Browsing by Author "Zhang, Yong"
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Item Anthropogenic and environmental drivers of the input and uptake of dissolved organic matter in temperate streams(University of Alabama Libraries, 2019) Shang, Peng; Lu, Yuehan; University of Alabama TuscaloosaDissolved organic matter (DOM) is a complex mixture of organic compounds and plays an essential role in regulating substrate and energy flows in aquatic ecosystems. However, environmental factors and biogeochemical mechanisms mediating the supply and uptake of DOM in streams are not well understood. The overarching goal of this dissertation is to assess the effects of the anthropogenic and natural drivers on the amount, source, composition, and fate of DOM in streams. The objective of Chapter II is to understand the effects of agricultural activities on DOM in a regional group of streams in Southeastern Alabama. The main finding is that agricultural land use increases DOC concentration and the proportions of terrestrial and microbial humic DOM compounds in streams, which suggests that agricultural activities accelerate the mobilization of organic matter from topsoils via enhancing oxidation, erosional transport, and shifting soil-to-stream flow paths. The objective of Chapter III is to identify the environmental drivers controlling the supply of terrestrial DOM in a Coastal Plain stream draining a forest-dominated watershed. The main finding is that discharge can be used to predict DOM supply across timescales, but other environmental drivers could be important at a given timescale. Specifically, the event-scale DOM supply is influenced by antecedent hydrological conditions and the duration of storms. At the diurnal scale, DOM variation is driven by physical dilution and concentration due to evapotranspiration. At the seasonal scale, DOM variation is mediated by organic matter availability from litterfall and discharge. The objective of Chapter IV is to determine the rates of natural DOM removal and identify the associated biogeochemical mechanisms in a second-order stream draining a forest-dominated watershed. The results provide the first record simultaneously measuring the uptake characters of humic-like and protein-like DOM, which demonstrates that humic-like DOM has a shorter uptake length and higher uptake velocity than protein-like DOM due to the preferential adsorption of humic-like compounds to benthic sediments. This dissertation improves our understandings of the supply and demand of DOM in subtropical streams in response to human land use and hydrological events, contributing to a greater understanding of the factors mediating the aquatic ecosystem response.Item Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19(Pergamon, 2020) Zhang, Yong; Yu, Xiangnan; Sun, HongGuang; Tick, Geoffrey R.; Wei, Wei; Jin, Bin; University of Alabama Tuscaloosa; Hohai University; Nanjing Normal University; Nanjing Medical UniversityFractional calculus provides a promising tool for modeling fractional dynamics in computational biology, and this study tests the applicability of fractional-derivative equations (FDEs) for modeling the dynamics and mitigation scenarios of the novel coronavirus for the first time. The coronavirus disease 2019 (COVID19) pandemic radically impacts our lives, while the evolution dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed over the last three months (up to 3/22/2020) in China. The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The validated SEIR model was then applied to predict COVID-19 evolution in the United States, Italy, Japan, and South Korea. In addition, a time FDE model based on the random walk particle tracking scheme, analogous to a mixing-limited bimolecular reaction model, was developed to evaluate non-pharmaceutical strategies to mitigate COVID-19 spread. Preliminary tests using the FDE model showed that self-quarantine may not be as efficient as strict social distancing in slowing COVID-19 spread. Therefore, caution is needed when applying FDEs to model the coronavirus outbreak, since specific COVID-19 kinetics may not exhibit nonlocal behavior. Particularly, the spread of COVID-19 may be affected by the rapid improvement of health care systems which may remove the memory impact in COVID-19 dynamics (resulting in a short-tailed recovery curve), while the death toll and mitigation of COVID-19 can be captured by the time FDEs due to the nonlocal, memory impact in fatality and human activities. (C) 2020 Elsevier Ltd. All rights reserved.Item Assessing the Robustness of Deep Learning Streamflow Models Under Climate Change(University of Alabama Libraries, 2022) Qualls, Logan Michelle; Tick, Geoffrey R.; University of Alabama TuscaloosaLong Short-Term Memory networks provide the most accurate rainfall-runoff predictions to-date, but their reliability under climate change is not well understood. We explore the robustness of these models under climate nonstationarity by creating train and test data splits thatare designed to simulate climate bias. By training on forcing data from hydrological years of high (low) aridity and testing on data from hydrological years of low (high) aridity, we can begin to quantify the performance and relative robustness of that performance under climate nonstationarity. We benchmark against a calibrated conceptual model (the Sacramento Soil Moisture Accounting model) and a calibrated process-based model (the NOAA National WaterModel), and found that LSTMs were generally more accurate than both, even when trained on climatologically biased data splits. The