different days. ...................................................................................................................... 25 Figure 2.5 Decay rate curves with and without the use of purifier at different flow rates based on (a) PM and (b) PN in classroom 3. The dotted curves represent exponential trendlines....................................................................................................................................... 27 Figure 2.6 Decay rate curves as a function of particle size in classroom 3 after switching off the NaCl source and operating the purifier at a flow rate of 267 m3 /h. The dotted curves represent exponential trendlines. .................................................................................................. 29 Figure 2.7 Air purifier removal efficiency as a function of particle size. The error bars indicate standard deviations of values measured in a single day. Values in x-axis are the mid-point diameters of each particle size range. .......................................................................... 30 Figure 3.1 The (a) two impaction stages and the (b) filter holder of the gelatin cascade impactor ........................................................................................................................................ 36 Figure 3.2 Experimental setup schematic for the laboratory characterization of the gelatin cascade impactor........................................................................................................................... 40 Figure 3.3 Pressure drop as a function of GCI flow rate for stages 1 and 2 ................................. 44 Figure 3.4 Collection efficiency curves of the GCI (a) first impaction stage and (b) second impaction stage using gelatin filters and various artificially generated aerosols. X-axis represents optical diameters for particles ≥ 0.3 µm and mobility diameters for particles < 0.3 µm. Error bars are standard deviations of repeated measurements. ............................................. 47 Figure 3.5 Collection efficiency curves of the GCI (a) first impaction stage and (b) second impaction stage using quartz filters and various artificially generated aerosols. X-axis represents optical diameters for particles ≥ 0.3 µm and mobility diameters for particles < 0.3 µm. Error bars are standard deviations of repeated measurements. ............................................. 49 Figure 3.6 The comparison of mass concentrations of collected particles using PCIS and GCI (a) first and (b) second impaction stages. Error bars are standard deviations of repeated measurements................................................................................................................................ 51 viii Figure 3.7 Inorganic ion (a) blank levels in gelatin substrate and (b) the normalized concentrations (per volume of air based on 24 hr sampling) of the blank gelatin in comparison with typical ambient PM levels in Los Angeles. Error bars are standard deviations. ..................................................................................................................................... 53 Figure 3.8 Metals and trace elements (a) blank levels in gelatin substrate in comparison with PTFE and (b) the normalized concentrations (per volume of air based on 24 hr sampling) of the blank gelatin in comparison with typical ambient PM levels in Los Angeles. Error bars are standard deviations.................................................................................................................. 55 Figure 3.9 DTT activity comparison between the blank gelatin substrate and PM collections in three major cities around the world. Error bars are standard deviations. ................................. 57 Figure 3.10 Particle number concentrations as a function of particle diameter for (a) gelatin and (b) UFP suspensions. X-axis represents mobility diameters measured by SMPS. Error bars are standard deviations of repeated measurements. .............................................................. 58 Figure 3.11 Ambient PM2.5 mass concentrations based on the GCI and PCIS. The error bars are standard deviations.................................................................................................................. 60 Figure 3.12 Redox activity of particles collected on gelatin filter using the GCI in comparison with particles collected on PTFE using the PCIS based on (a) DTT and (b) ROS assays. Error bars are standard deviations. ................................................................................... 61 Figure 4.1 Geographical representation of our sampling station in Kuwait. (Map source: Google Maps)................................................................................................................................ 67 Figure 4.2 Wind roses in three monitoring stations in Kuwait, including Sabriya, Ahmadi, and Kuwait Airport. ...................................................................................................................... 69 Figure 4.3 PM2.5 mass concentrations during summer, winter, and spring seasons in Kuwait. Error bars are standard deviations of the data............................................................................... 75 Figure 4.4 Mass concentrations (per volume of air) of EC and OC during summer, winter, and spring seasons. Error bars are standard deviations of the data............................................... 75 Figure 4.5 Mass concentrations (per volume of air) of metals and transition elements during summer, winter, and spring seasons. Error bars are standard deviations of the data.................... 77 Figure 4.6 Mass concentrations (per volume of air) of inorganic ions during summer, winter, and spring seasons. Error bars are standard deviations of the data............................................... 78 Figure 4.7 Annual average mass concentrations (per volume of air) of (a) PM2.5, inorganic ions, and carbonaceous species, and (b) metals and transition elements. Error bars are standard deviations of the data...................................................................................................... 81 Figure 4.8 Seasonal (per volume of air) DTT activity of PM2.5 in Kuwait. Error bars are standard deviations of the data...................................................................................................... 83 ix Figure 4.9 Major fossil fuel combustion sources in central and northern Kuwait........................ 88 Figure 4.10 The percentage contribution of emission sources to PM2.5 oxidative potential in Kuwait........................................................................................................................................... 90 x Abstract The unprecedented global socio-economic expansion, driven by extensive urban development, industrial evolution, and increased power generation, is notably deteriorating outdoor air quality, which in turn adversely impacts the air in indoor environments where people spend the majority of their time. Particulate matter (PM), a significant air pollutant originating from diverse sources and present in various sizes in the atmosphere, significantly contributes to adverse human health effects, including respiratory diseases, lung cancer, neurotoxicity, and cardiovascular illnesses. Ambient PM pollution can be managed by identifying emission sources of particles and understanding their physicochemical and toxicological characteristics in order to develop effective regulations and mitigation strategies. For indoor air quality, which remains unregulated, the sources of PM are diverse and fluctuate significantly, therefore, employing air filtration technologies becomes crucial to minimize exposure to these particulate pollutants. This dissertation investigated the effectiveness of simultaneously operated air purifiers and in-line filters within ventilation systems to mitigate PM pollution in indoor spaces. Additionally, it introduces the development and application of cascade impactors for particle collection and further analysis of their chemical composition and toxicological properties. Multivariate statistical analysis and multi-linear regression were also used to identify PM emission sources and their percentage contribution to the oxidative potential. Through a blend of experimental research and analytical exploration, this body of work provides essential insights into the characteristics, sources, and health implications of PM in indoor and outdoor settings, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution. 1 Chapter 1 - Introduction 1.1. Background Particulate matter (PM) air pollution has been linked to several health issues, including cardiovascular diseases, respiratory illnesses, lung inflammatory reactions, and increased mortality, even at relatively low concentrations and short-term exposure (Balluz et al., 2007; Pope & Dockery, 2006). Additionally, long-term exposure to high concentrations of particulate matter in the atmosphere can significantly decrease the average life expectancy by a year or more, mainly due to cardiopulmonary mortality and lung cancer (WHO, 2004). PM exposure has been also linked to systemic oxidative stress and cellular damage in humans and animals due to environmentally persistent free radicals, especially in combustion-generated particles, and organic compounds that can produce intracellular reactive oxygen species (ROS) (Gehling et al., 2014; Gehling & Dellinger, 2013; Kouassi et al., 2009; Longhin et al., 2013; Torres-Ramos et al., 2011). Given these health concerns, it is necessary to study the physicochemical and toxicological characteristics of indoor and outdoor particles and identify their primary sources in order to develop effective mitigation strategies and protect public health. The existence of high levels of indoor pollutants, including particulate matter, presents a considerable impediment to indoor gatherings in diverse environments, including educational institutions, workplaces, conference rooms, households, and other enclosed areas. In some cases, indoor air pollutant concentrations can be multiple times higher than those found outdoors (Cheek et al., 2021). Therefore, improving indoor air quality using air filtration technologies is essential since people spend most of their time in closed environments (Cheek et al., 2021; Cooper et al., 2021; Ma et al., 2016; Pacitto et al., 2020). Indoor air pollution leads to adverse health outcomes and almost 3.8 million premature deaths annually (WHO, 2021). Occupants’ 2 exposure to indoor particle pollutants can cause a number of health drawbacks, including respiratory illnesses, lung cancer, strokes, heart failure, asthma, and eye problems (Anderson et al., 2012; Hoskins, 2003; Oberdörster, 2000; Perez-Padilla et al., 2010; Polichetti et al., 2009). In addition, indoor pollution in working environments can lead to fatigue and a decline in focus and productivity (Chang et al., 2019). Although people spend most of their time in indoor environments, understanding the chemistry of outdoor particle pollutants and their associated emission sources is of equal importance since ambient pollution can significantly impact the quality of indoor air. Given the complexity of the physicochemical characteristics of real-life outdoor PM, it is very challenging to generate similar aerosols in the lab for chemical and toxicological analyses (Jacoby et al., 2011; Krieger et al., 2012). Therefore, collecting real-life PM directly from the ambient air is essential for numerous applications, including health assessment studies. Researchers have made significant advancements in developing PM sampling technologies to collect ambient aerosols either on filter substrates or in aqueous solutions (M. Chen et al., 2018; Demokritou, Gupta, et al., 2002; Taghvaee, Mousavi, et al., 2019). However, these sampling techniques were associated with several drawbacks, including setup-complexity, high-cost, losses of semi volatile species, and difficulties in extracting the collected particles from the filters (Misra, Singh, et al., 2002; Pirhadi, Mousavi, & Sioutas, 2020; Taghvaee, Mousavi, et al., 2019). It is imperative, therefore, to develop a new instrument that can operate without such drawbacks, particularly when it comes to the extraction of particulate matter. 3 1.2. Overview In the first study, we investigated the effectiveness of air purifiers and in-line filters in ventilation systems working simultaneously inside various classrooms at the University of Southern California (USC) main campus. We conducted real-time measurements of PM2.5 (diameter < 2.5 µm) particle mass (PM), particle number (PN), and carbon dioxide (CO2) concentrations in 9 classrooms during 2-hour lectures over a period of 5 months (i.e., from September 2021 to January 2022). The measurement campaign was carried out in three different phases: I) with no purifier in the classroom, II) with purifier at low flow rate of 267 m3 /hr, and III) with purifier at high flow rate of 748 m3 /hr, while the ventilation system was continuously working during all phases. Furthermore, a second measurement campaign was conducted in an empty classroom with an aerosol-generating source emitting sodium chloride. The results showed that the ventilation systems were adequate in providing sufficient outdoor air to dilute indoor CO2 concentrations due to the high air exchange rates (2.63-8.63 hr-1 ). The particle penetration coefficients (P) of the investigated classrooms were generally very low for PM (< 0.2) and PN (< 0.1), except one classroom, which corroborated the effectiveness of in-line filters in the ventilation systems in capturing ambient particle pollutants. Particle decay rate (K) values for the three phases were also investigated to calculate the purifier filtration efficiency. Operating the purifier at the maximum flow rate (i.e., 748 m3 /hr) increased the particle concentrations decay rates from 3.9-4.8 hr-1 (without using the purifier) to 6.5-6.7 hr-1 . Additionally, the results showed that the air purifier equipped with HEPA filter was efficient in removing both ultrafine and coarse particles with removal efficiency higher than 95%. However, the purifier has relatively lower efficiency in capturing particles in the intermediate size range with removal efficiency in the range of 82-88%. 4 The second study aimed to develop and evaluate a high-volume gelatin cascade impactor (GCI) that utilizes water-soluble gelatin substrates to capture various sizes of particulate matter. The GCI was developed with two impaction stages having cut-point diameters of 2.5 µm and 0.2 µm and designed with an operational flow rate of 100 lpm. The performance of the GCI was characterized in the laboratory, using artificially-generated aerosols, as well as in the field to investigate the efficiency of the sampler in collecting various multi-sized particles. The field experiments were conducted concurrently with a personal cascade impactor sampler (PCIS) to collect ambient PM2.5 for further toxicological assays, including dithiothreitol consumption (DTT) and macrophage-based reactive oxygen species (ROS). The results of the study indicated that the experimentally-measured cut-point diameters of both impaction stages matched the theoretical calculations, even with the use of alternative substrates, such as quartz filters, instead of the gelatin substrates. Moreover, the toxicological results of the field experiments revealed that the GCI was more capable of collecting PM-toxic constituents compared to the PCIS. The ROS and DTT activities of the PM2.5 collected using the GCI were 8813 μg Zymosan Units/mg PM and 26.4 nmol/min/mg PM, which were approximately more than twice the redox activity of particles collected using the PCIS. The GCI has made a significant technological contribution to the field of aerosol sampling due to its ability to collect considerable amounts of multi-sized toxic PM constituents, and its use of water-soluble filters to achieve 100% particle extraction efficiency for inhalation and toxicity studies. The final study aimed to investigate the seasonal variations, chemical composition, sources, and oxidative potential of ambient PM2.5 (particles with a diameter of less than 2.5 µm) in Kuwait City. The sampling campaign was conducted within the premises of Kuwait Institute for Scientific Research from June 2022 to May 2023, covering different seasons throughout the 5 year. The personal cascade impactor sampler (PCIS) operated at flow rate of 9 L/min was employed to collect weekly PM2.5 samples on PTFE and quarts filters. These collected samples were analyzed for carbonaceous species (i.e., elemental and organic carbon), metals and transition elements, inorganic ions, and DTT (dithiothreitol) redox activity. Furthermore, principal component analysis (PCA) and multi-linear regression (MLR) were used to identify the predominant emission sources and their percentage contribution to the redox activity of PM2.5 in Kuwait. The results of this study highlighted that the annual-averaged ambient PM2.5 mass concentrations in Kuwait (59.9 µg/m3 ) substantially exceeded the World Health Organization (WHO) guideline of 10 µg/m3 . Additionally, the summer season displayed the highest PM2.5 mass concentration (75.2 µg/m3 ) compared to other seasons, primarily due to frequent dust events exacerbated by high-speed winds. The PCA identified four primary PM2.5 sources: mineral dust, fossil fuel combustion, road traffic, and secondary aerosols. The mineral dust was found to be the predominant source, contributing 36.1% to the PM2.5 mass, followed by fossil fuel combustion and traffic emissions with contributions of 23.7% and 20.3%, respectively. The findings of MLR revealed that road traffic was the most significant contributor to PM2.5 oxidative potential, accounting for 47% of the total DTT activity. In conclusion, this comprehensive investigation provides essential insights into the sources and health implications of PM2.5 in Kuwait, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution in the region. 6 1.3. List of objectives The first study had the following objectives: • Exploring the role of portable air purifiers in filtering indoor particle pollutants and improving the indoor air quality inside university classrooms • Investigating the effectiveness of equipping indoor ventilation systems with in-line filters to capture ambient particles penetrating the indoor space. • Examining the performance of both air purifiers and ventilation systems working simultaneously under various realistic scenarios. • Highlighting the significance of maintaining sufficient air exchange rate inside closed congested environments, such as classrooms, in order to keep the indoor CO2 levels within acceptable standards. The second study had the following objectives: • Developing a high-flow-rate cascade impactor equipped with water-soluble gelatin substrates to collect coarse, fine, and ultrafine particle • Evaluating the performance of the gelatin cascade impactor in the lab to obtain the collection efficiency curves for impaction stages • Performing field experiments to compare the performance of the gelatin cascade impactor with the personal cascade impactor sampler in collecting ambient PM2.5 for further use in toxicological studies. The last study had the following objectives: • Investigating the seasonal variations in concentrations of various PM-constituents, including metals and trace elements, elemental and organic carbon, and inorganic ions in Kuwait. 7 • Exploring the seasonal variability in the toxicological characteristics of PM2.5 using dithiothreitol (DTT) assay. • Identifying the contribution of different urban sources to the toxicological behavior of ambient fine particle pollutants in Kuwait. 