Earth, Volume 6, Issue 4 (December 2025) – 30 articles

Land surface temperature (LST) is a key indicator reflecting the ecological environmental disturbance caused by open-pit coal mining activities and determining the ecological status in alpine permafrost regions. Thus, it is crucial to study the spatiotemporal variations and influencing mechanisms of LST throughout all stages of small-scale mining–large-scale land surface damage–ecological restoration. Landsat imagery over nine periods was extracted from the growing seasons between 1990 and 2024. This study retrieved LST while simultaneously calculating albedo, soil moisture, and normalized difference vegetation index (NDVI) for each time phase. By integrating land use/cover (LUCC) data, the spatiotemporal evolution patterns of LST in the mining area throughout all stages were revealed. Based on the Geodetector method, an identification approach for factors influencing LST spatial differentiation was established. This approach was applicable to the entire process characterized by significant land type transitions. The results indicate that the spatiotemporal variations in LST were significantly correlated with land surface damage and restoration caused by human activities in the mining area. With the implementation of ecological restoration, high and ultra-high temperatures decreased by about 25.98% compared to the period when the surface damage was the most severe. The main influencing factors of LST differentiation were identified for different land use types, i.e., natural and restored meadows (soil wetness, albedo, and NDVI), mine pits (albedo, aspect, and elevation), and mining waste dumps (aspect and albedo before restoration; aspect and NDVI after restoration). This study can provide a reference for monitoring the ecological environment changes and ecological restoration of global coalfields with the same climatic characteristics. Full article

Natural resource-endowed landscapes in many parts of the Global South play a crucial role in the livelihoods of communities. Such resource-endowed areas attract current and prospective resource-use actors, making them veritable hollow frontiers. Hollow frontiers, as crucial resource attractions in many parts of sub-Saharan Africa (SSA), have attracted significant interest in scientific and policy circles. While studies have explored the patterns of migration and population change around hollow frontiers, there is limited evidence on the resource-use dynamics and trajectories in hollow frontiers. This study uses the case of the Mungo Corridor of Cameroon, a hollow frontier par excellence, to (1) determine the variations in forestland resource-use practices, and (2) analyze changes in forestland resource space in the corridor. Data for this study was collected through key informant interviews (n = 37), focus group discussions (n = 15), household surveys using a structured questionnaire (n = 250), and Landsat images. Geospatial analysis, descriptive statistics, and the chi-square statistical technique were employed in the analysis. The study revealed that forestland resource-use practices (NTFPs harvesting) witnessed a significant decline due to the intensification of extraction rates. Furthermore, forestland witnessed a significant decline in Njombe-Penja and Loum (35.216% and 48.176%, respectively) between 1984 and 2024. The results provide novel insights on the pattern of resource use around hollow frontiers and further informs land management policy in the context of the regulation of land-based resources in the hollow frontiers of Cameroon and similar sub-Saharan African contexts. Future studies should explore forestland resource regeneration strategies in the Mungo Corridor. Full article

Wastewater contamination of freshwater ecosystems is a major driver of the spread of antibiotic resistance (AR). This preliminary study investigated the impact of wastewater pollution on the AR profiles of bacterial communities in the Oued–Zénati waterway, Algeria, across a pollution gradient. From September 2017 to May 2018, water samples were collected from an upstream reference site (P1), a site downstream of urban and hospital discharges (P2), and a downstream recovery site (P3). Physicochemical and microbiological analyses revealed a critical pollution hotspot at P2, with fecal coliform concentrations reaching 9.5 × 10⁵ MPN/100 mL, nearly 40 times higher than at P1. From a representative subset of 33 bacterial isolates characterized in this study, susceptibility testing showed a high prevalence of resistance, with observed trends matching the pollution gradient. Specifically, 100% of isolates from the polluted sites (P2 and P3) were resistant to ampicillin, and 60% of isolates from the hotspot (P2) were resistant to amoxicillin/clavulanic acid. Conversely, all isolates remained susceptible to gentamicin. These initial findings suggest that direct wastewater discharge is creating a significant reservoir for AR, highlighting potential risks to public and environmental health and underscoring the urgent need for improved wastewater management infrastructure. Full article

