Scientific advances in recent years have tremendously improved the predictive capabilities of domain-specific problems with the use of machine learning and artificial intelligence. An innovative exploration is performed to understand the root uptake of per- and polyfluoroalkyl substances (PFASs) by plants, focusing on the intricate interactions between PFAS compounds, crops and soil. We established a machine learning model which performs a regression task to accurately predict the root concentration factors (RCFs) values of the PFAs. Various machine learning models are trained and evaluated on various evaluation metrics, and the best model has an R^2 value of 0.9379. These models significantly outperformed the other existing models in predicting the logRCF values, indicating their robustness in capturing the complex dynamics of PFAS uptake by plants. For model development, around 300 instances (or data points) of root concentration factors (RCFs) that measure the amount of PFASs absorbed by the plant roots from the soil are used. The data also included 11 features, related to PFAS chemical structures, organic carbon content, crop and soil characteristics and cultivation conditions. The developed model is evaluated and interpreted to obtain the most important features, which highly contribute to predicting RCF values. Feature importance analysis was utilized to gain a greater understanding of the decision-making processes of the models and the significance of individual features. This study shed light on a detailed approach to predict and understand how plants absorb PFASs and also captured the essential variables that affect the uptake of PFAS, and offered insightful information about the various components that contribute to their occurrence.

Through rigorous thermodynamic analysis, the phenomenon of chemical synergy in complex chemical processes has been elucidated. Experimental methods in water sciences often involve studying the effects of various chemical combinations and concentrations on water quality and behavior. Understanding chemical synergy helps researchers design experiments that account for the combined effects of different chemical components, leading to more accurate and insightful results. Overall, chemical synergy informs and enhances numerical and experimental methods, as well as data analyses, in water sciences by providing a more comprehensive understanding of the complex interactions between chemical components in water systems. Chemical synergy manifests in various ways, depending on factors such as reactant structures and process energies. Essentially, any chemical interaction depends on the chemical composition of the reaction mixture, temperature, and pressure. Chemical synergy is governed by the intensity of these parameters, particularly temperature, pressure, and concentration ratios of chemical agents, that is the chemical composition. It is established that in complex processes, synergy leads to the formation of mixed compounds, enabling the exploration or prediction of necessary effects. The absence of synergy in some cases may result from the lack of measuring certain properties. Moving forward, software programs should account for mixed compound formation in seeking synergistic effects. Additionally, discovering synergy necessitates considering mixed compound or complex formation. These findings are pivotal for exploring and designing new synergistic processes. The deduced relationships will be extremely useful for the search and directed design of new synergistic processes with required properties. Future research will focus on isolating and characterizing mixed compounds, along with providing detailed thermodynamic and kinetic insights through theoretical calculations, complementing empirical observations.

The highly toxic carcinogenic potential of nitrites, resulting under certain conditions due to the reduction of nitrate, is well known and continuously studied. The main aim of this study was to exactly establish the amount of pure nitrite NO₂^- expressed in mg/L in eight different unknown groundwater sources by a new visible spectrophotometric method. Nitrate NO₃^- from unknown water samples was quantitatively reduced into NO₂^- nitrite anions using 10%-15% NH₂-OH hydroxylamine solution. The obtained nitrite NO₂^- was completely transformed into a diazonium salt by the existing β-naphthol in a 0.2% alcoholic solution, in a strongly acidic medium (HCl, 15%-20%), and in cold storage at 1º -7 ºC for 25 minutes. Then, the synthetized diazonium salt was quantitatively coupled with free β-naphthol in excess at double concentration in the solution, which led to the formation of an orange–intense orange azo dye that presented an absorption maximum at λ = 478 nm and was obtained in a quantitative proportion perfectly equivalent to the concentration of pure NO₂^- nitrite in the water samples. Through the spectrophotometric dosing of the orange–intense orange azoic dye formed at λ = 478 nm in relation to the absolute ethanol as a blank, the pure nitrite NO₂ ^- in the unknown water samples was directly determined. The eight water samples studied showed high nitrite concentrations (2.2092 mg/L; 3.0669 mg/L; 3.6109 mg/L; 3.6736 mg/L; 3.9038 mg/L; 3.7155 mg/L; 4.2385 mg/L; 5.1589 mg/L), which exceeded the maximum allowed official limit of nitrites in drinking water, at 0.5 mg/L, by approximately four to ten times. All of thewater samples studied cannot be intended, as such, for domestic consumption. The method was then subjected to complete statistical validation. The linearity, the limit of detection (LOD), the limit of quantitation (LOQ), the method and system's precision, and the robustness and accuracy of the analysis were calculated.

