Environmental and Earth Sciences

Abstract: This study examines the implications of El Niño on the Indian industrial economy in the context of climate change, with a focus on sectoral risks, economic disruptions, and emerging growth opportunities. The study adopts a qualitative and analytical approach using historical El Niño trends, secondary economic data, sectoral performance analysis, and climate-related industrial indicators to evaluate the impact on major industries in India. The findings indicate that El Niño negatively affects agriculture, commodity supply chains, and food inflation due to weak monsoon conditions and rising temperatures. However, industries related to cooling appliances, irrigation and water technologies, renewable energy backup systems, healthcare, and consumer durables show strong growth potential during El Niño years. Climate change is further accelerating the demand for climate-resilient infrastructure and adaptive industrial strategies. This study provides an integrated perspective linking climate phenomena with industrial economics in India. It highlights how El Niño acts not only as an environmental risk but also as a catalyst for industrial transformation, investment opportunities, and climate-resilient economic development.

Abstract: Bangladesh confronts a structural paradox: it is among the world’s most climate-vulnerable nations while simultaneously depending on resource-intensive industries — chiefly ready-made garments and agro-aquatic value chains — whose linear production logic accelerates the very environmental degradation that threatens its development gains. Direct transplantation of Scandinavian circular economy models is poorly matched to Bangladesh’s conditions of land scarcity, dense population, fragmented infrastructure, and constrained regulatory capacity. This article proposes an alternative conceptual framework, industrial symbiosis and deltaic resource optimization, which re-engineers circular economy principles around five resource streams intrinsic to Bangladesh’s geography and industrial structure: (1) structural valorization of textile residues (jhut) into high-performance composite building materials; (2) aquavoltaic systems integrating floating photovoltaics with pond-based aquaculture; (3) coastal seaweed bio-refineries producing biofuels and blue carbon credits; (4) integrated mangrove-shrimp cultivation generating premium organic seafood and carbon market revenue; and (5) decentralized urban anaerobic digestion combined with jute-based biopolymer manufacturing. Drawing on a narrative review of peer-reviewed studies, technical reports, and policy documents, the article synthesizes technical performance data, economic projections, and institutional barriers for each pathway. Evidence indicates that all five pathways are technically feasible with existing technologies and that pilots already demonstrate promising performance when embedded in supportive governance environments. The dominant barriers are institutional rather than technological: fragmented regulation, chronic under-enforcement of existing mandates, inadequate access to climate finance, and incentive structures that allow linear industrial models to externalize environmental costs. The article concludes with a phased implementation roadmap and targeted policy recommendations emphasizing coherent national strategy, enforcement capacity, and systematic engagement with global climate finance instruments.

Abstract: The Arteni volcanic complex (Armenia) represents a distinctive volcanic landscape characterized by well-preserved pyroclastic deposits, rhyolitic domes, extensive obsidian flows, and significant archaeological evidence. This study aims to evaluate the geoheritage value of the complex and to develop a scientifically grounded geotouristic trail model based on the targeted selection of representative viewpoints. Field-based investigations were integrated with semi-quantitative viewpoint assessment and GIS-supported spatial analysis, including morphometric, viewshed, and accessibility analyses. The results allowed the identification of key viewpoints (VP1–VP9), effectively representing the principal stages of volcanic evolution, including explosive eruptions, lava flow emplacement, and dome formation. Spatial analysis demonstrates that the selected view-points enable the development of a coherent, accessible, and scientifically meaningful geotouristic route while balancing scientific representativeness with visitor accessibility and safety. In addition, the widespread occurrence of obsidian and associated archaeological artifacts highlights the combined geological and cultural significance of the area. The proposed approach provides a transferable framework for the development of scientific geotourism in volcanic regions and contributes to geoheritage conservation, geoeducation, and sustainable regional development.

