Abstract: Costa Rica has experienced 37 tsunamis at both its Pacific and Caribbean coasts, with none to moderated impact. However, the coastal population has increased exponentially in the past decades, which might lead to higher impact from future tsunamis. In 2018 and 2019, IOC/UNESCO organized Experts Meetings of Tsunami Sources, Hazards, Risks and Uncertainties associated with the Tonga-Kermadec and Colombia-Ecuador subduction zones, where experts defined worst-case scenarios. Here we modeled the propagation of those tsunami scenarios to Costa Rica and their inundation for selected sites. We found that the Tonga-Kermadec scenarios provoked larger inundation than previous modeled sources from that region. However, the large travel time for those scenarios, about 14 hours, would allow for a timely evacuation. In the case of the Colombia-Ecuador scenarios, they provoked smaller inundation than previously modeled sources from that region, a good outcome as their arrival time is between 75 and 150 minutes. These new results required the update of tsunami evacuation maps and/or plans for many communities but provided more favorable conditions for tsunami preparedness. Yet, the short arrival times of the Colombia-Ecuador scenarios still require a prompt response from the population and authorities. For this, additional to updated tsunami evacuation maps and plans, it is recommended to have tsunami exercises in a regular basis.

Abstract: Acid mine drainage, a consequence of exposure of sulfide mining waste to weathering processes, results in significant water, sediment, and soil contamination. This contamination results in acidophilic ecosystems with elevated concentrations of sulfate and potentially toxic elements. The São Domingos mine, an abandoned site in the Iberian Pyrite Belt, lacks remediation measures and has numerous waste dumps, which are a major source of contamination to local water systems. This study examines sediment accumulation in five mine dams along the São Domingos stream that traverses the mine complex. Decades of sediment and waste transport since mine closure have resulted in dam-clogging processes. The geochemical, mineralogical, and magnetic properties of the sediments were analyzed to evaluate the mineralogical controls on potentially toxic elements mobilization. The sediments are dominated by iron oxides, oxyhydroxides, and hydroxysulfates, with jarosite playing a key role in binding high concentrations of iron and toxic elements. However, no considerable correlation was found between potentially toxic elements and magnetic parameters, highlighting the complex behavior of these contaminants in acid mine drainage-affected systems.

Abstract: In recent years, research on removal of radioactive elements from contaminated sources using microorganisms, has gained momentum. This study was focused on assessing the feasibility of removing cobalt, strontium, and cesium ions from an aqueous solution using microorganism or biomass. Arthrobacter nicotianae or persimmon tannin gel, known for its ability to remove cationic metal ions, was used for removing each metal. The metal-ion removal efficiency of A. nicotianae was affected by the solution pH, concentration of metals, and cell content. The amount of each metal ion removed increased with increasing pH (1–5) of the solution. The amount of each metal ion removed (mol metal ion/g dry wt. cells) increased with external metal ion content (mol metal ion), whereas the amount of each metal ion removed (%) decreased. By contrast, the each removed metal ion content (%) decreased with decreased cell content, whereas each metal removed (mol metal ion/g dry wt. cells) increased. The amounts of each metal removed (mol metal ion/g dry wt. cells) were fitted with a Langmuir isotherm. The removal of strontium using A. nicotianae was fast, and the removed amount almost reached equilibrium within 5 min. The amounts of cobalt and strontium removed were higher than that of cesium.

Abstract: Remote Sensing and GIS techniques employed in artificial groundwater recharge proved to be an effective and economic tool among the scientific community. Studies revealed that thematic layers with simple mathematical logics and weighted indexing techniques are used in the delineation of groundwater potential zone on several horizon conditions. In this study, the weighted modelling on hard rock terrain for groundwater recharge in Quetta valley is evaluated for its effectiveness due to its varying geological structure, considering parameters that include lithology, lineament density, slope, drainage density, and land cover. Imageries data of 30m resolution obtained from Landsat 8 and ASTER. Moreover, ground data of observation wells obtained from WAPDA, Pakistan. Our analysis identified the artificial replenishment sites for groundwater recharge which are correlated with ground data from water wells using Pearson’s correlation. Pearson’s value found 0.574 shows a positive correlation. Furthermore, categorizes the suitability of sites for groundwater recharge into four classes: most suitable, moderate, less, and not suitable. The output is evaluated against the structural density layer, hence, found the effectiveness of weighted modelling in groundwater recharge analysis in hard rock terrain for Quetta valley.

