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The capture and separation of carbon dioxide (CO2) has been the focus of a plethora of research in order to mitigate its emissions and contribute to global development. Given that CO2 is commonly found in natural gas streams, there have been efforts to seek more efficient materials to separate gaseous mixtures such as CO2/CH4. However, there are only a few reports regarding adsorption processes within pressurized systems. In the offshore scenario, natural gas streams still exhibit high moisture content, necessitating a greater understanding of processes in moist systems. In this article, a metal-organic framework synthesis based on zirconium (MOF-808) was carried out through a conventional solvothermal method and autoclave for the adsorption of CO2 and CH4 under different temperatures (45–65 °C) and pressures up to 100 bar. Furthermore, the adsorption of humid CO2 was evaluated using thermal analyses. The MOF-808 synthesized in autoclave showed a high surface area (1502 m2/g), a high capacity for CO2 adsorption at 50 bar and 45 °C and had a low selectivity to capture CH4 molecules. It also exhibited a fine stability after five cycles of CO2 adsorption and desorption at 50 bar and 45 °C − as confirmed by structural post-adsorption analyses while maintaining its adsorption capacity and crystallinity. Furthermore, it can be observed that the adsorption capacity increased in a humid environment, and that the adsorbent remained stable after adsorption cycles in the presence of moisture. Finally, it was possible to confirm the occurrence of physisorption processes through nuclear magnetic resonance (NMR) analyses, thus validating the choice of mild temperatures for regeneration and contributing to the reduction of energy consumption in processing plants.

Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future., Validerad;2024;Nivå 2;2024-04-04 (joosat);Full text license: CC BY-NC-ND

This study investigates the perceptions of three Swedish stakeholders towards the contested use of waste incineration bottom ash as aggregates outside landfills: waste producers, users and authorities. The interview study shows that the stakeholders lack incentives to circulate bottom ash. Waste producers have a functional outlet for bottom ash by using it as landfill cover. Since the transition from gravel to crushed rock, the availability of natural aggregates is enormous in Sweden at low environmental and economic costs. This causes the potential users of bottom ash to prioritize conventional aggregates. In cases where waste-based aggregates are nevertheless used, they are taken from project-internal sources. Hence, there are few incentives for the authorities to change the currently unfavorable regulations for using bottom ash as aggregates. With unclear environmental benefits and contested interests, the rationale for circulating bottom ash appears to be driven by morality., QC 20240214

This study investigates the perceptions of three Swedish stakeholders towards the contested use of waste incineration bottom ash as aggregates outside landfills: waste producers, users and authorities. The interview study shows that the stakeholders lack incentives to circulate bottom ash. Waste producers have a functional outlet for bottom ash by using it as landfill cover. Since the transition from gravel to crushed rock, the availability of natural aggregates is enormous in Sweden at low environmental and economic costs. This causes the potential users of bottom ash to prioritize conventional aggregates. In cases where waste-based aggregates are nevertheless used, they are taken from project-internal sources. Hence, there are few incentives for the authorities to change the currently unfavorable regulations for using bottom ash as aggregates. With unclear environmental benefits and contested interests, the rationale for circulating bottom ash appears to be driven by morality., QC 20240214

A machine vision driven sensor for estimating the instantaneous feeding rate of pelletized fuels was developed and tested experimentally in combustion and gasification processes. The feeding rate was determined from images of the pellets sliding on a transfer chute into the reactor. From the images the apparent area and velocity of the pellets were extracted. Area was determined by a segmentation model created using a machine learning framework and velocities by image registration of two subsequent images. The measured weight of the pelletized fuel passed through the feeding system was in good agreement with the weight estimated by the sensor. The observed variations in the fuel feeding correlated with the variations in the gaseous species concentrations measured in the reactor core and in the exhaust. Since the developed sensor measures the ingoing fuel feeding rate prior to the reactor, its signal could therefore help improve process control., Correspondence Address: Y. Ögren; RISE AB, Piteå, Box 726 SE-941 28, Sweden; . The Bio4Energy, a strategic research environment appointed by the Swedish government and the SwedishCenter for Gasification financed by the Swedish Energy Agency and member companies. The RE:source program finance by the Swedish Energy Agency, Vinnova and Formas. The Pulp&Fuel project financed by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 818011 and the TDLAS-AI project (Swedish energy agency project 50470-1).

This paper reviews digital tools for supporting the Circular Economy (CE) in the built environment. The study provides a bibliometric analysis and focuses on computer-aided design (CAD), building information modeling (BIM), and computational plugins that can be used by practitioners. While Life Cycle Assessment (LCA) is the primary methodology for evaluating buildings' environmental performance, the study identifies tools beyond LCA, including computational methods and circularity indicators, that can support the evaluation of circular design strategies. Our review highlights limitations in tools’ functionalities, including a lack of representative data for LCA and underdeveloped circularity indicators. The paper calls for further development of these tools in terms of interoperability aspects, integration of more sources of data for LCA and circularity, and possibilities for a comprehensive evaluation of design choices. Computational plugins offer greater flexibility, while BIM-LCA integrations have the potential to replace dedicated LCA software and spreadsheets. Additionally, the study identifies opportunities for novel digital methods, such as algorithms for circular design with various types of reused building elements, and sharing of digital twins and material passports. This research can inform future studies and support architects and engineers in their efforts to create a sustainable built environment., QC 20231204, ReCreate project (Horizon 2020 grant no. 958200)

This paper reviews digital tools for supporting the Circular Economy (CE) in the built environment. The study provides a bibliometric analysis and focuses on computer-aided design (CAD), building information modeling (BIM), and computational plugins that can be used by practitioners. While Life Cycle Assessment (LCA) is the primary methodology for evaluating buildings' environmental performance, the study identifies tools beyond LCA, including computational methods and circularity indicators, that can support the evaluation of circular design strategies. Our review highlights limitations in tools’ functionalities, including a lack of representative data for LCA and underdeveloped circularity indicators. The paper calls for further development of these tools in terms of interoperability aspects, integration of more sources of data for LCA and circularity, and possibilities for a comprehensive evaluation of design choices. Computational plugins offer greater flexibility, while BIM-LCA integrations have the potential to replace dedicated LCA software and spreadsheets. Additionally, the study identifies opportunities for novel digital methods, such as algorithms for circular design with various types of reused building elements, and sharing of digital twins and material passports. This research can inform future studies and support architects and engineers in their efforts to create a sustainable built environment., QC 20231204, ReCreate project (Horizon 2020 grant no. 958200)

Branched perfluorooctane sulfonic acid (PFOS) is recognized as a threatening environmental pollutant due to its high persistence and bioaccumulation in various environmental matrices as well as for its toxic effects on humans and wildlife even at very low concentrations. This study reports the first investigation of branched PFOS defluorination catalyzed by metal phthalocyanines. The reaction conditions were optimized using different reductants and temperatures. CobaltII phthalocyanine, when combined with TiIII citrate as a reducing agent, was able to defluorinate 10.9% of technical PFOS within 8 hours. In contrast, vitamin B12 only showed 2.4% defluorination during the same time period, under similar conditions. The defluorination mediated by the cobaltII phthalocyanine and TiIII citrate system corresponds to 54.5% of all branched PFOS isomers (br-PFOS isomers). Isomer-specific degradation was also investigated via high-resolution LC-orbitrap followed by their relative rates. The difference in catalytic efficacy of various phthalocyanine complexes is rationalized by their structures and electrochemical response. Lastly, a new defluorination mechanism is proposed based on the newly detected degradation products after the phthalocyanine treatment and previous studies. CobaltII phthalocyanine in the presence of TiIII citrate acts as an efficient reducing agent which was able to defluorinate 54.5% of all branched PFOS isomers.

Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future., Validerad;2024;Nivå 2;2024-04-04 (joosat);Full text license: CC BY-NC-ND

A machine vision driven sensor for estimating the instantaneous feeding rate of pelletized fuels was developed and tested experimentally in combustion and gasification processes. The feeding rate was determined from images of the pellets sliding on a transfer chute into the reactor. From the images the apparent area and velocity of the pellets were extracted. Area was determined by a segmentation model created using a machine learning framework and velocities by image registration of two subsequent images. The measured weight of the pelletized fuel passed through the feeding system was in good agreement with the weight estimated by the sensor. The observed variations in the fuel feeding correlated with the variations in the gaseous species concentrations measured in the reactor core and in the exhaust. Since the developed sensor measures the ingoing fuel feeding rate prior to the reactor, its signal could therefore help improve process control., Correspondence Address: Y. Ögren; RISE AB, Piteå, Box 726 SE-941 28, Sweden; . The Bio4Energy, a strategic research environment appointed by the Swedish government and the SwedishCenter for Gasification financed by the Swedish Energy Agency and member companies. The RE:source program finance by the Swedish Energy Agency, Vinnova and Formas. The Pulp&Fuel project financed by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 818011 and the TDLAS-AI project (Swedish energy agency project 50470-1).

This study investigates the perceptions of three Swedish stakeholders towards the contested use of waste incineration bottom ash as aggregates outside landfills: waste producers, users and authorities. The interview study shows that the stakeholders lack incentives to circulate bottom ash. Waste producers have a functional outlet for bottom ash by using it as landfill cover. Since the transition from gravel to crushed rock, the availability of natural aggregates is enormous in Sweden at low environmental and economic costs. This causes the potential users of bottom ash to prioritize conventional aggregates. In cases where waste-based aggregates are nevertheless used, they are taken from project-internal sources. Hence, there are few incentives for the authorities to change the currently unfavorable regulations for using bottom ash as aggregates. With unclear environmental benefits and contested interests, the rationale for circulating bottom ash appears to be driven by morality., QC 20240214

This paper reviews digital tools for supporting the Circular Economy (CE) in the built environment. The study provides a bibliometric analysis and focuses on computer-aided design (CAD), building information modeling (BIM), and computational plugins that can be used by practitioners. While Life Cycle Assessment (LCA) is the primary methodology for evaluating buildings' environmental performance, the study identifies tools beyond LCA, including computational methods and circularity indicators, that can support the evaluation of circular design strategies. Our review highlights limitations in tools’ functionalities, including a lack of representative data for LCA and underdeveloped circularity indicators. The paper calls for further development of these tools in terms of interoperability aspects, integration of more sources of data for LCA and circularity, and possibilities for a comprehensive evaluation of design choices. Computational plugins offer greater flexibility, while BIM-LCA integrations have the potential to replace dedicated LCA software and spreadsheets. Additionally, the study identifies opportunities for novel digital methods, such as algorithms for circular design with various types of reused building elements, and sharing of digital twins and material passports. This research can inform future studies and support architects and engineers in their efforts to create a sustainable built environment., QC 20231204, ReCreate project (Horizon 2020 grant no. 958200)

In Europe, increasing efforts on climate change mitigation, a sudden focus on energy independence after Russia’s invasion of Ukraine, and reported breakthroughs in nuclear fusion have sparked renewed interest in the potential of nuclear power. So-called small modular reactors (SMRs) are increasingly under development, and familiar promises about nuclear power’s potential are being revived. Nuclear power is routinely portrayed by proponents as the source of “limitless” amounts of carbonfree electricity. The rhetorical move from speaking about “renewable energy” to “fossil-free energy” is increasingly evident, and telling., Funding agencies: Svensk Kärnbränslehantering (grant no.24992), the Swedish Research Council (grants no. 2020-00623 and no. 2020-06548).

The Alta-Kvænangen Tectonic Window is part of a regional greenstone belt that formed around 2.3 Ga – 2.0 Ga. The Bergmark anticline in the area has gone through greenschist to amphibolite facies metamorphism and hosts Cu, Co and Au mineralizations in hydrothermal veins, albite felsite and chemical traps. The area was mined during the late 19th century mostly in trenches but it has still got exploration potential. This project will classify carbonate veins that are hosted within two gabbro units, compare orientations between the barren and mineralized veins as well as discuss the origin of the veins. The study area has been affected by multiple extensional and compressional events during the Svecokarelian orogeny (1.9 – 1.8 Ga) and regional hydrothermal alterations including an influx of Na + Ca ± K ± Fe. Major shear zones were activated and used for transporting fluids which are thought to have controlled the formation of mineralization in second- or even third-order structures. The veins were studied using field work, detailed mapping, optical microscopy as well as scanning electron microscopy (SEM) and structural analysis. Results gave three different types of hydrothermal veins: 1) Fe oxide veins, 2) calcite veins and 3) dolomite-plagioclase-quartz veins. All of them show strong signs of deformation and recrystallization all while the chalcopyrite mineralization is heterogeneously distributed and commonly more or less bornite-altered. The veins pinch and swell over short distances and are sinuous but with a general orientation. Albite haloes are common for the veins but far from all veins have haloes. There are at least two generations of veins as some were found to have offset others. Brecciation, tension gashes and faults are also found in the area apart from veins. From the field measurements, two groups of orientations could be determined: one with a SW-NE direction and the other in NW-SE direction, both with relatively steep dips. Considering all downhole m

