Earth, Volume 7, Issue 1 (February 2026) – 23 articles

Studies on land use and land cover changes are essential for predicting future trends and determining natural resource management decisions and the appropriate and precise detection of land use and land cover change is indispensable for obtaining detailed information. In this study, a purposive sampling technique was used for descriptive purposes. Geospatial approaches are powerful tools for analyzing these changes, offering precise, cost-effective, detailed, and advanced insights. This study focused on understanding the spatiotemporal dynamics of land use, land cover, and its drivers in Melokoza, utilizing Landsat images from 1993, 2013, and 2023, with a resolution of 30 m. Through supervised classification using the maximum likelihood method, this study identified six distinct land uses and land covers: forest, settlement, agriculture, shrubland, bare land, and water bodies. The findings revealed significant transformations, with a dramatic shift from natural forests to agriculture and settlements, which are driven by increasing human demands. Over the past three decades, forest and shrubland cover dropped to 29.89% and 12%, respectively, while settlement and agriculture increased by 154.6% and 231.9%. This transformation underscores the pressing need to address the conversion of formerly forested and shrub-covered areas into vibrant farming and settlement areas. To safeguard the stability and sustainability of our natural resources and ecosystems, stakeholders must focus on the pace of land use and land cover changes, mainly the deforestation linked to agricultural expansion and settlement growth. Full article

The global carbon cycle has become increasingly unbalanced over the past century as anthropogenic fluxes into the atmosphere far exceed the sequestration capacity of land and ocean systems. Data from 2025 show estimated annual anthropogenic emissions of ≈11.2 gigatonnes of carbon (GtC), while only ≈5.6 GtC are sequestered by land and ocean sinks mainly provided by photosynthetic CO₂ fixation. The resulting surplus of carbon emissions has led to a doubling of atmospheric CO₂ concentrations above pre-industrial values to ≈430 ppm, which is a major driver of increasingly erratic climatic phenomena. Recent data indicate that fossil fuel use will continue rising up to and beyond 2050, largely negating the drive to cut CO₂ emissions as recommended by the IPCC and other reputable transnational bodies. Hence, there is an urgent need to reduce atmospheric CO₂ levels via carbon sequestration. This review focuses on the proven capacity of biological mechanisms to sequester CO₂ at a global scale with an annual capacity in the range of gigatonnes of carbon. New measures such as re- and a-forestation, plus improved and more sustainable management of tropical tree crops, can further increase the carbon sequestration potential of these plants. By implementing these and other nature-based solutions, the highly productive tropical vegetation belt could contribute an additional 1–2 Gt of carbon sequestration via natural forests and perennial tree crops. In order to expedite this process, we examine the use of new modalities of transparent carbon trading systems that include selected tropical crops. As highlighted at COP30 in Brazil and elsewhere, this would enable tropical countries to derive benefit for costs incurred in land management changes such as reforestation, regenerative farming, and intercropping to benefit smallholders and other rural communities. In particular, carbon finance is emerging as a critical driver, with appropriately regulated and transparent carbon credit schemes offering fungible monetary compensation for climate-positive land management. Full article

Al-Musk Lake, an artificial waterbody of 2.9 km² formed by illegal dumping of 9.5 million cubic meters of raw sewage near Jeddah, Saudi Arabia, remains a significant subsurface environmental hazard after drainage activities in 2010. The current research employs a multidisciplinary approach, integrating geological mapping, aeromagnetic and electromagnetic surveys, Landsat imagery, and chemical analyses, to investigate contamination migration and accumulation. The objective is to delineate subsurface contamination pathways and assess their impact on soil and groundwater quality. Frequency-domain electromagnetic (FDEM) surveys identified areas of high apparent conductivity (up to 200 mS/m at 2000 kHz), indicative of deep contamination saturation. Chemical analysis of water and soil samples revealed distressing levels of heavy metals, Na⁺ up to 2400 mg/L, Ca²⁺ up to 3648 mg/L, and Fe up to 4150 mg/L, far exceeding irrigation safe standards. Findings locate two at-risk areas several kilometers from the lake, where contaminants accumulate through basement depressions controlled by faults. These pose immediate risks to adjacent residential areas and expanding agricultural belts. In short, subsurface contamination continues to spread westward. Short-term remedies include halting agricultural activities, treating in-storage water, and paving infiltration zones. A larger-scale geophysical survey, along with denser geochemical sampling and analysis, is necessary to guide long-term remediation and to protect public health. Full article

