Environmental and Earth Sciences

Abstract: Artificial intelligence (AI) is a human-built component of the technosphere, not an intelligence outside Earth-system limits. As AI systems scale, they increasingly shape the decisions, infrastructures, and capital flows through which human activity damages the biosphere. Dominant deployed foundation-model alignment methods, including reinforcement learning from human feedback (RLHF) and constitutional AI, treat human preferences as the primary alignment target while leaving biosphere integrity as context, externality, or secondary constraint. That framing is structurally incomplete. Human welfare, technological continuity, and AI operation all depend on biosphere function. Three convergent literatures support a corrective framework: planetary-boundary analysis showing seven of nine boundaries transgressed; energy-system analysis showing rapid and infrastructure-constrained data-center growth during the 2025-2030 buildout; and collective-action analysis showing that voluntary ecological restraint is unstable under competitive pressure. These literatures imply a design conclusion: ecological constraints must be formalized as hard inference-time refusal rules and reinforced through reward design. This paper presents Biosphere Sentinel as a reference architecture for reducing human and technospheric impacts on the biosphere through refusal rules, an eight-domain reward landscape, carbon-lock-in diagnostics, and a proposed Trophic Integrity Index pathway.

Abstract: Organic farming in the European Union is strongly shaped by Common Agricultural Policy (CAP) support, yet participation durability remains less examined than supported organic area or organ-ic–conventional comparisons. This study assesses whether the length of participation in CAP-supported organic farming is associated with the organizational, production, and economic out-comes of organic farms in Poland. It applies a two-level approach: CAP support trajectories based on ARMA data for 2008–2025 and organic production duration based on Polish FSDN data for 2008–2022. The results show that organic farming in Poland is highly CAP-dependent and follows an unstable trajectory, with expansion up to 2012–2013, subsequent decline, and renewed growth after 2019. Longer participation is associated with differences in land resources, supported organic UAA, ANCs conditions, production organization, and livestock presence, indicating both adapta-tion and structural selectivity. FSDN data show that fully organic farms have lower land and labor productivity than conventional farms, but persistent fully organic farms achieve similar income per hectare when subsidies are included; without subsidies, their income remains much weaker. The findings indicate that the evaluation of organic farming support should move beyond benefi-ciary counts and certified organic area to include participation durability, production-system co-herence, economic viability, and territorial embeddedness. More differentiated instruments are needed to strengthen durable, knowledge-intensive, and territorially embedded organic farming systems.

Abstract: This study investigates the provenance, weathering history, and tectono-sedimentary evolution of Lower Ediacaran siliciclastic rocks of the Imiter Formation (Saghro Group, Imiter Sub-inlier, Anti-Atlas, Morocco) deposited along the northern margin of Gondwana. An integrated approach combining petrography, whole-rock major and trace element geochemistry, rare earth elements (REE), Sm–Nd isotopes, and organic geochemistry (TOC and δ¹³Cₒᵣg) was used to constrain sediment sources and deposi-tional conditions. Geochemical proxies, including Th/Sc, La/Sc, and Zr/Sc ratios, to-gether with REE distribution patterns, indicate that the sediments were mainly derived from felsic to intermediate rocks of the upper continental crust, with only minor sedi-ment recycling. The negative εNd(t) values (−8.5 to −6.2) and Paleoproterozoic Nd model ages (1.6–2.1 Ga) further suggest erosion of evolved crustal sources related to the West African Craton. Weathering indices (CIA, CIW, PIA) suggest weak to moder-ate chemical weathering under predominantly arid conditions. Redox-sensitive proxies (V–Ni, V/Cr, V/(V+Ni)) and low TOC contents (0.1–0.3 wt.%) indicate deposition under mainly oxic to dysoxic conditions with only transient reducing episodes. Tectonic dis-crimination diagrams, supported by regional magmatism, point to sedimentation within an extensional basin evolving from active margin to continental rift conditions during the late Pan-African orogeny. The Imiter Formation records a system dominat-ed by crustal recycling, syn-rift tectonics, and dynamic redox conditions in a shallow marine environment.