The Alta-Kvænangen Tectonic Window is part of a regional greenstone belt that formed around 2.3 Ga – 2.0 Ga. The Bergmark anticline in the area has gone through greenschist to amphibolite facies metamorphism and hosts Cu, Co and Au mineralizations in hydrothermal veins, albite felsite and chemical traps. The area was mined during the late 19th century mostly in trenches but it has still got exploration potential. This project will classify carbonate veins that are hosted within two gabbro units, compare orientations between the barren and mineralized veins as well as discuss the origin of the veins. The study area has been affected by multiple extensional and compressional events during the Svecokarelian orogeny (1.9 – 1.8 Ga) and regional hydrothermal alterations including an influx of Na + Ca ± K ± Fe. Major shear zones were activated and used for transporting fluids which are thought to have controlled the formation of mineralization in second- or even third-order structures. The veins were studied using field work, detailed mapping, optical microscopy as well as scanning electron microscopy (SEM) and structural analysis. Results gave three different types of hydrothermal veins: 1) Fe oxide veins, 2) calcite veins and 3) dolomite-plagioclase-quartz veins. All of them show strong signs of deformation and recrystallization all while the chalcopyrite mineralization is heterogeneously distributed and commonly more or less bornite-altered. The veins pinch and swell over short distances and are sinuous but with a general orientation. Albite haloes are common for the veins but far from all veins have haloes. There are at least two generations of veins as some were found to have offset others. Brecciation, tension gashes and faults are also found in the area apart from veins. From the field measurements, two groups of orientations could be determined: one with a SW-NE direction and the other in NW-SE direction, both with relatively steep dips. Considering all downhole m

Th is method is developed for the purpose of helping suppliers to the automotive industry present a potential climate footprint of their proposed products to their customers in a quotation stage. The supplier/producer company is responsible for making a complete inventory of all inputs and outputs of the proposed product in accordance with the D ata C o llection T emplate. The method is based on the modelling of a climate footprint for a fictive average product, that can be used to present an estimated potential climate footprint for future product offers. Th e method was developed as integral parts of a climate footprint project coordinated by FKG, representing Swedish automotive component suppliers. The project resulted in this method, including an inventory tool to be used by supplier s to collect data and an average product model and calculator to estimate a potential clim ate footprint of the suppliers products. The inventory data includes information on supplied materials (types and qualities), transportation ( volume , mode, and distance), and energy sources (types and suppliers) used in production during 2021 or 2022 . This data can be used in simulations for future products. Life Cycle Assessment (LCA) consultants utilize the inventory to construct a simplified cradle to gate model in software tools like SimaPro, LCA for Experts (formerly GaBi )), or other LCA modelling software tool . This model, which employs a “simple cut off for recycled input materials and recyclable materials from production (“simple cut off” according to Ekvall et al. 2020 as recommended by EPD International see further chapter 2 and 3 for scope and modelling )), either utilizes certified climate data (e. EPDs) from sub suppliers or, more commonly, relies on general Ecoinvent data for materials and energy. A simplified LCA model for the average product from the previous year is documented and serves as the baseline for the calculation . Subsequently, a calculator is developed that ca

This comprehensive dissertation explores the real-world emissions of heavy-duty vehicles in goods movement, focusing on regions in California. Spanning five parts, the study assesses the emissions of nitrogen oxides (NOx), black carbon, and particulate matter (PM) from diesel and compressed natural gas (CNG) vehicles, as well as the emerging role of battery electric vehicles (BEVs) in the heavy-duty sector.Chapter 2 evaluates NOx and black carbon emissions from six heavy-duty diesel and CNG vehicles in California's San Joaquin Valley and Sacramento regions. Findings reveal that CNG vehicles emit significantly lower NOx but higher black carbon than diesel vehicles equipped with diesel oxidation catalysts (DOCs) and diesel particulate filters (DPFs). The study highlights the uneven distribution of emissions across urban areas, emphasizing the higher exposure of disadvantaged communities to these pollutants. In Chapter 3, a collaboration between the University of California Riverside and CALSART under the Volvo LIGHTS project assesses emissions from 15 different diesel vehicles. The study identifies variations in emissions based on vehicle types and operational conditions. Notably, yard tractors exhibit higher brake-specific NOx and THC emissions, while Class 8 trucks show significant emissions during idling. The results underscore the need for targeted emission reduction strategies in heavy-duty diesel vehicles. Chapter 4 addresses the potential of heavy-duty BEVs in reducing emissions in the South Coast Air Basin. It compares lab and real-world performance data for BEVs, highlighting their operational patterns, energy consumption, and range. This part emphasizes BEVs as a viable solution for meeting air quality and climate goals in freight transportation. Chapter 5 focuses on the particulate emissions of diesel vehicles in the South Coast Air Basin, particularly under DPF regeneration conditions. The study finds significant emissions of ultrafine particles in disadva

Hydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model-based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot-scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO-56 model), and (4) evaluating the performance of ERT-based ET computation. Our results showed that ERT was able to capture decimeter-scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point-based sensing, which can further our understanding of the hydraulic dynamics in the soil-plant-atmosphere continuum.

The presence of toxic metals in tobacco smoke is well documented. However, few studies have quantified trace metals in secondhand smoke (SHS) and thirdhand smoke (THS). Their presence in indoor air can contribute to nonsmokers’ exposures and health effects. In this study, emission and deposition rates of toxic trace metals were determined, and their airborne concentration in typical indoor scenarios was predicted. PM2.5 was collected on Teflon-coated filters at various times following a smoking event in a room-sized chamber over a 43 h period. The concentration of 28 trace metals was determined by extraction and analysis using inductively coupled plasma-triple quadrupole-mass spectrometry (ICP-QQQ-MS). Emission and indoor deposition rates of cadmium, arsenic, chromium, manganese, beryllium and selenium were determined, and used to predict concentrations expected in a smokers’ home and a smoking bar. In most of the considered scenarios, average indoor concentrations of Cd, As, and Cr exceeded their corresponding cancer risk thresholds and, in some cases, also noncancer reference exposure levels, more than 3 h after smoking ended. The fraction of cadmium that remained airborne was significantly higher than those of other metal traces and that of PM2.5, suggesting an association of Cd traces with small particles.

Monitoring wastewater for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—whether performed at a building, neighborhood, or city level—has emerged as a viable way to track the prevalence of Coronavirus Disease-2019 (COVID-19) in a population. This study assesses health equity implications for wastewater sampling paradigms at a sub-city (or sub-sewershed) scale. Many wastewater-based disease surveillance efforts established during the COVID-19 pandemic relied on convenient points of access to sampling locations within the sewer network and/or voluntary participation within a city or region. Sampling in this way may generate public health data that does not equitably represent diverse populations within the area of interest. In preparation for future pandemics, better strategies are needed to design wastewater sampling frameworks. We help address this knowledge gap by: (1) developing a geospatial analysis tool that probabilistically assigns demographic data for subgroup populations aggregated by race and age (which were cited as major risk factors for severe COVID-19 outcomes) from census blocks to sub-sewershed sampling zones; (2) evaluating the representativeness of subgroup populations and sub-sewershed wastewater data in Davis, California, within the sampling framework employed for COVID-19 disease surveillance; and (3) demonstrating a scenario planning strategy in which adaptive sampling prioritizes vulnerable populations (in this case, populations >65 years old). Extensive sub-sewershed wastewater monitoring data was collected in Davis from November 2021 through September 2022, with wastewater samples collected three times per week from 15 maintenance holes (nodes) and daily from the influent of the city’s centralized wastewater treatment plant (WWTP). The sub-city scale sampling achieved near complete coverage of the population, with spatial resolution that informed public health communication initiatives within the city. Wastewater data aggrega

