Search Results (2,826)

Most nations are shifting towards renewable energy sources to reduce energy-related emissions and achieve their net zero emissions targets by mid-century. Consequently, many attempts have been made to invest in clean, accessible, inexpensive, sustainable and reliable renewable energy sources while reducing dependency on fossil fuels. Recently, the production of biogas and upgrading it to produce biomethane is considered a sustainable way to reduce emissions from natural gas consumption. However, uncertainties in the biomass supply chain and less attention to decarbonising the natural gas grid have led to fewer investors in biomethane injection projects. Thus, researchers have applied Geographic Information System (GIS) as the best decision-making tool with spatial analytical and optimisation capabilities to address this issue. This study aims to review GIS-based applications on planning and optimising the biomass supply chain. Accordingly, this review covers different GIS-based biomass assessment methods with the evaluation of feedstock types, GIS-based approaches on selecting and optimising bioenergy plant locations and GIS-based applications on facilitating biomethane injection projects. This review identified four major biomass assessment approaches: Administrative division-based, location-based, cluster-based and grid-based. Sustainability criteria involved in site selection were also discussed, along with suitability and optimality techniques. Most of the optimising studies investigated cost optimisation based on a single objective. However, optimising the whole supply chain, including all operational components of the biomass supply chain, is still seldom investigated. Furthermore, it was found that most studies focus on site selection and logistics, neglecting biomethane process optimisation. Full article

The study of urban floods is increasingly crucial due to their growing frequency and impact on densely populated areas, often characterized by inadequate drainage and located in flood-prone zones. The consequences extend beyond physical damage, significantly affecting economies and livelihoods, necessitating substantial economic resources for recovery and infrastructure rebuilding. Urban planning now must integrate flood risk management, emphasizing not only infrastructural resilience but also comprehensive policies that address environmental and social vulnerabilities to better prepare and protect urban environments against future flood risks. This study addresses the critical issue of urban flood vulnerability through a focused analysis of Morelia, a city known for its susceptibility to flooding due to its geographical and hydrological characteristics and accelerated urban growth. Employing a multifaceted approach that integrates hydrological, socio-economic, and land use data within a Geographic Information Systems (GIS) framework, the research develops a Spatial Flood Vulnerability Index (SFVI). This index is meticulously applied at the urban block level, offering a precise mapping of flood risks across the city. By correlating the SFVI results with historical flood data, the study identifies the most vulnerable areas in Morelia, which are primarily impacted due to their proximity to water bodies, economic density, and infrastructural settings. The methodology not only highlights immediate flood risks but also aids in strategic urban planning to enhance resilience against future flooding events. This paper contributes a novel approach to flood risk assessment, providing a replicable model for similarly affected cities worldwide, aiming to balance structural measures with strategic planning tailored to local needs. Full article

This research provides a detailed analysis of earthquake-triggered landslides (ETLs) in Greece, spanning from antiquity to the present, with an emphasis on their temporal, spatial, and statistical characteristics. Supported by published scientific sources and geographic information systems (GIS) tools, we detected 673 landslides triggered from 144 earthquakes in Greece. With 166 ETLs associated with historical earthquakes and 507 with recent ones, the analysis reveals that regions in western Greece, including the Ionian Islands and the Peloponnese, exhibit the highest ETL frequencies, a trend strongly related to their seismotectonic regime. Most ETLs have occurred in geotectonic units belonging to the External Hellenides. Limestone-dominated lithologies and post-alpine deposits were identified as particularly susceptible to ETLs. These are strongly associated with earthquakes with magnitudes ranging from 5.5 to 7.0. Rockfalls constitute the most frequent type of ETLs in Greece, accounting for nearly half of all documented events. Coastal and offshore landslides, though less frequent, still pose unique risks for Greece. ETLs have mainly been observed in the very high and high susceptibility areas. The impacts of ETLs on both natural and built environments are profound, with destruction of buildings and infrastructure exacerbating the public health impact and socio-economic toll of such events. Full article