process-based model did not show as large of a performance gap as the conceptual and deep learning models, however (i) this model was not calibrated on a climate-biased data split and (ii) LSTMs always out-performed the process-based benchmark, even when the LSTM training data had climatological bias. We find that although all hydrologic models reported here degrade under nonstationarity, DL models demonstrate greater robustness. We also tested the hypothesis that dynamic climate attributes as inputs into the LSTM would improve performance under climate nonstationarity. We found no predictive value with the addition of dynamic, as opposed to static, climate attribute inputs.Item Assessment of Groundwater Susceptibility to Non-Point Source Contaminants Using Three-Dimensional Transient Indexes(MDPI, 2018) Zhang, Yong; Weissmann, Gary S.; Fogg, Graham E.; Lu, Bingqing; Sun, HongGuang; Zheng, Chunmiao; Hohai University; University of Alabama Tuscaloosa; University of New Mexico; University of California Davis; Southern University of Science & TechnologyGroundwater susceptibility to non-point source contamination is typically quantified by stable indexes, while groundwater quality evolution (or deterioration globally) can be a long-term process that may last for decades and exhibit strong temporal variations. This study proposes a three-dimensional (3-d), transient index map built upon physical models to characterize the complete temporal evolution of deep aquifer susceptibility. For illustration purposes, the previous travel time probability density (BTTPD) approach is extended to assess the 3-d deep groundwater susceptibility to non-point source contamination within a sequence stratigraphic framework observed in the Kings River fluvial fan (KRFF) aquifer. The BTTPD, which represents complete age distributions underlying a single groundwater sample in a regional-scale aquifer, is used as a quantitative, transient measure of aquifer susceptibility. The resultant 3-d imaging of susceptibility using the simulated BTTPDs in KRFF reveals the strong influence of regional-scale heterogeneity on susceptibility. The regional-scale incised-valley fill deposits increase the susceptibility of aquifers by enhancing rapid downward solute movement and displaying relatively narrow and young age distributions. In contrast, the regional-scale sequence-boundary paleosols within the open-fan deposits protect deep aquifers by slowing downward solute movement and displaying a relatively broad and old age distribution. Further comparison of the simulated susceptibility index maps to known contaminant distributions shows that these maps are generally consistent with the high concentration and quick evolution of 1,2-dibromo-3-chloropropane (DBCP) in groundwater around the incised-valley fill since the 1970s'. This application demonstrates that the BTTPDs can be used as quantitative and transient measures of deep aquifer susceptibility to non-point source contamination.Item Comparing the magnitude and mechanisms of submarine groundwater discharge (SGD) and associated nutrient fluxes in estuaries and coastal karst systems: the examples of mobile bay (usa) and maro-cerro gordo (spain)(University of Alabama Libraries, 2018) Montiel Martin, Daniel Agustin; Dimova, Natasha T.; University of Alabama TuscaloosaSubmarine groundwater discharge (SGD) is an important source of natural and anthropogenic nutrients and contaminants in coastal waters. Nutrient inputs from SGD can cause or exacerbate eutrophication, hypoxia, seagrass beds degradation, and harmful algal blooms (HABs), among other ecological impacts. Coastal karst systems and estuaries are among the most complex coastal areas, where the assessment of SGD and derived nutrient fluxes is particularly challenging. Their typically heterogeneous hydrogeology combined with temporal fluctuations of marine and terrestrial forcing result in large variations of SGD in both systems. In this dissertation I evaluated the magnitude and mechanisms driving SGD and its importance as a source of water and nutrients to Maro-Cerro Gordo (a coastal karst system in southern Spain) and Mobile Bay (an estuary of the northern Gulf of Mexico). In Maro-Cerro Gordo I found that SGD accounted for an important part of the water budget of the coastal karst aquifer, the only source of freshwater for nearby population and agricultural activities. Additionally, SGD served as a vector for NO3- fertilizers contamination to the sea, putting at risk the endangered species of the coastal ecosystem. In Mobile Bay I found that 1/4 of the nutrient budget is delivered by SGD during the dry season as NH4+ (56% of the total) and DON (15% of the total), mostly on the east shore, where Jubilees occur. I demonstrated that these SGD-derived nutrient inputs, in contrast to previous hypotheses, are originated naturally from organic matter mineralization in a peat layer found only on the east shore of the bay. In comparison, groundwater discharge in Maro-Cerro Gordo was primarily controlled by the terrestrially driven hydraulic gradient of the karst aquifer, while in Mobile Bay SGD was mainly marine-driven by sea level fluctuations (tidal pumping). Furthermore, the extremely fast groundwater flow of the karst aquifer in Maro-Cerro Gordo always created oxic conditions, allowing the NO3- contamination to reach the sea. In contrast, in the shallow coastal aquifer of Mobile Bay I found that, while the main form of nitrogen in inland fresh groundwater was NO3-, the SGD-derived nitrogen inputs to the bay was almost entirely in the form of NH4+. These large