Abstract: Extreme wind waves occurring during storms are a major natural hazard for the maritime economy on the one hand and a driver of various natural processes, e. g. the erosion of coastlines, on the other. Assessing the extreme values of wind wave heights and their long-term trends is crucial for coastal and offshore engineering and climate change studies. This study is dedicated to the values and long-term trends of extreme wave heights in the Black Sea. A 73-years long reanalysis is applied for the study. In order to additionally validate the reanalysis, data on wave heights form the Saral/AltiKa satellite altimeter is used. 19 locations along the coastline of the Black Sea are selected for the analysis of wind wave data. Maximal significant wave heights exceed 8.5 m along the southwestern coast of the Crimean Peninsula. Besides, 99.9th, 99th and 95th percentiles of significant wave heights are assessed. The long-term linear trends of these values are in general not statistically significant except of one location in case of maximal significant wave heights and two locations in case of 95th percentile of significant wave heights.

Abstract: Fish physiology is a key field of study in fisheries science, providing a solid foundation for understanding aquatic ecosystems and driving innovation in the aquaculture sector. As aquaculture continues to be a key source of food worldwide, study in this subject has increasingly focused on the implications of climate change, which poses considerable threats to fish and other aquatic species. This review outlines current studies on fish physiological responses to several stressors related with climate change, such as changes in temperature, salinity, disease occurrence, and oxygen levels. The combination of these abiotic and biotic variables is vital, as climate change accelerates pathogen fate and dispersion and alters nutrient dynamics, influencing fish growth and survival. This study also reviews mesocosm experiments and modelling research to illustrate the significance of these interactions in developing sustainable fisheries management and enhancing aquaculture methods. Furthermore, the study identifies current research trends and recommends areas for future research to address the ongoing problems posed by climate change to aquatic resources.

Abstract: Urban greenery and cooling initiatives have become top priorities for municipalities worldwide, as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology.

Abstract: The 210Pb-based dating method provides absolute ages determination in recent aquatic sediments at centennial scales. It is widely used to support a large variety of environmental studies. However, any empirical data set is compatible with an infinite number of chronologies that need to be constrained by a series of assumptions (models) on the particular sedimentary conditions of the studied environment, and validated with independent chronostratigraphic markers. During five decades, about thirty models have been developed to cope with the wide diversity of natural conditions, a good number of them appearing in recent years, along with new concepts such as model errors, attractors for χ-mapping, or kinetic reactive transport, which have changed common views and practices. This paper aims to present a comprehensive review of this dating method to provide to final users updated tools and a renewed understanding to improve the reliability of their applications. Models are classified in terms of their assumptions on the sedimentary systems, which are better understood from a revisited theory of early compaction and the description of the microcosms of saturated porous media, where composite fluxes of tracers undergo different deposition pathways in terms of physical and kinetic reactive transport. The article reviews empirical evidence on the natural variability in mass flows and initial activity concentrations. Some models allow analytical solutions, while others require numerical techniques. The review is illustrated with examples from real case studies.

Abstract: Background: Modern agriculture relies heavily on chemicals to ensure high yields and food security, but their overuse has led to health issues and pest resistance. Researchers are now exploring natural, eco-friendly alternatives for pest control. Methods: This study evaluated two ethanol-based formulations (12.5% and 25% v/v) derived from the tangerine peel (Citrus reticulata L. var 'Clementina') against conventional chemical treatments and untreated control in potato (Solanum tubevar. L. var. ‘Capiro’) cultivation. A randomised block design with three blocks per treatment, each containing 45 plants, was used during the wet season (February–April 2023). Results: Visual inspections and yellow traps followed weekly application from day 30 to 105 post-planting to monitor pest (e.g., Frankliniella occidentalis, Aphididae) and beneficial insect (e.g., Coccinellidae, Aphis mellifera) populations. The 25% formulation performed similarly to chemical treatments against pests but was harmless to beneficial insects. Post-harvest analysis showed that the formulations achieved 73% of conventional yields, with comparable tuber damage and Premnotrypes vorax larvae levels. Conclusions: Toxicological tests confirmed the formulations' eco-friendliness, making them suitable for small-scale Andean "chakras" for organic farming and honey production without chemicals.