The Mediterranean region is regarded as a hot spot on Earth because of its placement at the junction of many aerosols. Numerous studies have demonstrated that the North Atlantic Oscillation (NAO), which is closely related to the El Niño–Southern Oscillation (ENSO) phenomenon, influences the weather in the area. However, a recent study by the same author examined the ENSO effect on atmospheric processes in this area and discovered a slight but notable influence. This study builds on that earlier work, but it divides the Mediterranean region into four smaller regions during the same time span as the previous study, which is extended by two years, from 1980 to 2024. The division is based on geographical, climatological, and atmospheric process features. The findings demonstrate that volcanic eruptions significantly affect the total amount of aerosols. Additionally, the current study reveals that the Granger-causality test of the physical phenomena of solar activity, ENSO, and NAO indicates that all have a significant impact, either separately or in combination, on the atmospheric process over the four Mediterranean regions, and this effect can last up to six months. Moreover, a taxonomy of the different forms of aerosols across the four subregions is given. Full article

The understanding of coastal ecosystems regarding variability and resilience under climatic and anthropogenic forcing is reliant upon long-term ecological records. We examined the Helgoland Roads time series (1968–2017), which includes temperature, salinity, nutrients (nitrate, phosphate), and biological parameters (diatoms and Acartia spp.). We applied autocorrelation, multi-taper spectral analysis, and wavelet and cross-wavelet transforms to identify dominant temporal patterns and scale-dependent interactions. Sea surface temperature shows consistent long-term warming, and subdecadal (2–3-year) and decadal (7–8-year) oscillations reflect coherent patterns with the North Atlantic Oscillation and Arctic Oscillation. Salinity varied in anti-phase to Elbe River discharge at 6–7-year scales, reflecting control of seasonal, riverine freshwater, and salinity scenarios. Nutrients, as declining long-term trends (particularly phosphate), are associated with seasonal to multi-year variability linked to episodic discharge events. Biological parameters had strong annual periodicities reflective of bloom cycles but also variability above the annual limit. Diatoms responded to climatic, nutrient, and biological responses at the 3–5-year scale associated with this ecological context, particularly nitrate and phosphate; Acartia (spp.) respond to temperature, salinity, and resource availability (diatoms), reflecting climate/nutrient/trophic linkages. This study indicates that Helgoland Roads is represented as a multi-scale, non-stationary system, in which climate variability, riverine input, and ecological linkages are cascaded down to physical and chemical processes that structure biological communities. Spectral methods reveal scale-dependent synchrony and highlight the risks of trophic mismatch under climate change, emphasizing the importance of sustained high-frequency monitoring. Full article

Spatially explicit tools are essential for assessing biodiversity and guiding land use decisions at broad scales. This study presents a national-level approach for evaluating habitat quality as a proxy indicator for biodiversity, using Latvia as a case study. The approach integrates land use data, landscape structure, and habitat characteristics to generate habitat quality indices for agricultural and forest land. It addresses a common limitation in biodiversity planning, namely, the lack of consistent species-level data, by providing a comparative and conceptually robust way to assess how different land use types support biodiversity potential. The methodology was applied to assess current habitat quality and to simulate changes under two policy-relevant land use scenarios: the expansion of protected areas and a shift to organic farming. Results showed that expanding protected areas increased the national habitat quality index by 8.47%, while conversion to organic farming produced a smaller but still positive effect of 0.40%. Expansion of protected areas, therefore, led to a greater improvement in habitat quality compared to converting farmland to organic systems. However, both strategies offer complementary benefits for biodiversity at the landscape scale. Although national-level changes appear moderate, their spatial distribution enhances connectivity, particularly near existing protected areas, and may facilitate species movement. This approach enables national-level modelling of biodiversity outcomes under different policy measures. While it does not replace detailed species assessments, it provides a practical and scalable method for identifying conservation priorities, particularly in regions with limited biodiversity monitoring capacity. Full article

