Asthma has been reported in children as a leading chronic illness in the US and around the world. It is also the third leading cause of hospitalization among children under the age of 15, and is also one of the most common causes of school absenteeism. Children are at higher risk of asthma attacks and they pose a higher burden on health care system. Nearly 20.6% of middle and high school children in Florida have been told they have asthma, this prevalence has grown over 3% from 2006 to 2012. Changes in air pollutant levels are often related to health outcomes, e.g. prevalence of chronic asthma. Exposure to ambient air pollutants have been reported to exacerbate asthma attacks especially in children. Often agencies and governing bodies utilize national level health impact assessments (HIAs) to estimate local levels of health impacts. The US EPA (Environmental Protection Agency) developed the Benefit Mapping and Analysis Program (BenMAP) to estimate impacts on health due to changes in air pollution. Recent studies have shown that assessment of regional exposure is important to understand health impacts of pollutants at the local level. To use BenMAP effectively for HIA in Florida, one may have to update the prevalence rates and concentration response (CR) functions in BenMAP with Florida data. The main purpose of the research was to develop a method which can estimate impact of change in criteria pollutants on childhood asthma outcomes in Florida. The rates present in BenMAP are based on national estimates, which are higher than the rates for Florida. If these rates are used for the HIA method then the change in asthma emergency department visits estimated by BenMAP may be an overestimate with higher uncertainties. There are no baseline rates for asthma exacerbation ED visits in BenMAP, an asthma exacerbation is a more severe and poorly managed case of asthma. Asthma ED visit prevalence rates will tend to overestimate the asthma exacerbation rates by 64%, if used. Detailed review of US-EPA’s BenMAP software and peer reviewed literature was performed to identify the gaps in BenMAP for asthma assessments. The CR functions were developed using local pollutant and outcomes data. CR functions were added to BenMAP to bridge the gaps. The baseline prevalence and exacerbation rates at county level by age group, gender and race ethnicity were developed. This study highlights that an increase of 10 µg/m3 of PM2.5 contributes about 2% to asthma ED visit rate, in children 5-12 and is lower, for 13-18 olds (0.6%). The baseline prevalence and exacerbation rates at county level for asthma in children differed by race/ethnicity. This study publishes the ED rates by county and by gender, race and ethnicity from 2010 to 2014, which are recent rates and have not been published to such granularity by the State or by any other researcher. Current pollutant data in BenMAP is only available through 2008, and EPA has recommended it should be updated for analysis purposes. This study has updated the monitor data in BenMAP for Florida counties for 2010-2014. There are three major contributions of this study. Firstly, the study contributes to publishing childhood emergency department prevalence rates for asthma and exacerbation in the State of Florida by age group, race/ethnicity and gender. Secondly, development of concentration response functions specific to Florida using the time series analysis to show the impact of PM2.5 on asthma exacerbation emergency department visits, incorporating both temporal and spatial variability of PM2.5 during the study period. Finally, the study demonstrates the utility of using local (county-level) baseline asthma prevalence rates and local pollutant data for State HIA in Florida. The local PM2.5 data in BenMAP can be used for other health outcome assessments, researchers will only have to update the prevalence rates for the health outcome used in their study. Estimation using local data will be less prone to uncertainties using National level data, the use of local data has been emphasized by several researchers. The study recommends future work in refining spatial grid resolution in BenMAP to zip code level to facilitate studies at neighborhood level. Another recommendation is to further design research to study SES in context to dietary changes and better understand social injustices in areas with diverse population. A population-based study in conjunction with Florida Asthma Coalition (FAC) asthma cases from doctors’ offices is recommended which will be able to control for misclassifications, and include weather and allergens in analysis while studying individual pattern of exposure and diet.

Air pollution (in terms of PM2.5) is severe in developing countries, and the rapid population growth accompanied by urbanization may limit their potential economic development. This paper aims to investigate if urbanization and economic freedom cause higher levels of PM2.5 in developing countries. By measuring the potential effect of economic freedom on PM2.5 with the Ease of Doing Business-score by the World Bank, a new measure is introduced to the research on socioeconomic factors’ influence on air pollution. It is done by running both fixed effects- and system GMM regressions on a panel consisting of 63 low- and lower-middle-income economies between 2010-2017. The results indicate that PM2.5 is insensitive to changes in both variables and that urbanization’s effect on PM2.5 depends on the level of economic freedom and vice versa. However, both estimators may suffer from bias, and thus, the real relationship of urbanization and economic freedom on PM2.5 remains uncertain.