8 Chapter 2 - The role of portable air purifiers and effective ventilation in improving indoor air quality in university classrooms 2.1. Introduction Indoor particles may arise from both internal sources (e.g., cleaning activities, occupants) and external particles infiltrating indoor spaces. Human-generated particles (i.e., airborne aerosol particles released by the exhaled breath of humans) are major routes of airborne transmission of bacteria and viruses, including SARS-CoV-2, especially in confined environments with high population density, such as classrooms (Jayaweera et al., 2020; Kähler et al., 2020; Morawska & Cao, 2020; Piscitelli et al., 2022). The exhaled particles generally have an aerodynamic diameter of less than 1 μm, mostly in the range of 0.1 to 0.5 μm (Scheuch, 2020; Schwarz et al., 2015; Szabadi et al., 2022). The larger exhaled droplets settle on the ground within seconds due to the gravitational force and/or evaporate into smaller particles in a few seconds (Lindsley et al., 2014). The smaller particles remain suspended in the indoor environment for several hours and can be carried by air currents as far as several meters from their source (Piscitelli et al., 2022; Xie et al., 2007). These smaller particles have a greater capacity to increase the infection potential than large particles since they can travel longer distances (Fennelly, 2020). Given the health impacts caused by indoor airborne pollutants, employing air purification means in indoor spaces is essential for decreasing pollutant concentrations (Piscitelli et al., 2022; Szabadi et al., 2022). There are two main methods used to enhance indoor air quality and remove indoor particle pollutants, including in-line filters in ventilation systems and portable air purifiers (Küpper et al., 2019). Portable air purification units have been widely used in recent years due to their efficient removal of indoor pollutants (H. J. Kim et al., 2013; Shaughnessy & Sextro, 2006; Sultan et al., 2011). They have been placed in approximately 30% of private residential buildings 9 in developed countries, and a steady growth in the use of these cleaning devices is expected in the upcoming years (Ma et al., 2016; Shaughnessy & Sextro, 2006). The existence of in-line filtration in mechanical ventilation systems reduces the infiltration of outdoor particles to indoor spaces to a certain level depending on the filter’s characteristics, filter efficiency, and particle size 201(Batterman et al., 2005; Sublett, 2011; van der Zee et al., 2017). The effectiveness of these in-line filters in capturing ambient particles is assessed by the penetration coefficient (P), which describes the fraction of outdoor particles that successfully penetrate the building into the indoor environment (Barn et al., 2008; Lin & Peng, 2010; van der Zee et al., 2017). Moreover, the adequacy of the ventilation systems in bringing sufficient outdoor air to the indoor environment is assessed by the air exchange rate value (H. Guo et al., 2008; You et al., 2012). Air exchange rate (AER) is the rate at which indoor air is entirely replaced by outdoor air in a specific closed environment (e.g., classrooms). The replacement of indoor air with outdoor air occurs by various means, such as natural ventilation (e.g., doors and windows) and forced ventilation (e.g., mechanical ventilation systems). Indoor air quality can be improved by increasing the air exchange rate, since allowing more air to enter the space will dilute indoor pollution, except in cases where outdoor pollution is substantially high (H. Guo et al., 2008; Y. Li & Chen, 2003) in which the outdoor air needs to be purified by some sort of in-line filter. The main objective of this study was to explore the effectiveness of air purifiers and mechanical ventilation systems equipped with in-line filters in removing indoor airborne particles originating from outdoor and indoor sources in university classrooms. Several studies supported the effective work of the air purifier inside classrooms in improving indoor air quality and mitigating the transmission of bacteria and viruses (Burgmann & Janoske, 2021; Curtius et al., 2021; D. T. Liu et al., 2022; B. Zhao et al., 2020). However, this study provided additional 10 insights by examining the performance of both air purifiers and in-line filters in the ventilation systems working simultaneously inside various university classrooms with different characteristics. In addition, we investigated the adequacy of the ventilation systems in bringing sufficient outdoor air to the indoor environment. The findings of this work provide significant insights for public health officials, especially in educational institutions, to implement air pollution control systems and enhance the quality of air in indoor environments. 2.2. Methods 2.2.1. Measurement sites and protocol The measurements were conducted inside classrooms in the University of Southern California (USC) main campus area over a 5-month period from September 2021 to January 2022. Table 2.1 shows the details of the selected classrooms, including volume, floor area, and the total number of students. These classrooms were solely dependent on forced ventilation (i.e., mechanical ventilation systems) as the means of air exchange between outdoor and indoor environments. Natural ventilation was minimized in all classrooms by closing all doors and windows. The indoor monitoring was performed in two separate campaigns; the first campaign was held in all selected classrooms with students attending classes, while the second campaign was conducted in an empty classroom (i.e., classroom 3). In the first measurement campaign, indoor air quality measurements were conducted in three phases in 9 classrooms located in 7 different buildings; each phase had a different setting of an air purifier (Model Trio PlusTM, Field Controls, Kinston, NC, USA) equipped with H13 HEPA filters. The first phase was carried out without the presence of the portable air purifier to evaluate the effectiveness of ventilation systems equipped with in-line filters in reducing indoor pollutant levels without the interference of additional air-cleaning devices. In the second phase, we conducted the measurements while 11 both the classroom’s ventilation system and the air purifier (with flow rate of 267 m3 /h) were active. In the third phase, the air purifier was set at the highest possible flow rate (i.e., 748 m3 /h) while the ventilation system was operating simultaneously. We performed real-time measurements for indoor and outdoor PM2.5 mass concentrations (PM), particle number concentrations (PN), and CO2 levels during active 2-hour lectures in the presence of students. It should be noted that strong indoor particle generation sources (e.g., chalkboard dust and cleaning activities) were not present in the classrooms during the lectures. Pollutants’ monitoring in each classroom started 15 min before the beginning of the lecture and continued until 15 min after the end of the lecture. On the same day, we also monitored the outdoor pollutant concentrations for 15 min before and after the lecture to ensure that the outdoor concentration had not changed considerably while the lecture was ongoing. For each classroom, we repeated the previous procedure on three different days by changing the configuration of the purifier according to the three phases discussed earlier. Moreover, the location of the monitoring devices in the classrooms could affect the readings of the indoor pollutant concentrations. Therefore, we investigated the spatial variance in the pollutant concentrations by placing the monitoring devices in the middle and corners of the classrooms, the results of which are shown in Figure 2.1 for two different classrooms as an example (the rest of the classrooms yielded similar results). The results indicate overall spatial homogeneity for PM, PN, and CO2 concentrations inside the classrooms. This observation shows that the particle and gas pollutants are well mixed due to the overall high air exchange rates in the classrooms; thus, the location of the measuring devices in different spots within the classroom should not result in notable differences between the readings. According to the findings of Küpper et al. (2019) regarding the possible spatial variance in the purifier’s removal efficacy, changing the location of the purifier will provide 12 almost identical removal efficiencies and lead to the same distribution of clean air in the space. Therefore, we positioned the purifier in a fixed location (i.e., the center of the classroom) during the entire campaign. Table 2.1 Characteristics of the investigated classrooms. No. Classroom Building Name Number of Students Room Height (m) Floor Area (m2 ) Volume (m3 ) 1 OHE136 Olin Hall (OHE) 21 3.05 86.12 262.50 2 RTH 105 Tutor Hall (RTH) 7 3.05 97.73 297.89 3 SGM 226 Seeley G. Mudd Building (SGM) 8 3.05 63.64 193.97 4 GFS 221 Grace Ford Salvatori Hall (GFS) 20 3.05 36.42 111.00 5 GFS 205 Grace Ford Salvatori Hall (GFS) 12 3.05 36.51 111.29 6 KAP159 Kaprielian Hall (KAP) 20 3.05 37.63 114.68 7 OHE 120 Olin Hall (OHE) 6 3.05 56.49 172.17 8 KDC 236 Glorya Kaufman International Dance Center (KDC) 26 3.05 89.00 271.28 9 THH 118 Taper Hall (THH) 22 3.05 76.83 234.18 (a) (b) (c) Figure 2.1 Spatial variability in classrooms 3 and 6 based on (a) PM, (b) PN, and (c) CO2. The error bars indicate standard deviations of values measured in a single day. The second measurement campaign was carried out in classroom 3 in the presence of an indoor pollution source (i.e., sodium chloride aerosols) to simulate exhaled particles of humans. 13 Particles can be generated by humans through various activities, including breathing, speaking, coughing, and sneezing. The particle size that is generally produced by breathing ranges between 0.1 and 1.0 μm (Fabian et al., 2008; Holmgren et al., 2010; Johnson & Morawska, 2009; Scheuch, 2020). On the other hand, coughing, sneezing, and speaking generate larger particles compared to breathing; these particles are typically larger than 5 μm and will either settle gravitationally or evaporate to smaller particles (<1 μm) (Chao et al., 2009; Z. Y. Han et al., 2013; Lindsley et al., 2014; B. Zhao et al., 2020). To corroborate the use of sodium chloride (NaCl) as a representative for human exhaled particles, we measured the size distribution of NaCl particles by means of an optical particle sizer (OPS) (Model 3330, TSI, Shoreview, MN, USA) and a scanning mobility particle sizer (SMPS) (Model 3936, TSI, Shoreview, MN, USA). At first, we prepared a suspension by dissolving 50 mg of sodium chloride in 100 mL of ultrapure Milli-Q water to reach a concentration of 500 µg/mL. The suspension was sonicated in an ultrasonic bath for 30 min to achieve a homogenous solution. NaCl suspension was subsequently aerosolized using a commercially available nebulizer (Model 11310 HOPETM nebulizer, B&B Medical Technologies, Carlsbad, CA, USA) that was connected to a compressor pump (Model VP0625-V1014-P2-0511, Medo Inc., Roselle, IL, USA) equipped with a HEPA capsule (Model 12144, Pall Corporation, USA) to supply compressed filtered air to the nebulizer. The nebulizer was connected to both the SMPS and OPS by a clear vinyl tube to obtain the number-based particle size distribution. The particle size distribution showed that NaCl particles mostly fall in the range of 0.071 to 1.13 µm with a peak at 0.51 µm, which supports the use of NaCl as a representative of the particles generated by humans. A number of previously published studies used NaCl as the aerosol test agent to assess the effectiveness of air filtration means (Edwards et al., 2021; Elsaid & Ahmed, 2021; Heim et al., 2005; Szabadi et al., 2022; Zuraimi et 14 al., 2011). In addition, the National Institute for Occupational Safety and Health (NIOSH) considered NaCl as a standard test aerosol for measuring the effectiveness of respiratory protective equipment (e.g., N95 masks) (NIOSH, 2019). Following the same setup and sample preparation discussed earlier, NaCl suspension was aerosolized in classroom 3 to act as an indoor source of aerosols. 2.2.2. Instrumentation Various air quality monitors were used in this study to measure different pollutant concentrations. We employed the DiSCmini nanoparticle counter (Model Testo DiSCmini, Testo, West Chester, PA, USA) in our experiments to measure particle number concentrations. The TSI DustTrak monitor (Model 8520, TSI, Shoreview, MN, USA) was used to obtain realtime PM2.5 particle mass concentrations. In addition, we employed a Q-track device (Model 8551, TSI, Shoreview, MN, USA) to measure indoor and outdoor CO2 levels. One of the objectives of the second measurement campaign in the empty classroom was to estimate the purifier’s efficiency for each particle size. This was done using the optical particle sizer (OPS) (Model 3330, TSI, Shoreview, MN, USA) to obtain particle concentrations and size distributions. The OPS measures particle sizes from 0.3 to 10 μm, which are particles in the coarse and accumulation size ranges. 2.2.3. Additional calculations 2.2.3.1. Air exchange rate in the set of classrooms In this study, CO2 was chosen as the tracer gas since it is a non-reactive gas and was a readily available source (i.e., students) in our studied classrooms. The AER was calculated in this study based on the approach in one of our earlier studies by Fruin et al. (2011), in which AER values were determined inside moving vehicles instead of a classroom. After approximately 10 15 minutes from the beginning of each lecture, CO2 concentration increased until reaching a wellmixed steady-state condition. CO2 reached the equilibrium concentration when the production of CO2 by students was equal to the losses of CO2 due to air circulation in the ventilation system. The AER was calculated in the classrooms using the mass balance equation below: dCin dt = S V + (Cout − Cin)AER − Cink (2.1) where dCin/dt is the change of indoor CO2 concentration with time, S/V is the CO2 emission rate (ppm/hr), k is the deposition rate of CO2 (hr-1 ), AER is the air exchange rate (hr-1 ), Cin and Cout are the indoor and outdoor CO2 concentrations (ppm), respectively. CO2 is a conservative and non-reactive gas; therefore, it will remain suspended in the air since its decay is very slow (i.e., k = 0). The CO2 emission rate (S/V) was calculated by measuring the CO2 build-up rate from students. Before reaching the steady-state concentration, the increase in CO2 concentrations with time was linear and the build-up rate was determined as the slope of the line (Fruin et al., 2011; Hudda et al., 2011). Once CO2 equilibrium concentration is reached (i.e., dCin/dt = 0), Equation 2.1 can be rearranged to the following equation: AER = S V (Cin−Cout) (2.2) Equation 2.2 was used to calculate the air exchange rate in the 9 analyzed classrooms. For each classroom, the AER was calculated on three different days and then compared with the AER values received from the facilities and management department. 2.2.3.2. Indoor particle penetration (P) in the set of classrooms The indoor particle penetration (P) was calculated based on the steady-state approach, which is similar to that of Chao et al. (2003) (Chao et al., 2003). Treating the classroom as a closed system allows for the application of the mass balance equation. Equation 2.3 illustrates the mass balance applied in the tested classrooms: 16 dCin dt = S V + CoutAER (P) − CinAER − Cink (2.3) where dCin/dt is the change of indoor particle concentration with time, S represents the indoor particle production rate, V is the volume of the classroom (m3 ), k is the deposition rate of particles (h−1), and Cin and Cout are the indoor and outdoor particle concentrations, respectively. The indoor particle production rate in Equation 2.3 was neglected (i.e., S = 0) since there was no indoor source for particles in the studied classrooms during the active lectures. The presence of students inside the classrooms did not result in noticeable increases in the indoor particle concentrations since the particle emission rate by humans is negligible compared to the particles infiltrating from outdoor sources (Alsved et al., 2020; Asadi et al., 2020; Morawska et al., 2009). The indoor particle concentration in the classrooms reached a steady-state condition after 5–8 min from the beginning of the lecture (i.e., dCin/dt = 0), which means Equation 2.3 can be rearranged to the following equation: P = Cin(AER + k) CoutAER (2.4) The above expression has been widely used for the calculation of effective penetration or the steady-state indoor concentration (Cin) in numerous previous studies (Koutrakis et al., 1992). The calculation of particle penetration indoors was carried out in the first phase of measurements when the air purifier was switched off. The particle penetration should not be affected by using the air purifier in the second and third phases of measurements. However, to show the agreement of penetration coefficients in the three phases, the following equation was used when the air purifier was active: 17 P = Cin (AER + k + CADR V ) Cout AER (2.5) where CADR is the clean air delivery rate of the purifier (m3 /h), and V is the volume of the classroom (m3 ). Although the operation of an air purifier does not affect the penetration coefficient, it significantly affects the indoor-to-outdoor ratio. Equation (2.5) demonstrates that the addition of the (CADR/V) term will decrease the (Cin/Cout) ratio. Moreover, increasing the purifier’s flow rate leads to a further reduction in the indoor-to-outdoor ratio. The penetration coefficient in the studied classrooms was used as a metric for assessing the effectiveness of the in-line filters of the ventilation systems in capturing particles penetrating the building from the outdoor space. The air handling units in all tested classrooms, except classroom 3, were equipped with a dual direction 12-inch MERV 14 filter with a fiberglass media (Model Aerostar FP Mini-Pleat, Filtration Group, Santa Rosa, CA, USA). MERV 14 efficiently filters the outside air and the air returning from the indoor space. The air handling unit of classroom 3 had a 2-inch MERV 13 filter with a synthetic air media (Model Aerostar Green Pleat, Filtration Group, Santa Rosa, CA, USA). According to the manual of Aerostar filters, MERV 13 filters have lower particle removal efficiency than MERV 14 filters. 2.2.3.3. Air purifier’s efficiency in classroom 3 The second measurement campaign consisted of three stages leading to the determination of the purifier’s efficiency. In the first stage, the background indoor pollutant concentrations were measured without operating the pollution source and the purifier. The second stage started when the indoor pollutant generator was switched on until a stabilized particle concentration was reached. In the third and last stage, the indoor pollutant source was switched off, and the air purifier was switched on. The purpose of the third stage was to investigate the particle decay rate (Kpurifier) in the presence of the air purifier. The experiment was repeated three times by changing 18 the third-stage scenario. In the first scenario, the purifier was switched off in order to measure the natural decay rate of particles (Knatural) when the ventilation system was only switched on. In the second scenario, the purifier was operated at a flow rate of 267 m3 /h to obtain the particle decay rate (Kpurifier (low)). The last scenario was conducted while operating the purifier at a flow rate of 748 m3 /h to measure the decay rate at the purifier’s maximum fan speed (Kpurifier (high)). In order to calculate the particle decay rate after switching off the NaCl source, we treated the classroom as a closed system and applied the mass balance equation below: dCin dt = CoutAER (P) − Cin(K) (2.6) where dCin/dt is the change of the indoor particle concentration with time, K is the particle decay rate (h-1 ), AER is the air exchange rate (h-1 ), P is the particle penetration coefficient, and Cin and Cout are the indoor and outdoor particle concentrations, respectively. Based on the integration of Equation (2.6), the general equation for the indoor concentration is expressed as follows: Cin(t) = CoutAER (P) K (1 − e −(K)t ) + Cin(o)e −(K)t (2.7) where Cin(t) is the concentration of the particles at time t and Cin(o) represents the concentration of the particles at time 0. In order to analyze the decay of the particles (i.e., reduction in particle concentration) with time, we subtracted the concentration of the particles continuously infiltrating indoors (i.e., the first term of Equation (2.7)) from the measured concentrations during the decay period. This allowed us to use the exponential equation below to obtain the decay rate of the particles: Cin(t) = Cin(o)e −(K) t (2.8) The particle decay rate is a function of the air exchange rate, particle deposition rate, and particle removal rate by the purifier. Thus, Equations (2.9) and (2.10) were used to express the decay rate 19 in the natural condition (i.e., without the purifier) and in the presence of the purifier, respectively: KNatural = AER + k (2.9) KPurifier = AER + k + η Q V (2.10) where AER is the air exchange rate (h-1 ), k is the particle deposition rate (h−1), η represents the purifier efficiency, Q is the air volumetric flow rate of the purifier (m3 /h), and V represents the volume of the classroom (m3 ). By combining Equations (2.9) and (2.10), we can calculate the purifier’s efficiency, as shown in Equation (2.11): η = (KPurifier − KNatural) V Q (2.11) The decay in the particle mass and number concentrations was plotted as a function of time after switching off the aerosol source. Decay curves were obtained for a range of particle sizes (0.3– 10 