The Goreangab Reservoir was built in 1958 as part of the water reclamation plant to support the water supply in the Windhoek area, Namibia. It has not been used since 2002 due to deteriorated water quality. This work describes the occurrence of micropollutants in Goreangab Reservoir water and assesses the removal of a target steroid hormone micropollutant, 17β-estradiol (E2), with various emerging membrane processes. Results show that a variety of micropollutants exist in water, and many of them exceed the European guideline concentration values for surface water. While nanofiltration provides only moderate E2 removal (50 ± 9% for loose and 81 ± 4% for dense NF) from an initial concentration of 100 ng L-1, reactive photocatalytic membranes and a combination of ultrafiltration (UF) and adsorption by polymer-based spherical activated carbon (PBSAC) can remove 85-98% of E2, bringing the water quality closer to the target guideline (0.4 ng L-1). Water components such as organic matter, ions, and other micropollutants interfered with E2 removal to varying degrees, highlighting the requirement for pretreatment and process validation with real water. UF-PBSAC achieves good E2 removal at a comparatively low pressure, resulting in low energy consumption. The amount of energy required to apply light irradiation and voltage in photo- and electrocatalytic membrane systems is significant based on theoretical estimation.

Photo-biodegradation drives organic matter-mediated carbon-nitrogen-sulfur cycling in the reservoir's surface-layer: A multi-index analysis.

Assessing source water reservoirs as pre-treatment units for simultaneous control of autochthonous and runoff pollution through artificial mixing and aeration technology.

Algae penetration and taste and odor compounds production in drinking water treatment plants: seasonal variations and risk assessment.

Assembly of filament-like supraparticles in confined vortex rings.

A hybrid neural network-based concrete gravity dam seismic response prediction method quantifying reservoir water level uncertainty

Advancing reservoir water quality management by a depth-specific approach integrating Bayesian optimization and machine learning algorithms

Purpose Forecasting reservoir water levels plays a critical role in effective water resource management, contributing to the safety of hydraulic infrastructure and mitigating the impacts of droughts and floods. Current forecasting models work solely on satellite imagery, which has limitations on handling noise, particularly in peak values within the time-series streamflow from reservoir operation. Indeed, a multi-modal approach that integrates both satellite imagery and reservoir operation data is necessary to enhance the performance of forecasting. Design/methodology/approach This research presents a novel multi-modal forecasting model that integrates satellite imagery with historical water level data to improve prediction accuracy, particularly in forecasting abrupt changes in water levels. Image features are extracted using the histogram of oriented gradients (HOG) algorithm and normalized with the L2 norm to enhance training stability and reduce noise. A customized fusion function is developed to combine spatial features from satellite imagery with temporal features from water level time series, resulting in a unified composite feature vector. This vector, along with the historical water level sequence, is fed into a gated recurrent unit (GRU) model for forecasting. The fusion mechanism plays a crucial role in capturing sudden and abnormal variations in the data. Findings The model is assessed using satellite images and on-site water level measurements collected at the An Khe and Ka Nak Reservoir, Gia Lai, Vietnam, spanning January 2019 to December 2022. Experimental results demonstrate that the HOG-GRU variant significantly outperforms conventional deep learning models. The specific evaluation metrics are as follows: for the An Khe Reservoir, mean squared error (MSE) (0.08060), root mean squared error (RMSE) (0.28390), mean absolute error (MAE) (0.20446) and |Tracking Signal| (0.00032); whereas for the Ka Nak Reservoir, the corresponding values are MSE (0.20795), RMSE (0.45601), MAE (0.37937) and |Tracking Signal| (0.03985). These findings confirm the model's robustness and its practical applicability to real-world hydrological forecasting tasks. Originality/value This paper presents an original research contribution, offering novel insights to the academic domain of information technology, with all references comprehensively and accurately cited.

Unveiling the degradation behavior of naproxen in an enhanced UV222 advanced oxidation system: Mechanisms, efficiency gains, and actual water applicability.

Long-term fluctuations in reservoir water levels can lead to the deterioration of bank slope materials, representing a key trigger of instability. This study investigated the behavior of a slope–pile–sheet support structure at a site in Chongqing’s “Two Rivers and Four Banks” area through an integrated program of field monitoring and numerical simulation. The results demonstrated a strong correlation between slope displacement/settlement and water-level fluctuations, exhibiting a characteristic three-stage process. Rapid drawdown triggered substantial horizontal displacement with a one-month response lag, while settlement primarily occurred during water-level rise. Earth pressure behind the piles exhibited a non-linear R-shaped distribution, with a delayed response in shallow layers and a pronounced local pressure drop at 8 m depth indicative of seepage erosion. The pile bending moment showed a distinct S-shaped profile, with a maximum positive moment (1978.44 kN·m) at the rock-soil interface (13 m) and a negative moment zone below 21 m. The bending moment response also exhibited a one-month lag and was particularly sensitive to rapid drawdown. The identified contraflexure point at 21 m depth provides a basis for pile length optimization. The close agreement between numerical simulations and field data validates the strong hydro-mechanical coupling in the system. This research provides theoretical and practical support for the design and optimization of similar support structures in reservoir bank environments.

Isotopic exchange of tritium (3H) and 14C in gas/liquid systems.

