<p>  </p> <p>The transportation sector has emerged as one of the largest contributors to energy consumption and greenhouse gas emissions within the U.S. economy. As a consequence, transportation sustainability faces great challenges in automobile traffic congestion alleviation, air pollution reduction, and climate change adaptation. Emerging technologies bring new chances to resolve these issues. However, existing literature focusing on the sustainability assessment of emerging technologies often relies on the analysis of isolated systems using historical data. These studies neglected the complex interactions across different systems and failed to consider the potential impacts of future technology adoption. The sustainability performance of emerging transportation technologies is heavily dependent on competing and complementary relationships with existing transportation systems. Furthermore, the dynamics of system interactions can change with the diffusion of future technologies, as user behavior becomes more heterogeneous. Consequently, the future adoption of emerging technologies may lead to an uncertain urban sustainability outlook. Therefore, sustainability assessment and prospective system planning for emerging technologies necessitate a comprehensive examination of their interactions with urban transportation systems and the evolving landscape. </p> <p>The primary objective of this dissertation is to demonstrate the necessity and benefits of incorporating system interactions into sustainability assessments. To achieve this goal, this dissertation conducts four case studies, using various models inspired by machine learning, statistics, econometrics, and agent-based approaches, and applies them to two emerging technologies: shared mobility (including bike-sharing, shared e-scooters, and ride-hailing) and e-commerce. First, the interaction classification analysis shows that the current shared mobility primarily competes with public transit rather than complementing it, resulting in a significant bus ridership decline. Second, to enhance sustainability, it is crucial for shared mobility to substitute private car trips and integrate effectively with public transit. Understanding why current users do not sustainably use the system is the key. Results from the traveler mode choice behavior show that the travel cost and out-of-vehicle travel time (e.g., time spent on walking connection, waiting) of shared mobility are the major barriers for travelers to substitute car trips and use multimodal systems. Third, future system planning should improve the pricing mechanisms and fleet management to encourage travelers to use shared mobility in a sustainable way. Optimal pricing and fleet management strategies are sought through an agent-based simulation. Transit-oriented-development is proven to be the best fleet siting strategy and an optimal combination of fleet size and pricing for each shared mobility system is also solved for minimizing vehicle miles traveled (VMT) from urban transportation. Fourth, the penetration of e-commerce also reshapes urban mobility from personal travel demand changes, mode choice shifts, and goods delivery inclusion. We integrated the market segmentation and penetration of e-commerce into transportation simulation to comprehensively estimate its impact on urban mobility and transportation sustainability. </p> <p>Case studies from this dissertation demonstrate that the existing adoption of emerging technologies requires further actions in system design, user guidance, and operation management to obtain sustainability benefits. Knowledge from this dissertation supports decision-makers in their efforts to design and plan future emerging technologies toward a sustainable pathway. The findings and insights presented in the dissertation offer valuable guidance for policymakers, urban planners, and stakeholders involved in shaping the trajectory of these technologies.</p>

<p>Clean indoor air is a necessity, in the past opening a window or supplying outdoor air would suffice for removing indoor contaminated air. As humans live in more dense neighborhoods or urban areas, the need for energy efficient clean indoor air is important. As outdoor air pollution increases, a Biowall is a device to improve aesthetics and clean indoor air by pulling air through the root zone of plants in a loosely packed growth media. The Biowall is a sustainable supplement to a single use air filter. For this research a small Biowall was designed, fabricated, tested, and installed in the Children’s wing of a public library. The airflow simulation and watering for the Biowall was explored using Autodesk CFD to simulate the airflow through growth media and identify a hole pattern to distribute airflow evenly through the plant trays. Various watering line designs were also tested until the rectangular design proved to be more balanced at distributing the water evenly to the growth media. Finally, the air cleaning ability of the Biowall was tested using a Clean Air Delivery Rate (CADR) test to quantify the cleaning rate. The Biowall provided 12 cfm of clean air, or about 2 cfm/sq ft of plant tray area.  </p>

