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Browsing by Author "Hankey, Steven C."

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    Application of training data affects success in broad-scale local climate zone mapping
    Xu, Chunxue; Hystad, Perry; Chen, Rui; Van Den Hoek, Jamon; Hutchinson, Rebecca A.; Hankey, Steven C.; Kennedy, Robert (2021-12-01)
    Satellite imagery has been widely used to map urbanization processes. To address the urgent need for urban landscape mapping that goes beyond urban footprint analysis, the local climate zone (LCZ) scheme has been increasingly used to reveal the urban forms and functions important to urban heat islands and micro-climates across the globe. As with most supervised classification strategies, proper application of training data is critical for the success of LCZ classification models. However, the collection and application of LCZ training areas brings with it two challenges that may affect mapping success. First, because digitizing training areas is a timeconsuming task, there is a broad effort in the LCZ mapping community to create a crowdsourced data collection among different experts. However, this strategy likely leads to inconsistencies in labels that could weaken models. Second, the LCZ labeling process typically involves the delineation of large zones from which multiple training samples are drawn, but those samples are likely spatially autocorrelated and lead to overly optimistic estimates of model accuracy. Although both effects - inconsistent labeling and spatial autocorrelation - are theoretically possible, it is unknown whether they substantially affect accuracy. We investigated both issues, specifically asking: (i) how do the discrepancies of LCZ labeling by different experts impact broad-scale LCZ mapping? (ii) to what extent does spatial correlation affect model prediction power? We used two classifiers (Random Forests and ResNets) to map eight metropolitan areas in the US into LCZs, comparing training areas drawn by different or consistent interpreters, and data splitting strategy using rules that allow or reduce spatial autocorrelation. We found large discrepancies among results built from crowdsourced training areas digitized by different experts; improving the consistency of labels can lead to substantial improvements in LCZ classification accuracy. Second, we found that spatial autocorrelation can boost the apparent accuracy of the classifier by 16% to 21%, leading to erroneous interpretation of mapping results. The two effects interplay as well: spatial auto correlation in the raw data can lead to an underestimation of the model's predictive error when modeling with crowdsourced training areas of high inconsistency. Due to the uncertainty in the labeling process and spatial autocorrelation in derived training data, broad-scale LCZ mapping results should be interpreted with caution.
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    Assessing Barriers and Motivators for Use of a Trail for Active Transportation in a College Town
    FitzPatrick, Timothy Michael (Virginia Tech, 2017-05-30)
    A high amount of the US population is not physically active, contributing to rates of heart disease and obesity. One strategy to increase physical activity is to use more active transportation, defined as walking or biking for transit. Besides increasing physical activity levels, active transportation can provide other benefits such as decreased air pollution from cars. College campuses provide opportunities for active transportation as most residences are close to campus. Therefore, we examined reasons for use and barriers to active transportation in students living in a community connected to a large university via a 1.9 mile, paved protected trail. Two pedestrian and bicycle counters were placed to find the number of walkers and bikers on the trail per day and students were recruited to take an online survey. We found that more people used the trail during the weekday compared to the weekend. Students did not receive much support from their friends and family to use the trail. Users of the trail were more likely to believe that using active transportation helps protect the environment while non-users were uncomfortable using a bike. Barriers included the time it took to use the trail and the need to carry items. Both users and non-users indicated that a financial incentive would motivate them to use the trail more. We conclude that non-users may be uncomfortable using a bike and worry about carrying their items for class. Changing university parking fees, providing bike lessons, and placing signs with directions and time to campus may increase active transportation to the university via this trail.
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    Benefits, Burdens, Perceptions, and Planning: Developing a New Environmental Justice Assessment Toolkit for Long Range Transportation Plans
    Homer, Allison Kathleen (Virginia Tech, 2016-10-24)
    This research presents a new environmental justice assessment toolkit, the Equitable Environmental Justice Assessment Toolkit 2016 (EEJAT 2016). The purpose of this toolkit is to enable urban planners to more effectively measure whether environmental justice populations (low-income, non-white, Limited English proficiency, disabled, or elderly persons) are disproportionately burdened by long-range transportation plans. This toolkit is based on the concept that effective assessment of environmental justice (EJ) in transportation planning requires assessment frameworks that methodologically unify three sometimes divergent interests: those of federal and state bodies enforcing EJ assessment requirements, those of metropolitan planners who face capacity constraints and need guidance on how to conduct these assessments, and, most importantly, those of the protected populations themselves. This thesis involved analysis of current requirements, exploration of existing environmental justice assessment tools, case studies, decision theory, and principles of equity, and stakeholder engagement through surveys, interviews, and public meetings, all towards the development of the toolkit designed for the Roanoke Valley Transportation Planning Organization (RVTPO)'s Constrained Long-range Multimodal Transportation Plan 2040 (CLRMTP 2040) released in 2016. The resulting toolkit is a multi-step framework. The first step is a GIS map-based EJ Index, structured by normalized population distributions for each EJ demographic, and mapped by block group compared to regional (MPO) averages. This z-score based mapping was done in lieu of Roanoke's former linear model in effort to more systematically compare effects, and to more accurately represent the data, and by extension, the people. Second, the Community Profile expands upon the EJ Index to include documentation of community elements and social and economic systematic injustices in the area. Next, a Benefits and Burdens matrix guides planners to an appropriate model or method of assessment for each EJ effect for the project at hand, based on project scale and type, data availability, and skillsets of the assessor. The results of these assessments of each EJ effect are compiled for an overall Project Impact Assessment. Checks on assessor bias based on stakeholder feedback and decision theory are incorporated into this Project Impact Assessment. Cumulatively, the toolkit is designed to incorporate equity as a defining element of both processes and outcomes, to be flexible in order to be applicable to multiple projects, and to be usable by practitioners.