Abstract: This research investigates the adsorption potential of mango and pitanga tree pruning waste biochar produced at 300 °C and 500 °C for the uptake of Methylene Blue (MB) dye. The particle size of biochar and initial MB concentration were varied. Equilibrium data was modeled using Langmuir, Freundlich and Temkin equations. It was found that the pyrolysis process and the initial dye concentration directly influence the adsorption process. Increasing the temperature of the treatment resulted in an increase in the efficiency and adsorption capacity of the material. Finer particles (< 0.25 mm) were more efficient in adsorbing MB. Both materials presented similar modeled parameters for Langmuir Freundlich and Temkin isotherm equations. The adsorption at equilibrium of MB is best described by Langmuir and Freundlich models, and the modeled maximum adsorption capacity of both materials is 19.4 mg g^-1, proving its efficiency in the adsorption of MB and that the temperature of thermochemical process did not affect q_m.

Abstract: Accurate and timely earthquake magnitude estimation is critical for effective tsunami early warning, particularly in seismically active regions like Indonesia. Conventional seismic approaches often suffer from magnitude saturation for large events (Mw > 7.5), leading to underestimated magnitudes. To address this limitation, we propose a novel Peak Ground Displacement (PGD) Scaling Law based on regional high-rate GNSS (HR-GNSS) data from 21 moderate to large Indonesian earthquakes. Using 87 PGD measurements, we derive a regression model that relates PGD, hypocentral distance, and moment magnitude (Mw). The PGD-derived magnitudes (MPGD) show strong agreement with final moment magnitudes (Mw), achieving a mean absolute deviation (MAD) of 0.21—outperforming global models. Retrospective analyses demonstrate that the PGD estimates stabilize within 2–3 minutes for well-recorded events and remain robust even for great and tsunamigenic earthquakes. These findings confirm that HR-GNSS data can complement seismic networks, enabling rapid and reliable magnitude estimation in Indonesia.

Abstract: Mapping Soil Organic Carbon (SOC) at a regional scale is essential for assessing soil health and supporting sustainable land management. This study evaluates the potential of using Sentinel-2 imagery and regional calibration to predict SOC in salt-affected agricultural lands in Portugal, while also assessing the influence of soil properties, such as salinity, on SOC prediction. A per-pixel mosaicking approach was set to analyze the relation of spectral reflectance indices linked to bare soil conditions with SOC. SOC prediction models were developed using Linear Regression (LR) and Partial Least Squares Regression (PLSR). Among the tested approaches, the combination of maximum Bare Soil Index (maxBSI) with LR produced the most accurate SOC predictions, achieving moderate prediction performance (R² = 0.52, RMSE = 0.16%). This approach slightly outperformed the application of the 90th percentile of bare soil pixels (R90 reflectance) and the median approaches with PLSR. Notably, our findings indicate that soil salinity did not significantly affect SOC predictions, suggesting that within the observed salinity range of ECe between 1.2 and 10.4 dS m⁻¹ in topsoil, salinity had no statistical influence on SOC prediction. However, further case studies are needed to validate this observation across diverse agricultural conditions. In contrast, soil texture and moisture content emerged as the dominant factors influencing soil reflectance. This study demonstrates that Sentinel-2-derived indices, particularly maxBSI, combined with the proposed regional calibration approach, can be a cost-effective and scalable solution to monitor and regularly update SOC maps.

Abstract: The tree line is moving up the mountain slopes. Reasons include global warming and reduced grazing intensity. The effect of changes in plant cover on soil microbial abundance and diversity in subalpine meadows during the first stage of tree line shift is poorly understood. Soil samples from subalpine meadows on 3 reserved and 3 grazed slopes were mixed with chopped plant residues: 1) subalpine grass (GR) and 2) mixture of GR and birch leaves (BL) in a 1:1 ratio, mimicking early tree line shift. Soil samples with plant residues were placed in vessels, sealed and incubated for 1 year in dark with intermittent air exchange. The carbon, nitrogen (MBN) and phosphorus content of the soil microbial biomass and catabolic diversity index were assessed at the experiment’s beginning, after 6 months and after 1 year. The addition of BL with GR residues resulted in decrease in soil microbial biomass but increase in catabolic diversity. The increase in portion of recalcitrant fractions of plant residues (acid-insoluble and non-polar extractables) contributed to decrease in soil MBN content. It also decreased the proportion of soil microbial response to carbohydrates in total substrate-induced respiration, resulting in a more balanced and catabolically diverse microbial community.

