Search Results (219)

Railway transport plays a crucial role in sustainable and low-carbon mobility; however, the safety and resilience of railway systems are increasingly challenged by aging infrastructure, spatial inequality, and intensifying climate extremes. These challenges are particularly evident in Serbia, where railway stations in rural and peripheral areas often lack adequate safety infrastructure, accessibility, and climate-adaptive design, especially affecting women and other vulnerable passengers. The aim of this study is to develop a GIS-based spatial framework for assessing gender-sensitive railway safety under combined sociospatial and environmental pressures. The analysis integrates multiple geo-information sources, including railway infrastructure data, passenger statistics, safety incidents, and climate hazard indicators such as floods, heatwaves, heavy snowfall, and windstorms. Geographic Information System (GIS) techniques, including kernel density estimation, buffer and zonal statistics, spatial interpolation, and spatial regression, were applied to evaluate spatial safety patterns and environmental risks. The results reveal pronounced regional disparities, with southern and eastern Serbia representing the most vulnerable areas due to inactive stations, poor lighting, limited digital connectivity, and frequent exposure to extreme weather events. Rural railway stations are frequently located in climate risk zones, and many do not meet the minimum safety infrastructure standards. Based on these findings, this study recommends strengthening station lighting and surveillance systems, improving digital connectivity and emergency accessibility, and integrating climate-resilient infrastructure planning into railway modernization strategies. Overall, the findings highlight the importance of combining GIS-based spatial analysis, climate hazard assessment, and gender-sensitive planning to support safer, more inclusive, and climate-resilient railway infrastructure in Serbia. Full article

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

Understanding the spatiotemporal dynamics of medicinal plant distributions and their quality responses under climate change is essential for formulating forward-looking conservation and utilization strategies. In response to the increasing depletion of wild resources of Alpinia officinarum Hance, one of the ‘Ten Major Guangdong Medicinal Materials’, this study developed an integrated modeling platform incorporating nine algorithms. These included generalized linear models, machine learning techniques, and a MaxEnt model optimized using ENMeval (Regularization Multiplier (RM) = 3, Feature Class (FC) = LQH). The platform was applied to simulate habitat suitability evolution under current climatic conditions (1970–2000) and for two future periods (2050s: 2041–2060; 2090s: 2081–2100) across four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Furthermore, Co-kriging interpolation was coupled to conduct a comprehensive quality zoning based on the dual “ecological-chemical” dimension. Analysis of key environmental factors revealed that the distribution of A. officinarum is primarily constrained by hydrothermal conditions, with a suitable annual temperature ranges from 19.96 to 29.05 °C and a dry-season precipitation requirement between 56.64 and 185.65 mm. Model projections indicate that future warming does not promote habitat expansion; instead, it drives a latitudinal shift in the suitability centroid toward lower latitudes. The cumulative effects of different emission pathways vary markedly: the high-emission scenario (SSP585) triggers severe habitat contraction by the 2090s, while habitat loss under the SSP370 scenario remains relatively manageable. By overlaying the spatially heterogeneous distribution of galangin, this study delineated southeastern Yunnan, southeastern Guangxi, southwestern Guangdong, and northern Hainan as core “integrated quality regions”. These findings not only reveal the sensitivity and vulnerability of A. officinarum Hance to climate change but also provide spatially explicit guidance for in situ germplasm conservation and the selection of high-quality cultivation bases. Full article

Advanced time series interpolation techniques used for estimating marine environmental factors encounter challenges regarding their usability, practical implementation, and reproducibility outside of marine science laboratories. This study aimed to interpolate NIFS Serial Oceanographic Observations and develop a system for analyzing complex factors in offshore and coastal fishing ground formation in South Korea. Additionally, the study explored the potential for integration of spatiotemporally discontinuous in situ data with continuously available satellite data through interpolation methods. Specifically, daily sea temperature and salinity data were generated through conventional time series interpolation techniques such as linear, cubic spline, and STL + PCHIP, and spatial interpolation techniques such as IDW, kriging, and natural neighbor were used to construct monthly raster data. The generated data were compared with the output of the GOFS3.1 model, and statistical indices such as MAE, RMSE, R², and Pearson or Spearman correlation coefficients were used to evaluate the accuracy and reproducibility. Cubic spline temporal and kriging spatial interpolation methods demonstrated strong performance for the sea temperature data; however, the interpolation performance for the salinity data exhibited limited effectiveness owing to unique local variability. This study introduces techniques for transforming discontinuous in situ observational data into high-resolution data and demonstrates that the integrated use of in situ data can enhance our understanding of the fishing ground formation mechanisms and ecosystem-based fishery management. Full article