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    Combining expert and crowd-sourced training data to map urban form and functions for the continental US
    Demuzere, Matthias; Hankey, Steven C.; Mills, Gerald; Zhang, Wenwen; Lu, Tianjun; Bechtel, Benjamin (2020-08-11)
    Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.
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    Concentrations of criteria pollutants in the contiguous U.S., 1979 – 2015: Role of prediction model parsimony in integrated empirical geographic regression
    Kim, Sun-Young; Bechle, Matthew J.; Hankey, Steven C.; Sheppard, Lianne; Szpiro, Adam A.; Marshall, Julian D. (PLOS, 2020-02-01)
    National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.
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    Derelict to Dynamic: Examining Socioecological Productivity of Underutilized/Abandoned Industrial Infrastructure, and Application in Baltimore, Maryland
    Niland, Joseph Michael (Virginia Tech, 2018-06-25)
    With over 16,500 documented vacant commercial and residential units, roughly 20 miles of abandoned rail lines, a historic loss of approximately 330,000 residents, millions of gallons of annual surface water sewage discharges, and a decade-long failed water quality consent decree - Baltimore, Maryland lies at a crux of chronic challenges plaguing America’s formerly most economically and industrially powerful cities (Open Baltimore GIS [Vacancies Shapefile], 2017; “Harbor Water Alert” Blue Water Baltimore, 2017). Impending environmental threats in the “Anthropocene” (Crutzen, 2004) and increased attention to societal injustices warrant heightened inclusivity of social and natural urban functions. Socioecological inequities are often highly conspicuous in declining post-industrial American cities such as Baltimore. Chronic social, economic, and environmental perturbations have rendered some of once critical American infrastructure outdated, underutilized, and/or abandoned. Rivers, forests, rail corridors, as well as residential and industrial building stock are in significantly less demand than when America’s industrial age shaped urban landscapes in the late nineteenth/early twentieth centuries. Compounded by insensitive traditional urban development, these phenomena jeopardize urban social and ecological function. This thesis is an examination of contemporary urban ecology concepts as a systemic approach for revitalizing socially and ecologically marginalized urban areas, with an application in West Baltimore, Maryland neighborhoods. Through an examination of socioecological dilemmas and root causes, a conceptual procedure for urban blight mitigation along the Gwynns Falls corridor is proposed. Adopting an urban green infrastructure plan offers comprehensive alternative solutions for West Baltimore’s contemporary challenges. Master plans are proposed for the Shipley Hill, Carrollton Scott, and Mill Hill neighborhoods in West Baltimore. Site scale socioecological connections are suggested for the Shipley Hill neighborhood with contextual linkages in the surrounding neighborhoods. Additionally, policy considerations are explored for revitalizing Baltimore’s most vulnerable landscapes. By transforming derelict industrial infrastructure to dynamic socioecological patches and corridors, this work aims to enhance socioecological equity and connectivity. Negative aspects of Baltimore’s contemporary urban condition such as blight, high vacancy rates, ecological damage, population decline, and other symptoms of shrinking cities are deeply rooted in a complex evolution of social, environmental, and economic management. Current challenges facing Baltimore can be directly linked to a long history, specifically including industrialization and systematic segregation of neighborhoods. As the United States entered a period of stability following the industrial revolution, domestic manufacturing dwindled, causing a once strong workforce population to leave industrial mega-cities such as Baltimore. This population exodus left behind prior workforce housing and industrial infrastructure, much of which now nonessential to Baltimore’s contemporary urban functions. Housing vacancies and abandoned infrastructure are most noticeable in Baltimore’s predominately minority neighborhoods. Historically marginalized by systematic segregation tactics, “redlined” neighborhoods largely continue to lack sufficient social and economic capital for adaptation to a transformative new era in Baltimore’s history. Disparities in these minority neighborhoods have shown lasting consequences and continue to suffer from financial, social, and ecological neglect. However, progressive urban planning processes pose significant opportunity for equitable inclusion of historically marginalized urban communities through the introduction of green infrastructure. Because socioecological disparities in Baltimore are incredibly complex, an equally complex solution is necessary to adequately alleviate symptoms of declining cities. Although much research and literature has been cited in systemic solutions aiming to address the totality of these issues, practical implication of these strategies remains limited. This thesis aims to identify primary drivers of socioecological inequity as well as recommend policy and spatial solutions to alleviate symptoms of shrinking cites specific to Baltimore.