µm) to estimate the purifier’s efficiency in removing different particle sizes. In addition, the purifier removal efficiency for ultrafine particles was estimated using PN data obtained from the DiSCmini since it mainly detected particles with diameters below 0.3 µm. 2.3. Results and discussion 2.3.1. Indoor monitoring of PM, PN, and CO2 concentrations in the classrooms 2.3.1.1. Indoor CO2 levels Figure 2.2 demonstrates the actual air exchange rates in the selected classrooms, which showed a very good agreement with the AER received from the USC facilities and the management department. AER is the metric for assessing the adequacy of the applied ventilation (i.e., mechanical ventilation system) in bringing in sufficient outdoor air and diluting indoor CO2 concentrations. However, high air exchange rates will also increase the infiltration of outdoor 20 particulate pollutants, especially if the ventilation system operates without an in-line filtration system that removes a fraction of outdoor particle pollutants (H. Guo et al., 2008; van der Zee et al., 2017). As shown in Figure 2.2, the classrooms’ AER values ranged from 2.63 h−1 (Classroom 7) to 8.63 h−1 (Classroom 4). The American Society of Heating, Refrigerating and AirConditioning Engineers (ASHRAE) standard 62.1 (2016) recommended a minimum ventilation rate of 7.5 L/sec (27 m3 /h) per person in closed environments (ASHRAE, 2016). Figure 2.2 shows the alignment of the AER values with the ASHRAE’s recommendation in all investigated classrooms. Therefore, these AER values indicate sufficient outdoor-to-indoor air circulation and adequate ventilation. Figure 2.2 Air exchange rate (AER) values for the tested classrooms. The error bars indicate standard deviations of the values measured on three different days. After approximately 10 min from the beginning of each lecture, the indoor CO2 concentration reached a well-mixed steady-state condition when the production of CO2 by the students was equal to the losses of CO2 due to air circulation in the ventilation system. The ASHRAE standard 62.1 (2016) recommended that the indoor steady-state CO2 concentration should not exceed the outdoor CO2 level by more than 700 ppm (ASHRAE, 2016). Figure 2.3 21 presents the average outdoor and indoor CO2 concentrations during the three phases of measurements in the studied classrooms. The comparable indoor CO2 levels in the three phases confirm that the indoor CO2 concentrations are not affected by the use of air purifiers since the latter remove particulate and not gaseous air pollutants. Elevated concentrations of CO2 can impact productivity (Allen et al., 2016; MacNaughton et al., 2016; Simoni et al., 2010), lead to headaches and tiredness (Myhvold et al., 1996; Norbäck et al., 2013), and sick building syndrome (SBS) symptoms (e.g., difficulty in concentration, dizziness) (Azuma et al., 2018; J. Kim et al., 2020; Shriram et al., 2019; Vehviläinen et al., 2016). According to the recommended indoor CO2 level by ASHRAE (not exceeding the outdoor level by 700 ppm) and the measured outdoor CO2 level (400–500 ppm), the indoor CO2 levels in the tested classrooms should not exceed 1100–1200 ppm. This is consistent with our measurements inside the classrooms which showed values ranging between 500 ppm and 900 ppm. This observation corroborates that the ventilation systems in all the tested classrooms are adequate and provide sufficient outdoor air to dilute indoor CO2 concentrations as a result of the generally high air exchange rates (2.63–8.63 h −1) in each classroom (di Gilio et al., 2021; Hou et al., 2015; You et al., 2012). 22 Figure 2.3 Average outdoor and indoor CO2 levels during the three phases in the studied classrooms. The error bars indicate standard deviations of values measured in a single day. 2.3.1.2. Particle mass and number concentrations and indoor-to-outdoor ratios inside the classrooms Table 2.2 summarizes the ambient, indoor, and indoor-to-outdoor (I/O) ratios of PM2.5 mass concentrations and particle number concentrations in the occupied classrooms for the first, second, and third measurement phases. During the first phase, classroom 3 exhibited the highest indoor PM2.5 mass concentration (8.62 µg/m3 ), followed by classroom 9 with an indoor mass concentration of 2.43 µg/m3 . The indoor mass concentration during the three phases does not accurately reflect the effectiveness of the air purification unit in reducing indoor pollution because the ambient pollution has a significant influence on the indoor concentration. For example, classroom 9 showed a higher PM indoor concentration (2.43 µg/m3 ) compared to classroom 8 (0.95 µg/m3 ) in the first phase, while the corresponding outdoor levels were 21.67 and 5.69 µg/m3 , respectively. Therefore, we used the indoor-to-outdoor ratio as a metric for measuring the effectiveness of ventilation and air purifiers in reducing indoor pollutant levels. 23 Excluding classroom 3, all classrooms had PM and PN I/O ratios below 0.2 in the first phase without using the purification unit. This observation indicates that the ambient PM and PN were initially reduced by 80% or more in most classrooms by just the in-line filters of the ventilation system. In classroom 3, the ambient PM and PN concentrations in the first phase were reduced by 56 % and 65%, respectively. The low I/O values in the first phase did not allow for a proper investigation of the purifier’s efficiency in removing particles in the subsequent phases. For example, the PN I/O ratio in classroom 4 decreased from 0.05 in the first phase to 0.04 in the third phase when the purifier was operated at the maximum volumetric flow rate (748 m3 /h). Starting with a low I/O value did not allow the purifier to reduce the I/O ratio substantially and, more importantly, the indoor PM levels approached the limit of detection of the DustTrak, such as classrooms 2 and 7. Table 2.2 Indoor, ambient, and indoor-to-outdoor (I/O) ratios of PM and PN in the three measurement phases. LOD refers to the limit of detection of the employed instrument. PM2.5 Mass Concentration (PM) (µg/m3 ) First Phase Second Phase Third Phase Indoor Outdoor I/O Indoor Outdoor I/O Indoor Outdoor I/O Classroom 1 1.19 7.90 0.15 0.07 2.04 0.03 0.21 9.00 0.02 Classroom 2 0.31 8.2 0.04 0.48 46.01 0.01 <LOD 24.33 NA Classroom 3 8.62 19.55 0.44 1.25 3.04 0.41 10.97 42.39 0.26 Classroom 4 1.29 10.60 0.12 0.61 9.56 0.06 2.03 28.22 0.07 Classroom 5 1.24 13.44 0.09 1.04 10.31 0.10 0.73 6.60 0.11 Classroom 6 1.00 5.63 0.18 0.15 2.75 0.05 0.77 22.93 0.03 Classroom 7 0.27 7.83 0.03 <LOD 4.08 NA <LOD 11.67 NA Classroom 8 0.95 5.69 0.17 2.99 33.91 0.09 2.20 60.25 0.04 Classroom 9 2.43 21.67 0.11 1.92 18.71 0.10 0.87 11.85 0.07 Particle Number Concentration (PN) (particles/cm3 ) First Phase Second Phase Third Phase Indoor Outdoor I/O Indoor Outdoor I/O Indoor Outdoor I/O Classroom 1 207.4 8523.5 0.02 90.6 4798.5 0.02 84.0 2972.8 0.03 Classroom 2 113.73 2290.07 0.05 165.87 5285.31 0.03 42.76 3672.92 0.01 Classroom 3 2328.7 6693.6 0.35 1800.0 5557.3 0.32 1917.8 7050.2 0.27 Classroom 4 258.6 5537.9 0.05 306.2 6704.9 0.05 253.8 6944.3 0.04 Classroom 5 345.6 8453.6 0.04 151.0 8414.1 0.02 363.1 17704.5 0.02 Classroom 6 1389.0 14338.6 0.10 299.1 12158.4 0.02 89.4 10312.7 0.01 Classroom 7 76.36 6215.27 0.01 52.33 4425.42 0.01 53.41 5573.16 0.01 24 Classroom 8 388.0 13783.6 0.03 220.0 7050.2 0.03 90.4 5807.8 0.02 Classroom 9 1100.2 17763.5 0.06 673.5 14862.2 0.05 543.5 15294.1 0.04 The effective indoor penetration was measured for each classroom to assess the effectiveness of the in-line filtration in the air handling units. Figure 2.4 shows the penetration coefficients for PM and PN during each phase, as well as the average values throughout all three phases. Unlike the I/O ratio, the penetration coefficient values are independent of the purifier as corroborated by the comparable values in the three phases. The penetration coefficients for PM were higher than PN as the latter primarily consists of ultrafine particles (i.e., size < 0.3 µm), which are easier to remove by filters due to their diffusivity. The P values in the majority of classrooms were low for both PM (<0.2) and PN (<0.1), which can be attributed to the presence of efficient in-line filters (i.e., MERV 14) in the ventilation systems of almost all classrooms. Higher penetration coefficient values for PM (0.51) and PN (0.45) were observed in classroom 3 due to the less efficient in-line filter (i.e., MERV 13) used in its mechanical ventilation system. Based on the penetration values in classroom 3, the in-line filtration system could only reduce ambient PM and PN by approximately 49% and 55%, respectively. Therefore, we selected classroom 3 to conduct our experiments for the second measurement campaign. 25 (a) (b) Figure 2.4 Particle indoor penetration based on: (a) PM2.5 mass concentration and (b) particle number concentration. The error bars indicate standard deviations of the values measured on three different days. 2.3.2. Indoor monitoring of PM, PN, and CO2 concentrations in classroom 3 in the presence of indoor particle pollution source Real-time monitoring of PM, PN, and CO2 was conducted in the presence of an aerosolgenerating source emitting sodium chloride in classroom 3. As discussed earlier, classroom 3 26 was selected for the second measurement campaign due to its higher penetration coefficient compared to the other classrooms. The measured indoor CO2 level in classroom 3 was constant during the three stages due to the absence of indoor CO2 sources (e.g., students). Indoor CO2 levels were not affected by the generation of aerosols or the change of the purifier setting, as we would expect; however, PN and PM concentrations were heavily affected. 2.3.2.1. PM and PN decay rates with and without the use of air purifier at different volumetric flow rates air purifier’s efficiency in classroom 3 We conducted real-time measurements of PM and PN concentrations during three stages: (i) background condition, (ii) NaCl indoor source is switched on, (iii) purifier is switched on and the source is switched off. We repeated the experiment for different configurations of the third stage (i.e., without the purifier, the purifier at a low flow rate of 267 m3 /hr, and the purifier at a high flow rate of 748 m3 /h). Figure 2.5 shows the PM and PN particle decay curves in classroom 3, which were obtained and analyzed based on the third-stage data. The natural decay rates of the particles without the application of the air purifier were in the range of 3.9 to 4.8 h−1, where K values were 4.74 h−1 and 3.95 h−1 for PM and PN, respectively. When the purifier was switched on at a low flow rate (267 m3 /h), the decay rates increased to 5.0–5.3 h−1, with K values of 5.09 h −1 for PM and 5.26 h−1 for PN. Operating the purifier at the maximum air flow rate (748 m3 /h) resulted in a significant increase in the particle decay rates (6.5–6.7 h−1), with decay values of 6.70 h−1 and 6.58 h−1 for PM and PN, respectively. The theoretical values of the decay rates were calculated using Equations (2.9) and (2.10). According to Long et al. (2001) (Long et al., 2001), the deposition rate is dependent on the particle size and ranges between 0.10–0.25 h−1 for PM2.5 particles. Table 2.3 shows a good agreement between the theoretical and experimental decay rates for PM. 27 (a) (b) Figure 2.5 Decay rate curves with and without the use of purifier at different flow rates based on (a) PM and (b) PN in classroom 3. The dotted curves represent exponential trendlines. 28 Table 2.3 Theoretical versus experimental decay rates for particle mass (PM) with and without the use of purifier at different settings in classroom 3. Theoretical K (h−1) Experimental K (h−1) Without purifier (Knatural) 4.32 4.74  0.15 Purifier at low setting (Kpurifier (low)) 5.22 5.09  0.13 Purifier at high setting (Kpurifier (high)) 6.90 6.70  0.33 The quick reduction in particle concentrations clearly demonstrates the effectiveness of the air purifier. In the first stage, the initial PM and PN concentrations at the beginning of the decay period reached a 50% reduction after 35–40 min when only mechanical ventilation was on. Using the purifier at a low flow rate of 267 m3 /h and a high flow rate of 748 m3 /h reduced the particle number concentrations by 50% after 25–30 min and 10–15 min, respectively. According to Szabadi et al. (2022) (Szabadi et al., 2022), operating the purifier at the maximum flow rate caused a 50% reduction in the particle number concentration after 20 min of switching off the aerosol source, which is consistent with our study. Lower decay rates will result in longer particle residence times indoors and, if these aerosols contain viruses (e.g., SARS-CoV-2), the probability of transmission and infection will increase (Burgmann & Janoske, 2021; Curtius et al., 2021; Zhai et al., 2021; Zuraimi et al., 2011)Zuraimi et al. (2011) reported that using an air purifier at its maximum fan setting reduced the residence time of coughing and sneezing particles from 4–6 h to 30–40 min. All the aforementioned studies support the use of an air purifier at the maximum flow rate to increase the particle decay, which will decrease the risk of viruses’ transmission in case an infectious person is present in the classroom. 2.3.2.2. Removal efficiency as a function of particle size The measurements of the particle number concentration at the purifier’s flow rate of 267 m3 /h were used to determine the purifier’s removal efficiency as a function of particle size in classroom 3. Figure 2.6 presents different particle decay rates based on various particle size ranges. As shown in the figure, the increase in particle size is associated with a higher value of 29 particle decay rate. The efficiencies for each particle size range were calculated and shown in Figure 2.7. The particle removal efficiencies of the purifier for the size ranges (0.3–0.5 μm), (0.5–1 μm), (1–2 μm), (2–5 μm), and (5–10 μm) were 82.8%, 85.3%, 87.7%, 95.0%, and 99.4%, respectively. Higher efficiencies were achieved for coarse particles, which indicates the efficient performance of HEPA filters in capturing coarse particles. HEPA filters are less efficient in removing particles in the accumulation mode (0.3–2 μm), with removal efficiencies between 82% and 88%. Figure 2.6 Decay rate curves as a function of particle size in classroom 3 after switching off the NaCl source and operating the purifier at a flow rate of 267 m3 /h. The dotted curves represent exponential trendlines. 30 Figure 2.7 Air purifier removal efficiency as a function of particle size. The error bars indicate standard deviations of values measured in a single day. Values in x-axis are the mid-point diameters of each particle size range. The measurements of the particle number concentration in Figure 2.5b were used to assess the removal efficiency of ultrafine particles since PN data were dominated by particles with a size range of less than 0.3 μm. The decay rate at the purifier’s flow rate of 267 m3 /h was calculated as 5.26 h−1, corresponding to a removal efficiency value of 95.2%. These results lead to the conclusion that the air purifier equipped with HEPA filters is more efficient in removing both ultrafine particles (<0.3 μm) and coarse particles (2–10 μm). However, particles in the intermediate size range (0.3–2 μm) were somewhat less efficiently removed compared to those in the coarse and ultrafine ranges, although the removal efficiency even in that particle range was between 82 and 88%. These results are consistent with various previously published studies and are a result of the fact that smaller particles are easily removed by filters due to their high diffusivity, and larger particles are primarily removed because of their high interception and inertia impaction (Christopherson et al., 2020; Lowther et al., 2020). 31 2.4. Summary and conclusion This work investigated the effectiveness of air purifiers working in conjunction with inline filters of mechanical ventilation systems inside different classrooms and their role in improving air quality and capturing pollutants originating from both indoor and outdoor sources. The mechanical ventilation systems in all classrooms, except one, were equipped with 12-inch MERV 14 filters that significantly reduced ambient PM and PN concentrations by more than 80%. The less efficient in-line filter (MERV 13) in the ventilation system of classroom 3 reduced ambient PM and PN by 49% and 55%, respectively. The indoor CO2 levels in the analyzed classrooms (500–900 ppm) were below the ASHRAE 62.1 standard, indicating adequate ventilation and sufficient outdoor-to-indoor air circulation due to the high air exchange rates (2.63–8.63 h−1). Moreover, operating the purifier at the maximum flow rate (748 m3 /h) in classroom 3 resulted in increasing the particle decay rate from 3.9–4.8 h−1 (without the purifier) to 6.5–6.7 h−1, corresponding to a 50% reduction in indoor PM and PN after 10–15 min of switching off the aerosol source. The efficiency of the HEPA air purifier exceeded 95% in capturing ultrafine and coarse particles and ranged between 82–88% for particles in the accumulation range. This study highlighted the significance of mitigating indoor pollution in closed environments, especially in densely seated classrooms where the infection risk of viruses’ transmission is high. The findings of this study recommend the use of HEPA air purifiers in closed environments, especially when the ventilation system is not equipped with an efficient inline filter. 32 Chapter 3 - Development and performance evaluation of a two-stage cascade impactor equipped with gelatin filter substrates for the collection of multisized particulate matter 3.1. Introduction The collection of ambient aerosols with different particle sizes is accomplished by various inertial impaction technologies (M. Chen et al., 2018; H. Wang et al., 2017). Low-flowrate inertial impactors were used in various air sampling applications; however, they do not usually collect sufficient PM loadings for particle characterization and toxicological analysis because of their low operational flow rates (P. Patel et al., 2021). On the other hand, high-flowrate impactors can shorten the sampling duration and collect considerable amounts of PMtargeted constituents existing in the atmosphere (P. Patel et al., 2021; Sugita et al., 2019). Aerosol concentrators, such as versatile aerosol concentration enrichment system (VACES), have also been developed to enrich ambient PM concentration for conducting inhalation studies (S. Kim et al., 2000, 2001; Ning et al., 2006), however they impose several challenges. For instance, the operation of these concentrators is complex as it requires multiple processes in series, including saturation, condensation, and impaction (S. Kim et al., 2001). In addition, employing the concentrators in exposure studies can lead to inevitable instability in PM physical and chemical characteristics because of the variability of concentration and chemical composition of ambient PM (Taghvaee, Mousavi, et al., 2019). While aerosol concentrators have been widely used for the collection of ambient PM, employing high-flow-rate inertial impactors leads to a simpler setup with a lower cost (Kavouras et al., 2000; Misra, Kim, et al., 2002). Previous publications used inertial impactors to collect ambient particles on various types of substrates, including PTFE (Teflon) and quartz filters (Biswas & Gupta, 2017; George et al., 33 2012). The collected particles on these different substrates are usually extracted in a particular solvent (e.g., ultrapure Milli-Q water) using a sonication device in order to generate liquid solutions for PM characterization or using them in exposure studies (Varga et al., 2001). However, the efficiency of extracting the collected particles in Milli-Q water is often much lower than 100% due to the insolubility of some particles, making it difficult to extract all the collected particles in the sample (Huang et al., 2020; Taghvaee, Mousavi, et al., 2019). Some of these water-insoluble species (e.g., transition metals and organics such as polycyclic aromatic hydrocarbons (PAH)) are particularly toxic components of the ambient aerosol and their exclusion compromises the integrity of toxicological studies (Gerlofs-Nijland et al., 2009). To overcome this challenge, the use of water-soluble particle collection media such as filters and impaction substrates is highly desired since these substrates will completely dissolve in water (i.e., 100% extraction efficiency), allowing for the extraction of all possible particles, including water-insoluble fractions. As an example, gelatin filters are one of the commercially available water-soluble filters and have been widely used for collecting airborne microbes and viruses (i.e., bioaerosol sampling) since they can efficiently maintain microorganisms(Appert et al., 2012; Chan et al., 2020; Fabian et al., 2009; Y. Liu et al., 2020; Scherwing & Patzelt, 2020) However, to the best of our knowledge, no previous studies have developed a high-flow-rate cascade impactor for collecting multi-sized ambient PM (i.e., coarse, accumulation, and ultrafine particles) on gelatin filters to use in toxicological studies. The main objective of this study was to develop and evaluate a high-flow-rate gelatin cascade impactor (GCI) for the collection of different ambient PM fractions on gelatin substrates and filters. The performance of GCI was evaluated using laboratory-generated aerosols as well as in field experiments to collect ambient PM for further toxicological studies. The GCI operates at 34 a high flow rate (100 lpm) and is designed to separate ambient particles into three groups: coarse (> 2.5 µm), accumulation (0.2 - 2.5 µm), and ultrafine particles (< 0.2 µm). The development of the GCI offers a simple and powerful tool for numerous applications requiring PM aqueous solutions, including toxicity assays and inhalation studies. In addition, the use of water-soluble gelatin substrates permits the complete extraction of water-insoluble species, which provides a better understanding of the toxicological properties of the collected PM samples. Using gelatin filters in field sampling experiments is simpler, cheaper, and more efficient in terms of PM slurry preparations in comparison with conventional filters (e.g., PTFE and quartz) that pose considerable difficulties in extracting the collected particles into aqueous solutions (Huang et al., 2020; Taghvaee, Mousavi, et al., 2019). 