The article notes an increase in the share of renewable energy use in European countries and the reasons for the lag of the Russian Federation in this area A promising area of application of non-traditional sources of thermal energy for heating using heat pumps is noted. Despite the constant growth in the use of low-potential geothermal systems in foreign countries, the share of geothermal energy in Russia is negligible. The analysis of climatic conditions and characteristics of aerothermal heat pumps allowed to note their low reliability in regions with a long heating season while reducing the temperature to –15 °C. The versatility and popularity of geothermal pumps has been pointed out. Calculations of thermal energy consumption of residential buildings with individual gas heating were carried out. Based on long-term observations, seasonal changes in the temperature of the soil mass at various depths have been determined. The results obtained were used in the design of a geothermal heating system with heat extraction from the soil mass. The required pump capacity and collector area for heat collection are defined. When calculating the parameters of a soil collector laid at a shallow depth, insufficient heat content of the soil was noted, which will lead to a gradual decrease in the temperature of the array and, accordingly, a decrease in heat extraction. High costs for purchasing heating equipment and installing a geofield reduce the interest of individual consumers in using alternative heat sources. The most realistic area of application of geothermal heat pumps is for heating apartment buildings and social facilities under construction with heat extraction at depths of more than 50 m.
Bentonite of which major mineral is montmorillonite will be used as a buffer material for the geological disposal of a high-level radioactive waste. It has been reported that the interlayer aperture of montmorillonite decreases with increasing salt water concentration. In this study, a model was improved based on a thermodynamic model to quantitatively analyze the effect of salinity on the number of hydration layers of Na-and Ca-montmorillonite. We calculated average interlayer distance as a function of montmorillonite partial density based on the specific surface area of montmorillonite, and calculated the change in montmorillonite partial density versus salinity based on the thermodynamic model. Furthermore, we considered the number of hydration layers versus salinity. In addition, in the analysis, the montmorillonite content in Kunigel-V1 was taken into consideration, and consistency with swelling stress and swelling rate was confirmed, before an empirical equation for the relationship between water content and the relative partial molar Gibbs free energy of interlayer water was revised. The number of hydration layers in the interlayer decreased with increasing salinity. This is qualitatively consistent with the measured data by X-ray diffraction (XRD).
Bentonite is used as a buffer material in the geological disposal of high-level radioactive waste, which is considered for co-location with part of TRU waste. Bentonite swells in contact with groundwater to fill gaps and resist rock pressure by swelling stress. Many studies have been conducted on swelling stress, including data acquisition so far. The authors have proposed a thermodynamic model in the past and are testing its practicality in parallel with the acquisition of thermodynamic data for various conditions. The application of the model also requires thermodynamic data for groundwater in contact. For example, if nitrate (NaNO3) contained in some TRU wastes or Ca(OH)2 in cement used as support material for tunnels excavated in the repository and as filling material around TRU wastes dissolve into groundwater, the eluate may come into contact with buffer material or bentonite mixed soil, etc., but thermodynamic data for those waters are scarce. In this study, the activity of water and the relative partial molar Gibbs free energy, which are necessary for the analysis of swelling stress of bentonite, were obtained as parameters of NaNO3 and Ca(OH)2 concentration and temperature, and the relative partial molar enthalpies were calculated. The results showed that the relative partial molar enthalpies were almost zero in NaNO3 solutions of 1–5 M and Ca(OH)2 solutions of pH 12.07–12.47 (equivalent to concentrations of 0.0017–0.005 M).