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    Emotional Agents: Modeling Travel Satisfaction, Affinity, and Travel Demand  Using a Smartphone Travel Survey
    Le, Huyen Thi Khanh (Virginia Tech, 2019-06-28)
    This dissertation seeks to understand travel satisfaction, travel affinity, and other psychological factors in relation to travel demand, such as the desire for trip making, willingness to spend time traveling, and choice of travel mode. The research was based on the Mood State in Transport Environments survey of 247 Android users (about 6,000 completed trip surveys) in the Blacksburg-Roanoke, VA, Washington, DC, and Minneapolis, MN metropolitan areas from fall 2016 to spring 2018. Respondents answered an entry survey, tracked their travel for 7 days, and answered a trip survey associated with each trip. The dataset provides opportunities to examine travel and activities during travel at the within- and between-person levels. Three studies in this dissertation examined three measures of the positive utility of travel and their relationship with travel behavior. I quantified (1) the desirability of trip making, (2) the ideal travel time related to different travel characteristics, and (3) the effect of satisfaction on commute mode choice. The first study examines the patterns of travel affinity with various travel modes, trip purposes, and activities during the trip. Travel affinity was measured by asking the willingness to forgo a trip when there is an opportunity to do so. I found that this is a valid and strong measure of the positive utility of travel. Travelers were more willing to make trips when they traveled on foot or bicycle, talked with someone during the trip, and took shorter trips. Additionally, commute trips were less likely to be enjoyed as compared to other, non-commute trips. The second study focused on (1) testing the validity of the "ideal travel time" measurement and (2) measuring factors associated with the willingness to spend time traveling. I found that although ideal travel time was a strong measure of the positive utility of travel, it was very weakly associated with the desirability of trip making and satisfaction with trips. Although few people wanted zero commute time (3%), the number of trips that had zero ideal travel time was much higher (16%), indicating that the desired travel amount may vary across different trip and environmental characteristics and purpose. Ideal travel time was longer for active travel trips, leisure trips, when conducting activities during trips (e.g., talking, using the phone, looking at the landscape), when traveling with companions and during the weekend. The third study investigated the role of travel satisfaction and attitude in mode choice behavior. This is one of the very few studies that have considered the role of these psychological factors in multimodal mode choice based on revealed preference data. I found that satisfaction and attitude toward modes and travel played a significant role in the choice model; it also modified the role of travel time in the models. However, the perception of travel time usefulness was insignificant in the model. Scenario analyses based on the model results showed that it is optimal to invest in active transportation and public transit at the same time in order to shift car drivers to these sustainable modes. These studies contribute to the small but growing body of literature on the positive utility of travel and transrational decision making in transportation. It is the only study that employed a smartphone survey with a repeated measure of trips over the course of 1-2 weeks. The third study is among the earliest attempts to include satisfaction and attitude together into mode choice models. This dissertation has several implications for research and practice. First, it calls for better measurements of well-being and satisfaction. Second, models with appropriate psychological factors would more realistically resemble actual travel behavior. Including satisfaction in the choice model changes the coefficient of travel time (and potentially cost), which modifies the value of travel time savings, a basis of most benefit-cost analyses in transportation planning and engineering. Better mode choice and trip generation models will generate more reliable predictions of future infrastructure use and investment. Third, studies of travel affinity (positive utility of travel) have implications for demand modeling and management practice. Practitioners should reevaluate the effectiveness of travel demand management strategies aimed at reducing travel time and trips, such as congestion pricing (e.g., tolls), online shopping, and telecommuting.