In the context of the global transition to low-carbon energy systems, addressing the challenges posed by the intermittent nature of large-scale renewable energy sources and the repurposing of abandoned mine resources is crucial. Underground tunnel reservoirs, as vital components of pumped-storage power stations, require a comprehensive understanding of how the intrinsic characteristics of the tunnels influence water flow behavior to ensure operational efficiency and safety. This study identifies five key factors that affect water flow characteristics in tunnels. A hydraulic model of a pumped-storage power station in abandoned mines is employed, and a single-factor comparison method is utilized to investigate the flow velocity, static water pressure, and head loss in the underground reservoir under varying conditions. Furthermore, using the response surface methodology, a multi-factor analysis is conducted to assess the combined impact of three critical factors—inlet flow velocity, curvature, and slope—on head loss. The results indicate that the influence of these factors on head loss follows the order: curvature > slope > inlet flow velocity. This study provides valuable decision-making support for the transformation of underground spaces and the selection of tunnels for underground reservoirs in abandoned mine pumped-storage power station projects. It offers crucial insights to facilitate the transition of mining areas and the development of new energy storage industries.

Modeling capillary bound water in limestone reservoirs

Characterization and modeling approach for planning restoration strategies in a complex basin affected by acid mine drainage.

Reservoir water quality planning based on a robust multi-criteria decision-making approach under deep uncertainty.

Comprehensive review of optimization and surrogate models for agricultural water resources and reservoir water management

Accessible reservoir water quality monitoring: An integrated google earth engine and machine learning framework

One of the most significant cases for evaluating oil recovery factor and sweep efficiency involves reservoir water imbibition through fractures, exemplified by the Ekofisk oil field in the northern Norwegian sector of the North Sea. Gel treatment has been successfully applied in fields at their late stages of development and fracture reservoirs to enhance oil recovery and reduce water production. In order to predict the use of microgels, this study proposes a novel mathematical model for use in reservoirs during their late production stages, specifically for plugging fractures with network structural heterogeneity. This model was modified by correlating the screen test model results with the fracture model experiment results. The pressure gradient and effective viscosity through fractures with network structural heterogeneity extend the economic life of these assets. Simulation models were executed using the Nimezida 2014 hydrodynamic simulator to simulate the naturally fractured reservoir of this case study. The tangible impact of gel injection was revealed through a comparison of two scenarios: with a pumped screen, accumulated oil production increased from 102 to 200 days to approximately 7995.2 tons. In contrast, the effect of gel injection into the network fracture during the same period had a minimal impact on accumulated oil production, amounting to about 5 tons. However, the study presents more optimistic prospects over an extended period, considering that at least 40% of the injected gel remains in the fracture. These findings affirm the utility of microgel technology in sealing fractures and mitigating water flow in low-permeability zones, offering promising implications for enhancing reservoir performance in carbonate formations, including those in Iraqi oil fields.

This study aims to determine the rainwater harvesting (RWH) potential at the campus scale within the Düzce University Konuralp Campus. The amount of collectable rainwater was calculated based on the surface areas of rooftops, impervious grounds, and green spaces across the campus, using 2024 meteorological precipitation data and runoff coefficients corresponding to surface types. According to the results, the annual RWH potential is approximately 197,201 m3, of which 76% originates from rooftops, 23% from impervious surfaces, and 1% from green areas. It was found that the harvested rainwater could fully meet the irrigation, ornamental pond, and reservoir water demands within the campus. Additionally, this implementation could enable approximately 94,907 m3 of water savings, 151,851 kWh of energy savings, and a reduction of 199,998 kg in CO2 emissions annually. The study emphasizes that system design and management tailored to local climatic conditions are critical for the sustainable use of water resources. The findings demonstrate that RWH systems offer an effective environmental and economic solution for university campuses and similar institutional sites.

As a sink for microplastics (MPs) in the aquatic environment, sediments have garnered considerable attention. However, the occurrence characteristics of MPs in sediments of different water seasons are not clear, especially for reservoir sediment cores. This study aimed to elucidate the occurrence, spatial and vertical distribution, fragmentation and pollution risk of MPs in the sediment cores of the Xiangxi River, Three Gorges Reservoir (TGR) during different seasons. In sediment cores, the average abundance of MPs was 8.57 × 103 ± 5.65 × 103 items/kg DW in the wet season (WS) and 7.98 × 103 ± 4.00 × 103 items/kg DW in the dry season (DS), respectively. The abundance of MPs in surface sediments and sediment cores exhibited spatial heterogeneity, reflecting seasonally contrasting hydrodynamic conditions between sites S1 and S3. However, the abundance of MPs in the river estuary was the highest, both in surface sediments and sediment cores. Interestingly, the occurrence characteristics of MPs in surface sediments indicated that in addition to anthropogenic activity, hydrological conditions of the river can also have an impact on the spatial distribution of MP abundance in surface sediments. Polypropylene (PP), polyethylene (PE), polystyrene (PS), and polyethylene-propylene copolymer (EPM) were identified as the dominant polymer types (57–99%), with small-sized microplastics (SMPs, 0–300 μm) being the most prevalent. Water seasons influenced the size distribution of MPs in surface sediments. Using a conditional fragmentation model, MP sources were inferred by comparing fragmentation parameters (λ and α) in sediments with those reported for atmospheric deposition, reservoir water, and water-level fluctuation zone soils. Furthermore, the pollution load index (PLI) exceeded 1, indicating MP accumulation in the sediments. The pollution risk index (PRI) values indicated a considerable (300 < PRI < 1000) pollution risk in two water seasons, primarily due to the presence of polyvinyl chloride (PVC). This study enhances the understanding of MP behavior and associated environmental risks in reservoir sediments, offering valuable insights for future research and pollution mitigation efforts.