Petroleum sludge, a hazardous by-product derived from petroleum extraction and refining process, it may cause significant environmental risks and potential health hazards for humans because of its high content of petroleum hydrocarbons, heavy metals, and other toxic compounds if handled improperly. To extract residual oil from Tahe Oilfield sludge, an alkali-surfactant compound was employed in this study. The key parameters encompassing surfactant concentration, alkali loading, and washing temperature were investigated and optimized. After two washing cycles with a solution containing, cocamidopropyl hydroxysulfate betaine (CPSB) at 0.5 wt
The specific hydro-climatic conditions of the upper Yellow River are important for endemic plateau fish. Hydropower development alters water flows and habitats, with its long-term effects on native cold-water fish remaining unclear. This study examines the fish community's response in the Hukou–Erduo River section of the upper Yellow River before and after the Maerdang Hydropower Station’s impoundment. Utilizing a comprehensive 20-year dataset (2005–2024) that incorporates multi-season fishery surveys alongside plankton and benthic monitoring, this study analyzed temporal variations in species composition, abundance, and community dominance. The results indicate that the fish species richness increased from 15 to 21 after impoundment of Maerdang Hydropower Station, with assemblages consistently dominated by Schizopygopsis pylzovi and Gymnocypris eckloni, alongside other native cold-water taxa such as Triplophysa spp. The overall community structure remained stable, and no evident proliferation of exotic species was observed. Despite local transformations from rapid-flow to lentic habitats within the reservoir, tributary connectivity and fish passage facilities preserved essential ecological linkages. These findings demonstrate that, under conditions of intact tributary habitats and effective ecological management, early-stage hydropower operation exerts limited disturbance on fish community stability.
The planning and construction of the section from Longyang Gorge to Qingtong Gorge have spanned half a century and are largely finalized. With the continuous development and construction of hydropower cascades in this section, the ecological environment and ecosystem balance have undergone significant changes. To clarify the impact of hydropower project construction on fish resources in the Longyangxia–Qingtongxia reach, this study compares historical and recent survey data to analyze the effects of hydropower development on fish resources in this section. The findings indicate that the fish habitat in the reach has shifted from flowing water habitats to predominantly lentic reservoir habitats, accompanied by significant habitat fragmentation. These changes pose substantial survival threats to indigenous fish species. Therefore, it is imperative to further strengthen fish resource conservation efforts in the reach to protect the fish resources in the upper Yellow River. This study provides a theoretical foundation for the conservation of fish species in this river section.
Buffer material composing engineered barrier in the geological disposal of a high-level radioactive waste develops swelling stress by penetration of groundwater from the surrounding rock mass. In previous studies, we proposed a model to analyze the swelling stress of bentonite based on thermodynamic theory. However, the thermodynamic data of interlayer water in montmorillonite which is the major component of bentonite are quite limited. In this study, we have determined the activity of interlayer water and the relative partial molar Gibbs free energy of interlayer water by measuring relative humidity (RH) and temperature. We have also analyzed the swelling stress of bentonite based on the thermodynamic model and compared to the measured data. Kunigel-V1 and Kunipia-F of which montmorillonite contents are approximately 57
This article is devoted to the study of mesophilic and thermophilic regimes of anaerobic digestion of sewage sludge in a bioreactor. The research aim is the development of a technology for obtaining clean soil from sewage sludge treated in a bioreactor. The article presents methods for processing experimental results using ash content determination techniques. The stages of experimental studies on anaerobic digestion of sewage sludge are outlined. The methodology for determining the ash content of the studied clean soil samples is provided. Analyses conducted in the “Biotechnology” laboratory are aimed at determining the chemical and toxicological characteristics of sewage sludge. As a result of the experimental studies, comparisons of mesophilic and thermophilic digestion regimes are presented. For a comparative analysis of the digestion regimes, the article employs methods for determining the mass fraction of organic matter and ash content indicators in samples of sewage sludge treated in a bioreactor. Technological regimes of anaerobic digestion of sewage sludge in a bioreactor are analyzed for optimization purposes. Based on the results of the experimental studies, the application of sewage sludge treated in a bioreactor is recommended as clean soil for the reclamation of disturbed lands during the greening of populated areas.