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    An Equity Analysis of the U.S. Public Transportation System Based on Job Accessibility
    Jeddi Yeganeh, Armin (Virginia Tech, 2017-05-09)
    Background: Access to quality public transportation is critical for employment, especially for low-income and minority populations. This research contributes to previous work on equity analysis of the U.S. public transportation system by covering the 45 largest Metropolitan Statistical Areas (MSAs) and their counties. Objective: This study analyzes job accessibility of transit commuters in the 45 largest MSAs to assess the existing differences in accessibility between Census-defined socioeconomic status (SES) categories. Method: 2014 Census demographic data were matched to a previously published 2014 dataset of transit job accessibility at the Census Block Group level. Transit equality and justice analyses were performed based on population-weighted mean job accessibility and SES variables. Results: The findings suggest that within individual MSAs, the low-income populations and people of color have the highest transit job accessibility. However, in certain MSAs with high job accessibility, such as New York, Washington, D.C., Chicago, and Houston, there is a significantly disproportionate access to public transportation based on income. Variables such as income, and the use of personal vehicle, are found to have a statistically significant negative impact on job accessibility in almost all MSAs. The percentage of White workers has a significant impact on job accessibility in upper-mid-density MSAs and high-density MSAs. The percentage of the population with limited English speaking ability is not a significant determinant of job accessibility except in lower-mid-density MSAs. Disparities by income are greater than disparities by race. Racial disparities increase by MSA size and density controlling for income. The findings suggest that planning for public transportation should take into account risks, benefits, and other equally important aspects of public transportation such as frequency, connectivity, and quality of service.
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    Green Affordable Housing: Cost-Benefit Analysis for Zoning Incentives
    Jeddi Yeganeh, Armin; McCoy, Andrew P.; Hankey, Steven C. (MDPI, 2019-11-08)
    In the year 2017, about 89% of the total energy consumed in the US was produced using non-renewable energy sources, and about 43% of tenant households were cost burdened. Local governments are in a unique position to facilitate green affordable housing, that could reduce cost burdens, environmental degradation, and environmental injustice. Nonetheless, limited studies have made progress on the costs and benefits of green affordable housing, to guide decision-making, particularly in small communities. This study investigates density bonus options for green affordable housing by analyzing construction costs, transaction prices, and spillover effects of green certifications and affordable housing units. The authors employ pooled cross-sectional construction cost and price data from 422 Low-Income Housing Tax Credit (LIHTC) projects and 11,016 Multiple Listing Service (MLS) transactions in Virginia. Using hedonic regression analyses controlling for mediating factors, the study finds that the new construction of market-rate green certified houses is associated with small upfront costs, but large and statistically significant price premiums. In addition, the construction of market-rate green certified houses has large and statistically significant spillover effects on existing non-certified houses. Existing non-certified affordable housing units show small and often insignificant negative price impacts on the transaction prices of surrounding properties. The study concludes that the magnitude of social benefits associated with green building justifies the local provision of voluntary programs for green affordable housing, where housing is expensive relative to its basic cost of production.
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    Impact of Road Proximity and other Determinants of Air Quality along Multi-Use Trails in the National Capital Region
    Tushar, Md Shazalal (Virginia Tech, 2024-06-04)
    Active travel can provide short-term and long-term health benefits and has the ability to reduce the negative externalities of vehicular traffic, for example, congestion, land consumption, and air pollution. However, exposure to air pollution is higher for pedestrians and cyclists than other road users when considering inhalation rate and travel distance. Route choice for active travel is a potential strategy to reduce the adverse impact of exposure to air pollution. Multi-use trails could be an effective way to reduce health impacts as the pollutant concentration is typically lower on trails, however, proximity to nearby roadways can deteriorate the air quality in multi-use trails. The goal of this study is to investigate the air pollutant concentrations on multi-use trails adjacent to different roadway classification and identify the factors that influence air quality in multi-use trails. I collected pollutant concentrations of PM2.5, particle number, and black carbon using mobile monitoring on an e-bike. I identified five trail routes that run parallel to an interstate highway, principal arterial, and local roads for this study and collected pollutant concentrations during morning, afternoon, and weekend afternoon peak hours. The average concentration of PM2.5, particle number, and black carbon was 15.62 µg/m3, 9,857 pt/cc, and 595.36 ng/m3 respectively among all the trail routes used for this study. I observed higher pollutant concentrations during morning peak hours than afternoon peak hours. Also, concentrations were lower on weekends than weekdays. The pollutant concentrations were different among multi-use trails based on their proximity and characteristics of nearby roadways. The pollutant concentrations significantly declined when the trail segment was 50-100 meters away as compared to segments within 50 meters of nearby interstates, freeways, or collectors. Concentrations increased significantly for trail segments having a nearby road Annual Average Daily Travel (AADT) of more than 32,000. The regression models explain 65%, 59%, and 52% of variability in the PM2.5, particle number, and black carbon concentrations respectively. Nearby road AADT and road density were found to be significant for PM2.5, particle number, and black carbon concentrations. Cooking place (rest areas with barbeque grills) and construction sites were significant and positively associated with PM2.5 concentrations. Airport and construction sites near trails showed a positive relation to the particle number concentration. Parking spaces near trails increase the concentration of black carbon along trails. This study shows the impact of roadway proximity on the air quality of trails which should be considered by municipalities while planning for multi-use trail network to mitigate health risks of pedestrians and bicyclists on trails.
