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Outdoor air pollution is an increasing public health burden. Fine particulate matter (PM2.5) is a pollutant of major concern for human health, and it also affects the climate and ecosystems. Understanding and quantifying emission sources and their impact on particulate air pollution is critical for improving global health and for informing climate action. Poor air quality across the globe disproportionately affects middle- and lower-income countries. Delhi, the capital of India, is one of the most populated and polluted megacities in the world, where in 2017 almost 12,000 premature deaths were attributed to outdoor air pollution. My thesis aims to advance our understanding of outdoor air pollution in Delhi megacity, with a focus on the impact of on-road transport emissions on surface levels of PM2.5 and its implications for air quality policymaking. To do this, I use a combination of a state of the art regional atmospheric chemistry transport model, recently developed local emissions inventories, and sensitivity analysis techniques. In the first research chapter I use the WRF-Chem atmospheric chemical transport model to understand the regional influence on air quality over Delhi. As part of this work, I characterise seasonal anthropogenic, pyrogenic, and biogenic influences on fine particulate matter and one of its main constituents, organic aerosol (OA), over the Indo-Gangetic Plain (IGP). My results show that anthropogenic emissions influence the magnitude and distribution of PM2.5 and OA throughout the year, especially over cities including Delhi, while pyrogenic emissions from crop residues burning result in localized contributions over the central and upper parts of IGP in all non-monsoonal seasons, with the highest impact during the post-monsoon season that correspond to the post-harvest season in the agricultural calendar. Biogenic emissions play an important role in the magnitude and distribution of PM2.5 and OA during the monsoon season, particularly over the lower IGP. In all seasons mean values of PM2.5 still exceed the recommended levels, indicating that air pollution is a year-round problem. In the second research chapter I develop the WRF-Chem model used in my first chapter to include local emission inventories, in order to quantify the contribution of the on-road transport sector to surface PM2.5 over Delhi during the highly polluted post-monsoon season. This contribution is compared to the contributions of other local (within Delhi) and regional (within the National Capital Region, NCR) anthropogenic sectors. My results show that emissions from the local transport sector contribute typically less than 10% to daily mean PM2.5 values over Delhi, rising to 17% when regional transport sources are included. The contribution from the local transport sector is largest (18%) during the evening traffic peak. The total transport impact is dominated by contributions from twoand three-wheelers (50%) and heavy-duty vehicles (30%). The largest individual contributions to daily mean PM2.5 values are found to be from regional power and industry (14%) and domestic (11%) sectors. In the third research chapter I drive the WRF-Chem model with future transport emissions scenarios to investigate the potential impact of electric and clean-fuel vehicles on surface PM2.5 and ozone (O3) over Delhi for two contrasting seasons, pre-monsoon and post-monsoon. My results show that the conversion of diesel vehicles to compressed natural gas (CNG) brings a greater reduction in PM2.5 concentrations than the full electrification of two- and three-wheelers. However, the maximum reduction of daily mean PM2.5 concentrations for all scenarios is within 5% compared to baseline values for both seasons. Electrification of two- and three-wheelers increases average 8-hour daily maximum (MDA8) ozone (1.3-3.5% in pre-monsoon 5-13% in post-monsoon) compared to baseline values. On the other hand, conversion of all diesel vehicles to CNG reduces MDA8 O3 in both seasons (by 2.3-5.3% in pre-monsoon and by 1-1.5% in post-monsoon) compared to baseline values. In conclusion, the findings of my thesis highlight different factors that can be relevant for designing effective policies to meet PM2.5 air quality standards over Delhi megacity, with a focus on mitigating the impact from the on-road transport sector. First, air quality over Delhi is strongly influenced by regional and seasonal pollution sources from the IGP. As such, effective mitigation of PM2.5 pollution over Delhi will require a range of regional and state-level policies. In particular, cooperative mitigation strategies between the Delhi megacity and the broader NCR is needed if PM2.5 pollution is to be reduced. Second, two-and three-wheelers and heavy-duty vehicles dominate on-road transport impact on PM2.5, thus emissions reductions from these vehicles should be given priority, both within Delhi and in the NCR. Third, cleaner mobility plans of electrification of two- and threewheelers should be accompanied by diesel vehicles conversion to compressed natural gas, to limit ozone pollution increase and further reduce PM2.5 concentrations. This also highlights the importance of coordinated control of PM2.5 and other pollutants such as O3 when considering emission control strategies for transport over Delhi.