Abstract: The valorization of food waste, particularly in a manner that is decentralized has significant potential to enhance to reuse of organic wastes. A novel technology which converts raw organic waste, particularly vegetable and food waste a de-graded material in approximately 24 hours as a bio fertiliser. The material which is pathogen free and stable and has a texture similar to humic soil conditioner and an aromatic characteristic similar to that of coffee grind . This material is created through the use of a bio-digester. During the process, the food digester or bio-digester does not require any additional fresh water and has little to no grey water discharge to the sewers, environmental conditions dependent. A cultivated and unique consortium of hydrolytic and mesophilic enzymes are added to the pre-conditioned chamber that aggressively breaks down the raw material. This chamber is controlled and monitored to maintain optimum mesophilic conditions throughout the 24-hour period. During the processing of food waste, emissions were recorded as being minimal. The addition of the enzyme had a significant effect on the organic matter characteristics and on the nutrient profile on the material. The nutrient profile, pH, electrical conductivity, heavy metal content, and other parameters of the material produced from the process over a number of years was determined. These data indicate very low levels of heavy metals and reasonable levels of nitrogen (N), phosphorus (P) and potassium (K). Controlled growth studies were conducted to ascertain the comparative growth performance of a model crop, perennial rye grass when grown in soil treated with the material, biowaste compost (BWC) and composted green waste (CGW), all applied at three rates. Overall, the biomass from pots treated with degraded food waste material were higher than pots treated with a biowaste and green waste compost at com-parative application rates on a volume basis. Analysis of the grass for agronom-ically important macronutrients, such as N, P and K, indicated higher uptake rates of these nutrients in the experimental material treated pots over four harvests. Despite higher uptake the residual total nitrogen (N) and available phosphorus (P) and potassium (K) were higher in the soil after the 4th harvest. The product produced from the food waste processing has a high potential as a viable organ-ic/biofertilizer fertilizer and fits in with the EU strong policy of the Circular Economy and new EU Fertiliser Regulations which promotes organic fertilizer as against mineral fertilizer. The most substantial benefits of such a technology are both the very short turn around and the reduced emissions of GHG gasses re-leased during the processing.

Abstract: Mining has caused severe pollution, particularly in areas with poor regulation. The former Ávalos Foundry in Chihuahua City left toxic contamination after its closure in 1997, affecting nearby settlements. This study examines the socio-environmental impact on residents adjacent to the former foundry. The study area considered 5,773 dwellings, with 4,634 inhabited by 14,187 persons. A survey of 465 residents assessed sociodemographic aspects, environmental perceptions, and community participation. Tap water samples from 70 homes were analyzed for toxic metals, compared to Mexican, American and European regulatory. Using ArcGIS and interpolation, the study modeled water pollutant dispersion. Findings show residents face economic, social, environmental, and health issues due to ongoing contamination. Several suffer from respiratory and skin diseases linked to mining waste. While the majority consider their live comfortable or very comfortable, many would not have moved there if aware of the risks before. Despite concerns, most residents are reluctant to engage in community efforts to address pollution. Water tests revealed high levels of, arsenic, copper, chromium, iron, manganese, and nickel posing serious health risks. The study calls for immediate action, including awareness campaigns, health monitoring, environmental remediation, and collaboration among public, social, and educational sectors to secure funding and long-term solutions.