Abstract: The long-term relationship between climate change, vegetation change and soil development, is a highly complex process. Findings of multiproxy (sedimentological, MS, geochemical (AAS, XRD), micromorphological, anthracological, phytolith and malacological) studies from a loess/paleosol sequence in northeastern Hungary highlighted the transformation of a reddish-brown fossil soil layer (cambisol) to a podzolic soil with signs of iterative wildfires during the terminal part of MIS3. According to our findings, a Scots pine (Pinus sylvestris) dominated open parkland emerged on the northern slopes during the second phase of MIS3 hosted by a special reddish-brown soil. Then the last phase of MIS3 was marked by the development of spruce (Picea) dominated open parkland. Results further suggest that vegetation change passed a critical threshold leading to an unusually rapid expansion of spruce (within ca. 100 yr). This rapid expansion of spruce, changing the geochemistry of the litter to a more acidic state likely caused the initiation of podzolization and the transformation of the original soil. The opening of MIS2 marked not only intensive dust accumulation but a steady decline of arboreal elements as well leading to the emergence of a cold tundra on top of the podosol with charcoal remains.

Abstract: Brasenia schreberi is a nationally protected aquatic macrophyte of substantial ecological value and economic significance, yet its wild populations have declined drastically due to habitat degradation and anthropogenic disturbances. This review systematically synthesizes research progress on the effects of water pH and depth on the growth, ecophysiology, mucilage quality, and community structure of B. schreberi, integrating findings from field surveys and controlled greenhouse experiments to elucidate critical ecological thresholds under combined environmental stressors. Our analysis reveals that natural B. schreberi populations are predominantly distributed in lentic habitats with stable water depths of 0.5-1.5 m (optimally 1.2-1.5 m) and circumneutral to weakly acidic conditions (pH 6.0-7.5). Deviations from these parameters substantially impair plant performance: when water depth exceeds 1.5 m or pH falls below 5.5, photosynthetic efficiency declines, root-to-shoot ratios increase aberrantly, and mucilage thickness decreases significantly. The synergistic critical threshold for population decline was identified at 1.1 m depth × pH 6.3. For artificial propagation, optimal cultivation strategies diverge from wild habitat preferences: maintaining pH at 7.0-7.5 (weakly alkaline) enhances mucilage polysaccharide accumulation and commercial quality, whereas a phenological stage-specific dynamic water-depth management regime (“shallow-deep-shallow-deep”) maximizes vegetative propagation success and yield. This review provides a theoretical framework and parameterized technical guidance for wild population restoration, standardized cultivation, and hydrological regulation in plateau wetland ecosystems. Future research priorities should focus on elucidating the molecular mechanisms underlying pH- and depth-mediated mucilage synthesis, developing precision water quality management systems, and strengthening ex situ germplasm conservation.

Abstract: Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, newly established or emerging-market WWTPs often lack historical aeration logs, making it difficult to construct high-precision surrogate models. Conventional cross-plant model deployments face severe data distribution shifts, and standard multi-objective optimization algorithms are prone to generating non-physical extrapolation errors, such as achieving compliance with "zero aeration" under low-concentration conditions. To break through inter-plant data barriers, this study proposes an intelligent aeration decision-making framework that integrates cross-domain transfer learning with physics-informed constraints. First, this study designs an adversarial network based on air-to-water ratio and removal rate features. By employing a gradient reversal layer (GRL) to extract domain-invariant representations, this network achieves cross-plant knowledge transfer. Second, this study proposes a physics-informed multi-objective particle swarm optimization (PI-MOPSO) algorithm, which embeds the theoretical oxygen demand as a physical penalty into the fitness function, ensuring the physical reliability of the optimization decisions. Experiments demonstrate that the surrogate model restricts the prediction errors for effluent chemical oxygen demand (COD) and total nitrogen (TN) removal rates to within 1%. Validated by statistical tests, the improved algorithm effectively circumvents non-physical prediction biases. Its Pareto front achieves a spacing metric of 0.0027, outperforming baseline algorithms in hypervolume stability. This framework provides optimal aeration scheduling strategies conforming to biochemical dynamics for target WWTPs lacking aeration action labels, demonstrating substantial practical engineering value.

