Search Results (775)

Urban flooding represents a growing global concern, especially in areas with rapid urbanization, unregulated urban sprawl and climate change conditions. Conventional flood modeling approaches do not effectively capture the complex dynamics between natural watershed behavior and urban infrastructure; they typically simulate these domains in isolation. This study introduces the Watershed-BIM methodology, a three-dimensional simulation framework that integrates Building and City Information Modeling (BIM/CIM), Geographic Information Systems (GIS), Flood Risk Assessment (FRA), and Flood Risk Management (FRM) into a single framework. Autodesk InfraWorks 2024, Civil 3D 2024, and RiverFlow2D v8.14 software are incorporated in the development. The methodology enhances interoperability and prediction accuracy by bridging hydrological processes with detailed urban-scale data. The framework was tested on a real-world flash flood event in Mandra, Greece, an area frequently exposed to extreme rainfall and runoff events. A specific comparison with observed flood characteristics indicates improved accuracy in comparison to other hydrological analyses (e.g., by HEC-RAS simulation). Beyond flood depth, the model offers additional insights into flow direction, duration, and localized water accumulation around buildings and infrastructure. In this context, integrated tools such as Watershed-BIM stand out as essential instruments for translating complex flood dynamics into actionable, city-scale resilience planning. Full article

Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram statistics from 12 soil surface photographs spanning 3.83% to 19.75% SWC under controlled lighting. For each image, pixel-level values of red, green, blue (RGB) channels and hue, saturation, value (HSV) channels were extracted to compute per-channel histograms, whose empirical means and standard deviations were used to parameterize Gaussian probability density functions. Linear interpolation of these parameters yielded synthetic histograms and corresponding images at 1% SWC increments across the 4–19% range. Validation against the original dataset, using dice score (DS), Bhattacharyya distance (BD), and Earth Mover’s Distance (EMD) metrics, demonstrated that the interpolated images closely matched observed color distributions. Average BD was below 0.014, DS above 0.885, and EMD below 0.015 for RGB channels. For HSV channels, average BD was below 0.074, DS above 0.746, and EMD below 0.022. These results indicate that the proposed method reliably generates intermediate SWC data without additional direct measurements, especially with RGB. By reducing reliance on exhaustive sampling and offering a cost-effective dataset augmentation, this approach facilitates large-scale, noninvasive soil moisture estimation and supports machine learning applications where field data are scarce. Full article

This study examined the spatial distribution and environmental determinants of hairtail (Trichiurus lepturus) catch volumes in the southern coastal waters of South Korea, employing a Spatial Durbin Model (SDM) based on grid-level data collected from 2020 to 2022. Key explanatory variables included chlorophyll-a concentration, dissolved oxygen, salinity, sea surface temperature, and fishing effort. Spatial autocorrelation was confirmed through Moran’s I test, justifying the application of a spatial econometric framework. Among the environmental factors, salinity exhibited the strongest positive direct effect on catch volumes, whereas dissolved oxygen consistently showed a negative effect. Chlorophyll-a concentration exhibited significant positive effects both within local grids and in neighboring areas. Sea surface temperature also had a modest but significant direct effect on catch volumes. Additionally, higher fishing effort was associated with increased catch volumes, emphasizing the spatial impact of human activities on fishery resources. These findings reveal that hairtail tend to aggregate in high-salinity, low-oxygen environments and respond to seasonal oceanographic variations. Overall, the results highlight the value of spatial econometric models in fisheries research by revealing how environmental and anthropogenic factors influence fish catch through both direct and indirect effects. The spatial framework offers deeper insight into the mechanisms driving hairtail distribution, particularly in ecologically complex regions like the Jeju Strait. Full article

