Browsing by Author "Lim, Theodore C."

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    Assessing variability and uncertainty in green infrastructure planning using a high-resolution surface-subsurface hydrological model and site-monitored flow data
    Lim, Theodore C.; Welty, Claire (Frontiers, 2018-11-26)
    Green infrastructure (GI) is increasingly being used in urban areas to supplement the function of conventional drainage infrastructure. GI relies on the “natural” hydrological processes of infiltration and evapotranspiration to treat surface runoff close to where it is generated, alleviating loading on the conventional infrastructure systems. This research addresses growing interest in identification and quantification of uncertainties with distributed, infiltration-based stormwater control measures, retrofitted on private and public properties and in right-of-ways in existing urban areas. We identify four major sources of variability and uncertainty in cumulative performance of systems that rely on extensive implementation of distributed GI: non-additive effects of individual best management practices (BMPs) at the catchment scale; the spatial configuration of fine-scale land use and land cover changes; performance changes due to climate change; and noise levels present in urban flow monitoring programs. Using a three-dimensional coupled surface-subsurface hydrological model of a residential sewershed in Washington DC, we find that prolonged, large-magnitude rain events affect various spatial configurations of GI networks differently. Runoff peaks and volumes can both be influenced by the spatial permutations of infiltration opportunities in addition to the absolute magnitude of treated area. However, the magnitude of the last source of uncertainty—noise levels in urban flow monitoring programs—may be larger than sources of variability associated with spatial changes in fine-scale land use and land cover. Changes associated with climate change– more frequent and larger rainfall events– will likely intensify performance differences between spatial configurations of GI but also increase noise levels in urban flow monitoring programs.
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    Community-engaged heat resilience planning: Lessons from a youth smart city STEM program
    Lim, Theodore C.; Wilson, Bev; Grohs, Jacob R.; Pingel, Thomas (Elsevier, 2022-10-01)
    While recognition of the dangers of extreme heat in cities continues to grow, heat resilience remains a relatively new area of urban planning. One barrier to the creation and successful implementation of neighborhood-scale heat resilience plans has been a lack of reliable strategies for resident engagement. In this research, the authors designed a two-week summer STEM module for youth ages 12 to 14 in Roanoke, Virginia in the Southeastern United States. Participants collected and analyzed temperature and thermal comfort data of varying types, including from infrared thermal cameras and point sensors, handheld weather sensors, drones, and satellites, vehicle traverses, and student peer interviews. Based on primary data gathered during the program, we offer insights that may assist planners seeking to engage residents in neighborhood-scale heat resilience planning efforts. These lessons include recognizing: (1) the problem of heat in neighborhoods and the social justice aspects of heat distribution may not be immediately apparent to residents; (2) a need to shift perceived responsibility of heat exposure from the personal and home-based to include the social and landscape-based; (3) the inextricability of solutions for thermal comfort from general issues of safety and comfort in neighborhoods; and (4) that smart city technologies and high resolution data are helpful “hooks” to engagement, but may be insufficient for shifting perception of heat as something that can be mitigated through decisions about the built environment.
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    Comparison of different spatial temperature data sources and resolutions for use in understanding intra-urban heat variation
    Kianmehr, Ayda; Lim, Theodore C.; Li, Xiaojiang (Elsevier, 2023-09)
    In this study, we investigate the compatibility of specific vulnerability indicators and heat exposure data and the suitability of spatial temperature-related data at a range of resolutions, to represent spatial temperature variations within cities using data from Atlanta, Georgia. For this purpose, we include various types of known and theoretically based vulnerability indicators such as specific street-level landscape features and urban form metrics, population-based and zone-based variables as predictors, and different measures of temperature, including air temperature (as vector-based data), land surface temperature (at resolution ranges from 30 m to 305 m), and mean radiant temperature (at resolution ranges from 1 m to 39 m) as dependent variables. Using regression analysis, we examine how different sets of predictors and spatial resolutions can explain spatial heat variation. Our findings suggest that the lower resolution of land surface temperature data, up to 152 m, and mean radiant temperature data, up to 15 m, may still satisfactorily represent spatial urban temperature variation caused by landscape elements. The results of this study have important implications for heat-related policies and planning by providing insights into the appropriate sets of data and relevant resolution of temperature measurements for representing spatial urban heat variations.