River pollution has emerged as a critical environmental issue worldwide, affecting water quality, aquatic ecosystems, and human health. Timely and accurate detection of pollution is essential for effective water resource management and environmental protection. This study investigates the application of machine learning (ML) algorithms for real-time monitoring and prediction of river pollution using key water quality parameters, including temperature, pH, turbidity, electrical conductivity (EC), and dissolved oxygen (DO). Three ML models such as: Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), were employed to investigate continuous sensor data collected from the Mahanadi River in Cuttack City, Odisha. The performance of the models was evaluated using accuracy, precision, recall, and F1-score, and the relative importance of each water quality parameter was assessed to identify critical contributors to pollution detection. Findings indicate that Neural Networks outperformed DT and SVM, achieving an accuracy of 92
To mitigate habitat disturbances caused by the Phase II of the Hanjiang-to-Weihe River Water Diversion Project (HWRP-II) and to reconstruct ecological functions, this study developed an integrated technical framework for wetland restoration specifically tailored for sandy substrates under drought-flood alternation. A 40 m × 20 m floodplain within the project-affected area was designated as a demonstration site. To create hydrological micro-habitats, a 300 m2 meandering side channel was excavated to divert river water. Additionally, riparian slopes were stabilized using a combination of stone-plant composites, geocells, and stepped gabions. In terms of the configuration of the vegetation community, eight indigenous species, such as Salix matsudana, Taxodium ascendens, and Phragmites australis, were selected to encompass a range of functional groups, including trees, shrubs, and emergent macrophytes. The findings indicate that the integrated strategy of “hydrological micro-habitats—riparian bio-engineering—drought/flood-tolerant vegetation” offers a replicable technical approach for the swift ecological restoration of intermittent river wetlands after engineering disturbances.
Persistent organic pollutants such as synthetic dyes represent a serious environmental challenge due to their toxicity and resistance to biodegradation, while conventional wastewater treatment methods often exhibit limited efficiency at low pollutant concentrations. Adsorption has therefore emerged as an effective alternative owing to its simplicity, low energy requirements, and versatility. In this study, magnetic chitosan nanoparticles were synthesized via an in-situ co-precipitation method and evaluated for the adsorption of methyl orange (MO) as a model azo dye. Structural and surface characterization by FTIR, XRD, SEM, and BET analyses confirmed successful magnetic functionalization and surface properties favorable for adsorption. Batch adsorption experiments conducted at an initial MO concentration of 30 mg L−1, adsorbent dosage of 1 g L−1, solution volume of 100 mL, near-neutral pH (≈ 6.9), and room temperature demonstrated rapid dye uptake, with equilibrium attained within 30 min. The equilibrium adsorption capacity reached 29.32 mg g−1, corresponding to a removal efficiency of 97.7
We studied the aquatic macroinvertebrate community and the environmental drivers in the Baćička River. The research in 2016 and the research in 2017 found a group of the 31 taxa. The Gastropoda, the Plecoptera and the Turbellaria dominated the 31 taxa. Data analysis found the water chemistry factors that shape the macroinvertebrate community. The analysis showed a link between the Ephemeroptera and the water temperature. The analysis also showed that the Turbellaria had a link, with the pH and the total hardness. Strong biotic interactions were observed, including a perfect positive link between the Hirudinea and the Oligochaeta. A significant faunistic finding was made with the discovery of Metreletus balcanicus here for the first time. This finding shows that the karst aquifer has different living things. Regression models were used to test how non-living and living variables can predict each other. The regression models showed that the combination of non-living and living variables can predict the relationship between the Hirudinea and the Oligochaeta, with perfect accuracy (R2 = 0.999). The findings establish an ecosystem baseline and validate the use of specific macroinvertebrate groups as robust bioindicators for monitoring the ecological status of vulnerable karstic freshwater systems in the Balkan region. The implemented research, based on a seasonal monitoring design, not only provides detailed insight into the community structure but also offers robust models for assessing future changes, thereby clearly highlighting its applied value in the protection and sustainable management of these unique habitats.
Situated within the governance framework of ecological civilization under China’s 14th Five-Year Plan and the SDG 6 agenda, this study analyses People’s Daily reporting on water environment governance. Using a research design that integrates content analysis and framing analysis, we develop a 10-item coding scheme based on Zang Guoren’s macro–meso–micro (high–middle–low) three-level framing model. A database search yielded 875 reports; after manual verification and deduplication, 289 reports were retained for analysis. Results show that, at the macro level, coverage is organized around a dominant river-basin governance frame, is largely narrated at cross-regional/national scales, and displays a strongly positive tone. At the meso level, sources are concentrated in government and party-state institutions, and genres are primarily straight news and narrative reports, with relatively limited public deliberation and watchdog-oriented discussion. At the micro level, long-form narratives predominate, while direct quotations and visualization remain scarce, constraining explanations of governance mechanisms, risk communication, and the verifiable presentation of performance outcomes. Accordingly, we argue that mainstream media should maintain an authoritative and constructive tone while better foregrounding local variation, incorporating multi-stakeholder voices, and strengthening evidence-based reporting to enhance public deliberation and participation mobilization around water environment governance.