Aridity represents a fundamental climatic constraint governing water resources, ecosystem functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the Johansson Continentality Index (JCI), the De Martonne Aridity Index (I_DM), and the Pinna Combinative Index (I_P). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temperature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A moderate positive association between I_DM and I_P (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Macedonia, establishing a first climatological baseline of aridity conditions based on multiple indices applied to homogenized observations, and contributing to regional assessments of hydroclimatic variability relevant to climate adaptation planning. Full article

Understanding landform evolution is essential for assessing how terrain responds to geomorphic drivers such as weathering, fluvial erosion, hillslope processes, and tectonic uplift. This is particularly important in applications such as constructed post-mining landform rehabilitation, where predicting long-term erosional stability is vital for sustainable closure planning. In addition to long-term average erosion rates, the spatial patterns of gullies, rills, and channels are critical for assessing landform stability. This review examines Digital Elevation Model (DEM)—driven, process-based Landform Evolution Models (LEMs), with a primary focus on SIBERIA, CAESAR-Lisflood, and SSSPAM, which are widely used to evaluate the erosional behaviour of constructed post-mining landforms, each with distinct characteristics. These models are systematically compared in terms of input requirements, process representations, parameterisation, and predictive capabilities. Recent advances in high-spatial resolution DEMs (e.g., LiDAR, SRTM), along with digital soil and rainfall databases and satellite-derived vegetation indices, have improved the parameterisation of erosion, hydrological, and sediment-transport processes of the LEMs. A brief comparative case study is presented to demonstrate how these LEMs simulate 1000-year erosional behaviour along a linear hillslope. This review synthesises the current capabilities and limitations of DEM-driven LEMs, providing guidance for researchers, land managers, and practitioners in selecting appropriate models to support sustainable post-mining landform management, as well as outlining potential future advancements. Full article

This paper presents the development and pilot validation of an enhanced water governance assessment tool developed within the European InnWater project. Grounded in OECD Principles on Water Governance, the study combines literature review, thematic framework development, and pilot validation with stakeholders. The tool expands existing governance assessment frameworks by explicitly integrating four cross-cutting dimensions that are often insufficiently addressed: circular economy, environmental resilience, local empowerment, and procedural equity with particular attention to vulnerable groups. The assessment framework is organised across five governance domains—mega-trends and resilience; policy, institutions and regulation; financing; data, monitoring and evaluation; and engagement and accountability—and operationalised through a structured questionnaire applied in selected European pilot sites. Insights from initial pilot applications illustrate the tool’s capacity to reveal institutional coordination gaps, capacity constraints, and barriers to inclusive and adaptive governance. The paper contributes a replicable, policy-relevant governance assessment methodology designed to support reflexive learning and institutional improvement in diverse European water governance contexts. Full article

This study presents a machine learning framework to automate the mapping of the Integrated Relative Anthropization Index (INRA, by its Spanish acronym). A predictive model was developed to estimate the degree of anthropization in the basin of Lake Tota, Colombia, using the XGBoost machine learning algorithm and remote sensing data. This research, part of a broader wetland monitoring project, aimed to identify the optimal spatial scale for analysis and the most influential predictor variables. Methodologically, models were tested at resolutions from 20 m to 500 m. The results indicate that a 50 m spatial scale provides the optimal balance between predictive accuracy and computational efficiency, achieving robust performance in identifying highly anthropized areas (sensitivity: 0.83, balanced accuracy: 0.91). SHAP analysis identified proximity to infrastructure and specific Sentinel-2 spectral bands as the most influential predictors in the INRA emulation model. The main result is a robust, replicable model that produces a detailed anthropization map, serving as a practical tool for monitoring human impact and supporting sustainable management strategies in threatened high-Andean ecosystems. Rather than a simple classification exercise, this approach serves to deconstruct the INRA methodology, using SHAP analysis to reveal the latent non-linear relationships between spectral variables and human impact, providing a transferable and explainable monitoring tool. Full article