This dissertation consists of three separate research projects. Each paper uses a different applied econometric technique to investigate problems related to air quality economics. The first chapter is a general introduction to all three studies. The second chapter explores adopting an environmentally-friendly public transportation system in Europe. The Bayesian econometric methods show that willingness to pay for a new public transportation system is primarily driven by improvements to public goods, such as air quality and greenhouse gas emission reduction. The third chapter uses the red tide-related stated experience and satellite imagery of chlorophyll-a concentration as well as field data of respiratory irritation. This chapter illustrates that ancillary scientific information can be efficiently combined with choice experimental data. The fourth chapter uses panel fixed-effect models to investigate the short-term effect of air pollution on students' cognitive performance in China. It is shown that PM2.5 has a significantly negative impact on students' exam performance.

Air pollution is considered the greatest current environmental health threat to humanity, with an estimated mortality burden of 7 million per year. More than half the world’s population is exposed to increasing air pollution. Reduction of air pollution is essential to global health and can be expected to generate long-term societal benefits. Receptor models are efficient mathematical tools for identification of sources of air pollution. A popular receptor modeling technique is Positive Matrix Factorization (PMF). However, PMF is limited by the assumption of constant source profiles throughout the modeling period—while the contribution of each source is modeled to change over time, its profile (e.g., mass spectrum, when PMF is applied to mass spectrometer data) stays constant. PMF is frequently applied to data on air pollution from fine particulate matter (PM), particularly in megacities. Megacities are centers of economic activity, harbor very large populations, and have high PM levels, especially in the developing world, posing acute challenges to public health. One such city is Delhi, India. Delhi is the second most populated city in the world and routinely experiences some of the highest particulate matter concentrations of any megacity on the planet. However, the current understanding of the sources and dynamics of PM pollution in Delhi is limited. Measurements at the Delhi Aerosol Supersite (DAS) provide long-term chemical characterization of ambient submicron aerosol in Delhi, with near-continuous online measurements of aerosol composition. In this dissertation, I apply PMF on data collected in the DAS study to characterize sources and atmospheric dynamics of submicron aerosols in Delhi. In study 1 (chapter 2), I report on source apportionment based on unsupervised (unconstrained) positive matrix factorization (PMF), conducted on 15 months of highly time-resolved speciated submicron non-refractory PM₁ (NR-PM₁) between January 2017 and March 2018. This dataset was collected in the DAS study. I report on seasonal variability across four seasons of 2017 and interannual variability using data from the two winters and springs of 2017 and 2018. I also show that a modified tracer-based organic component analysis provides an opportunity for a real-time source apportionment approach for organics in Delhi. Phase equilibrium modeling of aerosols using the extended aerosol inorganics model (E-AIM) predicts equilibrium gas-phase concentrations and allows evaluation of the importance of the ventilation coefficient (VC) and temperature in controlling primary and secondary organic aerosol. I also find that primary aerosol dominates severe air pollution episodes, and secondary aerosol dominates seasonal averages. An edited version of this chapter has been published in Atmospheric Chemistry and Physics. In study 2 (chapter 3), we develop the approach of conducting supervised (constrained) PMF on long-term datasets separated into 4 hour periods with limited variability in emissions and meteorology and statistically demonstrate its viability. I apply this time-of-day PMF approach on two seasons of highly time-resolved NR-PM₁ organics. This approach improves upon the seasonal source apportionment previously employed in Delhi by capturing the diurnal variability in source mass spectral profiles and retaining low computational intensity. Use of the EPA PMF tool allows application of constraints and quantifies random errors and rotational ambiguity in PMF solutions. Results in this study demonstrate that time-of-day PMF approach gives a greater number of more appropriate PMF factors compared to the traditional seasonal PMF approach. The time-of-day PMF approach fits data better, improving fits at specific time points, and at key m/zs. Portions of this chapter will be submitted to Atmospheric Measurement Techniques. Previous