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ItemAn observational and modeling study of energy, water, and carbon transport in eco-hydro-meteorological systems(Georgia Institute of Technology, 2023-12-12) Zhu, ModiEco-hydro-meteorological systems play a critical role in regulating the Earth's energy, water, and carbon cycles. Understanding the physical mechanisms driving ecosystem functioning is essential for predicting and mitigating the impacts of global environmental change. The primary objective of this study is to understand the complex mechanisms and interactions that govern the transport of energy, carbon, and water in various eco-hydro-meteorological systems. However, the mechanisms in different eco-hydro-meteorological systems are quite different. This study, by employing a blend of observational data and modeling techniques, investigates the physical transportation of energy, water, and carbon within diverse ecosystems --forest, permafrost, and lake --each with its distinct mechanisms, and develops a comprehensive understanding of how these ecosystems function and respond to environmental changes. In the observational phase, data is gathered using flux towers that measure the exchange of energy, water, and carbon between the Earth's surface and the atmosphere. Datasets from multiple flux towers across forest, permafrost, and lake ecosystems are scrutinized to discern patterns and drivers of eco-hydro-meteorological system processes. The observations have revealed the differences of how energy, water, and carbon are transported in different eco-hydro-meteorological systems and the importance of further study. In the modeling phase, the past traditional models of energy, water, and carbon transport of eco-hydro-meteorological systems have been carefully reviewed. The non-gradient models are widely applied in modeling the meteorological processes in recent decades. This study utilizes Maximum Entropy Production (MEP) Model and Half-order Derivative (HOD) Methods together with newly proposed inference models to simulate the eco-hydro-meteorological processes, which yielded consistent results compared to field experiments. Overall, this study has significant implications for our understanding of how eco-hydro-meteorological systems function and how they respond to environmental changes. The knowledge gained from this research could inform the development of policies and strategies to promote environmental sustainability and protect these vital ecosystems for future generations.
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ItemAn observational and modeling study of energy, water, and carbon transport in eco-hydro-meteorological systems(Georgia Institute of Technology, 2023-12-11) Zhu, ModiEco-hydro-meteorological systems play a critical role in regulating the Earth's energy, water, and carbon cycles. Understanding the physical mechanisms driving ecosystem functioning is essential for predicting and mitigating the impacts of global environmental change. The primary objective of this study is to understand the complex mechanisms and interactions that govern the transport of energy, carbon, and water in various eco-hydro-meteorological systems. However, the mechanisms in different eco-hydro-meteorological systems are quite different. This study, by employing a blend of observational data and modeling techniques, investigates the physical transportation of energy, water, and carbon within diverse ecosystems --forest, permafrost, and lake --each with its distinct mechanisms, and develops a comprehensive understanding of how these ecosystems function and respond to environmental changes. In the observational phase, data is gathered using flux towers that measure the exchange of energy, water, and carbon between the Earth's surface and the atmosphere. Datasets from multiple flux towers across forest, permafrost, and lake ecosystems are scrutinized to discern patterns and drivers of eco-hydro-meteorological system processes. The observations have revealed the differences of how energy, water, and carbon are transported in different eco-hydro-meteorological systems and the importance of further study. In the modeling phase, the past traditional models of energy, water, and carbon transport of eco-hydro-meteorological systems have been carefully reviewed. The non-gradient models are widely applied in modeling the meteorological processes in recent decades. This study utilizes Maximum Entropy Production (MEP) Model and Half-order Derivative (HOD) Methods together with newly proposed inference models to simulate the eco-hydro-meteorological processes, which yielded consistent results compared to field experiments. Overall, this study has significant implications for our understanding of how eco-hydro-meteorological systems function and how they respond to environmental changes. The knowledge gained from this research could inform the development of policies and strategies to promote environmental sustainability and protect these vital ecosystems for future generations.