fluxes of NH4+ were produced by two main processes: organic matter mineralization and dissimilatory nitrate reduction to ammonium (DNRA).Item Concentration-Discharge Responses of Water Quality Metrics to Hydrological Events in a Coastal Plain Forested Watershed(University of Alabama Libraries, 2023) Ahmad, Sakinat Mojisola; Lu, YuehanRecent studies have continued to support the forecast that the Gulf Coast will experience more devastating storms in the coming years. This would enormously affect water quality, including material export from watersheds to downstream ecosystems. Also, streams in the southeastern United States are hot spots for organic carbon export, an important component in carbon budgeting and climate change prediction. To improve our understanding of stream water quality's response to storms, this study assessed the patterns of the concentration-discharge (C-Q) hysteresis during storms using high-resolution data of fluorescence Dissolved Organic Matter (fDOM), specific conductance, chlorophyll-a, Dissolved Oxygen, pH, and turbidity in a forested coastal plain stream in west central Alabama, United States. We employed wavelet analysis to assess how each water quality variable covaried with discharge while using Hysteresis and Flushing Index to evaluate solute transport patterns. Results show that source proximity and constituent availability collectively controlled the response sequence of water quality variables, with mechanisms varying across variables. For most storms, pH was exported first, followed by chlorophyll, turbidity, specific conductance, fDOM, and finally, dissolved oxygen. The mobilization mechanism, i.e., the export of hydrogen ions from adjacent sources, governed pH's response to storms. In contrast, the dilution mechanism seemed to dominate the response of chlorophyll-a, turbidity, and fDOM. Equal percentage of storms was governed by the mobilization mechanism and the dilution mechanism for specific conductance. Seasonal variation played a significant role in the hysteretic index of pH and chlorophyll-a, whereas antecedent discharge significantly impacted the Flushing Index of pH, chlorophyll-a, and turbidity. Dry period duration before a storm significantly increased the flushing of ions into the creek, as seen in the increased hysteresis index. These findings highlight the considerable influence of storms on stream biogeochemistry. However, different water quality components react differently, highlighting complexity in predicting how water quality will be affected by hydrological events and necessitating continuous, high-resolution monitoring.Item Deep Learning for Operational Streamflow Forecasts, Or More Specifically: Long Short-Term Memory Networks As a Rainfall-Runoff Modulefor the U.S. National Water Model(University of Alabama Libraries, 2022) Frame, Jonathan Martin; Zhang, Yong; University of Alabama TuscaloosaThis dissertation investigates deep learning (DL) and combining hydrologic process-based (PB) models with DL for a hybrid (HB) modeling approach (often referred to as ''physics-informed machine learning" or ''theory-guided learning") for improving the predictive performance of streamflow in the U.S. National Water Model. An in-depth analysis is made of the benefits of DL and the potential drawbacks of the HB models. No evidence is found supporting the use HB models over the "pure" DL models in the use cases analyzed. The performance of the HB models is found to degrade in ungauged basins, whereas the DL models do not. The DL models are the best performing models for predicting extremely high runoff events, even when such events are not included in the training set. Adding physics inspired constraints to data-driven models causes a loss of system information relative to the DL models. As such, a "pure" DL model, specifically the Long Short-Term Memory (LSTM), is chosen as one of the core modules for the Next Generation (Nextgen) U.S. National Water Model. The LSTM (via Nextgen) is applied to simulate streamflow for a three-year period across the 191,020 km^2 New England region.Item Efficient removal of sulfamerazine (SMR) by ozonation in acetic acid solution after enrichment SMR from water using granular activated carbon(Royal Society of Chemistry, 2019) Yao, Youru; Mi, Na; Zhu, Yongqing; Yin, Li; Zhang, Yong; Li, Shiyin; Nanjing Normal University; University of Alabama Tuscaloosa; Anhui Normal UniversitySulfamerazine (SMR) as a persistent organic pollutant in waste streams is of growing environmental concern. This study explores the extraction SMR from water into an acetic acid (AA) solution using granular activated carbon (GAC), and removal of SMR by ozonation in AA solution. Systematic experiments have shown that GAC can be used as an adsorbent to transfer sulfamerazine from water to AA solution. SMR removal efficiency is 99.5% in 10% AA aqueous solution, which is better than in water. The removal rate of SMR in the AA solution decreased as the initial molar ratio of SMR and O-3 increased. The removal rate of SMR decreased with Fe3+ present in the reactive system. The removal of SMR is dominated by indirect ozonation in water, while the SMR removal is an effect of both direct and indirect ozonation in AA solution. It is a very efficient process for the degradation of SMR in micro polluted water when using combined GAC adsorption-desorption in AA solution and ozonation of the resulting solution.Item Fractal nature of groundwater level fluctuations affected by riparian zone vegetation water use and river stage variations(Nature Portfolio, 