Search Results (17,352)

Decades of cultivation and the often exclusive use of mineral fertilisers as a substitute for organic inputs have reduced the soil organic carbon (SOC) content of agricultural soils, meaning they now represent a potential sink for carbon sequestration to mitigate climate change and improve soil function. As well as being a legacy of management, SOC will also be dependent on local scale climate, topography, and soil properties; accounting for this local context is important when benchmarking fields and quantifying the potential for additional carbon sequestration. We developed a landscape-scale methodology, using a handheld infrared device, for baselining SOC stocks in the top 30 cm across a 45,000 ha farm cluster in the UK. The cluster is exploring opportunities for landscape-scale environmental improvement with a focus on natural flood protection and water pollution reduction through conversion of arable land to permanent grassland. We used the baseline data to estimate additional benefits of arable reversion for soil carbon sequestration. Because all the farms in the cluster share the same pedoclimatic conditions, variance in SOC at the field scale could be confidently attributed to differences in soil type and land use. Average SOC stocks in arable and permanent pasture fields were 103.9 and 140.3 Mg C ha⁻¹, respectively. Variance in %SOC was modelled using soil series, sample depth, land use, and clay content, and fields were benchmarked based on deviation from the expected value. The fields with the largest SOC stocks were identified and used as references to predict future potential sequestration. The conversion of arable land to permanent pasture resulted in a predicted average uplift in SOC of 55.0 Mg C ha⁻¹. Our landscape-scale methodology provides robust evidence on current and future carbon stocks for public subsidy schemes and natural capital markets that account for local constraints and opportunities. Full article

The research analysed the space-use and habitat-preference characteristics of red deer (Cervus elaphus) in the Sopron Mountains, Hungary, utilising high-resolution Global Positioning System (GPS) telemetry data and two distinct land-cover databases. Hourly location data from 10 individuals were processed using the minimum convex polygon (MCP) and kernel home range (KHR) methods. Additionally, a relative stability index (RSI) was developed to describe seasonal shifts in area use. Significant sexual dimorphism was identified in the extent of annual home ranges: the mean space use of stags (3381 ha) significantly exceeded that of hinds (1391 ha). Geomatical analyses highlighted the seasonality of space use: the smallest extent was recorded in June, and shifts in home ranges within a single year were significant, while the winter period exhibited the least seasonal variation. Regarding habitat selection, significant seasonality was observed in hinds, reflecting temporal changes in resource availability, whereas this pattern was not observed in stags. The study concluded that the applied methods are appropriate for gathering baseline information; however, integrating high-precision databases is essential for accurate modelling of deer–forest interactions. Full article

Climate change, rapid urbanization, land-use changes, and the creation of a multi-layered risk environment by multiple disaster hazards have made interdisciplinary educational models—capable of integrating resilience knowledge into planning and design education—all the more essential. Nevertheless, the systematic and competency-based integration of scientific knowledge generated in the fields of ecological planning, nature-based solutions, multi-hazard analysis, and digital planning tools into higher education curricula remains limited. This study aims to develop a competency-based curriculum model for ecological planning and disaster-resilient cities by adapting the resilience literature into a modular educational model. Literature mapping, thematic clustering, gap identification, competence framework building, and curricular architecture development are the steps of the study’s design-based analytical framework. Studies published between 2015 and 2025 were examined in terms of disaster types, analytical tools, and planning approaches; they were then reorganized based on three competency areas: green skills, digital skills, and resilience skills. The findings have resulted in a modular curriculum comprising 35 modules and 105 topics, structured within a three-tiered framework consisting of conceptual content, practical application, and case-based learning. The original contribution of this study is its proposal of a structured educational model that bridges the gap between the production of scientific knowledge and curriculum design. The proposed framework provides a scalable and adaptable foundation for undergraduate, graduate, and professional education contexts; it also establishes a foundation for AI-supported personalized learning pathways in ecological planning and disaster resilience education. Full article

This paper presents a physics-informed Deep Q-Network (DQN) framework for optimizing the deployment of 100 vortex bladeless turbines (VBTs) in a Saharan microgrid. The proposed approach integrates wake interaction modeling, land-use constraints, techno-economic factors, and power quality (PQ) indicators at the point of common coupling. The novelty lies in coupling aerodynamic modeling with reinforcement learning and grid constraints. Results show that dense layouts (≤400 m²) yield up to 41% gains but degrade PQ (Pst > 1.0, THD > 5%). An optimal range of 500–800 m² achieves stable performance with moderate gains (6–9%) and acceptable PQ. Larger surfaces (>1000 m²) show limited benefits (<4%). The framework supports efficient and sustainable wind deployment in constrained environments. Full article

