Search Results (62)

Water resources are fundamental for human development in every possible sense; from natural development, since they are the main biological factor necessary for the development of life, to economic development, since they are essential for a large number of productive systems, especially in the primary and secondary sectors. This makes them a resource which, although at first glance may seem unlimited, is critical since their scarcity and unavailability compromise the whole of human development, greatly limiting productive and economic activity and, ultimately, social welfare. The current development of IoT technology, on the other hand, provides tools to face this problem in a technical way, allowing the adoption of distributed and automated solutions that, together with the knowledge provided by disciplines such as agricultural and alimentary engineering, make viable the development of a system that allows us to monitor and control water distribution networks (WDNs). Next, the situations that involve the mentioned problem will be detailed and different aspects will be proposed in which the implementation of the presented system is intended to have a direct impact. Full article

Floods are among the most destructive natural disasters and can devastate human life, infrastructure, and mobility in urban areas. It is necessary to develop a simulation model suitable for disaster management to prepare for flooding and facilitate rapid response interventions. The advantage of a three-dimensional (3D) geographic information system (GIS) is that it allows researchers to perform more successful spatial analyses than traditional two-dimensional (2D) systems. In this study, real-time 3D flood simulations were created for the Bozkurt district of Kastamonu, Türkiye, integrating GIS and game engine technologies. Land use land cover (LU/LC) map, digital elevation model (DEM), soil properties and climate data of the study region constitute the input data for the hydrological model. DEM and building footprints are also used to create 3D models of the buildings in the region. Through the Soil and Water Assessment Tool (SWAT) analysis, a hydrological model that included environmental factors such as precipitation, runoff, and soil erosion was created. The average flow rate for the same period, obtained from flow monitoring stations in the Bozkurt district, was 4.64 m³/s, while the flow rate obtained with the SWAT+ model was 4.12 m³/s. Using the flow parameters obtained with SWAT, 3D flood models were developed on Unreal Engine (UE). The flood simulation created with UE and the flood disaster experienced in 2021 in the region were compared on an area basis. The obtained simulation accuracy was 88%. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

As a crucial component of the Beijing–Hangzhou Grand Canal’s hydraulic engineering, the Jiangnan Canal has historically played a pivotal role in China’s development as a key hydraulic infrastructure. This water conservancy project, connecting northern and southern water systems, not only facilitated regional economic integration but also nurtured unique cultural landscapes along its course. The Jiangnan Canal and its adjacent cities were selected as the study area to systematically investigate 334 tangible cultural heritage (TCH) sites and 420 intangible cultural heritage (ICH) elements. Through integrated Geographical Information System (GIS) spatial analyses—encompassing nearest neighbor index, kernel density estimation, standard deviation ellipse assessment, multi-ring buffer zoning, and Geodetector modeling, the spatiotemporal distribution features of cultural heritage were quantitatively characterized, with a focus on identifying the underlying driving factors shaping its spatial configuration. The analysis yields four main findings: (1) both TCH and ICH exhibit significant spatial clustering patterns across historical periods, with TCH distribution displaying an axis-core structure centered on the canal, whereas ICH evolved from dispersed to clustered configurations. (2) The center of gravity of TCH is primarily around Taihu Lake, while that of ICH is mainly on the south side of Taihu Lake, and the direction of distribution of both is consistent with the direction of the canal. (3) Multi-ring buffer analysis indicates that 77.2% of TCH and 49.8% of ICH clusters are concentrated within 0–10 km of the canal, demonstrating distinct spatial patterns: TCH exhibits a gradual canal-dependent density decrease with distance, whereas ICH reveals multifactorial spatial dynamics. (4) Human activity factors, particularly nighttime light intensity, are identified as predominant drivers of heritage distribution patterns, with natural environmental factors exerting comparatively weaker influence. These findings provide empirical support for developing differentiated conservation strategies for canal-related cultural heritage. The methodology offers replicable frameworks for analyzing heritage corridors in complex historical landscapes, contributing to both applied conservation practices and theoretical advancements in cultural geography. Full article