Abstract: China’s accelerating industrialization has led to worsening air pollution, characterized by recurrent haze episodes. Accurate quantification of PM2.5 distribution is crucial for air quality assessment and public health management. Although traditional prediction models can effectively identify PM2.5 concentration fluctuations with moderate accuracy, their dependence rely heavily on extensive ground-based monitoring station data, limiting their applicability in areas with sparse monitoring coverage. To address this limitation, this study proposes a novel algorithm for high-precision PM2.5 concentration prediction, termed VDMS (Vision Transformer with DLSTM Multi-Head Self-Attention and Self-supervision). Based on the traditional Vision Transformer (ViT) architecture, VDMS incorporates a Double-Layered Long Short-Term Memory (DLSTM) network and a Multi-Head Self-Attention mechanism to enhance the model’s capacity to capture temporal sequence features and global dependencies. These enhancements contribute to greater stability and robustness in feature representation, ultimately improving prediction performance. Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM2.5 concentration prediction tasks, achieving a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 4.05 µg/m³, and a mean absolute error (MAE) of 3.23 µg/m³. Furthermore, experiments conducted in areas with sparse ground monitoring stations demonstrate that the model maintains high predictive accuracy, further validating its applicability and generalization capability in data-limited scenarios. Moreover, the VDMS model adopts a modular design, offering strong scalability that allows its architecture to be adjusted according to specific requirements. This adaptability renders it suitable for monitoring various atmospheric pollutants, providing essential technical support for precise environmental management and air quality forecasting.

Abstract: The processing of metallurgical slags is an urgent task, as they contain residual amounts of precious and non-ferrous metals such as gold, silver and copper. The efficiency of extraction of these metals directly depends on the granulometric composition of the processed material, which determines the need for its detailed analysis. The purpose of this study is to study the effect of the granulometric composition of slags on the efficiency of extraction of precious metals using various enrichment methods. For this purpose, studies were carried out, including granulometric analysis, chemical composition analysis, flotation tests using Na₂S and 3418A reagents, as well as magnetic separation. The analysis showed that the main part of the slag consists of particles less than 3.36 mm, while the content of copper is 0.60%, zinc – 2.37%, gold – 0.1 g/t, and silver – 7.2 g/t. Flotation experiments have confirmed that the use of Na₂S and 3418A increases the recoverability of copper and silver, and reducing the particle size to d80 &lt;10 microns increases the efficiency of copper extraction by 7%. Magnetic separation did not have a significant effect on the concentration of non-ferrous metals, but it allowed us to isolate an iron-containing fraction suitable for further processing. Thus, optimization of flotation processes and control of granulometric composition make it possible to increase the efficiency of metallurgical waste processing, reduce losses of valuable metals and reduce the environmental burden.

Abstract: The seasonal variations, spatial distribution, and health risk assessment of 13 volatile organic compounds (VOCs), particularly benzene, toluene, ethyl benzene, and xylene (BTEX), in the ambient air of Dilovası, a Turkish city with unplanned urbanization and its share of environmental issues, were presented in this study. At 22 locations in Dilovası, air samples were collected separately for the summer and winter using passive tube sampling, and concentrations were measured using thermal desorption GC-MS. Pollution maps were created using the Golden Software Surfer Program and QGIS Desktop 3.42.0 Software. A health risk evaluation was conducted using the US Environmental Protection Agency's (USEPA) approach. The study's findings demonstrated that the atmospheric VOC concentrations at the sampling locations varied significantly by season and location. According to the Carcinogenic Risk Assessment, residents in this area may be more susceptible to cancer if they are exposed to benzene, ethylbenzene, and naphthalene over an extended period. It was determined by the Non-Carcigonenic Risk (HQ) evaluation that, while there was no significant risk for health effects at 21 measurement points, there was a substantial risk for non-cancer health effects at 1 measurement point. The significance of regulatory policies and pollution control technologies has once again emerged in this context.

Abstract: Climate change threatens a wide range of ecosystem processes that underpin the Pacific's coral reefs. While conservation actions such as managing fishing pressure and water quality have demonstrated resilience benefits, motivated and ecologically literate practi-tioners are essential to implement and adapt future strategies effectively. This paper ex-plores how a gamified learning tool based on the board game 'Snakes and Ladders' can enhance understanding of stochastic events and resilience concepts in coral reef ecology. We align this tool with intended learning outcomes (ILOs) from a third-year Coral Reef Ecology course and describe how gameplay, discussion, and simulation were integrated into the curriculum.