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    Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit data
    Lu, Tianjun; Lansing, Jennifer; Zhang, Wenwen; Bechle, Matthew J.; Hankey, Steven C. (2019-08-10)
    Land Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R-2: 0.56; Root Mean Square Error [RMSE]: 032 mu g/m(3)) as compared to the permit data model (0.42; 037) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data. (C) 2019 The Authors. Published by Elsevier B.V.
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    Leveraging Street View and Remote Sensing Imagery to Enhance Air Quality Modeling through Computer Vision and Machine Learning
    Qi, Meng (Virginia Tech, 2024-02-14)
    Air pollution is associated with various adverse health impacts and is identified as one of the leading risk factors for global disease burden. Further, air pollution is one of the pathways through which climate change could negatively impact health. Field studies have shown that air pollution has high spatiotemporal variability and pollutant concentrations vary substantially within neighborhoods. Characterizing air pollution at a fine-grained level is essential for accurately estimating human exposure, assessing its impact to human health, and further aiding localized air pollution policy. Air quality models are developed to estimate air pollution at locations and time periods without monitors, and these estimates are commonly used for exposure and health effects studies. Traditional land use regression [LUR] models are one of the cost-effective empirical air quality models. LUR typically relies on fixed-site measurements, GIS-derived variables with limited spatial resolution, and captures linear relationships. In recent years, innovative open-source imagery datasets and their associated features (e.g., street view imagery, remote sensing imagery) have emerged and show potential to augment or replace traditional LUR predictors. Such imagery data sources embody abundant information of natural and built environment features. Advanced computer vision techniques enable feature extraction and quantification through these extensive imagery datasets. The overarching objective of this dissertation is to investigate the feasibility of leveraging open-source imagery datasets (i.e., Google Street View [GSV] imagery, Landsat imagery, etc.) and advanced machine learning algorithms to develop image-based empirical air quality models at both local and national scale. The first study of this work established a pipeline of feature extraction through street view imagery sematic segmentation. The resulting street view features were used to predict street-level particulate air pollution for a single city. The results showed that solely using GSV-derived features can achieve comparable model fits as using traditional GIS-derived variables. Feature engineering improved model stability and interpretability through reducing spurious variables from potential misclassifications from computer vision algorithms. The second study further developed GSV-based models at national scale across multiple years. Random forest models were developed to capture the nonlinear relationship between air pollution and its impacting factors. The results showed that with sufficient street view images, GSV imagery alone may explain the variation of long-term national NO2 concentrations. Adding satellite-derived aerosol estimates (i.e., OMI column density) can significantly boost model performance when GSV images are insufficient, but the addition narrows when more GSV images are available. Our systematic assessment of the impact of image availability on model performance suggested that a parsimonious image sampling strategy (i.e., one GSV image per 100m grid) may be sufficient and most cost-effective for model development and application. Our third study explored the feasibility of combining street view and remote sensing derived features for national NO2 and PM2.5 modeling and projection at high spatial resolution. We found that GSV-based models captured both the highest and lowest pollutant concentrations while remote sensing features tended to smooth the air pollution variations. The results suggested that GSV features may have the capability to better capture fine-scale air pollution variability. The resulting air pollution prediction product may serve a variety of applications, including providing new insights into environmental justice and epidemiological studies due to its high spatial resolution (i.e., street level). Collectively, the result of this dissertation suggests that GSV imagery, processed with computer vision techniques, is a promising data source to develop empirical air quality models with high spatial resolution and consistent predictor variables processing protocol. Image-based features assisted with advanced ML approaches have the potential to greatly improve air quality modeling estimates, and successfully show comparable and even superior model performance than other modeling studies. Moreover, the ever-growing public imagery data sources are particularly promising for remote or less developed areas where traditional curated geodatabases are sparse or nonexistent.
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    A Multi-level Analysis of Extreme Heat in Cities
    Kianmehr, Ayda (Virginia Tech, 2023-09-01)
    As a result of climate change and urbanization, rising temperatures are causing increasing concern about extreme heat in cities worldwide. Urban extreme heat like other climate-related phenomena is a complex problem that requires expertise from a range of disciplines and multi-faceted solutions. Therefore, this study aims to develop a comprehensive understanding of urban heat issue by taking a multi-level approach that integrates science, technology, and policy. Throughout the three main papers of this dissertation, a variety of quantitative and qualitative methods, such as microclimate modeling, machine learning, statistical analysis, and policy content analysis, are used to analyze urban heat from different perspectives. The first paper of this dissertation focuses on the street canyon scale, aiming to identify the physical and vegetation parameters that have the greatest impact on changing thermal conditions in urban environments and to understand how these parameters interact with each other. Moving towards identifying applicable heat-related data and measurement techniques, the second paper assesses whether lower-resolution temperature data and novel sources of vulnerability indicators can effectively explain intra-urban heat variations. Lastly, the third paper of this dissertation reviews heat-related plans and policies at the Planning Districts level in Virginia, providing insights into how extreme heat is framed and addressed at the regional and local levels. This analysis is particularly important for states such as Virginia, which historically have not experienced multiple days of extreme heat during summers, as is common in southern and southwestern states of the United States. The results of this study provide insights into the contributing and mitigating factors associated with extreme heat exposure, novel heat-related data and measurement techniques, and the types of analysis and information that should be included in local climate-related plans to better address extreme heat. This dissertation explores new avenues for measuring, understanding, and planning extreme heat in cities, thereby contributing to the advancement of knowledge in this field.