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    Comparison of machine learning algorithms for emulation of a gridded hydrological model given spatially explicit inputs
    Lim, Theodore C.; Wang, Kaidi (Elsevier, 2022-02-01)
    This study compares the performance of several machine learning algorithms in reproducing the spatial and temporal outputs of the process-based, hydrological model, ParFlow.CLM. Emulators or surrogate models are often used to reduce complexity and simulation times of complex models, and have typically been applied to evaluate parameter sensitivity or for model parameter tuning, without explicit treatment of variation resulting from spatially explicit inputs to the model. Here we present a case study in which we evaluate candidate machine learning algorithms for suitability emulating model outputs given spatially explicit inputs. We find that among random forest, gaussian process, k-nearest neighbors, and deep neural networks, the random forest algorithm performs the best on small training sets, is not as sensitive to hyperparameters chosen for the machine learning model, and can be trained quickly. Although deep neural networks were hypothesized to be able to better capture the potential nonlinear interactions in ParFlow.CLM, they also required more training data and much more refined tuning of hyperparameters to achieve the potential benefits of the algorithm.
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    A Coupled Hydrologic-Economic Modeling Framework for Evaluating Alternative Options for Reducing Watershed Impacts in Response to Future Development Patterns
    Amaya, Maria Teresa (Virginia Tech, 2022-04-28)
    Economic input-output (I-O) and watershed models provide useful results but when seeking to integrate these systems, the structural, spatial, and temporal differences between these models must be carefully considered. To reconcile these differences, a hydrologic-economic modeling framework is designed to couple an economic model with a watershed model. A physically constrained, I-O model, RCOT, is used to represent the economic system in this framework because it provides sectoral detail for a regional economy and calculates physical resource quantities used by these sectors. Uniquely, it also allows for technology options for all sectors and minimizes the resource use based on environmental constraints imposed by the watershed, which adds complexity to the representation of the economic system and its interactions with the watershed system. To represent the watershed system in this framework, the Hydrological Simulation Program-Fortran (HSPF) is used. An HSPF model has been calibrated to represent the hydrological processes of Cedar Run Watershed by the Occoquan Watershed Monitoring Laboratory (OWML). Thus, the capabilities of this framework are demonstrated using strategic scenarios developed to examine future development patterns that may occur within Fauquier County, northern Virginia, and its local basin, Cedar Run Watershed. The scenarios evaluate both the downstream and seasonal impacts on water flow and nitrogen concentration within the watershed, and the changes made within the economic system in response to these impacts. For these scenarios, the most efficient solution is the one that minimizes the use of resource inputs within the economic sectors, including developed land, water withdrawn, and applied nitrogen, which in turn inform watershed health. The scenario results demonstrate that this coupled hydrologic-economic modeling framework can overcome the spatial differences of the individual models and can capture the interactions between watershed and economic systems at a temporal resolution that expands the types of questions one can address beyond those that can be analyzed using these models separately.