Abstract: Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to assess the robustness of multispectral satellite imagery and topographic attributes derived from airborne LiDAR data to predict the density of three vegetation layers in a wet eucalypt forest at the Warra Supersite in Tasmania, Australia. We deployed multiple variables derived from airborne LiDAR data, medium-resolution Landsat-8 Operational Land Imager (OLI) surface reflectance, and high-resolution WorldView-3 satellite imagery. These datasets were combined with topographic attributes extracted from resampled LiDAR data and validated with vegetation density layers extracted from high-density LiDAR. Using spectral bands, indices, texture features, a geology layer, and topographic attributes as predictor variables, we evaluated the predictive power of 13 data schemes at three different pixel sizes (1.6 m, 7.5 m, and 30 m). The schemes of the 30 m Landsat-8 (OLI) dataset provided better model accuracy than the WorldView-3 dataset across all three pixel sizes (R2 values from 0.15 to 0.65) and all three vegetation layers. For predicting the density of the overstorey vegetation, spectral indices (R2 = 0.48) and texture features (R2 = 0.47) were useful, and when both were combined, they produced higher model accuracy (R2 = 0.56) than either dataset alone. Model prediction improved further when all five data sources were included (R2 = 0.65). The best models for mid-storey (R2 = 0.46) and understorey (R2 = 0.44) vegetation had lower predictive capacity than for the overstorey. The models validated using an independent dataset confirmed the robustness. The model accuracies increased with an increase in the number of predictor variables. The spectral indices and texture features derived from the Landsat data products integrated with the low-density LiDAR data can provide valuable information on the forest structure of larger geographical areas for sustainable management and monitoring of forest landscape.

Abstract: In recent years, a market for chemical additives to improve the quality of plastic recyclates has been established. High growth opportunities are expected for these additives. The products are very diverse and can be used, for example, for the post-stabilization of recyclates or to reduce unpleasant odors. There are also products on the market that can repair damaged polymers. And additives are available that improve the miscibility of inhomogeneous sorting fractions. On behalf of a plastics recycler, the authors have attempted to find out the chemical identity and frame formulation of these plastic additives. However, this information is mostly not disclosed. Even in the available safety data sheets, the composition of these products was regularly not included. Only in individual cases, individual substances which are subject to declaration (REACH/CLP) have been specified. However, it is known from discussions and the literature that highly reactive substances are sometimes used for these products, which are to be added directly to the hot melt in the extruder. Now that the SME recycling industry is already confronted with plastic waste containing banned additives from the past (risk cycle, legacy chemicals), a new problem is emerging for the future: are these additives sufficiently safe? The authors advise caution here and therefore at least against the use of recycling additives whose chemical identity and frame formulation are not disclosed. This can lead to the 'recycling privilege' under REACH no longer applying to plastic recyclates, with serious consequences for recyclers. Scientific studies should also be carried out to determine whether these recycled additives pose any risks to consumers.

Abstract: Reforestation is widely promoted as a nature-based solution for climate change, yet little attention is given to its potential unintended consequences: deforestation leakage, where agricultural activities are displaced rather than reduced. This study provides a empirical quantification of reforestation-induced leakage in Brazil, using panel data and spatial econometric models to estimate both the magnitude and geographic extent of displaced deforestation. We estimate 12% of deforestation leakage due to reforestation displacing agricultural areas, with effects propagating up to 150 km. We also test whether livestock intensification mitigates this spillover effect - without significant effects on offset leakage. Our results suggest that carbon credit accounting methodologies could improve their statistical evidence to avoid inflate or miscalculate net carbon benefits of reforestation projects. These findings have direct implications for carbon offset standards (e.g., Verra, ART-TREES) and the design of reforestation projects worldwide.

Abstract: We propose and validate three linear regression models, using maximum air temperature and retrievals of LST of the MYD21A1D data product (LSTMYD21A1D) as response and predictor variables, for the three major biomes in the São Francisco River Basin as defined by the Instituto Brasileiro de Geografia e Estatística (IBGE) in the Map of Biomes of Brazil (MBB), which is often used to support public policies. A dataset of 94,544 observations of ground-based measurements and retrievals of LST is used to build the models. The efficacy and performance of the models are tested by means of cross-validation and of benchmarking with the linear regression model for the entire basin, regardless the type of biomes. The three proposed linear regression models indicated that LSTMYD21A1D explains well the variations in air Tmax for all the types of biomes in the SFRB (R2: from 0.46 to 0.54). The results of the cross-validation revealed that the three models were equally good at providing estimates of air Tmax across the basin (RMSE < 2.66oC and MAE < 2.14oC), even though they were built with subsets of data sampled in a specific biome. The benchmark test indicated that the models, when applied to their specific biome, provide better estimates of air temperature than the regression model for the entire basin.