This study evaluates the spatiotemporal variations in surface water quality in An Giang province, a key upstream region of the Vietnamese Mekong Delta (VMD), under the influence of hydrological alterations and climate change impacts. Water quality data from 2010 to 2023 were collected from 10 monitoring stations along the Tien and Hau Rivers, focusing on key parameters including pH, temperature, Dissolved Oxygen (DO), Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammonium (N-NH₄⁺), Nitrate (NO₃⁻), orthophosphate (P-PO₄³⁻), and Coliforms. The Mann–Kendall test and Sen’s slope estimator were employed to detect long-term trends and quantify the magnitude of changes. The findings indicated that the Hau River exhibits significant organic pollution, evidenced by elevated levels of BOD and COD, alongside diminished levels of DO. The Tien River exhibits elevated concentrations of NH₄⁺ and total suspended solids (TSS). The MK test indicated that BOD, COD, and NH₄⁺ levels were increasing at most locations in a statistically significant manner. This indicates that the water quality deteriorated over time. The study revealed that the majority of pollutants exhibited statistically significant increasing trends (p ≤ 0.05). The Tien River’s COD is increasing by 1.6 mg/L annually, whereas the Hau River’s COD is escalating by 1.7 mg/L per year. The biochemical oxygen demand on both rivers is increasing by 0.5 mg/L each year. The diminishing quantities of dissolved oxygen indicated a decline in water quality. Pollutant concentrations demonstrated significant positive associations with maximum temperature (r = 0.47–0.64) and hours of sunshine (r ≈ 0.50–0.64). A significant negative correlation with river discharge was observed, particularly during the dry season (r = −0.79 to −0.88), when diminished flows resulted in elevated pollution concentrations. The findings offer measurable evidence that increasing temperatures and decreasing river flows significantly affect water quality, underscoring the necessity of adapting water resource management in the Mekong Delta. Full article

Aerosol microphysical and optical properties play a crucial role in cloud microphysics, precipitation physics, and flood formation over areas characterized by complex monsoon regimes. This research presents a multi-source data integration approach to analyzing the spatio-temporal interaction between precipitation, aerosols, and flooding in the state of Kerala, incorporating an air mass trajectory analysis to examine its potential contribution to flooding. The results show that the Aerosol Optical Depth (AOD) values were high in the coastal districts (>0.8) in the La Niña year (2021) but low in the El Niño year (2015). On the precipitation side, 2018 and 2021 were both years with a high degree of anomalies, resulting in heavy rainfall that led to widespread flooding in the Thrissur district, among others. The trajectory analysis revealed that the Indian Ocean controls the precipitation during the southwest monsoon and the pre-monsoon. The post-monsoon precipitation is mainly sourced from the Arabian Peninsula and Arabian Sea, transferring marine aerosols along with desert aerosols. The overall study shows that the variability in aerosols and precipitation is more subject to change by the meteorological dynamics, as well as influenced by the regional changes in land use and land cover, causing fluxes in the land–atmosphere interactions. In conclusion, the present study highlights the possible interactive functions of atmospheric dynamics and anthropogenic land use modifications in generating a flood hazard. It provides essential information for land management policies and disaster risk reduction. Full article

Climate change-induced extreme weather events increasingly threaten public health, with a particularly acute impact on the mental well-being of urban populations. This study evaluates regional disparities in mental health outcomes associated with climate-induced extreme weather in urban environments, where social and infrastructural vulnerabilities exacerbate environmental stressors. We synthesized data from cohort and cross-sectional studies using both traditional frequentist and Bayesian meta-analytic frameworks to assess the mental health sequelae of extreme weather events (e.g., heatwaves, floods, droughts, and storms). The traditional meta-analysis indicated a significant increase in the odds of adverse mental health outcomes (OR = 1.32, 95% CI: 1.07–1.57). However, this global estimate was characterized by extreme heterogeneity (I² = 95.8%), indicating that the risk is not uniform but highly context-dependent. Subgroup analyses revealed that this risk is concentrated in specific regions; the strongest associations were observed in Africa (OR = 2.23) and Europe (OR = 2.26). Conversely, the Bayesian analysis yielded a conservative estimate, suggesting a slight reduction in odds (mean OR = 0.92, 95% CrI: 0.87–0.98). This divergence is driven by the Bayesian model’s shrinkage of high-magnitude outliers toward the high-precision data observed in resilient, high-income settings (e.g., USA). Given the extreme heterogeneity observed (I² = 95.8%), we caution against interpreting either pooled estimate as a universal effect size. Instead, the regional subgroup findings—particularly the consistently elevated risks in Africa and Europe—offer more stable and policy-relevant conclusions. These findings emphasize urgent, context-specific interventions in urban areas facing compounded climate social risks. Full article