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    The Dynamics of the Impacts of Automated Vehicles: Urban Form, Mode Choice, and Energy Demand Distribution
    Wang, Kaidi (Virginia Tech, 2021-08-24)
    The commercial deployment of automated vehicles (AVs) is around the corner. With the development of automation technology, automobile and IT companies have started to test automated vehicles. Waymo, an automated driving technology development company, has recently opened the self-driving service to the public. The advancement in this emerging mobility option also drives transportation reasearchers and urban planners to conduct automated vehicle-related research, especially to gain insights on the impact of automated vehicles (AVs) in order to inform policymaking. However, the variation with urban form, the heterogeneity of mode choice, and the impacts at disaggregated levels lead to the dynamics of the impacts of AVs, which not comprehensively understood yet. Therefore, this dissertation extends existing knowledge base by understanding the dynamics of the impacts from three perspectives: (1) examining the role of urban form in the performance of SAV systems; (2) exploring the heterogeneity of AV mode choices across regions; and (3) investigating the distribution of energy consumption in the era of AVs. To examine the first aspect, Shared AV (SAV) systems are simulated for 286 cities and the simulation outcomes are regressed on urban form variables that measure density, diversity, and design. It is suggested that the compact development, a multi-core city pattern, high level of diversity, as well as more pedestrian-oriented networks can promote the performance of SAVs measured using service efficiency, trip pooling success rate, and extra VMT generation. The AV mode choice behaviors of private conventional vehicle (PCV) users in Seattle and Knasas City metropolitan areas are examined using an interpretable machine learning framework based on an AV mode choice survey. It is suggested that attitudes and trip and mode-specific attributes are the most predictive. Positive attitudes can promote the adoption of PAVs. Longer PAV in-vehicle time encourages the residents to keep the PCVs. Longer walking distance promotes the usage of SAVs. In addition, the effects of in-vehicle time and walking distance vary across the two examined regions due to distinct urban form, transportation infrustructure and cultural backgrounds. Kansas City residents can tolerate shorter walking distance before switching to SAV choices due to the car-oriented environment while Seattle residents are more sensitive to in-vehicle travel time because of the local congestion levels. The final part of the dissertation examines the demand for energy of AVs at disaggregated levels incorporating heterogeneity of AV mode choices. A three-step framework is employed including the prediction of mode choice, the determination of vehicle trajectories, and the estimation of the demand for energy. It is suggested that the AV scenario can generate -0.36% to 2.91% extra emissions and consume 2.9% more energy if gasoline is used. The revealed distribution of traffic volume suggests that the demand for charging is concentrated around the downtown areas and on highways if AVs consume electricity. In summary, the dissertation demonstrates that there is a dynamics with regard to the impacts and performance of AVs across regions due to various urban form, infrastructure and cultural environment, and the spatial heterogeneity within cities.
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    Effects of spatial configuration of imperviousness and green infrastructure networks on hydrologic response in a residential sewershed
    Lim, Theodore C.; Welty, Claire (American Geophysical Union, 2017-09-01)
    Green infrastructure (GI) is an approach to stormwater management that promotes natural processes of infiltration and evapotranspiration, reducing surface runoff to conventional stormwater drainage infrastructure. As more urban areas incorporate GI into their stormwater management plans, greater understanding is needed on the effects of spatial configuration of GI networks on hydrological performance, especially in the context of potential subsurface and lateral interactions between distributed facilities. In this research, we apply a three-dimensional, coupled surface-subsurface, land-atmosphere model, ParFlow.CLM, to a residential urban sewershed in Washington DC that was retrofitted with a network of GI installations between 2009 and 2015. The model was used to test nine additional GI and imperviousness spatial network configurations for the site and was compared with monitored pipe-flow data. Results from the simulations show that GI located in higher flow-accumulation areas of the site intercepted more surface runoff, even during wetter and multiday events. However, a comparison of the differences between scenarios and levels of variation and noise in monitored data suggests that the differences would only be detectable between the most and least optimal GI/imperviousness configurations.
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    An empirical study of spatial-temporal growth patterns of a voluntary residential green infrastructure program
    Lim, Theodore C. (Routledge, 2018-01-01)
    Voluntary residential Green Infrastructure (GI) stormwater management retrofit programs can help cities comply with environmental regulations while also improving quality of life. Previous research has identified influential factors in residents’ willingness to adopt GI, but few have simultaneously studied the spatial and temporal dynamics of GI. I use a six-year record of participation in a voluntary residential GI program in Washington DC to explore how neighborhood characteristics and social influence affect GI adoption over time. Statistical regression and Monte Carlo permutation resampling techniques are used to explain the spatial-temporal patterns of growth of the program. I demonstrate empirical evidence that participation location is increasingly determined by the locations of previous participants. These findings suggest that past participants will increasingly influence spatial clustering of GI in the city.