Background: Urban planning documents at metropolitan scale typically demand large, cloud-hosted language models that limit their adoption in Global South contexts. This study deploys Moondream, a 1.7-billion-parameter vision-language model (VLM) runnable locally via Ollama, for extracting geographic knowledge from Planes Reguladores Comunales (PRCs) across 29 processed Gran Santiago municipalities. The pipeline combines native PDF text extraction, keyword-based multi-label classification across six thematic axes, and VLM-based optical character recognition and cartographic interpretation. Results: The pipeline processes 2289 PRC articles in 4.3 min at an estimated energy cost of 0.000866 kWh and zero marginal monetary cost. Zoning (53.3%) and land use (43.1%) dominate PRC content, while social housing provisions appear in only 4.0% of articles; normative gap analysis identifies five municipalities where social housing is entirely absent from regulatory text. A comparative evaluation of Moondream against keyword baseline on an 88-article validation sample yields macro-F1 = 0.355 and mean Cohen’s κ = 0.004, confirming that generalist VLMs require domain fine-tuning for specialized legal text. It is argued that the cost asymmetry between industrial-scale and small-model approaches constitutes an epistemic asymmetry with direct consequences for the geographic distribution of urban data infrastructure. Full article

With the continuous reductions in anthropogenic NOx emissions and persistent surface O₃ pollution in China, soil NO emissions have become an increasingly important component of the regional NOx budget. In this study, an updated Berkeley–Dalhousie Soil NO Parameterization model driven by MERRA-2 reanalysis data was used to develop a 20-year soil NO emission inventory for China from 2001 to 2020. Multiple sensitivity scenarios were designed to quantify the relative contributions of nitrogen fertilizer application, meteorological variations, land use changes, and canopy factors on the interannual variations in soil NO emissions. The results showed that soil NO emissions exhibited an overall pattern of initial increase followed by fluctuating decline, with an average annual emission of 0.92 ± 0.05 Tg N year⁻¹ and a peak of 0.98 Tg N year⁻¹ in 2014. Summer was the dominant emission season, accounting for 57.7–61.9% of annual emissions. Spatially, emissions were concentrated in agriculturally intensive regions, particularly East China and Central China. With the decline in anthropogenic NOx emissions, the relative contribution of soil NO to total NOx emissions showed a recovery after 2012, indicating its increasing importance in future NOx budget assessments. Driver attribution analysis showed that nitrogen fertilizer application determined the long-term emission potential, whereas meteorological conditions regulated interannual and seasonal variability. These findings highlight the need to incorporate soil NO emissions into sustainable nitrogen management and ozone-related air quality assessments. Full article

The article presents a comprehensive methodology for optimizing ground impact zones of spent rocket stages based on the integration of geoinformation analysis, remote sensing of Earth, ballistic modeling, and ecosystem sustainability assessment. An information and analytical system (IAS) has been developed and tested, providing automated selection of environmentally sustainable landing points within acceptable dispersion zones. The methodology includes the use of the NDVI, digital terrain models, soil quality assessments, fire hazard assessments, and environmental damage calculations. For the first time, a system for classifying operational-territorial units according to their level of resilience to man-made impacts has been formed. The results suggest the potential for the reduction of the dangerous impact zone under modeled conditions. The system architecture is designed to be scalable and applicable to other spaceports located in continental regions. The presented methodology contributes to the development of an environmentally oriented approach to aerospace infrastructure management. Full article

Rooftop rainwater harvesting (RWH) offers a practical adaptation option for Mediterranean cities where water scarcity is amplified by seasonal rainfall and climate variability. This study reports early findings from a simplified monthly water balance screening model for a typical residential building, driven by ERA5-Land monthly precipitation for Antalya and İzmir (Türkiye). Scenarios cover roof areas of 250–3000 m² and practical tank capacities of 2–100 m³ under a fixed non-potable demand of 0.20 m³/day. The model tracks monthly storage dynamics and supply demand in order to compute demand coverage and monthly reliability (i.e., fraction of months in which full demand is met). Reliability-based storage thresholds (≥0.80) are derived for four evaluation windows (1996–2010, 2011–2025, 1996–2025, 1950–2025) to explore climate sensitivity. In parallel, a guideline-style sizing which is consistent with the Turkish rainwater harvesting guideline is implemented using a three-day storage rule based on the wettest month potential. To enable a like-for-like comparison, the collection losses are harmonized by setting loss to 0.10 in the simulation and efficiency to 0.90 in the guideline method. The results show stable thresholds for Antalya but stronger period sensitivity in İzmir. They also quantify cases where guideline sizing does not achieve the target reliability under dry season constraints. This approach supports the rapid, climate-aware pre-design of small- to medium-scale urban RWH systems. Full article