2019) Sun, HongGuang; Gu, Xiufen; Zhu, Jianting; Yu, Zhongbo; Zhang, Yong; Hohai University; University of Wyoming; University of Alabama TuscaloosaGroundwater systems affected by various factors can exhibit complex fractal behaviors, whose reliable characterization however is not straightforward. This study explores the fractal scaling behavior of the groundwater systems affected by plant water use and river stage fluctuations in the riparian zone, using multifractal detrended fluctuation analysis (MFDFA). The multifractal spectrum based on the local Hurst exponent is used to quantify the complexity of fractal nature. Results show that the water level variations at the riparian zone of the Colorado River, USA, exhibit multifractal characteristics mainly caused by the memory of time series of the water level fluctuations. The groundwater level at the monitoring well close to the river characterizes the season-dependent scaling behavior, including persistence from December to February and anti-persistence from March to November. For the site with high-density plants (Tamarisk ramosissima, which requires direct access to groundwater as its source of water), the groundwater level fluctuation becomes persistent in spring and summer, since the plants have the most significant and sustained influence on the groundwater in these seasons, which can result in stronger memory of the water level fluctuation. Results also show that the high-density plants weaken the complexity of the multifractal property of the groundwater system. In addition, the groundwater level variations at the site close to the river exhibit the most complex multifractality due to the influence of the river stage fluctuation.Item Modeling COVID-19 spreading dynamics and unemployment rate evolution in rural and urban counties of Alabama and New York using fractional derivative models(Elsevier, 2021) Yu, Xiangnan; Zhang, Yong; Sun, HongGuang; Hohai University; University of Alabama TuscaloosaThe COVID-19 pandemic has been affecting the United States (U.S.) since the outbreak documented on 2/29/2020, and understanding its dynamics is critical for pandemic mitigation and economic recovery. This study proposed and applied novel time fractional derivative models (FDMs) to quantify the spatiotemporal dynamics of the COVID-19 pandemic spreading in the states of Alabama and New York, U.S., two states with quite different population compositions, urbanization, and industry structures. Model applications revealed that the pandemic evolving in the two states exhibited an overall similar time-dependent trend with subtle differences in propagation rates. Alabama may have more inter-county communications in rural areas than urban areas, while the opposite may be true for the New York State. Further analysis using the space FDM showed that the COVID-19 pandemic spread in rural/urban areas of the two states by following the tempered stable density distributions with different indexes, while the number of the state's pandemic epicenters affected the pattern of the COVID-19 pandemic spreading in space. Finally, applications of a novel time FDM revealed that the evolution of the economy, represented by the weekly unemployment insurance claims in the two states, exhibited different spreading and recovery rates, most likely due to their different exposures and responses to the pandemic. Therefore, COVID-19 spreading dynamics exhibited strong and subtly different spatiotemporal memories in rural and urban areas in the Alabama and New York States, motivating the application of FDMs.Item A new global scale river's slope dataset(University of Alabama Libraries, 2016) Wan, Tong; Cohen, Sagy; University of Alabama TuscaloosaRiver’s slope (bed and flow) is a key parameter in fluvial hydrology, hydraulics and geomorphology. It affects many important fluvial variables such as flow velocity and sediment transport, especially bedload. Limitation in river’s slope data confined fluvial modeling, particularly at large scales. Traditional slope calculation algorithms cannot accurately predict river’s slopes as these are based on cell-by-cell calculation, which is only suitable for hillslopes and steep mountainous streams. This paper presents a new algorithm for calculating global river’s slope and a procedure to upscale it for relatively course resolution global hydrological modeling. The algorithm is based on a simple principle of calculating slope from elevation depression over the length of a river segment. The algorithm automates this calculation for global rivers. In this paper, the HydroSHEDS 15 arc-sec Digital Elevation Model is used for calculating global river network and retrieving the elevation values. A sensitivity analysis is conducted in order to examine the effect of maximum river segment length on slope predictions. An analysis of the accuracy of this dataset has been conducted by comparing the new dataset against observed slope data collected from the literature and an independent high-resolution stream network layer for the contiguous United States. The results show that this algorithm is able to accurately calculate global river’s slope. Applications of the resulting dataset are proposed.Item One step closer to fully automated structure interpretation in 3D seismic data(University of Alabama Libraries, 2020) Lou, Yihuai; Zhang, Bo; University of Alabama TuscaloosaSeismic structure interpretation is the compulsory step for 3D seismic structure modeling, stratigraphic features analysis, and 3D reservoir modeling. The modern 3D seismic surveys usually cover up to hundreds of square kilometers with thousands