Antibiotic resistance has emerged as a critical global health threat in the 21st century, leading to untreatable infectious diseases and significantly increasing human morbidity and mortality. Rapid, portable, and user-friendly detection of antibiotics, microbial pathogens, and antibiotic-resistant organisms in aquatic environment plays a key role in controlling the spread of diseases and combating the emergence of antibiotic resistance. Unfortunately, current environmental monitoring practices are tedious, expensive and slow. These limitations impede timely detection and response to emerging threats, allowing the spread of diseases and antibiotic-resistant pathogens to human populations. This dissertation research aims to develop environmental monitoring strategies that streamline the current monitoring timeline and make it accessible to a wider range of users. This effort will contribute to more frequent and efficient monitoring of antibiotics, microbial pathogens, and antibiotic-resistant organisms in water matrices. Through this research, I first investigated the application of Surface Enhanced Raman Scattering (SERS) for label-free monitoring of antibiotics in wastewater. By utilizing carefully designed SERS substrates and artificial intelligence (AI) algorithms, I successfully quantified the target antibiotics even in the presence of other organic and inorganic molecules in wastewater. Next, I developed a portable and semi-automatic virus detection centrifugal microfluidic disc (CD) that integrates sample concentration, purification, amplification, and quantification steps for environmental water monitoring. The on-CD assay is completed in less than 1.5 hours, and its performance is comparable to that of the benchtop in-tube assay. Lastly, building upon the foundation of the virus detection CD system, I further designed a microfluidic CD that integrates the phenotypic bacterial culture with genotypic pathogen identification for monitoring of antibiotic resista

Wildfires are becoming more frequent and intense due to climate change, especially in the southwestern US. Wildfires occur in vulnerable urban areas prone to intense droughts and extreme floods, exacerbating water scarcity issues. Polluted runoff from wildfires can contaminate the limited surface water available for drinking water. To protect downstream water quality, green infrastructure such as stormwater biofilters could be implemented to remove pollutants from post-fire runoff. However, the impacts of wildfire residues on their functions remain unknown. This dissertation aims to understand how green infrastructure can mitigate wildfire impacts on water quality and how to design innovative wildfire-resilient stormwater biofilters.This dissertation specifically addresses four research questions. (1) How and to what extent are wildfires impacting surface water quality? (2) How do wildfire residues affect the physical and chemical functions of stormwater biofilters? (3) How do wildfire residues affect the biological function of stormwater biofilters? (4) Can waste-derived amendments remove pollutants from wildfire residues? Finally, a study explored whether a cohort-based research experience can increase STEM engagement and persistence. To address these questions, I used a combination of field experiments, bench-scale laboratory experiments with column setups, physical-chemical processes, and geochemical and spectroscopic techniques, as well as quantitative and qualitative research methods. Natural stormwater was collected from Ballona Creek in Los Angeles, CA, and wildfire residues were collected from the Santa Monica Mountains, CA. Results showed that wildfires can have a lasting impact on water quality, releasing pollutants into surface water even years after the wildfire. Deposited wildfire residues can negatively affect the infiltration capacity of biofilters by clogging filter media but do not affect their pollutant removal capacity. Wildfire residues did not

The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamfl

In this thesis, the methodology of tailoring and characterizing pore size distribution (PSD) of phase-inverted porous polymeric membranes is conducted with theoretical model simulations and experimental verification. The investigative journey begins with the revealing of the significance of pore size and pore size distribution for membrane performance with the CFD and other classical model analysis and culminates in the systematic fabrication matrix for membrane pore manipulation, which is strongly supported by precise characterizations and distinguished by the illustration of classical hydrodynamic models.Chapter 2 initiates the effects of PSD mean, shape, and scale on water permeance and solute rejection of membranes by using classical analytical models and computational fluid dynamics (CFD), which elaborates on the importance of precisely manipulating membrane pore size and pore size distribution from a theoretical perspective, emphasizing the need to avoid excessively large or small pores. Chapter 3 systematically examines the influence of polymeric membrane sample preparation techniques on their morphologies and structures as revealed by scanning electron microscopy (SEM) to develop a sample characterization method that not only serves as a guide for accurate SEM characterization of polymeric membranes but also as a stepping stone towards standardizing protocols. Chapter 4 involves the fabrication of porous membranes and integrates the surface porosity characterization via SEM and the data derived from contact angle measurements (with dry-wet adjustment ratio applied in wet conditions), establishing a novel method for quantifying the surface porosity of porous membranes through contact angle measurements. Chapter 5 investigates the water permeability and solute rejection of microfiltration (MF) and ultrafiltration (UF) membranes in pressure-driven filtration by integrating the surface porosity, mean pore size, pore size distribution, and skin layer thickness of

Decisions on water allocation to humans and the environment depend on physical engineering structures, various operations and allocation policies, supplies, and demands of numerous end-users. Different assumptions of current and future scenarios can anticipate decisions that best meet human and environmental objectives, under different stressors (e.g., climate change, increased demands). Environmental water allocation especially presents intricate challenges, given the interplay of various regulations and the complexities of managing water resources across different regions. Therefore, the goal of this collection of studies is to provide new insights on reservoir operations, hydropower generation and water management in the Central Sierra Nevada, California, aiming to balance human demands, while achieving greater environmental benefits. This work involves the use of a novel method for water-power modeling with a specific application to the Central Sierra Nevada, California introduced in Chapter 1, and used in Chapters 2 and 4. The modeling framework includes more detailed and facility-specific information to provide a more comprehensive and finer temporal resolution (daily time-step) of water allocation decisions than those found in most modeling efforts. This is a potentially crucial method for modeling water management, due to the reconciliation of water and power systems through the integration of hydroeconomic needs (e.g., hydropower operations) and rule-based simulation (e.g., instream flow requirements), which is one of the biggest challenges in modeling water systems. Better representation of real-world systems is essential to address the difficulties in water management and to analyze solutions. These models are made available for use in a broad range of scenario analyses, including different hydrological inputs (historical and future climates), electricity prices, and a variety of management objectives. Chapter 2 delves into the nuanced landscape of environmental flow (e-flows) requirements, primarily anchored on water year types (WYTs), to understand the efficacy and adaptability of current strategies. Through an extensive examination of pertinent hydropower licensing documents, the research identifies a lack of standardized adoption of WYTs in many river reaches, manifesting as minimal variation across different year types and limited seasonal fluctuations. Incorporating climate change projections from multiple Global Circulations Models, the study reveals significant variability in WYT distributions under existing management strategies. This variability has led to inconsistencies in e-flow management, exacerbating potential conflicts among stakeholders. To address these challenges, an adaptive strategy is proposed, employing a method to recalibrate WYT thresholds, aiming to bolster the reliability and resilience of e-flows. As a result, Chapter 3 critically analyzes the systemic barriers hindering the effective implementation of e-flows. A comprehensive systematic review and bibliometric analysis were conducted, yielding insights into the major impediments such as competing priorities of human water uses, data deficiencies, and resource and capacity limitations. To enhance the successful implementation of e-flows, the dissertation recommends a system analysis approach, utilizing modeling tools to navigate competing demands and foster holistic flow allocations based on hydroecological principles. In turn, Chapter 4 evaluates the resilience of water systems and hydropower against climate whiplash. Through 200 synthetic hydrologic sequences of different lengths of dry-wet-dry combinations, the research underscores the vulnerability of water storage and the implications for water resource management, offering policy suggestions to enhance system flexibility and resilience against climatic shocks. Finally, Chapter 5 concludes by providing policy insights and recommendations based on these studies to help inform stakeholders and decision-makers in the search for sustainable solutions to water management problems.

