Earth

Glaciers are significant sources of fresh water on planet Earth. The Hindukush–Karakoram–Himalayan (HKH) glaciers provide the water supply to more than half of the human population of the globe, for agricultural activities, biodiversity survival, and ecosystem services. In recent years, the loss of glacial ice has been forecasted to cause problems such as sea level rise, changes in water availability, and release of contaminants that reside in the surfaces of glaciers or within them. In this regard, mineralogical sediments play a significant role in the geochemistry of glaciers and element cycling. This study analyzed elemental pollutants found in the glaciers of Pakistan and investigated the diverse bacterial communities residing therein. Samples of ice and sediments were collected from the Gilgit, Hunza, and Swat glaciers in northern Pakistan. Nine elements, including co-factors, heavy metals, and nutrients, were assessed using atomic absorption spectrophotometry. The research findings indicate higher concentrations of the elements K, Fe, Cu, and Cr in Hunza glacier ice (Hgi) and Ni, Zn, As, and Cd in Gilgit glacier ice (Ggi). In terms of glacier sediments, Swat (Sgs), Gilgit (Ggs), and Hunza (Hgs) samples showed the highest concentrations of K, Cu, Ni, Zn, As, Pb, Cd, and, respectively, of Fe, and Cr. The amount of Cu and Cr is the same in Swat glacier ice and Swat glacier foot. However, the concentration of some elements (As, K, Pb, Zn) is higher in Swat glacier ice, while the amount of some elements (Cd, Ni) is greater in Swat glacier foot. Furthermore, microbial cultivation techniques revealed diverse bacterial communities inhabiting the sampled glaciers. Phylogenetic analysis of the bacterial isolates, based on 16S rRNA gene sequences, showed high homology (99–100%) with previously reported species. The resultant phylogenetic tree grouped the bacterial isolates, such as Serratia marcescens, Cupriavidus sp., and Bacillus cereus, with closely related species known for their roles in nutrient cycling, environmental resilience, and metal tolerance. These findings highlight the ecological significance and adaptive potential of microbial communities in glacier environments, emphasizing their role in elemental cycling and environmental resilience. Full article

Danube River represents a critical axis of ecological and economic importance for the countries along its course. From this perspective, this paper aims to assess the most significant characteristics of the river and of its main tributaries, as well as its impact on the environmental sustainability and socio-economic development. Navigation and the economic contribution of the Danube River are the key issues of this work, emphasizing its importance as an international transport artery that facilitates trade and tourism, and develops the energy industry through hydropower plants. The study includes an analysis of the volume of goods transported from 2019 to 2023, as well as an analysis of the goods traffic in the busiest port on the Danube. Furthermore, climate change affects the hydrological regime of the Danube, as well as the ecosystems, economy, and energy security of the riparian countries. Main impacts include changes in the hydrological regime, increased frequency of droughts and floods, reduced water quality, deterioration of biodiversity, and disruption of the economic activities dependent on the river, such as navigation, agriculture, and hydropower production. Thus, hydrological risks and challenges are investigated, focusing on the extreme events of the last two decades and the awareness of their repercussions. In this context, the national and international institutions responsible for monitoring and managing the Danube are presented, and their role in promoting a sustainable river policy is explored. Methods and technologies are shown to be essential tools for monitoring and prediction studies. The Danube includes an extensive network of hydrometric stations that help to prevent and manage the most significant risks. Finally, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of the development of the hydrological studies was conducted, highlighting the potential of the river. Full article

Flooding poses significant challenges in semi-arid regions, where irregular rainfall patterns increase environmental vulnerability. This study explicitly aims to improve flood susceptibility mapping by integrating advanced machine learning (ML) algorithms with geographic information systems (GIS) and remote-sensing data. Using data from the Upper Drâa Basin in southern Morocco, we applied boosting algorithms, including XGBoost, CatBoost, LightGBM, and Hist Gradient Boosting, to enhance the accuracy of flood risk assessment. Quantitative model evaluation shows that Hist Gradient Boosting achieved the best performance, with the lowest mean squared error (MSE = 0.06897) and root mean squared error (RMSE = 0.2626). It also attained the highest F1 score (0.8), overall accuracy (93.1%), and area under the curve (AUC = 0.833), indicating its superior predictive capability. These findings highlight the strong potential of novel boosting ensemble learning methods in flood susceptibility prediction and contribute valuable, data-driven insights for policymakers and urban planners to support effective flood mitigation strategies in southern Morocco. Full article