Access to safe drinking water, recognized as a human right by the United Nations General Assembly and the Human Rights Council in 2010, remains a major public health challenge, particularly in low- and middle-income countries, where water treatment infrastructure is often inadequate or non-existent. The growing urbanization and industrialization have greatly affected the water quality of the Mahanadi River of Cuttack city, Odisha, which is a threat to environmental sustainability, aquatic ecosystems, and public health. It is important to accurately predict the quality of water in order to manage water resources and control pollution, as early prediction enables proactive decision-making, timely interventions, and effective regulatory planning. This study proposes a combined AI (artificial intelligence) model of forecasting water quality in urban river, addressing the limitations of traditional statistical and deterministic models, which often struggle with nonlinear and complex environmental interactions. The suggested system integrates different machine learning methods, including artificial neural networks (ANNs), support vector machines (SVM), and decision trees (DT), to forecast major water quality indices, including pH, dissolved oxygen (DO), and turbidity. By combining multiple algorithms, the hybrid framework leverages the strengths of each model while minimizing individual weaknesses, resulting in improved robustness and generalization capability. The model is trained with the historical data of urban river, the environmental factors, temperature, precipitation, and discharge are used as the input features to capture seasonal variability, hydrological dynamics, and climatic influences on water quality. The findings prove that the combined AI model is much more efficient than conventional approaches in terms of prediction accuracy, stability, and adaptability to changing environmental conditions. Performance evaluation using standard statistical metrics demonstrates significant improvements in predictive reliability, making the model suitable for real-time applications. This integrated AI-based approach can provide a trustworthy means of monitoring and controlling water quality in urban river systems, supporting sustainable water resource management, pollution mitigation strategies, and policy formulation. Furthermore, the proposed framework has the potential to be scaled and adapted to other urban watersheds, contributing to smarter and data-driven environmental management practices.
Under intensifying global climate change, sudden water crises pose significant threats to achieving Sustainable Development Goal 6 (SDG 6). News media play a pivotal role in constructing risk perception and mobilizing societal response. Adopting quantitative content analysis based on Semetko and Valkenburg’s generic frames, this study compares news coverage in Singapore and Thailand from 2015 to 2025 to unveil divergent risk communication patterns. The results reveal two distinct models: Singapore exhibits a “Technocratic-Moral” model, characterized by the dominance of Attribution of Responsibility and significant Morality frames, reinforcing state authority and civic duty aligned with Target 6.5 (IWRM). Conversely, Thailand demonstrates a “Livelihood-Adaptive” model, where Human Interest and Conflict frames are strongly correlated, reflecting social struggles over resource allocation and Target 6.1 (Safe Access). We argue these divergences stem from the “Hydrological-Political Nexus” within each nation. The findings provide empirical insights for optimizing localized risk communication strategies to advance SDG 6 in diverse developmental contexts.