This paper demonstrates seasonal variations in the generalised extreme value (GEV) distribution shape parameter and discrepancies in GEV types within the same location. Daily rainfall data from 26 rain gauge stations located in Tasmania were used as a case study. Four GEV distribution parameter estimation techniques, such as MLE, GMLE, Bayesian, and L-moments, were used to determine the shape parameter of the distribution. With the estimated shape parameter, the spatial variations under different seasons were investigated through GIS interpolation maps. As there is strong evidence that shape parameters potentially vary across locations, spatial analysis focusing on the shape parameter across Tasmania (Australia) was performed. The outcomes of the analysis revealed that the shape parameters exhibit their highest and lowest values in winter, with a range from −0.234 to 0.529. The analysis of the rainfall data revealed that there is significant variation in the shape parameters among the seasons. The magnitude of the shape parameter decreases with elevation, and a non-linear relationship exists between these two parameters. This study extends knowledge on the current framework of GEV distribution shape parameter estimation techniques at the regional scale, enabling the adoption of appropriate GEV types and, thus, the appropriate determination of design rainfall to reduce hazards and protect our environments. Full article

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

Soil micronutrient assessment is crucial for ensuring sustainable crop production and environmental quality, particularly in intensively cultivated regions. This study aimed to evaluate and map the spatial distribution of Diethylenetriamine Pentaacetic Acid (DTPA)-extractable micronutrients (Fe, Mn, Zn and Cu) in agricultural lands of Thirunallar commune, Karaikal, for augmenting site-specific nutrient management. A total of 233 geo-referenced surface soil samples (0–20 cm) were collected using a handheld GPS on a pre-defined grid and analyzed for available micronutrients. The spatial variability and distribution patterns were generated in ArcGIS 10.8.2 using semivariogram-based kriging interpolation. The results indicated that Fe, Mn and Cu were sufficient across the study area, with concentrations ranging from 4.74 to 99.80 ppm, 3.70–97.40 ppm, and 1.46–12.40 ppm, respectively, mainly due to the presence of iron-rich minerals, reduced manganese forms, and continuous application of copper-based inputs. Zinc showed greater variability (0.52–17.20 ppm), ranging from deficient to sufficient levels, likely influenced by fertilizer application and organic matter additions. The findings emphasize the importance of site-specific nutrient management to optimize fertilizer usage and crop productivity, particularly in fine-textured clay soils. This study demonstrates the effectiveness of geostatistical approaches for supporting precision agriculture in micronutrient-deficient areas. Full article

Population growth and agricultural expansion are threatening groundwater resources in the Maze Zenti catchment, Southern Ethiopia. This study evaluated groundwater suitability for drinking and irrigation by analyzing 30 samples using an integrated approach. This approach included GIS-based IDW interpolation, hydrochemical characterization, drinking water quality index, entropy weight, pollution index of groundwater, multivariate statistics, Piper, Gibbs, and Wilcox diagrams, ANOVA, and irrigation indices based on WHO standards. The correlation matrix revealed strong associations between Na⁺-TDS (r = 0.77) and Na⁺-Ca²⁺ (r = 0.68), indicating mineral dissolution, ion exchange, and agricultural inputs as key factors. Weak correlations were found for NO₃⁻ and F⁻, reflecting localized anthropogenic and geogenic influences. Component analysis identified four components explaining 78.2% (wet season) and 81.2% (dry season) of the variance, highlighting mineralization and anthropogenic inputs. Hydrochemical facies were mainly Ca-Mg-HCO₃ with some localized Na-HCO₃, suggesting that rock–water interactions are the primary source of geochemical control. Drinking water quality assessment showed that, during the wet season, 52.8% of the catchment had excellent water quality, 45.8% was good, and 1.4% was poor–very poor. In the dry season, 51.6% was excellent, 47.4% was good, 0.8% was poor, and 0.2% was very poor. The results of the entropy-weighted analysis indicated seasonal improvement, with excellent areas increasing from 13.1% to 31.4% and poor zones decreasing from 7.5% to 3.4%. Irrigation indices (Na%, PI, MAR, SAR) and Wilcox analysis (86.4% C2S1) suggested low sodicity and salinity hazards. This study provides the first integrated seasonal mapping of drinking and irrigation water quality, entropy-weighted water quality, and pollution index for the Maze Zenti catchment, establishing a hydrogeochemical baseline. Overall, groundwater in the area is generally suitable for drinking and irrigation. However, localized monitoring and sustainable land-use practices are recommended to mitigate contamination risks. Full article