This study aimed to evaluate the performance and robustness of the GR2m “Génie Rural à 2 paramètres au pas du temps Mensuel” rainfall–runoff model for simulating streamflow under past and future hydrometeorological shifts in the Medjerda, a data-scarce Mediterranean catchment in northern Tunisia characterized by limited hydrometeorological records and high climate variability. The evaluation was conducted across three subcatchments characterized by contrasting climatic conditions and representing the hydrometeorological pattern of the Medjerda catchment. To assess the model’s robustness, a calibration–validation process was applied. This method alternated between dry and wet periods and evaluated model performance through various criteria. Subsequently, GR2m was adopted to simulate projected discharge, using projections from the “Model for Interdisciplinary Research on Climate 5” (MIROC5) under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Standardized climate indices (SCIs) were employed to assess climate change impacts. The results demonstrate that GR2m performs well in simulating streamflow across different climatic conditions within the Medjerda catchment and maintains satisfactory performance when calibrated over a non-stationary climate period. The findings indicate a continuous decline in projected runoff and suggest a significant increase in extreme drought events. Full article

Artisanal small-scale gold mining (ASGM) is the largest source of global mercury (Hg) emissions. This study investigated Hg contamination in water, soil, sediment, fish, and cassava plants around ASGM sites in Gunung Pongkor, West Java, Indonesia. Hg concentration ranged from 0.06 to 4.49 µg/L in water; 0.420 to 144 mg/kg dw in soil; 0.920 to 150 mg/kg dw in sediment; 0.259 to 1.23 mg/kg dw in fish; 0.097 to 5.09 mg/kg dw in cassava root; and 0.350 to 8.84 mg/kg dw in cassava leaf. Geo-accumulation index (Igeo) analysis revealed moderate to heavy soil contamination upstream, likely due to direct ASGM input. In contrast, sediment Igeo values indicated heavy contamination downstream, suggesting Hg transport and sedimentation. Bioconcentration factors (BCFs) in fish were predominantly high in downstream and midstream areas, indicating enhanced Hg bioavailability. Bioaccumulation factors (BAFs) in cassava were higher in upstream areas. Health risk assessment, based on the Hazard Quotient (HQ) and Hazard Index (HI), identified ingestion as the primary exposure route, with children exhibiting significantly higher risks than adults. These findings highlight the significant Hg contamination associated with ASGM in Gunung Pongkor and emphasize the need for targeted mitigation strategies to protect human and environmental health. Full article

This study presents the first comprehensive nationwide assessment of mangrove ecosystems in the Maldives. Surveys were conducted across 162 islands in 20 administrative atolls, integrating field data, the literature, and secondary sources to map mangrove distribution, confirm species presence, and classify habitat types. Twelve true mangrove species were identified, with Bruguiera cylindrica, Rhizophora mucronata, and Lumnitzera racemosa emerging as dominant. Species diversity was evaluated using Shannon (H′), Margalef (d′), Pielou’s evenness (J′), and Simpson’s dominance (λ′) indices. Atolls within the northern and southern regions, particularly Laamu, Noonu, and Shaviyani, exhibited the highest diversity and evenness, while central atolls such as Ari and Faafu supported mono-specific or degraded stands. Mangrove habitats were classified into four geomorphological types: marsh based, pond based, embayment, and fringing systems. Field sampling was conducted using standardized belt transects and quadrats, with species verified using photographic documentation and expert validation. Species distributions showed strong habitat associations, with B. cylindrica dominant in marshes, R. mucronata and B. gymnorrhiza in ponds, and Ceriops tagal and L. racemosa in embayments. Rare species like Bruguiera hainesii and Heritiera littoralis were confined to stable hydrological niches. This study establishes a critical, island-level baseline for mangrove conservation and ecosystem-based planning in the Maldives, providing a reference point for tracking future responses to climate change, sea-level rise, and hydrological disturbances, emphasizing the need for habitat-specific strategies to protect biodiversity. Full article

Sustainable development is increasingly challenged by the growing threats of climate change. There is a close relationship between climate change, public health, and Sustainable Development Goals (SDGs). This study investigates the temperature anomalies in the Kingdom of Bahrain and their potential effects on human health. Furthermore, it proposes solutions to support Bahrain’s SDG-related goals. Data were collected from global studies and statistics and the Bahrain Meteorological Directorate over 50 years, which were then used to calculate the temperature anomalies and the heat indices, thereby exploring the past and present monthly and annual national temperature and sociated risks to human health. The results show that Bahrain is located in an area of high temperature anomalies and high rates of cardiovascular diseases. Furthermore, anomaly calculations indicate a critical rise in temperature, ranging from 1 to 4 °C higher than the averages recorded in the 1960s, 1970s, and 1980s. Such an increase could significantly affect human health, particularly since the heat index results show that summers consistently fall within the extreme danger ranges. In contrast, other seasons have occasionally reached the danger level or required extreme caution in certain years. Consequently, this study offers recommendations to help mitigate the rise in temperature and associated risks in the future. Full article