The health effects of particulate air pollution and the evaluation of mitigation efforts to address them have been focused in the past on measurements of bulk mass concentrations of aerosol particles (particulate matter or PM) at fixed locations instead of more traffic-related PM such as black carbon (BC). A more appropriate investigation of the spatial and temporal variabilities of these pollutants is necessary to effectively estimate realistic pedestrian exposure. In this work, three novel scientific contributions are presented with an overarching goal of quantifying the influence of environmental factors on the spatial and temporal distributions of BC and PM2.5 (all particles smaller than 2.5 micrometers) in urban areas. Mass concentrations of BC and PM2.5 were obtained with a mobile platform called the “aerosol backpack”. With this tool, strategic mobile measurement field campaigns were conducted at multiple sites in four countries to achieve the scientific objectives of this work. First, a concept was developed to optimize the mobile measurement strategy for obtaining high-quality data for scientific analyses including a traceable way to reconstruct and calculate PM2.5 mass concentrations from an optical particle size spectrometer. Second, an entire investigation was done on the field performance of the most widely-used portable absorption photometer for measuring BC mass concentrations, the AE51. Results show that these instruments are robust and reliable across different environments. Third, a statistical approach based on a Bayesian distributional model was developed and refined to suitably analyze mobile measurement datasets and extract reliable information. Through this model, the differences between the effects of human activities and other environmental factors on BC and PM2.5 have been quantified. These results quantitatively confirm that spatial and temporal characteristics related to human activities have stronger effects on the variability of the BC mass concentration than on the regulated PM2.5 – consequently, having more influence on pedestrian exposure. This study highlights the importance of high data quality for mobile measurements to make them useful in exposure assessment, particularly to pollutants that are highly variable in space. Finally, this study contributes to the growing evidence of the importance of including more traffic-related pollutants to monitor air quality in urban areas and create appropriate mitigation strategies to combat the adverse health effects of air pollution.:Table of Contents Bibliographic Description .................................................................................................. i Bibliografische Beschreibung ........................................................................................... ii 1. Introduction ................................................................................................................... 1 1.1 Black carbon ....................................................................................................... 2 1.2 Mobile measurements ........................................................................................ 5 1.3 Objectives ............................................................................................................... 6 2. Methodology ................................................................................................................. 9 2.1 TROPOS Aerosol backpack ................................................................................... 9 2.1.1 Instrumentation .............................................................................................. 10 2.2 Mobile measurement strategy ........................................................................... 12 2.3 Phase 1 – Pilot studies .......................................................................................... 12 2.3.1 MACE-2015, Manila Philippines (Master thesis) ......................................... 13 2.3.2 Saxony Soot Project 2016, Leipzig and Dresden, Germany .......................... 15 2.4 Phase 2 – Optimization of MM and quality assurance ......................................... 18 2.4.1 CARE-2017, Rome, Italy .............................................................................. 18 2.4.2 Other datasets ................................................................................................. 19 2.5 Phase 3 – Data analysis ......................................................................................... 20 2.5.1 Statistical model: lognormal distributional regression .................................. 21 3. Results and Discussion ............................................................................................... 27 3.1 First publication .................................................................................................... 27 3.1.1 Methodology for high-quality mobile measurement with focus on black carbon and particle mass concentrations ............................................................................ 27 3.2 Second publication ................................................................................................ 45 3.2.1 Performance of microAethalometers: Real-world field intercomparisons from multiple mobile measurement campaigns in different atmospheric environments 45 3.3 Third Publication .................................................................................................. 73 iv 3.3.1 Pedestrian exposure to black carbon and PM2.5 emissions in urban hotspots: New findings using mobile measurement techniques and flexible Bayesian regression models .................................................................................................... 73 4. Summary and Conclusions ....................................................................................... 101 5. Outlook ..................................................................................................................... 107 Appendix ....................................................................................................................... 109 A.1 Publications included in the Doctoral Thesis and Author’s contributions ......... 109 A.2 Other Publications as First Author and Co-author during PhD ......................... 