3.2. Methods 3.2.1. Gelatin cascade impactor design The gelatin cascade impactor (shown in Figure 3.1) consists of two sequential impaction stages along with a filter holder placed downstream of the impactor and is operated at flow rate of 100 lpm. Gelatin filters with diameters of 47 mm and 80 mm (3.0 μm pore size, Sartorius AG, Germany) were used as particle collection media in the two impaction stages and the after-filter stage, respectively. The detailed specifications of the GCI are expressed in Table 3.1 which presents the design parameters and the physical characteristics of the impactor based on the theoretical calculations. As presented in Table 3.1, the critical cut-point diameters (d50) for the first and second impaction stages of GCI were 2.5 µm and 0.2 µm, respectively. They were determined first theoretically for each impaction stage based on the critical Stokes number and then confirmed in laboratory experiments as discussed later in section 3.1.1. Following the same procedure, the pressure drop values were determined theoretically as 1 in H2O (0.25 kPa) and 12 35 in H2O (2.98 kPa) for the first and second impaction stages, respectively, and then were verified experimentally. Furthermore, the first impaction stage was designed with one slit nozzle having width and length of 0.33 cm and 2.48 cm, respectively, while the second stage was designed with 6 equally spaced slit nozzles having equal width (0.013 cm) and length (3.12 cm). Table 3.1 The physical characteristics and design parameters of the gelatin cascade impactor First stage Second stage After-filter Substrate material Gelatin filter Gelatin filter Gelatin filter Substrate diameter (mm) 47 47 80 Critical Stokes number St50 0.25 0.25 - √St50 0.5 0.5 - Cut-point d50 (µm) 2.50 0.20 - △P (kPa) 0.25 2.98 - Jet velocity Uj (cm/s) 2021 7001 - Flow rate (lpm) 100 100 100 Slit nozzle width W (cm) 0.33 0.013 - Slit nozzle length (cm) 2.48 3.12 - Number of slit nozzles 1 6 - 36 (a) (b) Figure 3.1 The (a) two impaction stages and the (b) filter holder of the gelatin cascade impactor 37 3.2.2. Impaction theory Inertial impaction theory has been widely used for the development of air particulate capturing technologies (e.g., cascade impactors) (Gotoh & Masuda, 2000; Maeng et al., 2007; Marple et al., 1990) and was used for the development of the GCI in this study. We employed the following Stokes equation for designing the cut-point diameter and nozzle dimensions of impactor stages based on the Stokes number of a particle having a 50% impaction probability (St50) (Marple et al., 1990, 1991; Sioutas, Koutrakis, & Olson, 1994)1994): St50 = ρpUjCcdp 2 9µW (3.1) where ρp is the particle density (g/cm3 ), Uj is the average velocity of the jet (cm/s), µ is the dynamic viscosity of the air (g/(cm.s)), dp is the particle diameter (cm), W is the nozzle width (cm), and Cc is Cunningham slip correction factor, which was estimated using Equation 3.2 (Hinds, 1999): Cc = 1 + 2.52λ dp (3.2) where λ is the mean free path of air molecules (cm). Moreover, the jet velocities for both impaction stages in Table 3.1 were calculated by dividing the GCI flow rate by the nozzle cross-sectional area. The theoretical pressure drop across each impactor stage was calculated based on the following Bernoulli’s equation: ∆P = 1 2 ρaUj 2 (3.3) where △P is the pressure drop (dyn/cm2 ) and the density of air (ρa) is equal to 0.0012 g/cm3 . 38 3.2.3. Laboratory characterization of the first and second impaction stages of the gelatin cascade impactor 3.2.3.1. Experimental setup Monodisperse polystyrene latex (PSL) particles along with three different polydisperse aerosols (i.e., sodium chloride (NaCl), ammonium sulfate ((NH4)2SO4), and ammonium nitrate (NH4NO3)) were used to determine the particle collection efficiency for each stage of the gelatin cascade impactor. These monodisperse and polydisperse particles were generated in the lab by aerosolizing their aqueous solution using a nebulization system (Figure 3.2) (Han et al., 2009; Lim et al., 2020; Soo et al., 2016). Figure 3.2 illustrates the schematic of the experimental setup used for the characterization of impaction stages of the GCI. At first, the liquid suspensions were converted to airborne aerosols by supplying compressed filtered air to a nebulizer (Model 11310 HOPETM nebulizer, B&B Medical Technologies, USA) using a compressor pump (Model VP0625-V1014-P2-0511, Medo Inc., USA) equipped with HEPA capsule (Model 12144, Pall Corporation, USA). The aerosolized particles were drawn to a silica-gel diffusion dryer (Model 3062, TSI Inc., USA) to remove the moisture of particles, followed by a glass container having Po-210 neutralizers (Model 2U500, NRD Inc., USA) to minimize the electrical charges of particles. The airborne particles entered the GCI operating with a flow rate of 100 lpm using a high-capacity pump (Model 0523-101-G588NDX, Gast Manufacturing Inc., USA). Particle collection efficiency (1 – particle penetration) was determined as a function of particle size using an optical particle sizer (OPS) (Model 3330, TSI Inc., USA) and a scanning mobility particle sizer (SMPS) (Model 3936, TSI Inc., USA). Since the GCI was designed to allow for decoupling of the first impaction stage from the second stage, we individually characterized each stage to obtain the particle collection efficiency curves using the experimental 39 setup shown in Figure 3.2. Given that the OPS and SMPS mainly detect particles in size range of 0.3 to 10 µm and 0.01 to 0.7 µm, respectively, we characterized the first impaction stage using the OPS only while the second impaction stage was characterized using both instruments simultaneously (i.e., OPS and SMPS). As shown in the experimental setup schematic, both particle counter instruments were connected upstream and downstream of the impaction stage to measure the difference in particle number concentrations before and after the impactor. Equation 3.4 was used for calculating the collection efficiency of particles for each impaction stage: 𝐶𝐸 = 𝑃𝑁𝑢 − 𝑃𝑁𝑑 𝑃𝑁𝑢 × 100% (3.4) where CE is the particle collection efficiency and PNu and PNd are particle number concentrations upstream and downstream of the impactor, respectively. Additionally, the pressure was also experimentally measured upstream and downstream of each impaction stage using Magnehelic pressure gauge (Model series 2000, Dwyer Instruments Inc., USA) to calculate the pressure drop as a function of the air flow rate. 40 Figure 3.2 Experimental setup schematic for the laboratory characterization of the gelatin cascade impactor 3.2.3.2. Mass loading tests In addition to the laboratory characterization of GCI described above, the performance of GCI equipped with gelatin filter was compared with a personal cascade impactor sampler (PCIS) (Model 225-370, SKC Inc., USA) equipped with PTFE (Teflon) filter (Pall Life Sciences Inc., USA). We performed laboratory experiments employing both impactors in parallel to collect artificially generated test aerosols (i.e., sodium chloride, ammonium nitrate, and glutaric acid) and then calculate the mass concentrations (µg/m3 ) of the collected particles. For each test aerosol, the experiments were carried out to compare the particle mass concentrations measured by the GCI first and second impaction stages with those determined by the PCIS 2.5 µm and 0.25 µm cut-point stages, respectively. Prior to the above-mentioned laboratory tests, all filters and substrates were kept in a room with a controlled temperature of 22 - 24 ºC and relative humidity 41 of 40 - 50% to equilibrate and then were weighed before and after the experiments using Mettler 5 microbalance (MT5, Mettler Toledo Inc., USA) to determine the collected mass from the difference between their final and initial weights. 3.2.4. Analysis of blank gelatin filters 3.2.4.1. Chemical and toxicological analyses Blank gelatin and PTFE filters having equal size (47 mm) were analyzed in the Wisconsin State Lab of Hygiene (WSLH) at the University of Wisconsin-Madison. The blank filters were analyzed for their inorganic ions, metals, and trace elements. The inorganic ions were assessed using ion chromatography (IC), while the metals and trace elements were measured by inductively coupled plasma mass spectroscopy (ICP-MS) (Karthikeyan & Balasubramanian, 2006; Lough et al., 2005). Furthermore, the redox activity of the filters was assessed by means of the macrophage-based reactive oxygen species (ROS) and dithiothreitol consumption (DTT) assays. At first, aqueous solutions of the filters were prepared using Type 1 ultrapure Milli-Q water (resistivity 18.2 MΩ·cm at 25 °C, total organic carbon (TOC) ≤ 5 ppb) for the use in both assays. ROS assay was performed by exposing highly responsive rat alveolar macrophage cells to the liquid solution and using dichlorodihydrofluorescein diacetate (DCFHDA) as the fluorescent probe to assess the ROS activity. DCFH-DA was employed in ROS assay since it is responsive to main reactive oxygen species, including hydroxyl, peroxide, and superoxide radicals (Carranza & Pantano, 2003; Schoonen et al., 2006). More details related to ROS assay are described in Landreman et al. (2008). Furthermore, the DTT assay has been widely used for measuring the redox activity of PM samples (Delfino et al., 2013; Kumagai & Shimojo, 2002; Shima et al., 2006). This assay measures the DTT consumption in the filter extract and its conversion to the disulfide form, in which the linear rate of DTT depletion is 42 proportional to the oxidative potential (toxicity) of the analyzed sample. Further details regarding the DTT assay can be found in Kumagai et al. (2002) and Shafer et al. (2016). 3.2.4.2. Particle number concentrations A blank gelatin filter with an average mass of 63.20 mg was dissolved in 220 ml of ultrapure Milli-Q water using an ultrasonic bath for 30 min to form a homogenous solution with a concentration of approximately 287 µg/ml. The blank gelatin slurry was analyzed for the particle number concentrations using the nebulization system setup described previously, in which the slurry was aerosolized and then drawn through the SMPS inlet port to measure the number-based size distribution. For the purpose of comparison, we collected ambient ultrafine PM (UFP) on PTFE filter (20 × 25 cm, 2.0 μm pore size, PALL Life Sciences, USA) using a high-flow-rate PM sampler that has a cut-point diameter of 0.18 µm to separate accumulation from ultrafine PM (Misra, Kim, et al., 2002). The collected particles were extracted in an ultrapure Milli-Q water to form a liquid suspension having a concentration of 275 µg/ml (i.e., similar to the concentration of the blank gelatin suspension) that was re-aerosolized using the previously discussed nebulization system. 3.2.5. Field evaluation of gelatin cascade impactor In addition to the laboratory characterization of the GCI, field tests were conducted using GCI and PCIS equipped with gelatin and PTFE filters, respectively. The field location was near the University of Southern California (USC) main campus in downtown Los Angeles, CA, and in close proximity to a major highway (I-110). This location site has been extensively used in previous publications since it represents a mixture of various urban sources emitting PM in different sizes and chemical compositions (Moore et al., 2007; Ning et al., 2007; Sardar et al., 2005). The aerosol samplers (i.e., GCI and PCIS) were placed in a controlled indoor environment 43 (i.e., a sampling unit) and connected to the outdoor environment using an aluminum tube to sample ambient air, which will equilibrate to room temperature once it enters the controlled indoor space. It should be noted that placing the GCI in an outdoor environment to conduct field experiments might be problematic, especially in fall and winter seasons, because the higher relative humidity prevailing during these periods might compromise the quality of the gelatin substrates. Before initiating these field experiments, the PTFE and gelatin filters were kept in a standard laboratory condition (i.e., temperature of 22 - 24 ºC and relative humidity of 40 - 50%) to equilibrate and then obtain their pre-sampling weights. After sampling, the filters were weighed to calculate the collected mass on each filter from the difference between the postsampling and the pre-sampling weights. Prior to starting the field sampling, we conducted a series of field tests to assess the durability of gelatin filters in withstanding long-term sampling durations. These field experiments were carried out with different sampling durations (e.g., 1 day, 2 days, 5 days) to collect ambient particles on gelatin substrates using the GCI. We realized that exceeding 24-hr of sampling resulted in damaging the gelatin filters as they were torn apart and separated into smaller pieces. Therefore, we performed short-term sampling (24 hr) to compare the oxidative potential of PM2.5 (dp < 2.5 µm) samples using the GCI and the PCIS simultaneously. For the collection of PM2.5, we employed the GCI first impaction stage and the PCIS 2.5 µm cut-point stage and placed an 80 mm gelatin filter and a 37 mm PTFE filter in their after-filter stages, respectively. Given that the PCIS operates at low flow rate (i.e., 9 lpm), we used two PCIS simultaneously on each sampling day to allow for the collection of sufficient PM mass loadings (> 300 µg) on two PTFE filters, which were composed together to form a slurry for the toxicological analysis. The mass loading was not an issue in GCI since it was operating at flow 44 rate of 100 lpm, allowing for an average collection of more than 2 mg of PM2.5 each day. After concluding five daily field experiments, the collected samples were sent to Wisconsin state lab of hygiene to measure the redox activity of particles using DTT and ROS assays. 3.3. Results and discussion 3.3.1. Laboratory experiments using artificially generated test aerosols 3.3.1.1. Pressure drop and collection efficiency curves Using the experimental setup discussed earlier, the pressure drop was measured for both impaction stages and plotted as a function of GCI air flow rate as shown in Figure 3.3. By operating the GCI at a flow rate of 100 lpm, the pressure-drop values for the first and second impaction stages were experimentally measured as 1.4 in H2O (0.35 kPa) and 14.2 in H2O (3.53 kPa), respectively. These pressure-drop values agreed with the theoretical predictions (Table 3.1) of the first and second impaction stages. Maintaining low pressure drop (≤ 3.5 kPa) in both impaction stages was necessary to minimize evaporation losses of volatile components (Furuuchi et al., 2010). Figure 3.3 Pressure drop as a function of GCI flow rate for stages 1 and 2 45 Additionally, Figure 3.4 shows the particle collection efficiency curves for both impaction stages using gelatin filters as impaction substrates. Figure 3.4 (a) shows the collection efficiency data as a function of particle size for the first impaction stage using three laboratorygenerated aerosols, including PSL, sodium chloride, and ammonium nitrate. The collection efficiency increased rapidly in the range of 2 - 3 µm and was higher than 90 - 95 % for particles larger than 5 µm, corroborating the fact that the GCI equipped with gelatin substrate efficiently minimized coarse particle bouncing and re-entrainment losses. All three test aerosols showed a very good agreement in the critical (50%) cut-point diameter of the first impaction stage, which was approximately 2.4 µm, corresponding to a Stokes number of 0.27 (√St50 =0.52). The figure also demonstrated that the lab aerosols had the same particle size distribution, except of the monodisperse PSL particles (size range of 1.9 - 7 µm) which were coarse particles and used for the characterization of the first impaction stage only. Transitioning to the second impaction stage, Figure 3.4 (b) shows the collection efficiency curves using three polydisperse aerosols, including ammonium sulfate, ammonium nitrate, and sodium chloride. The collection efficiency increased sharply in the particle size range of 0.16 – 0.27 µm and approached approximately 99% for particles larger than 0.3 µm, which also indicated that the particle losses and bounce-off were not significant in the GCI. Similar to the first stage, the 50% cut-point diameter was in good agreement across the three analyzed aerosols and was, on average, 0.21 µm, corresponding to a Stokes number of 0.29 (√St50 =0.54). The experimental values of the 50% cut-point diameters for both impaction stages were very consistent with the theoretical predictions shown in Table 3.1. Additionally, the experimentally measured square root of Stokes numbers (√St50) for the first and second impaction stages were 0.52 and 0.54, respectively, and were consistent with previous studies that 46 reported experimentally determined √St50 values in the range of 0.4 - 0.6 for impactors with rectangular nozzle (Demokritou et al., 2004; Demokritou, Kavouras, et al., 2002; Misra, Kim, et al., 2002; Sioutas, Koutrakis, & Burton, 1994). However, they were lower than the theoretical √St50 value (0.77) calculated for a slit-nozzle impactor with a flat rigid substrate (Hinds, 1999). The lower √St50 with respect to the rigid surface is most likely due to the penetration of particles into the pores of the gelatin substrate, causing a slight increase in the collection efficiency and minimizing the particle losses and bouncing-off. Previous studies reported similar results by achieving lower √St50 values than the theoretically calculated for rigid surfaces because of the use of polyurethane foam (PUF) as an impaction substrate (Kavouras et al., 2000; Kavouras & Koutrakis, 2001)2001). The sharpness of the collection efficiency curves was assessed by the geometric standard deviation (GSD) value obtained from the square root of the ratio of particle diameter corresponding to 84.1% collection efficiency to the diameter of 15.9% collection effic(Demokritou, Kavouras, et al., 2002; Kang et al., 2012; Marple et al., 2004). The GSD of the collection efficiency curves of the test aerosols in the first and second impaction stages were in the range of 1.4 - 1.5, indicating sharp inertial separation of particles. 47 (a) (b) Figure 3.4 Collection efficiency curves of the GCI (a) first impaction stage and (b) second impaction stage using gelatin filters and various artificially generated aerosols. X-axis represents optical diameters for particles ≥ 0.3 µm and mobility diameters for particles < 0.3 µm. Error bars are standard deviations of repeated measurements. 48 In addition to the use of gelatin as impaction substrates, we employed 47 mm quartz filters in both impaction stages to obtain particle collection efficiency curves using the same procedure and laboratory-generated aerosols described earlier. Figure 3.5 shows that the use of quartz filters led to similar collection efficiency patterns and the same 50% cut-point diameters in comparison with gelatin substrates. Therefore, GCI can also be used with different substrates (e.g., quartz, Teflon) without any changes in its technical specifications and particle separation characteristics. 