Search Results (642)

This article examines the effects of climate change on the 32 million inhabitants of the Megalopolis of Central Mexico (MCM), which is threatened by chaotic urbanization, land-use changes, the deforestation of the Forest of Water by organized crime, unsustainable agriculture, and biodiversity loss. Expensive hydraulic management extracting water from deep aquifers, long pipes exploiting water from neighboring states, and sewage discharged outside the endorheic basin result in expensive pumping costs and air pollution. This mismanagement has increased water scarcity. The overexploitation of aquifers and the pollution by toxic industrial and domestic sewage mixed with rainfall has increased the ground subsidence, damaging urban infrastructure and flooding marginal neighborhoods with toxic sewage. A system approach, satellite data, and participative research methodology were used to explore potential water scarcity and weakened water security for 32 million inhabitants. An alternative nature-based approach involves recovering the Forest of Water (FW) with IWRM, including the management of Natural Protected Areas, the rainfall recharge of aquifers, and cleaning domestic sewage inside the valley where the MCM is found. This involves recovering groundwater, reducing the overexploitation of aquifers, and limiting floods. Citizen participation in treating domestic wastewater with eco-techniques, rainfall collection, and purification filters improves water availability, while the greening of urban areas limits the risk of climate disasters. The government is repairing the broken drinking water supply and drainage systems affected by multiple earthquakes. Adaptation to water scarcity and climate risks requires the recognition of unpaid female domestic activities and the role of indigenous people in protecting the Forest of Water with the involvement of three state authorities. A digital platform for water security, urban planning, citizen audits against water authority corruption, and aquifer recharge through nature-based solutions provided by the System of Natural Protected Areas, Biological and Hydrological Corridors [SAMBA] are improving livelihoods for the MCM’s inhabitants and marginal neighborhoods, with greater equity and safety. Full article

Shaft misalignment is among the most common faults in rotating machinery, and although many diagnostic methods have been proposed, reliably detecting it under varying load conditions remains a major challenge for vibration-based techniques. To address this issue, this study proposes a new vibration-based misalignment detection framework that leverages cointegration analysis. The approach examines both the stationarity of vibration signals and the residuals derived from the cointegration process. Specifically, it combines the Augmented Dickey–Fuller (ADF) test with cointegration analysis in three stages: (1) applying the ADF test to raw vibration data before cointegration, (2) performing cointegration on the vibration time series, and (3) reapplying the ADF test to the post-cointegrated data. The method was validated using experimental measurements collected from a laboratory-scale test rig comprising a motor, gearbox, and hydraulic gear pump, tested under both healthy and misaligned states with varying degrees of severity. Vibration signals were recorded across multiple load conditions. The results demonstrate that the proposed method can successfully detect misalignment despite load variations, while also providing insights into fault severity. In addition, the residuals from the cointegration process proved to be highly sensitive to damage, highlighting their value as features for vibration-based condition monitoring. Full article

Total knee replacement (TKR) is a reliable treatment for end-stage degenerative conditions of the knee. Patient-reported outcome measures (PROMs) are central to assessing TKR outcomes, but they have limitations. Activities of daily living (ADLs) in the early post-operative period complement PROMs for holistic patient assessment. This study presents a method for capturing ADL parameters from data generated by inertial measurement unit (IMU) devices embedded in TKR prosthesis. A conventional posterior stabilized TKR was modified to create chambers in the femoral and tibial components. The prosthesis was implanted into a cadaver knee and movement was simulated using a hydraulic actuated knee simulator (AMTI, VIVO, MA, USA). A powered IMU device was placed in each of the chambers. The simulator was activated for various ADLs and the generated data was collected wirelessly. The pre-processed data was fed into a novel multimodal deep learning artificial intelligence model created to recognize specific ADL (trained on 70% of the data, with 30% reserved for validation and testing). The model achieved 95.68% overall accuracy, with 100% for sitting, standing, stance, and knee bending. Walking, stair navigation, and jogging showed F1 scores of 0.98, 0.92, 0.91, and 0.89, respectively. This technology enables seamless knee activity recognition and reporting with positive implications for patient-specific rehabilitation protocols. Full article

Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making. Full article

Tight conglomerate reservoirs are characterized by dense lithology, significant compositional contrasts between cement and gravel, strong stress gravel content, strong heterogeneity, and uneven spatial distribution, which collectively result in low porosity, complex pore–throat structures, and low permeability. After hydraulic fracturing, the stress sensitivity of tight conglomerate reservoirs is jointly governed by the rock matrix and induced fractures. In this study, the Mahu tight conglomerate reservoir in the Xinjiang Oilfield was selected as the research target. Stress sensitivity experiments were conducted on conglomerate matrix cores and on cores with varying fracture conditions. After stress loading, the degrees of permeability damage of the matrix, through-fracture, double short-fracture, and microfracture cores were 41%, 69%, 93%, and 97%, respectively. The matrix exhibited moderate-to-weak stress sensitivity, the through-fracture cores showed moderate-to-strong stress sensitivity, while the double short-fracture and microfracture cores exhibited strong stress sensitivity. Experimental results indicate that when fractures are present, the stress sensitivity of the core is primarily controlled by fracture closure and matrix compression. As fracture development increases, core permeability is significantly enhanced; however, stress sensitivity also increases accordingly. Under net stress, gravel protrusions embed into fracture surfaces, reducing surface roughness, while irreversible alteration of fracture geometry becomes the dominant factor driving stress sensitivity in fractured cores. These findings provide a scientific basis for predicting stress-sensitivity-induced damage in tight conglomerate reservoirs. Full article

This study investigates flood risk trends using rainfall data collected from 13 districts of Diyarbakır Province, Turkey, with a focus on supporting sustainability-oriented climate adaptation. Both annual and seasonal precipitation variations were examined, with particular emphasis on the role of maximum daily rainfall in driving flood potential. In addition, the analysis integrates extreme precipitation patterns with regional hazard characteristics to provide a more comprehensive flood risk assessment framework. Non-parametric statistical methods, including the Mann–Kendall trend test and Spearman’s Rho correlation, were applied to detect trends in annual and seasonal datasets. Flood magnitudes were estimated using the Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) approaches. The dataset covers varying periods between 2009 and 2023, depending on station availability. The results show a statistically significant increase in both annual and winter precipitation at Bismil, and a significant winter increase at Çermik. Other stations displayed upward trends that were not statistically significant. Çüngüş, Lice, and Kulp were identified as particularly susceptible to extreme rainfall. Although the relatively short observation period poses a limitation, consistent patterns of intensified precipitation were detected. Previous studies in Turkey have demonstrated that such events often cause severe infrastructure damage and displacement of vulnerable communities. The findings of this study provide practical insights for national and regional authorities, including the Disaster and Emergency Management Authority (AFAD), the General Directorate of State Hydraulic Works (DSİ), and the Ministry of Environment, Urbanization, and Climate Change, to strengthen sustainable climate adaptation planning and disaster risk reduction strategies. Overall, this research highlights the importance of integrating extreme precipitation analysis into sustainable flood management, resilient infrastructure development, and long-term sustainability policies, thereby reinforcing the connection between hydrological risk assessment and sustainability science. Full article

This study aims to investigate the sedimentary facies characteristics of the coal seam roof in the Qinglong and Longfeng coal mines and their control over water abundance. By collecting core samples and well logging data from both mining areas, multiple methods were employed, including core observation, thin-section analysis, sedimentary microfacies distribution mapping, nitrogen adsorption tests, and nuclear magnetic resonance analysis, to systematically analyze the depositional environments, types of sedimentary microfacies, and their distribution patterns. Results indicate that the roof of Qinglong Coal Mine is predominantly composed of sandy microfacies with well-developed faults, which not only increase fracture porosity but also provide water-conducting pathways between surface water and aquifers, significantly enhancing water abundance. In contrast, Longfeng Coal Mine is characterized mainly by muddy microfacies, with small-scale faults exhibiting weak water-conducting capacity and relatively low water abundance. Hydrochemical analysis indicates that consistent water quality between Qinglong’s working face, karst water, and goaf water confirms fault-induced aquifer–surface water connectivity, whereas Longfeng’s water quality suggests weak aquifer–coal seam hydraulic connectivity. The difference in water hazard threats between the two mining areas primarily stems from variations in sedimentary microfacies and fault structures. Full article