The analysis of specific habitat conditions involves a systematic assessment of environmental variables such as temperature, hydrology, and vegetation, to clarify species’ ecological requirements and develop conservation strategies. Common approaches include statistical modelling, various Habitat Suitability Index (HSI) models, and GIS-based spatial analyses, which quantify factors like topography, land cover and anthropogenic pressures. Today, machine learning (ML) methods are widely applied across engineering disciplines, including water resources management. In this study, ML methods, particularly model trees, are employed to model and predict key abiotic factors relevant to fish communities. The research focuses on the bioindicator species Barbus balcanicus (brook barbel), which inhabits the middle part of the Sutla River (transboundary river basin between Croatia and Slovenia) and serves as an indicator of ecological conditions in this system. Using ML, models for water depth, water velocity, and water temperature were developed and applied together with SWAT (Soil and Water Assessment Tool) data to determine the HSI for future scenarios to support habitat assessment and water management planning. Full article

Bioenergy crops are widely regarded as a promising approach to support renewable energy production, diversify farm income, and enhance land-use efficiency. Despite these potential benefits, adoption rates remain low, and empirical understanding of landowners’ decision-making processes is still emerging. This study examines landowners’ interest in and likelihood of adopting bioenergy crops, explicitly differentiating between early-stage interest and near-term adoption intentions. Survey data from 207 landowners are analyzed using a bivariate probit model to identify key factors influencing both outcomes. The results reveal a marked disparity between expressed interest and adoption likelihood, with a significantly greater proportion of landowners indicating interest than those willing to adopt in the near term. Economic orientation increases adoption interest by 9.5 percentage points, while identity orientation increases adoption likelihood by 6.6 percentage points. Determinants such as increased awareness, land size, experience, and participation in conservation programs exert varying influences across different decision stages. These findings suggest that stated interest and stated near-term adoption likelihood represent related but distinct dimensions of adoption readiness, shaped by different economic, identity-based, and institutional factors. Effective promotion of bioenergy crops requires more than general awareness campaigns. Policies should combine financial incentives, technical assistance, market development support, and outreach strategies that present bioenergy crops as compatible with landowners’ economic goals, stewardship values, recreational uses, and long-term attachment to their land. Full article

Scientific studies of mine closure and ecosystem management have become very important since the rate of coal mine closures in China has increased rapidly over the last decade. This study first analyzed spatiotemporal changes in land use and ecosystem services value (ESV) during the period 2000–2020 around the Kailuan Mining Area in Tangshan City. The area has a history of over 100 years of continuous mining activities in the region. The analyses used the PLUS model, multi-scenario simulation, and ESV equivalent factor method and multi-source data on land use, mining activities, socioeconomic factors, and climatic conditions. The study then projected land-use changes and spatiotemporal ESV characteristics for the year 2030 for two alternatives: (1) the Current Development Scenario (CDS), representing the current pace of development without mine closure; and (2) the Ecological Restoration Scenario (ERS), representing mine closure and ecological restoration. Key results include: (1) during 2000–2020, cultivated land and construction land were the primary land uses, with the overall trends showing decrease in cultivated, forest, pasture, and unused lands, varying water use areas, and continuously increasing construction land; (2) the revised ESV results show that total ESV declined from 31.27 million USD in 2000 to 25.30 million USD in 2020, a net decrease of 6.19 million USD, mainly because of cropland loss and degradation of forest and grassland; and (3) for 2030, the CDS projected a continued decline in total ESV to 24.30 million USD, whereas the ERS increased total ESV to 26.50 million USD, which is 2.19 million USD higher than the CDS and 1.20 million USD higher than the 2020 baseline. Compared with the CDS, the ERS increased cropland by 13.20 km² and reduced construction land by 10.06 km², indicating that reclaiming subsided water bodies and idle construction land into cropland and restored ecological land can enhance ecosystem services while mitigating subsidence-related risks. The framework can support data-driven post-mining land-use planning and ecological management in resource-based regions. Full article

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

This study assesses the spatio-temporal distribution and transition dynamics of land use and land cover (LULC) in Egypt using satellite-based MODIS observations (SAT-MODIS) and WRF static datasets (WRF-MODIS) from 2001 to 2020. Dominant LULC types, barren areas (BAs), cropland (CR), urban and built-up land (UBL), water bodies (WBs), grassland (GR), and open shrubland (OS), exhibited notable changes associated with agricultural expansion, urbanization, and land reclamation due to human-induced activities. BAs remained dominant, covering more than 94% of Egypt throughout the study period. Comparative analysis between the three WRF-MODIS options (WRF-Opt1, WRF-Opt2, and WRF-Opt3) and SAT-MODIS revealed LULC classification discrepancies, which may be due to differences in algorithms, temporal representation, and spatial resolution. WRF-Opt3 showed the highest spatial consistency with SAT-MODIS, particularly before and around 2010. The findings highlight limitations of static WRF land cover datasets and emphasize the need for higher-resolution and dynamically updated LULC datasets to improve regional climate and land–atmosphere modeling applications over Egypt. Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article