Abstract: The current paper contributes to the utilization of social media data for extracting meaningful information for hydrological management. Specifically, the author analysed a mash-up of social media datasets, from Instagram, X, Flickr and YouTube, regarding the medicane Ianos (September 2020). The total number of features was 7915 texts, 2949 photos and 753 videos. Initially, the author classified the text strings into five categories: I. Ianos identification; II. Consequences; III. Disaster Management; IV. Weather Info.; and V. Emotions, Opinions and Other. Classification was performed by using Long Short Term Memory – Recurrent Neural Network (LSTM-RNN) and transformers. Sequentially photos from Instagram and Flickr were classified into two categories: I. Related and II. Not related. Three models, a VGG-19, a ResNet101 and an Efficient-Net were fine-tuned, and complementary used in an ensemble method, which increased the accuracy radically. The author further extracted 160,162 and 8,332 video frames YouTube, and Instagram videos respectively, while a relevant share video index (RSVI) was defined for assessing how much was the related content of the videos. The final part of the research employed location entity recognition (LER), geoparsing, geocoding and GIS for mapping. The output of the research, is presented in tables, figures and maps.

Abstract: Accurate estimation of forest canopy height and understory terrain in mountainous regions is crucial for carbon stock assessment under the Paris Agreement but remains challenging. This study aimed to evaluate ICESat-2's performance in these complex environments. To achieve this, ICESat-2 ATL03 Version 6 photon data were processed using a novel adaptive DBSCAN algorithm (BDT-ADBSCAN) in Pu'er City, China, a biodiversity hotspot, and results were validated against airborne LiDAR. ICESat-2 achieved high terrain retrieval accuracy (R2=1.00, RMSE=0.91 m), primarily affected by slope, while canopy height retrieval was less accurate (R2=0.53, RMSE=6.45 m) with systematic underestimation, mainly influenced by canopy height itself. Nighttime strong beam acquisitions substantially improved accuracies for both products. This research demonstrates ICESat-2's viability for high-resolution digital terrain modeling and provides quality control thresholds for forest structure estimation in challenging regions, addressing validation gaps in Asian biodiversity hotspots and supporting carbon monitoring for UN Sustainable Development Goals.

Abstract: The aim of this article is to analyse the context of climate change and the need for public policies from a global perspective in the aspects of degradation and impact, as well as the identification of more effective methodologies for the formulation of public policies. The method used in this article was a literature review, and information was obtained from 61 scientific articles indexed mostly in the Scopus and Dialnet databases. The results show large-scale deforestation in industrialized countries, plus 35% in the Alps, which has led to a reduction of more than 80% in the length of most Alpine glaciers. In conclusion, climate change is generating devastating effects worldwide such as rising temperatures, shrinking glaciers, water loss, deforestation and black carbon emissions, which are affecting local economies and natural resources, and water scarcity is looming in the future, which will generate a war for water resources between dominant states due to low agricultural production and food shortages.

Abstract: This study explored the potential of Digital Earth Africa (DE Africa) coastline products for coastal vulnerability index (CVI) assessment. Although, CVI can be calculated from several dataset, objective of this study is to assess the CVI using remotely sensed data including the DE Africa coastline products and the Advanced Space borne thermal Emission and Reflection Radiometer Global Digital Elevation Model version 2 (ASTER GDEM), geological map, and geographic information system (GIS) techniques. The study has been conducted on the Atlantic coast of Morocco to evaluate the variability of the coastline of Casablanca from 2000 to 2023. The research was conducted by analyzing DE Africa coastline products namely annual shorelines and rates of coastal change along the Casablanca coastline. These products were used to extract coastal erosion metrics namely annual shoreline distances, shoreline change envelope (SCE), net shoreline movement (NSM), linear regression rate (LRR) and end point rate (EPR). The process involved the assessment of shoreline dynamics based on coastal erosion metrics and their variations especially for six main beaches located on the coast of Casablanca. These metrics were used in conjunction with information related to coastal elevation, coastal geomorphology and coastal slope, extracted from geologic map and ASTER GDEM, to compute the CVI in the GIS environment. The analyses of these datasets indicated that the major areas of Casablanca coastline, especially on the sand beaches, situated at elevation less than 12 m are threatened by the coastal hazards and are under pressure of coastal erosion at -1.5 m/yr rate. The results indicated that around 44 % and 23 % of the studied area have respectively a very high to high vulnerability index and they are vulnerable to natural phenomena such as inundation, coastal flooding and submersion, while 8 % and 25 % have respectively moderate and low vulnerability. This study showed that DE Africa coastline products combined with local landscape information constitute an invaluable tool for the studies of coastal monitoring.