<p>    </p> <p>Electronic waste is one of the fastest growing waste streams, spurred by their rising market and demand. However, these devices contain an array of metals that is recyclable for economic and environmental benefit through secondary manufacturing. As the turnaround rate for newer models quickens, consumers are motivated to purchase novel devices, leaving their current ones behind. Focusing on how United States (U.S.) households manage their electronics, a top-down approach stock and flow STELLA model was created to model the lifecycle of eight common electronics. Input data for the model came from a public online survey directed to U.S. household owning adults. From the model, a metallic stock and flow analysis was conducted to quantify the trends, environmental footprint, and economic value of stored devices in U.S. households and how it compares to devices being used, disposed, and recycled. The number of stored devices in the U.S. was found to be increasing annually with a stored amount of over 757 million stored individual electronic devices, nearly half of which originate from cell phones, carrying an economic value of 32.6 billion US dollars (USD) and carbon emissions of 7.6 billion kilograms (kg) from their metallic components alone for the year 2020. Most of the pollution and economic value stems from precious metals (PMs) and in a circular economy, these stored metals can have a significant impact to the environment and economy through recycled. Also, with advancing capabilities of smartphones, the metallic composition for device components of Samsung galaxy smartphones was quantified to assess their evolving metallic content. With the growing market of electronic devices, knowing the value and importance of devices currently in U.S. households is critical. This underlies the influence of sustainable design through a circular economy to push initiatives to manufacture recyclable friendly devices, expand the metal recycling industry, and motivate citizens to properly handle their stored devices. </p>

<p>To accurately estimate the potential recovery of metals from electronic devices, various tools such as mass flow analysis, dynamic models, and forecasting models have been employed. However, the reliability of the model-generated outputs hinges on the accuracy of the input data. To ensure accurate data collection, it is imperative to examine and compare different methodologies. Although surveys have conventionally been used in information and telecommunications technologies to gather consumer information, their validity is seldom contrasted with alternative methods due to the lack thereof. In response, a new mixed methodology has been developed to obtain primary consumer data through tangible information, offering new avenues for data acquisition. The methodology involves quantitative and qualitative approaches taking direct physical measurements (dimensions, weight, and quantity) of devices and including a ten question semi-structured interview to discussed consumer devices use, stock and transfer patterns, composition changes of the electronic devices, and disposal behavior. As a result, it was found new methodology measured 79% of the devices directly at laboratories and 21% of the remaining devices were self-reported by the participant. The devices on consumer stage frequently have the same type and number of components as fabric and a positive difference of about 17.99 g compared with the mass reported by fabricants or literature. The sequential steps undertaken by participants in the new methodology to acquire consumer-stage data offer distinct advantages over surveys, particularly in capturing a more comprehensive inventory of devices in storage.</p><p>Additional results indicate that the proposed methodology can provide valuable insights into the stock of electronic devices. Nevertheless, further research is required to understand the implications of surveys versus direct measurements in accurately representing mass flows during the user stage. Additionally, the relevance of external power supply or charging systems on storage will be explored as part of the supplementary findings. By improving the accuracy of metal recovery estimation and exploring more effective data collection methods, we can optimize closed-loop projects and contribute to sustainable resource management.</p>