Soil erosion affects agricultural and environmental sustainability and needs to be addressed. The Cagayan de Oro River Basin (CDORB), one of the major river basins in the Philippines, provides economic, social, and environmental services to the city and municipalities inside the basin. More than 70% of the area of the river basin is devoted to various forms of agricultural production. Land cover critically influences erosion dynamics as vegetation reduces rainfall impact, enhances infiltration, and limits sediment transport. This study employs the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) integrated with the Modified Universal Soil Loss Equation (MUSLE) to evaluate soil erosion under different rainfall return periods (5, 10, 25, 50, 100 years) and four land cover scenarios: No Reforestation Intervention (NI), Maximum Forest Cover (MF), Slope-Based Land Use (SB), and Reforestation on Public Domain (PD). Model results showed that soil loss increased with rainfall intensity, with NI yielding the highest average erosion of 1443 t ha⁻¹. Conservation scenarios reduced erosion by up to 53% compared to NI. Among the conservation scenarios, MF, SB, and PD yielded average erosion of 21, 716, and 1304 t ha⁻¹, respectively. While the MF scenario had the least soil loss, no space was assigned for economic production. On the other hand, the SB approach offered the best balance, halving erosion across all rainfall return periods, but at the same time has sufficient space available for economic production. These findings demonstrate the scientific value of integrating HEC-HMS and MUSLE for event-based erosion modeling and highlight how comparing multiple land-cover scenarios can inform data-driven land use planning and policy formulation for sustainable watershed management. Full article

This study estimated the relative risks (RRs) of respiratory and cardiovascular mortality and morbidity due to short-term ozone exposure in 13 polluted communes across Chile’s Santiago Metropolitan and Valparaíso regions. Data on daily ozone, meteorology, and pollutants were sourced from the National Air Quality Information System (NAQIS), while health outcomes (mortality, hospital admissions, and emergency visits) were obtained from the Department of Health Statistics. A Poisson regression model, adjusted for trends, meteorology, day-of-week effects, and pollutants, quantified RRs for a 10 ppb ozone increase, ranging from 1.004 to 1.198 (95% CI). The highest risks were in Santiago’s Eastern zone (cerebrovascular, RR 1.171, 95% CI: 1.018–1.347), Western zone (cardiovascular, RR 1.198, 95% CI: 1.049–1.369), and Valparaíso’s Viña del Mar (ischemic heart disease, RR 1.127, 95% CI: 1.017–1.248). The 5–64-year age group was most affected, particularly in terms of emergency visits. Reducing ozone to the WHO guideline (100 µg/m³) could avoid 837,498 cases in Santiago and 17,992 in Valparaíso annually, resulting in economic savings of $7,439,930,640 and $1,044,568,800, respectively. These results highlight the need for stricter air quality policies to reduce ozone-related health burdens. Full article

Tropical weather cities, such as Mérida in Yucatán, Mexico, are perceived as air pollution-free environments. This study aimed to evaluate the air quality in Mérida City over five years, focusing on PM_2.5 and PM₁₀ as well as spatial and temporal factors. A government-accredited monitoring station for PM_2.5 (2018–2022) and economic air sensors for PM_2.5 and PM₁₀ (2023) were used. Results showed the maximum daily (90 μg m⁻³) and annual PM_2.5 (23 μg m⁻³) averages for 2020 exceeded the Mexican regulations. Sensors indicated that the fixed pollution sources influenced PM_2.5 and PM₁₀. Spatially and temporally, the southwest of the city in the dry season of 2023 showed the highest PM_2.5 and PM₁₀. Tropical conditions (solar radiation and temperature) increased PM, while high humidity and precipitation decreased it. Air quality improved during the rainy season. The southwest zone had the highest density of diesel vehicles and fixed pollution sources, which contributed to the highest PM concentration. The monitoring showed that air quality related to PM in Mérida City is a concern. Local and external factors are affecting the air quality. It is mandatory to regulate air emissions from fixed sources and implement vehicle verification, even in tropical weather cities. Full article

The Artificial Intelligence Forecasting System (AIFS), recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), represents a paradigm shift in global weather prediction by replacing traditional physically based methods with machine learning-based approaches. This study examines the sensitivity of the Weather Research and Forecasting (WRF) model to differentiate initial and boundary conditions, comparing the new AIFS with two well-established global models: IFS and GFS. The analysis focuses on the implications for air quality applications, particularly the influence of each global model on key meteorological variables involved in pollutant dispersion modelling. While overall forecast accuracy is comparable across models, some differences emerge in the spatial pattern of the wind field and vertical profiles of temperature and wind speed, which can lead to divergent interpretations in source attribution and dispersion pathways. Full article