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    Examining privilege and power in US urban parks and open space during the double crises of antiblack racism and COVID-19
    Hoover, Fushcia-Ann; Lim, Theodore C. (Springer, 2020-11-24)
    In this perspective, we argue that creating the positive outcomes socio-ecological researchers and practitioners seek for urban areas requires acknowledging and addressing the interactions of race and systemic racism in parks, open and green spaces. Racial experiences are inseparable from physical landscapes and the processes of designing, managing, or studying them. From COVID-19 to the Black Lives Matter movement and protests, the events of 2020 in the United States underscore how considerations of social justice must extend beyond the conventional distributional focus of environmental justice. It must incorporate an understanding of how the built environment is racialized spatially, but not always readily quantified through the proximity-based measurements frequently used in research and practice. The perspective is organized in three main parts. The first part presents a series of vignettes to frame the ways cities and individuals participate, respond, and interact under COVID-19 with racial segregation as the backdrop. The second part suggests a stepwise approach to building an understanding of racial inequities in socio-ecological systems (SES) research and practice including four entry points: (1) racialized spatial distribution of hazards and amenities, (2) racialized qualities of space, (3) racialized people in space, and (4) racialized creation of space. Finally, the third part proposes actions the SES community can take to enhance our commitment to community recovery, improvement, and thrivability. This perspective cautions practitioners and researchers against opportunistic or quick-fix solutions, and instead challenges our colleagues to be inclusive of disenfranchised voices in shaping socio-ecological goals, now more than ever. The goal of this perspective is to spark engagement with power and privilege in parks and open space using the example of COVID-19 and race in the US.
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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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    Interdisciplinary inquiry and spatial green stormwater infrastructure research
    Huang, Lechuan; Lim, Theodore C.; Misra, Shalini (MDPI, 2022-01-21)
    The use of vegetation and infiltration into soils to manage stormwater and water quality— called green stormwater infrastructure (GSI)—is now widely recognized as a viable alternative or supplement to the pipes and pumps of conventional, or “gray”, drainage infrastructure. Over the years, much research has emerged regarding spatial aspects of GSI implemented at large scales, including where it is located, where it should be located, and what metrics best represent the benefits it brings to different locations. Research in these areas involves expertise from multiple academic disciplines, but it is unclear whether and how researchers from different disciplines identify and approach questions related to the spatiality of GSI. By adopting the explanatory sequential mixed method design, we identified four categories of spatial GSI studies through a literature review of over 120 research papers: empirical, ecological, decision support systems, and optimization. Here, we present representative examples of these categories of spatial GSI studies, as well as associations between the academic disciplines represented in these categories of spatial GSI papers. Then, we conducted semi-structured interviews with a sample of GSI researchers which revealed the value of interdisciplinary training and knowledge. Finally, in this paper, we identify several gaps that could be addressed to improve interdisciplinary research on GSI implementation, and sustainability transitions in general.
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    Land, Water, Infrastructure And People: Considerations Of Planning For Distributed Stormwater Management Systems
    Lim, Theodore C. (University of Pennsylvania, 2017)
    When urbanization occurs, the removal of vegetation, compaction of soil and construction of impervious surfaces—roofs, asphalt, and concrete—and drainage infrastructure result in drastic changes to the natural hydrological cycle. Stormwater runoff occurs when rain does not infiltrate into soil. Instead it ponds at the surface and forms shallow channels of overland flow. The result is increased peak flows and pollutant loads, eroded streambanks, and decreased biodiversity in aquatic habitat. In urban areas, runoff is typically directed into catch basins and underground pipe systems to prevent flooding, however such systems are also failing to meet modern environmental goals. Green infrastructure is the widely evocative idea that development practices and stormwater management infrastructure can do better to mimic the natural hydrological conditions through distributed vegetation and source control measures that prevent runoff from being produced in the first place. This dissertation uses statistics and high-resolution, coupled surfacesubsurface hydrologic simulation (ParFlow.CLM) to examine three understudied aspects of green infrastructure planning. First, I examine how development characteristics affect the runoff response in urban catchments. I find that instead of focusing on site imperviousness, planners should aim to preserve the ecosystem functions of infiltration and evapotranspiration that are lost even with low density development. Second, I look at how the spatial configuration of green infrastructure at the neighborhood scale affects runoff generation. While spatial configuration of green infrastructure does result in statistically significant differences in performance, such differences are not likely to be detectable above noise levels present in empirical monitoring data. In this study, there was no evidence of reduced hydrological effectiveness for green infrastructure located at sag points in the topography. Lastly, using six years of empirical data from a voluntary residential green infrastructure program, I show how the spread of green infrastructure depends on the demographic and physical characteristics of neighborhoods as well as spatially-dependent social processes (such as the spread of information). This dissertation advances the science of green infrastructure planning at multiple scales and in multiple sectors to improve the practice of urban water resource management and sustainable development.