Flood hazards and their disastrous consequences disrupt economic activity and threaten human lives globally. From a remote sensing perspective, since floods are often triggered by extreme climatic events, such as heavy rainstorms or tropical cyclones, the efficacy of using optical remote sensing data for disaster and damage mapping is significantly compromised. In many flood events, obtaining cloud-free images covering the affected area remains challenging. Nonetheless, considering that floods are the most frequent type of natural disaster on Earth, optical remote sensing data should be fully exploited. In this article, firstly, we will present a critical review of remote sensing data and machine learning methods for global flood-induced damage detection and mapping. We will primarily consider two types of remote sensing data: moderate-resolution multi-spectral data and high-resolution true-color or panchromatic data. Big and semantic databases available for advanced machine learning to date will be introduced. We will develop a set of best-use case scenarios for using these two data types to conduct water-body and built-up area mapping with no to moderate cloud coverage. We will cross-verify traditional machine learning and current deep learning methods and provide both benchmark databases and algorithms for the research community. Last, with this suite of data and algorithms, we will demonstrate the development of a cloud-computing-supported computing gateway, which houses the services of both our remote-sensing-based machine learning engine and a web-based user interface. Under this gateway, optical satellite data will be retrieved based on a global flood alerting system. Near-real-time pre- and post-event flood analytics are then showcased for end-user decision-making, providing insights such as the extent of severely flooded areas, an estimated number of affected buildings, and spatial trends of damage. In summary, this paper’s novel contributions include (1) a critical synthesis of operational readiness in flood mapping, (2) a multi-sensor-aware review of optical limitations, (3) the deployment of a lightweight ML pipeline for near-real-time mapping, and (4) a proposal of the GloFIM platform for field-level disaster support. Full article

Water pollution is a significant threat for human health, particularly in developed countries. This study advances the mathematical understanding of WP transmission dynamics by developing a fractional–fractal derivative framework with non-singular kernels and the Mittage–Leffler function, which successfully preserves the non-local behavior of pollutants. The fractional–fractal derivatives in sense of the Atangana–Baleanu–Caputo formulation inherently captures the non-local and memory-dependent behavior of pollutant diffusion, addressing limitations of classical differential operators. A novel parameter, $\gamma$, is introduced to represent the recovery rate of water systems through treatment processes, explicitly modeling the bridge between natural purification mechanisms and engineered remediation efforts. Furthermore, this study establishes stability analysis, and the existence and uniqueness of the solution are established through fixed-point theory to ensure the mathematical stability of the system. Moreover, a numerical scheme based on the Newton polynomial is formulated, by obtaining significant simulations of pollution dynamics under various conditions. Graphical results show the effect of important parameters on pollutant evolution, providing useful information about the behavior of the system. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

This study assesses the environmental status and water quality of the Saraswati River, an ancient and endangered waterway in Bengal, using an integrated approach. By combining traditional knowledge, advanced geospatial tools, and field analysis, it examines natural and human-induced factors driving the river’s degradation and proposes sustainable restoration strategies. Tools such as the Garmin Global Positioning System (GPS) eTrex10, Google Earth Pro, Landsat imagery, ArcGIS 10.8, and Google Earth Engine (GEE) were used to map the river’s trajectory and estimate its water quality. Remote sensing-derived indices, including the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Salinity Index (NDSI), Normalized Difference Turbidity Index (NDTI), Floating Algae Index (FAI), and Normalized Difference Chlorophyll Index (NDCI), Total Dissolved Solids (TDS), were computed to evaluate parameters such as the salinity, turbidity, chlorophyll content, and water extent. Additionally, field data from 27 sampling locations were analyzed for 11 critical water quality parameters, such as the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and microbial content, using an arithmetic weighted water quality index (WQI). The results highlight significant spatial variation in water quality, with WQI values ranging from 86.427 at Jatrasudhi (indicating relatively better conditions) to 358.918 at Gobra Station Road (signaling severe contamination). The pollution is primarily driven by urban solid waste, industrial effluents, agricultural runoff, and untreated sewage. A microbial analysis revealed the presence of harmful species, including Escherichia coli (E. coli), Bacillus, and Entamoeba, with elevated concentrations in regions like Bajra, Chinsurah, and Chandannagar. The study detected heavy metals, fertilizers, and pesticides, highlighting significant anthropogenic impacts. The recommended mitigation measures include debris removal, silt extraction, riverbank stabilization, modern hydraulic structures, improved waste management, systematic removal of water hyacinth and decomposed materials, and spoil bank design in spilling zones to restore the river’s natural flow. Full article