49 (a) (b) Figure 3.5 Collection efficiency curves of the GCI (a) first impaction stage and (b) second impaction stage using quartz filters and various artificially generated aerosols. X-axis represents optical diameters for particles ≥ 0.3 µm and mobility diameters for particles < 0.3 µm. Error bars are standard deviations of repeated measurements. 50 3.3.1.2. The comparison of particle loading between GCI and PCIS Figure 3.6 shows the average mass concentrations of particles collected on gelatin and PTFE substrates using GCI and PCIS, respectively. The data shown in the figure are based on the average value of the three test aerosols (i.e., sodium chloride, ammonium nitrate, and glutaric acid) used in the laboratory experiments. Figure 3.6 (a) presents the mass concentration results based on the first impaction stage of GCI and the 2.5 µm cut-point stage of the PCIS. Given that both stages had 50% cut-point diameters of approximately 2.5 µm, the findings show an excellent agreement between both impactors with a minimal variability of 3.3% and 4.2% for the impaction substrate and after-filter, respectively. Moreover, Figure 3.6 (b) also demonstrates consistency in mass concentration between both impactors using 0.25 µm cut-point stage of the PCIS and the second impaction stage of the GCI, along with their after-filter stages. The variability in mass concentrations between the GCI and PCIS were 8.1% and 9.3% for the impaction substrate and after-filter, respectively. The higher mass concentration variability between the GCI and PCIS in this experiment was attributed to the slight difference in the 50% cut-point diameter between the PCIS 0.25 µm stage and GCI second impaction stage, which also explains the higher mass concentration of the PCIS over the GCI in the after-filter stage. 51 (a) (b) Figure 3.6 The comparison of mass concentrations of collected particles using PCIS and GCI (a) first and (b) second impaction stages. Error bars are standard deviations of repeated measurements. 52 3.3.2. Filter blank analysis 3.3.2.1. Chemical and toxicological analyses The IC analysis results are presented in Figure 3.7 (a), which illustrates the levels of inorganic ions found on blank gelatin filter. The levels of inorganic ions in the blank gelatin were approximately less than 150 µg/filter, excluding sulfate which showed a concentration of 860 µg/filter. Considering the maximum sampling duration (i.e., 24 hr) of the gelatin filter and the operational flow rate (i.e., 100 lpm) of the GCI, the levels of inorganic ions in the blank gelatin filter were converted to airborne concentrations in units of mass per volume of air (m3 ). The comparison of volume-based values with typical inorganic ion content in ambient PM in Los Angeles (Pirhadi, Mousavi, Taghvaee, et al., 2020) is shown in Figure 3.7 (b). According to the figure, the inorganic ion levels in the blank gelatin (< 0.05 µg/m3 ) were substantially lower than the levels in ambient PM, except sulfate (5.97 µg/m3 ) which was more than twice its level in the ambient air (1.73 µg/m3 ) in Los Angeles. Considering that ammonium sulfate is a major constituent of PM2.5, these results preclude the use of gelatin substrates for inorganic PM ion analysis, but as we noted earlier, the main purpose of using these substates was for toxicological analysis and more suitable substrates (e.g., quartz or Teflon) can be used with the GCI for the characterization of the inorganic ion content of PM. 53 (a) (b) Figure 3.7 Inorganic ion (a) blank levels in gelatin substrate and (b) the normalized concentrations (per volume of air based on 24 hr sampling) of the blank gelatin in comparison with typical ambient PM levels in Los Angeles. Error bars are standard deviations. 54 Figure 3.8 (a) shows the results of ICP-MS analysis for metals and trace elements in blank gelatin and PTFE filters having equal size (i.e., 47 mm). The levels of most metals and trace elements in the blank gelatin were approximately in the range of 10 – 10,000 ng/filter, with the exception of sodium (Na) and potassium (K) which were much higher than other metals with concentrations of 176,758 and 11,668 ng/filter, respectively. It is worth noting that some of these selected metals had negligible values (less than 10 ng/filter), including lithium (Li), manganese (Mn), nickel (Ni), barium (Ba), mercury (Hg), and lead (Pb). The findings also highlight that the concentrations of several metals in gelatin exceeded that of PTFE, including potassium (K), vanadium (V), sodium (Na), and chrome (Cr), especially Na whose concentration in gelatin was 735 times its concentration in the PTFE filter. It is also notable that the blank levels of most redox-active metals (i.e., Fe, Cu, Ti, Zn, Pb, Ba, Mn) were in a comparable range in the two filters. Following the same procedure discussed earlier to convert blank levels to airborne concentrations per volume of air, we expressed the blank metals and trace elements in units of ng/m3 of air in Figure 3.8 (b) and compared them with the ambient PM in Los Angeles (Pirhadi, Mousavi, Taghvaee, et al., 2020). As shown in the figure, the levels of most metals and trace elements in the blank gelatin (< 1 ng/m3 ) were lower than those in the ambient air by one order of magnitude or higher. However, the concentrations of sodium and potassium in the blank gelatin were in a comparable range with the ambient levels. 55 (a) (b) Figure 3.8 Metals and trace elements (a) blank levels in gelatin substrate in comparison with PTFE and (b) the normalized concentrations (per volume of air based on 24 hr sampling) of the blank gelatin in comparison with typical ambient PM levels in Los Angeles. Error bars are standard deviations. The toxicological analysis showed that the average oxidative potential levels in the blank gelatin filter based on DTT and ROS assays were 5.19  0.88 nmoles/min/filter and 367.3  89.5 56 µg Zymosan/filter, respectively. The blank redox activity values of the gelatin filter were in a comparable range with the blank PTFE filter, which had DTT and ROS activities of 3.92  0.67 nmoles/min/filter and 283.4  72.9 µg Zymosan/filter, respectively. Furthermore, extensive studies used DTT assay to determine the oxidative potential of PM collected samples in different locations around the globe (i.e., Milan, Los Angeles, and Riyadh) (Altuwayjiri et al., 2022; Cho et al., 2005; Farahani et al., 2022; Hakimzadeh et al., 2020; Saffari et al., 2014). Figure 3.9 shows the comparison of the normalized DTT activity (per volume of air) of the blank gelatin filter with the ambient PM in the three major cities. According to the figure, the DTT activity of the blank gelatin (0.036  0.006 nmoles/min/m3 ) is much lower than the ambient PM in Milan, Los Angeles, and Riyadh which had average oxidative potential levels of 2.4  0.43, 0.88  0.29, and 1.2  0.32 nmoles/min/m3 , respectively. Therefore, gelatin filters can be used as substrates for the collection of PM samples for toxicological analysis since their blank redox activity values were in a comparable range with other substrates (i.e., PTFE) and were considerably low compared to the DTT responses recorded in typical ambient PM around the world. 57 Figure 3.9 DTT activity comparison between the blank gelatin substrate and PM collections in three major cities around the world. Error bars are standard deviations. 3.3.2.2. Particle number concentrations of re-aerosolized gelatin blank filters and filters with ambient PM The SMPS was employed to assess the particle number concentrations of the two suspensions (i.e., blank gelatin and UFP) by aerosolizing the slurries using the typical nebulization system illustrated in the methodology section. Accordingly, the particle number distribution curves as a function of particle diameter were obtained and presented in Figure 3.10 for the blank gelatin and UFP slurries. As clearly shown in the figure, the particle number concentration for UFP exceeded the blank gelatin by approximately two orders of magnitudes. The total particle number concentrations for blank gelatin and UFP suspensions were 6,012  248 and 360,379  21,757 particles/cm3 , respectively. This considerably low particle number concentration of the blank gelatin in comparison with UFP further corroborates the use of gelatin filters in the field of aerosol sampling. 58 (a) (b) Figure 3.10 Particle number concentrations as a function of particle diameter for (a) gelatin and (b) UFP suspensions. X-axis represents mobility diameters measured by SMPS. Error bars are standard deviations of repeated measurements. 59 3.3.3. Field comparison between the oxidative potential of particles Before performing the toxicological analysis on the collected PM2.5 samples, the ambient particle mass concentrations obtained by the GCI and PCIS were compared in Figure 3.11, which shows a very good agreement between the two samplers (i.e., minimal variability of less than 3%). The consistency between the GCI equipped with gelatin substrate and the PCIS equipped with PTFE substrate corroborates the suitable use of gelatin filters in field sampling without any issues related to water absorption. The collected PM2.5 samples were then analyzed using DTT and ROS assays in order to compare the redox activity of particles collected on gelatin filters using the GCI with particles collected on PTFE filters using the PCIS. It should be noted that the blank redox activity values of gelatin and PTFE filters were subtracted from the redox activity of the collected PM2.5 samples. For comparison purposes, we normalized the redox activity to PM mass, obtained from the gravimetric analysis, to express the values in units of µg Zymosan Units/mg PM and nmoles/min/mg PM for ROS and DTT, respectively. Figure 3.12 (a) shows the results of the DTT assay in which the oxidative potential of particles collected using the GCI was approximately twice the PCIS, with redox activities of 26.44 nmoles/min/mg PM and 13.56 nmoles/min/mg PM for the GCI and PCIS, respectively. In addition, the ROS results shown in Figure 3.12 (b) further corroborate the ability of the GCI to achieve higher particle redox activity values in comparison with the PCIS. As illustrated in the figure, the ROS activity of particles collected using GCI equipped with gelatin filter (8813.2 µg Zymosan Units/mg PM) is more than 4 times the redox activity of particles collected on PTFE filter using PCIS (1909.1 µg Zymosan Units/mg PM). The higher PM oxidative potential of the GCI compared to the PCIS might be attributed to the superiority of the GCI in capturing water-insoluble redox-active PM species (e.g., elemental carbon, PAH, other insoluble organics, and transition metals such as iron, lead, 60 nickel). Previous studies have reported the significant contribution of water-insoluble compounds to the oxidative potential of PM2.5 (Daher et al., 2011; Pirhadi, Mousavi, & Sioutas, 2020). D. Wang et al. (2013) conducted an experiment using aerosol-into-liquid collector to compare the ROS levels of two PM2.5 suspensions, one of which was filtered from the waterinsoluble species. They concluded that the filtered suspension resulted in approximately 30% lower ROS activity than the unfiltered slurry, further underscoring the effective role of insoluble compounds in the overall redox activity of PM2.5. These observations support the efficiency of using the GCI in collecting ambient PM for further toxicological studies and in-vivo and in-vitro assays. Figure 3.11 Ambient PM2.5 mass concentrations based on the GCI and PCIS. The error bars are standard deviations. 61 (a) (b) Figure 3.12 Redox activity of particles collected on gelatin filter using the GCI in comparison with particles collected on PTFE using the PCIS based on (a) DTT and (b) ROS assays. Error bars are standard deviations. 62 3.4. Summary and conclusion In this study, a two-stage gelatin cascade impactor was developed and evaluated in the laboratory as well as in field experiments. The GCI operates at a high flow rate of 100 lpm and consists of two impaction stages with 2.5 µm and 0.2 µm cut-point diameters, respectively. The pressure drop values in both impaction stages were low (≤ 3.5 kPa) in order to minimize evaporation losses of volatile components. Furthermore, laboratory experiments were conducted using artificially produced aerosols to corroborate the agreement of the GCI with the PCIS in terms of the mass concentration of collected particles, which showed a minimal variability of less than 10% between both impactors. In addition to the lab experiments, field experiments were carried out to collect PM2.5 particles on PTFE and gelatin filters using PCIS and GCI, respectively. Since the GCI was operated with a high sampling flow rate (100 lpm), it was able to collect considerable amounts of PM in 24 hr intervals. The field tests corroborated the higher particle oxidative potential levels measured by the GCI in comparison with the PCIS based on both DTT and ROS assays. The DTT and ROS activities of particles collected using the GCI were 26.44 nmoles/min/mg PM and 8813.2 µg Zymosan Units/mg PM, respectively, which were more than twice the redox activities of particles collected by the PCIS. This can be attributed to the superiority of the GCI in capturing water-insoluble redox-active PM species on gelatin filters, which were dissolved in water to extract all collected particles. Although gelatin filters offer significant advantages to the field of aerosol sampling, they might not be suitable for PM chemical characterization studies due to the elevated blank levels of some metals and inorganic ions (e.g., sulfate, sodium). In addition, field experiments to collect ambient PM on gelatin filters should be limited to 24-hr sampling duration to avoid any possible damage to the filter media. If PM chemical analysis or long-term sampling durations are required, the GCI can be equipped 63 with different types of substrates without any significant change in its technical specifications or particle separation characteristics, as was verified experimentally by the use of quartz and gelatin substrates. The GCI is a significant technological contribution to air pollution studies and the field of aerosol sampling due to its ability to achieve high-volume collection of multi-sized PM without significant particle bouncing and re-entrainment losses. It also enables researchers in the field of environmental health to conduct inhalation and toxicological studies using water-soluble filters (i.e., gelatin) that can easily dissolve in water and achieve a particle extraction efficiency of 100%. The GCI equipped with gelatin filters is a powerful replacement for traditional cascade impactors due to its advantages in collecting considerable amounts of real-life PM redox-active constituents and preserving their chemical compositions for further toxicity assays. 64 Chapter 4 - Identifying urban emission sources and their contribution to the oxidative potential of fine particulate matter (PM2.5) in Kuwait. 4.1. Introduction Kuwait, a small country in the southwestern part of Asia and at the northeastern edge of the Arabian Peninsula, is characterized by its desert environment and extremely hot and arid climate. Kuwait is experiencing unprecedented high ambient air temperatures, with the Mutriba area reaching 54°C in 2016, which is the highest recorded temperature in the last 76 years (Alahmad et al., 2019; Merlone et al., 2019). In addition to the elevated temperatures, dust is a common phenomenon in the region due to frequent dust episodes caused by strong winds carrying loose sand and dirt from dry surfaces (AL-Harbi, 2015; Yassin et al., 2018). Previous studies have shown that Kuwait experienced high levels of PM originating from local and regional anthropogenic and natural sources, with daily PM2.5 and PM10 concentrations potentially exceeding 300 µg/m3 and 2000 µg/m3 , respectively, in extreme dust episodes (Alahmad et al., 2021; Al-Hemoud et al., 2018; Brown et al., 2008). According to a comprehensive global air quality report published by the World Health Organization (WHO) in 2016, the average annual PM2.5 mass concentration in Kuwait was 75 µg/m3 , exceeding several neighboring countries, including Iraq (50 µg/m3 ), Oman (48 µg/m3 ), Bahrain (60 µg/m3 ), United Arab Emirates (64 µg/m3 ), and Iran (42 µg/m3 ) Click or tap here to enter text. (Chee. In the last four decades, Kuwait has experienced remarkable socio-economic growth, driven by the construction of extensive urban freeways and bridges as well as the development of the industrial infrastructure (Al-Awadhi, 2014). This expansion necessitates the implementation of effective regulatory strategies focusing on environmental concerns, particularly the high levels of particulate pollution, to prevent environmental and health deterioration in the region. 65 Multivariate factor analysis models, including Principal Component Analysis/Multiple Linear Regression (PCA/MLR), UNMIX, and Positive Matrix Factorization (PMF), have been utilized for identifying emission sources as well as quantifying their impacts on any targeted PM species (Deng et al., 2018; Shi et al., 2014; Z. Wang et al., 2012). Several previous research studies have combined the results of PCA analysis with MLR in order to identify the main emission sources and their percentage contribution to target PM species in different regions around the globe (Argyropoulos et al., 2016; Deka et al., 2016; X. Guo et al., 2016; Shirmohammadi et al., 2015; Taghvaee, Sowlat, et al., 2019; Ul-Saufie et al., 2013). To date, only two source apportionment studies have been conducted in the state of Kuwait, aiming to identify the potential sources of ambient PM2.5 (Alahmad et al., 2021; Alolayan et al., 2013). These two studies have reported a wide range of PM2.5 sources in Kuwait City, with soil dust accounting for the majority (54%) of PM2.5 mass during 2004-2005, and regional pollution being the major contributor (44%) during 2017-2019. Given the limited information regarding PM2.5 sources in Kuwait, this study provides a comprehensive analysis of the potential emission sources and their contributions to the ambient PM2.5 toxicity. Furthermore, to the best of our knowledge, there has been no prior research conducted to investigate the toxicological characteristics of ambient particles in Kuwait. The objective of this study is to explore the chemical and toxicological properties as well as potential sources of ambient PM2.5 in an urban site located in Kuwait City, Kuwait. PM2.5 samples were collected from June 2022 to May 2023, covering all different seasons. The collected samples were analyzed after each season to determine the PM chemical composition (i.e., metals, inorganic ions, carbonaceous species) and oxidative potential (i.e., redox activity). 66 We then employed PCA analysis coupled with MLR to study the contribution of pollution sources to the PM2.5 oxidative potential in Kuwait. 4.2. Methods 4.2.1. Description of sampling location Our sampling site, illustrated in Figure 4.1, is located within the premises of Kuwait Institute for Scientific Research (KISR) in Kuwait City, which is the capital of Kuwait. Kuwait City is the center of the country and includes residential and industrial areas, governmental offices, and numerous private sector corporations. Given the central location and the close proximity to major residential areas where the majority of the population resides, our sampling site is representative of population exposure to a wide range of pollutants originating from nearby emission sources, including Doha east and west power stations (10 km), Shuwaikh desalination plant (4 km), and Shuwaikh sea port (2 km). In addition, the site is located at a distance of 600 m from Highway 85, which connects the far west residential areas to downtown Kuwait in the east side and serves primarily for workers commuting between their homes and workplaces. Previous studies have conducted their sampling campaigns in the heart of Kuwait City due to its representation of a diverse urban pollutant sources and its high population density (Al-Awadhi, 2014; Alolayan et al., 2013; Brown et al., 2008). 