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Indiana University-Purdue University Indianapolis (IUPUI) / Rise in air pollution resulting in negative health externalities for humans has created an urgent need for cities and communities to monitor it regularly. At present we have insufficient ground passive and active monitoring networks in place which presents a huge challenge. Satellite imagery has been used extensively for such analysis, but its resolution and methodology present other challenges in estimating pollution burden. The objective of this study was to propose three low-cost methods to fill in the gaps that exist currently. First, EPA grade sensors were used in 11 cities across the U.S. to examine NO2. This is a simplistic way to assess the burden of air pollution in a region. However, this technique cannot be applied to fine scale analysis, which resulted in the next two components of this research study. Second, a citizen science network was established on the east side of Indianapolis, IN who hosted 32 Ogawa passive sensors to examine NO2 and O3 at a finer scale. These low-cost passive sensors, not requiring power, and very little maintenance, have historically tracked very closely with Federal Reference Monitors. Third, a low-cost PurpleAir PA-II-SD active sensors measuring PM2.5 were housed with the citizen scientists identified above. This data was uploaded via Wi-Fi and available via a crowd sourced site established by PurpleAir. These data sets were analyzed to examine the burden of air pollution. The second and third research studies enabled granular analyses utilizing citizen science, tree canopy data, and traffic data, thus accommodating some of the present limitations. Advancement in low-cost sensor technology, along with ease of use and maintenance, presents an opportunity for not just communities, but cities to take charge of some of these analyses to help them examine health equity impacts on their citizens because of air pollution.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第24153号 / 地環博第231号 / 新制||地環||44(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 梶井 克純, 教授 高野 裕久, 准教授 田中 周平 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

<p> Exposure to traffic-related air pollutants has been found to be damaging to human health. Nitrogen dioxide (NO2) levels, commonly used to indicate traffic-related pollution levels, vary significantly over small areas with higher levels found near sources such as major roads and industrial areas. The temporal and spatial variability in NO2 levels is partly caused by fluctuations in meteorological variables, and better understanding of these: meteorological influences can be used to enhance exposure assessment in health effects models.</p> <p> In this study, the interaction between measured hourly NO2 concentrations and climate variables at 11 locations in the Toronto-Hamilton Urban Airshed (THUAS) is examined. Analysis of meteorological data shows that two large urban heat islands (UHI) are present in the THUAS, centred on the downtown areas of Toronto and Hamilton. Lake breezes are found to occur frequently in the region, on up to 50% of summer days at lakeshore locations. These temperature and wind patterns influence NO2 and pollutant distributions. NO2 concentrations are highest in the early morning and late evening. Mean concentrations are highest in winter, although individual 1-hour NO2 concentrations are found to be highest in summer because of higher production rates. Wind direction is the strongest control on hourly NO2 concentration, and temperature and wind speed also have an effect. Seasonal variations in meteorology and emissions mean that the degree of spatial variability in NO2 concentrations changes from season-to-season in the THUAS resulting in variable exposure of urban populations.</p> <p> An attempt to improve an existing Land Use Regression (LUR) model, used for predicting nitrogen dioxide (NO2) concentrations and estimating human exposure, was made by incorporating high resolution interpolated observed up- and downwind effects of wind transport on NO2 concentrations around major roadways. Incorporation of observed wind direction effects in the LUR model slightly improved the accuracy of NO2 concentration estimates in densely populated, high traffic, and industrial/business areas in both Toronto and Hamilton. However the short-term nature of initial NO2 concentration data limits the utility of the model in light of the significant seasonal variation in climate parameters in the THUAS and their influence on NO2 transport and distribution.</p> / Thesis / Master of Science (MSc)