Mid–long-term streamflow prediction (MLSP) plays a critical role in water resource planning amid growing hydroclimatic and anthropogenic uncertainties. Although AI-based models have demonstrated strong performance in MLSP, their capacity to quantify predictive uncertainty remains limited. To address this challenge, a DeepAR-based probabilistic modeling framework is developed, enabling direct estimation of streamflow distribution parameters and flexible selection of output distributions. The framework is applied to two case studies with distinct hydrological characteristics, where combinations of recurrent model structures (GRU and LSTM) and output distributions (Normal, Student’s t, and Gamma) are systematically evaluated. The results indicate that the choice of output distribution is the most critical factor for predictive performance. The Gamma distribution consistently outperformed those using Normal and Student’s t distributions, due to its ability to better capture the skewed, non-negative nature of streamflow data. Notably, the magnitude of performance gain from using the Gamma distribution is itself region-dependent, proving more significant in the basin with higher streamflow skewness. For instance, in the more skewed Upper Wudongde Reservoir area, the model using LSTM structure and Gamma distribution reduces RMSE by over 27% compared to its Normal-distribution counterpart (from 1407.77 m³/s to 1016.54 m³/s). Furthermore, the Gamma-based models yield superior probabilistic forecasts, achieving not only lower CRPS values but also a more effective balance between high reliability (PICP) and forecast sharpness (MPIW). In contrast, the relative performance between GRU and LSTM architectures was found to be less significant and inconsistent across the different basins. These findings highlight that the DeepAR-based framework delivers consistent enhancement in forecasting accuracy by prioritizing the selection of a physically plausible output distribution, thereby providing stronger and more reliable support for practical applications. Full article

This study delivers a pioneering, high-resolution hydrogeochemical assessment of surface waters in the Upper Velhas and Upper Paraopeba river basins within Brazil’s Iron Quadrangle—an area of critical socioeconomic importance marked by intensive mining and urbanization. Through a dense sampling network of 315 surface water points (one every 23 km²), the research generates an unprecedented spatial dataset, enabling the identification of contamination hotspots and the differentiation between lithogenic and anthropogenic sources of potentially toxic elements (PTEs). Statistical methods, including exploratory data analysis and cluster analysis, were applied to determine background and anomalous concentrations of potentially toxic elements (PTEs). Geospatial distribution maps were generated using GIS. The results revealed widespread contamination by As, Cd, Cr, Ni, Pb, and Zn, with many samples exceeding Brazilian, European, and global drinking water standards. Arsenic and cadmium anomalies in rural and peri-urban communities raise concerns due to the direct consumption of contaminated water. The innovative application of dense spatial sampling and integrated geostatistical methods offers new insights into the pathways and sources of PTE pollution, identifying specific lithological units (e.g., gold schists, mafic intrusions) and land uses (e.g., urban effluents, mining sites) associated with elevated contaminant levels. By establishing robust regional geochemical baselines and source attributions, this study sets a new standard for environmental monitoring in mining-impacted watersheds and provides a replicable framework for water governance, environmental licensing, and risk management in similar regions worldwide. Full article

The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks ICM two-dimensional hydrodynamic modeling and systematic resilience assessment. The framework makes three key innovations: (1) multi-scale temporal stress scenarios combining short-duration extreme events (1–2 h) with long-duration persistent events (24 h) and historical extremes; (2) integrated infrastructure–drainage stress analysis that explicitly models roads’ dual role as critical infrastructure and emergency drainage channels; and (3) dynamic resilience quantification under multiple stressors across 15 systematically designed stress conditions. Using Western Beijing as a case study, the model is validated, achieving Nash–Sutcliffe efficiency values exceeding 0.9, demonstrating its robust capability in simulating complex mountainous terrain flood processes. Through systematic analysis of fifteen rainfall scenarios designed based on Chicago rainfall patterns and historical events (including the July 2023 Haihe River basin flood), encompassing various intensities (30–200 mm/h), durations (1 h, 2 h, 24 h), and return periods (10, 50, 100 years), the key findings include the following: (1) A rainfall intensity of 60 mm/h represents a crucial threshold for system performance, beyond which significant impacts on community infrastructure emerge, with built-up areas experiencing inundation depths of 0.27–0.4 m that exceed safe passage limits. (2) Road networks become primary drainage channels during intense precipitation, with velocities exceeding 5 m/s in village roads and exceeding 5 m/s in country road sections, creating significant hazard potential. (3) Four major risk spots were identified with distinct waterlogging patterns, characterized by maximum depths ranging from 0.8 to 2.0 m and recovery periods varying from 2 to 12 hours depending on the topographic confluence effects and drainage efficiency. (4) The system demonstrates strong recovery capability, achieving >90% recovery within 3–6 hours for short-duration events, while showing vulnerability to extreme scenarios, with performance declining to 0.75–0.80, highlighting the coupling effects between water depth and flow velocity in steep terrain. This research provides quantitative insights for flood risk management and for enhancing community resilience in mountainous regions, offering valuable guidance for infrastructure improvement, emergency response optimization, and sustainable community development. This study primarily focuses on physical resilience aspects, with socioeconomic and institutional dimensions representing important directions for future research. Full article