Coastal erosion is a critical environmental challenge in the Upper Gulf of Thailand, driven by both natural processes and human activities. This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). The results show that the Random Forest algorithm, utilizing spectral bands and indices (NDVI, NDWI, MNDWI, SAVI), achieved the highest classification accuracy (98.17%) and a Kappa coefficient of 0.9432, enabling reliable delineation of land and water boundaries. The extracted annual shorelines were validated with high accuracy, yielding RMSE values of 13.59 m (2018) and 8.90 m (2023). The DSAS analysis identified significant spatial and temporal variations in shoreline erosion and accretion. Between 1988 and 2006, the most intense erosion occurred in regions 4 and 5, influenced by sea-level rise, strong monsoonal currents, and human activities. However, from 2006 to 2018, erosion rates declined significantly, attributed to coastal protection structures and mangrove restoration. The period 2018–2023 exhibited a combination of erosion and accretion, reflecting dynamic sediment transport processes and the impact of coastal management measures. Over time, erosion rates declined due to the implementation of protective structures (e.g., bamboo fences, rock revetments) and the natural expansion of mangrove forests. However, localized erosion remains persistent in low-lying, vulnerable areas, exacerbated by tidal forces, rising sea levels, and seasonal monsoons. Anthropogenic activities, including urban development, mangrove deforestation, and aquaculture expansion, continue to destabilize shorelines. The findings underscore the importance of sustainable coastal management strategies, such as mangrove restoration, soft engineering coastal protection, and integrated land-use planning. This study demonstrates the effectiveness of combining machine learning and geoinformatics for shoreline monitoring and provides valuable insights for coastal erosion mitigation and enhancing coastal resilience in the Upper Gulf of Thailand. Full article

This study explores the fabrication of tannic acid-crosslinked gelatin nanofibers via electrospinning, followed by helium and nitrogen plasma treatment to enhance their biofunctionality, which was assessed using fibroblast cells. The nanofibers were characterized using scanning electron microscopy, atomic force microscopy, attenuated total reflection Fourier transform infrared spectroscopy, X-ray diffraction, and water contact angle measurements before and after treatment. Helium and nitrogen gas plasma were employed to modify the nanofiber surfaces. Results indicated that helium and nitrogen plasma treatment significantly increased the hydrophilicity and biofunctionality of the nanofibers by 5.1° ± 0.6 and 15.6° ± 2.2, respectively, making them more suitable for human skin fibroblast applications. To investigate the impact of plasma treatment on gelatin, we employed a computational model using density functional theory with the B3LYP/6-31+G(d) method. This model represented gelatin as an amino acid chain composed of glycine, hydroxyproline, and proline, interacting with plasma particles. Vibrational analysis of these systems was used to interpret the vibrational spectra of untreated and plasma-treated gelatin. To further correlate with experimental findings, molecular dynamics simulations were performed on a system of three interacting gelatin chains. These simulations explored changes in amino acid bonding. The computational results align with experimental observations. Comprehensive analyses confirmed that these treatments improved hydrophilicity and biofunctionality, supporting the use of plasma-treated gelatin nanofibers in skin tissue engineering applications. Gelatin’s natural biopolymer properties and the versatility of plasma surface modification techniques underscore its potential in regenerating cartilage, skin, circulatory tissues, and hamstrings. Full article

Due to their indisputable biocompatibility and abundant source, biopolymers are widely used to prepare hydrogels for skin tissue engineering. Among them, cellulose is a great option for this challenging application due to its increased water retention capacity, mechanical strength, versatility and unlimited availability. Since algae are an unexploited source of cellulose, the novelty of this study is the decellularization of two different species, freshly collected from the Black Sea coast, using two different chemical surfactants (sodium dodecyl sulphate and Triton X-100), and characterisation of the resulted complex biopolymeric 3D matrices. The algae nature and decellularization agent significantly influenced the matrices porosity, while the values obtained for the hydration degree included them in hydrogel class. Moreover, their capacity to retain and then controllably release an anti-inflammatory drug, ibuprofen, led us to recommend the obtained structures as drug delivery systems. The decellularized macroalgae hydrogels are bioadhesive and cytocompatible in direct contact with human keratinocytes and represent a great support for cells. Finally, it was noticed that human keratinocytes (HaCaT cell line) adhered and populated the structures during a monitoring period of 14 days. Full article