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    New Methods for Measuring Spatial, Temporal and Chemical Distributions of Volatile Organic Compounds
    Hurley, James Franklin (Virginia Tech, 2023-01-20)
    Volatile organic compounds (VOCs) are those chemical species having sufficiently high vapor pressures to exist largely or entirely in the gaseous phase, whereas reactive organic carbon (ROC) encompasses all organics except methane. ROC can be emitted biogenically and anthropogenically, usually in a pure hydrocarbon form that is susceptible to reaction with common atmospheric oxidants such as hydroxyl and ozone in the initial steps to the formation of particulate matter, the criteria pollutant most strongly implicated in human mortality. The diversity of both the emitted VOCs and their possible atmospheric reactions yields countless different compounds existing in the atmosphere with a correspondingly wide range of volatility, solubility, reactivity, etc.. Moreover, the temporal and spatial variability of a given analyte is often large. Real-time chemical characterization of gaseous and particulate organic compounds can be achieved by instrumentation utilizing chromatographic and/or mass spectrometric techniques, but these methods are expensive, often logistically challenging, and require high levels of skills for both operation and data analysis. Conversely, filter-based measurements for organic particulates are inexpensive and straightforward, but do not give real-time data and analytes may be lost or transformed before analysis. There is a niche for robust, low-maintenance, moderate-cost instrumentation that offers chemical information on atmospheric carbon. Presented here are two projects that develop and validate instrumentation for measuring ROC. The first combines flame ionization detection (FID) with a CO2 detector to estimate the O/C ratios of sampled gases and particulates. O/C ratios are a particularly valuable piece of chemical information as higher ratios give lower volatility and higher solubility, meaning increased propensity to partition into the condensed phase. The second project utilizes portable VOC samplers with sorbent tubes that trap and protect analytes for detailed analysis. The samplers' portability and programmable microcontrollers offers the investigator great flexibility, both spatially and temporally. A third project analyzed the chemical composition of commercially available fragrance mixtures and modeled their emissions' impact on oxidant reactivity. It was observed that terpenes, despite their low mole fractions in the mixtures, represent the vast majority of emitted reactivity and are quantitatively evolved from the mixtures in a matter of hours.
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    New Opportunities in Crowd-Sourced Monitoring and Non-government Data Mining for Developing Urban Air Quality Models in the US
    Lu, Tianjun (Virginia Tech, 2020-05-15)
    Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR development, and (3) new/open data extracted from non-government sponsored platforms could serve as alternative datasets to traditional predictor variable sources (e.g., land use and geographic features) in LUR models. In Chapter 3, I developed LUR models using community-based sampling (n = 50) for 60 volatile organic compounds (VOC) in the city of Minneapolis, US. I assessed whether adding area source-related features improves LUR model performance and compared model performance using variables featuring area sources from government vs. non-government sponsored platforms. I developed three sets of models: (1) base-case models with land use and transportation variables, (2) base-case models adding area source variables from local business permit data (government sponsored platform), and (3) base-case models adding Google point of interest (POI) data for area sources. Models with Google POI data performed the best; for example, the total VOC (TVOC) model had better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). This work suggests that VOC LUR models can be developed using community-based samples and adding Google POI could improve model performance as compared to using local business permit data. In Chapter 4, I evaluated a national LUR model using annual average PM2.5 concentrations from low-cost sensors (i.e., PurpleAir platform) in 6 US urban areas (n = 149) and tested the feasibility of using low-cost sensor data for developing LUR models. I compared LUR models using only the PurpleAir sensors vs. hybrid LUR models (combining both the EPA regulatory monitors and the PurpleAir sensors). I found that the low-cost sensor network could serve as a promising alternative to fill the gaps of existing regulatory networks. For example, the national regulatory monitor-based LUR (i.e., CACES LUR developed as part of the Center for Air, Climate, and Energy Solutions) may fail to capture locations with high PM2.5 concentrations and the within-city spatial variability. Developing LUR models using the PurpleAir sensors was reasonable (PurpleAir sensors only: 10-fold CV R2 = 0.66, MAE = 2.01 µg/m3; PurpleAir and regulatory monitors: R2 = 0.85, MAE = 1.02 µg/m3). I also observed that incorporating PurpleAir sensor data into LUR models could help capture within-city variability and merit further investigation on areas of disagreement with the regulatory monitors. This work suggests that the use of crowd-sourced low-cost sensor networks for LUR models could potentially help exposure assessment and inform environmental and health policies, particularly for places (e.g., developing countries) where regulatory monitoring network