The aim of this study was to conduct a precise analysis through a new spectrophotometric method that uses nitrate anions, NO₃^-, as species with significant toxic potential covertly existing in the composition of several samples of water intended for domestic consumption. Nitrates, NO₃^-, from the studied groundwater samples quantitatively reacted with phenol disulfonic acid, which led to the synthesis of an intense bright yellow nitro-phenol disulfonic acid compound in the presence of a solution of NaOH, 2 M, and ammonia, 10-12%, 2:1. Nitro-phenol disulfonic acid showed an absorption maximum at a wavelength of ʎ = 402 nm and was obtained in a quantitative proportion perfectly equivalent to the concentration of pure NO₃^- nitrate anions obtained from the water samples. Through spectrophotometrically dosing the bright-yellow intense nitro-derivative formed at λ = 402 nm in relation to double-distilled H₂O as a control, the concentrations expressed in mg/L of pure nitrate NO₃^- ions in the unknown water samples were directly determined. Following this method, the NO₃^- nitrate concentrations from all eight unknown groundwater sources were found to be below the maximum allowed reference limit of 50 mg/L. The method proposed was linear over the entire concentration range of the chosen standard solutions (4,00 µg/mL- 40 µg/mL). The linear regression coefficient was R² = 0.9995198, R² ≥ 0.9990, and the correlation coefficient was R = 0,9997599, R > 0.9990. The standard error of _the regression line was SE = 0,0059577, SE <<1. The detection limit was LOD = 0,8456 μg/ mL, LOD < 1, and the quantitation limit was LOQ = 2,8093 μg/ mL, LOQ < 3. The method used for the visible spectrophotometric analysis of nitrates was subsequently subjected to complete statistical validation.

The need for urgent climate action and achieving net-zero emissions drives the development of CCS technologies, including CO₂ injection and storage in saline aquifers. This aligns perfectly with Thailand's ambitious commitment to combat climate change and achieve net-zero emissions, reflecting the global imperative for efficient CCS initiatives. Geologically unique formations like the Mae Moh basin in Thailand offer significant potential for carbon storage because it’s a coal-fired power plant is located nearby which releases about 10 Mtpa, making them pivotal players in supporting Thailand's net-zero target. The current study focuses on an examination of well placement strategies, a crucial element in optimizing CO₂ storage efficiency within saline aquifers. By targeting the geological context of Mae Moh basin, we investigated and compared the performance of four distinct well-pattern scenarios: Single Vertical Injection (SVI), Single Horizontal Injection (SHI), Two Vertical Injection (TVI), and Two Vertical Injection and Brine Production (TVIP) wells. Each of these scenarios serves as an indicator of well-placement efficiency tailored to the specific geological intricacies of Mae Moh basin. Over a 30-year period of injection, the TVI is remarkably the most effective configuration, injecting as much as 1.70 MtCO₂, which is attributed to the presence of two vertical injection wells that facilitating efficient and rapid CO₂ co-injection. The TVIP introduces an innovative approach by combining CO₂ injection with brine production, resulting in a cumulative injection of approximately 0.86 MtCO₂. Additionally, the analysis of CO₂ trapping mechanisms reveals a dynamic interplay, of which structural and residual gas trapping mechanisms dominate the early stage of injection before transitions to solubility trapping over time. The current investigation emphasizes the adaptability required for CCS project operating in challenging geological conditions.

The conveyance of water typically involves the transport of dissolved air, air bubbles, or air pockets that naturally occur or are introduced during the operation of the system. Air management in pressurized systems is complex due to various intervening factors, making comprehensive solutions challenging to achieve. Solutions include taking advantage of the dissolution of air in water, utilizing flow drag to carry air, employing protective equipment like air valves, and implementing monitoring strategies and operational controls. The wide range of potential situations involving air, along with their dependence on system characteristics and operational procedures—which can vary considerably during the system's lifetime—highlights the complexity of air management in pressurized systems. The interactions between air and water—either beneficial or detrimental—during unsteady, quasi-steady, and steady flows, including air admission, expulsion, forward and backward air movement, stagnation, and dissolution and release, remain critical areas of scientific and practical interest. This work aims to systematize the inspection and assessment of pressurized systems to identify air sources and management solutions, ultimately providing a framework for enhancing system efficiency, reliability, and safety. Moreover, this work provides practical examples from engineering practice and from experimental and numerical studies that highlight relevant issues related to air in pressurized water systems.

Water distribution network models are crucial for the effective management and operation of water distribution systems. Current technology enables the creation of detailed models of complex networks, often requiring the use of optimization tools for the repeated analysis of network behavior. Simplifying these models offers several advantages, including reduced computational time and easier decision-making for network managers. This paper presents a methodology based on six simplification algorithms that achieve up to an 80% reduction in network elements, while maintaining a high efficiency index for validation and using set point curves to represent the simplified elements.