Gastrodia elata has been a valuable medicinal resource in the East for approximately 3000 years. In South Korea, G. elata is cultivated in open-fields or greenhouses near residential areas. However, due to severe continuous damage, cultivation sites need to be frequently relocated, leading to a shortage of available cultivation areas. Alternatively, farmers are focusing on mountain cultivation. This study analyzed suitable cultivation sites for G. elata in mountainous areas using a geographic information system (GIS) and applied various classification methods to identify their characteristics and similarities. The analysis showed that the Natural Breaks (Jenks) classification method maximized the differences between grades, whereas the Quantile method reclassified the area of suitable sites to a relatively high proportion. In contrast, the Equal Interval method reclassified the areas of suitable and unsuitable sites to a lower proportion, whereas the Geometric Interval method best demonstrated extreme-temperature regions as unsuitable sites. Among the classification methods, the Natural Breaks (Jenks) and Geometric Interval methods yielded the most similar results. These findings provide critical methodological outcomes for G. elata cultivation and sustainable agriculture and forestry. Future empirical research and the application of climate change scenarios are necessary to enhance the sustainability of the G. elata cultivation industry. Full article

The intensive use of fossil fuels for energy production harms the environment. The adoption of sustainable energy systems can reduce the damage. Wind energy is one of the most widely used renewable sources. The most important problem in establishing new wind power plants (WPPs) is estimating the wind energy potential (WEP) in potential installation locations where there are no measured data. Many geographic information system (GIS)-based studies have been conducted on this subject. In this study, based on the technical specifications of a wind turbine selected for the Kırklareli Province of Türkiye, wind speed maps at 125 m height were created using many station points with known locations and wind speeds and the WEP of Kırklareli was calculated. In addition, the WEP map of Kırklareli was created by first determining the areas where WPPs cannot be installed and creating the wind speed map. After removing exclusion areas where wind turbines cannot be installed, the wind speeds at 125 m ranged between 3.12 m/s and 8.51 m/s. The wind speed was found to be higher in the south of the province, and the total WEP in areas with wind speeds higher than 6 m/sec was 6628.21 MW. Full article

A novel atmospheric pollutant transport quantification model (APTQM) has been developed to analyze and quantify cross-regional air pollutant transport pathways and fluxes. The model integrates high-resolution numerical simulations, Geographic Information System (GIS) capabilities, and advanced statistical evaluation metrics with boundary pixel decomposition methods to effectively characterize complex pollutant transport dynamics while ensuring computational efficiency. To evaluate its performance, the model was applied to a representative winter pollution event in Beijing in December 2021, using fine particulate matter (PM_2.5) as the target pollutant. The results underscore the model’s capability to accurately capture spatial and temporal variations in pollutant dispersion, effectively identify major transport pathways, and quantify the contributions of inter-regional sources. Cross-validation with established methods reveals strong spatial and temporal correlations, further substantiating its accuracy. APTQM demonstrates unique strengths in resolving dynamic transport processes within the boundary layer, particularly in scenarios involving complex cross-regional pollutant exchanges. However, the model’s reliance on a simplified chemical framework constrains its applicability to pollutants significantly influenced by secondary chemical transformations, such as ozone and nitrate. Consequently, APTQM is currently optimized for the quantification of primary pollutant transport rather than modeling complex atmospheric chemical processes. Overall, this study presents APTQM as a reliable and computationally efficient tool for quantifying inter-regional air pollutant transport, offering critical insights to support regional air quality management and policy development. Full article

Due to the increasing demand for agricultural production and the depletion of natural resources, the rational and efficient use of resources in agriculture becomes essential. Thus, Agriculture 4.0 or precision agriculture (PA) was born, which leverages advanced technologies such as Geographic Information Systems (GIS), Artificial Intelligence (AI), sensors and remote sensing techniques to optimize agricultural practices. This study focuses on an innovative approach integrating data from different sources, within a GIS platform, including data from an experimental atmospheric simulator and from a wireless sensor network, to identify the most suitable areas for future crops. In addition, we also calculate the optimal path of a drone for crop monitoring and for a farm machine for agricultural operations, improving efficiency and sustainability in relation to agricultural practices and applications. Expected and obtained results of the conducted study in a specific area of Reggio Calabria (Italy) include increased accuracy in agricultural planning, reduced resource and pesticide use, as well as increased yields and more sustainable management of natural resources. Full article