Abstract: Under global climate change, shifts in the suitable distribution of forest vegetation have become an important issue in ecology and biogeography. Birch forests are widely distributed across cold-temperate, temperate, and montane regions in China, but different birch forest types may vary in their environmental adaptations and spatial responses to climate change. In this study, three representative birch forest vegetation types in China, namely Betula utilis forest, Betula albosinensis forest, and Betula ermanii krummholz, were selected for comparative analysis. Based on vegetation distribution records and environmental variables, an optimized MaxEnt model was constructed using ENMeval to identify current suitable distribution patterns, key environmental drivers, and future habitat changes under climate change scenarios.The results showed that the three birch forest types differed markedly in current suitable distribution patterns. Betula utilis forest was mainly concentrated in the Qinling Mountains, Betula albosinensis forest showed a broader montane distribution pattern, and Betula ermanii krummholz was restricted to high-altitude or high-latitude cold habitats. Climatic factors were the dominant drivers of suitability, but the key environmental variables differed among the three vegetation types, indicating niche differentiation along temperature, precipitation, and elevation gradients. Under future climate scenarios, the suitable habitats of the three types showed type-specific changes in area, spatial stability, and centroid migration. Betula utilis forest and Betula albosinensis forest mainly exhibited regional spatial adjustment and partial expansion, whereas Betula ermanii krummholz showed stronger dependence on high-elevation cold habitats and more limited spatial adjustment capacity. These findings indicate that different birch forest vegetation types in China do not respond uniformly to climate change. The study provides a vegetation-type-specific basis for identifying stable suitable areas, potential expansion areas, and climate-sensitive habitats, and can support adaptive management and conservation planning for montane forest vegetation under future climate change.

Abstract: Floodplain wetlands are dynamic and biodiverse environments that provide important ecosystem services. This study analyzes the temporal and spatial dynamics of hydrogeomorphological attributes, vegetation, and water in floodplain wetlands. The methodology consisted of applying PCA in temporal (T) and spatial (S) modes, decomposing spectral indices (NDVI, NDMI, MNDWI) to identify variability patterns associated with ENSO events. The results revealed that C2 was the main descriptor of hydrological anomalies, with strong temporal synchrony between vegetation vigor (NDVI) and the expansion of the water surface (MNDWI), contrasting with the water stress response captured by NDMI. PCA highlighted environmental heterogeneity within the floodplain, with peatland areas standing out as zones of high spatial complexity and greater water retention capacity. Temporal variability responded primarily to climatic extremes, whereas spatial variability was modulated by hydrogeomorphology.

Abstract: Tropospheric ozone (O³) is a phytotoxic air pollutant that can impair visible foliar injury (O³ VFI) and reduces photosynthesis in sensitive forest species. Viburnum lantana L. has been widely used as an in situ bioindicator of O³ pollution in mountainous areas of Europe; however, field-observed O³-induced VFI as well as critical levels (CLs) established to protect forests, have not been validated. This study validated field-observed O³ effects in V. lantana through experiments carried out in a Free-air O³ eXposure infrastructure (FO₃X) and determined which O³ metric (exposure-based—AOT40 or flux-based—POD₁) best explains O³ effect on leaf physiology and VFI. V. lantana saplings were subjected to ambient air (AA) conditions and elevated O³ levels at 1.5× and 2.0× AA. Throughout the experimental period (T1: 2-month and T2: 3.5-month O³ exposure) measurements were taken for the Plant Injury Index (PII), light-saturated net photosynthetic rate (A_sat), stomatal conductance (g_s), leaf color index (SPAD), and the maximum photochemical efficiency of photosystem II (Fv/Fm). O³ VFI was first observed in 2.0× after 16 days. As a result, O³ treatment influenced PII, which was significantly higher in the 2.0× (9.06 ± 3.24) than in the 1.5× and AA treatments (1.31 ± 0.62 and 1.29 ± 0.71) at T2. The A_sat, SPAD, and Fv/Fm were significantly affected by O³ treatments; no significant difference in g_s was found. POD1 better explained variability in O³ VFI and physiological parameters, with CLs proposed for V. lantana of 1.61 mmol m^–2 and 1.22 mmol m^–2 for a 4% reduction of A^sat and g_s, and a CL of 7.82 mmol m^–2 for the onset of O³ VFI.