The growing threat of antimicrobial resistance has necessitated the development of alternative, non-antibiotic therapies for effective microbial control. Antimicrobial photodynamic therapy, which uses photosensitizers activated by light to generate reactive oxygen species, offers a promising solution. Among natural photosensitizers, curcumin, a polyphenolic compound from Curcuma longa, has demonstrated broad-spectrum antimicrobial activity through reactive oxygen species-mediated membrane disruption and intracellular damage. However, curcumin’s poor water solubility, low stability, and limited bioavailability hinder its clinical utility. Nanotechnology has emerged as a transformative strategy to overcome these limitations. This review comprehensively explores advances in nanocurcumin- and curcumin-loaded nanoparticles, highlighting their physicochemical enhancements, photodynamic mechanisms, and antimicrobial efficacy against multidrug-resistant and biofilm-associated pathogens. A range of nanocarriers, including chitosan, liposomes, nanobubbles, hybrid metal composites, metal–organic frameworks, and covalent organic frameworks, demonstrate improved microbial targeting, light activation efficiency, and therapeutic outcomes. Applications span wound healing, dental disinfection, food preservation, water treatment, and medical device sterilization. Conclusions and future directions are given, emphasizing the integration of smart nanocarriers and combinatorial therapies to enhance curcumin’s clinical translation. Full article

The primary objective of the present study is to evaluate the effect of conglomerate microorganisms on nitrification in activated sludge. The present study compares this process with activated-sludge technology to explore the variables that influence the complex biochemical processes taking place in bioreactors. The research under consideration involves monitoring the effectiveness of optimizing the wastewater treatment process using kinetic modeling for the nitrification and denitrification processes. The system is designed to simulate various operating scenarios and adjust process parameters in real time. The nitrification rate demonstrates a 99.03% performance, while the denitrification rate ranges from 19.08% to 91.01%. A substantial correlation has been demonstrated between this variable and the temperature of the treated wastewater. This provides the possibility of accurately assessing the ammonium oxidation potential. Furthermore, kinetic equations facilitate the estimation of parameters that are not typically measured, yet are essential for optimizing operational parameters (e.g., dissolved oxygen levels in the aeration tank, sludge dosage, and influent flow rate). This estimation is crucial for enhancing the effectiveness of the process and attaining the desired or anticipated outcomes. This validation underscores the efficacy of the technology, thereby establishing a foundational framework for subsequent research endeavors. These research efforts are directed towards providing decision-makers and stakeholders with actionable insights. The validation underscores the significance of optimized practices in the context of water resource protection. Moreover, it signifies a substantial advancement in the instrumentation of wastewater treatment plants. Full article

Packaging demand currently exceeds 144 Mt per year, of which >90% is conventional plastic, generating over 100 Mt of waste and 1.8 Gt CO₂-eq emissions annually. In this review, we systematically survey three classes of lignocellulosic feedstocks, agricultural residues, fruit and vegetable by-products, and forestry wastes, with respect to their physicochemical composition (cellulose crystallinity, hemicellulose ratio, and lignin content) and key processing pathways. We then examine fabrication routes (solvent casting, extrusion, and compression molding) and quantify how compositional variables translate into film performance: tensile strength, elongation at break (4–10%), water vapor transmission rate, thermal stability, and biodegradation kinetics. Highlighted case studies include the reinforcement of poly(vinyl alcohol) (PVA) with 7 wt% oxidized nanocellulose, yielding a >90% increase in tensile strength and a 50% reduction in water vapor transmission rate (WVTR), as well as pilot-scale extrusion of rice straw/polylactic acid (PLA) blends. We also assess techno-economic metrics and life-cycle impacts. Finally, we identify four priority research directions: harmonizing pretreatment protocols to reduce batch variability, scaling up nanocellulose extraction and film casting, improving marine-environment biodegradation, and integrating circular economy supply chains through regional collaboration and policy frameworks. Full article