Eutrophication remains a persistent challenge in the Romanian Black Sea coastal zone, driven by excess nutrient inputs from riverine and coastal sources and further intensified by climate change. This study assesses eutrophication dynamics and explores mitigation options using an integrated framework that combines in situ observations, satellite-derived chlorophyll a data, machine learning, and system dynamics modelling. Water samples collected during two field campaigns (2023–2024) were analyzed for nutrient concentrations and linked with chlorophyll a products from the Copernicus Marine Service. Random Forest analysis identified dissolved inorganic nitrogen, phosphate, salinity, and temperature as the most influential predictors of chlorophyll a distribution. A system dynamics model was subsequently used to explore relative ecosystem responses under multiple management scenarios, including nutrient reduction, enhanced zooplankton grazing, and combined interventions. Scenario-based simulations indicate that nutrient reduction alone produces a moderate decrease in chlorophyll a (45% relative to baseline conditions), while restoration of grazing pressure yields a comparable response. The strongest reduction is achieved under the combined scenario, which integrates nutrient reduction with biological control and lowers normalized chlorophyll a levels by approximately two thirds (71%) relative to baseline. In contrast, a bloom-favourable scenario results in a several-fold increase in chlorophyll a of 160%. Spatial analysis highlights persistent eutrophication hotspots near the Danube mouths and urban discharge areas. These results demonstrate that integrated strategies combining nutrient source control with ecological restoration are substantially more effective than single-measure interventions. The proposed framework provides a scenario-based decision-support tool for ecosystem-based management and supports progress toward achieving Good Environmental Status under the Marine Strategy Framework Directive. Full article

The sustainability of resources and ecological integrity are significantly influenced by land use and land cover change (LULCC) dynamics, particularly in ecotonal semi-arid regions where biome transitions are highly sensitive to anthropogenic disturbance and climatic variability. This study aims to assess historical LULCC dynamics and spatial reconfiguration across nine classes (grassland, shrubland, wetlands, forestland, waterbodies, farmed land, built-up land, bare land, and mines/quarries) in the C5 Secondary Drainage Region of South Africa over the three periods 1990–2014, 2014–2022, and 1990–2022. Using the South African National Land Cover datasets and the TerrSet liberaGIS v20.03 Land Change Modeller, this research applied post-classification comparison, transition matrices, asymmetric gain–loss metrics, and patch-based landscape analysis to quantify the magnitude, direction, source–sink dynamics, and spatial reconfiguration of LULCC. Results showed that between 1990 and 2014, Shrubland expanded markedly (+49.1%), primarily at the expense of Grassland, Wetlands, and Bare land, indicating bush encroachment and hydrological stress. From 2014 to 2022, the trend reversed as Grassland increased substantially (+261.2%) while Shrubland declined sharply (−99.3%). Forestland also regenerated extensively (+186%) along riparian corridors, and Waterbodies expanded more than fivefold (+384.6 km²). Over the long period between 1990 and 2022, Built-up land (+30.6%), Cultivated land (+16%), Forestland (+140%), Grassland (+94.4%), and Waterbodies (+25.6%) increased, while Bare land (−58.1%), Mines and Quarries (−56.1%), Shrubland (−98.9%), and Wetlands (−82.5%) decreased. Asymmetric analysis revealed strongly directional transitions, with early Grassland-to-Shrubland conversion likely driven by grazing pressure, fire suppression, and climate variability, followed by a later Shrubland-to-Grassland reversal consistent with fire, herbivory, and ecotonal climate sensitivity. LULC dynamics in the C5 catchment show class-specific spatial reconfiguration, declining landscape diversity (SHDI 1.3 → 0.9; SIDI 0.7 → 0.43), and patch metrics indicating urban and cultivated fragmentation, shrubland loss, and grassland consolidation. Based on these quantified trajectories, we recommend targeted catchment-scale land management, shrubland restoration, and monitoring of anthropogenic hotspots to support ecosystem services, hydrological stability, and sustainable land use in ecotonal regions. Full article