Abstract: The Aral Sea crisis has severely impacted water resources in the Republic of Karakalpakstan, making groundwater a critical alternative source for drinking and irrigation. This study presents a hydroecological assessment of brackish groundwater in the Karauzyak district based on field investigations conducted in 2025. Results showed that groundwater mineralization ranges from 2.1 to 4.8 g/L (predominantly 2.2–3.8 g/L), classifying the water as brackish to highly brackish. The dominant hydrochemical type is sodium-chloride and mixed sodium-sulfate-chloride. Most samples exhibited pH values of 7.1–8.3, moderate to high hardness (6.5–26.5 mg-eq/L), and elevated sulfate and chloride levels. Concentrations of toxic microelements (Pb, Cd, As, Hg, etc.) remained below maximum permissible limits. However, the overall salinity significantly restricts direct use for drinking water supply and limits agricultural application without additional management. Piper diagram analysis revealed distinct hydrochemical facies, reflecting the influence of natural salinization processes, irrigation seepage, and evaporative concentration under arid conditions. The findings highlight both the potential and limitations of local groundwater resources and underscore the need for desalination technologies, improved drainage, and continuous monitoring to ensure sustainable use in the Aral Sea region.

Abstract: Particle pollution has been recognized as a major part of environmental pollution. More specifically, the inhalation of very small (ultrafine) airborne particulate matter (PM) that is emitted from the burning of fossil fuels poses the most serious threat to human health. High-efficiency retention of these particles is one of the most challenging environmental problems, since conventional techniques like electrostatic precipitators, bag filters or cyclones have low collection efficiency in the respirable range (0.1 μm–1.0 μm). Acoustically induced agglomeration of ultrafine particles is a promising technique to increase the size of small particles before they enter a conventional filter. During this process, high-intensity acoustic fields are applied to the flue gas stream, inducing interaction effects among suspended particles that give rise to collisions and agglomeration. The preconditioned aerosol can then be filtered within conventional filters with higher collection efficiency. The present work reports the results of a numerical investigation of the effect of ultrasound preconditioning on the particle size distribution as a function of parameters related to the ultrasound system design, such as the acoustic frequency and intensity, and the initial mass loading. Particle agglomeration is modeled via the solution of the population balance equation (PBE) with the Multi-Monte Carlo (MMC) method. Results show that acoustic agglomeration can shift particle size distribution towards larger values of diameters and reduce the total number concentration of particles, thus leading to increased capture efficiency of conventional filters.

Abstract: AlphaEarth Foundations (AEF) unifies multi-modal and multi-temporal observations into analysis-ready low-dimensional representations, introducing a representation-driven paradigm that addresses key limitations of traditional task-centric approaches, including high engineering complexity, limited cross-regional transferability, and strong dependence on labeled data. This paper presents the first systematic review of AEF, synthesizing 23 research articles published up to March 2026 from three perspectives: conceptual positioning, technical framework, and application practices. It first clarifies the distinctions between AEF and related paradigms, including foundation models, remote sensing large models, and existing unified embedding approaches. It then summarizes the organization of its data system and key technical components. Based on a systematic literature survey, the paper further provides a structured synthesis of current studies in terms of thematic and regional distribution, scientific questions, usage patterns, and evaluation methods. The analysis indicates that AEF represents an important step toward a paradigm shift from task-driven to representation-driven Earth observation, offering clear advantages in reducing engineering barriers, enabling cross-regional transfer, and establishing a unified environmental semantic foundation. However, limitations remain in temporal resolution, semantic interpretability, and performance in specific task scenarios. Future work should focus on enhancing dynamic representation capabilities, cross-domain adaptation mechanisms, multi-source integration frameworks, systematic evaluation and annotation systems, and asset-oriented applications. By delineating the capability boundaries and applicable contexts of AEF from both methodological and empirical perspectives, this study provides a systematic reference for the standardized development and rational application of unified surface embeddings as an emerging geospatial information infrastructure.

