Browsing by Subject "Air pollution"
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Item Advances and application of positive matrix factorization for source attribution of air pollution in megacities(2021-02-22) Bhandari, Sahil; Hildebrandt Ruiz, Lea; Apte, Joshua S.; Sharma, Mukul M; Allen, David TAir 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 PhysicsItem Air quality in the Houston Ship Channel region : an environmental and land use analysis(2008-08) Nasser, Omar Maher; Sletto, BjørnDespite federal, state, and local efforts to combat environmental injustices resulting from heavy industrial activity and high air pollution levels, there is a widespread tendency for hazardous industrial activities to locate near low-income, underrepresented ethnic populations in the United States. The Houston Ship Channel, a port containing the largest concentration of Petrochemical Facilities in the United States, evidences this tendency and provides a stellar example of the nexus between poverty, race, industrial location, and air pollution levels. As a result of the heavy industrial activities in the East Houston area adjacent to the Houston Ship Channel, the surrounding residential area’s air quality levels are significantly poor in relation to federal, state, and local standards. Not coincidentally, these neighborhoods are predominantly low-income and Hispanic in makeup. Unfortunately, there exist few or no federal or state accountability and enforcement mechanisms to resolve this serious problem. In addition, Houston’s lack of zoning and weak land use regulations provides little opportunity for the situation to improve. Although community organization efforts have succeeded in terms of mobilization, education, and consensus building, more effective local planning tools, supported by federal regulations and applied research, would serve to remove the roadblocks that have hindered the advancement of policies promoting enhanced air quality controls, and thus improve the quality of life of the residents of East Houston.Item Factors influencing ambient particulate matter : from Texas to New Delhi(2022-05-03) Patel, Kanan; Hildebrandt Ruiz, Lea; Allen, David T; Apte, Joshua S; Sharma, Mukul MLong term exposure to particulate matter (PM) has been linked to an increase in mortality and cardiorespiratory diseases. In addition, PM affects Earth’s radiative balance, and is one of the main sources of uncertainty in climate change predictions. Hence, it is imperative to understand PM composition and concentrations and the factors contributing to their variability. Different parts of the world experience different levels of air pollution, due to an interplay between various factors including sources, meteorological factors, and chemical transformations. PM can either be directly emitted into the atmosphere (primary) or can be generated as result of oxidation of gas-phase precursors leading to the formation and partitioning of low volatility products to the particle phase (secondary). The nature, sources and dynamics of PM can be estimated by combining ambient field measurements with receptor modeling, machine learning and statistical analysis tools. The objective of my thesis is to understand the factors influencing PM concentration and composition in different environments. In chapter 2, I have reported the results of the measurements in Austin, Texas, one of the fastest growing metropolitan cities in the U.S. I used several modeling and data analysis tools to understand the sources and formation of particulate matter in Austin including positive matrix factorization (PMF), the Extended Aerosol Thermodynamics Model (E-AIM) and air back trajectory analysis using HYSPLIT. Through my analysis, I demonstrated that photochemistry is an important factor in governing PM composition in Austin. We observed rapid photochemical processing of traffic emissions, H₂SO₄-driven new particle formation (NPF) events, production of organic nitrate, and daytime peaks in the locally formed oxidized organic aerosol during the summer period. My analysis also suggested that SO₂ emissions from cement kilns may be the main source of particulate sulfate observed at this receptor site, pointing toward the need for measurements at the source to investigate this further. This chapter has been published in ACS Earth and Space Chemistry. Meanwhile, Delhi (India) is the most polluted megacity in the world and routinely experiences extreme pollution episodes. Our group is one of the first in the world to measure long term PM composition at high time resolution in the city. As part of the Delhi Aerosol Supersite (DAS) study, we have recorded over five years of near-continuous PM composition to understand inter-seasonal as well as inter-annual variability in the PM concentrations and the factors influencing them. I have studied specific “special” events which have implications for policy decisions. In chapter 3, I have investigated the factors influencing high PM concentrations observed during the autumn (~Sep – Nov) season which experiences some of the most extreme pollution episodes observed anywhere in the world. I combined our measurements with data obtained from regulatory monitoring sites (CO, NOₓ, PM₂.