111 A.3 PhD Committee .................................................................................................. 113 A.4 Supervision Committee ...................................................................................... 114 List of Figures ............................................................................................................... 115 List of Tables ................................................................................................................ 116 Abbreviations ................................................................................................................ 117 Bibliography ................................................................................................................. 119 Acknowledgement ........................................................................................................ 129 / Die gesundheitlichen Auswirkungen der Luftverschmutzung durch Feinstaub und die Bewertung von Maßnahmen zu ihrer Eindämmung konzentrierten sich bisher auf Messungen der Massenkonzentration von Aerosolpartikeln (PM; Particulate Matter) an festen Standorten und nicht auf verkehrsbedingte Aerosolpartikel wie z. B. Ruß (BC; Black Carbon). Eine zielgerichtete Untersuchung der räumlichen und zeitlichen Variabilität dieser Schadstoffe ist notwendig, um die realistische Exposition von Fußgängern effektiv abzuschätzen. In dieser Arbeit werden drei neue wissenschaftliche Ansätze mit dem übergreifenden Ziel vorgestellt, den Einfluss von Umweltfaktoren auf die räumliche und zeitliche Verteilung von BC und PM2,5 in städtischen Gebieten zu quantifizieren. Die Massenkonzentrationen von BC und PM2,5 (alle Partikel kleiner 2,5 Mikrometer) wurden mit einer mobilen Plattform, dem Aerosol-Rucksack, gemessen. Damit wurden strategische mobile Messkampagnen an mehreren Standorten in verschiedenen Ländern durchgeführt, um die wissenschaftlichen Ziele dieser Arbeit zu erreichen. Dazu wurde zunächst ein Konzept zur Optimierung der mobilen Messstrategie entwickelt, um qualitativ hochwertige Daten für wissenschaftliche Analysen zu erhalten, einschließlich einer nachvollziehbaren Methode zur Rekonstruktion und Berechnung von PM2.5-Massekonzentrationen aus Messungen mit einem optischen Partikelgrößenspektrometer. Zweitens wurde die Leistungsfähigkeit der am häufigsten verwendeten tragbaren Absorptionsphotometers zur Messung der BCMassekonzentration unter realistischen Bedingungen untersucht. Diese Ergebnisse zeigen, dass die verwendeten Geräte in den unterschiedlichsten Umgebungen robust und zuverlässig einsetzbar sind. Drittens wurde ein statistischer Ansatz entwickelt und angepasst, um mobile Messdatensätze in geeigneter Weise zu analysieren und weitere nützliche Informationen zu gewinnen. Mithilfe dieses Modells wurden die Unterschiede zwischen den Auswirkungen menschlicher Aktivitäten und anderer Umweltfaktoren auf BC und PM2,5 quantifiziert. Diese Ergebnisse bestätigen quantitativ, dass räumliche und zeitliche Merkmale im Zusammenhang mit menschlichen Aktivitäten stärkere Auswirkungen auf die Variabilität der BC-Massekonzentration haben als auf die regulierte PM2,5-Konzentration - und folglich auch einen größeren Einfluss auf die Exposition von Fußgängern. Diese Studie unterstreicht die Bedeutung hoher Datenqualität bei mobilen Messungen zur Expositionsabschätzung, insbesondere bei Schadstoffen, die räumlich sehr variabel sind. Insbesondere trägt diese Studie dazu bei, die Notwendigkeit hervorzuheben, in städtischen Gebieten mehr verkehrsbedingte Luftschadstoffe in die Überwachung der Luftqualität einzubeziehen. Darüber hinaus sollen geeignete Strategien, zur Bekämpfung der gesundheitsschädlichen Auswirkungen der Luftverschmutzung, entwickelt werden.:Table of Contents Bibliographic Description .................................................................................................. i Bibliografische Beschreibung ........................................................................................... ii 1. Introduction ................................................................................................................... 1 1.1 Black carbon ....................................................................................................... 2 1.2 Mobile measurements ........................................................................................ 5 1.3 Objectives ............................................................................................................... 6 2. Methodology ................................................................................................................. 9 2.1 TROPOS Aerosol backpack ................................................................................... 9 2.1.1 Instrumentation .............................................................................................. 10 2.2 Mobile measurement strategy ........................................................................... 12 2.3 Phase 1 – Pilot studies .......................................................................................... 12 2.3.1 MACE-2015, Manila Philippines (Master thesis) ......................................... 13 2.3.2 Saxony Soot Project 2016, Leipzig and Dresden, Germany .......................... 15 2.4 Phase 2 – Optimization of MM and quality assurance ......................................... 18 2.4.1 CARE-2017, Rome, Italy .............................................................................. 18 2.4.2 Other datasets ................................................................................................. 19 2.5 Phase 3 – Data analysis ......................................................................................... 20 2.5.1 Statistical model: lognormal distributional regression .................................. 21 3. Results and Discussion ............................................................................................... 27 3.1 First publication .................................................................................................... 27 3.1.1 Methodology for high-quality mobile measurement with focus on black carbon and particle mass concentrations ............................................................................ 