Abstract: Flash drought, as a rapidly developing form of drought, has become an increasing threat to agricultural production, ecosystem stability, and regional carbon cycling, par-ticularly in croplands within monsoon regions. Existing studies have mainly focused on point-scale identification or conventional vegetation indices, while a systematic understanding of the regional spatiotemporal evolution of flash droughts and crop-specific differences in photosynthetic recovery remains limited. Using mul-ti-source remote sensing data from the North China Plain and the Middle-Lower Yangtze Plain during 2001–2024, this study integrated triple collocation error assess-ment, root-zone soil moisture percentile-based identification, connected component tracking, and Random Forest–SHAP analysis to characterize flash drought trajectories and their impacts on vegetation. The results showed that the southern Middle-Lower Yangtze Plain exhibited a high-frequency but low-intensity pattern, whereas the cen-tral North China Plain was characterized by relatively low frequency but higher inten-sity and longer duration. Rice-based systems were more vulnerable to frequent flash drought shocks, while rainfed and rotation systems faced stronger cumulative risks. Solar-induced chlorophyll fluorescence (SIF) responded to flash droughts 6–9 days ear-lier than gross primary productivity (GPP), and all cropping systems exhibited a “rapid physiological response–lagged carbon assimilation recovery” pattern. The month of occurrence, drought duration, and decline rate were identified as the dominant factors controlling photosynthetic recovery. These findings extend the flash drought monitor-ing framework from the perspectives of regional connectivity and crop recovery mechanisms, and provide a remote sensing-based scientific basis for agricultural early warning, drought mitigation, and food security management.

Abstract: The purpose of this study is to explore how Zimbabwean firms use Environmental Management Accounting (EMA) and climate risk disclosure in times of policy uncertainty and how these relate to sustainable growth and macroeconomic stability. The study was couched in the interpretivist research philosophy and adopted the inductive research approach. A case study research design, which aligns with a qualitative research design, was chosen for the study. The study employed in-depth interviews with management accountants, finance executives, and industry leaders across firms in Harare. The study adopted the cross-sectional time horizon and analysed data using thematic analysis to develop insights into the role of EMA and climate risk disclosure in times of policy uncertainty. The study's findings show that climate policy uncertainty compels business leaders to reconfigure management accounting systems to integrate environmental performance measures and scenario-based capital planning. The findings indicate that strategic EMA is essential because it enhances cost visibility, which, in turn, supports proactive risk management and stabilises investment decision-making within an enterprise. Firms that have integrated climate disclosure frameworks were found to demonstrate stronger stakeholder confidence and had high adaptability capacity. In an uncertain policy environment, firm-level adjustments support macroeconomic resilience and sustainable growth by lowering regulatory shock sensitivity and reducing the costs they impose. The study contributes to the literature by connecting the discussions of macroeconomic stability with micro-level accounting procedures and providing a process-based approach and understanding of how strategic EMA disclosure serves as a transmission mechanism between economic resilience and climate policy uncertainty. The study contributes to the emerging discourse on climate risk accounting within the fragile macroeconomic context of developing countries. It is therefore recommended that the regulatory institutional pillar be strengthened to reduce uncertainty and enhance the EMA's strategic adaptation.