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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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    Linking GIS, youth environmental literacy, and city government functions to define and catalyze community heat resilience planning in Roanoke, VA
    Dillon, Maxwell Stewart (Virginia Tech, 2022-06-10)
    Statistics show that chronic heat exposure and extreme heat waves are the leading cause of death amongst natural disasters in urban spaces across the United States, outpacing the likes of more notable phenomena such as hurricanes, tornadoes, and earthquakes. Heat in urban spaces is not distributed equally due to the urban heat island effect, a phenomenon which significantly elevates temperatures due to the various absorption characteristics of built environment features. Historical discriminatory mortgage lending schemes and planning practices that targeted communities of color have intensified that issue, endangering the health and well-being of marginalized neighborhoods to this day. Although generating feasible design solutions to mitigate the impact of heat in urban spaces represents a substantial challenge, utilizing readily available data sources to garner the social and political support required for actionable change is likely the more complex issue. Because youth are typically less jaded by external social and political influences and will either enjoy the benefits or suffer the consequences related to the built environment for their entire adult life, they possess a unique potential to serve as a vehicle for generating community momentum for the implementation of heat resilience solutions. This thesis explores the spatial distribution of heat throughout neighborhoods in Roanoke, Virginia by exploring both land surface temperature and air temperature discrepancies by Home Owners' Loan Corporation (HOLC) classification and census tract. I find that HOLC polygons not labeled "A" possess a considerably higher average temperature than the most "desirable" classification, and that there is a statistically significant inverse relationship between mean land surface temperature (aggregation of Landsat raster files) and census tract socio demographic characteristics such as median household income and percentage of residents aged 65 and over. This thesis also examines the potential of youth-focused science education programs to catalyze the political will necessary to enact resilience planning efforts that no single governmental agency is responsible for. I analyzed the various impacts that artifacts produced by a 2021 science education program conducted with Roanoke City middle school students inflicted on a 2022 focus group comprised of influential Roanoke public officials. I show the reasoning which supports that four primary opportunity and challenge categories – Breaking Down Silos, Spreading Awareness, Places and Venues, and Resources and Funding – can serve as foundational discussion components for heat resilience planning panels in the future. This thesis advances the awareness of disproportionate exposure to heat in urban spaces and contributes to theories attempting to trigger heat resilience planning efforts.
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    Model emulators and complexity management at the environmental science-action interface
    Lim, Theodore C. (Elsevier, 2021-01-01)
    As our understanding of the interactions present in socio-ecological systems advance, emulation modeling can help reduce the complexity and required computational resources of the models used to represent these systems. While emulation is commonly used in model meta-analyses and parameterization, it has been less explored in the context of environmental management. In this research, I analyze the reflections of a group of watershed modelers on environmental model emulation. I find that decreased simulation run-times are an important motivation because emulators enable stakeholders to interact directly with the model. However, participants also reported that criteria for an emulator in an environmental management context should also assess its capability to act as a platform for learning and to manage stakeholder perceptions of the modeling process. Further, at the science-action interface, stakeholder perceptions play a significant role in the approach to model emulation through determining acceptable levels complexity in model processes and inputs.