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

This dissertation investigates the synthesis, characterization, and application of electrically conducting carbon nanotube (CNT) porous structures for electromagnetic interference (EMI) shielding and chemical production. The study addresses two critical challenges: enhancing the electrical conductivity and mechanical strength of CNT films for effective EMI shielding, and developing energy-efficient methods for caustic (NaOH) production.First, we present a facile one-step method for fabricating pure and metal-decorated densified CNT films. These films exhibit high electrical conductivity (~106 S m-1) and exceptional EMI shielding effectiveness (EMI SE) of 99.999992% (71 dB) at frequencies between 8.2 GHz and 12.4 GHz with a thickness of 14.3µm. The densification process reduces contact resistance between neighboring CNTs, significantly enhancing the overall conductivity and mechanical strength of the films. Furthermore, decorating the CNT films with a thin layer of gold increases the EMI SE from 56.67 dB to 66.12 dB, demonstrating the potential of these materials for protecting modern lightweight and compact electronic devices from electromagnetic interference.Second, we explore the electrochemical production of caustic using a flow-through membrane/cathode electrolysis process. Traditional chlor-alkali processes for caustic production are energy-intensive and require high-purity feedstocks, high concentrations of NaCl, and elevated operating temperatures. Our innovative process produces caustic solutions (pH 10.22-12.26) at a specific energy consumption (SEC) of 1.71 kWhe/kg NaOH using a 3.5% NaCl solution at room temperature, while also generating pure hydrogen. The high flow rates through the cathode enhance mass transport and facilitate hydrogen gas stripping, reducing energy consumption and operational complexity. This method is particularly suitable for on-site caustic production in carbon capture and sequestration (CCS) applications, where increasing the alkal

Eulerian-Lagrangian (EL) models are developed that account for stochasticity and randomness in tracers of inertial particles forced by a carrier flow phase. Central to the novelty of the models is a forcing formulation that uses a series expansion with random coefficients to account for epistemic and aleatoric uncertainties, in lieu of commonly used stochastic, random-walk processes.Starting from randomly forced ordinary differential equations that govern the Lagrangian inertial point-particle tracer dynamics, Lagrangian cloud and Liouville models are derived. Both cloud and Liouville models are closed and are shown to more accurately and computationally efficiently predict the propagation of the forcing randomness into confidence intervals of the particle phase solution as compared to Monte Carlo sampling methods.The closed and predictive particle cloud tracer models the mean motion and deformation of a cloud of inertial particles at a singular point in space and along its Lagrangian trajectory in time. The tracer builds upon the Subgrid Particle-Averaged Reynolds Stress Equivalent (SPARSE) formulation first introduced in Davis et al. (2017) for the tracing of particle clouds. Using a combination of the forcing models, averaging and a truncated Taylor series expansion to estimate the statistical correlations in the cloud region, the SPARSE model is closed and achieves a third convergence for the confidence interval with respect the number of samples.The Liouville models are rigorously derived with the method of distributions and do not require truncation or ad-hoc assumptions. The deterministic PDF models are described by hyperbolic partial differential equations (PDEs). In Eulerian form, the PDEs are solved with grid-based spectral methods. To recover the Lagrangian character of the disperse phase, the method of characteristics is employed to derive a PDF formulation based on the computation of flow maps, circumventing difficulties of solving high-dimensional PDE

We present a new pore-scale model for multicomponent advective-diffusive transport with coupled mineral dissolution and precipitation. Both dissolution and precipitation are captured simultaneously by introducing a phase transformation vector field representing the direction and magnitude of the overall phase change. An effective viscosity model is adopted in simulating fluid flow during mineral dissolution-precipitation that can accurately capture the velocity field without introducing any empirical parameters. The proposed approach is validated against analytical solutions and interface tracking simulations in simplified structures. After validation, the proposed approach is employed in modeling realistic rocks where mineral dissolution and precipitation are dominant at different locations. We have identified three regimes for mineral dissolution-precipitation coupling: (a) compact dissolution-precipitation where dissolution is dominant near the inlet and precipitation is dominant near the outlet, (b) wormhole dissolution with clustered precipitation where dissolution generates wormholes in the main flow paths and precipitation clogs the secondary flow paths, and (c) dissolution dominant where all solid grains are gradually dissolved. In the three regimes, the proposed approach provides reliable porosity-permeability relationships that cannot be described well by traditional macroscale models. We find that the permeability can increase while the overall porosity decreases when the main flow paths are expanded by dissolution and adjacent pore spaces are clogged by precipitation.

The long-lived radiotoxicity of the high-level radioactive waste generated by nuclear power plants requires safe isolation from the biosphere for many hundreds of thousands of years. An international consensus has emerged that such isolation can best be provided by disposal in mined geologic repositories, a strategy that today is pursued by most countries dealing with radioactive waste. However, the need to predict the performance of such repositories over very long time periods generates large uncertainties that have to be accounted for in safety assessments. The findings from such safety assessments need to be conveyed to all stakeholders in a clear way, such that public confidence in geologic disposal solutions can be achieved. It is suggested here that close international collaboration on the technical aspects of geologic waste disposal has helped, and will continue to help, building trust and increasing confidence. This paper discusses a particular international collaboration initiative referred to as DECOVALEX, which brings together multiple teams and disciplines to collectively tackle complex experimental and modeling challenges related to geologic disposal. By describing how DECOVALEX works and by providing joint research examples, a case is made that such international collaboration contributes to knowledge transfer and confidence building in radioactive waste disposal science.

The rise of global temperatures and greenhouse gas emissions establish the critical importance of sustainable development strategies, particularly in material efficiency. The Building sector is a large contributor of global CO2 emissions. The growing global population drives demand for residential construction, amplifying the need for alternative building materials and methods to mitigate carbon emissions while meeting housing needs. One potential solution lies in repurposing shipping containers, with millions circulating globally each year, making it an ideal resource for structural construction material. This thesis aims to quantify and compare the environmental impact of framing materials used in residential construction, specifically standard wood framing and Intermodal Steel Building Unit (ISBU) framing. ISBUs are the term for ISO shipping containers repurposed for building construction. We model our dimensions from an off-grid shipping container home designed and built by Team UCSC, EcoHus, to conduct an Eco Audit of manufacturing stages of the building lifecycle using Ansys®Granta EduPack 2021 software. We found that the materials used for the standard wood frame home require 2.67 times more energy and emit 2.31 times more CO2 than the ISBU framing. Additionally, we examine the sustainable attributes of EcoHus, including its integration of renewable energy and water systems. While acknowledging the considerable work ahead, this research contributes to the exploration of advancing sustainable practices in residential building construction and aims to guide future endeavors in decarbonizing the building sector through sustainable design strategies.