This thesis presents methods to place landing sites for the Urban Air Mobility (UAM) concept. The analysis shows an integrated approach to establish UAM landing site requirements, place landing sites based on predicted demand, and estimate the costs associated with UAM landing sites. This thesis also makes estimates of fares associated with UAM operations. The methods presented are applied to three large urban centers in the United States. The analysis assumes an all-electric, advanced multi-rotor aircraft with autonomous navigational and Vertical Takeoff and Landing (VTOL) capabilities to estimate UAM landing site requirements. The thesis presents the land area requirements of UAM landing sites using Federal Aviation Administration heliport design criteria considering ground-taxi configurations. The analysis performed employs a UAM vehicle with an equivalent Rotor Diameter (RD) of 43 feet. In this thesis, UAM demand is estimated using a mode choice model developed in a companion study (UAM Scenario Analysis). The methodology to locate UAM landing sites includes splitting and consolidation of UAM landing sites considering the Zillow Transaction and Assessment Dataset (ZTRAX) to introduce land-use size and cost constraints. The sites are split using a K-Means clustering method and are consolidated using a simple center of mass approach where the demand of each landing site is analogous to mass. The results presented in this thesis apply to 75 and 200 landing sites in each region and assume passenger Cost-Per-Mile (CPM) of $1.20 and $1.80, respectively. This thesis presents a comparative study on how the availability of land affects the splitting, consolidation, and relocation of UAM landing sites for each region, the number of landing sites, and the cost per passenger-mile. / Master of Science / This thesis aims at the landing sites for the Urban Air Mobility (UAM) concept for commuting passengers in Northern California (17 counties), Southern California (9 counties), and Dallas-Fort Worth (12 counties) region. The aircraft for this service is designed to be an all-electric advanced multi-rotor aircraft with autonomous navigational and Vertical Takeoff and Landing (VTOL) capabilities. The commuting trips considered is focused on passengers traveling to work from home and back. This thesis presents the land area requirements of these landing sites, which are calculated from the Federal Aviation Administration's (FAA) Advisory Circular 150/5390-2C using ground-taxi configuration for a typical representative aircraft of an equivalent rotor diameter (RD) of 43 feet. The landing sites are then split into smaller sites and consolidated into larger sites. This thesis also presents a list of plots of land located within the 0.5 statute-mile boundaries of the landing sites for relocation. This entire analysis is based on the availability of land from the Zillow Transaction and Assessment Dataset (ZTRAX). The results presented in this thesis are for 75 and 200 landing sites set in the study area for a passenger Cost-Per-Mile (CPM) of $1.2 and $1.8, respectively. The results show how the availability of land changes for different CPM for a set of landing sites and affects the splitting, consolidation, and relocation of landing sites for each region. In the end, the thesis presents conclusions and recommendations unique to each region.

Urban Air mobility (UAM) promises reduced congestion on roads, reduced travel times and stronger overall efficiency in densely populated areas. However several challenges arise when wanting to implement UAM such as safety and social acceptance. The aim of this paper is to gain valuable insights how to implement safe and socially accepted UAM into society. Current regulations are discussed as well as X, Y and Z volumes in U-space, flight separations with ellipsoidal safety buffers, high speed corridors, landing separation at vertiports and airspace partition with voronoi diagrams are proposed and discussed. Social acceptance is addressed with some of the most prominent concerns e.g. safety, privacy and noise. Examples are set in Stockholm, Sweden, resulting in a maximum airspace occupation of 1 % which means 210 UAS (Unmanned Aircraft Systems) on each flight level. Sensitive areas and people with privacy concerns should have the option to opt-out of having their properties under the flight paths of UAM-vehicles. Concerns with UAM from the public has to be taken into great consideration for a successful implementation of UAM.

Spatial and temporal variations and factors affecting these variations in concentrations of measured parameters at two different groups of stations in Ankara are investigated in this study. The first group consists of three curbside stations that are under the direct influence of traffic emissions, on the other hand, the other group is composed of eight residential stations in which domestic heating is the main emission source. State of air quality in Ankara is determined through comparison with air quality data generated in other countries and available air quality standards. Based on this comparison, although air quality has improved in recent years, it is still not one of the clean cities around the world. Although measured concentrations of pollutants comply with standards in the Turkish Air Quality Regulation, further reduction in concentrations will be necessary, if EU directives become effective in the country. Relation between measured concentrations and meteorological parameters are also investigated. Wind speed and mixing height are the two parameters that are most closely related to measured concentrations at residential stations. However, at curbside stations concentrations are determined by emissions. Qualitative comparison of two groups of stations emphasizes the contribution of motor vehicle emissions on residential areas. Seasonal and diurnal variations of measured parameters and lower winter-to-summer ratio of SO2 indicate contribution of diesel vehicle emissions to SO2 levels at curbside stations. Contribution of traffic emissions are also observed in terms of well defined bimodal traffic pattern of SO2 and PM-10 at non-curbside stations. Seasonal and diurnal pollutant ratios are investigated to apportion different source types that are effective in each group of station. PM-to-SO2, NO-to-NO2, PM-10-to-NOX and SO2-to-NOX ratios are found to be good tracers for qualitative assessment of source groups, namely traffic and domestic heating. Different statistical methodologies are demonstrated to determine the source regions of pollutants with respect to wind direction. Air quality level of Ankara, instead of air quality level in each station, is determined in terms of daily API. One by one correlation between API and meteorological factors are investigated, maximum wind speed and daily thermic excursion is found to be the highest correlated variables. The relation between API and these variables is analyzed by multiple linear regression method and then air pollution forecast model highly correlated with API and meteorological variables is developed. The assimilative capacity of Ankara is calculated in terms of ventilation coefficient and found to be highest in summer and lowest in winter. In winter poor dispersion conditions favor the poor air quality in the city.