Soil piping is the process by which subsurface water creates and enlarges channels, or “pipes,” within soil, enabling rapid and preferential flow beneath the surface. The collapse of these eroded pipes can lead to land degradation, gully formation, and potential damage to overlying infrastructure. While the structural consequences of pipe collapse are well recognized, there is limited understanding of the factors controlling pipe collapse and how water within the pipe influences moisture levels within a slope. This study used physical models of unsaturated slopes to examine how compaction conditions, pipe characteristics, and hydraulic conditions affect the progression of internal erosion. Models were created with different initial pipe sizes, moisture contents, densities at compaction and levels of pipe connectivity. Volumetric water content (VWC) sensors and cameras were used to monitor the slope response to subsurface flow, and measurements of pipe geometry were collected after the tests. Results showed that lower initial soil water content was more susceptible to pipe collapse, while higher water content showed improved pipe stability and sustained preferential flow. Fully connected pipes grew through erosion due to the pipe flow, while disconnected pipes grew mainly through local pipe collapse. Hydraulic equilibrium and soil erodibility affected the final pipe morphology more than the initial pipe size. These experimental results demonstrate that soil fabric and hydraulic connectivity of the pipe control the progression of piping, likelihood of collapse, and movement of water within the soil matrix. Full article

Anaerobic digestion (AD) technology is universally acknowledged as the most economically viable and efficient approach for energy recovery from livestock manure. To validate the efficacy of riboflavin-functionalized carbon-based conductive materials (CCM-RF) in enhancing methane production at pilot scale, three pilot-scale upflow anaerobic sludge blanket (UASB) reactors were constructed and separately supplemented with carbon cloth (CC), granular activated carbon (GAC), and a combination of CC and GAC. During reactor initialization, riboflavin and a concentrated inoculum of Methanosarcina barkeri (M. barkeri) were introduced to investigate the mechanistic role of CCM-RF in promoting direct interspecies electron transfer (DIET) and optimizing treatment efficiency during anaerobic digestion of cattle manure wastewater. The results showed that all reactors improved AD performance and maintained stable operation at the OLR of 15.66 ± 1.95 kg COD/(m³·d), with a maximum OLR of 20 kg COD/(m³·d) and the HRT as short as 5 days. Among the configurations, the CC reactor outperformed the others, achieving a methane volumetric yield of 6.42 m³/(m³·d), which represents an eight-fold increase compared to conventional AD systems. Microbial community analysis revealed that, although M. barkeri was initially inoculated in large quantities, Methanothrix—a methanogen with DIET capability—eventually became the dominant species. The enrichment of Methanothrix and the simultaneous enhancement in sludge conductivity collectively verified the mechanistic role of CCM-RF in promoting CO₂-reductive methanogenesis through strengthened DIET pathways. Notably, M. barkeri showed progressive proliferation under conditions of high organic loading rates (OLR) and short hydraulic retention time (HRT). This phenomenon provides a critical theoretical basis for the development of future strategies aimed at the targeted enrichment of Methanosarcina-dominant microbial consortia. Full article

Constructed wetlands (CWs) are sustainable and cost-effective systems that utilise plant–microbe interactions and natural processes for wastewater treatment. Microbial communities play a pivotal role in pollutant removal by crucial processes like nitrogen transformations, phosphorus cycling, organic matter degradation and the breakdown of emerging contaminants. Dominant phyla, such as Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes, collectively orchestrate these biogeochemical functions. Advances in molecular tools, including high-throughput sequencing and metagenomics, have revealed the diversity and functional potential of wetland microbiomes, while environmental factors, i.e., temperature, pH and hydraulic retention time, strongly influence their performance. Phosphorus removal efficiency is often lower than nitrogen, and large land requirements and long start-up times restrict broader application. Microplastic accumulation, the spread of antibiotic resistance genes and greenhouse gas emissions (methane, nitrous oxide) present additional challenges. The possible persistence of pathogenic microbes further complicates system safety. Future research should integrate engineered substrates, biochar amendments, optimised plant–microbe interactions and hybrid CW designs to enhance treatment performance and resilience in the era of climate change. By acknowledging the potential and constraints, CWs can be further developed as next-generation, nature-based solutions for sustainable water management in the years to come. Full article