Mining may cause devastating environmental impacts through large-scale land transformations. However, mining-induced land transformations are poorly understood relative to a mine’s productivity or life cycle. We integrated satellite imagery, geographic information systems (GISs), and mine site production data (ore, concentration, and waste) to conduct a detailed spatiotemporal analysis of 15 open-pit copper mines in Chile, distinguishing six types of features. Although the occupied area (9.90 to 149.61 km² in 2020) and composition vary across mines, facilities for waste storage occupy the largest proportion (>50%) of the transformed land area, emphasizing the need for proper waste management. The analysis of land transformation factors (the transformed land area per unit production) showed high variation (0.006178 to 0.372798 m²/kg-Cu) between mines over time. This reveals a significant problem in the historical practice of using averages from life cycle assessment (LCA) databases. This research reveals the significance of geospatial analyses in assessing mining-induced land transformation, and it provides geospatial data for land-related LCA. Mining companies are encouraged to disclose GIS information regarding land transformation to foster transparency and social responsibility, as well as to promote responsible and sustainable mining. Full article

With global climate change causing temperature increases, even cooler regions like Finland are facing increasing heat risks. The city of Lahti is expected to experience a higher-than-average temperature increase, making heat risk mitigation essential. This study aims to assess present and future heat risks in Lahti using exposure and social vulnerability indicators to identify heat risk hotspots and provide strategies for mitigation within the city’s urban planning framework. The method utilizes a combination of Land Surface Temperature (LST) data (2014–2024), climate projections, and microclimate analysis to identify heat risk in the city. Geographic Information Systems (GIS) and ENVI-met modeling were employed to assess the relationship between land surface temperatures (LST), urban structure, and green infrastructure. Risk assessments were conducted using social and environmental vulnerability indicators, and future projections were based on a combined SSP2-4.5 scenario. The results show a significant increase in high-risk areas by 2040, rising from 9.79% to 23.65% of Lahti’s core urban area. Although the current urban planning framework of the city (Masterplan 2035) is effective in terms of maintaining exposure levels, the continued increase in projected air temperatures, as modeled based on outputs of the EC-Earth3-veg GCM, remains a concern. Microclimate modeling confirmed that urban greenery significantly reduces heat stress and improves thermal comfort. To address future heat risks, Lahti must integrate more green infrastructure into its urban design and identify seasonal heat mitigation methodologies. Additionally, the findings emphasize the need for adaptive planning strategies to mitigate rising temperatures and ensure urban resilience. Full article

Trachelospermum jasminoides (Lindl.) Lem. is a well-known herb with important medicinal and economic values. It is widely used in the treatment of inflammations in China. As global climate change intensifies, the ecological niche of plants has correspondingly shifted. Therefore, understanding the distribution of suitable habitats for T. jasminoides under different climate conditions is of great significance for its cultivation, introduction, and conservation. This research utilizes the MaxEnt model in combination with the Geographic Information System (ArcGIS) to analyze the present and future potential habitat distributions of T. jasminoides. Based on 227 documented occurrence points and 15 ecological variables, the results emphasize that the key environmental limitations influencing the optimal habitats of T. jasminoides are the precipitation during the coldest quarter, the mean temperature of the driest quarter, precipitation in the warmest quarter, temperature seasonality (standard deviation × 100), and the human impact index. At present, the combined area of suitable and highly suitable habitats for T. jasminoides amounts to 15.76 × 104 km², with the highly suitable habitats predominantly situated in East and Central China. Based on climate scenario forecasts, within the SSP1-2.6 climate scenario, the total suitable habitat area for T. jasminoides is projected to increase relative to the current situation. Nevertheless, in the SSP2-4.5 and SSP5-8.5 climate scenarios, the suitable habitat area is anticipated to initially rise and then decline. The distribution center is mainly concentrated in the provinces of Hunan and Jiangxi, with the centroid shifting southeastward compared to the current situation. The findings of this research offer valuable insights for the effective cultivation, preservation, and sustainable use of T. jasminoides resources. Full article