Machine learning models are increasingly used for streamflow prediction due to their promising performance. However, their data-driven nature makes interpretation challenging. This study explores the interpretability of a Random Forest model trained on high streamflow events from a hydrological perspective, comparing methods for assessing feature influence. The results show that the mean decrease accuracy, mean decrease impurity, Shapley additive explanations, and Tornado methods identify similar key features, though Tornado presents the most notable discrepancies. Despite the model being trained with events of considerable temporal variability, the last observed streamflow is the most relevant feature accounting for over 20% of importance. Moreover, the results suggest that the model identifies a catchment region with a runoff that significantly affects the outlet flow. Accumulated local effects and partial dependence plots may represent first infiltration losses and soil saturation before precipitation sharply impacts streamflow. However, only accumulated local effects depict the influence of the scarce highest accumulated precipitation on the streamflow. Shapley additive explanations are simpler to apply than the local interpretable model-agnostic explanations, which require a tuning process, though both offer similar insights. They show that short-period accumulated precipitation is crucial during the steep rising limb of the hydrograph, reaching 72% of importance on average among the top features. As the peak approaches, previous streamflow values become the most influential feature, continuing into the falling limb. When the hydrograph goes down, the model confers a moderate influence on the accumulated precipitation of several hours back of distant regions, suggesting that the runoff from these areas is arriving. Machine learning models may interpret the catchment system reasonably and provide useful insights about hydrological characteristics. Full article

Although Bangladesh is frequently regarded as ‘ground zero’ for climate change, the Chittagong Hill Tracts (CHTs) have only recently been acknowledged for their environmental vulnerabilities, especially after the devastating rainfall and landslides of 2017. However, attributing these risks solely to climate change overlooks their entanglement with structural inequalities, extractive development, deforestation, and long-standing marginalization. The study examines how climate variability intersects with broader environmental risks through a mixed-methods approach, integrating 30 years of NASA TRMM_3B42_daily rainfall data with a household survey (n = 400), life stories, focus group discussions, and key informant interviews conducted across all three CHT districts. Findings do not support a singular attribution to climate change. Rather, they reveal compounded vulnerabilities shaped by land degradation, water scarcity, flash flooding, and landslides—often linked to deforestation and neoliberal development interventions. We argue that the CHT exemplifies ecological entanglement, shaped by climate variability and structural inequalities rooted in land governance and Indigenous dispossession. By integrating spatially disaggregated climate data with historically grounded local experiential narratives, this study contributes to climate justice debates through relational, place-based understandings of vulnerability in the Global South. Full article

A vital step for any hydrochemical assessment is properly carrying out quality assurance and quality control (QA/QC) techniques to evaluate data confidence before performing the assessment. Understanding the processes governing groundwater evolution in coastal aquifers is critical for managing freshwater resources under increasing anthropogenic and climatic pressures. This study reassesses the hydrochemical and isotopic data from the Deep Confined Aquifer System (DCAS) in the Jiangsu Coastal Plain, China, by firstly applying QA/QC protocols. Anomalously high Fe and Mn concentrations in several samples were identified and excluded, yielding a refined dataset that enabled a more accurate interpretation of hydrogeochemical processes. Using hierarchical cluster analysis (HCA), principal component analysis (PCA), and stable and radioactive isotope data (δ²H, δ¹⁸O, ³H, and ¹⁴C), we identify three dominant drivers of groundwater evolution: water–rock interaction, evaporation, and seawater intrusion. In contrast to earlier interpretations, we present clear evidence of active seawater intrusion into the DCAS, supported by salinity patterns, isotopic signatures, and local hydrodynamics. Furthermore, inconsistencies between tritium- and radiocarbon-derived residence times—modern recharge indicated by ³H versus Pleistocene ages from ¹⁴C—highlight the unreliability of previous paleoclimatic reconstructions based on unvalidated datasets. These findings underscore the crucial role of robust QA/QC and integrated tracer analysis in groundwater studies. Full article