is limited. In Chapter 5, I developed national LUR models to predict annual average concentrations of 6 criteria pollutants (NO2, PM2.5, O3, CO, SO2 and PM10) in the US to compare models using new data (Google POI, Google Street View [GSV] and Local Climate Zone [LCZ]) vs. traditional geographic variables (e.g., road lengths, area of built land) based on different modeling approaches (partial least square [PLS], stepwise regression and machine learning [ML] with and without Kriging effect). Model performance was similar for both variable scenarios (e.g., random 10-fold CV R2 of ML-kriging models for NO2, new vs. traditional: 0.89 vs. 0.91); whereas adding the new variables to the traditional LUR models didn't necessarily improve model performance. Models with kriging effect outperformed those without (e.g., CV R2 for PM2.5 using the new variables, ML-kriging vs. ML: 0.83 vs. 0.67). The importance of the new variables to LUR models highlights the potential of substituting traditional variables, thus enabling LUR models for areas with limited or no data (e.g., developing countries) and across cities. The dissertation presents the integration of new/open data from non-government sponsored platform and crowd-sourced low-cost sensor networks in LUR models based on different modeling approaches for predicting ambient air pollution. The analyses provide evidence that using new data sources of both air quality and predictor variables could serve as promising strategies to improve LUR models for tracking exposures more accurately. The results could inform environment scientists, health policy makers, as well as urban planners interested in promoting healthy communities.
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    Pathways to Sustainable Housing
    Jeddi Yeganeh, Armin (Virginia Tech, 2021-04-19)
    The world is observing unprecedented, devastating, yet growing effects of climate change. GDP has been slow for decades; Covid-19 has disturbed the economy; energy prices are rising; unemployment remains high; consumer debt and budget deficit are climbing; wealth inequality is at an all-time high. Still, 89% of the energy consumed in the United States comes from non-renewable sources. Amid this challenging time, the question this work tries to answer is how can we protect our climate and environment through innovative development policies and practices that concurrently promote social equity and preserve economic viability? To answer this question, I explore five sustainable housing goals: climate protection, policy innovation, environmental protection, social equity, and economic growth. I use data and empirical analysis to show sustainable development challenges and conflicts are significant. I share lessons learned from cities and states that act as pioneers of climate and environmental protection; I explore a balanced integration of economic, environmental, and social goals through zero-energy building in the traditionally siloed policy sector of low-income housing; I show that a lack of consideration for social equity can turn environmental initiatives into luxury goods that surrender equity to profitability; I show that a lack of consideration for economic viability can lead to underinvestment in environmental and social equity initiatives. The overall insights derived from this study suggest that state housing agencies and local governments, particularly in large cities and in communities that are more vulnerable to environmental risks, are in a unique position to stimulate and drive climate and environmental protection. Significant between-agency differences in housing policy innovation persist, and future policy innovation research should explore factors that impact the utility of policy innovation and barriers the environmental sustainability movement faces at the organization level and beyond. Existing challenges to distributed energy generation need further study. This research highlights the need for greater policy attention to affordable housing needs in core urban areas, neighborhood diversity, and costs of gentrification.
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    Planning for walking and cycling in an autonomous-vehicle future
    Botello, Bryan; Buehler, Ralph; Hankey, Steven C.; Mondschein, Andrew; Jiang, Zhiqiu (Elsevier, 2019-05-30)
    Over the last few decades, walking and cycling have increased in the United States, especially in large cities. Future efforts to promote active travel will occur during a time when automated vehicles will increasingly perform driving tasks without human input. Little is known about impacts of an automated vehicle fleet on pedestrians and cyclists. This study uses semi-structured interviews with experts from academia as well as the public and private sectors in the United States to (1) explore potential synergies and conflicts between increasingly automated motorized vehicles and active travel; and (2) highlight planning and policy priorities for active travel in a time of emerging connected and automated vehicles (C/AVs). Our interviews indicate that while C/AVs promise to make roadways safer for motorists, cyclists, and pedestrians, some potential hazards exist related to communication, behavior, and technical capabilities in the near term. In the long-term, C/AVs may have drastic impacts on infrastructure, the built environment, and land use, but these impacts are likely to vary by locality. Federal and state governments will play a role in ensuring that connected and automated vehicles operate safely, but local governments will ultimately determine how automated vehicles are integrated into the transportation network.