<p>  </p> <p>Plastics are the most common polymers used in various industries. However, million tons of plastics are produced and disposed every year around the world, and part of them end up entering the environment and agricultural ecosystems in the form of microplastics. Microplastics have become an environmental and health threat to aquatic species and humans because they are small and can easily reach water bodies for municipal and agricultural uses. Microplastics have been traced in food commodities and products derived from animals and even found in bottles of drinking water. As an approach to permanently remediating microplastics, current microplastic degradation techniques, however, require high energy inputs and thus are generally not cost-efficient. High-voltage atmospheric cold plasma (HVACP) is a low-cost energy-efficient technology to produce highly reactive species that can induce physicochemical changes in polymers. This study, for the first time, used HVACP as a novel remediation strategy for microplastics. HVACP was generated by dielectric barrier discharge at 50 kV using oxygen, nitrogen, or their mixture as working gas. Two types of microplastics, polypropylene (PP) and low-density polyethylene (LDPE), were treated for 30 min, and the effect of 24-h post-treatment was also studied. The properties of HVACP-treated microplastics, including weight, particle size, crystallinity, melting point, carbonyl index (CI), and surface morphology, were comprehensively analyzed. HVACP treatments were found effective in degrading both PP and LDPE microplastics. A larger extent of degradation was observed with PP microplastics treated by O/N mixture plasma, but the nitrogen plasma-treated sample showed a higher degree of oxidation according to its CI. For PE microplastics, oxygen plasma caused more degradation, but post-treatment did not promote further oxidation. The results indicated two potential mechanisms for microplastic degradation by HVACP. LDPE microplastics were degraded by oxidative reactions caused by highly reactive oxygen species, and PP microplastics followed a hydrolytic pathway of degradation as they became more hydrophilic after HVACP treatment. This study proved that HVACP is a promising method for microplastic degradation, and thus has great potential for addressing the severe challenges of microplastics that the food and agriculture sectors are currently facing.</p>

<p>  </p> <p>The integration of Industry 4.0 technologies such as cyber-physical systems, the internet of things, and artificial intelligence has revolutionized the traditional manufacturing systems, making them smart and digital. Maintenance, a critical component of manufacturing, has been incorporated with data-driven strategies such as prognostic and health management (PHM) to improve production efficiency and reliability. This is achieved by real-time sensing and AI-based modeling, which monitor the health condition of operational equipment for fault detection or failure prediction. The results generated by these models provide crucial support for decision-making processes in manufacturing, ranging from maintenance scheduling to production management.</p> <p>This research focuses on data-driven machine health models based on deep learning in manufacturing systems and explores three directions towards the practical implementation of PHM: model interpretation, model adaptability and robustness enhancement, and cost-benefit analysis of maintenance strategies. In terms of model interpretation, the RNN-LSTM-based model prediction on bearing health estimation was analyzed, and the relationship between the model input and output was investigated. The adoption of the LRP technique improved the explainability of the LSTM model beyond predictive maintenance applications. To enhance model adaptability and robustness, a Transformer-based method was developed for fault diagnosis and novel fault detection, which achieved superior performance compared to conventional fault classification AI-based models. The decision-making aspect of PHM was addressed by conducting a cost-benefit analysis on different maintenance strategies, which provided a new perspective for decision-makers in maintenance management.</p>

<p dir="ltr">Improving environmental sustainability in energy production and waste management are of critical importance. Anaerobic digestion (AD) uses microbes to biologically decompose organic waste and produce biogas, which can be used for various forms of sustainable energy. It can be particularly valuable for livestock facilities considering AD of their manure, and potentially other feedstocks as well, a process known as co-digestion. Improved understanding of co-digestion of agro-industrial feedstocks is critical for these facilities. Understanding the macromolecular composition (carbohydrate, protein, and lipid portions) of potential AD feedstocks has the potential to provide important information for predicting important parameters of AD behavior. However, the stability of these macromolecules in AD samples during long-term storage must be confirmed. Furthermore, synergistic and antagonistic impacts of co-digestion on methane production and digestate composition need to be more thoroughly explored.</p><p dir="ltr">This dissertation investigates the impact of storage at refrigeration temperatures (4°C) for up to one-year on the macromolecular composition of various agro-industrial feedstocks (beef manure, starch, slaughterhouse waste, soap stock, and filter press slurry) and anaerobic co-digestion samples. These same feedstocks were co-digested with manure in batch digesters at different proportions, using two or three feedstocks to determine possible synergistic effects.</p><p dir="ltr">The findings show that minimal macromolecular degradation occurred in AD samples during storage at refrigeration temperatures for up to one-year. A major exception was samples containing high concentrations of readily biodegradable starches, which did experience >50% carbohydrate degradation. This indicates a need for methodological rigor during sample storage and reporting experimental design.</p><p dir="ltr">Furthermore, the co-digestion experiments demonstrated frequent improvements or synergy in specific methane yield, methane production rate, and a wide variety of physical and chemical parameters in the digester effluent. Specific methane yield was shown to be at least additive, with improvement ranging from 3-168%. Some improvements in kinetic performance were also observed and quantified. Statistical results suggest that influent characteristics could be useful as predictors for methane production. This research could catalyze additional work needed to optimize co-digestion feeding strategies for full-scale digesters.</p>