Sediment transport in alluvial channels is strongly controlled by the grain-size distribution of bed and suspended materials. This, in turn, influences river morphology by modifying the cross-sectional area and course of the channel. Statistical parameters such as mean, standard deviation, skewness, and kurtosis provide quantitative indicators of the energy conditions that control sediment transport and deposition. This study examines the depositional characteristics of sediments in the Ganga River in Varanasi City, India, employing a novel combination of linear discriminant function (LDF) and sediment transport index (STI). The LDF results reveal distinct depositional environments: Y₁ and Y₂ values indicate deposition in a low-energy fluvial environment similar to beaches, Y₃ values suggest shallow marine settings, and Y₄ values point to mixed deltaic and turbid current depositional environments. Additionally, CM diagrams show rolling and suspension as the dominant sediment transport mechanisms. Shear stress analysis combined with STI highlights significant depositional features, with minimal erosion observed throughout the study area. The study provides an operational framework for mapping erosion-deposition patterns on alluvial point bars that are transferable to other sand-bed rivers worldwide where detailed hydraulic data are limited but detailed grain-size and DEM information are available. Full article

Field capacity (FC) and permanent wilting point (PWP) thresholds are critical parameters in climate-smart agriculture because they directly relate to soil water availability, which is essential for optimizing water use, improving crop yields, and ensuring resilience against climate variability. Using the continuous mosaic of SoilGrids data, pedotransfer functions based on bulk density, clay content, and sand content were applied to estimate the threshold values of FC and PWP across Mexico utilizing random forest (RF) algorithms. The selection of these parameters was based on their positive contribution to the model’s prediction: bulk density (0.51), clay content (0.21), and sand content (0.16). Soil organic carbon (SOC) contributed negatively; this negative importance score warrants careful interpretation. The 30–60 cm depth was chosen based on the assumption that it is reasonably uniform across other depths and lies below the highly variable surface horizon, which is strongly influenced by management practices and organic matter dynamics. Here we address key technical and scientific critiques regarding the use of SoilGrids for generating FC and PWP data. Additionally, the relevant role of FC and PWP thresholds in the context of climate-smart agriculture is highlighted, from the calculation of available soil water to their role in achieving sustainable development goals. Full article

Regional green development requires balancing anthropogenic carbon emissions (CEs) with vegetation carbon sequestration (VCS). Using the CASA model and plant photosynthesis equation, we estimated VCS from net primary productivity (NPP) and proposed a Carbon Balance Pressure Index (CBPI) to quantify the imbalance between carbon sources and sinks. Spatial analysis and a geographic detector were applied to examine influencing factors of CBPI across Sichuan–Chongqing from 2001 to 2017. Results show that CE increased by 178%, while VCS rose by 27%. Regional CBPI thus enhanced from 0.35 to 0.76, aligning with CE trends. The CBPI presented a clear west-low (0–0.2, except Panzhihua), center-high (peak 3.1 in Chengdu), moderate-east (0.1–0.8) pattern. Geographic detector reveals that economic development and urbanization accounted for 80% of CBPI heterogeneity, followed by transportation (65%). Energy-intensive industries dominated developed areas, while construction-land expansion prevailed in developing regions. This study underscores region-specific emission-sink pathways and provides an empirical basis for differentiated low-carbon strategies in similar rapidly urbanizing regions in China. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