<p>The research effort addressed important gaps in the modeling to simulate Urban Air Mobility (UAM) operations and couple optimization analyses for vehicle design, fleet allocations, and operational choices for next generation urban travel. Urban Air Mobility is expected to be a \$1 trillion dollar industry by 2040, but operators and designers have limited models and tools to estimate fleet performance, cost metrics, emissions performance, and profit for a given concept under future concepts of operations. A review of the literature reveals 14 modeling gaps related to infrastructure, operations, airspace, vehicles, and customers. In addition, the UAM industry requires better understanding of how operational choices may impact vehicle design and fleet allocations in a market with significant economic barriers and infrastructure needs. To address those needs, this effort proposed alternatives to address modeling challenges and develop studies to evaluate UAM vehicle concepts and concepts of operations in ways once not possible using the enhanced modeling tools. The research findings revealed that modeling coupled design/fleet and operational choices can affect daily profitability potential by 2-4\times\, for piloted and autonomous operations and affect the fleet size from between 12-50 vehicles across small, medium, and large metropolitan areas. The modeling capability provided by the improvements in UAM operations simulations and accessing vehicle and fleet metrics enables future studies to address UAM in a holistic manner. The increased capability could benefit the UAM community and inform future operations and concepts of operations in preparation for ubiquitous operations.</p>

<p>Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace re-<br> search. Their rise in popularity spurred the growth of interest in urban air mobility (UAM)<br> across the world. UAM promises the next generation of transportation and logistics to be<br> handled by UASs that operate closer to where people live and work. Therefore safety and<br> security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially<br> challenging in urban canyon environments where traditional radar systems used for air traffic<br> control (ATC) are limited by their line of sight (LOS).<br> This thesis explores the design and preliminary results of a target tracking system for<br> urban canyon environments based on a network of camera nodes. A network of stationary<br> camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems<br> as well as cover considerable urban airspace. A camera node consists of a camera sensor, a<br> beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and<br> an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons,<br> an automatic calibration process of the proposed system is devised to simplify the time-<br> consuming and tedious calibration of a traditional camera network present in motion capture<br> (MoCap) systems. Through edge computing devices, the tracking system combines machine<br> learning techniques and motion detection as hybrid measurement modes for potential targets.<br> Then particle filters are used to estimate target tracks in real-time within the airspace from<br> measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking<br> volume shows an estimation error within 0.5m when tracking multiple targets. Moreover,<br> a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking<br> error within 0.3m on a micro-UAS in real time.</p>