Rooftop and community solar are alternative product classes for residential solar in the United States. Community solar, where multiple households buy solar from shared systems, could make solar more accessible by reducing initial costs and removing adoption barriers for renters and multifamily building occupants. Here we test whether community solar has expanded solar access in the United States. On the basis of a sample of 11 states, we find that community solar adopters are about 6.1 times more likely to live in multifamily buildings than rooftop solar adopters, 4.4 times more likely to rent and earn 23% less annual income. We do not find that community solar expands access in terms of race. These differences are driven, roughly evenly, by inherent differences between the two solar products and by policies to promote low-income community solar adoption. The results suggest that alternative solar products can expand solar access and that policy could augment such benefits.

Correction to: Nature Energyhttps://doi.org/10.1038/s41560-024-01546-2, published online 3 June 2024 In the version of the article initially published, data in Fig. 5b–d were displayed incorrectly and have now been amended in the HTML and PDF versions of the article. The original and corrected figures can be seen below in Fig. 1. (Figure presented.)

Advancements in air quality research and the demand for more accurate environmental health assessments have highlighted the need for methodologies that bridge the gap between laboratory findings and real-world conditions. This dissertation includes a retrospective of maritime emissions, and a deep analysis of the operational and emission parameters on newest container ships, all with the goal of improving regulations for ship air pollution and reducing personal exposure. The thesis then provides evaluation to establish a novel aerosol delivery method for health studies, aiming to enhance the management of air pollution and its health implications.Maritime shipping, a major fossil fuel and criteria emissions source, has been historically understudied, and so emissions controls and policy have been misapplied. A historic technical review was conducted, utilizing an extensive internal dataset to develop new insights into the emissions profile of ocean-going vessels (OGVs). This analysis provides an understanding of the pollutants released by these ships across decades of regulatory changes and suggests pathways for more effective regulatory strategies. In the subsequent chapter, this work applies GPS monitoring on active container ships, combined with in-use engine operational data and emissions testing, to develop an understanding of how vessel operations modify the contributions to air pollution. This approach offers a significant advancement over traditional engine load-based emission estimations, providing guidance to improve the accounting of emissions in real-time and historically, under real-world operating conditions, and identifying areas of regulatory oversight. This granularity enables the identification of specific maneuvers and activities, particularly in environmentally sensitive areas, that disproportionately affect emission levels.The final chapter evaluates a recently introduced method for aerosol delivery in biomedical studies, designed to mimic real

On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others' energy demands has considerable decarbonization potential.

This dissertation provides investigation and evaluation of different types of renewable and alternative fuels impact on reducing emissions of critical pollutants on light-duty vehicles and heavy-duty and off-road equipment and marine related emissions. It is important to understand fuel impact on emissions characteristics and marine related emissions to be able to facilitate policy making. This dissertation assessed the potential of increasing ethanol content in California reformulated gasoline (CaRFG) by investigating the exhaust emissions from a fleet of 20 light-duty vehicles. A baseline CaRFG E10 (10% ethanol by volume) fuel was splash blended with denatured ethanol to create a E15 fuel. Each vehicle/fuel combination were exercised over triplicate Federal Test Procedure (FTP) cycles on a chassis dynamometer. Emissions measurement included nitrogen oxides (NOx), total hydrocarbons (THC), non-methane hydrocarbons (NMHC), carbon monoxide (CO), particulate matter (PM) and solid particle number (SPN), greenhouse gases (GHG) and volatile organic compounds (VOCs). Life cycle GHG emissions and ozone forming potential analysis were also performed. The dissertation also discussed gaseous and particulate emissions from natural gas and diesel-powered yard tractors tested on different driving cycles, some being representative of typical yard tractor operation. The natural gas yard tractors were operated with fuel blends of varying compositions, including a renewable natural gas (RNG) fuel. Emissions measurement included NOx, THC, CO, PM, SPN, GHG. Life cycle GHG emissions analysis was performed. This thesis also quantified emissions and performance effects, using an engine dynamometer, with RD fuel and RD/BD fuel blend relative to a petroleum-derived conventional ultra-low sulfur diesel (conventional CARB ULSD) reference fuel. This study was conducted using an agricultural engine without selective catalytic reduction (SCR) exhaust treatment and without a diesel particulate f

Community solar, a business model where multiple customers buy output from shared solar systems, has expanded solar access among multifamily housing occupants, renters, and low-income households. Policies to enable community solar could be expanded and benefits of access augmented through targeted measures to support community solar adoption in underserved communities.

In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high-resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station-based SWE observations are used as the prediction target. All DL models produce higher median Nash-Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

For a diverse area of research, spanning from financial markets to weather and climate systems to experiments conducted in turbulent flows, the most common form of data belongs to the category of time series. In the context of turbulence research, the time series analysis techniques have mostly focused on spectral approach, where Fourier wavenumbers or frequencies are associated with eddy length or time scales. In this thesis, I propose an event-based approach as an alternative to the spectral one. This approach is used to address three important but fundamental problems in wall turbulence. First, identification of coherent structures in the flow from single-point time series measurements. Second, unravelling the scales associated with intermittency in wall turbulence. Third, the quantification of small-scale anisotropy in wall-bounded turbulent flows. To address these three objectives, different novel time-series analysis techniques have been introduced. Regarding the first objective, I introduce a level-crossing method and show how specific features of a turbulent time series associated with coherent structures can be objectively identified, without assigning a priori any arbitrary threshold. For the second and third objectives, I introduce a scale-dependent event framework, where the turbulent time series is considered to consist of a chronicle of events with finite size and duration across multiple scales of the flow. Overall, this dissertation provides a novel contribution towards data-driven modelling of turbulent flows with widespread applications, especially for atmospheric systems exhibiting complex temporal interactions at scales from seconds to decades.