Forests play a vital role in the global carbon cycle but face growing anthropogenic pressures, with climate change and forest fragmentation among the most critical. In West Africa, particularly in Ghana, the interaction between increasing aridity and forest fragmentation remains underexplored, despite its significance for forest biomass dynamics and carbon storage processes. This study examined how spatial variation in climatic aridity (Aridity Index, AI) affects above-ground biomass (AGB) in Ghana’s ecological zones, both directly and indirectly through forest fragmentation and biodiversity, using structural equation modeling (SEM) and generalized additive models (GAMs). Results from this study show that AGB declines along the aridity gradient, with humid zones supporting the highest biomass and semi-arid zones the lowest. The SEM analysis revealed that areas with a lower aridity index (drier conditions) had significantly lower AGB, indicating that arid conditions are associated with lower forest biomass. Fragmentation patterns align with this relationship, while biodiversity (as measured by species richness) showed weak associations, likely reflecting both ecological and data limitations. GAMs highlighted nonlinear fragmentation effects: mean patch area (AREA_MN) was the strongest predictor, showing a unimodal relationship with biomass, whereas number of patches (NP), edge density (ED), and landscape shape index (LSI) reduced AGB. Overall, these findings demonstrate that aridity and spatial configuration jointly control biomass, with fragmentation acting as a key mediator of this relationship. Dry and transitional forests emerge as particularly vulnerable, emphasizing the need for management strategies that maintain large, connected forest patches and integrate restoration into climate adaptation policies. Full article

Many rural communities are struggling to understand the changing climate and how to mitigate and adapt to its negative effects. “Climate literacy” (i.e., an understanding of the climate system, how human actions influence it, and how it affects society and the Earth) may be a necessary precursor to climate action (i.e., steps that help to mitigate or adapt to climate change). For rural communities in the Brazilian Amazon, where access to formal education is limited, grasping abstract concepts like greenhouse gas emissions can be particularly challenging. We asked: Is climate literacy a necessary precursor to climate action? We conducted 22 semi-structured interviews with forest extractivists living within the Chico Mendes Extractive Reserve in the state of Acre, Brazil. We found that forest extractivists are experiencing the impacts of climate change but lack an understanding of its causes and forms of mitigation and are unaware of ways to adapt to it. Improved educational opportunities could support both climate literacy and, in turn, climate action. Full article

Lake Batur, located within a volcanic caldera in Bali, Indonesia, is subjected to anthropogenic pressures related to agriculture, aquaculture, tourism, and religious activities, which may affect its water quality and ecology condition. This study investigates the physicochemical properties of lake water and diatom assemblages preserved in lake sediments to provide insight into environmental conditions in this volcanic alkaline ecosystem. Water quality parameters, including pH, temperature, electrical conductivity (EC), and total dissolved solids (TDS), were measured. Vertical profiles of temperature and conductivity revealed stable stratification, with minimal variation below 20 m water depth. Elevated nitrogen concentrations, including nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonium (NH₄⁺), were observed, particularly in the southern basin, suggesting localized nutrient enrichment. Scanning electron microscopy (SEM) analysis of lake sediment samples identified ten diatom genera, including Ulnaria, Denticula, and Discostella, which are commonly associated with nutrient-enriched freshwater environments. Overall, the results indicate that Lake Batur exhibits conditions consistent with early-stage eutrophication in localized areas, highlighting the importance of continuous monitoring and targeted management strategies to protect the ecological integrity of this volcanic lake system. Full article

This study addresses the global challenge of superficial soil contamination by heavy metals, focusing on differentiating natural geogenic sources from anthropogenic contributions in complex industrial–urban environments. We develop an integrated geostatistical and multivariate framework combining soil metal concentration analysis with AERMOD atmospheric dispersion modeling using a comparative multi-model machine learning approach (including Extreme Gradient Boosting, Random Forest, and Ridge Regression). Applied to the industrialized area of Taranto, Southern Italy, this approach incorporates spatial autocorrelation and multiple environmental predictors to identify contamination patterns and sources. The results reveal variable predictive accuracy across metals, with RF generally outperforming the other algorithms. The model achieved its highest performance for copper (R² = 0.58, RMSE = 25.82), Tin (R² = 0.53, RMSE = 5.95), and chromium, while showing instability for others. These disparities highlight the differential influence of remote sensing data on contamination mapping. The framework advances the quantitative assessment of soil pollution by linking atmospheric deposition and spatial processes with causal interpretability. Full article