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts. Full article

Nowadays, much attention has been paid to the study of the specific activity of radionuclides on the surface of glaciers. This work is devoted to the study of specific activity of natural (K-40, U-235, U-238, Th-232) and anthropogenic (Cs-137) radionuclides in cryoconites of glaciers of the Central Caucasus. The work shows that the activity of the investigated natural radionuclides in the cryoconites of the glaciers we studied is comparable to data from Arctic glaciers, somewhat lower than on Mount Elbrus and Transcaucasia, and significantly lower than on Alpine glaciers. The study revealed that the glaciers of the Central Caucasus (Tsey, Skazka, and Bezengi) exhibit low values of specific activity of anthropogenic radionuclide Cs-137 and average from 0.51 Bq/kg (Bezengi) to 2.61 Bq/kg (Skazka). On the contrary, high and very high concentrations of this radionuclide were revealed in cryoconites of glaciers from other regions, especially in the Alps, the Arctic, and Transcaucasia. Thus, our data confirm the results of previous studies conducted on glaciers of the Central Caucasus, which indicated that the activity of natural radionuclides in cryoconites of glaciers of the Central Caucasus is comparable to the world average values, while the anthropogenic radionuclide Cs-137 is much lower. Full article

Background: This study aimed to evaluate risk perception about climate change and the sustainable behaviors adopted by adults. Methods: An anonymous questionnaire was administered between February and April 2024 among parents of adolescents in southern Italy. Results: A total of 765 parents agreed to participate in the survey, and the average respondents’ age was 47.3 years (28–78). Overall, 67.6% of respondents were extremely concerned that heat waves will negatively affect future generations. This concern was higher among respondents aged 41–50 years and >50 years, males, those who did not need additional information about climate change, and those who had received information about climate change. Regarding climate change concerns, 25.9% and 52.1% of participants were very or extremely concerned about climate change, respectively. Males, those who had one child, respondents aged 41–50 years and >50 years, and those who did not need additional information about climate change were significantly more likely to have concerns about climate change. Moreover, 34.5% of respondents engaged in sustainable behaviors. Finally, those who believed that climate change will negatively affect future generations and those who were extremely concerned that heat waves will negatively affect future generations were significantly more likely to engage in behaviors that mitigate the impact of climate change than those who were not concerned. Conclusions: These findings highlight the need to plan and design interventions on this topic. Full article

Soil erosion is a significant environmental issue that threatens the stability of land and agricultural productivity. In Eritrea, erosion remains understudied, limiting effective land management. This study assesses soil erosion and maps erosion risk in the Mai Nefhi watershed using the Revised Universal Soil Loss Equation (RUSLE), integrated with Geographic Information System (GIS) and remote sensing (RS) data. Key parameters were analyzed, including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and conservation practice (P). A severity classification identified five risk levels: low (0–7), moderate (7–22), high (22–45), very high (45–90), and severe (90–250) t ha⁻¹ yr⁻¹ with an area coverage of 61.93%, 22.05%, 5.62%, 6.43%, and 3.94%, respectively. Among all the parameters, the LS factor was identified as the dominant driver of soil loss, with erosion rates increasing sharply on slopes above 30%. There was a weak inverse relationship between soil organic matter and erosion (R² = 0.279), indicating that only 27.9% of the variability in soil erosion rates can be explained by SOM content alone. This result further suggests other dominant factors like slope and land use. The findings underscore the need for slope-sensitive conservation strategies, including terracing, agroforestry, and restrictions on hillside cultivation. Full article

Africa’s water security is inextricable from the recent patterns of climate variability. Effective formulation and implementation of climate policies require efficient integration of disaster mitigation and sustainable development to eliminate maladaptation and enhance water security. However, practical implementation is still lacking in many African countries. This study aims to examine practical measures and strategies for the integration of sustainable development and disaster mitigation into climate policies to enhance water security in Africa. A systematic bibliometric and thematic analysis was conducted using 95 peer-reviewed articles within the period 2010 to 2025 from the Scopus database. Software and frameworks such as VOSviewer and the Preferred Reporting Items for Systematic Reviews (PRISMA) were employed to analyze publication trends, co-authorship networks, keyword co-occurrence, and themes. The study revealed current research themes such as nature-based solutions for water security, climate-resilient infrastructure and technologies, and practical measures, such as eco-based adaptation and water energy food nexus, as some components of climate policy integration that can enhance water security in Africa. The study offers key policy recommendations for policymakers in the implementation of integrated climate policies adequate and effective enough to deal with water security issues in Africa. Full article