The insufficient availability of safe water has emerged as a prevalent issue severely impacting public health in developing nations. Moreover, studies reporting the efficacy of treatment plants (TPs)—specifically Phragmites karka and Typha latifolia—in removing toxic elements in aquaculture wastewater are scanty. Therefore, this study is aimed at investigating the effects of hydraulic retention time (HRT), seasonal variations, and TPs on the removal efficiency of pollutants from a vertical subsurface flow constructed wetland (VSSF-CW) in Nigeria. The experiments spanned three seasons (November–December–January—NDJ; March–April–May—MAM; and July–August–September—JAS) of the year, with samples collected from the CW at 7 day intervals for analysis. The aquaculture wastewater was analyzed in the laboratory to determine its chemical and toxic compositions before and after the introduction of treatment plants. Three-way ANOVA was used to analyze the main and interactive effects between HRT, seasons, and TPs on the physicochemical properties of the CW’s effluents. The removal efficiency was determined to evaluate the performance of the constructed wetland in comparison to the treatment plants. Results showed that these constructed wetlands effectively removed contaminants, with significant differences (p < 0.05) mostly observed in the effects of treatment plant types and seasons on the chemical and heavy metal concentrations. This was further confirmed by the main effects of HRT, seasons, and treatment plant choice, which significantly (p < 0.05) influenced treatment efficiency. Removal efficiencies increased with longer HRTs, reaching peak removal efficiencies of approximately 69, 67, and 61% for Na, K, and Ca, respectively. The BOD and COD reached 85 and 90% removal efficiency, while removal efficiency of 100% was achieved for most heavy metals at 21 day retention time. In summary, the study found that TPs (Phragmites karka and Typha latifolia), HRT, and seasonal variation are important for treating integrated poultry and aquaculture wastewater in a VSSF CWs. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article

This paper aims to develop a robust empirical–analytical model using the statistics of mine water level rebound in abandoned mines and the basic physical principles of underground hydraulics. The data collected and treated included the time series of the mine water level for 35 closed and flooded mines from four European countries. Within the developed model, mine water level evolution is governed by an ordinary differential equation with one fitting parameter that depends on the floodable cavity volume in a mine and water inflow before flooding begins. The model assumes that rock properties and residual void distribution are homogeneous, and the mines being flooded are almost isolated hydraulically from the neighboring ones. The exponential formula, as the governing equation solution, was found to be the most suitable for fitting the measurements. The calculated exponential curves allow for excellent or very good fitting of the measured water levels for 17 of 35 mines, and acceptable fitting for 11 mines in terms of minimizing mean-square-root deviation. The proposed approach can be applied to preliminary assessments of mine water level rebound in developing and calibrating sophisticated numerical flow models. Full article

Frozen ground regions have recently experienced increasing construction activity due to the vast undeveloped resources they contain. However, frozen soils exhibit thermal and mechanical properties that differ substantially from those of temperate soils, leading to a range of engineering challenges. This study investigates the influence of organic matter content on the freezing behavior of soils through a series of laboratory experiments and machine learning (ML) modeling. Soil samples were collected from Alberta, Canada, and Gangwon Province, South Korea, and their organic matter contents were adjusted using the loss-on-ignition method combined with peat moss addition. Standard Proctor compaction tests and uniaxial compression tests under subzero conditions were performed to evaluate compaction characteristics and strength development. The unfrozen water content was measured at different subzero temperatures to assess thermal and hydraulic responses. The resulting experimental dataset was then used to develop ensemble ML models—random forest (RF) and extreme gradient boosting (XGB)—for predicting unfrozen water content. The results indicate that higher organic matter contents reduce compaction efficiency, increase residual unfrozen water content, and influence strength development under freezing conditions. Both RF and XGB achieved high predictive accuracy, demonstrating their potential as reliable tools for evaluating the freezing behavior of organic soils. Full article