Ensuring safe drinking water is a basic need of human which align with SDG 6 but still remains a major concern in northern Bangladesh, where groundwater from tubewell is the primary water source. This study aimed to predict current potability of drinking water and mapping the contamination hotspot in Thakurgaon district within 40 tubewells sampling for lead. Each tubewell was pumped for five minutes before collecting samples. Samples were analyzed inorganic elements and trace metal including lead (Pb). To enhance predictive assessment, Support Vector Machine (SVM), Rasndom Forest (RF), and XGBoost machine learning (ML) models were developed using preprocessed water-quality data where RF was the best fit and was used for further spatial analysis. The RF model achieved an accuracy of 0.917, classifying 25
To evaluate the effectiveness of water diversion and exchange measures in improving water quality in plain river network cities, this study focuses on the ancient district of Suzhou City. Based on long-term monitoring data from 26 water quality sections collected between 2020 and 2023, temporal variation, seasonal patterns, and spatial heterogeneity of river network water quality were systematically analyzed. DO, CODMn, NH3–N, and TP were selected as key indicators, and FCE, the M–K trend test, and the NPI were applied to assess water quality status and trends. The results show that overall river network water quality improved continuously during the study period, with the comprehensive grade shifting from predominantly Class IV to mainly Class III or better. The proportion of monitoring sections meeting higher quality standards increased significantly, while inferior Class V sections were nearly eliminated. Water diversion and exchange measures effectively increased DO and reduced CODMn and NH3–N concentrations, but their impact on TP control was relatively limited, with signs of potential rebound. Clear seasonal variations were observed, with poorer water quality in summer, indicating the continued influence of rainfall runoff. Spatially, water quality was generally better in the northern area than in the southern area, and different rivers showed varied responses to diversion measures. Overall, water diversion and exchange projects contribute positively to urban river network water quality improvement, but alone are insufficient to address nutrient accumulation and non-point source pollution. Integrated source control and refined water environment management are therefore required for sustained improvement.
Unionidae, Corbiculidae, Pisidiidae, Euglesidae, Lymnaidae, Planorbidae and Astacidae in the rivers of Uzbekistan, it was revealed for the first time that 20 species and 2 subspecies are distributed in the Zarafshan River, 17 species and 1 subspecies in the Kashkadarya, and 15 species and 1 subspecies in the Akhangaron River. When analyzing the distribution of species in biotopes in the Zarafshan, Kashkadarya and Akhangaron rivers, it was found that 2 species occur in rocky soils, 11 in sandy soils and 13 in muddy soils. During the researches, the species distributed in the studied water ecosystems belong to 7 different ecological groups, of which peloreophiles are 8 species 31
Two surveys of avian resources were conducted in the study area of the Duobu Hydropower Station on the Nyang River in Tibet. An analysis was performed on bird species, population size, migratory characteristics, and avian community diversity. Based on these findings, the study examined the impact of the Duobu Hydropower Station project on bird populations and proposed corresponding conservation measures. The results showed that a total of 60 bird species from 29 families and 11 orders were recorded in the region, comprising 2193 individuals. During the breeding season, 35 species from 20 families and 8 orders were observed, while in the wintering season, 39 species from 19 families and 7 orders were recorded. Among these, Passeriformes were the most abundant order. In terms of residency status, resident birds dominated, with 41 species (accounting for 68.33
Water contamination poses a persistent threat to the sustainability of river systems and agricultural irrigation, as poor-quality water accelerates soil degradation through salinity, sodicity, alkalinity, and toxic ion accumulation. This study provides a seasonally resolved assessment of irrigation water quality along the Periyar River using Fuzzy Irrigation Water Quality Index (FIWQI) and machine learning techniques. Monthly data from March 2017 to August 2024 were analyzed for pH, Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Chloride (Cl−), Magnesium to Calcium ratio (Mg2+/Ca2+), Fluoride, and Boron; collected from eight monitoring stations spanning the upper catchment to downstream industrial zones. Unlike conventional threshold-based indices, FIWQI employs fuzzy logic to capture gradual transitions and uncertainty, enabling realistic seasonal evaluation. Results reveal clear temporal and spatial variability influenced by seasonal hydrodynamics and anthropogenic activities. Monsoon and postmonsoon periods consistently exhibited high irrigation suitability across all stations due to dilution effects. During premonsoon low-flow conditions, localized declines in FIWQI occurred at industrially impacted stations (17 and 2336) and downstream station (2334), marked by elevated EC and chloride levels. Overall, most stations were classified as good to excellent year-round, with limited seasonal declines. To enhance predictive capacity, Random Forest Regression (RFR) models were developed using progressively ranked input parameters, achieving high accuracy (testing R2 ≈ 0.95; CV_R2 > 0.96). Chloride, boron, and fluoride emerged as key contributors, demonstrating the effectiveness of combining fuzzy logic and machine learning for dynamic irrigation water quality assessment and adaptive management.