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ItemShaping The New Normal: The Role of Air Quality on Human Activities for Informed Decision Making(Georgia Institute of Technology, 2023-08-02) Xu, LeiAir pollution remains a complex global issue with severe societal and public health consequences. Despite extensive efforts, air quality policies in the United States have achieved limited success. The intricate relationship between human activities, transportation, and air quality has prompted academic and governmental efforts to adopt information policies, encouraging public involvement in transportation behavior changes and exploring the link between air pollution exposure and disease transmission. The significant shifts in transportation and public health, partly due to technological advancements and the COVID-19 pandemic, present an opportunity to investigate the influence of air quality and related policies on these societal changes. In this dissertation, I examine the role of air quality in shaping the "New Normal" by evaluating the impact of existing air quality policies on the adoption of micromobility and analyzing the potential contribution of air pollution to the spread of COVID-19. The first study explores the association between short-term air pollution exposure and COVID-19 infection rates in the U.S., aiming to raise awareness and inform decision-making for policymakers and the public. The second study assesses the effectiveness of air quality alerts on micromobility usage, examining public behavioral responses and evaluating policy success. The third study compares the spatiotemporal responses of micromobility and traditional driving to air quality information, analyzing potential replacement effects between these two transportation modes. Drawing from diverse fields such as air quality analytics, public policy, and public health, this research offers valuable insights and supports decision-making in the pursuit of a sustainable future. Amidst unprecedented changes and the potentially critical role of air pollution in reshaping daily life, this dissertation contributes to ongoing efforts to address air pollution and its complex societal implications.
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ItemThe teleworking treatment effect on travel behavior: An in-depth exploration of endogenous switching regression models(Georgia Institute of Technology, 2023-07-30) Wang, XinyiThe idea of teleworking (TWing) was proposed in the 1960s as a way of reducing travel, and the number of teleworkers has steadily climbed over the years. In the past three years, the COVID-19 pandemic has boosted teleworking to unprecedented levels. As the pandemic appears to have been brought under control, many “pandemic teleworkers” have chosen to keep teleworking even though this work arrangement is no longer required by their employers and/or the risk of serious illness from the coronavirus has been dramatically reduced. The reshaped teleworker composition (e.g., in terms of sociodemographic traits, occupation, TWing frequency, and TWing motives) will unavoidably lead to travel demand changes (e.g., in net volume, peak-hour distribution, mode distribution). The objectives of this dissertation fall into two categories: policy-oriented and methodology-oriented. The policy-oriented objectives aim to understand TWing-related motives, factors influencing teleworking frequency, and the behavioral outcome of teleworking. The methodology-oriented objectives aim to improve and compare endogenous switching regression models, which we adopt to quantify the impact of teleworking on vehicle-miles driven (VMD). Specifically, to understand “why do people telework?”, we apply a latent class choice model and identify five heterogeneous teleworker segments based on TWing-related motives, namely travel-dominant, flexibility-dominant, career-dominant, workplace-discouraged, and family-dominant. With the same model, we also identify factors that influence the teleworking frequency for each motive segment, which explains “what influences teleworking frequency?” In particular, we find that factors such as gender, education, and job characteristics have heterogeneous impacts on teleworkers with different motives. Next, to deepen the understanding of teleworking-induced behavioral changes on VMD (“what are the teleworking outcomes?”), we adopt three endogenous switching regression models (ESRMs), namely, binary probit switching regression, ordered probit switching regression, and multinomial logit switching regression. Compared to non-teleworkers, we consider the behavioral difference between “non-usual” and “usual” teleworkers. Also, we separate teleworkers based on teleworking-related motives (specifically, travel stressed or not) to compare results when the outcome variable (VMD) likely conforms to the teleworking motivation versus when it does not. Moreover, we expand the application of endogenous switching regression models, such as by allowing more flexible model specifications; developing analytical representations of, and simulation procedures for estimating, the back-log-transformed treatment effect; and improving estimator efficiency by implementing simultaneous maximum likelihood estimation instead of using the prevalent two-stage estimation approach. The results of the dissertation provide a unique perspective on understanding the post-pandemic teleworker segmentation, factors that have heterogeneous impacts on teleworking frequency, and teleworking treatment effects on travel behavior accounting for the influence of TWing-related motivations and frequency. The application, improvement, and comparison of ESRMs will also be relevant to numerous instances of self-selection beyond the present context.