Abstract: This study utilizes ENVI-met simulations to examine the effectiveness of nature-based urban design strategies in enhancing outdoor thermal comfort for senior citizens during Mediterranean heat waves. The research focuses on a high-density, post-refugee neighborhood in Greater Athens, assessing both baseline and optimized urban scenarios featuring mature trees and water elements. Simulations conducted on July 23, 2023—the hottest recorded day of the year—demonstrate substantial reductions in Physiologically Equivalent Temperature (PET), with improvements ranging from 11.17 K to 22.45 K in the morning, 2.17 K to 14.55 K in the afternoon, and 0.56 K to 4.78 K in the evening. Furthermore, dynamic comfort analysis reveals a reduction in energy balance of up to 191.92 W during peak heat hours, alleviating thermal strain on elderly individuals. These findings highlight the pivotal role of shading and evaporative cooling strategies in mitigating urban heat stress, particularly for vulnerable populations. The study underscores the urgency of integrating climate-responsive urban interventions into policy frameworks to enhance resilience against extreme heat events.

Abstract: Mesoscale eddies play a critical role in ocean circulation and biogeochemical processes, yet predicting their dynamic characteristics remains challenging due to nonlinear interactions and background errors in traditional methods. This study proposes a physics based Long Short-Term Memory (LSTM) network to predict key eddy features, amplitude, radius, and maximum circularly averaged speed (MCAs), by integrating multi-source observational data and hydrodynamic principles. Utilizing 28 years (1993-2020) of daily eddy trajectories from the global META3.1exp atlas and high-resolution reanalysis data (JCOPE2M) in the Northwest Pacific (15°-35°N, 115°-135°E), we systematically evaluate the effects of temporal sequence length and physical variables on prediction performance. The model demonstrates superior accuracy compared to conventional LSTM approaches, with mean absolute errors (MAE) for 1-7 day predictions increasing from 0.72 cm to 1.37 cm (amplitude), 8.85 km to 18.02 km (radius), and 0.80 cm/s to 2.46 cm/s (MCA). Key innovations include: 1) Dynamic reconstruction of spatiotemporal label-feature relationships to mitigate error accumulation, 2) Incorporation of sea surface temperature (SST) and height (SSH), which improve prediction accuracy by 5.33-5.92% and 3.65-5.47%, respectively, outperforming eddy kinetic energy inputs. Seasonal analysis reveals lower model accuracy in summer versus winter, particularly for amplitude (MAE: 1.29 cm vs 1.03 cm) and radius (15.3 km vs 13.2 km). Interannual error patterns correlate with El Niño events, highlighting climate-ocean coupling effects. This work advances eddy prediction through physics-guided machine learning, providing a framework for operational ocean forecasting. Future extensions could incorporate three-dimensional eddy structures and additional environmental drivers to enhance predictive capability.