67 Figure 4.1 Geographical representation of our sampling station in Kuwait. (Map source: Google Maps) 4.2.2. Sampling campaign and meteorological conditions We collected PM2.5 samples on a weekly basis, specifically on weekdays, throughout all different seasons to provide a comprehensive representation of the entire year (i.e., June 2022 to May 2023). Since the fall season is very short in Kuwait, spanning only four weeks from early November to early December, it was challenging to analyze the seasonal trend within this short period. Therefore, the fall period was considered as a part of the winter season in this study, with a sampling duration spanning from early November 2022 to late January 2023. The sampling campaign of the summer season was carried out from June to July 2022, while the spring period 68 samples were collected from March to May 2023. During our sampling period, the meteorological parameters, including temperature and relative humidity, were obtained from the meteorological department of the Directorate General of Civil Aviation of Kuwait (DGCA). The average temperature and relative humidity during the warm season (i.e., March to July) were recorded as 36.1  8.4ºC and 19.7  3.9%. In contrast, the temperature and relative humidity during the cold season (i.e., November to January) were observed to be 14.3  5.7ºC and 52.8  6.4%. Furthermore, wind roses were obtained from three DGCA monitoring stations, strategically located in widely separated sites across the country, to obtain the predominant wind direction data during our sampling period. Figure 4.2 shows our sampling location along with the wind direction, frequency, and speed in the three selected stations: the north site in Sabriya, the south site in Ahmadi, and an intermediate location at Kuwait Airport. All monitoring stations indicated that the prevailing wind was originating from the northwesterly direction during our study period, with a noticeable influence from the north direction as well. 69 Figure 4.2 Wind roses in three monitoring stations in Kuwait, including Sabriya, Ahmadi, and Kuwait Airport. 4.2.3. Instrumentation We used the personal cascade impactor samplers (PCIS) (Model 225-370, SKC Inc., Eighty Four, PA, USA) (Misra, Singh, et al., 2002; Singh et al., 2003) operated at a flow rate of 9 L/min and equipped with 2.5 cut-point stage to collect PM2.5 samples. We employed two samplers simultaneously and connected in parallel to collect particles on both 37 mm PTFE (Pall Life Sciences Inc., Ann Arbor, MI, USA) and quartz (Whatman company, Marlborough, MA, USA) filters. To minimize particle bouncing and re-entrainment, we applied grease to the impaction surface of the 2.5 µm stage to capture coarse particles and prevent them from bouncing to the subsequent stage. This enabled the collection of fine particles (dp < 2.5 µm) in the after-filter stage, where the PTFE or quartz filters were placed. Before starting the field 70 sampling, both PTFE and quartz filters were maintained under standard laboratory conditions, specifically a temperature range of 22 - 24 ºC and relative humidity of 40 - 50%, in order to achieve equilibration and subsequently determine their pre-sampling weights using Mettler 5 microbalance (MT5, Mettler Toledo Inc., USA). After completing the sampling of each season, the filters were reweighed to obtain the collected PM2.5 mass on each filter by subtracting the pre-sampling from the post-sampling weight. 4.2.4. Chemical and toxicological analysis The collected PM2.5 samples were sent to the Desert Research Institute (DRI) in Reno, Nevada for comprehensive analysis of the chemical composition, including metals and trace elements, carbonaceous species, and inorganic ions. The samples were analyzed for the content of metals and trace elements using inductively coupled plasma mass spectroscopy (ICP-MS), employing a digestion method for the PTFE filter to extract particles into an acidic solution, which was subsequently aerosolized and introduced to the ICP-MS instrument, as detailed by Herner et al. (2006). In addition, the inorganic ion content was measured using ion chromatography (IC), which is a process that involves particle extraction into ultrapure deionized water via sonication, filtration of the liquid solution, and subsequent quantification of ion concentrations, as elaborated by Karthikeyan and Balasubramanian (2006). Moreover, the collected quartz filters were analyzed to obtain the concentration of organic carbon (OC) and elemental carbon (EC) using DRI multiwavelength thermal/optical carbon analyzer (Magee Scientific, Berkeley, CA, USA). This instrument employs a dual-optical system and a multi-stage thermal process to separate and measure different forms of carbon. Further details regarding the design, calibration, and operation of the multiwavelength thermal/optical carbon analyzer can be found in Chen et al. (2015). Sections of the PTFE filters were also shipped to Illinois lab for 71 aerosol research at the University of Illinois Urbana-Champaign for the purpose of performing toxicological analysis. Dithiothreitol (DTT) assay is one of the most common and wellestablished techniques employed to quantify the oxidative stress induced by redox-active PM species and has been extensively used in previous aerosol research studies (Aldekheel et al., 2023; Cho et al., 2005; Gao et al., 2017; Verma et al., 2009). At first, each PM2.5 sample was extracted in an ultrapure water, followed by the addition of the reducing agent (DTT) and the incubation at 37 ºC in potassium phosphate buffer. During the incubation, the PM redox-active compounds in the sample reacted with DTT, leading to the oxidation of DTT into its disulfide form. The linear rate of DTT consumption in the filter extract is proportional to the oxidative potential of redox species present in the aerosol sample, which can generate reactive oxygen species and damage the biological systems. Further detailed information related to the methodology of DTT assay are available in Cho et al. (2005) and Kumagai et al. (2002). 4.2.5. Principal component analysis and multi-linear regression approach PCA is a statistical tool utilized to simplify complex datasets by reducing the number of variables and identifying patterns and correlations in the data to create new variables (principal components). In the current study, we used Statistical Package for Social Science (SPSS) software (version 25) to perform the PCA analysis using the mass concentrations of EC, OC, selected metals (i.e., Si, Fe, Al, Mg, Ca), and inorganic ions (i.e., sulfate, ammonium). PCA groups highly correlated chemical species into different components, implying that these species originate from the same specific emission source of PM2.5. The varimax orthogonal rotation was employed to improve the fit and interpretability of PCA results by adjusting the factor loadings to ensure that each principal component predominantly corresponds to a single factor (Dallarosa et al., 2005). An eigenvalue greater than 1 was utilized as a threshold to determine the inclusion 72 of potential source factors in the analysis (Argyropoulos et al., 2016). In order to ensure the suitability of the data for the PCA analysis and the reliability of source factors, the KaiserMeyer-Olkin (KMO) value was set to be above 0.5 according to the literature (Altuwayjiri et al., 2022; Argyropoulos et al., 2016). Before performing the PCA analysis, the mass concentrations were standardized using equation 4.1 to ensure that all variables are on a similar scale and can be compared fairly: 𝑍𝑖𝑗 = 𝐶𝑖𝑗 − 𝐶𝑗 𝜎𝑗 (4.1) where Zij represents the standardized dimensionless value of the jth species in the ith sample, Cij is the mass concentration of the jth species in the ith sample, Cj refers to the mean mass concentration of species j, and σj represents standard deviation of species j. After data standardization, the PCA analysis was performed based on the following equation: 𝑍𝑖𝑗 = ∑𝑔𝑖𝑘𝑓𝑘𝑗 + 𝑒𝑖𝑗 𝑃 𝑘=1 (4.2) Where P is the number of source factors in the analysis, fkj is the loading of the jth species on the source, gik represents the contribution of the source in the ith sample (factor score), and eij is the unexplained residual that is not captured by the principal components. After identifying the source factors, we used the MLR approach to quantify the contribution of each emission source to the oxidative potential of PM2.5 during the investigated period (Zuo et al., 2007). The obtained factor scores were used as independent variables in a multi-linear regression analysis, with the extrinsic DTT redox activity as the dependent variable. The standardized regression coefficients (Beta) and the derived regression coefficient (R2 ) were then used to determine the proportional contributions from various sources to the PM2.5 redox activity. 73 4.3. Results and discussion 4.3.1. Seasonal variations in PM2.5 mass concentrations and chemical composition 4.3.1.1. Mass concentrations and carbonaceous species Figure 4.3 illustrates the measured mass concentrations of ambient PM2.5 during the summer, winter, and spring seasons in Kuwait. The results indicate a noticeably higher PM2.5 mass concentration (75.2  8.5 µg/m3 ) during the summer compared to the winter period (60.1  10.8 µg/m3 ), mainly due to the substantially elevated summer concentrations of crustal elements originating from soil dust (Nava et al., 2012; P. Zhao et al., 2006). During our summer sampling campaign, we encountered frequent dust events, which is a common occurrence in the region during the summer season (Al-Hemoud et al., 2018; J. Li et al., 2020). The meteorological department of DGCA in Kuwait reported a higher mean wind speed during the summer season (6.1  2.3 m/s) compared to the spring (4.1  1.2 m/s) and winter seasons (3.3  0.9 m/s). The combination of strong summer winds, known as Shamal, and arid surface conditions facilitated the resuspension of dust from the desert areas (Alahmad et al., 2021; AL-Harbi, 2015; J. Li et al., 2020; Parolari et al., 2016). The ambient PM2.5 mass concentration in the spring season (44.5  10.8 µg/m3 ) was lower than both summer and winter seasons. This resulted from a combination of factors: the spring period lacked the same frequency of dust episodes seen in the summer months, and it also experienced higher temperature and mixing height compared to the winter months, which led to the reduction of PM2.5 concentrations in the spring season. Moreover, the annual average PM2.5 mass concentrations (59.9 µg/m3 ) was substantially higher than the air quality guidelines set by the World Health Organization (WHO), which recommended an annual mean PM2.5 concentrations of 10 µg/m3 (WHO, 2006). Our reported PM2.5 mass concentrations have also exceeded the national ambient air quality standards (NAAQS) established by the US 74 Environmental Protection Agency (EPA), which specified an annual mean concentration of 12 µg/m3 . The extrinsic mass concentrations of OC and EC during different seasons are demonstrated in Figure 4.4. It is evident from the graph that the lowest EC concentration was observed during the summer season (1.79 µg/m3 ), followed by spring (2.26 µg/m3 ) and winter (3.36 µg/m3 ). Considering that EC primarily originates from transportation activities, particularly the exhaust emissions of vehicles due to incomplete fuel combustion (Cyrys et al., 2003; Keuken et al., 2012), the decrease in its concentration during the summer season can be mainly attributed to reduced traffic volume. This reduction in traffic was primarily due to the closure of schools and the fact that the majority of the population choose to leave the country during extremely hot summer months (i.e., June – August) (Al-Awadhi, 2014). During winter, the concentrations of EC increased as a result of atmospheric stability conditions that led to a decrease in the boundary layer height, restricting the vertical dispersion of pollutants and increasing their concentrations (K. Patel et al., 2021; Schwartz et al., 2018). Moreover, the OC concentrations have also shown the same seasonal trend, with values of 5.18 µg/m3 , 5.51 µg/m3 , and 5.77 µg/m3 during summer, spring, and winter periods, respectively. The seasonal variation in OC concentrations is less pronounced compared to the EC, primary because OC can originate from primary sources (e.g., transportation and oil combustion) as well as secondary sources (i.e., atmospheric chemical reactions) (Gianini et al., 2013; Yu et al., 2004). Therefore, the reduced traffic in summer was counterbalanced by the enhanced secondary formation of OC, resulting in a reduced seasonal variability between summer and winter. 75 Figure 4.3 PM2.5 mass concentrations during summer, winter, and spring seasons in Kuwait. Error bars are standard deviations of the data. Figure 4.4 Mass concentrations (per volume of air) of EC and OC during summer, winter, and spring seasons. Error bars are standard deviations of the data. 76 4.3.1.2. Metals and transition elements Figure 4.5 depicts the measured extrinsic mass concentrations of metals and transition elements throughout the investigated seasons. During the summer season, we observed significantly higher concentrations of silicon (Si), lithium (Li), magnesium (Mg), aluminum (Al), iron (Fe), calcium (Ca), titanium (Ti), and manganese (Mn) compared to the winter period. In particular, the concentration of silicon during summer (11885.9 ng/m3 ) was approximately one order of magnitude higher than the winter period (957.3 ng/m3 ). The higher concentrations of soil elements during summer can be attributed to the prevailing high-speed wind from the northwest, passing through flat and arid desert regions, which enhances long-range transport of soil particles and contributes to dust events (Al-Dousari et al., 2017; Al-Hemoud et al., 2017). In the winter season, we observed increased concentrations of copper (Cu) and lead (Pb), following a similar seasonal trend to the EC mass concentrations illustrated in Figure 4.4. This indicates that the reduction in non-tailpipe (i.e., brake and tire wear) emissions during summer can be attributed to the same factors discussed earlier, specifically the decreased traffic volume during the summer months. Furthermore, the mass concentrations of sulfur (S) and vanadium (V) exhibited higher levels during the winter season in comparison to summer and spring, which can be attributed to various meteorological factors. Primarily, the decreased wind velocity prevalent in the colder months exacerbated the impact of nearby local sources, such as Doha oil-based power plants situated approximately 10 km from our sampling site. Additionally, the formation of a temperature inversion layer near the surface, predominantly occurring in winter, led the accumulation of particulate pollutants in the lower atmosphere (Beard et al., 2012; Trivedi et al., 2014). Furthermore, the sodium (Na) concentrations remained consistent across all seasons due 77 to the close proximity (< 50 m) of our sampling station to the gulf, where sea salt particles are continuously influx to our sampling point, eliminating any significant seasonal variation. Figure 4.5 Mass concentrations (per volume of air) of metals and transition elements during summer, winter, and spring seasons. Error bars are standard deviations of the data. 4.3.1.3. Inorganic ions The extrinsic mass concentrations of PM2.5-bound inorganic ions (i.e., sulfate, nitrate, and ammonium) during the investigated period are depicted in Figure 4.6. During the summer season, the nitrate concentration was 1.03 µg/m3 , significantly lower than the 3.53 µg/m3 observed during the winter season. The extremely high summer temperatures significantly contributed to the dissociation of ammonium nitrate, while the lower winter temperatures coupled with higher relative humidity favored its partitioning into the particulate phase (Argyropoulos et al., 2016; Samara et al., 2016). Sulfate mass concentrations exhibited less seasonal variation compared to nitrate, with higher levels observed during the winter months compared to summer and spring. Despite the enhancement of secondary sulfate formation during 78 warm periods with high solar radiation, the lower summer concentrations can primarily be ascribed to the strong northwestern winds that transport locally generated SO2 away from Kuwait City (Al-Awadhi, 2014; Brown et al., 2008). Alolayan et al., (2013) and Brown et al. (2008) reported that sulfate concentrations in Kuwait were substantially higher during the fall season (14 – 15 µg/m3 ) compared to the summer (8 – 9 µg/m3 ), attributing this trend primarily to the low-speed winds that intensified the influence of local sulfate sources in fall. Furthermore, the reduced ammonium concentration in the summer can be explained by its reaction with sodium chloride (NaCl) under increased temperatures, leading to the formation of volatile ammonium chloride (NH4Cl), as reported by Aldabe et al. (2011) and Artı́ñano et al. (2003). Figure 4.6 Mass concentrations (per volume of air) of inorganic ions during summer, winter, and spring seasons. Error bars are standard deviations of the data. 4.3.1.4. Comparison with previous studies conducted in Kuwait City Alolayan et al. (2013) and Brown et al. (2008) have analyzed the chemical composition of ambient PM2.5 during the period of 2004 - 2005 in Kuwait City, given its proximity to most 79 residential areas and the presence of a wide range of urban PM2.5 emission sources. Figure 4.7 shows a comparison between our current research and previous studies on the annual average mass concentrations of PM2.5, carbonaceous species, inorganic ions, and metal elements. The annual average PM2.5 mass concentration in the current study (59.9 µg/m3 ) increased compared to previous years (i.e., 2004 - 2005), potentially due to the urbanization, industrial expansion, and economic development in the region. Despite the growth in population and subsequent rise in the number of vehicles in Kuwait over the past two decades, the current study revealed a relatively lower concentration of EC (2.3 µg/m3 ). This can be attributed to the location of our sampling site, which is adjacent to a less congested highway (i.e., Highway 85), while previous studies conducted their sampling at the intersection of two of the most heavily trafficked highways in Kuwait (i.e., Fourth Ring Road and Highway 50). Another contributing factor could be the recent construction of a new bridge above and parallel to Highway 85, which has substantially alleviated traffic congestion in the area. Although we reported a decrease in EC concentrations, the increase in OC concentration in our research supports its origin from a wide range of sources, including industrial activities and secondary formation in the atmosphere (Altuwayjiri et al., 2021; Saarikoski et al., 2008). Moreover, the increased mass concentration of sulfate observed in our current study compared to the level of 2004-2005 can be attributed to the increased electricity generation from burning fossil fuels, driven by economic and population growth over the past 20 years. Over the past two decades, the electricity generation capacity in Kuwait has significantly increased, predominantly (> 98%) relying on fossil fuel, and it is projected to further increase by 70% in 2035 (32 GW) compared to the level recorded in 2018 (18.8 GW), according to the report published by the Energy Building and Research Center at Kuwait Institute for Scientific Research (2019). Furthermore, the mass concentration of several 80 crustal metal elements (e.g., Si, Ca, K, Mg) were comparable to previous years. Conversely, the concentration of non-tailpipe tracers such as Zn and Cu has decreased, in alignment with the factors contributing to the decrease in EC concentrations. (a) (b) 81 Figure 4.7 Annual average mass concentrations (per volume of air) of (a) PM2.5, inorganic ions, and carbonaceous species, and (b) metals and transition elements. Error bars are standard deviations of the data. 4.3.2. PM2.5 oxidative potential Figure 4.8 presents PM2.5 oxidative potential in terms of extrinsic DTT activity across different seasons. The per m3 of air volume normalized DTT activity showed a higher value of 1.31  0.21 nmol/min/m3 during the winter season compared to the summer (0.65  0.18 nmol/min/m3 ) and spring (0.88  0.24 nmol/min/m3 ). The annual average extrinsic DTT activity in Kuwait (0.95  0.31 nmol/min/m3 ) exceeded the PM2.5 DTT levels in Los Angeles (0.35 nmol/min/m3 ) (Shirmohammadi et al., 2016) and Atlanta (0.31 nmol/min/m3 ) (Verma et al., 2014), but remained lower than Milan (3.38 nmol/min/m3 ) (Hakimzadeh et al., 2020), Athens (5.62 nmol/min/m3 ) and Beirut (3.51 nmol/min/m3 ) (Farahani et al., 2022). Saffari et al. (2014) have comprehensively analyzed PM2.5 toxicological properties using DTT assay in ten distinct locations in southern California. The authors observed higher extrinsic DTT activity during the winter and fall seasons (0.5 – 1.3 nmol/min/m3 ) compared to the summer and spring (0.2 – 0.6 82 nmol/min/m3 ), which aligns with the seasonal trend observed in the current study. The increased PM2.5 oxidative potential during the winter months in Kuwait can be mostly attributed to the increase in EC and OC concentrations, as they demonstrated a similar pattern to the DTT activity throughout the seasons. To confirm this argument, we conducted a Spearman correlation analysis (Table 4.1), which revealed a significant correlation between DTT activity and EC (R = 0.86), OC (R = 0.65), Pb (R = 0.87), and Cu (R = 0.65), suggesting a strong contribution of vehicular activities to the oxidative potential of ambient PM2.5 in Kuwait as it will be discussed later in section 3.3.2. Previous research has consistently demonstrated a robust correlation between DTT activity and organic compounds, including OC, water-soluble OC, water-insoluble OC, PAH, and EC, leading to the conclusion that carbon and organic species play a significant role in driving the oxidative potential of particulate matter (Cho et al., 2005; Saffari et al., 2014; Steenhof et al., 2011). Given the importance of these organics in PM oxidative potential, another potential explanation for the lower summer DTT level is the evaporation of semi-volatile organic compounds (SVOCs) caused by extreme heat. However, the winter period enhanced the partitioning of SVOCs to the particulate phase, resulting in increased DTT activity as also argued by Saffari et al. (2014) who made similar observations in Los Angeles. 