The methodology employs graph theory to analyze and optimize water distribution networks. Simplifications range from the elimination of parallel pipes and terminal nodes to the sectorization and simplification of blocks with multiple inputs and outputs. The simplification process ensures hydraulic equivalence between the simplified model and the original network.

The impact of network simplification on the management and operation of water distribution systems is evaluated through various operational scenarios with different control elements and allowable error percentages. The results indicate that the proposed simplification methodology provides practical tools for network managers, contributing to better practices in water resource management and enhancing the efficiency and effectiveness of water distribution network operations.

Gravitational Water Vortex Power Plants (GWVPPs) are an innovative and sustainable energy solution that harnesses the kinetic energy of water vortices created in basin structures, which can be either cylindrical or conical. This study aims to enhance the performance of the vortex turbine, a critical component of GWVPPs, by optimizing its design parameters. In this study, we used the conical basin structure. The research was conducted using Computational Fluid Dynamics (CFD) simulations in COMSOL Multiphysics, focusing on various configurations of the turbine blades. Specifically, we analyzed the effects of blade length, blade inclination angle, and turbine height on the turbine's efficiency and flow behavior. Through detailed simulation studies, we observed that the performance of the vortex turbine is significantly influenced by these parameters. The optimal configuration was found to be a blade inclination angle of 8 degrees, a blade length of 28 cm, and a turbine height of 35 cm. This configuration achieved the highest efficiency, reaching up to 55%. The simulation results provide valuable insights into the relationship between the turbine's geometric design and its performance metrics, highlighting the potential of GWVPPs as a low-cost, environmentally friendly power generation option. This study's findings contribute to the advancement of renewable energy technologies by demonstrating the feasibility and efficiency of using optimized vortex turbines in GWVPPs, thereby supporting sustainable energy initiatives.

The developed model is a mathematical algorithm that manages different energy sources for the water–energy nexus. The work comprises the definition of the model and the mathematical algorithm that executes the system's simulation. Subsequently, the optimization algorithm is defined for Excel Solver (single-objective) and Python (multi-objective). The model is designed to be applicable to a diverse range of hybrid energy systems, particularly in conjunction with pumped hydropower storage. It focuses on the energy and water balance between subsystems to manage and simulate the performance throughout time. To conclude the algorithm development, the model was tested on a case study. The experiment was conducted on a 24-hour consumption irrigation field. The model was used to explore a variety of scenarios involving different energy sources, including photovoltaic, wind, PHS, grid, and battery (off-grid) technologies. The results demonstrated that a system integrating photovoltaic, wind, PHS, and the grid is the most economically and environmentally viable option, exhibiting the lowest grid consumption compared to a scenario solely relying on PV and PHS. A third scenario was tested, in which the grid was replaced with a battery energy storage system (BESS), thus providing an off-grid solution. Despite reducing carbon emissions, the system encountered challenges in meeting the full demand for water and energy. The scenarios that envisage the implementation of the electric grid permit the sale of surplus renewable energy, including photovoltaic (PV) and wind, during periods of low demand. This contributes to a reduction in costs. It is the scenario with both solar and wind that benefits the most from these conditions, as it can reproduce a positive grid cash flow throughout the year for higher water allocations.

Computational fluid dynamics has been used as a tool to simulate multiphase fluids in pressurized pipelines. This tool has been limited for the simulation of experimental-scale processes by different authors to understand transient phenomena in pipes with pressurized flow with entrapped air. Today, few CFD models have been used in the industrial sector and in research for hydraulic operations in large-scale pressurized pipelines with entrapped air, since there are some gaps in the pursuit of an adequate protocol for the simulation of hydraulic processes in pressurized pipelines from an industrial approach and cases that can be used to test CFD modelling.

This study shows the use of a two-dimensional CFD model to understand the filling processes of a large-scale pipeline, taking hydraulic conduction in Valencia, Spain, which has a length of 485 m and an air valve, as a case study. This CFD model is performed using a VoF method for air and water. The model is also calibrated by means of mesh quality analysis, according to some hydraulic modelling protocols stipulated by some expert researchers in hydraulic infrastructure simulation, and subsequently validated with pressure and velocity measurements. This CFD model shows relevant information on the processes of interaction between air and water, allowing pressurized flow conditions, and free flow in some sections of the pipeline, as well as air expulsion processes through the air valve, to be identified.

The study contributes to the foundation of the hydraulic modeling of large-scale networks using CFD tools, allowing the visualization of air--water interactions, which is difficult to analyze in existing or planned pipeline infrastructures, as well as the identification of new insights that can improve the design and operational strategies of large-scale pipeline infrastructures.