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

Groundwater in northwest Tunisia plays a vital role in supporting the domestic, agriculture, industry, and tourism sectors. However, climate change and over-exploitation have led to significant degradation in groundwater quality and quantity. Traditional spatial analysis techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS) are frequently used for assessing groundwater potential and water quality. Yet, these methods are limited by data availability. The integration of Geospatial Artificial Intelligence (Geo-AI) offers improved precision in groundwater potential zone (GWPZ) delineation. This study compares the effectiveness of the Analytical Hierarchy Process (AHP) and advanced Geo-AI techniques using deep learning to map GWPZ in the Majerda transboundary basin, shared between Tunisia and Algeria. By incorporating thematic layers such as rainfall, slope, drainage density, land use/land cover (LU/LC), lithology, and soil, a comprehensive analysis was conducted to assess groundwater recharge potential. The results revealed that both methods effectively delineated GWPZ; however, the Geo-AI approach demonstrated superior accuracy with a classification accuracy rate of approximately 92%, compared to 85% for the AHP method. This indicates that Geo-AI not only enhances the quality of groundwater potential assessments but also offers a reliable alternative to traditional methods. The findings underscore the importance of adopting innovative technologies in groundwater exploration efforts in this critical region, ultimately contributing to more effective and sustainable water resource management strategies. Full article

Forecasting particulate matter with a diameter of 2.5 μm (PM_2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM_2.5 data, their limited number and geographic coverage present significant challenges. Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. To address these challenges, this study leverages geographic information systems (GIS), remote sensing (RS), and a hybrid LSTM architecture to predict PM_2.5 concentrations. Training LSTM models, however, is an NP-hard problem, with gradient-based methods facing limitations such as getting trapped in local minima, high computational costs, and the need for continuous objective functions. To overcome these issues, we propose integrating the novel orchard algorithm (OA) with LSTM to optimize air pollution forecasting. This paper utilizes meteorological data, topographical features, PM_2.5 pollution levels, and satellite imagery from the city of Tehran. Data preparation processes include noise reduction, spatial interpolation, and addressing missing data. The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. The proposed OA-LSTM model achieved the lowest root mean square error (RMSE) value of 3.01 µg/m³ and the highest coefficient of determination (R²) value of 0.88, underscoring its effectiveness compared to other models. This paper employs a binary OA method for sensitivity analysis, optimizing feature selection by minimizing prediction error while retaining critical predictors through a penalty-based objective function. The generated maps reveal higher PM_2.5 concentrations in autumn and winter compared to spring and summer, with northern and central areas showing the highest pollution levels. Full article

The precise selection of agricultural land is essential for guaranteeing global food security and sustainable development. Additionally, agricultural land suitability (AgLS) analysis is crucial for tackling issues including resource scarcity, environmental degradation, and rising food demands. This research examines the synergies and trade-offs among the sustainable development goals (SDGs) using a hybrid geographic information system (GIS)–fuzzy analytic hierarchy process (FAHP)–geostatistical framework for AgLS analysis in Attur Taluk, India. The area was chosen for its varied agro-climatic conditions, riverine habitats, and agricultural importance. Accordingly, data from ten topographical, climatic, and soil physiochemical variables, such as slope, temperature, and soil texture, were obtained and analyzed to carry out the study. The geostatistical analysis demonstrated the spatial variability of soil parameters, providing essential insights into key factors in the study area. Based on the receiver operating characteristic curve analysis, the results showed that the FAHP method (AUC = 0.71) outperformed the equal-weighting scheme (AUC = 0.602). Moreover, suitability mapping designated 17.31% of the study area as highly suitable (S1), 41.32% as moderately suitable (S2), and 7.82% as permanently unsuitable (N2). The research identified reinforcing and conflicting correlations with SDGs, emphasizing the need for policies to address trade-offs. The findings showed 40% alignment to climate action (SDG 13) via improved resilience, 33% to clean water (SDG 6) by identifying low-salinity zones, and 50% to zero hunger (SDG 2) through sustainable food systems. Conflicts arose with SDG 13 (20%) due to reliance on rain-fed agriculture, SDG 15 (11%) from soil degradation, and SDG 2 (13%) due to inefficiencies in low-productivity zones. A sustainable action plan (SAP) can tackle these issues by promoting drought-resistant crops, nutrient management, and participatory land-use planning. This study can provide a replicable framework for integrating agriculture with global sustainability objectives worldwide. Full article

This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches—r.sun and the Point Cloud Solar Radiation Tool (PCSRT)—in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments. Full article