The quality of water in rivers and their self-purification capacity are critical for maintaining healthy aquatic ecosystems. This study aims to analyze and compare various mathematical models of self-purification, assessing their applicability in restoring water quality and proposing recommendations for their improved use. A systematic review of the scientific literature was conducted following PRISMA 2020 guidelines to ensure a rigorous approach. Research questions were framed using the PICO model, which includes Population, Intervention, Comparison, and Outcomes. Relevant studies published between 2015 and 2024 regarding mathematical models of river self-purification were selected. Inclusion and exclusion criteria were applied, and a critical analysis of findings was performed, highlighting methodologies and results. The results indicate that the effectiveness of self-purification models varies significantly depending on environmental and geographic characteristics. A need for more specific models and the integration of local variables was identified as a research gap that requires attention in future studies. Furthermore, recommendations were made to enhance model calibration and validation, as well as to incorporate innovative approaches for optimizing water quality management in rivers. These mathematical models are essential tools for managing river water quality, promoting public health, and contributing to the achievement of Sustainable Development Goal 6 (SDG 6). Full article

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article

Fine spatial information of precipitation plays a significant role in regional eco-hydrological studies but remain challenging to derive from satellite observations, especially in complex terrain areas. Sichuan Province, located in the southwest of China, has a highly variable terrain, and the spatial distribution of precipitation exhibits extreme heterogeneity and strong autocorrelation. Multi-scale Geographically Weighted Regression (MGWR) and Random Forest (RF) were employed for downscaling the Global Precipitation Measurement Mission (GPM) products based on high spatial resolution terrain, vegetation, and meteorological data in Sichuan province, and their specific effects on gauged precipitation accuracy and spatial precipitation distributions have been analyzed based on the influences of environmental variables. Results show that the influence of each environmental factor on the distribution of precipitation at different scales was well represented in the MGWR model. The downscaled data showed good spatial sharpening effects; additionally, the biases in the overestimated region were well corrected after downscaling. However, when based on spatial autocorrelation and considering adjacent influences, the MGWR performed poorly in correcting outlier sites adjacent to the high–high clusters. Compared with MGWR, relying on independently constructed decision trees and powerful regression capabilities, superior correction for outlier sites has been achieved in RF. Nevertheless, the influence of environmental variables reflected in RF differs from actual conditions, and detailed characteristics of precipitation spatial distribution have been lost in the downscaled results. MGWR and RF demonstrate varying applicability when downscaling GPM products in complex terrain areas, as they both improve the ability to finely depict spatial information but differ in terms of texture property expression and precipitation bias correction. Full article

REDD+ has emerged as a global strategy for reducing CO₂ emissions from deforestation and forest degradation and shows great promise for the Extractive Reserves of the Brazilian Amazon (RESEX). It is essential to assess whether REDD+ projects have effectively contributed to the conservation of these areas over time. To address this issue, we analyzed land use and cover dynamics in the RESEX Rio Preto-Jacundá (Rondônia) and its surroundings from 2004 to 2020 to evaluate the impacts of a certified REDD+ project. The following two trend scenarios were simulated: (i) pre-implementation (2004–2012), projected to 2020, and (ii) post-implementation (2012–2020), projected to 2028. Historical maps were derived from the TerraClass dataset, and future projections were generated using Markov Chains combined with Cellular Automata. Forest conservation was evaluated through structural metrics such as the number, size, and shape of forest fragments, and the type, frequency, and length of boundaries with other land uses, using ArcGIS tools and Patch Analyst. Carbon sequestration was estimated from the aboveground biomass values of primary and secondary forests. The results showed that the REDD+ mechanism did not achieve the expected environmental benefits, with a decrease in carbon stocks over time and potential negative effects on the richness and composition of local flora. Full article