of inline and crossline vertical slices. Manual seismic structure interpretation (horizon and fault interpretations) on thousands of inline and crossline vertical slices is a time-consuming and tedious task. My dissertation focuses on developing new algorithms and workflows to automatically extract horizon surfaces and fault surfaces from the 3D seismic data. Most automatic horizon extraction algorithms are based on the seismic reflector dip attribute. The quality of extracted horizons is highly affected by the accuracy of the seismic reflector dip. However, the seismic reflector dip attribute is usually inaccurate near discontinuous zones such as faults and unconformities. Moreover, the accuracy of an extracted horizon increases with increasing user interpreted control points. I improve the automatic seismic horizon interpretation from three aspects: (1) improving the accuracy of the seismic reflector dip attribute, (2) tracking a horizon using multiple seismic attributes, and (3) automatically generating control points prior the automatic tracking horizons. The extracted seismic horizons strictly follow the local seismic reflection events over the whole seismic survey. Automatic or semi-automatic fault surface construction is still a challenges task although seismic fault attributes are widely used in assisting seismic fault interpretation in 3D seismic survey. The staircase and undesired sequence stratigraphic artifacts are the main factors that hinder researchers from automatically constructing fault surfaces. I improve the automatic seismic fault interpretation from two aspects: (1) generating a new seismic fault attribute without staircase and undesired sequence stratigraphic artifacts, and (2) automatically constructing fault surfaces by analyzing the topological features of the new seismic fault attribute on time and vertical slices. The proposed fault surface construction workflow successfully constructs fault surfaces and computes corresponding fault parameters such as fault dip and strike and even conjugate faults within the seismic survey.Item Quantifying Groundwater Storage and Vulnerability for the State of Alabama Using Comprehensive Flow and Particle Tracking Models(University of Alabama Libraries, 2023) Ponprasit, Chaloemporn; Zhang, YongGroundwater is a vital natural resource whose sustainability and vulnerability can affect the local society, economy, and/or ecosystem. Although Alabama has abundant groundwater resources (~533 trillion gallons), a reliable quantification of the long-term evolution of groundwater quantity and quality has not been made using process-based physical models. To fill this knowledge gap, this thesis quantitatively evaluated groundwater sustainability and vulnerability for the state of Alabama, by integrating the states-scale groundwater flow and particle tracking models, local-scale hydrological statistical analysis, and state-wide radioactive tracer ages using three studies (or chapters). The first study calculated backward probabilities of groundwater pollutants in three-dimensional, intermediate-scale heterogeneous aquifers (where the backward travel time probability leads to the index of groundwater vulnerability), by developing a backward particle tracking approach. Numerical experiments showed that the backward probabilities were sensitive to the vertical location and length of screened intervals, resulting in the (vertical) scale effect in backward probabilities which should be considered for evaluating Alabama's groundwater vulnerability to non-point source contamination.The second study evaluated the vulnerability of regional-scale aquifer-aquitard systems in southern Alabama using a process-based approach focusing on groundwater flow and forward/backward particle tracking modeling. These large-scale models, as well as statistical analysis and radioactive isotope dating, suggested that shallow groundwater in southern Alabama is mainly in Anthropocene age and hence susceptible to surface contamination, while deep aquifers are mainly "fossil" aquifers and may be "safe" from modern contamination. It is important to consider hydrologic conditions and intermediate-scale aquifer-aquitard architectures in the regional-scale models when interpreting three-dimensional aquifer vulnerability maps.The third study explored the transient dynamics of groundwater systems and developed effective management strategies for groundwater resources by incorporating the changing climate and anthropogenic impacts. The impact of pumping rate, well location, and recharge rate on the groundwater system were systematically assessed through three scenarios. Effective strategies for maintaining sustainable groundwater supplies in vulnerable areas require careful management of groundwater resources, where a sustainable balance is needed between recharge and pumping. Well locations and boundary conditions were found to be sensitive to management strategies for groundwater resources in Alabama.These studies assessed groundwater sustainability and vulnerability, and highlighted the importance of considering various factors in sustainable groundwater management. The findings of this thesis may eventually inform policy and decision-making related to groundwater resources management in Alabama.Item Quantifying mass transfer processes in groundwater as a function of molecular structure variation for multicomponent NAPL sources(University of Alabama Libraries, 2018) Abbott, Joe Boone; Tick, Geoffrey R.; University of Alabama TuscaloosaThe presence of nonaqueous phase liquids (NAPLs) in soil and groundwater