<p>Biodegradation of untreated cotton, linen, and hemp textiles as three substrates – measured through biogas production – was studied to compare digestion yield and the ability of anaerobic sludge as inoculum to utilize the sugars in these textiles without pretreatment. Digestion of these textile substrates was carried out over a 26-day study, with daily sampling of biogas production, to measure biogas production rate and accumulation. The flasks were maintained at 37 °C and 150 RPM with a substrate to inoculum ratio (SIR) of 0.5 g sugars from substrate g-1 VSinoculum from anaerobic sludge. Biogas samples were analyzed through gas chromatography (GC) to determine general biogas composition produced by each textile. Biogas production was notable after the four-day mark; with first peaks occurring on day five (hemp, cellulose), day seven (cotton), and day nine (linen). Production of biogas in the control largely outperformed trials with no added substrate, but overall the methane fractions of the gas was lower than expected, indicating that pretreatment is likely necessary for more complete biodegradation of natural textiles. </p>

<p>Biomass liquefaction is a nascent field within biorefinery research and has arisen in response to the bottleneck created from materials handling at the front end of the biorefinery. The basic concept is that if the biomass were to be converted into a flowable slurry at the front-end of the process, then the material could smoothly flow into the biorefinery pretreatment and down time due to the material forming a plug would be minimized or eliminated. Three liquefaction routes were studied in this work. These routes were: enzyme route, enzyme mimetic route, and a combined route of enzyme and enzyme mimetic. Through a Techno-economic assessment (TEA), it is possible to determine which route is most-economical to scale up and also to understand the extent to which liquefaction increases/decreases of the price of the biorefinery product.</p> <p>Gasification is a bio-based technology that has recently acquired more attention as it is an efficient conversion process for a variety of feedstocks. As new techniques and process routes are discovered, it is important to analyze which process technique is feasible for commercial scale up, as the highest performing technique may not be the most economical option to pursue. Along the same philosophy, a process concept was developed on Aspen Plus® to treat syn-gas impurities and also recycle the spent solvents. A TEA study was performed to determine the unit cost of treatment and to explore avenues of cost saving.</p>

<p dir="ltr">Smart windows, dynamically adjusting optical transmittance, face global adoption challenges due to climatic and economic variability. Aiming these issues, we synthesized a methyl cellulose (MC) salt system with high tunability for intrinsic optical transmittance (89.3%), which can be applied globally to various locations. Specifically, the MC window has superior heat shielding potential below transition temperatures while turning opaque at temperatures above the Lower Critical Solution Temperature (LCST), reducing the solar heat gain by 55%. Such optical tunability is attributable to the particle size change triggered by the temperature-induced reversible coil-to-globular transition. This leads to effective refractive index and scattering modulation, making them prospective solutions for light management systems, an application ahead of intelligent fenestration systems. MC-based windows demonstrated a 9°C temperature decrease compared to double-pane windows on sunny days and a 5°C increase during winters in field tests, while simulations predict an 11% energy savings.</p><p dir="ltr">Incorporating MC-based phase change materials in passive solar panels indicated optimized energy efficiency, offering a sustainable alternative. Real-time simulations validate practical applicability in large-scale solar panels. Furthermore, a temperature-responsive sorbent with a dark layer demonstrates an optimal optical and water uptake performance. Transitioning between radiative cooling and solar heating, the sorbent exhibits high water harvesting efficiency in lab and field tests. With an adjustable LCST at 38 ℃, the cellulose-based sorbent presents a potential solution for atmospheric water harvesting, combining optical switching and temperature responsiveness for sustainable water access. Furthermore, the ubiquitous availability of materials, low cost, and ease-of-manufacturing will provide technological equity and foster our ambition towards net-zero buildings and sustainable future.</p>