83 Figure 4.8 Seasonal (per volume of air) DTT activity of PM2.5 in Kuwait. Error bars are standard deviations of the data. Table 4.1 Spearman bivariate correlation between the extrinsic DTT activity (nmoles/min/m3 ) and the concentrations of selected chemical species. Chemical species Correlation coefficient (R) Chemical species Correlation coefficient (R) EC 0.858** Si -0.270 OC 0.647** Mg -0.558 Cu 0.645** Al -0.500 Pb 0.869** Ca -0.502 S 0.710** Fe -0.552 V 0.632** Sulfate 0.720** Ni 0.650** Ammonium 0.569** ( **) indicates that the correlation is significant at the 0.01 level. 84 4.3.3. PM2.5 emission sources and their contribution to the oxidative potential. 4.3.3.1. Source apportionment of PM2.5 using PCA Table 4.2 summarize the results of the PCA analysis performed using the volumenormalized mass concentrations of carbonaceous compounds, chemical species, and inorganic ions for the entire study period. Our analysis revealed four distinct PM2.5 emissions sources in Kuwait, covering in total 93.92% of the variance in the data. The first factor was predominantly associated with crustal element including Si, Al, Ca, Fe, and Mg, meaning that this source can be identified as mineral dust emissions. According to previous research studies, these specific metals and transition elements are commonly considered as tracers for soil dust in different regions around the world (Altuwayjiri et al., 2022; Sowlat et al., 2012; Tian et al., 2016). ALHarbi (2015) have reported that the annual dust deposits in Kuwait consisted of a substantial amount of silica which accounted for approximately 17% of the falling dust, further highlighting the importance of this element as a marker for soil dust emissions. In our PCA analysis, the contribution of the mineral dust factor was 36.06% of the total PM2.5 in Kuwait, which aligns with our expectations due the large desert regions covering the majority of the country. ALHarbi (2015) have performed a comprehensive study to investigate the levels of dust deposition in Kuwait, which was found to be approximately 53.7 ton km-2 month-1 and significantly higher than various worldwide locations, including North India , Yazd (Iran), Texas (USA), Lanzhou (China), and California (USA), with values of 21, 6.5, 8.5, 11.1, and 1.6 ton km-2 month-1 , respectively. Moreover, Al-Hemoud et al. (2018) estimated a total of 7367 dust episodes (i.e., dust storms, rising dust, and suspended dust) in Kuwait during 1962 to 2015, corresponding to a remarkable number of 136 dust episodes, on average, per year. 85 The second factor was primarily associated with high loadings of S, V, and Ni, which are main markers of fossil fuel combustion (Cheng et al., 2018; Maciejczyk et al., 2021). The oil burned in Kuwait power stations contains a high sulfur content as well as measurable levels of Ni and V (Alolayan et al., 2013; A. A. Ramadan et al., 2008). As shown in Figure 4.9, our sampling site was significantly affected by numerous fossil fuel combustion sources in central and northern Kuwait, including Doha east and west power stations, Doha and Shuwaikh seawater desalination plants, and northern oil fields. According to Ramadan (2022), Doha power stations mainly burn crude oil and heavy fuel oil (HFO) while Shuwaikh seawater desalination plant completely rely on natural gas combustion. The abovementioned sources were the major generators of fuel-combustion tracers (i.e., S, V, Ni), which were transported by the dominant northwesterly winds to Kuwait City. Moreover, the oil and gas operations played a crucial role in emitting sulfur dioxide (SO2) gas which is a precursor of the particulate sulfur in the atmosphere (Al-Awadhi, 2014). In our PCA results, the fossil fuel combustion factor has shown a relatively less contribution (23.69%) to the PM2.5 mass concentration in Kuwait City compared to the mineral dust factor. Alahmad et al. (2021) have investigated the sources of PM2.5 in Kuwait City and revealed a notably high concentration of sulfur in one of the identified factors, which was named as “regional pollution”. The authors highlighted that while a portion of the sulfur concentration was derived from local power plants burning high-sulfur oil, the majority was attributed to regional oil-based power stations in neighboring countries (Alahmad et al., 2021; Brown et al., 2008). The third factor was identified as traffic emissions due to the presence of tailpipe and non-tailpipe markers, including EC, Pb, and Cu. A large number of previous studies in the literature have considered EC as a major marker for tailpipe emissions (Jain et al., 2018; Yin et 86 al., 2010), while Cu and Pb have been identified as tracers for non-tailpipe emissions (Harrison et al., 2012; Jeong et al., 2022; Querol et al., 2008; Shirmohammadi et al., 2016). This factor accounted for 20.32% of the total PM2.5 mass in Kuwait City, indicating a comparable contribution to the fossil fuel combustion factor. It is worth mentioning that OC loadings were comparable in all factors, except mineral dust factor, supporting the fact that OC originates from diverse primary (i.e., traffic and oil combustion) and secondary sources (Taghvaee, Sowlat, et al., 2019). The fourth factor was named as “secondary aerosols” due to the elevated loadings of sulfate (SO4 2−) and ammonium (NH4 + ), accounting for about 13.85% of the total PM2.5 concentrations in Kuwait. Previous research studies have used these two species (i.e., ammonium and sulfate) as markers of secondary aerosol formation (Altuwayjiri et al., 2022; Jain et al., 2020; Sricharoenvech et al., 2020). Our sampling site in KISR is located adjacent to Sulaibikhat Bay, which unfortunately suffers from the release of industrial, domestic, and hospital sewage from several discharge points (Alshraifi et al., 2009; Mydlarczyk et al., 2020). The decomposition of organic compounds in wastewater releases odorous hydrogen sulfide (H2S) and ammonia gases into the atmosphere (Widiana et al., 2017), which normally undergo a series of atmospheric chemical reactions that contribute to the formation of secondary ammonium sulfate aerosols. Briefly, H2S can be rapidly oxidized in the atmosphere to sulfur dioxide (SO2), which can then be further oxidized to sulfur trioxide (SO3) that reacts with water droplets, forming sulfuric acid (H2SO4) (Abdul Raheem et al., 2009). Ammonium sulfate is formed secondarily from the gasphase reaction of sulfuric acid (H2SO4) with ammonia (NH3) (Seinfeld & Pandis, 2016; Sinanis et al., 2008). 87 Table 4.2 Principal components and loadings of PM chemical species. Loadings > 0.6 are in bold. Principal components Mineral dust Fossil fuel combustion Traffic Secondary aerosols EC -0.156 0.342 0.883 0.119 OC -0.320 0.474 0.503 0.430 Sulfate -0.320 0.320 0.334 0.727 Ammonium -0.442 0.411 -0.034 0.752 Cu -0.480 0.372 0.716 -0.209 Pb -0.366 0.088 0.811 0.338 Si 0.920 -0.211 -0.213 -0.192 Mg 0.880 -0.289 -0.275 -0.242 Al 0.893 -0.284 -0.258 -0.231 Ca 0.896 -0.259 -0.256 -0.209 Fe 0.863 -0.295 -0.259 -0.294 S -0.219 0.864 0.291 0.330 V -0.335 0.835 0.236 0.280 Ni -0.365 0.868 0.261 0.149 Variance (%) 36.06 23.69 20.32 13.85 Cumulative (%) 36.06 59.75 80.07 93.92 88 Figure 4.9 Major fossil fuel combustion sources in central and northern Kuwait. 4.3.3.2. The contribution of PM2.5 sources to the oxidative potential using MLR approach Multi-linear regression was performed between the air volume-normalized DTT activity and the resolved factor scores obtained from the PCA analysis. Table 4.3 presents the MLR results, including the standardized coefficients and R2 value, which were used to quantify the relative contribution of emission sources to the oxidative potential of PM2.5. It should be noted that the mineral dust factor was excluded from the MLR analysis due to the negative correlation observed between soil dust tracers (e.g., Si, Al) and DTT activity as was presented in Table 4.1. According to the MLR results, the road traffic factor was the most significant source influencing the oxidative potential of ambient PM2.5, with a contribution of 47% (standardized Beta 89 coefficient = 0.795) to the DTT activity in Kuwait (Figure 5). This aligned with the significant correlation highlighted previously in Table 4.1 between DTT activity and traffic emissions, including tailpipe and non-tailpipe markers. It was also supported by previous research studies corroborating the significant effect of vehicular exhaust and road dust (i.e., non-tailpipe) emissions on the redox activity of ambient PM (Hu et al., 2008; Shirmohammadi et al., 2015, 2016). Previous research carried out by Taghvaee et al. (2019) reported that vehicular emissions were responsible for 44% of the overall PM-induced redox activity in Athens, Greece. Furthermore, a study conducted by J. Wang et al. (2020) in Bangkok, Thailand, revealed that fossil fuel combustion, including vehicular exhausts, was the major contributor (63%) to the total PM oxidative potential in the region. Moreover, as shown in Figure 4.10, the contributions of fossil fuel combustion and secondary aerosols were 19% and 13% to the overall DTT activity, respectively. To further support our MLR findings, Fadel et al. (2023) investigated the predominant sources in Beirut (Lebanon) and revealed that tracers of heavy fuel combustion (e.g., V, Ni) demonstrated a notable contribution (>14%) to the ambient PM oxidative potential. Additionally, Romano et al. (2020) and J. Wang et al. (2020) identified strong correlations between tracers of secondary aerosols (i.e., sulfate, ammonium) and PM oxidative potential. Table 2. Table 4.3 Output of multi-linear regression (MLR) analysis between PCA factor scores (independent variables) and extrinsic DTT activity (dependent variable). Source Unstandardized coefficient ( Std. error) Standardized coefficient P-value R 2 Constant 0.914  0.048 0.000 0.79 Fossil fuel combustion 0.147  0.049 0.329 0.007 Traffic 0.356  0.047 0.795 0.000 Secondary aerosols 0.097  0.049 0.216 0.063 90 Figure 4.10 The percentage contribution of emission sources to PM2.5 oxidative potential in Kuwait. 4.4. Summary and conclusion This study provides a comprehensive insight into the seasonal variability, chemical composition, and source apportionment of PM2.5 concentrations in Kuwait, emphasizing the crucial link between emission sources and PM oxidative potential. The data revealed notable seasonal variations in ambient PM2.5 levels, with summer season marked by the highest PM2.5 concentration (75 µg/m3 ) primarily due to the increase in soil dust elements. The intense summer winds (known as Shamal) combined with dry ground conditions facilitated the resuspension of dust from the widespread desert regions. To identify the predominant PM2.5 sources in Kuwait City, PCA was employed and effectively disentangled four distinct principal components (i.e., emission sources), covering 93.9% of the variance in the data, which were mineral dust emissions, fossil fuel combustion, road traffic, and secondary aerosols. Mineral dust, largely attributed to the vast desert landscapes of Kuwait, was the dominant contributor (36.1%), 91 highlighting the region's vulnerability to frequent dust episodes. The fossil fuel combustion factor had a contribution of 23.7%, which emphasized the influence of local power plants and the country's heavy dependence on oil-based energy sources. The road traffic factor, underscored by markers such as EC, Pb, and Cu, revealed a contribution of 20.3% from both tailpipe and nontailpipe emissions. Secondary aerosols, particularly characterized by sulfate and ammonium ions, highlighted the influence of the indirect emissions resulting from complex atmospheric chemical reactions. Using the MLR approach, this research quantified the contribution of the identified emission sources to the oxidative potential of PM2.5. Road traffic was the most potent factor affecting PM2.5 oxidative potential (47%), corroborating findings from previous research studies and underscoring the need for vehicular emission controls. Moreover, this work highlighted that the annual average PM2.5 concentrations in Kuwait (59.9 µg/m3 ) considerably exceeded both WHO and US EPA guidelines, which underscores the urgent need for strategic interventions to enhance air quality. It is imperative to note that while the natural origins of mineral dust might be challenging to control, the other identified sources can be managed with stringent policy measures, technological advancements, and public awareness campaigns. This study offers a robust foundation for future air quality management strategies in Kuwait, underlining the importance of a harmonized approach that encompasses monitoring, source identification, and targeted mitigation measures. 92 Chapter 5 - Conclusion The purpose of the first study was to examine how using air purifier together with in-line ventilation filters in various classrooms could improve air quality and capture particulate pollutants. Most of the classrooms had 12-inch MERV 14 filters which were effective in reducing ambient PM and PN concentrations by over 80%. However, one classroom had a less efficient MERV 13 filter which only reduced PM and PN by 49% and 55%, respectively. The air purifier, equipped with a HEPA filter, was highly efficient at removing ultrafine and coarse particles (efficiency was > 95%), but relatively less so for particles in the intermediate size range. The indoor CO2 levels were within standards in all classrooms, indicating adequate ventilation and outdoor-to-indoor air circulation due to high air exchange rates. Overall, as the world continues to grapple with the SARS-CoV-2 pandemic and highly contagious viruses, the importance of maintaining healthy indoor air quality cannot be overstated. This study underscores the need for effective indoor air quality management strategies to ensure the safety and well-being of building occupants. Moving forward, more research is needed to investigate the long-term effectiveness of air purifiers and in-line filters in different indoor environments and under various conditions (e.g., ventilation systems without in-line filters). Additionally, efforts should be made to increase public awareness of the importance of indoor air quality and encourage the adoption of effective indoor air quality management practices. The aim of the second study was to develop and evaluate a gelatin cascade impactor (GCI) that could capture various sizes of particulate matter using water-soluble gelatin substrates. The GCI has a high flow rate of 100 lpm and consists of two impaction stages with cut-point diameters of 2.5 µm and 0.2 µm. Results of the field experiments revealed that the GCI was more capable of collecting PM-toxic constituents compared to the PCIS. The ROS and DTT 93 activities of the PM2.5 collected using the GCI were 8813 μg Zymosan Units/mg PM and 26.4 nmol/min/mg PM, which were approximately more than twice the redox activity of particles collected using the PCIS. It should be noted that the gelatin filters may not be suitable for PM chemical characterization studies due to the elevated blank levels of some metals and inorganic ions. To overcome this limitation, the GCI can be equipped with different types of substrates without significant changes in its technical specifications or particle separation characteristics, making it an ideal tool for long-term sampling durations and PM chemical analysis studies. The GCI equipped with gelatin filter is a valuable addition to the field of environmental health research since it is an accurate and powerful tool for sampling particle pollutants for future studies aimed at evaluating PM effects on human health. The last study explored the seasonal variability in the chemical and toxicological properties as well as the mass concentrations of ambient PM2.5 collected in Kuwait. We analyzed the collected samples for their metals, inorganic ions, organic species, and DTT activity. Afterward, PCA coupled with MLR was employed to investigate the contribution of urban emission sources to the oxidative potential of PM2.5 in Kuwait. PCA effectively disentangled four distinct principal components (i.e., emission sources), covering 93.9% of the variance in the data, which were mineral dust emissions, fossil fuel combustion, road traffic, and secondary aerosols. The road traffic was the most potent factor affecting PM2.5 oxidative potential (47%), corroborating findings from previous research studies and underscoring the need for vehicular emission controls. Moreover, this work highlighted that the annual average PM2.5 concentrations in Kuwait (59.9 µg/m3 ) considerably exceeded both WHO and US EPA guidelines, which underscores the urgent need for strategic interventions to enhance air quality. It is imperative to note that while the natural origins of mineral dust might be challenging to control, the other 94 identified sources can be managed with stringent policy measures, technological advancements, and public awareness campaigns. 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These error bars can either (1) illustrate the standard deviation of a set of data points, aiding in the depiction of the spread or variability of data points within a dataset, or (2) illustrate the standard deviation derived from repeated measurements with the same measurement method to indicate the degree of confidence or precision. Each figure's caption offers an explanation of the error bars used. The standard deviation was computed using the following general equation: 𝑆𝑚 = √ ∑ (𝐶𝑖 − 𝐶𝑎𝑣𝑔) 𝑁 2 1 𝑁 − 1 (A.1) where 𝑆𝑚 represent the standard deviation, 𝐶𝑖 is the individual data point in the dataset, 𝐶𝑎𝑣𝑔 is the arithmetic average of the data, and 𝑁 is the total number of data points. Additionally, it was imperative to assess the reliability and uncertainty inherent in each data point within our datasets. Since our primary focus involves the measurement of PM concentrations (equation A.2) in our field of study, we quantified the uncertainty associated with individual data points by considering the uncertainties associated with both PM mass and air volume measurements. Through the principles of uncertainty propagation, we utilized equation A.3 to quantitatively assess the uncertainty linked to individual concentration data points: 𝐶 = 𝑀 − 𝐵 𝑉 (A.2) ( 𝑆𝐶 𝐶 ) 2 = 𝑆𝑀 2 + 𝑆𝐵 2 (𝑀 − 𝐵) 2 + ( 𝑆𝑉 𝑉 ) 2 (A.3) where C is the concentration of PM (µg/m3 ), M is the measured PM mass (µg), B is the dynamic (system) blank (µg), V is the measured volume of air (m3 ), 𝑆𝐶 represents the uncertainty of the 120 PM concentration, 𝑆𝑀 is the PM mass standard deviation of repeated measurements, 𝑆𝐵 is the precision of the system blank and can be estimated from the standard deviation of N dynamic blank measurements. In addition, the precision of the measured volume of air (𝑆𝑉) can be calculated using equation A.4, given that the volume of air is obtained through the multiplication of air sampling flow rate (Q) and duration of the sampling (t): ( 𝑆𝑉 𝑉 ) 2 = ( 𝑆𝑄 𝑄 ) 2 + ( 𝑆𝑡 𝑡 ) 2 (A.4) where 𝑆𝑄is the absolute precision of air flow rate and 𝑆𝑡 is the absolute precision in measuring time. Moreover, as a part of data cleaning and validation, we employed the interquartile range (IQR) method for identifying and handling outliers as it is an important step before processing the data. IQR was calculated as the difference between the third quartile (Q3) and the first quartile (Q1) of the dataset. The range Q1 - 1.5(IQR) to Q3 + 1.5(IQR) was used to identify potential outliers in a dataset. Data points that fell below Q1 - 1.5(IQR) or above Q3 + 1.5(IQR) were considered outliers. This method provided a systematic way to detect values that are significantly different from the bulk of the data.