Bangladesh is among the world’s most climate-vulnerable countries, facing recurrent hazards that disrupt lives and livelihoods. Among these, heatwaves and cold snaps strongly affect electricity consumption, representing a key socio-economic impact of climate extremes. In this study, we used meteorological and electricity data from six sub-regions of Bangladesh to examine long-term changes in extreme temperature days and their effects on electricity usage. Results showed that western inland stations (Chuadanga, Jashore) experienced hotter summers and colder winters, whereas coastal sites (Barishal, Patuakhali) were moderated by maritime influences. Trend analysis revealed significant increases in hot-day frequency since 1961 (up to 1.8 days yr⁻¹ at coastal areas, while cold-day frequencies generally declined but with regional variability. Electricity demand followed a clear pattern, being highest on hot days, lowest on cold days, and intermediate on normal days. Among the regions, Khulna consistently recorded the greatest demand (up to 161 MWh), while Patuakhali remained the lowest (~19–32 MWh). Regression analysis further showed that demand rises with maximum temperature, with slopes up to 5.7 MWh °C⁻¹ and moderate correlations (r = 0.27–0.47). Importantly, the temperature–demand relationship has strengthened in recent years, as similar climatic conditions now correspond to higher electricity use, reflecting both climatic pressures and socio-economic growth. These findings highlight the challenge of temperature extremes for electricity demand and the need to integrate climate–energy linkages into adaptation planning. Full article

Constructed wetlands (CWs) are eco-technologies used for wastewater treatment, where vegetation is a key component. In recent decades, the adaptability and phytoremediation functions of ornamental plants (OPs)—which are not typically found in natural wetlands—have been tested. However, few comprehensive studies address the processes or mechanisms by which these OPs enhance pollutant removal in CWs and their artisanal applications. It is also vital to understand the most used OP species in CWs and their advantages in phytoremediation. Thus, this study enhances the understanding of the processes involved in pollutant degradation within CWs, specifically focusing on absorption, adsorption, translocation, radial oxygen loss, and root exudates. It reaffirms the essential role that plants play in these systems, as suggested in previous scientific reports, and discusses some potential applications for the ornamental plants produced in CWs, such as flower arrangements and crafts. This also includes the social aspect, emphasizing community engagement through social capital initiatives for the adoption and appropriation of the ecotechnology. Additionally, a bibliometric analysis revealed that Mexico is among the countries with the most significant research on OPs in CWs, particularly concerning commercially valuable species, followed by India and China. These findings can be instrumental in planning future community projects focused on wastewater treatment using CWs using OPs. Full article

From 2011 to 2021, marine and brackish water aquaculture production in the Mediterranean and Black Seas increased by 91.3% in volume and 74.5% in value, primarily due to the rise in finfish marine aquaculture. In the Aegean Sea, a significant aquaculture hotspot, Greece and Turkey lead in fish farm numbers and production volume. This study uses Google Earth satellite imagery to map and analyze fish farming cages along the Aegean Sea, comparing findings with the EMODnet dataset. By cataloging fish farm cages along the Greek and Turkish coastlines, we identified 4729 cages in Greece and 2349 in Turkey, with Turkey’s cages occupying a larger area (1.64 km²) than Greece (1.35 km²) due to their larger average size. The analysis revealed significant discrepancies between satellite-derived data and EMODnet records, particularly along the Greek coastline, highlighting gaps in existing datasets. Our findings underscore the need for improved marine spatial planning and management as well as for consistent data collection to support sustainable aquaculture. Full article

Coastal boulder deposits (CBDs) are important geomorphic indicators of extreme wave activity, yet integrated morphometric and hydrodynamic analyses remain limited along the Moroccan Atlantic coast. This study characterizes the morphology, spatial distribution, and transport thresholds of supratidal boulders at Oued Cherrat and Mansouria, and quantifies the wave energy required for their mobilization. Between 2021 and 2025, 85 boulders were surveyed, supported by lithological analyses, GPS mapping, and pre-/post-storm photographic documentation. At Oued Cherrat, boulders ranged from 0.01 to 3.56 m³ (≤7.84 t), with solitary blocks located 30–94 m inland and larger imbricated clasts up to 150.5 m. At Mansouria, dimensions reached 22 × 20 × 3.5 m (>2032 t), positioned 5–140 m from the shoreline. Storms in January and March 2025 displaced boulders up to 4.5 m at Oued Cherrat (e.g., 6.39 t) and up to 3 m at Mansouria (e.g., 21.42 t), with new blocks deposited and megaboulders showing slight in situ rotations. Hydrodynamic modelling estimated sliding thresholds of 1.1–4.0 m/s at Oued Cherrat and 2.7–11.0 m/s at Mansouria, while rolling thresholds reached 18.23 m/s. These values confirm the dependence of transport on boulder mass, imbrications, and topography. The findings demonstrate that extreme storms can rapidly reorganize multi-tonne CBDs, while the largest megaboulders require rare, exceptionally high-energy events. Full article