Abstract: Irish potato (Solanum tuberosum) is a critical crop for food security and economic growth in Tsangano and Angónia districts, central Mozambique. Challenges like inconsistent yields and variable quality are often linked to suboptimal soil conditions, and limit production. This study aimed to classify and evaluate the suitability of soils for potato cultivation, filling gaps in detailed soil assessments for sustainable agriculture. Using combined pedological approach and laboratory soil analysis, four pedons TSA-P01 and TSA-P02 in Tsangano, and ANGO-P01 and ANGO-P02 in Angónia were analyzed for bulk density, texture, pH, organic carbon, and nutrient content. The pedons were very deep (> 150 cm) and had textures ranging from sandy clay loam to sandy loam. TSA-P02 had the lowest bulk density (0.78 Mg m-3) and the highest available water capacity (182.0 mm m-1). Soil pH ranged from 5.6 to 7.9, indicating neutral to slightly acidic conditions. Nutrient analysis revealed low total nitrogen (0.12 - 0.22%), varying soil organic carbon (0.16 - 2.73%), and cation exchange capacity (10.1 - 11.33 cmol(+) kg-1). Pedons TSA-P01, ANGO-P1, and ANGO-P02 were characterized by eluviation and illuviation as dominant pedogenic processes while in pedon TSA-P02 shrinking and swelling were the pedo-genic processes. Weathering indices identified ANGO-P01 as the most highly weathered, while TSA-P02 was the least weathered and had better fertility indicators. According to USDA Taxonomy, the soils were classified as Ultisols, Vertisols, and Alfisols, corresponding to Acrisols, Alisols, Vertisols, and Luvisols in the WRB for Soil Resources. All studied soils were marginally suitable for potato production (S3f) due to dominant fertility constraints but with varying minor limitations in climate, topography, and soil physical properties. The findings hence recommended targeted soil fertility management to enhance productivity and sustainability in potato cultivation.

Abstract: The equation of time (EoT) tracks daily deviations in length between the solar day and the mean day. Since the length of the mean day remains constant throughout the year, the EoT must mirror daily fluctuations in the length of the solar day. Furthermore, if the Sun meridian declination (SMD) is dynamically linked to Earth’s rotational speed (ERS) the EoT must obey to oscillations in ERS. This document examines the position, velocity, acceleration, and net drive of the mean-time Sun within a solar sundial noon analemma considering both its vertical and horizontal dimensions: the SMD and the EoT. Evidence supports that ERS decreases monotonically along two trans-equinoctial analemmatic phases in which the net drives of the EoT and SMD become coordi-nated (either both accelerating or both decelerating) within the SMD interval of −16 to +19 arcdeg, centered at +3. Conversely, ERS increases monotonically along two trans-solstitial analemmatic phases in which the net drives of the EoT and SMD become opposed, outside the specified interval of SMD. The ERS reaches its minima and maxima at the troughs and crests of the EoT.

Abstract: Positional accuracy in cadastral data is fundamental for secure land tenure and efficient land administration. However, many land administration systems, experience difficulties to meet accuracy standards, particularly in areas with digitized historical maps, leading to disruptions in land transactions. This study investigates the use of unsupervised clustering algorithms in order to identify and characterize systematic spatial error patterns in cadastral maps. We compare Fuzzy c-means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMM) in clustering error vectors derived from 500 homologous points. These points were obtained by comparing cadastral data with a higher-accuracy land survey within a 7 km² area in Ioannina, Greece, known for its inaccuracies in the Greek National Cadastre. The optimal number of clusters for each algorithm was determined. Results show that DBSCAN and GMM successfully captured a central area of random errors surrounded by a region exhibiting a systematic, counter-clockwise rotational error, whereas FCM did not capture this pattern. DBSCAN, with its ability to isolate noise points in the center of the study area, provided the most interpretable results. This clustering approach can be integrated into automated cadastral map improvement methods, contributing to progressive cadastral renewal efforts.

Abstract: In this study, we analyze the suitability of using CHIRPS, CMORPH and TRMM platforms (PPS) in monitoring precipitation extreme events, precipitation-runoff relationships, and seasonal/year-to-year variability on the Saltito semiarid subbasin in the Mexican state of Durango. Satellite precipitation products (PPS) in 16 sites were contrasted point to point with data from rainfall gauge stations and with daily temporal resolution for the period of four years (2015-2019). Using this information, we construct Rx1d, Rx2d, R25 mm, and RR95 extreme rainfall indices. For precipitation-runoff relationships, a runoff model based on the Storm Water Management Model (SWMM) was calibrated and validated with gauge data, and we get Qx1d, Qx2d, and Qx3d runoff indices. We use Bias Volume (%), MSE, Correlation coefficient, and Median vias to evaluate the ability of satellite products to detect and analyze extreme precipitation and run flow events. Although these sensors tend to overestimate both precipitation levels and the occurrence of extreme precipitation events, their high spatial and temporal resolution makes them a reliable tool for analyzing trends in climate change indices. As a result, they serve as a useful resource for evaluating the intensity of climate change in the region, particularly in terms of precipitation patterns. They also allow hydrological modeling and observe precipitation-runoff relationships acceptably. It is relevant in the absence of precipitation and hydrometric information, which is usually common in vast regions of the developing world.