Abstract: Monitoring dynamic post-catastrophic landscapes necessitates unsupervised classification approaches capable of incorporating newly emerging landscape-cover states without relying on predefined classes. Within this framework, the temporal matching of independently derived spectral clusters presents a critical methodological challenge. This study compared alternative temporal matching approaches for multi-temporal Sentinel-2 imagery of the post-catastrophic floodplain landscape of Khortytsia Island (Ukraine) from 2021 to 2026. In addition to conventional methods based on centroid distance, Mahalanobis distance, Linear Discriminant Analysis, and Random Forest, geometrically oriented approaches employing the elongation and principal-axis orientation of spectral point clouds were evaluated. A series of tests assessed matching accuracy, robustness to seasonal and interannual drift, graph connectivity, and consensus structure among alternative matching solutions. The results demonstrated that geometrically oriented approaches preserved temporal correspondence among landscape-cover states with high stability despite phenological and interannual variability. In particular, axis-based matching more effectively maintained separation between corresponding and competing clusters amid progressive temporal divergence. Consensus analysis revealed that disagreement among methods was concentrated in ecotonal and actively transforming zones, indicating areas of increased landscape instability. This study shows that the geometry of spectral trajectories contains valuable information for temporal matching and provides a promising foundation for monitoring dynamic post-catastrophic landscape systems.

Abstract: Accurate information on atmospheric temperature profiles is crucial for improving numerical weather forecasting and short-term numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which fails to meet the high spatial resolution required for NWP. In this study, we present a method to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) using data from the polar-orbiting satellite FY-3C. The aim is to provide high-resolution atmospheric structure for NWP in the TP. The temperature profile in VASS exhibits temporal and spatial heterogeneity due to the significant impact of clouds on the radiative transfer mode (RTM). To address this, we employ a combination of variation and artificial neural network (Var-ANN) methods to calibrate the satellite product and improve its compatibility with the model. To confirm the feasibility of our method, we compare the calibrated results with the observed data from 121 radiosonde soundings and 2400 meteorological stations in China, both of which represent conditions closest to the real atmospheric states. The calibrated temperature shows improvements over the original temperature, with a root mean square error, bias, and agreement with radiosonde soundings of 2.11, -0.72, and 0.998, respectively. We also select two classical cases involving the eastward movement of the plateau vortex (PV) and the formation of precipitation to verify the applicability of the calibration in NWP. The results demonstrate that the performance of NWP improves after assimilating the calibrated data, with the Var-ANN data assimilation scheme achieving the highest threat score of 66.9 and 66.7 for case 1 and case 2, respectively. These findings suggest that the Var-ANN method is suitable for calibrating satellite temperature profiles, and the calibrated data holds potential for precipitation forecasting. Furthermore, the novel method can also be applied in global temperature profile correction and satellite cross-calibration.

Abstract: The cross-border Lima River Basin, shared between Portugal and Spain, is prone to recurrent meteorological droughts, which are projected to intensify under climate change. This trend underscores the need for robust early-warning systems to support proactive water management. Under the EU-funded RISC_PLUS project—aimed at strengthening resilience to hydro-climatic risks in the cross-border Minho–Lima River Basins—this study develops a regionalised forecasting framework to evaluate meteorological drought forecast skill using precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Seasonal Forecasting System 5 (SEAS5) for the Portuguese section of the Lima River Basin. The 12-month Standardized Precipitation Index (SPI12) is employed as a long-term drought indicator, computed from hybrid 12-month accumulations that combine observed monthly precipitation (October 1979 to February 2025) and SEAS5 forecasts (October 2018 to February 2025). These data are integrated into four hybrid configurations (1 to 6 months lead time) to maximise forecast skill while preserving observed drought memory: 11 months of observations plus 1 month of forecast (11 obs + 1 fcst), 10 obs + 2 fcsts, 9 obs + 3 fcsts, and 6 obs + 6 fcsts. Forecast performance is assessed over the period October 2018 to February 2025. Deterministic SPI12 forecasts and categorical drought classifications are evaluated using a suite of regression-based metrics (e.g., Pearson correlation, root mean square error (RMSE), and skill scores) and contingency-table-based metrics (e.g., false alarm rate (FAR) and F1-score), across SEAS5 ensemble members, percentiles, and spread-based indicators. The 11 obs + 1 fcst configuration, particularly when using the Dry Spread (SpD; defined as the Q10 + Q25 percentiles) and the Q75 percentile, exhibits the highest skill, achieving a Pearson correlation coefficient of r=0.97, an RMSE of approximately 0.17, and near-perfect categorical performance (probability of detection (POD) = 1.00; FAR = 0.00). Conversely, longer lead-time configurations (9 obs + 3 fcsts and 6 obs + 6 fcsts) exhibit degraded performance, with the 6 + 6 configuration providing limited added value relative to climatology. These results demonstrate that SEAS5 precipitation forecasts can provide skilful drought predictions at lead times of up to six months in the Lima River Basin when integrated within the SPI12 framework. The proposed blending methodology therefore provides a robust technical basis for the operational early-warning system being developed under the RISC_PLUS project to support transboundary drought risk management in the Minho–Lima region.