is difficult and expensive to remediate. Complications exist for remediation of multicomponent NAPL sources due to differences in dissolution behavior at the molecular level. The dissolution behavior of two contaminants of concern (COC), trichloroethene (TCE) and toluene (TOL), was compared as binary mixtures within hexane, decane, and hexadecane. The relative ideality of mass transfer processes for TCE and TOL from the binary NAPL mixtures was evaluated by comparing aqueous-phase COC concentrations calculated using Raoult’s Law to the observed equilibrium aqueous-phase COC concentrations for a series of batch dissolution experiments. As mole fraction ratios of the COCs (i.e., TCE and TOL) within the NAPL source decrease, dissolution nonideality generally increases for such multicomponent NAPL mixtures. A series of comprehensive equilibrium batch experiments was conducted to understand and quantify the systematic influence of bulk NAPL carbon-chain length on the dissolution behavior of TCE and TOL. The differences between the observed COC equilibrium and Raoult’s Law-predicted concentrations are likely due to specific intra-NAPL component interactions that occur and thereby affect mass transfer dynamics from the multicomponent NAPL mixture. However, no particular correlation between the observed COC aqueous-phase equilibrium concentrations (via dissolution) and the COC-NAPL mixture’s bulk NAPL carbon chain length was determined. A static equilibrium-solubility model was used to estimate activity coefficients for TCE and TOL within various carbon-length aliphatic bulk NAPL mixtures (i.e., hexane, decane, hexadecane). The xlUNIFAC Model was used to simulate the mixtures for comparison to the batch experimental systems, following the UNIFAC group contribution methods for estimating phase equilibrium. TOL (aromatic structure) showed greater nonideal dissolution behavior than TCE (aliphatic structure) in the presence of the different bulk-NAPL components used for this study. The results of this work suggest that the prediction of aqueous phase concentrations in groundwater of COCs from complex multicomponent NAPL sources is highly dependent upon both compositional and molecular structural variations. Such impacts should be taken into account when designing and evaluating a particular remediation strategy and/or predicting COC concentrations from a NAPL source zone region.Item Quantifying non-Fickian transport in porous and fractured media using fractional-calculus based stochastic models(University of Alabama Libraries, 2019) Lu, Bingqing; Zhang, Yong; University of Alabama TuscaloosaNon-Fickian or “anomalous” transport, where the target’s spatial variance grows nonlinearly in time, describes the pollutant dynamics widely observed in heterogeneous geological media deviating significantly from that described by the classical advection dispersion equation (ADE). The ADE describes the Fickian-type of transport, with symmetric snapshots like the Gaussian distribution in space (Berkowitz et al., 2006). Non-Fickian transport can be observed at all scales. Non-Fickian transport is typically characterized by apparent (as heavy as power-law) early arrivals and late time tailing behaviors in the tracer breakthrough curves (BTCs). Non-Fickian transport is well known to be affected by medium heterogeneity. Heterogeneity can refer to variations in the distribution of geometrical properties, as well as variations in the biogeochemical properties of the medium, which cannot be mapped exhaustively at all relevant scales. Complex geometric structures and intrinsic heterogeneity in geological formations affect predictions of tracer transport and further challenge remediation analyses. Hence, efficient quantification of non-Fickian transport requires parsimonious models such as the fractional engine based physical models. In this dissertation, I first compared three types of time non-local transport models, which include the multi-rate mass transfer (MRMT) model, the continuous time random walk (CTRW) framework, and the tempered time fractional advection dispersion equation (tt-fADE) model. I then found that tt-fADE can model the rate-limited diffusion and sorption-desorption of Arsenic in soil. Additionally, non-Fickian dynamics for pollutant transport in field-scale discrete fracture networks (DFNs) were explored. Monte Carlo simulations of water flow were then conducted through field-scale DFNs to identify non-Darcian flow and non-Fickian pressure propagation. Finally, to address non-Fickian transport for reactive pollutants, I proposed a time fractional derivative model with the reaction term. Findings of this dissertation improve our understanding of the nature of water flow and pollutant transport in porous and fractured media at different scales. The correlated parameters and relationships between media properties and parameters can enhance the applicability of fractional partial differential equations that can be parameterized using the measurable media characteristics. This provides one of the most likely ways to improve the model predictability, which remained the most challenge for stochastic hydrologic models.Item Recovery of crude oil from saturated porous media as a function of geochemistry and wetting-phase dynamics for multiple remediation flushing strategies(University of Alabama Libraries, 2018) Booth, Joe; Tick, Geoffrey R.; University of Alabama TuscaloosaA comprehensive study was performed to evaluate the mechanisms controlling mobilization of trapped nonaqueous phase liquid (NAPL) crude oil as a function of wetting phase