This study evaluates the quality and irrigation suitability of drainage water in the Al-Jouf Region, Saudi Arabia, where water scarcity necessitates the reuse of nonconventional resources. Eighteen drainage water samples were analyzed for physicochemical parameters and irrigation indices, including electrical conductivity (EC), sodium percentage (Na⁺%), sodium adsorption ratio (SAR), magnesium hazard (MH), Kelly’s ratio (KR), permeability index (PS), and irrigation water quality index (IWQI). Multivariate statistical tools were applied to identify dominant hydrogeochemical processes. Inverse Distance Weighting (IDW) interpolation in ArcGIS Desktop 10.8 was employed to map significant physicochemical data and irrigation indicators. Results revealed that while EC values indicated low to moderate salinity (0.74–25.2 μS/cm), most samples showed high Na⁺%, SAR, and KR, classifying them as doubtful to unsuitable for irrigation. The IWQI ranged from 84.47 to 1617.87, indicating poor to inferior quality due to evaporation, fertilizer leaching, and sodium accumulation. Furthermore, the results highlight the importance of precise geographic modeling in determining whether drainage water is suitable for long-term agricultural use in arid regions such as Al-Jouf. Sustainable reuse of such drainage water requires freshwater blending, gypsum application, and the cultivation of salt-tolerant crops, aligning with Saudi Vision 2030 objectives for sustainable water management in arid regions. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Soil degradation in high-altitude livestock systems—driven by acidification, compaction, low water retention and nutrient loss—reduces forage productivity and limits the sustainability of grazing-based production. These constraints highlight the need for spatial tools capable of prioritising soil interventions and guiding more efficient land management. The objective of this study was to develop a spatial analysis model to identify and rank soil management priorities in a high-altitude livestock farm. A total of 441 georeferenced observations were collected using portable sensors to measure pH, electrical conductivity, water retention capacity and soil compaction. The data were processed through GIS interpolation, cartographic overlay and reclassification techniques to assign intervention levels across the landscape. The results indicated that 70% of the area presented moderately acidic soils, 32% required improvements in water retention, and 67% exhibited moderate compaction. The proposed model is replicable, operationally simple and suitable for site-specific decision-making. Overall, this study provides a technical tool that supports extension programmes, territorial planning and sustainable livestock management. Full article

The growing frequency of extreme weather, earthquakes, fires, and environmental hazards underscores the need for real-time monitoring and predictive management at the urban scale. Conventional three-dimensional spatial information systems, which rely on orthophotos and ground surveys, often suffer from computational inefficiency and data overload when processing large and heterogeneous datasets. To address these limitations, this study introduces a three-dimensional GeoHash-based geocoding algorithm designed for lightweight, real-time, and attribute-driven digital twin operations. The proposed method comprises five integrated steps: generation of 3D GeoHash grids using longitude, latitude, and altitude coordinates; integration with GIS-based urban 3D models; level optimization using the Shape Overlap Ratio (SOR) with a threshold of 0.90; representative object labeling through weighted volume ratios; and altitude correction using DEM interpolation. Validation using a testbed in Sillim-dong, Seoul (10.19 km²), demonstrated that the framework achieved approximately 9.8 times faster 3D modeling performance than conventional orthophoto-based methods, while maintaining complete object recognition accuracy. The results confirm that the 3D GeoHash framework provides a unified spatial key structure that enhances data interoperability across querying, visualization, and simulation. This approach offers a practical foundation for operational digital twins, supporting high-efficiency 3D mapping and predictive disaster management toward resilient and data-driven urban systems. Full article