Abstract: Recent studies have investigated whether the rate of global warming has changed since the 1970s, with particular attention to the role of natural variability and its removal from temperature time series. In particular, Foster and Rahmstorf (2026) analyzed global mean surface temperature series, adjusted for natural variability. However, their procedure might produce spurious changepoints, since it does not appropriately handle the autocorrelation present in the residuals of the models considered. In this study, we revisit the same adjusted temperature series using a different methodology (the Quandt likelihood ratio test) while properly accounting for the presence of autocorrelation. We find evidence that global temperature has departed from its previous path since around 2013-2014. Our results provide a robust proof of a clear recent increase in the temperature trend, at a rate of warming that has doubled since that date.

Abstract: Forest edge effects are commonly interpreted as radial gradients from the edge toward the interior, but this assumption may oversimplify the spatial organization of heterogeneous tropical forest fragments. Here, we integrated field-based phytosociological data with Sentinel-2 spectral indices to evaluate whether edge-effect interpretation depends on analytical scale in Semideciduous Seasonal Forest fragments embedded in the Brazilian Cerrado. Five fragments were analyzed using transect-based plots and continuous pixel-level modeling. Basal area showed a strong positive correlation with NDVI (r = 0.95), supporting its use as the main spectral proxy for vegetation structure. Plot-level segmented regression detected edge-to-interior transitions, with breakpoints ranging from approximately 13 to 39 m. However, pixel-level modeling revealed scale-dependent responses, including shallow gradients in IF and AC, an intermediate transition in CN, a deeper gradient in IP, and high internal heterogeneity without a single dominant radial transition in Panga. The first two PCA axes explained approximately 81% of the total variance, reinforcing the structural–spectral correspondence. These findings show that edge effects are detectable but not adequately represented by fixed radial zones alone. Pixel-level Sentinel-2 modeling improves the spatial interpretation of fragmented tropical forests.

Abstract: Accurate rainfall data are essential for hydrological forecasting and climate modeling. However, many developing regions, including Tunisia, struggle with significant data gaps in rainfall measurements, particularly from gauge stations. These missing data impair climate model validation and reduce forecasting accuracy across both spatial and temporal dimensions. To overcome these limitations, we conduct a comprehensive evaluation of novel deep learning (DL) architectures designed for imputing missing rainfall gauge data and generating monthly rainfall forecasts. Our framework systematically compares multiple DL approaches: Long Short-Term Memory (LSTM), a hybrid Bidirectional LSTM with a Transformer attention mechanism (BiLSTM-Transformer), and a pure Transformer model. Subsequently, we employ Principal Component Analysis (PCA), K-Means clustering, and quantile techniques to further refine DL model outputs. The processed data are then analyzed using Light Gradient Boosting Machine (LightGBM) to produce final results. Our rigorous evaluation across 47 Tunisian gauges covering 1983–2012 (70% training, 30% testing) demonstrates that the BiLSTM-Transformer hybrid delivers superior performance, achieving an 18.4% reduction in root mean squared errors (RMSE) compared to conventional interpolation methods (14.2 mm versus 17.4 mm monthly error) and improving R2 values by 0.15–0.23 across all test stations. The model shows particular strength in capturing Mediterranean rainfall patterns, correctly predicting 83% of extreme rainfall events (greater than 95th percentile). Furthermore, spatial graph networks boost performance at data-sparse stations by 12.7% through explicit modeling of topographic influences.