In the construction of water conveyance tunnels with the shield method, accidents have occurred from time to time, such as collapses and explosions, and it is of practical significance to explore the cause mechanism of the accident. However, previous research has not considered the effects of dependence between risks on the risk spread. In response, we propose a method based on the Natural Killing Model (the N-K Model) and complex network theory to analyze the cause of shield construction accidents in water conveyance tunnels. By deeply exploring the transmission mechanism and action intensity between system risks, this method can scientifically clarify the accident cause mechanism and provide support for engineering construction safety management. The method constructs a risk index system. Secondly, we introduce the N-K model to reveal the risk coupling mechanism. Then, based on complex network theory, we construct the incident causation model and revise the node’s centrality with the coupling value. Finally, the network topology parameters are calculated to quantitatively describe the causal characteristics of accidents, revealing the risk evolution process and critical causes. The research results indicate that the key causes of accidents are failure to construct according to regulations, inadequate emergency measures, poor ability of judgment and decision-making, and insufficient understanding of abnormal situations. The front end of critical links is subject to human or management risks and should be carefully controlled during construction. Full article

Soil erosion has been causing an imbalance in nature and the environment. It is mainly caused naturally but is also due to human interventions leading to desertification and possible contamination. Therefore, engineering, geography, and cartography have been allies in applying erosion models to predict, address, and remediate the impacts. Therefore, the Revised Universal Soil Loss Equation (RUSLE) and Soil and Water Assessment Tool (SWAT) linked to Geographic Information Systems (GISs) could boost decision making as tools to mitigate issues. This study applies the RUSLE and SWAT models from a geotechnical point of view to analyze a sub-watershed at Idanha-a-Nova (Portugal) over 4 years, showing a predominant erosion risk class with losses lower than 5 t.ha⁻¹.year⁻¹ (60 to 86%), characterized as very low risk. The modeling permitted the development of soils erosion susceptibility charts, in addition to material availability and the suitability for construction areas, exposing a replicable methodology that could contribute to minimizing environmental impacts while encouraging a more intelligent use of the land towards a greener exploration. Full article

Restoring the structures and functions of tissues along with organs in human bodies is a topic gathering attention nowadays. These issues are widely discussed in the context of regenerative medicine. Excipients/delivery systems play a key role in this topic, guaranteeing a positive impact on the effectiveness of the drugs or therapeutic substances supplied. Advances in materials engineering, particularly in the development of hydrogel biomaterials, have influenced the idea of creating an innovative material that could serve as a carrier for active substances while ensuring biocompatibility and meeting all the stringent requirements imposed on medical materials. This work presents the preparation of a natural polymeric material based on pullulan modified with silymarin, which belongs to the group of flavonoids and derives from a plant called Silybum marianum. Under UV light, matrices with a previously prepared composition were crosslinked. Before proceeding to the next stage of the research, the purity of the composition of the matrices was checked using Fourier-transform infrared (FT-IR) spectroscopy. Incubation tests lasting 19 days were carried out using incubation fluids such as simulated body fluid (SBF), Ringer’s solution, and artificial saliva. Changes in pH, electrolytic conductivity, and weight were observed and then used to determine the sorption capacity. During incubation, SBF proved to be the most stable fluid, with a pH level of 7.6–7.8. Sorption tests showed a high sorption capacity of samples incubated in both Ringer’s solution and artificial saliva (approximately 350%) and SBF (approximately 300%). After incubation, the surface morphology was analyzed using an optical microscope for samples demonstrating the greatest changes over time. The active substance, silymarin, was released using a water bath, and then the antioxidant capacity was determined using the Folin–Ciocâlteu test. The tests carried out proved that the material produced is active and harmless, which was shown by the incubation analysis. The continuous release of the active ingredient increases the biological value of the biomaterial. The material requires further research, including a more detailed assessment of its balance; however, it demonstrates promising potential for further experiments. Full article