Local Climate Zones (LCZs) have become a foundational framework for urban climate modeling, yet their use across model families has not been systematically evaluated. Crucially, the LCZ framework itself has served as a developmental basis, revealing the progression of urban canopy parameterizations (UCP) from early models to the diverse model families currently in use. This evolution is exemplified by systems like the Weather Research and Forecasting (WRF) model, where the application of LCZ has fundamentally shifted from an experimental add-on to a basic, built-in feature of its urban-modeling capabilities. This review synthesizes a decade of LCZ-based studies to clarify how LCZ improves surface representation, enhances comparability, and supports multiscale modeling workflows. It provides a comprehensive overview of peer-reviewed work up to the end of 2024, offering a baseline for understanding the field’s rapid recent growth. Using a structured evidence-mapping approach, we categorize applications into three maturity stages: testing and measurement, operational and planning-oriented applications, and expansions beyond urban climate to chemistry, hydrology, and Earth-system modeling. The assessment covers various iterations of mesoscale systems (WRF, SURFEX/TEB, COSMO), local-scale climatologies (MUKLIMO-3, UrbClim), microscale models (ENVI-met, CFD), and supporting tools including SUEWS, SOLWEIG, RayMan, VCWG, and CESM-CLMU. Results show clear divisions of labor: WRF and SURFEX/TEB anchor process-rich regional simulations; MUKLIMO-3 and UrbClim offer computationally efficient long-horizon or multi-city assessments; ENVI-met and CFD provide design-scale insight when parameterized with LCZ archetypes. Across all families, model skill is strongly constrained by LCZ data quality and by inconsistencies in LCZ to UCP translation. We conclude that advancing LCZ-based urban climate modeling will depend on improved LCZ products, standardized parameter libraries, and formalized cross-scale model couplings that allow existing tools to interoperate more reliably under growing urban-climate pressures. Full article

Europe is dealing with environmental problems that require cooperation beyond national and regional borders. Air pollution, water pollution, biodiversity loss, and waste management are the major issues that are not only complex but also cross borders. Therefore, it is necessary to provide collaborative responses that go beyond the capacity of individual countries. This inquiry centers on the question of what the best way is to set up and govern the transnational cooperation in Europe to confront these major environmental challenges. A systematic bibliographic review of the research conducted between 2010 and 2025 forms the basis of this work. The research combines semantic analysis and Latent Dirichlet Allocation (LDA) modeling to study 80 selected publications to find the tenets of the themes discussed. The identified topics are urban climate change adaptation and mitigation, climate policy and management, adaptation and vulnerability frameworks, land use and biodiversity impacts, and future climate projections and assessment. The findings show that there are strong synergies between biodiversity and climate adaptation, resilience, and environmental governance, as well as the great influence of climate change on the water management sector. The study has unveiled the significance of institutional policy frameworks in bringing about environmental cooperation across borders. In addition, it depicts the relationship between local urban projects and supra-regional policy strategies, in which the two can merge and function efficiently as long as they are working towards the common goal of environmental sustainability. This study is meant to shed more light on the area of environmental governance research, discovering areas that need more exploration, and providing some signposts on how to improve environmental involvement in Europe. Full article

This study investigates temporal and spatial variation in torrential flood hazards and sediment dynamics in two ungauged watersheds in southeastern Serbia from 1991 to 2023. By integrating classical hydrological models with modern geospatial and photogrammetric techniques, watershed responses to environmental and anthropogenic changes are quantified. Torrential flood potential was estimated and peak discharges were calculated using both the rational and SCS-Unit hydrograph methods, while sediment transport was assessed through Gavrilović’s erosion potential model and a modified Poljakov model. A key innovation is the use of UAV-based and close-range photogrammetry for 3D grain-size analysis, marking the first such application in Serbia. The mean torrential flood potential decreased by 4.4% in the Petrova Watershed and 4.2% in the Rasnička Watershed. Specific peak discharges for a 100-year return period declined from 1.62 to 1.07 m³·s⁻¹·km⁻² in Petrova and from 1.60 to 1.34 m³·s⁻¹·km⁻² in Rasnička. Sediment transport during a 1% probability flood was reduced from 4.97 to 2.53 m³·s⁻¹ in Petrova and from 13.87 to 9.48 m³·s⁻¹ in Rasnička. Grain-size analyses revealed immobile coarse bedload in the Petrova and active sediment transport in the Rasnička River, where D50 and D90 decreased between 2023 and 2024. The findings highlight the effectiveness of a synergistic methodological approach for analyzing complex watershed processes in data-scarce regions. The study provides a replicable model for flood hazard assessment and erosion control planning in similar mountainous environments undergoing socio-environmental transitions. Full article