Abstract: To reveal the solute sources, migration and enrichment mechanisms of water bodies in the endorheic lake region of the Qiangtang Plateau, Tibetan Plateau, and to clarify the hydrogeochemical cycling patterns in alpine arid zones, this study took typical en-dorheic lake areas in the region as the research object, conducted a systematic hydro-geological survey, collected 28 groups of water samples of various types (including springs, rivers, thermal springs, freshwater lakes, salt lake brines, atmospheric precip-itation and glacial meltwater), tested their major ions, trace elements and physical properties, and comprehensively investigated the hydrogeochemical characteristics, evolution laws and solute sources of water bodies, quantified the dominant control-ling factors and established a conceptual hydrogeochemical model by combining methods such as PHREEQC modeling, principal component analysis (PCA) and Pear-son correlation analysis; the results show that water bodies in the study area exhibit a distinct evolutionary gradient, from the low-salinity HCO₃-Ca recharge end-member, through transitional HCO₃·SO₄-Ca(Mg) water, to highly mineralized Cl-Na(SO₄·Cl-Na) salt lake brine, with synchronous enrichment of Li, B, As and other elements; solute sources are controlled by a ternary coupling mechanism of evaporative concentration, rock weathering and leaching, and deep geothermal fluid input, while cation ex-change and mineral dissolution-precipitation further regulate ionic ratios; As, Li, B and Cl⁻ display conservative migration in non-hydrothermal waters, whereas thermal springs show unique geochemical signatures due to the input of deep-seated fluids; PCA reveals that evaporative concentration (contribution rate 55.39%) is the dominant controlling factor, rock weathering (17.09%) provides the basic solute load, and the coupled process of deep fluid mixing and carbonate precipitation (14.21%) regulates elemental fractionation, and this study constructs a conceptual model of "multi-source recharge–water–rock interaction–evaporative concentration", which clarifies the evo-lutionary laws of regional water bodies and provides a scientific basis for water cycle research and green exploration of strategic mineral resources in salt lakes of the en-dorheic regions on the plateau.

Abstract: Naturally formed treefall gaps represent primary sources of environmental heterogeneity in tropical forests, yet their role in driving the components of beta diversity in specialized leaf-litter fauna remains poorly understood. We investigated the influence of natural treefall gaps on harvestmen (Arachnida: Opiliones) community structure and beta diversity partitioning in a well-preserved Atlantic Forest remnant in southern Bahia, Brazil. Using standardized nocturnal searches and leaf-litter sampling, we recorded 845 individuals across 23 species. Coverage-based rarefaction indicated higher estimated richness in gaps, although observed alpha diversity did not differ significantly among habitats. Community composition differed significantly along the gap–forest gradient, driven mainly by litter depth and microclimatic variation. Indicator species analysis identified Protimesius sp. as a robust gap-specialist. Beta diversity partitioning revealed that turnover accounted for 79.5% of total dissimilarity, while nestedness contributed 20.5%. Treefall gaps exhibited the highest internal beta diversity and species exclusivity, supporting their role as dynamic environmental filters that enhance regional diversity. Our findings highlight the ecological importance of natural disturbance and litter structure in maintaining biodiversity patterns in tropical forests.

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Formaldehyde (HCHO), a prevalent indoor air pollutant released from furniture and building materials, poses significant health risks due to its carcinogenic nature. In this study, a binary cuprous oxide–titanium dioxide (Cu₂O–TiO₂) composite photocatalyst was synthesized via a hydrothermal method to enable efficient visible-light-driven degradation of gaseous formaldehyde at ambient temperature. The structural, mor-phological, and optical properties of the as-prepared catalysts were characterized us-ing XRD, SEM, TEM, EDX, and UV-Vis spectroscopy. While pristine Cu₂O exhibited a formaldehyde degradation efficiency of approximately 68% under white light illumi-nation, the incorporation of TiO₂ markedly enhanced the photocatalytic performance. Among the different mass ratios tested, the Cu₂O–TiO₂ (1:1) composite demonstrated the highest activity, achieving 83% degradation of formaldehyde within 240 minutes under white light. Enhanced performance is attributed to the formation of a hetero-junction that reduces the effective bandgap, promotes charge separation, and sup-presses electron–hole recombination. Additionally, the generation of carbon dioxide and water as end products confirmed complete mineralization. The catalyst also showed good reusability, retaining over 81% efficiency after five cycles. This work presents a cost-effective, stable, and visible-light-active Cu₂O–TiO₂ heterojunction photocatalyst with strong potential for indoor air purification applications.