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ItemNumerical modeling of discontinuous processes in geomaterials and geosystems(Georgia Institute of Technology, 2023-07-28) He, HaozhouThis doctoral research advances numerical methods for simulating discontinuous processes such as crack propagation, surface debonding and large deformation in geomechanics. Two novel approaches are presented to address the limitations of traditional Finite Element Methods (FEM) when dealing with discrete failure problems and soil-structure interactions. We first study multi-scale crack propagation in rocks. Most discrete fractures in rock propagate in combination with smaller defects (micro-cracks) that form a damaged zone around the discrete fracture surfaces. To account for the effects of micro-crack propagation, a new two dimensional 4-node cohesive element is defined to couple Cohesive Zone Method (CZM) technique to a Continuum Damage Mechanics (CDM) model. The proposed CDM based CZ element is validated by single-element simulations, borehole breakout simulations, and biaxial compression simulations of textured materials. Next, we propose a novel approach to couple the Smooth Particle Hydrodynamics (SPH) method with the FEM to study large deformation processes in granular media that interact with solids. The SPH+FEM model is implemented in MATLAB to simulate the interaction between a solid (modeled with the FEM) and a host particulate medium (modeled with SPH). We use the proposed SPH+FEM to simulate various stress paths and boundary value problems of interest in geomechanics, e.g., problems of biaxial compression, sand column collapse, shallow foundation bearing capacity and intrusion mechanisms of deformable compound anchors.
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ItemA Scalable and Adaptable Coastal-Urban Flood Modeling Framework for Changing Climates(Georgia Institute of Technology, 2023-07-24) Son, YoungjunCoastal communities in the United States are threatened by a diverse range of flood risks, such as high tides, storm surges, heavy rainfall, and groundwater floods. In addition, global climate change further exacerbates the severity and frequency of floods by raising sea levels and intensifying extreme weather events. Urban flood models are vital for coastal communities to effectively assess the emerging risks of floods and prepare resilience strategies in response to changing climates. In the present research, a flood modeling framework is developed for applications in coastal-urban systems. The framework introduces an accessible urban flood model for coastal applications, called WRF-Hydro-CUFA, which combines two open-source models, namely WRF-Hydro and SWMM. In a pilot study for the City of Tybee Island in Georgia, USA, the WRF-Hydro-CUFA model simulations successfully reproduce two distinct flood events: nuisance flooding caused by the perigean spring tides in 2012 and extreme flooding resulting from Hurricane Irma in 2017. Furthermore, a web-based dashboard is built for operational flood predictions, integrating modeling information and existing flood-related resources, such as real-time camera feeds and nearby water level measurements. The platform aims to facilitate the integration of flood-related knowledge and observations from researchers, local experts, and community practitioners. To leverage the ongoing deployments of hyper-local water level sensors along the U.S. Georgia coasts, the flood modeling framework includes the development of a physics-based empirical modeling approach to assimilate estuarine water levels directly using the sensor observations. The physics-based empirical modeling approach implements the Objective Analysis procedure, which combines empirical observations from the water level monitoring network with spatial covariance statistics derived from physics-based model simulations. The efficient assimilation of coastal water levels enables community officials to reliably identify localized flood threats, particularly to critical infrastructures in coastal regions, such as bridges and marinas. The established flood modeling framework provides coastal communities with an accessible option to understand emerging flood risks, which can empower them to identify effective and sustainable resilience strategies informed by scientific insights.
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ItemAn Assessment of EV Adoption and Potential Growth under Evolving Techno-policy Scenarios(Georgia Institute of Technology, 2023-05-22) Dai, ZiyiReaching net-zero carbon emissions by 2050 is an overarching goal for sustainable development in the United States. Electric Vehicles (EVs) are being developed to reduce energy consumption during on-road operations and have become the cornerstone of sustainable transportation systems. This study explores how has EV adoption increased over the past years and predicts how will the EV market continue to expand and penetrate in the future years using a case study with regional granular travel survey data. This study first conducted a comprehensive and detailed analysis of the historical EV ownership and use from the year 2000 to 2021 under the Puget Sound region, covering aspects of household demographics and travel pattern differences between EV-households and non-EV-households, as well as the trend identification and quantification of multiple factors in deciding EV adoption and use. Following evidence from the analysis a modeling framework was developed and experimented for future year EV growth prediction at both macroscale and microscale levels, the macroscale model forecasts the total number of EV sales in specific future years based on EV technology development, EV market production and supply, the existence of supporting policy and rebates program, as well as external environmental factors, then the microscale model identifies the candidate EV-purchasing households through the measurement of similarities between EV-households and non-EV-households, with the dynamics of the factor influence reflected in the rescaling of the weights while calculating the similarity. Following the model outputs multiple scenarios regarding technology, policy and market were designed and proposed for model sensitivity analysis, specifically, how are the outputs affected based on changes in different input components. The proposed research methodology will supplement the existing studies on EV expansion and penetration over time, and will specifically account for parameter-driven preference dynamics. The analysis results will provide substantial details on the identification of influential factors on EV adoption, while prediction results will also provide substantial research findings on understanding the future EV market and possible impacts and turbulences.