receptor modeling studies have identified vehicular emissions and fossil fuel combustion as prevalent factors contributing to fine PM pollution in Delhi. However, cooking and biomass burning have not been consistently identified in ambient studies. Bottom-up (source-oriented) studies have recognized the high exposure to residential energy emissions from cooking and heating and associated biomass burning emissions. In study 3 (chapter 4), I address these limitations of receptor modeling studies by applying PMF on two seasons of highly time-resolved NR-PM₁ organics. I utilize the time-of-day PMF approach (chapter 3) to separate primary organics into component primary factors. Hydrocarbon-like organic aerosol, or HOA, the fuel combustion and traffic primary organic aerosol surrogate, occurs in every season, and shows strong diurnal patterns. Biomass burning organic aerosol, or BBOA, separates only in winter, and exhibits time series peaks associated with space heating and solid-fuel combustion. Cooking organic aerosol, or COA, separates only in monsoon and reports stable diurnal patterns, suggesting the presence of cooking sources all-day. Equilibrium modeling of organic aerosols using volatility basis sets (VBS) suggests that differences in ventilation coefficient and temperature can explain the differences in factor separation between winter and monsoon. Overall, I show that traffic, and cooking and biomass burning contribute almost equally to the primary organic aerosol burden in Delhi, in broad agreement with several bottom-up studies. Portions of this chapter will be submitted to Atmospheric Chemistry and Physics

ABSTRACT Exposure to ambient air pollution is a well-recognized risk factor for cardiovascular morbidity and mortality. Studies have shown that air pollution, especially acute exposure to traffic and industrial sources, can influence the autonomic nervous system and in turn affect heart rate variability leading to arrhythmias. While some studies examine acute air pollution effects on ventricular arrhythmias or heart rate corrected QT interval (QTc), few have explored both acute and long-term effects in a mixture of components. Studies tend to focus on fine particulate matter, which can penetrate deep into the lungs due to its smaller size and deposit a large spectrum of organic and inorganic elements1. However, fewer studies have examined which specific elements of fine particulate matter can contribute to cardiovascular toxicity. This dissertation investigates how multiple components of ambient air pollution can impact cardiovascular health. We hypothesized that different components of fine particulate matter may have a direct impact on arrythmias and ventricular repolarization. In particular, we theorized that all the PM2.5 components would either increase the risk for ventricular arrythmias or prolong QT interval, but we expected PM2.5 mass, lead, nickel and elemental carbon to have the largest adverse effects based on past literature. In our first study, we assessed the association of the onset of ventricular arrhythmias with 0-21 day moving averages of PM2.5 and particle radioactivity using time-stratified case-crossover analyses among 176 patients with dual-chamber implanted cardioverter-defibrillators in Boston, Massachusetts. We found that in this high-risk population, independently of particle radioactivity, 21-day PM2.5 exposure was associated with higher odds of a ventricular arrhythmia event onset among patients with known cardiac disease and indication for ICD implantation. In our second study, we utilized time-varying linear mixed-effects regressions with a random intercept for each participant to analyze associations between QTc interval and moving averages (0 to 7 day moving averages) of 24-hour mean concentrations of PM2.5 metal components (vanadium, nickel, copper, zinc and lead) in the Normative Aging Study. We found that exposure to metals (especially lead and copper) contained in PM2.5 were associated with acute changes in ventricular repolarization as indicated by prolonged QT interval length. Finally, we utilized time-varying linear mixed-effects regressions to examine associations between acute (0-3 day), intermediate (4-28 day) and long-term (1 year) exposure to components of fine particulate air pollution (PM2.5 mass, elemental carbon, organic carbon, nitrate, sulfate, ozone), temperature and heart-rate corrected QT interval (QTc). We also evaluated whether diabetic status would modify the association between the PM2.5 components and QTc interval. We found consistent results that higher sulfate levels were associated with significant longer QTc across all moving averages and that organic carbon also increased QTc interval, but for different time periods depending on the model. We found that diabetic status could amplify the association between certain PM2.5 components (elemental carbon, nitrate, organic carbon and sulfate) and QTc interval.