27 3.2 Second publication ................................................................................................ 45 3.2.1 Performance of microAethalometers: Real-world field intercomparisons from multiple mobile measurement campaigns in different atmospheric environments 45 3.3 Third Publication .................................................................................................. 73 iv 3.3.1 Pedestrian exposure to black carbon and PM2.5 emissions in urban hotspots: New findings using mobile measurement techniques and flexible Bayesian regression models .................................................................................................... 73 4. Summary and Conclusions ....................................................................................... 101 5. Outlook ..................................................................................................................... 107 Appendix ....................................................................................................................... 109 A.1 Publications included in the Doctoral Thesis and Author’s contributions ......... 109 A.2 Other Publications as First Author and Co-author during PhD ......................... 111 A.3 PhD Committee .................................................................................................. 113 A.4 Supervision Committee ...................................................................................... 114 List of Figures ............................................................................................................... 115 List of Tables ................................................................................................................ 116 Abbreviations ................................................................................................................ 117 Bibliography ................................................................................................................. 119 Acknowledgement ........................................................................................................ 129

Cache County and the Wasatch Front, Utah have persistently experienced some of the nation’s worst air quality over the past decade. Elevated PM2.5 concentrations during wintertime “red air day” episodes frequently exceed the National Ambient Air Quality Standards (NAAQS). We investigate the possible effects of two different economic policies in controlling these regional problems. Adapting a model originally developed to calculate the social investment necessary to control nationwide disease outbreaks, we estimate an optimal preventative capital stock (for example, investment in public transportation) of between $4.1 million and $14.1 million to control red air day episodes in Cache County, and $133 million to $1.6 billion dollars to control such episodes in the Wasatch Front. Further, we find that a seasonal gasoline tax rate of roughly $8 per gallon is necessary for policy makers in the Wasatch Front to impose at the pump if their goal is to maintain concentrations below the NAAQS on average during a typical winter-inversion season. This rate is roughly $2 more than the rate calculated for Cache County in a previously published study.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20690号 / 工博第4387号 / 新制||工||1682(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 高野 裕久, 教授 米田 稔, 准教授 上田 佳代 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Tropospheric fine particulate matter (PM) poses serious health risks and has a significant impact on global climate change. The measurement of various aspects of PM is challenging due to its complex chemical nature. This dissertation addresses various aspects of PM, including composition, measurement, and visibility. The U.S. Environmental Protection Agency (EPA) proposed a new secondary standard based on visibility in urban areas using 24-h averaged measurements of either light scatter or PM concentration. However shorter averaging times may better represent human perception of visibility. Data from two studies conducted in Lindon, UT, 2012, and Rubidoux, CA, 2003, were used to compare different techniques to estimate visibility, particularly the effect of relative humidity on visibility estimations. Particle composition was measured in Salt Lake City during January-February of 2009. One-hour averaged concentrations of several gas phase and particle phase inorganic species were measured. The results indicate ammonium nitrate averages 40% of the total PM2.5 mass in the absence of inversions and up to 69% during strong inversions. Also, the formation of ammonium nitrate is nitric acid limited, while the formation of ozone appears to be oxidant and volatile organic carbon (VOC) limited. Reduction of NOx will reduce ammonium nitrate secondary particle formation, however, a decrease in NOx may increase ozone concentration.Due to the complexity of PM it is poorly characterized. A large fraction of PM is composed of organic compounds, but these compounds are not regularly monitored due to limitations in current sampling techniques. The GC-MS Organic Aerosol Monitor (OAM) combines a collection device with thermal desorption, gas chromatography and mass spectrometry to quantitatively measure the carbonaceous components of PM on an hourly averaged basis. A compact GC and simple pre-concentrator were developed for the system to decouple separation from manual injection and enhance separation of environmentally-relevant polar organic compounds, such as levoglucosan. The GC-MS OAM is fully automated and has been successfully deployed in the field. It uses a chemically deactivated filter for collection followed by thermal desorption and GC-MS analysis. Laboratory tests show that detection limits range from 0.2 to 3 ng for many atmospherically relevant compounds. The GC-MS OAM was deployed in the field for semi-continuous measurement of the organic markers, levoglucosan, dehydroabietic acid, and several polycyclic aromatic hydrocarbons (PAHs) during winter (January to March), 2015 and 2016. Results illustrate the significance of this monitoring technique to more fully characterize the organic components of PM and identify sources of pollution.