Abstract: Pharmaceuticals and microplastics are persistent emerging pollutants that pose serious threats to aquatic ecosystems and ecological health. This review provides a thorough and comprehensive examination of their predominant pathways, sources, and distribution, highlighting wastewater disposal, agricultural runoff, and atmospheric deposition. The toxicological effects of these pollutants on aquatic organisms, particularly fish, are discussed, with emphasis on bioaccumulation and biomagnification in the food chain, physiological effects including effects on growth, reproduction, immune system performance and behavioral changes. The ecological consequences, including disruptions to trophic dynamics and ecosystem stability, are also addressed. Although valuable efforts, mitigation and remediation strategies remain inadequate and further research is needed because they do not capture the scale and complexity of these hazards. This review highlights the urgent need to advance treatment technologies, establish comprehensive regulatory frameworks and organize intensive research on long-term ecological impacts to address the environmental threats posed by pharmaceuticals and microplastics.

Abstract: Wuhan is dotted with lakes, known as “ City with Hundreds of Lakes”, the development of the city is inseparable from the river and lake waters, the evolution of the lakes has been affecting the construction and layout of the city. Since the 20th century, the lake evolution in the main urban area of Wuhan is the most intense and the urban development is also the most rapid. Therefore, based on the precious high-precision historical maps of Wuhan in the early and mid-20th century, combined with the information of lakes in Wuhan obtained from satellite remote sensing images, the evolution characteristics of lakes in Wuhan in the past 100 years (1920~2019) were investigated through the theory of landscape fractal, and the response mechanism of lake evolution to urban expansion was further explored by combining with the trajectory of urban expansion. The results show that the area of lakes in Wuhan declined from 2,133.5 km² in 1920 to 550.8 km² in 2019, with a total decrease of 1,582.7 km², an area shrinkage rate of 74.18 % , and a strong amplitude of area change. The changes of fractal dimension and shoreline development coefficient of lakes in Wuhan city show synchronization as a whole, with occasional fluctuations, but on the whole the fractal dimension and shoreline development coefficient of lakes are getting smaller in a century. Specifically, the evolution of lakes in the Hankou area is mainly affected by the construction of dykes and lake filling, and most of the lakes are resolved and fragmented under the influence of urban expansion, whereas the evolution of lakes in Wuchang and Hanyang is mainly caused by the urban construction around the lakes, and many lake branches are cut for various urban constructions, and the shape of the lake tends to be simple and regular under the influence of urban expansion. This study is of great significance to fill in the history of lake evolution in Wuhan before the popularization of remote sensing, and to guide the rational development of lakes in Wuhan and the sustainable and healthy development of Wuhan.

Abstract: In this paper an integrated approach by using microwave and optical satellite data, respectively acquired by Sentinel-1 (S-1) and Sentinel-2 (S-2) was presented for monitoring vegetation status. Experimental data and electromagnetic models have been combined to relate backscattering from S-1 and optical indices from S-2 t to plant conditions, which were evaluated by measuring the vegetation photochemical reflectance index (PRI), the vegetation and the soil water content. Field data were collected in two sorghum fields close to Florence in Tuscany (Central Italy) dur-ing summers 2022 and 2023. The results show significant correlations between microwave and optical data with respect to field measurements, highlighting the potential of remote sensing techniques for agricultural monitoring and management, also in response to climate change.