Exposure to fine particulate matter air pollution (PM2.5) is the largest environmental risk factor for death in the United States and globally. Reducing these deaths is facilitated by better understanding how specific emissions sources affect PM2.5 concentrations, but traditional methods for doing so are computationally demanding and resource intensive. In this presentation, I describe my dissertation research into air quality-related human health impacts through the development and use of reduced complexity models (RCMs) that rapidly estimate changes in PM2.5 concentrations and associated deaths. For my first chapter, I use an RCM (InMAP) to estimate the potential air quality-related human health impacts of growing switchgrass, an important bioenergy feedstock. I find that life cycle air quality-related health impacts of switchgrass production vary greatly by location and fertilizer type, and are driven primarily by ammonia emissions from fertilizer application. For my second chapter, I use InMAP and two other RCMs to estimate the air quality impacts of all domestic, human-caused emissions in the United States to identify promising targets for reducing air quality-related deaths. I find that half of the deaths are attributable to 5 human activities, all in different sectors. Promising policy decisions for reducing the deaths include targets of historical focus, such as coal-powered electricity generation, and emerging targets, such as agricultural emissions and residential solvent use. For my third chapter, I describe the development of an open source RCM (Global InMAP) for use on a global spatial domain. I generate global chemical and meteorological inputs to parametrize Global InMAP, configure its computational grid, and run InMAP on a global emissions inventory to demonstrate its use. Overall, its performance against ground observations is comparable to current global models, but at greatly reduced computational intensity. Global InMAP can be used to further inform policy decisions for reducing air quality-related deaths worldwide.

This dissertation focuses on three subjects related to aerosol vertical profile, future climate change impacts on surface aerosol concentration and its effects on premature mortality and drought risk over California (CA). In Chapter 1, I investigate how convective mass flux and precipitation (wet removal) are associated with the aerosol vertical profile over the globe using Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observation (CALIPSO), Modern-Era Retrospective Analysis for Research and Applications (MERRA), European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) and Global Precipitation Climatology Project (GPCP). The results indicate that convective mass flux is an important mechanism that influences global aerosol vertical distribution. In the tropics, more convective mass flux is associated with more aerosol above 500 hPa while less aerosol is related to less convective mass flux. More convective mass flux over the Northern Hemisphere mid-latitude emission sources is associated with more aerosol aloft (downwind). More (less) downward CMF is associated with more (less) aerosol in the lower troposphere and less (more) aerosol in the upper troposphere and vertical dispersivity over NH Pacific. In Chapter 2, the response of surface fine particulate matter (PM2.5) concentration to greenhouse gas (GHG) induced warming at the end of century, and its effects on premature mortality related to respiratory diseases, is estimated. To conduct this work I use simulated PM2.5 from seven Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) models and five projected populations by Shared Socioeconomic Pathways (SSPs). Future climate change has a significant impact on surface PM2.5 concentration and the corresponding premature mortality. Both surface PM2.5 and premature mortality will likely increase at end of the 21st century under business-as-usual warming. In Chapter 3, I investigate future drought risk over California using the Community Earth System Model Large Ensemble Community Project (CESM-LENS). This model is used because 1. it captures the observed teleconnection between El-Nino and California (CA) precipitation and 2. allows an assessment of the role of natural climate variability. The results suggest that under business-as-usual GHG emissions, drought risk during the 21st century does not significantly increase.

Emission inventories of fine particulate matter (PM) were compared to estimates of emissions based on observational data emerging from the EPA Particulate Matter Supersites and other field programs. Six source categories for fine PM emissions were considered: on-road mobile sources, non-road mobile sources, cooking, biomass combustion, fugitive dust and stationary sources. Regional emission inventories of PM in the exhaust from on-road and non-road sources were generally consistent with ambient observations. In contrast, emission inventories of road dust were an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although a comprehensive nationwide emission inventory of fine PM from cooking sources and biomass burning is not yet available, observational data in urban areas suggest that cooking sources account for roughly 5-20% of total primary emissions (excluding dust) and the magnitude of biomass burning emissions are highly dependent on the region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for fine PM are substantial. Because of these uncertainties, the design of fine PM control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods, as demonstrated in this work. This approach was used in the development of a primary PM emissions inventory for air quality modeling. This emissions inventory improved upon previous inventories by updating some source strengths, temporal allocations, and chemical speciation profiles; in addition, size resolution information was incorporated into the inventory.