This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and environmental impact of the Urban Air Mobility (UAM) or Advanced Air Mobility (AAM). UAM is a concept aerial transportation mode designed for intracity transport of passengers and cargo utilizing autonomous (or piloted) electric vehicles capable of Vertical Take-Off and Landing (VTOL) from dense and congested areas. While the industry is preparing to introduce this revolutionary mode in urban areas, realizing the scope and understanding the factors affecting the attractiveness of this mode is essential. The success of UAM depends on its operational efficiency and the relative utility it offers to current travelers. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior using revealed preference data and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. Chapter II presents a methodology to estimate commuter demand for UAM operations in the Northern California region. A mode-choice model is calibrated from the commuter mode-choice behavior observed in the survey data. An integrated demand estimation framework is developed utilizing the calibrated mode-choice model to estimate UAM demand and place vertiports. The feasibility of commuter UAM operations in Northern California is further analyzed through a series of sensitivity analyses. This study was published in Transportation Research Part A: Policy and Practice journal. In an effort to analyze the feasibility of UAM operations in different use cases, demand estimation frameworks are developed to estimate UAM demand in the airport access trips segment. Chapter III and Chapter IV focus on developing the UAM Concept of Operations (ConOps) and demand estimation methodology for airport access trips to Dallas-Fort Worth International Airport (DFW)/Dallas Love Field Airport (DAL) and Los Angeles International Airport (LAX), respectively. Both studies utilize the latest available originating passenger survey data to understand arriving passengers' mode-choice behavior at the airport. Mode-choice conditional logit models are calibrated from the survey data, further used to estimate UAM demand. The former study is published in the AIAA Aviation 2021 Conference proceeding, and the latter is published in ICNS 2021 Conference proceedings. UAM vertiport capacity may be a barrier to the scalability of UAM operations. A heavy concentration of UAM demand is observed in specific areas such as Central Business Districts (CBD) during the spatial analysis of estimated UAM demand. However, vertiport size could be limited due to land availability and high infrastructure costs in CBDs. Therefore, operational efficiency is critical for capturing maximum UAM demand with limited vertiport size. The study included in Chapter V focuses on analyzing factors impacting vertiport capacity. A discrete-event simulation model is developed to simulate a full day of commuter operations at the San Francisco Financial District's busiest vertiport. Besides calculating the capacity of different fundamental vertiport designs, sensitivity analyses are carried to understand the impact of several assumptions such as service time at landing pads, service time at parking stall, charging rate, etc. The study explores the importance of pre-positioning UAM vehicles during the time of imbalance between arrival and departure requests. This study is published in ICNS 2021 Conference proceedings. Community annoyance from aviation noise has often been a reason for limiting commercial operations at several major airports globally. Busy airports are located in urban areas with high population densities where noise levels in nearby communities could govern capacity constraints. Commercial aviation noise is only a concern during landing and take-offs. Hence, the impact is limited to communities close to the airport. However, UAM vehicles would be operated at much lower altitudes and have more frequent taking-off and landing operations. Since the UAM operations would mostly be over dense urban spaces, the noise potential is significantly high. Chapter VI includes a study on preliminary estimation of noise levels from commuter UAM operations in Northern California and the Dallas-Fort Worth region. This study is published in the AIAA Aviation 2021 Conference proceedings. The final chapter in this dissertation explores the impact of airspace restrictions on UAM demand potential in New York City. Integration of UAM operations in the current National Airspace System (NAS) has been recognized as critical in developing the UAM ecosystem. Several pieces of urban airspace are currently controlled by Air Traffic Control (ATC), where commercial operation density is high. Even though the initial operations are expected to be controlled by the current ATC, the extent to which UAM operations would be allowed in the controlled spaces is still unclear. As the UAM system matures and the ecosystem evolves, integrating UAM traffic with other airspace management might relax certain airspace restrictions. Relaxation of airspace restrictions could increase the attractiveness of UAM due to a decrease in travel time/cost and relatively more optimal placement of vertiports. Quantifying the impact of different levels of airspace restrictions requires an integrated framework that can capture utility changes for UAM under different operational ConOps. This analysis uses a calibrated mode-choice model, restriction-sensitive vertiport placement methodology, and demand estimation process. This study has been submitted for ICNS 2022 Conference. / Doctor of Philosophy / Urban Air Mobility (UAM) or Advanced Air Mobility (AAM) are concept transportation modes currently in development. It proposes transporting passengers and cargo in urban areas using all-electric Vertical Take-Off and Landing (eVTOL) vehicles. UAM is a multi-modal concept involving low-altitude aerial transport. The high capital costs involved in developing vehicles and infrastructure suggests the need for meticulous planning and strong strategy development in the rolling out of UAM. Moreover, urban travelers are relatively more sensitive to travel time savings and travel time reliability; therefore, the efficiency of UAM is critical for its success. This dissertation comprises multiple studies surrounding demand estimation, feasibility and capacity analysis, and the environmental impact of UAM. To estimate the potential for UAM, we need first to understand the mode-choice making behavior of urban travelers and then estimate the relative utility UAM could possibly offer. The studies presented in this dissertation primarily focus on analyzing urban travelers' current behavior and estimating the potential UAM demand for different trip purposes in multiple U.S. urban areas. The system planners would need to know the individual or combined effect of various parameters in the system, such as cost of UAM, network size of UAM, etc., on UAM potential. Therefore, sensitivity analyses with respect to UAM demand are performed against various framework parameters. Capacity constraints are not initially considered for potential demand estimation. However, like any other transportation mode, UAM could suffer from capacity issues that can cause operational delays. A simulation study is dedicated to model UAM operations at a vertiport and estimating factors affecting vertiport capacity. After observing the demand potential for certain optimistic scenarios, we realized the possibility of a large number of low-flying vehicles, which could cause annoyance and environmental impacts. Therefore, the following study focuses on developing a noise estimation framework from a full-day of UAM operations and estimating a highly annoyed population in the Bay Area and Dallas-Fort Worth Region. In our studies, modeling restricted airspaces (due to commercial operations at large airports) was always a critical part of the analysis. The urban airspaces are already quite congested in some urban areas, and we assumed that UAM would not operate in the restricted airspaces. The last study in this dissertation focuses on quantifying the impact of different levels of airspace restrictions on UAM demand potential in New York. It would help system planners gauge the level of integration required between the UAM and National Airspace System (NAS).