PM2.5 variability within the neighborhood scale has not been thoroughly studied for wood burning communities. High variability in near-field PM2.5 concentration may lead to harmful public exposure since monitoring does not occur on that scale. This study measures near-field PM2.5 variability by measuring black carbon (BC), a component of PM2.5, in a 1 km2 area located in Cambria, California. BC and meteorological data (when meteorological instruments were available) were measured over thirteen 12-hour intensive operation periods (IOPs) occurring over the winters of 2009 and 2010. Near-field BC variability was measured to understand the type of exposures found in communities where many homes are burning wood simultaneously within a small area. In addition, relationships between meteorological, geographical, and burning source characteristics and BC were observed as tools for understanding BC concentration. The computer air dispersion modeling programs, ISC-PRIME and ISCST3, were also evaluated for applicability to the near field. BC concentrations were measured using 1- to 2-minute resolution aethalometers and 12 hour resolution Personal Environmental Monitors (PEMs). On average, over all IOPs and sites, aethalometer and PEM BC averages were very similar, ranging between 200 and 250 ng/m3, or 4 and 5 µg/m3 for PM2.5, and standard deviations were often high. Averaging all BC measurements, aethalometer BC standard deviation values were 360 percent of the average BC concentration and PEM BC standard deviations were 120 percent the average BC concentration. The average standard deviation detected during each IOP was 190 percent of the average BC concentration for aethalometers and 79 percent of the average BC concentration for PEMs. The average standard deviation detected at each site was 220 percent of the average BC concentration for aethalometers and 76 percent of the average BC concentration for PEMs. The larger standard deviations measured by higher resolution aethalometers demonstrated that low resolution instruments, such as PEMs, are unable to detect high concentrations that may occur. In addition to examining BC variability, multiple linear regression analyses were conducted to determine the impact of meteorological variables and geographic and burning source characteristics on BC concentration and a weighted BC deviation function (BC standard deviation divided by average BC concentration). Time impacts, humidity, and wind speed, accounted for about 50 percent of variability in aethalometer average BC and BC deviation. However, because all model assumptions were not satisfied, improvements are needed. Regression models based on PEM BC found wind speed and direction to account for about 80 percent of average PEM BC variability and number of burning sources to account for about 30 percent of PEM BC deviation. Although PEM BC models accounted for a high percentage of BC variability, few data points were available for the PEM analyses and more IOPs are needed to determine their accuracy. When evaluating correlations between geographic and burning source characteristics and PEM BC concentrations, specific IOP and PEM sampling location explained almost 70 percent of variability in BC concentration, though model residuals suggested model bias. IOP likely explained variation in burning patterns and meteorology over each night while sampling location was likely a proxy for housing density, tree coverage, and/or elevation. Because all regression model assumptions could not be satisfied, the predictors were also observed graphically. Plotting BC concentration versus the number of burning sources suggested that number of burning sources may affect BC concentration in areas of low tree coverage and high housing density and in the case that the level of surrounding vegetation and structures are minimal. More data points will be needed to determine whether or not these relationships are significant. ISC-PRIME and ISCST3 modeling overall tended to under predict BC concentrations with average modeled-to-measured ratios averaging 0.25 and 0.15, for ISC-PRIME and ISCST3, respectively. Correction factors of 9.75 and 18.2 for ISC-PRIME and ISCST3, respectively, were determined to bring modeled BC concentrations closer to unity, but the range of ratios was still high. Both programs were unable to consistently capture BC variability in the area and more investigation will be needed to improve models. The results of the study indicate high BC variability exists on the near-field scale, but that the variability is not clearly explained by existing regression and air dispersion models. To prevent public exposure to harmful concentrations, more investigation will be needed to determine factors that largely influence pollutant variability on the neighborhood scale.

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