dynamics and geochemistry during enhanced-flushing conditions. Synchrotron X-ray microtomography (SXM) was used to perform pore-scale examination of NAPL fragmentation and changes in blob morphology, and recovery using three different advective flushing methods: surface-active agent (surfactant) flushing, alkaline flushing, and sequential alkaline-surfactant flushing. This set of experiments was conducted to understand effects on such processes (fragmentation and recovery) as a function of media composition (geochemical/mineralogical) and pH alterations due to calcium-carbonate fraction. The sequential flushing technique (alkaline→ surfactant) yielded the highest recovery, 32% after 5 pore volumes (PV) of flushing. The crude oil (NAPL) distribution varied due to differences in heterogeneity and type of fluid (i.e. surfactant vs. alkaline) used for flushing. Drop shape analysis was also performed to measure contact angle across the solid-liquid-liquid (S-L-L) junction for a multiphase system for three different API° gravity (or density) crude oils within aqueous solutions of varying pH and in contact with two different types of media including silica (glass plate) and limestone (calcium carbonate plate). Interfacial tension (IFT) was also measured during drop shape analysis for the three different density oils, as a function of pH. Higher pH values showed the greatest change in contact angle (i.e. high to low) and subsequently the lowest IFT measurements. During alkaline flushing, buoyancy forces predominantly controlled mobilization of NAPL (oil), and during surfactant flushing, viscous forces yielded relative greater control on crude oil (NAPL) mobilization. Calculated capillary force values showed significant variation for the set of experiments due to the changes in interfacial tension between the three crude oils tested and the alkaline aqueous phase (of varying pH). The results from this research can be used to aid in the understanding of physical and chemical parameters/properties that control mobilization of crude in porous saturated media. This can help reduce time and cost during remediation of contaminated sites that contain crude oil or less dense NAPL derivatives consistent with fuel-type petroleum hydrocarbons.Item Reservoir fluid-rock interactions during a co₂ eor/ccs pilot test at citronelle oil field, alabama(University of Alabama Libraries, 2020-12) Rheams, Erik; Donahoe, Rona; University of Alabama TuscaloosaWith the world’s expanding need for energy, new sources of petroleum or technologies to extend current petroleum reserves are required. However, concerns about global warming are increasing as atmospheric CO2 levels continue to rise worldwide due to the burning of fossil fuels. Enhanced oil recovery (EOR) provides a method for expanding existing petroleum reserves by prolonging the life of older oil fields where primary production methods have been exhausted. EOR also opens an avenue for using CO2 captured from point sources such as power plants for beneficial purpose, thus preventing its release into the atmosphere and sequestering the CO2 in deep geologic formations that also serve as petroleum reservoirs. Citronelle Oil Field, located in Mobile County, Alabama, was the site for a 2008-2012 SECARB pilot project funded by the U.S. Department of Energy (DOE) that was aimed at testing CO2 flood for enhanced oil recovery and carbon sequestration. Citronelle Field is the largest and oldest oil play in the state of Alabama with reserves originally estimated at about 500 million barrels in place, less than half of which had been produced between its discovery in 1955 and the start of the pilot project in 2008. The field’s primary producing units are the Upper and Lower Donovan Sands within the Rodessa Formation. The work performed for this study was funded by the DOE to examine the fluid-rock interactions induced in the reservoir by the injection of supercritical CO2. Water samples were collected from four production wells located around the CO2 injection well between June 2010 and February 2012, and water chemistry was analyzed by ICP-OES and IC. Temporal trends for water sample compositional variation are presented, and compositional similarities and differences between the water samples collected from the four wells are discussed. Geochemical modeling was employed to determine the fluid-rock interactions taking place within the reservoir and thus provide potential explanations for the observed water sample compositional trends. Finally, the impact of an over pressuring event that created preferred flow paths within the system and its impact on water chemistry and oil production is discussed.Item Scale effect of contaminant transport in saturated porous media identified by the time fractional advection-dispersion equation(University of Alabama Libraries, 2017) Garrard, Rhiannon Maire; Zhang, Yong; University of Alabama TuscaloosaTime nonlocal transport models such as the time fractional advection-dispersion equation (t-fADE) were proposed to capture well-documented non-Fickian dynamics for conservative solutes transport in heterogeneous media, with the underlying assumption that the time nonlocality (which means that the current concentration change is affected by previous concentration load) embedded in the physical models can release the effective dispersion coefficient from scale dependency. This assumption however has never been systematically examined using real data. This study fills this historical knowledge gap by capturing non-Fickian transport (likely due to solute retention) documented in