₅) to gain insights from the temporal trends of the pollutants and to demonstrate the differences between autumn and winter, which also experiences high concentrations. I incorporated receptor models and non-parametric wind regression to understand the nature and sources of PM during this period. Further, I used meteorological data such as temperature, planetary boundary layer height, wind speed/direction and relative humidity to understand their impact on PM using statistical hypothesis testing. Using these tools, I demonstrated the influence of regional agricultural burning (from the neighboring states) and fireworks during the festival of Diwali on PM during this season. Overall, my analysis provided detailed insights into the sources and dynamics of PM during one of the most polluted seasons in Delhi (and in the world) and provided a direction for future studies in the region. This chapter has also been published in ACS Earth and Space Chemistry. In chapter 4, I have investigated the impact of COVID-19 lock-down on Delhi's air quality by combining PM and gas phase data of over four years with robust statistical analysis, including the method of “robust differences” to account for seasonal variability in the pollutant concentrations. My analysis suggests that future large-scale modification of activity restrictions in Delhi may impact the primary pollutants (NOₓ, CO, black carbon) more than the secondary pollutants, emphasizing the fundamental importance of secondary or regional pollutants on air quality in Delhi. I showed that overall, future strict activity reductions may lead to only a moderate reduction in PM₁, reflective of complex PM₁ chemistry and the need for integrative, multiscale, and multisectoral policies to address the major air pollution challenge in Delhi. This chapter has been published in ACS Environmental Science & Technology Letters. Because of the interplay between sources and meteorology in Delhi, in chapter 5 I have developed machine learning models incorporating random forest regression that estimate the concentrations of PM₁ and its constituents by using meteorology and emission proxies. I have demonstrated the applicability of these models to capture temporal variability of the PM₁ species, to understand the influence of individual factors via sensitivity analyses, and to separate impacts of the COVID-19 lockdowns and associated activity restrictions from impacts of other factors. Overall, these models provide new insights into the factors influencing ambient PM₁ in New Delhi, India, demonstrating the power of machine learning models in atmospheric science applications. This chapter will be submitted to Aerosol Science and Technology. My research has advanced our understanding about PM formation and processing in different environments. These novel measurements and analyses will help guide future studies aimed at understanding and improving ambient air quality in these regions. Furthermore, the results of my scientific analyses may help guide policy decisions aimed at reducing PM levels in the atmosphere, thus helping improve the lives of millions of people.Item Insights into the functions of RNA post-transcriptional modifications gained through studies in cellular stress(2017-08-29) Baldridge, Kevin Charles; Contreras, Lydia M.; Sanchez, Isaac C., 1941-; Alper, Hal; Georgiou, George; Ren, PengyuIn recent years, the explosion of knowledge regarding functional roles for RNA in biology have uncovered the need for detailed molecular understanding of the relationship between the chemistry of RNA and its function. Post-transcriptional modifications in RNA are changes to the chemistry of the four basic RNA nucleotides, which can be thought of as expansions to the genetic alphabet of RNA. By subtly changing the chemistry of the standard RNA nucleotides, post-transcriptional modifications (PTMs) can act to modulate RNA function through a variety of mechanisms ranging from altering the basic physicochemical properties of an RNA molecule to fine-tuning complex regulation processes in higher organisms such as humans. While a number of functions have been ascribed to RNA post-transcriptional modifications, we still lack a comprehensive picture of how subtle changes in RNA chemistry can alter cellular function. Therefore, my dissertation focuses on applying cellular stress models as a way to interrogate how RNA post-transcriptional modifications can modulate RNA function. First, I provide a thorough discussion of how ribosomal RNA methylations help Escherichia coli adapt to stressful environmental conditions by protecting the vital protein translation process. Furthermore, by performing a targeted meta-analysis of publicly available stress-induced gene expression data, I identify specific ribosomal RNA methylations which may be important for E. coli adaptation to particular stresses including oxidative, heat, and cold stresses. Next, I examined the question of how RNA chemistry is affected directly by an external stress using a model system for simulated air pollution exposure with human lung cell cultures to demonstrate that cellular RNA oxidation is a reliable consistent indicator of oxidative stress from air pollution exposure. Building upon this air pollution exposure model system, I further explored how chemical oxidation of RNAs can be specifically enriched in certain RNA transcripts under oxidative stress induced by air pollution exposure. This work highlights specific oxidation of mRNAs involved in regulatory pathways as a possible mechanism for air-pollution related disease, suggesting that accumulation of oxidized mRNAs might interfere with processing or gene regulation. Additionally, examination of specific RNA oxidation associated with pre- and post-stress differential gene expression after exposure to air pollution highlights the potential that the oxidized RNA base 8-oxoguanosine might function as an epitranscriptomic mark affecting transcription. Lastly, I characterized how RNA modification machinery can contribute to cellular stress associated with expression of engineered transfer RNAs for