This study aimed to forecast monthly PM_2.5 concentrations in Zabol, one of the world’s most dust-prone regions, using four time series models: SARIMA, SARIMAX enhanced with Fourier terms (selected based on spectral peak analysis), TBATS, and a novel hybrid ensemble. Spectral analysis identified a dominant annual cycle (frequency 0.083), which justified the inclusion of two Fourier harmonics in the SARIMAX model. Results demonstrated that the hybrid model, which optimally combined forecasts from the three individual models (with weights ω₂ = 0.628 for SARIMAX, ω₃ = 0.263 for TBATS, and ω₁ = 0.109 for SARIMA), outperformed all others across all evaluation metrics, achieving the lowest AIC (1835.04), BIC (1842.08), RMSE (9.42 μg/m³), and MAE (7.43 μg/m³). It was also the only model exhibiting no significant residual autocorrelation (Ljung–Box p-value = 0.882). Forecast uncertainty bands were constant across the prediction horizon, with widths of approximately ±11.39 μg/m³ for the 80% confidence interval and ±22.25 μg/m³ for the 95% confidence interval, reflecting fixed absolute uncertainty in the multi-step forecasts. The proposed hybrid framework provides a robust foundation for early warning systems and public health management in dust-affected arid regions. Full article

Climate change research is increasingly important in regions vulnerable to extreme hydrometeorological events like droughts, which pose significant socio-economic and environmental challenges. This study examines future variability of meteorological drought in northernmost South America using the Standardized Precipitation Index (SPI) and precipitation projections from CMIP6 models. We first evaluated model performance by comparing historical simulations with observational data from the Climate Hazards Group InfraRed Precipitation with Station dataset for 1981–2014. Among the models, CNRM-CM6-1-HR was selected for its superior accuracy, demonstrated by the lowest errors and highest correlation with observed data—specifically, a correlation coefficient of 0.60, a normalized root mean square error of 1.08, and a mean absolute error of 61.37 mm/month. Under SSP1-2.6 and SSP5-8.5 scenarios, projections show decreased rainfall during the wet months in the western Perijá mountains, with reductions of 3% to 26% between 2025 and 2100. Conversely, the Sierra Nevada of Santa Marta is expected to see increases of up to 33% under SSP1-2.6. During dry months, northern Colombia and Venezuela—particularly coastal lowlands—are projected to experience rainfall decreases of 10% to 17% under SSP1-2.6 and 13% to 20% under SSP5-8.5. These areas are likely to face severe drought conditions in the mid and late 21st century. These findings are essential for guiding water resource management, enabling adaptive strategies, and informing policies to mitigate drought impacts in the region. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

The Nile, Indus, and Yellow River deltas are historically significant and have experienced extensive shoreline changes over the past 50 years, yet the roles of human interventions and natural events remain unclear. In this study, the Net Shoreline Movement and End Point Rate (EPR) were calculated to quantify the erosion and accretion of the shoreline, respectively. Subsequently, linear trend analysis was employed to identify potential directional shifts in shoreline behavior. These measures are combined with segment-scale cumulative area and the EPR trend to reveal where erosion or accretion intensifies, weakens, or reverses through time. Results show distinct, system-specific trajectories, the Nile lost ~27 km² from 1972 to1997 as a result of the dam construction and sediment reduction, and lost only ~3 km² more from 1997 to 2022, with local stabilization. The Indus switched from intermittent gains before 1990s to sustained loss after that, totaling ~300 km² of cumulative land loss mainly due to upstream dam constructions and storm events. The Yellow River gained ~500 km² from 1973 to 1996 then lost ~200 km² after main-channel relocation and reduced sediment supply despite active-mouth management. These outcomes indicate that deltas are very vulnerable to system wide human activities and natural events. Combined, satellite-derived metrics can help prioritize locations, guide feasible interventions, establish annual monitoring and trigger action. A major caveat of this study is that yearly shoreline rates and 5–10-yearaverages can mask short-lived or very local shifts. Targeted field surveys and finer-scale modeling (hydrodynamics, subsidence monitoring, bathymetry) are therefore needed to refine the design and inform better policy choices. Full article