literature (Huang et al. 1995) and observed in our laboratory from small to intermediate spatial scale using the promising, tempered t-fADE model. Fitting exercises show that the effective dispersion coefficient in the t-fADE, although differing subtly from the dispersion coefficient in the standard advection-dispersion equation, increases nonlinearly with the travel distance (varying from 0.5 to 12 m) for both heterogeneous and macroscopically homogeneous sand columns. Further analysis reveals that, while solute retention in relatively immobile zones can be efficiently captured by the time nonlocal parameters in the t-fADE, the retention-independent solute movement in the mobile zone is affected by the spatial evolution of local velocities in the host medium, resulting in a scale-dependent dispersion coefficient. The same result may be found for the other standard time nonlocal transport models, such as the well-known multi-rate mass transfer (MRMT) model and the hydrologic version of continuous time random walk (CTRW), that separate solute retention and jumps (i.e., displacement). Therefore, the t-fADE with a constant dispersion coefficient cannot capture scale-dependent dispersion in saturated porous media, challenging the application for stochastic hydrogeology methods in quantifying real-world, pre-asymptotic transport. Hence, improvements on time nonlocal models using, for example the novel subordination approach, are necessary to incorporate the spatial evolution of local velocities without adding cumbersome parameters. Future improvements are also explored, given knowledge obtained in this study.Item Seismic interpretation with the aid of deep learning(University of Alabama Libraries, 2019) Wu, Hao; Zhang, Bo; University of Alabama TuscaloosaNowadays one of the biggest challenges for geoscientists is effectively extracting the useful information from massive geo-datasets. Deep learning algorithms have become incredibly good at analyzing and identifying pieces of objects from massive data. The application of deep learning in seismic exploration has become one of the hottest research topics in recently two-years. My dissertation focuses on developing new workflows for seismic data processing and interpretation with the aid of deep learning algorithms. Picking the first arrival of seismic data is one of the most time-consuming tasks in the seismic data processing. The first arrival segments the seismic traces into two parts. Each part of the seismic traces can be viewed as a unique object. I automatically identify the two objects of the seismic trace by using a state-of-art pixel-wise convolutional image segmentation method. The boundary of the two objects is regarded as the first arrivals of seismic data. Noise filtering is another important step in the seismic data processing. I proposed to filter the noise in seismic data by integrating deep learning and variational mode decomposition. My new method does not require prior information about the noise which is one of the compulsory inputs for image de-noising using deep learning. My method not only effectively removes the random noise in the seismic image but also the coherence noise such as migration artifacts which is beyond the capability of current filtering methods. The process of seismic horizon interpretation can be treated as dividing the seismic traces into several segments. I proposed a workflow to perform semi-automated horizon interpretation method by using the encoder-decoder convolutional neural network. There are two main parts of my workflow. The first part is segmenting the seismic traces into different parts using deep learning and treat the boundary of two nearby parts as the horizon. The second part is refining the horizons using a two-step filtering. My method does not require seismic attributes such as the dip and azimuth of a seismic reflector as the inputs which are compulsory for current horizon picking algorithms.Item Simulating PFAS adsorption kinetics, adsorption isotherms, and nonideal transport in saturated soil with tempered one-sided stable density (TOSD) based models(Elsevier, 2021) Zhou, Dongbao; Brusseau, Mark L.; Zhang, Yong; Li, Shiyin; Wei, Wei; Sun, HongGuang; Zheng, Chunmiao; Hohai University; University of Arizona; University of Alabama Tuscaloosa; Nanjing Normal University; Southern University of Science & TechnologyReliable quantification of per- and polyfluoroalkyl substances (PFAS) adsorption and mobility in geomedia provides critical information (i.e., evaluation and prediction) for risk characterization and mitigation strategy development. Given the limited PFAS data available and various competing theories for modeling pollutant kinetics, it is indispensable to better understand and quantify the adsorption and transport of PFAS in geomedia using generalized models built upon a consistent physical theory. This study proposed a universal physical law (called the tempered stable law) in PFAS adsorption/transport by interpreting PFAS adsorption kinetics and nonideal transport as a nonequilibrium process dominated by adsorption/desorption with multiple rates following the tempered one-sided stable density (TOSD) distribution. This universal TOSD function led to novel TOSD-based models which were then tested by successfully simulating PFAS adsorption kinetics, adsorption isotherms, and nonideal transport data reported in the literature. Model comparisons and extensions were also discussed to further check the feasibility of the TOSD models and their adaptability to capture PFAS transport in more complex geomedia at all scales.