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ItemHousing for Resilience and Equity: Accounting for the Effects of Electrification and Climate-Induced Human Mobility in Decision Support Tools(Georgia Institute of Technology, 2023-04-30) Maxim, AlexandraThis dissertation explores the relationship between climate change, climate-induced human mobility, and decarbonization. The research finds that climate change will cause regional population shifts and increase housing needs in certain areas exacerbating existing challenges related to energy burden, demographic disparities, safety, and health issues in the current housing stock. Additionally, the decarbonization of the electric grid and the electrification of households will impact grid resilience, particularly on extremely cold days. The research in this dissertation highlights the urgent need for comprehensive policies and strategies that address the intersection of these complex challenges, including the development of resilient and energy-efficient housing, the integration of climate adaptation, migration planning, and the enhancement of grid resilience through innovative solutions. The findings provide insights for policymakers, planners, and practitioners working on climate change, housing, and energy with implications for sustainable and equitable urban development in the face of climate change.
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ItemEmergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations(Georgia Institute of Technology, 2023-04-30) Roy, SomdutEmergency-Response-Vehicles (ERVs) operate with the purpose of saving lives and mitigating property damage. Emergency-response Vehicle Preemption (EVP) is implemented to provide the right-of-way to ERVs by displaying the green indications along the ERV route. Two EVP strategies were developed as part of this effort. First, a strategy was developed, defined as “Dynamic-Preemption” (DP), that utilizes Connected-Vehicle (CV) technology to detect, in real time, the need for preemption prior to the ERV reaching the vicinity of an intersection. The DP strategy is based on several generalized traffic demand and simplified traffic flow assumptions. Second, a machine learning approach was utilized to develop an EVP call strategy that sought to (1) preemptively clear queues at intersections prior to ERV arrival, (2) create a "delay-free" path for the ERV, and (3) minimize excess delay to the conflicting traffic in the event of an EVP call. The ML approach utilizes currently available vehicle detection data streams and is trained based on simulated EVP scenarios. Existing field strategies and the developed strategies were tested under varying scenarios, on a simulated signalized corridor testbed. It was observed that the proposed methodologies showed tangible improvement over the existing baseline algorithms for EVP, both in terms of ERV travel time and delay to the conflicting movements. In summary, this research is expected to lay the foundation for use of novel computational approaches in solving the EVP problem in traffic ecosystems with limited CV penetration, with the aid of microsimulation.
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ItemModeling of factors impacting atmospheric formic acid, ozone and PM2.5 dynamics(Georgia Institute of Technology, 2023-04-27) Gao, ZiqiElevated concentrations of air pollutants have been linked to an increased risk of respiratory diseases, cardiovascular diseases, lung cancer, and mortality rate. Multiple atmospheric processes, such as emissions, chemical reactions, transport, and deposition, can affect ozone formation, particulate matter (PM2.5), and organic acids. Although the government has set many regulations to reduce the emissions of pollutant precursors, the actual impacts of these regulations are highly sensitive to external factors such as local meteorology, source distributions, and large-scale climatic patterns, such that emissions reductions could be ineffective or could even result in worse air pollution. The effectiveness of the regulations can be evaluated after isolating the impacts of external factors. This can help policymakers make future regulations to mitigate air pollutant concentrations more effective and efficient. This work applied several models, ranging from chemical transport, box, and empirical models, to assess the impact of existing national and state emission reduction regulations on emissions and air pollutants concentrations. These models were also used to evaluate and quantify the impact of the main drivers and processes (e.g., emissions, meteorological impacts, chemical reactions, etc.) that impact air quality and predict potential air pollution concentrations in future years. In looking at the impact on both anthropogenic and biogenic emissions, we found that 1) A bi-directional emission-deposition process has more impact on formic acid formation than photooxidation reactions, 2) Emissions largely control peak ozone and PM2.5 concentrations as well as PM2.5 chemical composition trends, though meteorology impacts daily variability and can lead to increases in annual peak ozone concentrations despite emissions reductions. 3) In the future, meteorological impacts will significantly impact all air pollutant concentrations with the emissions reduction and affect the attainment of pollutant standards. Thus, while historical and future air pollution regulations can (or, in some cases, have) attain pollutant standards, meteorology, and climatic effects may endanger consistent attainment.