The tasks and objectives of grassland management have changed significantly in recent decades. One of the key elements of adapting to climatic and economic challenges is the optimal use and future sustainability of grasslands. Ferenc Balázs’s plant stand assessment method is a fast, efficient and widely applicable method for evaluating the quantitative and qualitative characteristics of forage in grasslands, as well as the economic value of pastures. This study is based on a three-dimensional coenological survey which is low-cost, does not require technical infrastructure, and empirically considers the species’ preference by livestock. As a result of our extended criteria approach, we assigned modified forage value (k-value) categories to 2310 vascular plant species. Based on our investigations in the presented case study, the Balázs method was proven to be well suited for estimating the yield of grasslands and determining the relative forage value of grasslands with a high degree of confidence in practice. As this method is non-destructive and involves little trampling, it is particularly suitable for monitoring grassland habitats with a high density of protected plant and animal species. Full article

Comprehension of spatio-temporal groundwater recharge (GWR) under climate change is imperative to enhance water resources availability and management. The main aim of this study is to examine climate change’s effects on spatio-temporal GWR. This study was done by ensembling five climate models and the physically-based WetSpass-M model to estimate GWR during baseline (1986 to 2015), mid-term (2031 to 2060), and long-term (2071 to 2100) periods for the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In comparison to the Identification of unit Hydrographs and Component flows from Rainfall, Evaporation, and Streamflow (IHACRES)’s baseflow and direct runoff with corresponding WetSpass-M model outputs, the statistical indices showed good performance in simulating water balance components. Projected future temperature and rainfall will likely increase dramatically compared to the baseline period for RCP4.5 and RCP8.5. In comparison to the baseline period, the annual GWR had been projected to increase by 4.28 mm for RCP4.5 for the mid-term (MidT4.5), 15.27 mm for the long-term (LongT4.5), 2.38 mm for the mid-term (MidT8.5), and 13.11 mm for the long-term for RCP8.5 (LongT8.5), respectively. The seasonal GWR findings showed an increasing pattern during winter and spring, whereas it declined in autumn and summer. The mean monthly GWR for MidT4.5, LongT4.5, MidT8.5, and LongT8.5 will increase by 0.34, 1.26, 0.18, and 1.07 mm, respectively. The watershed’s downstream areas were receiving the lowest amount of GWR, and prone to drought. Therefore, this study advocates and recommends that stakeholders participate intensively in developing and implementing climate change resilience initiatives and water resources management strategies to offset the detrimental effects in the downstream areas. Full article

The sustainable management of water resources requires experts and also citizens who understand the hydrosphere and its key functions. To educate the public about water-related issues, various water literacy concepts have been developed. However, many of these concepts are too complex for people to understand. In contrast, the ocean literacy framework effectively translates knowledge into behavioral changes and actions. The Danube River, known as the world’s most international river, has a catchment area shared by 19 countries. This river basin has experienced unprecedented landscape alterations, floods, droughts, and pollution events, highlighting the need for a new approach to environmental education. Additionally, globally, more people live near rivers than by the ocean. To empower members of riverside communities with water literacy, we aimed to adapt the ocean literacy principles into river literacy principles. In this study, we introduce a novel concept of river literacy, consisting of seven principles. This framework aims to support sustainable development goals through education and to restore and revive damaged freshwater habitats more effectively. The principles were tested in formal education across five countries. The results indicate that participants in river literacy programs became more motivated to protect rivers, and their understanding of fluvial geography and riverine pollution improved. Full article