The southeastern portion of North Carolina is a dense crop and animal (swine) agricultural region which previous research suggests emits a significant portion of the state's nitrogen emissions. These findings indicated that transporting air over this region can effect nitrogen concentrations in precipitation at sites at least 50 miles away. The present study was able to combine isotope compositional information with the concept of back trajectory analysis to examine the relationship between this regional nitrogen emission source and receptors independent of pollutant concentration information. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to determine potential sources of nitrogen in rainwater collected at an urban receptor site in Raleigh, North Carolina, during the first three quarters of 2004. The delta 15N isotope ratio signatures of each sample were used to further differentiate between sources of the rainwater nitrogen. This study examined the importance of pollutant sources such as animal agricultural activity and meteorology on rainfall chemistry as well as their implications on fine particulate matter formation. Additionally, meteorological conditions associated with anomalous springtime fine particulate matter concentrations found in coastal New Hanover County, North Carolina were investigated. Samples that transited the dense crop and animal (swine) agricultural region of east-southeastern North Carolina (i.e. the source region) had lower delta 15N isotope ratios in the nitrate ion (average = -2.1 ± 1.7 per mil) than those from a counterpart nonagricultural region (average = 0.1 ± 3.0 per mil.) However, the limited data set does not offer any conclusive evidence of similar patterns in ammonium ions. An increase in PM fine mass concentrations in the urban receptor site (yearly average = 15.1 ± 5.8 μg/m3) was also found to correspond to air transport over the dense agricultural region relative to air which was not (yearly average = 11.7 ± 5.8 μg/m3). Fine particulate matter concentrations over the course of four years in four counties around the state were examined, and peak levels of PM2.5 were found in the summer at 3 of the 4 sites. The fourth site, located in costal New Hanover County had a fine particulate matter peak in the spring, which appeared to be unusual as most literature indicates that conditions for high PM2.5 levels are generally found in the summer. Investigation of the phenomenon revealed that historically, meteorological conditions including relative humidity, temperature, and wind direction allow for a peak to exist much earlier in the year at such a location. Transport of pollutants from the dense swine region to the Northwest of New Hanover County is more common in the springtime, along with relative humidity that is in a range witch allows an increase in particulate matter to occur.

Particulate matter in the atmosphere with aerodynamic diameter 2.5 micrometer or less (PM[subscript 2.5]) has been studied extensively due to its impact on human health, climate, and other environmental effects. PM[subscript 2.5]) in the southeast US as a whole and particularly in North Carolina (NC) are the focus of this research. The main body of this study includes two parts. The first part focuses on regional i.e. Southeast U.S. PM[subscript 2.5]) analysis consisting of two urban inland sites including locations N.Birmingham, AL (BHM) and Jefferson, GA (JST); two urban coastal sites viz., Gulfport, MS (GFP), Pensacola and FL (PNS); and three rural sites including Yorkville, GA (YRK), Centreville, AL (CTR), and Oak Grove, MS (OAK) (Chapter 2). The second part focuses on North Carolina counties consisting of three urban sites and two rural/agriculture sites in NC. The three urban N.C. sites are located in Forsyth County, Mecklenburg County, and Wake County, respectively; and the two rural/agriculture sites are located in Lenoir County and Caswell County, respectively (Chapter 3). The analysis of data and results indicate that the annual National Ambient Air Quality Standard (NAAQS) for PM[subscript 2.5]) (annual standard: 15 mg/m³) is exceeded in all of urban inland areas and one agriculture site which is surrounded by several power plants, but the annual average of PM[subscript 2.5]) is less than the annual standard at all of the other agricultural sites and urban coastal sites. High annual average concentrations are dominated by elevated values during summer. The 24-hour standard (65 mg/m³) for PM[subscript 2.5]) is not exceeded at any of the urban and/or agriculture sites. In the first part, analysis of chemical species revealed that yearly average fractions of organic matter (OM) with yearly average 4.26