Abstract (if available)

Abstract Full title: Assessing indoor and outdoor air quality by characterizing the physicochemical and toxicological properties of particulate matter using well-developed sampling technologies and multivariate statistical analysis. Abstract: The unprecedented global socio-economic expansion, driven by extensive urban development, industrial evolution, and increased power generation, is notably deteriorating outdoor air quality, which in turn adversely impacts the air in indoor environments where people spend the majority of their time. Particulate matter (PM), a significant air pollutant originating from diverse sources and present in various sizes in the atmosphere, significantly contributes to adverse human health effects, including respiratory diseases, lung cancer, neurotoxicity, and cardiovascular illnesses. Ambient PM pollution can be managed by identifying emission sources of particles and understanding their physicochemical and toxicological characteristics in order to develop effective regulations and mitigation strategies. For indoor air quality, which remains unregulated, the sources of PM are diverse and fluctuate significantly, therefore, employing air filtration technologies becomes crucial to minimize exposure to these particulate pollutants. This dissertation investigated the effectiveness of simultaneously operated air purifiers and in-line filters within ventilation systems to mitigate PM pollution in indoor spaces. Additionally, it introduces the development and application of cascade impactors for particle collection and further analysis of their chemical composition and toxicological properties. Multivariate statistical analysis and multi-linear regression were also used to identify PM emission sources and their percentage contribution to the oxidative potential. Through a blend of experimental research and analytical exploration, this body of work provides essential insights into the characteristics, sources, and health implications of PM in indoor and outdoor settings, underscoring the critical need for effective air quality management strategies to mitigate the impacts of particulate pollution.

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Asset Metadata

Creator Aldekheel, Mohammad (author)

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

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Engineering (Environmental Engineering)

Degree Conferral Date 2024-08

Publication Date 06/19/2024

Defense Date 06/04/2024

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

Tag aerosol sampling,cascade impactor,indoor air quality,OAI-PMH Harvest,oxidative potential,particulate matter,source apportionment,toxicity

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sioutas, Constantinos (committee chair), Egolfopolous, Fokion (committee member), McCurry, Daniel (committee member)

Creator Email aldekhee@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113996WPX

Unique identifier UC113996WPX

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Legacy Identifier etd-AldekheelM-13115

Document Type Dissertation

Format theses (aat)

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