The ongoing climate crisis and growing water scarcity are exerting increasing pressure on agriculture in Southern and Western Europe, leading to reduced crop yields, greater risk of land abandonment, and deterioration of soil quality. Despite the extensive literature on irrigation and water use in agriculture, the specific relationship between irrigation availability and changes in the type of farming (ToF) remains insufficiently investigated. This study aims to address this gap by analyzing data from the 2010 and 2020 Italian General Agricultural Censuses conducted by ISTAT, in combination with microdata from the Italian Farm Accountancy Data Network (FADN), focusing on irrigated farms in Southern Italy. Descriptive analysis reveals that over 60% of irrigated farms specialize in permanent crops and predominantly adopt efficient irrigation systems such as drip irrigation. Between 2010 and 2020, approximately 23.8% of farms changed their ToF; however, these transitions were not generally associated with improvements in productivity or profitability. Results from logistic regression models suggest that structural variables—including Utilized Agricultural Area (UAA), economic size, and demographic characteristics such as the farmer’s gender—play a more significant role in determining changes in the ToF than the presence of irrigation itself. Moreover, the adoption of organic practices and larger farm sizes are positively associated with ToF changes. These findings suggest that while there has been some shift toward less water-demanding and potentially more sustainable crops, the impact of irrigation on such transitions remains limited. The results underscore the need for more targeted agricultural policies and improved data collection to support effective climate adaptation strategies in the sector. Full article

Ubiquitous and accelerating mass loss from the Greenland Ice Sheet (GrIS) has been widely reported in recent scientific studies, implying rapid changes in the Arctic cryosphere. However, while numerous studies provide accounts of glacial mass loss and consequent sea level change, a qualitative assessment of the implications is conspicuously absent. This scoping review addresses that gap by synthesizing the recent scientific literature related to cryospheric change in Greenland and its implications for key species and ecological processes; and highlights the necessity of understanding the bigger picture of how multiple ecological processes, abiotic-biotic assemblages, and cryosphere-human interactions with the environment are rapidly changing and pushing the Arctic into a possible no-analog scenario in recent geological times. It is also argued that this situation presents a novel challenge for planetary sustainability and warrants the identification of new research priorities that can generate a holistic understanding of the complexity of the Arctic cryosphere, interactions between biotic and abiotic components, and local lifeworlds—all of which are related to the well-being of the Earth itself. Full article

This study presented a comprehensive physical–statistical analysis of atmospheric particulate matter (PM₁₀ and PM_2.5) and trace gases (SO₂ and O₃) over Faial Island in the Azores archipelago during 2024. We collected real-time data at the Espalhafatos rural background station, covering 35,137 observations per pollutant, with 15 min intervals. Descriptive statistics, probability distribution fitting (Normal, Lognormal, Weibull, Gamma), and correlation analyses were employed to characterize pollutant dynamics and identify extreme pollution episodes. The results revealed that PM_2.5 (fine particles) concentrations are best modeled by a Lognormal distribution, while PM₁₀ concentrations fit a Gamma distribution, highlighting the presence of heavy-tailed, positively skewed behavior in both cases. Seasonal and episodic variability was significant, with multiple Saharan dust transport events contributing to PM exceedances, particularly during winter and spring months. These events, confirmed by CAMS and SKIRON dust dispersion models, affected not only southern Europe but also the Northeast Atlantic, including the Azores region. Weak to moderate correlations were observed between PM concentrations and meteorological variables, indicating complex interactions influenced by atmospheric stability and long-range transport processes. Linear regression analyses between SO₂ and O₃, and between SO₂ and PM_2.5, showed statistically significant but low-explanatory relationships, suggesting that other meteorological and chemical factors play a dominant role. This result highlights the importance of developing air quality policies that address both local emissions and long-range transport phenomena. They support the implementation of early warning systems and health risk assessments based on probabilistic modeling of particulate matter concentrations, even in remote Atlantic locations such as the Azores. Full article

In conventional hydrology, a short-duration design rainstorm is typically used to estimate the design discharge in urban sewer systems. The reason for using a short duration is that engineers believe the time of concentration in urban watersheds is relatively small. The short-duration hyetograph is supposed to generate a flow hydrograph that accurately reflects the rainfall-runoff processes. In this study, we developed a street-sewer runoff model for an urban district of 2470 hectares. Detailed field flooding records were utilized to verify the stormwater model’s capability for inundation simulations. Subsequently, different rainfall series extracted from the recorded rainstorm data were used to investigate the causes of flooding corresponding to different durations of rainstorms. The results indicate that a 90 min main concentrated rainstorm causes small-scale flooding only; however, a 24 h rainfall series results in an extensive range of inundations. We further conducted similar short- and long-duration hyetograph tests in 16 urban drainage partitions (ranging from 2.3 to 193.5 hectares) to confirm the above findings. The results indicate that the maximum discharge in most partitions can only be found when the hyetograph duration exceeds 1080 min, which essentially contradicts previous engineering designs in urban watersheds in Taiwan. Full article