Natural conversion of ferrihydrite (Fh), a widespread Fe(iii)-oxyhydroxide mineral at the Earth's surface, to thermodynamically more stable iron oxides such as goethite (Gt) and hematite (Hm) is a slow process that spans months to years. Here we examined the effects of synthesis and storage conditions on the hydration, the ratio of tetrahedral to octahedral iron sites, and the transformation of naturally aged 2-line Fh at room temperature and mildly acidic pH over an ageing period of 5 years. Fh samples synthesized and aged in either aerobic or anaerobic conditions were characterized over time by XRD, SEM, thermogravimetric analysis - mass spectroscopy (TGA-MS), and X-ray absorption spectroscopies (XANES and XMCD). The findings show that the ratio of tetrahedral to octahedral Fe(iii) sites in Fh is correlated to its extent of hydration, with fresher Fh samples exhibiting a higher ratio and more bound water. Fresh Fh aged in aerobic conditions has similar bound inorganic carbon, is more hydrated, and has less tetrahedral Fe(iii) than that aged in anaerobic conditions. Hence, for relatively fresh Fh there is a link between Fh properties and storage conditions. However, the long-term ageing characteristics, such as the transformation rate and relative phase fraction of Gt and Hm products, are not noticeably impacted by storage conditions. TGA-MS measurements coupled with O K-edge XANES spectra confirm that Fh tends to lose its hydration as it ages, as expected. Corresponding Fe L2,3-edge XMCD spectra reveal that this dehydration is coupled to a steady decrease in the ratio of tetrahedral to octahedral Fe(iii) sites. In addition to the obvious constraints these findings place on making comparisons across Fh samples of different age and environmental settings, they also highlight that Fh structure, and consequently magnetism, are linked to its bound water content.

Untargeted sequencing of total RNA in wastewater, known as metatranscriptomics, has recently garnered interest due to its potential for detecting outbreaks and circulation of pathogens and their variants, especially those of RNA viruses. Both the liquid and settled solids fractions of wastewater influent have been successfully used and compared as starting material for community-level surveillance of select targets such as SARS-CoV-2 using quantitative PCR and to a limited extent, targeted sequencing. We seek to complement such comparative studies using a holistic, i.e. untargeted microbiome sequencing, approach to understand the differences in microbial diversity that can be captured between the two sample types, which may inform our decision surrounding choice of sample type for future routine PCR-based surveillance programs which aim to include a broader range of targets. We present a feasibility study for using total untargeted RNA sequencing for the differentiation of microbial abundance and diversity within liquid influent and settled solids samples. Raw wastewater samples were collected from two wastewater treatment plants—City of Davis and University of California at Davis—in January 2022 and March 2022. Extraction of total nucleic acids from liquid influent and settled solids were performed using protocols optimized for PCR-based surveillance of SARS-COV-2, followed by mRNA capture and untargeted library preparation for Illumina shotgun sequencing. For data analysis, we evaluated two open access tools requiring varying skill levels: Chan Zuckerberg ID (CZID), a cloud-based analysis and visualization pipeline with a web-based interface and Kraken2, a script-based pipeline that requires knowledge of the command line interface. We found no significant differences in overall microbial richness, alpha diversity and relative abundance between influent and settled solids samples, though beta diversity analysis suggested samples within a sample group (combination o

Limiting the rise of global mean temperatures and stabilizing Earth’s climate will require achieving net-zero emissions of long-lived greenhouse gases (GHGs) [1]. However, ongoing (residual) emissions from difficult-to-decarbonize sources, such as those from chemical reactions for commonly used products, [2,3] will need to be balanced by removal of carbon dioxide (CO2) or other GHGs from the atmosphere (hereinafter “CDR”) [4]. Enabling CDR via materials is a logical first step given the already large and growing demand for materials, eliminating the need to develop new industries for CDR. One such hard-to-carbonize material is plastic, with 99% of plastic materials made from fossil fuels. Driven by growing single-use consumer applications, the rate at which plastics are produced and disposed of is outpacing most other human-made materials. Producing plastic from bio-based feedstocks is a commonly discussed method to mitigate impacts of fossil-based plastic production and, more recently, act as a CDR mechanism. In this work, numerous pathways that could support bio-based plastics acting as a CDR strategy on a global scale by 2050 are presented. Due to their high technology-readiness and to promote a circular bioeconomy, this study focuses on the utilization of non-edible biomass resources as feedstocks for plastic production, rather than the formation of plastics from CO2 capture and utilization. Pathways are presented which consider the level of bio-based plastic market replacement, the type of energy resources used for production, as well as the prevalence of different waste management practices to systematically assess what levers need to be pulled to enable CDR in plastics. Production pathways and associated life cycle inventories for bio-based plastics from 2nd and 3rd generation feedstocks resulting in CDR are derived. To model end-of-life (EoL) impacts, a review is conducted to examine the biodegradation behavior and associated GHG emissions from bio-based pla

As the second most populous urban area in the United States, the South Coast Air Basin (SoCAB) of California has long suffered threats from degraded air quality, which is harmful to human health. While regulation efforts have significantly improved air quality over the past six decades, the SoCAB remains in violation of both ozone (O3) and fine particulate matter (PM2.5) National Ambient Air Quality Standards. Moreover, the inequality of air pollutant exposure in disadvantaged communities has emerged as a prominent concern while there is a lack of robust community-scale air quality modeling for addressing the equality in prior studies. In this dissertation, the contribution of meteorological variability and anthropogenic emission changes to deteriorated O3 pollution are examined through state-wide CMAQ simulations (4×4 km). Further, a high-resolution (1×1 km) CMAQ model is developed to assess changes in air pollutant levels and exposure disparities for various racial/ethnic groups over the SoCAB between 2012 and 2018, aiming at revealing the effectiveness of regulation policies in reducing air pollution and improving environmental justice. A selected set of future deep decarbonization scenarios are adopted to examine local impacts of decarbonization on the SoCAB and disadvantaged communities, particularly for a community situated near the Ports of Long Beach. The results show that regulation efforts have achieved substantial decreases in nitrogen dioxide (NO2) and O3 concentrations over inland regions of SoCAB, while PM10, PM1, and ultrafine fine particles (UFPs) exhibited slight increases. Further analysis focused on exposure among different racial/ethnic groups pinpoints that, although absolute disparities of air pollution have fallen while relative disparities persist over time. The co-benefits of air quality and human health in future scenarios indicate that deep decarbonization could significantly diminish PM2.5 concentrations, subsequently leading to reduced m

The continued increase in global demand for construction materials is driving notable environmental burdens from their production, particularly greenhouse gas (GHG) emissions. In turn, there is growing interest from policy makers, industry sectors, and academics to derive emissions mitigation strategies that can support achieving the global goal of net-zero CO2 emissions by mid-century. To meet these aims, collective and swift action is required to evaluate the efficacy, feasibility, and equity of GHG mitigation strategies. Yet, current methods do not consistently support systematic assessments leveraging open data sharing platforms, integration of potential co-benefits or unintended consequences of decarbonization efforts on other environmental impacts, the implications for neighboring communities near industrial facilities, or routes to train the next generation of engineers to adequately deal with these complexities. This research aims to address these research gaps through the formulation of open-datasets and tools, a net-zero emissions roadmap that integrates technology readiness, cost, and strategy efficacy, a geographic-driven investigation of disproportionate impacts, and engineering curricula advancements. In the first stage and second of this research, the cement and concrete industries are examined. Namely, systematic, quantitative methods for addressing the role of varying parameters to alter cement and concrete GHG emissions are derived using openly accessible data. Building from these methods, routes to reaching net-zero GHG emissions are examined for the cement industry in California, where there is a mandate to mitigate these emissions by 2045. In the analysis of routes to net-zero emissions, the effects of decarbonization strategies on the creation of air pollutants, which are a local impact that burden neighboring communities (unlike climate change, which is a global impact), are also considered. Noting the imperative that decarbonization efforts d