applications in expanding the genetic code in E. coli. Using a suite of suppressor tRNAs designed by directed evolution, I demonstrated that the evolved lower expression level minimizes the impact of stress caused by interactions with E. coli post-transcriptional modification machinery, resulting in high performing tRNAs that remain functional regardless of their interactions with host PTM machinery. Collectively, the studies described in this dissertation demonstrate the power of employing cellular stress models to interrogate functional roles of RNA post-transcriptional modifications and highlight the myriad roles that PTMs can play in responding to and contributing to cellular stress.Item Multiscale spatial patterns of outdoor air pollution in California : drivers of variability and implications for exposure and environmental justice(2021-04-06) Chambliss, Sarah Elisabeth; Apte, Joshua S.; Kinney, Kerry A.; Marshall, Julian D; Passalacqua, Paola; Misztal, Pawel KExposure to air pollution causes diseases of the lungs, cardiovascular system, brain, and numerous other systems, and is a leading environmental health risk worldwide. The burden of air pollution exposure is not distributed evenly across the population of the United States, and often falls more heavily on low-income groups and people of color. An accurate understanding of how air pollution levels vary on multiple spatial scales is critical for shaping effective policies to improve air quality for the highest exposed communities. Pollutants with primary and secondary contributions like fine particulate matter (PM₂.₅) vary significantly within urban areas on length scales of 1 km but are influenced by emissions at scales of 100 km or more, while other pollutant categories exhibit strong near-source decay at length scales of 100 m. In this dissertation I apply two complementary approaches to assess multiscale spatial patterns for five health-relevant pollutants: PM₂.₅, black carbon (BC), ultrafine particles (UFP), nitrogen oxide (NO), and nitrogen dioxide (NO₂). Using a reduced-complexity chemical transport model I show that current emissions patterns lead to significant PM₂.₅ exposure disparity among racial-ethnic groups, income categories, and other socioeconomic groupings, driven by the systematically higher proximity to emissions from on-road mobile sources, industry, natural gas and petroleum development, and other major sources. To estimate exposure disparity for pollutants that vary at very fine spatial scales and follow difficult-to-model patterns driven by complex characteristics of the urban landscape (BC, UFP, NO, and NO₂), I use data collected via mobile monitoring to construct empirical air pollution maps for a variety of neighborhoods in the San Francisco Bay Area. These measurements show high exposure disparities both within and among racial-ethnic groups, with disparity in mean concentrations driven by differences in neighborhood background concentrations but higher within-group disparity driven by highly localized near-source gradients. I also assess sources of uncertainty in mobile monitoring-based mapping techniques. These complementary approaches provide a broad picture of causes of urban exposure disparity in California and can inform future mitigation measures.Item Multivariate GLS meta-analysis on ambient air pollution and congenital heart anomalies(2014-05) Wang, Ni; Beretvas, Susan NatashaThe effects of air pollutants CO, NO₂, O₃, PM₁₀ and SO₂ on congenital heart anomalies are represented by the odds ratio of each disease per unit increase in the concentration of each pollutant. In this study, the effects of air pollutants are summarized using multivariate GLS approach with correlation between outcomes being taken into account, where the correlations are sampled from uniform [-1,1]. Meta-analysis conducted here found no statistically significant increase in odds ratio of any disease. This result is different from what Vrijheid et al. 2011 suggested when correlation is not considered using the same set of data. The difference in conclusions from the two meta-analysis indicate that correlation between outcomes may play an important role when synthesizing effect sizes. Thus, before conduct meta-analysis, a thorough consideration about whether to incorporate the correlation in synthesizing should be given.Item Presentation: Big, Beautiful Sky: The State and Future of Texas' Air(Environmental Science Institute, 2003-09-05) Environmental Science Institute; Allen, David T.Item Responsiveness and feedback under authoritarianism : the public, city governments, and air pollution in China(2018-09-11) Buchanan, Ross Ardley; Jones, Bryan D.Recent scholarship on authoritarian responsiveness has found limited, ad hoc responsiveness to public concern by local governments in China. However, almost nothing is known about the outcomes of this responsiveness. We do not know if the outcomes are ever substantively meaningful for the public, nor whether they “feed back” on public concern in authoritarian systems. To address this gap, I propose a novel responsiveness-feedback model with outcomes, and apply it to air pollution in Chinese cities because of the issue’s importance and objectively measurable severity. I estimate the relationships between pubic concern, government action, and air pollution levels in 273 cities from 2013-2015 using structural equation models to account for feedback, and find evidence of substantively meaningful responsiveness and outcomes-based feedback. I speculate that our model also applies to other topic areas that can be addressed by local governments and have outcomes the public can directly observe.Item