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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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    Patterns in environmental priorities revealed through government open data portals
    Lim, Theodore C. (Elsevier, 2021-11-01)
    The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance–the proliferation of government open data portals (ODPs)–has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.
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    Predictors of urban variable source area: a cross-sectional analysis of urbanized catchments in the United States
    Lim, Theodore C. (Wiley, 2016-12-15)
    Many studies have empirically confirmed the relationship between urbanization and changes to the hydrologic cycle and degraded aquatic habitats. While much of the literature focuses on extent and configuration of impervious area as a causal determinant of degradation, in this article, I do not attribute causes of decreased watershed storage on impervious area a priori. Rather, adapting the concept of variable source area (VSA) and its relationship to incremental storage to the particular conditions of urbanized catchments, I develop a statistically robust linear regression-based methodology to detect evidence of VSA-dominant response. Using the physical and meteorological characteristics of the catchments as explanatory variables, I then use logistic regression to statistically analyze significant predictors of the VSA classification. I find that the strongest predictor of VSA-type response is the percent of undeveloped area in the catchment. Characteristics of developed areas, including total impervious area, percent-developed open space and the type of drainage infrastructure, do not add to the explanatory power of undeveloped land in predicting VSA-type response. Within only developed areas, I find that total impervious area and percent-developed open space both decrease the odds of a catchment exhibiting evidence of VSA-type response and the effect of developed open space is more similar to that of total impervious area than undeveloped land in predicting VSA response. Different types of stormwater management infrastructure, including combined sewer systems and infiltration, retention and detention infrastructure are not found to have strong statistically significant effects on probability of VSA-type response. VSA-type response is also found to be stronger during the growing season than the dormant season. These findings are consistent across a national cross-section of urbanized watersheds, a higher resolution dataset of Baltimore Metropolitan Area watersheds and a subsample of watersheds confirmed not to be served by (combined sewer systems).
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    Quantifying Interactive Cooling Effects of Morphological Parameters and Vegetation-Related Landscape Features during an Extreme Heat Event
    Kianmehr, Ayda; Lim, Theodore C. (MDPI, 2022-04-09)
    In this study, we apply the ENVI-met model to evaluate the effects of combinations of morphological and vegetation-related landscape features on urban temperatures and thermal comfort. We simulated the thermal conditions of 126 scenarios, varying the aspect ratios of street canyons, vegetation cover and density, surface materials, and orientations toward the prevalent winds under an extreme heat situation. Our results show how the effects of physical and vegetation parameters interact and moderate each other. We also demonstrate how sensitive thermal comfort indices such as temperature and relative humidity are to the built environment parameters during different hours of a day. This study’s findings highlight the necessity of prioritizing heat mitigation interventions based on the site’s physical characteristics and landscape features and avoiding generic strategies for all types of urban environments.
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    Revitalizing Urban Neighborhoods by Adopting Green Infrastructure: The Case of Washington DC
    Lim, Theodore C. (Urban Planning International, 2018-06-19)
    The concept of Green Infrastructure (GI), or using the natural processes of evapotranspiration and infiltration to manage stormwater runoff close to where rain falls is a popular concept among urbanists. In addition to providing the ecosystem services of flood management, GI realizes other goals of increasing urban livability, through mitigating urban heat island effect, providing community amenity, purifying air, and even reducing crime. At the same time, GI has been shown to be primarily driven by federal-level stormwater management regulations to make expensive improvements to aging infrastructure. GI is one way that cities may achieve this goal more efficiently. In this paper, I trace the history of stormwater infrastructure regulation and urban sustainability in the US, how this national context influenced local policy in Washington DC neighborhoods. In addition to the popular narrative that GI can spur neighborhood revitalization, I identify the market-driven urban processes that determine GI locations in already revitalizing neighborhoods. Using an overlay analysis of these factors—centrally-driven planning processes, distributed voluntary participation and distributed development patterns—I show how different neighborhoods throughout the District are likely to have different distributions of Green Infrastructure adoption rates, with areas experiencing high re-investment showing the highest levels of probable GI adoption.