Abstract: Diesel particulate matter, primarily ultrafine particles (UFP), defined as particles smaller than 0.1 µm, are released by diesel-powered vehicles, especially those used in heavy-duty hauling, and are linked to serious health hazards. While much of the existing research on traffic-related air pollution focuses on urban environments, limited attention has been paid to how complex the topography influences the concentration of UFPs, particularly in areas with significant truck traffic. With a focus on Morgantown, West Virginia, an area distinguished by steep topography, this study investigates how travel over two different terrain conditions effect UFP concentrations close to roadways. Specifically, we sought to determine if truck count can be used as a surrogate allowing for varying topography for the concentration of UFPs. This study shows that “TRUCK COUNT” does result in a linear relationship and yields a possible surrogate measure of lung dose of UFP number concentration.

Abstract: Climate change in recent decades has made exceptional rainfall increasingly frequent, with the consequent risk of flooding of watercourses. Streams and rivers characterized by short flow times are subject to rapid and impressive floods; for this reason, the modeling of their beds is of fundamental importance for the execution of hydraulic calculations capable of predicting the flow rates and identifying the points where floods may occur. In the context of studies conducted on three watercourses in Calabria (Italy), different survey and restitution techniques were used (aerial Lidar, terrestrial laser scanner, GNSS, Photogrammetry). By integrating these methodologies, multi-resolution models were obtained, which constitute the basis of the hydraulic calculations performed. The representations of models and effects that hydraulic calculations have predicted in the event of exceptional rainfall (flow, speed, flooded areas and critical points in the banks) constitute useful tools for designing appropriate defense works and preparing evacuation plans in the event of an alarm, with the aim of mitigating hydrogeological risk.

Abstract: The increasing anthropogenic pressures on rural landscapes, particularly those arising from agricultural intensification, necessitate robust methods to assess environmental sustainability. This study proposes a multi-criteria approach for evaluating the environmental sustainability of rural landscape in northern Benin, using satellite-derived land cover data. The methodology was applied to 3 sites representative of rural landscapes in northern Benin. A 12-class land cover map, produced via the Moringa processing chain, was reclassified into Human Disturbance Coefficients (HDC) based on nine weighted environmental impact criteria. These were then spatially aggregated into 1 km² grid cells to produce the Landscape Environmental Sustainability Index (LESI). Results indicate that most areas exhibit moderate anthropogenic impact (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low impact (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include refining evaluation criteria and introducing variable weighting schemes by land cover or region.

Abstract: China's urban development is transitioning from an extensive expansion model to an intensive improvement approach. Promoting urban renewal through stock land resources has become a key focus in contemporary urban spatial governance. As cities enter the stock development era, accurately identifying the quantity and spatial distribution of inefficient stock land is crucial for improving land use efficiency. However, existing identification methods for inefficient existing space face challenges including limited data sources, over-reliance on expert experience and insufficient identification accuracy. These factors impede the realization of large-scale, high-precision, and universally applicable identification frameworks. This study proposes an identification method integrating multi-source big data, including land surveys, socioeconomic data, spatiotemporal trajectories, and air quality metrics. Using Shenzhen as a case study, we developed an index system with three dimensions (social, economic, and ecological) and eight indicators. The entropy weight method determined indicator weights, while mean-standardization and Moran’s I index characterized spatial distribution. The study identified a total of 65.37 km² of inefficient existing land in Shenzhen, accounting for approximately 7% of the city's construction land in 2019. Clusters mainly appear in the northwestern part of the city, including administrative boundary zones and urban fringe areas. Longgang District (21.11 km²) and Baoan District (12.57 km²) collectively contribute 51.5% of the inefficient land. Shenzhen's inefficient existing land exhibits an "edge aggregation and corridor extension" spatial pattern, and its formation is subject to the compound influence of historical development patterns, ecological control policies, and urban spatial structure. This study provides a scientific basis for urban renewal and inefficient land redevelopment through the integration of multidimensional big data and spatial statistical analysis. The proposed framework offers replicable technical support for sustainable urban governance and precision regeneration strategies in high-density Chinese cities.