Rapid urbanization and climate change demand innovative green solutions in city planning. Tiny forests—small artificial wooded areas in urban or peri-urban settings—are gaining attention. This paper explores the use of the Miyawaki method to establish such forests in Italy, highlighting their environmental and educational benefits. The study defines micro-forests (100–200 m²) and mini-forests (200–2000 m²) per legislative standards and describes the qualitative features needed for self-sustaining ecosystems. Mimicking natural succession, these forests support biodiversity, reduce urban heat, improve air quality, and act as carbon sinks. Beyond ecological functions, they offer strong pedagogical value, fostering naturalistic intelligence and reconnecting people with natural rhythms and ecosystems. Case studies from Vigevano and Rome show practical applications, demonstrating how tiny forests can enhance sustainability, community well-being, and environmental awareness in cities. Full article

Aerosols from natural and anthropogenic sources exert significant yet highly variable influences on the Earth’s radiative balance characterized by pronounced spatial and temporal heterogeneity. Accurate quantification of these effects is crucial for enhancing climate projections and informing effective mitigation strategies. In this study, we evaluated the performance of three widely used aerosol optical depth (AOD) datasets—MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2), MODIS Aqua, and MODIS Terra—by comparing them against ground-based AERONET observations from ten stations located within the dust belt region. Statistical assessments included coefficient of determination (R²), correlation coefficient (R), Index of Agreement (IOA), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Mean Bias (RMB), and standard deviation (SD). The results indicate that MERRA-2 showed the highest agreement (R = 0.76), followed by MODIS Aqua (R = 0.75) and MODIS Terra (R = 0.73). Seasonal and annual AOD climatology maps revealed comparable spatial patterns across datasets, although MODIS Terra consistently reported slightly higher AOD values. These findings provide a robust assessment and reanalysis of satellite AOD products over arid regions, offering critical guidance for aerosol modeling, data assimilation, and climate impact studies. Full article

In Mexico, 95% of the population has access to drinking water sources, but only about 65% of domestic waste water is treated to safe levels. This study analyzes forty years of Mexican scientific production on water and waste water treatment through a bibliometric and conceptual approach, evaluating its contribution Sustainable Development Goal (SDG) 6. The analysis identified three major research clusters: (1) biological processes for water treatment, (2) development and optimization of physical–chemical processes, and (3) water quality and management. These themes reflect the evolution of biological approaches for identifying and removing organic contaminants, the application of advanced techniques for improving water quality, and the promotion of sustainable water use. The study also highlights the growing attention to emerging contaminants, nanotechnology, integrated water resource management, and persistent challenges in sanitation. With respect to SDG 6, Mexican research has mainly focused on targets 6.1 (universal and equitable access to drinking water), 6.3 (water quality), and 6.5 (water resources management), while targets 6.2 (sanitation), 6.a (international cooperation), and 6.b (community participation) remain underrepresented compared with the international benchmarks, where the research trend is on water management, resources, and the water–food–energy nexus. Finally, the findings also show synergies with SDGs 11 (sustainable cities and communities), 9 (industry, innovation, and infrastructure), and 3 (good health and well-being), although gaps persist in addressing equitable access to water and society participation. Full article

Natural geogenic effects lead to alterations in soil heavy metal concentrations. This study assesses the presence of elevated trace-element concentrations in the Oued Irriri watershed in southeastern Morocco. ASTER satellite imagery, geochemical, and aeromagnetic data are combined to determine the origin of these anomalies. Processing of ASTER images delineated alteration zones coinciding with areas of high heavy metal anomalies by detecting hydrothermal alteration minerals, including muscovite, montmorillonite, illite, hematite, jarosite, chlorite, and epidote. Principal Component Analysis (PCA) of geochemical data distribution in soils enabled the characterization of variations in trace-element concentrations, the extraction of geochemical anomalies, and the identification of potential sources of contamination. Comparing satellite image processing results with geochemical analyses facilitated the production of a geogenic enrichment map. The study results indicate high enrichment levels of zinc, Molybdenum, and bismuth in the western basin, of purely lithological origin. Hydrothermal alteration surfaces intersect geochemical anomaly zones in the north and northeast, primarily showing the impact of fault rooting on the surface deposition of Cu, Ba, Hg, and Pb-rich deposits. This study developed a geogenic enrichment map indicating naturally affected areas, identifying potential risks to eco-environmental systems, and better preventing the effects of geogenic enrichment. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article