Abstract: The increasing accumulation of nano-/microplastics (NMPs) in agricultural soils has become an emerging environmental concern, posing risks to soil health, crop productivity, and food safety. Due to their persistence and small size, NMPs can disrupt soil structure, alter microbial communities, and facilitate the transport and uptake of contaminants by plants. In this context, biochar has attracted significant attention as a climate-smart soil amendment capable of improving soil quality while mitigating emerging pollutants. This review explores the potential role of biochar, including modified biochar, as a sustainable strategy for enhancing soil health and reducing the risks associated with NMPs contamination in agricultural systems. The unique physicochemical properties of biochar—such as its high surface area, porous structure, and abundant functional groups—enable interactions with plastic particles and associated contaminants through adsorption, aggregation, and immobilization processes. These interactions can reduce mobility, bioavailability, and plant uptake of NMPs in soil. In addition, biochar contributes to soil fertility improvement by enhancing nutrient retention, increasing water holding capacity, improving soil structure, and stimulating beneficial microbial activity. Biochar application also plays an important role in climate change mitigation by stabilizing carbon in soils and reducing greenhouse gas emissions from agricultural systems. Although biochar is considered a promising material for sustainability, some types of biochar may have adverse effects in saline–alkaline soils due to their high pH and salinity, particularly when produced at high pyrolysis temperatures. Overall, integrating biochar or modified biochar into sustainable agricultural practices offers multiple co-benefits, including soil restoration, pollutant mitigation, improved soil health, and enhanced climate resilience. This review synthesizes recent advances in understanding the mechanisms by which biochar influences NMPs behavior in soil–plant systems and highlights current knowledge gaps and future research directions needed to support its effective application in sustainable agriculture.

Abstract: Geochemical anomaly detection plays a critical role in mineral exploration, yet conven-tional methods are often limited by compositional effects, sensitivity to outliers, and in-sufficient consideration of spatial relationships. To address these issues, this study pro-poses an integrated analytical framework that combines compositional data analysis and spatial statistics for robust geochemical anomaly identification. The framework incor-porates isometric log-ratio (ILR) transformation to eliminate the closure effect, robust principal component analysis (RPCA) to extract stable geochemical patterns, local indi-cators of spatial association (LISA) to characterize spatial clustering, and compositional balance analysis (CoBA) to enhance anomaly signals. The method is applied to the Barkol Lake area in the Eastern Tianshan, a key metallogenic belt within the Central Asian Orogenic Belt. The results reveal significant geochemical anomalies characterized by Cu-associated element assemblages (e.g., Cu–Ni–Cr), which are spatially correlated with major fault zones and volcanic–intrusive complexes. The identified anomalies show strong consistency with known mineral occurrences and delineate several prospective targets for copper polymetallic mineralization. Compared with conventional approaches, the proposed framework demonstrates improved robustness to outliers, enhanced sensi-tivity to weak anomalies, and better integration of compositional and spatial constraints. These advantages highlight its effectiveness for geochemical anomaly detection and mineral prospectivity mapping in complex geological settings.