Groundwater provides 40–60% of California's water supply. However, domestic wellwater quality is not regulated in California due to its decentralized nature and high cost of testing. Many well water users rely on regional groundwater monitoring data (e.g. Groundwater Ambient Monitoring and Assessment) for information about their water quality but there remains a need for an integrated approach that combines place-based quantitative analysis with cancer risk assessment and community engagement. Our pilot study aims to build relationships and science literacy with private well water users through compensated community sampling of California well water and provision of quantitative reports including 22 inorganic constituents (e.g. lead, arsenic, nitrate) with primary and secondary MCLs. Additionally, the pilot study seeks to investigate the impacts of seasonality, land use, and climate change phenomena (e.g. wildfire, drought, floods) on well water quality. Participants (n=113) were recruited in collaboration with our community partner, the Environmental Justice Coalition for Water. Chemical analyses were performed at UC Davis and used to generate water quality reports and to assess potential public health risks and resources for remediation (see Figure A6 in Appendix). Of the 113 well samples tested, 14 (12.4%) had at least one constituent exceedance of Primary Maximum Contaminant Levels (MCLs), 74 (64.5%) had at least one constituent exceedance of Secondary MCLs, and all had at least one constituent exceedance of Public Health Goals (PHGs) and Notification Levels (NLs) (e.g. Lead (57/57), Uranium (28/57), Arsenic (12/57), Cadmium (11/57)). This study will help study participants assess the health risks associated with consumption of their well water and provides the infrastructure to scale up to a larger community-based groundwater quality monitoring program.

This thesis introduces the Functional Flows Adaptive Implementation Model (FFAIM), a framework for distributing a variable environmental flow budget throughout the year using a Functional Flows approach to prioritize key features of the natural flow regime that structure hydrologic seasonality for healthy ecosystem self-regulation. This framework provides (1) continuous scaling of functional environmental flow schedules by water year percentile and (2) an optimization structure for real-time adaptive operation of an annually varying environmental flow budget with periodically updated unimpaired flow forecasts. This functional flow implementation shows how unimpaired flow forecasts can inform environmental flow operations that preserve natural patterns of interannual and seasonal flow variability with a limited proportional environmental flow budget. This framework is demonstrated for the Lower Tuolumne River to shape and shift flows in accordance with the 2018 Water Quality Control Plan for the San Francisco Bay/Sacramento-San Joaquin Delta Estuary or Bay-Delta Plan (Bay-Delta Plan).

Salinity and nitrate leaching in California's Central Valley significantly impair agricultural productivity and groundwater sustainability. Through extensive biophysical and agrohydrological modeling and empirical field data, the research quantifies the impacts of these environmental stressors and offers evidence-based strategies for mitigating their effects. A biophysical model that incorporates soil, weather, crop data, water cost, and production costs predicted that salinity levels exceeding 5 dS/m can reduce crop yields by up to 45% for highly sensitive crops like almonds and table grapes. On the other hand, these crops could profit growers more because of their market values. The economic analysis shows that the profitability of agricultural production under different salinity scenarios varies significantly, with potential annual economic losses amounting to hundreds of millions of dollars, underscoring the critical need for tailored salinity management strategies. A salinity decision-support web tool was developed. This tool enables users to input data and predict crop yields and economic outcomes. The tool's application in real-world scenarios has shown that it can effectively assist farmers and policymakers in making informed decisions that optimize economic returns and environmental sustainability.Concurrently, the dissertation evaluates the effectiveness of various conservation practices, such as cover crops, irrigation nitrogen credits, and high-frequency low fertigation, in reducing nitrate leaching. Utilizing the APEX model, the results demonstrate that these practices can reduce nitrate leaching of the root zone by up to 90%, thereby substantially decreasing the risk of groundwater contamination. This study also evaluated an integrated AMRS model to assess and manage nitrate nitrogen (NO3-N) leaching into groundwater at a field scale. The model was evaluated at the deep vadose zone and shallow groundwater and showed that conservation practices such as

Terrestrial production and export of dissolved organic and inorganic carbon (DOC and DIC) to streams depends on water flow and biogeochemical processes in and beneath soils. Yet, understanding of these processes in a rapidly changing climate is limited. Using the watershed-scale reactive-transport model BioRT-HBV and stream data from a snow-dominated catchment in the Rockies, we show deeper groundwater flow averaged about 20% of annual discharge, rising to ∼35% in drier years. DOC and DIC production and export peaked during snowmelt and wet years, driven more by hydrology than temperature. DOC was primarily produced in shallow soils (1.94 ± 1.45 gC/m2/year), stored via sorption, and flushed out during snowmelt. Some DOC was recharged to and further consumed in the deeper subsurface via respiration (−0.27 ± 0.02 gC/m2/year), therefore reducing concentrations in deeper groundwater and stream DOC concentrations at low discharge. Consequently, DOC was primarily exported from the shallow zone (1.62 ± 0.96 gC/m2/year, compared to 0.12 ± 0.02 gC/m2/year from the deeper zone). DIC was produced in both zones but at higher rates in shallow soils (1.34 ± 1.00 gC/m2/year) than in the deep subsurface (0.36 ± 0.02 gC/m2/year). Deep respiration elevated DIC concentrations in the deep zone and stream DIC concentrations at low discharge. In other words, deep respiration is responsible for the commonly-observed increasing DOC concentrations (flushing) and decreasing DIC concentrations (dilution) with increasing discharge. DIC export from the shallow zone was ~66% of annual export but can drop to ∼53% in drier years. Numerical experiments suggest lower carbon production and export in a warmer, drier future, and a higher proportion from deeper flow and respiration processes. These results underscore the often-overlooked but growing importance of deeper processes in a warming climate.

Most of the available data on diffusion in natural clayey rocks consider tracer diffusion in the absence of a salinity gradient despite the fact that such gradients are frequently found in natural and engineered subsurface environments. To assess the role of such gradients on the diffusion properties of clayey materials, through-diffusion experiments were carried out in the presence and absence of a salinity gradient using salt-diffusion and radioisotope tracer techniques. The experiments were carried out with vermiculite samples that contained equal proportions of interparticle and interlayer porosities so as to assess also the role played by the two types of porosities on the diffusion of water and ions. Data were interpreted using both a classical Fickian diffusion model and with a reactive transport code, CrunchClay that can handle multi-porosity diffusion processes in the presence of charged surfaces. By combining experimental and simulated data, we demonstrated that (i) the flux of water diffusing through vermiculite interlayer porosity was minor compared to that diffusing through the interparticle porosity, and (ii) a model considering at least three types of porous volumes (interlayer, interparticle diffuse layer, and bulk interparticle) was necessary to reproduce consistently the variations of neutral and charged species diffusion as a function of salinity gradient conditions.