Setting the agenda on air pollution : examining the traditional and social media agendas and their relationships 2011-2015(2017-08) Zheng, Pei; Chen, Gina Masullo; McCombs, Maxwell; Chen, Wenhong; Chyi, Hsiang; Johnson, ThomasUnder the theoretical frameworks of agenda-setting and authoritarian environmentalism , this dissertation examined traditional and social media agendas on the air pollution issue in China from 2011 to 2015. It adopted Granger’s causality analysis to test the causal relationships among four traditional media outlets (N = 1,147), six types of actors on the Chinese social media platform called Weibo (N = 4,045), and between agendas of traditional media outlets and social media actors. The results showed most of news stories were framed under “publicity and government trust” frame between 2011 and 2012, and under “war on pollution” and “science” frames after 2013. Government officials, environmental scientists and researchers dominated media sources. The state-owned media, People’s Daily, set the agenda for other local and commercial media outlets. Agendas on social media were fragmented with media setting the agenda for NGOs and verified individual’s accounts. Agenda-setting effects existed only between traditional media and media’s Weibo accounts, and between traditional media and verified individuals’ Weibo accounts. The agendas of ordinary people on Weibo were independent of the agendas other social media actors and of traditional media. The opinion leaders on Weibo were mostly business leaders and celebrities. This dissertation is the first study to provide a holistic view and clear trajectory of agendas on air pollution over five years. It explained authoritarian environmentalism from a media perspective and contributed to agenda-setting theory by capturing the fragmented nature of the social media agenda. Methodologically, this dissertation advanced existing study by applying a computer-assisted social media data collection method and conducting a more rigorous causality analysis called Granger’s causality.Item Spatiotemporal climate risk vulnerability assessment to extreme heat and particulate matter : combining realtime concentration of de facto population(2022-04-29) Choi, Seung Jun; Jiao, JunfengThe recurring summer heatwaves and particulate matter in Seoul, S. Korea, have been considered the "New Normal" that calls for perpetual attention. Climate change has been augmenting the frequency and intensity of extreme heat and air pollution issues. Their threats to Seoul's living environment and public health concerns, including the safety of vulnerable populations sensitive to climate risks, are snowballing. Exposure and damage to heat-related casualties or particulate matter are more detrimental to the elderly and the children. Studies have investigated the occurrence of heatwaves or particulate matter based on spatial information using urban climate IoT sensing devices in Seoul. However, little research has examined the spatiotemporal exposure patterns of vulnerable groups by using real-time big floating population data captured by mobile phone signals. This study traced the high-resolution climate hazard risk, focusing on extreme heat and particulate matters, using IoT environmental sensors named S-DoTs deployed throughout Seoul. We combined spatiotemporal climate hazard vulnerability assessment in hour periods with floating population data classified in different age groups. A framework for urban climate sensing application is suggested for future climate vulnerability assessment research. Then, this study discussed policy implications to be better equipped with dealing with recurring climate hazardsItem Urban aerosol : role of sources and atmospheric processes(2022-08-30) Gani, Shahzad; Apte, Joshua S.; Passalacqua, Paola; Kinney, Kerry A; Hildebrandt Ruiz, LeaOutdoor air pollution has detrimental health effects resulting in substantial global and regional decrements in life expectancy. More than half of the world's population lives in urban areas making it important to understand the sources and processes that drive urban air pollution. Some of the most polluted cities in the world are in India and as of 2019, Delhi is the world's most polluted megacity. While cities in the USA generally have an order of magnitude lower aerosol mass loadings than those observed in Delhi, particle number (PN) concentrations often tend to be similarly high. In this dissertation, I provide the overview of the Delhi Aerosol Supersite (DAS) study--a site that we set up in Delhi for long-term continuous online aerosol composition and size distribution measurement using state-of-art instruments. In Chapter 2 and 3 of this dissertation, I investigate the aerosol composition and aerosol size distribution observed in Delhi respectively. I also use long-term fixed site and mobile monitoring datasets to investigate the spatiotemporal variation of ultrafine particle (UFP, D [subscript p] <100 nm) concentrations in the San Francisco (SF) Bay Area, USA. For both Delhi and the SF Bay Area, I have distilled insights on physicochemical processes on the basis of field observations complemented with satellite and modeling datasets. Delhi, India routinely experiences some of the world's highest urban particulate matter concentrations. We established the DAS study to provide long-term characterization of the ambient submicron aerosol composition in Delhi. In chapter 2, we report on 1.25 years of highly time resolved speciated submicron particulate matter (PM₁) data, including black carbon (BC) and non-refractory PM₁ (NR-PM₁), which we combine to develop a composition-based estimate of PM₁ ("C-PM₁" = BC + NR-PM₁) concentrations. We observed marked seasonal and diurnal variability in the concentration and composition of PM₁ owing to the interactions of sources and atmospheric processes. Winter was the most polluted period of the year with average C-PM₁ mass concentrations of ~210 μg m⁻³. Monsoon was hot and rainy, consequently making it the least polluted (C-PM₁ ~50 μg m⁻³) period. Organics constituted more than half of the C-PM₁ for all seasons and times of day. While ammonium, chloride and nitrate each were ~10% of the C-PM₁ for the cooler months, BC and sulfate contributed ~5% each. For the warmer periods, the fractional contribution of BC and sulfate to C-PM₁ increased and the chloride contribution decreased to less than 2%. The seasonal and diurnal variation in absolute mass loadings were generally consistent with changes in ventilation coefficients, with higher concentrations for periods with unfavorable meteorology--low planetary boundary layer height and low wind speeds. However, the variation in C-PM₁ composition was influenced by temporally varying sources, photochemistry and gas-particle partitioning. During cool periods when wind was from the northwest, episodic hourly averaged chloride concentrations reached 50-100 μg m⁻³, ranking among the highest chloride concentrations reported anywhere in the world. We estimated the contribution of primary emissions and secondary processes to Delhi's submicron aerosol. Secondary species contributed almost 50-70% of Delhi's C-PM₁ mass for the winter and spring months, and up to 60-80% for the warmer summer and monsoon months. For the cooler months that had the highest C-PM₁ concentrations, the nighttime sources were skewed towards primary sources, while the daytime C-PM₁ was dominated by secondary species. Overall, these findings point to the important effects of both primary emissions and more regional atmospheric chemistry on influencing the extreme particle concentrations that impact the Delhi megacity region. Future air quality strategies considering Delhi's situation in both a regional and local context will be more effective than policies targeting only local, primary air pollutants. While fine particulate matter (PM [subscript 2.5]) mass concentrations in Delhi are at least an order of magnitude higher than in many western cities, the PN concentrations are not similarly elevated. Here we report on 1.25 years of highly time resolved particle size distributions (PSD) data in the size range of 12-560 nm. We observed that the large number of accumulation mode particles--that constitute most of the PM [subscript 2.5] mass--also contributed substantially to the PN concentrations. The UFP fraction of PN was higher during the traffic rush hours and for daytimes of warmer seasons--consistent with traffic and nucleation events being major sources of urban UFP. UFP concentrations were found to be relatively lower during periods with some of the highest mass concentrations. Calculations based on measured PSD and coagulation theory suggest UFP concentrations suppression by a rapid coagulation sink during polluted periods when large concentrations of particles in the accumulation mode result in high surface area concentrations. A smaller accumulation mode for warmer months result in increased UFP fraction, likely owing to a comparatively smaller coagulation sink. We also see evidence suggestive of nucleation which may also contribute to the increased UFP proportion during the warmer seasons. Even though coagulation does not affect mass concentrations, it can significantly govern PN levels with important health and policy implications. Implications of a strong accumulation mode coagulation sink for future air quality control efforts in Delhi are that a reduction in mass concentration, especially in winter, may not produce proportional reduction in PN concentrations. Strategies that only target accumulation mode particles (which constitute much of the fine PM [subscript 2.5] mass) may even lead to an increase in the UFP concentrations as the coagulation sink decreases. The health risks of UFP exposure are an important subject of current investigation in air pollution epidemiology. In the absence of routine monitoring of UFP, air pollution epidemiology studies often use other co-emitted pollutants as proxy for UFP, with NO [subscript x] (NO+NO₂) considered a good choice. In chapter 4, we use long term fixed site measurements along with extensive mobile monitoring data to evaluate the spatiotemporal correlation of UFP and NO [subscript x]. We incorporate 4-6 years of hourly PN concentration data from multiple fixed sites across the San Francisco Bay Area that include near-highway, urban, suburban and rural sites. In addition, we incorporate observations from a 32-month mobile monitoring campaign comprising >3,000 h of coverage of a range of road types and land uses. Across all fixed sites, PN measurements show prominent mid-day peaks during the summer--characteristic of new particle formation--which are not observed for other co-emitted pollutants (NO subscript x], BC, CO). While we found moderate correlation in diurnal patterns of NO [subscript x] and UFP at sites with high traffic, the correlation dropped significantly for low traffic areas--especially during high insolation (e.g., summer daytime) periods. Mobile monitoring data yielded similar results: NO [subscript x] was observed to have weaker correlations with UFP for non-highway roads during high insolation periods. The spatiotemporal profiles of UFP can differ strongly from other traffic-related air pollutants when new particle formation from nucleation contribute to a significant fraction of UFP.