Abstract: Precise assessment of landslide potential in tectonically active mountain areas like Darjeeling Sikkim Himalaya (DSH) is a scientific challenge due to the complexity of different landslide conditioning factors that control the slope stability. Despite several studies for landslide susceptibility mapping, most of the conventional methods struggle to capture the nonlinear relationships and spatial heterogeneity that characterize landslides. Besides, the current use of pixel-based methods is insufficient to depict geomorphological units and slope-scale processes, thus limiting their effectiveness in boundary demarcation of landslide-prone areas. These limitations highlight the need for more robust machine learning frameworks that integrate geomorphology-based terrain segmentation with advanced machine learning models, which would not only facilitate modeling the multifaceted interactions among environmental components but also improve the understanding of the landslide driving forces. In this study, we have used slope unit based landslide susceptibility mapping with 4380 slope units integrated with 17 conditioning factors, and 8373 total updated inventories using six models Random Forest (RF), Generalized Additive Model (GAM), Categorical Boosting (CatBoost), Tabular Neural Network (TabNet), Bayesian Additive Regression Trees (BART), and Convolutional Neural Network (CNN). The model hyperparameters were optimized using Bayesian optimization, except for the BART model. Among the six models, RF (AUC = 0.848) and CatBoost (AUC = 0.846) were the best two performing models. Furthermore, SHAP analysis reveals that elevation, aspect, slope, distance to faults, NDVI, and proximity to roads and drainage networks are the main landslide controlling factors in DSH. The interaction analysis using SHAP indicates that the occurrence of landslides is controlled by nonlinear and threshold-dependent relations, especially among slope-rainfall, rainfall-soil moisture, and slope-distance to roads and faults, which represents a complex interaction between the hydrological triggering factor, geomorphic processes, tectonic activity, and human interventions.

Abstract: Humanity faces not isolated problems but a PolyCrisis, which is a set of 26 tightly interwoven existential crises spanning ecological collapse, planetary overheating, chronic disease epidemics, institutional fragility and social breakdown. Each crisis amplifies the others through cascading feedback loops, and sixteen possess the independent capacity to cause human extinction. We are not entering an emergency, but we are already in a state of emergency. Multiple planetary boundaries have been transgressed, and climate tipping points are being crossed now. Extinction rates match historical great mass extinction events, while our food systems, primarily responsible for almost half these crises, simultaneously drive hunger, obesity and chronic diseases. This PolyCrisis is not the result of isolated failures, but the predictable outcome of Planet A, the Operating System of our mainstream civilization, characterized by economics of unbounded extraction and hoarding, violence-based and profit-based food systems, short-term thinking, and unlimited growth imperatives on a finite planet. Planet B is our proposed PolySolution framework, a complete alternative Operating System grounded in empirical reality and proven solutions. It integrates animal-free food systems releasing up to 5 billion hectares for rewilding, regenerative economics measuring non-violence and biocapacity, circular economy minimizing waste, technological restraint with democratic governance, seven-generation thinking, and PolyCommunity coordination, collaboration and co-creation of the PolySolution. It calls for the immediate emergency implementation of two planetary-scale MegaSolutions: a) Hungerless, implementing universal, free access to gourmet whole-foods, plant-based Vegan meals worldwide, eliminating hunger and accelerating food and health systems transformation, and b) Cool, halting planetary overheating through agricultural emissions elimination, massive rewilding for carbon sequestration, and comprehensive stabilization of the life-support systems of our planet.

Abstract: The introduction of sustainable practices into waste management can have favorable environment impact, increase resource value, and economic gains. Hydrothermal technologies have strong potential for the production of up-cycled ingredients from biowaste (amino acids, sugars, phenols, pharmacologically-active compounds, etc.), enabling additionally high energy recovery (50-80%) from biowaste with net-negative carbon emission. This review discusses the use of subcritical and supercritical water technologies for sustainable valorization of biowaste and conversion of biomass into high-value chemicals and biofuels. The potential for the extraction/generation of bioactive compounds from plant and animal waste is presented, emphasizing the efficiency, compound stability, and bioactivity of fractions obtained. The possibilities of simultaneous extraction of added-value compounds and hydrolysis of feedstock biopolymers by said technologies are elaborated. The review further addresses the production of biofuels through hydrothermal carbonization for solid fuels, hydrothermal waste liquefaction for liquid fuels, and supercritical water gasification for gaseous fuels. The paper highlights the environmental and economic advantages of technologies based on sub- and supercritical water, over conventional chemical and fermentative routes, emphasizing their contribution to circular bioeconomy by converting biowaste into value-added products and sustainable energy sources.