In recent years, the accurate evaluation of artificial fracture networks has become a key challenge in enhancing the effectiveness of reservoir stimulation in shale gas development. This paper systematically reviews the research progress on evaluation methods for artificial fracture networks in shale gas horizontal wells, covering two major technical systems: direct monitoring and dynamic inversion. Direct monitoring methods focus on technologies such as microseismic monitoring, tracers, wide-field electromagnetic methods, and distributed fiber optics. Dynamic inversion methods utilize data from fracturing construction curves, shut-in water hammer effects, and flowback production, and combine numerical simulations with artificial intelligence algorithms to infer fracture network parameters, although the issue of non-uniqueness in solutions remains to be addressed. Research shows that no single technology can comprehensively characterize fracture network features. Future directions should involve the integration of multi-source data (geophysical, chemical, fiber-optic, and dynamic production data) to construct intelligent evaluation frameworks, validated by field experiments and dynamic data simulations. The introduction of artificial intelligence and big data technologies provides new ideas for fracture network parameter inversion, but their effectiveness still requires support from more case studies. This paper provides theoretical guidance and practical reference for the optimization and integration of fracture network evaluation technologies in efficient shale gas development. Full article

The global scarcity of freshwater, driven by population growth and the unequal distribution of water resources, has intensified the need for alternative water supply technologies. Among the most promising solutions, adsorption-based atmospheric water harvesting (AWH) systems offer the ability to extract water vapor directly from ambient air, even under low-humidity conditions. This review presents a comprehensive overview of the thermodynamic principles and material characteristics governing these systems, with particular emphasis on adsorption isotherms and their role in predicting and optimizing system performance. A generalized theoretical framework is proposed to assess the energy efficiency of thermally driven AWH devices, based on key material parameters. Recent developments in sorbent materials, especially metal–organic frameworks (MOFs) and advanced zeolites, are examined for their high-water uptake, regeneration efficiency, and potential for operation under real climatic conditions. The Dubinin–Astakhov and modified Langmuir isotherm models are reviewed for their effectiveness in describing nonlinear sorption behaviors critical to performance modeling. In addition, component-level design strategies for adsorption-based AWH systems are discussed. The integration of solar energy is also discussed, highlighting recent prototypes and design strategies that have achieved water yields ranging from 0.1 to 2.5 L m⁻²/day and specific productivities up to 2.8 L kg⁻¹ using MOF-801 at 20% RH. Despite notable progress, challenges remain, including limited productivity in non-optimized setups, thermal losses, long-term material stability, and scalability. This review concludes by identifying future directions for material development, system integration, and modeling approaches to advance the practical deployment of efficient and scalable AWH technologies. Full article

Monitoring water quality parameters in coastal and estuarine environments is critical for assessing their ecological status and addressing environmental challenges. However, traditional in situ sampling programs are often constrained by limited spatial and temporal coverage, making it difficult to capture the complex variability in these dynamic systems. This study introduces a novel upscaling framework that leverages limited in situ measurements and airborne hyperspectral data to generate multiple conditional realizations of water quality parameter fields. These pseudo-measurements are statistically consistent with the original data and are used to calibrate inversion algorithms that relate satellite-derived reflectance data to water quality parameters. The approach was applied to Kaštela Bay, a semi-enclosed coastal area in the eastern Adriatic Sea, to map seasonal variations in water quality parameters such as Chlorophyll-a. The upscaling framework captured spatial patterns that were absent in sparse in situ observations and enabled regional mapping using Sentinel-2A satellite data at the appropriate spatial scale. By generating realistic pseudo-measurements, the method improved the stability and performance of satellite-based retrieval algorithms, particularly in periods of high productivity. Overall, this methodology addresses data scarcity challenges in coastal water monitoring and its application could benefit the implementation of European water quality directives through enhanced regional-scale mapping capabilities. Full article