Snowmelt recharge critically affects water table fluctuations in cold-region unconfined aquifers, where it serves as a primary source of groundwater. This study investigates the temporal and spatial variations in water table responses to snowmelt events in the Oakes Aquifer, North Dakota. Climatic data, including winter snowfall and temperature, were collected from the North Dakota Agricultural Weather Network (NDAWN), as well as the National Weather Service (NWS) and National Oceanic and Atmospheric Administration (NOAA) stations. Observation well data (1991–2023) were analyzed, and Inverse Distance Weighting (IDW) interpolation in ArcGIS Pro 3.6 was used to generate continuous spatial maps of groundwater level rises during spring. Results indicate that snowmelt significantly drives water table fluctuations, with higher snowfall associated with larger rises. Spatial variability in responses reflects differences in soil permeability, and land cover, with high-permeability soils showing more pronounced increases. Temperature strongly influenced the magnitude of snowmelt-induced groundwater rise, with warmer winters generally associated with greater recharge, while colder periods limited infiltration, likely due to frozen soil effects. These findings underscore the role of snowmelt as a key recharge source in cold-region unconfined aquifers, with variations controlled by local hydrogeological and climatic conditions. Understanding these dynamics is critical for groundwater management, particularly under changing climate scenarios. Future studies should focus on long-term monitoring, climate modeling, and cross-regional comparisons to improve predictions of snowmelt-driven recharge. Full article

Invasive alien species (IAS) are a significant threat to ecosystems worldwide, in particular island ecosystems where ecological resilience is limited. Spatially explicit and locally informed strategies are crucial on small islands to effectively manage IAS. The present study uses an integrated approach to map and manage IAS on Rodrigues Island, Mauritius, using a combination of field surveys, participatory mapping, and spatial analysis tools. Field data was collected in four sites on Rodrigues, namely Cascade Pigeon, Cascade St Louis, Mourouk Valley, and Golden Bat Reserve, supported by participatory mapping and Inverse Distance Weighting (IDW) interpolation in ArcGIS. The results revealed firstly that invasion hotspots were concentrated in previously disturbed areas, especially in Mourouk Valley and Cascade Pigeon, where Furcraea foetida, Leucaena leucocephala, and Millettia pinnata co-occur. Secondly, grassland zones exhibited minimal invasion, indicating their potential as natural buffer zones for conservation. Thirdly, the integration of stakeholder knowledge through Participatory GIS (PGIS) enhanced the accuracy and contextual understanding of the spatial analysis. Fourthly, the IDW interpolation method demonstrated high precision with low root mean square error (RMSE) values and minimal spatial error (≤0.5 m). Finally, the study underscores the importance of adaptive, site-specific monitoring and management strategies that combine spatial tools and local knowledge. These findings provide a replicable framework for IAS management in other island ecosystems facing similar ecological challenges, contributing to national and international biodiversity conservation efforts, including Sustainable Development Goal 15—Life on Land. Full article

Electromobility is a key element of efforts to reduce transport emissions at points where transport tasks are carried out (e.g., along roads, in urban areas). At the same time, the implementation of electromobility, as a whole, encompasses the movement of people and cargo using electric vehicles (EVs), is strongly dependent on the deployment of EV charging points, which are part of the alternative fuel infrastructure. At the current stage of electromobility development, the process of deploying alternative fuel infrastructure along the TEN-T (Trans-European transport network) is underway, a process mandated by the AFIR (Regulation for the Deployment of Alternative Fuels Infrastructure). The AFIR regulation assumes the construction of infrastructure adapted to serve low- and zero-emission vehicles along the TEN-T network. The elements of the infrastructure under construction include a recharging pool, a recharging station, a recharging point for electric vehicles (EVs), and hydrogen refueling stations for fuel cell electric vehicles (FCEVs). It should be noted that infrastructure elements must be adapted to support light-duty electric vehicles (eLDVs) and heavy-duty electric vehicles (eHDVs). This approach expands the possibilities of using electric vehicles in passenger and freight transport within the TEN-T network. The aim of this article is to estimate the impact of electric vehicle charging points on electromobility in a selected area. During the research phase, spatial interpolation of electric vehicle charging points was conducted using GIS tools. The spatial interpolation of electric vehicle charging points presented in the article represents an innovative approach at the stage of analysis and development of alternative fuel infrastructure along the TEN-T network. Full article