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    Population-Level Exposure to Particulate Air Pollution during Active Travel: Planning for Low-Exposure, Health-Promoting Cities
    Hankey, Steven C.; Lindsey, Greg; Marshall, Julian D. (Environmental Health Perspectives, 2016-10-07)
    Background: Providing infrastructure and land uses to encourage active travel (i.e., bicycling and walking) are promising strategies for designing health-promoting cities. Population-level exposure to air pollution during active travel is understudied. Objectives: Our goals were a) to investigate population-level patterns in exposure during active travel, based on spatial estimates of bicycle traffic, pedestrian traffic, and particulate concentrations; and b) to assess how those exposure patterns are associated with the built environment. Methods: We employed facility–demand models (active travel) and land use regression models (particulate concentrations) to estimate block-level (n = 13,604) exposure during rush-hour (1600–1800 hours) in Minneapolis, Minnesota. We used the model-derived estimates to identify land use patterns and characteristics of the street network that are health promoting. We also assessed how exposure is correlated with indicators of health disparities (e.g., household income, proportion of nonwhite residents). Our work uses population-level rates of active travel (i.e., traffic flows) rather than the probability of walking or biking (i.e., “walkability” or “bikeability”) to assess exposure. Results: Active travel often occurs on high-traffic streets or near activity centers where particulate concentrations are highest (i.e., 20–42% of active travel occurs on blocks with high population-level exposure). Only 2–3% of blocks (3–8% of total active travel) are “sweet spots” (i.e., high active travel, low particulate concentrations); sweet spots are located a) near but slightly removed from the city-center or b) on off-street trails. We identified 1,721 blocks (~ 20% of local roads) where shifting active travel from high-traffic roads to adjacent low-traffic roads would reduce exposure by ~ 15%. Active travel is correlated with population density, land use mix, open space, and retail area; particulate concentrations were mostly unchanged with land use. Conclusions: Public health officials and urban planners may use our findings to promote healthy transportation choices. When designing health-promoting cities, benefits (physical activity) as well as hazards (air pollution) should be evaluated.
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    Prototyping as a User-Centered and Risk Reduction Approach to the Planning, Design, and Construction of More Sustainable Infrastructure
    Guerra Moscoso, Miguel Andres (Virginia Tech, 2019-07-03)
    Designing for sustainability is a complex process that requires to reduce the perceived risk of designing out of the traditional method, to prioritize the end-users' needs and preferences in the design, while considering the product-service dual-nature of infrastructure systems. To address such complexities, this research looks into prototyping from design thinking. Prototyping is a feedback mechanism that enriches the design process by emphasizing user experience and removing designers' fear of failure. This critical step is often absent during the design of physical infrastructure (e.g., transportation systems, water systems), in part, because of the size and complexity of these socio-technical systems. This research aims to understand how civil engineers can adopt prototyping design for large-scale and complex urban infrastructure systems and how prototyping influence design cognition among infrastructure stakeholder groups. To measure the effect of physical prototypes on users and designers, the researchers conducted nineteen interviews with community members, engineers, planners, and city officials in two prototyped projects: a road network in Macon, Georgia and a re-designed city block in Akron, Ohio. The researchers coded the interviews for evidence of how prototyping enhanced citizen engagement and how the design team was willing to adopt unconventional designs after prototyping. Improved understanding of prototyping as a design methodology for infrastructure can lead to more user-centered and innovative solutions. This research provides tools to manage design decisions in engineering and urban planning better, and new approaches for urban infrastructure problem-solving. Future research can compare how this process may inform design if immersive virtual experiences are used to prototype.
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    Racial-ethnic exposure disparities to airborne ultrafine particles in the United States
    Saha, Provat K.; Presto, Albert A.; Hankey, Steven C.; Marshall, Julian D.; Robinson, Allen L. (IOP Publishing, 2022-10)
    Ultrafine particles ('UFP'; <100 nm in diameter) are a subset of fine particulate matter (PM2.5); they have different sources and spatial patterns. Toxicological studies suggest UFP may be more toxic per mass than PM2.5. Racial-ethnic exposure disparities for PM2.5 are well documented; national exposure disparities for UFP remain unexplored due to a lack of national exposure estimates. Here, we combine high-spatial-resolution (census block level) national-scale estimates of long-term, ambient particle number concentrations (PNC; a measure of UFP) with publicly available demographic data (census block-group level) to investigate exposure disparities by race-ethnicity and income across the continental United States. PNC exposure for racial-ethnic minorities (Asian, Black, Hispanic) is 35% higher than the overall national mean. The magnitudes of exposure disparities vary spatially. Disparities are generally larger in densely populated metropolitan areas. The magnitudes of disparities are much larger for PNC than for PM2.5; PM2.5 exposure for racial-ethnic minorities is 9% higher than the overall national mean. Our analysis shows that PNC exposure disparities cannot be explained by differences in income. Whites of all incomes, including low-income Whites, have substantially lower average PNC exposures than people of color of all incomes. A higher proportion of traffic and other PNC sources are located near many minority communities. This means that the exposure disparities are structural and strongly tied to where certain subsets of the population live and that simply reducing PNC emissions nationwide will not reduce these disparities.