Due to the increasing demand for agricultural water, the water availability for winter and spring irrigation of cotton fields has decreased. Consequently, dry seeding followed by irrigation (DSSI) has become a widespread cotton cultivation technique in Xinjiang. This study focused on the interaction between soil particles and cotton seeding equipment under DSSI in Xinjiang. The discrete element method (DEM) simulation framework was employed to compare the performance of the Johnson-Kendall-Roberts (JKR) model and Bonding model in simulating contact between soil particles. The models’ ability to simulate the angle of repose was investigated, and shear tests were conducted. The simulation results showed that both models had comparable repose angles, with relative errors of 0.59% for the JKR model and 0.36% for the contact model. However, the contact model demonstrated superior predictive accuracy in simulating direct shear test results, predicting an internal friction angle of 35.8°, with a relative error of 5.8% compared to experimental measurements. In contrast, the JKR model exhibited a larger error. The Bonding model provides a more accurate description of soil particle contact. Subsoiler penetration tests showed that the maximum penetration force was 467.2 N, closely matching the simulation result of 485.3 N, which validates the reliability of the model parameters. The proposed soil simulation framework and calibrated parameters accurately represented soil mechanical properties, providing a robust basis for discrete element modeling and structural optimization of soil-tool interactions in cotton field tillage machinery. Full article

Payments for Watershed services (PWSs) have been emerging as a critical tool for environmental governance in watershed, yet their comparative effectiveness across implementation models has remained poorly understood. Based on a comparative analysis of Eco-Compensation (EC) and Payments for Ecosystem Services (PESs) frameworks, examining both theoretical foundations and implementation practices, this study aims to quantitatively assess and compare the effectiveness of two dominant PWSs models—the EC-like model (Phase I: October 2008–April 2017) and the PESs-like model (Phase II: 2017–December 2021). Using the Liaohe River in China as a case study, utilizing ecosystem service value (ESV) as an indicator and employing the corrected unit-value transfer method, we compare the effectiveness of different PWSs models from October 2008 to December 2021. The results reveal the following: (1) Policy Efficiency: The PESs-like model demonstrated significantly greater effectiveness than the EC-like model, with annual average increases in ESV of 3.23 billion CNY (491 million USD) and 1.79 billion CNY (272 million USD). (2) Functional Drivers: Water regulation (45.1% of total ESV growth) and climate regulation (24.3%) were dominant services, with PESs-like interventions enhancing multifunctionality. (3) Stakeholder Impact: In the PESs-like model, the cities implementing inter-county direct payment showed higher growth efficiency than those without it. The operational efficiency of PWSs increases with the number of participating stakeholders, which explains why the PESs-like model demonstrates higher effectiveness than the EC-like model. Our findings offer empirical evidence and actionable policy implications for designing effective PWSs models across global watershed ecosystems. Full article

The increasing use of pharmaceutically active compounds (PhACs), endocrine-disrupting compounds (EDCs), and personal care products (PCPs) has led to the widespread presence of organic micropollutants (OMPs) in aquatic environments, posing a significant global challenge for environmental conservation. In recent years, advanced materials-based nanofiltration (NF) technologies have emerged as a promising solution for water and wastewater treatment. This review begins by examining the sources of OMPs, as well as the risk of OMPs. Subsequently, the key criteria of NF membranes for OMPs are discussed, with a focus on the roles of pore size, charge property, molecular interaction, and hydrophilicity in the separation performance. Against that background, this review summarizes and analyzes recent advancements in materials such as metal organic frameworks (MOFs), covalent organic frameworks (COFs), graphene oxide (GO), MXenes, hybrid materials, and environmentally friendly materials. It highlights the porous nature and structural diversity of organic framework materials, the advantage of inorganic layered materials in forming controllable nanochannels through stacking, the synergistic effects of hybrid materials, and the importance of green materials. Finally, the challenges related to the performance optimization, scalable fabrication, environmental sustainability, and complex separation of advanced materials-based membranes for OMP removal are discussed, along with future research directions and potential breakthroughs. Full article