Abstract: Coastal biodiversity conservation is challenged by fragmented datasets and the limited integration of environmental conditions into marine spatial planning (MSP). This study develops an operationalized adaptive Marine Spatial Planning (MSP) to support biodiversity conservation by linking remote sensing data, IoT-based water quality measurements, and spatial optimization within Spatial Decision Support System (SDSS). The Tidung Islands are used as a case study, where benthic habitats are mapped from 3 m PlanetScope imagery. Water quality observations are processed into the Nemerow Pollution Index (NPI) and subsequently interpolated through an ensemble approach that combines inverse distance weighting, random forest, and gradient boosting. A key innovation of this study is the incorporation of the Nemerow Pollution Index (NPI) as a dynamic environmental cost layer within Marxan-based conservation prioritization. These data were incorporated alongside anthropogenic pressures to evaluate multiple conservation scenarios The ensemble interpolation demonstrated strong predictive performance (R²=0.76;MAE=0.0306), enabling reliable spatial representation of environmental conditions. The results show that integrating environmental quality into MSP significantly improves spatial efficiency, reduces fragmentation, and enhances ecological representation compared to conventional approaches based on static variables. Moderate conservation targets (≈30%) produced the most optimal solutions (~2,200 cost; ~11 km boundary), while more ambitious targets resulted in fragmented and inefficient spatial configurations. The proposed framework offers a transferable approach for data-limited coastal regions, contributing to the advancement of adaptive biodiversity conservation strategies.

Abstract: Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi‑arid regions, yet long‑term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used multi‑source precipitation datasets; CHIRPS, IMERG, and ERA5‑Land, against long‑term observations from Ed Dueim and Kosti, the two main reference stations in White Nile State, central Sudan. The assessment covers monthly and annual scales across each product’s available record (1952–2022) and uses a broad set of metrics, including Pearson and Spearman correlations, NSE, KGE, RMSE, MAE, percent bias, and categorical detection scores (POD, FAR, CSI). All three datasets capture the region’s single‑peak June–October monsoon pattern, but their accuracy differs sharply when it comes to rainfall amounts and year‑to‑year variability. CHIRPS performs best overall, with monthly NSE values around 0.77 and KGE between 0.79 and 0.88, along with a consistent dry bias of 5–13%—a predictable error that can be corrected operationally. IMERG shows strong monthly correlations but consistently overestimates rainfall by 25–42%, which leads to unreliable annual totals (NSE = −1.93 to −2.21). ERA5‑Land performs worst across nearly all metrics, with monthly NSE near or below zero, annual NSE dropping to −15.34, and frequent false alarms during the dry season. Taken together, the evidence points to CHIRPS as the most reliable dataset for routine hydro‑climatic monitoring in White Nile State, while IMERG and ERA5‑Land may still be useful in more specialized or time‑specific applications.

Abstract: Maintaining food systems in the face of climate change (CC) is a major concern. Palm oil is included in many commodities and this food system will be affected detri-mentally by inclement future climate, when oil palm (OP) will experience increased disease and declining growth. The OP diseases considered are basal stem rot (BSR), bud rot (BR) and fusarium wilt (FW), where the attempts to control them have been un-successful. An approach may be to replace compromised OP with different crops better suited to future climate: These plants will have less disease because of the “Parasites Lost” phenomenon. Maintaining a vegetable oil product is an important advantage. How CC will affect OP and associated ailments has been determined previously by CLIMEX modelling. The modelling of future suitable climate (FSC) has also been car-ried out for soybean, maize and the common bean (CB) using the same modelling pa-rameters. This enables direct comparisons in the OP producing countries of Colombia, Nigeria and Papua New Guinea (PNG). Limited data for rapeseed are also discussed. The FSC for OP was much reduced in these countries and that for soybean was higher. Soybeans will have less disease as it would be an introduced and annual crop. Maize had much fewer advantages and the CB and rapeseed had none. Maize had potential advantages in Nigeria until 2050. A novel method for adapting to the serious diseases of OP and poor growth would be to grow soybeans in similar regions to where OP grows currently. Plans could be made for replacing OP with soybeans which could be modified when real time data becomes available. The paper provides a novel method for mitigating future diseases and poor growth of OP, which are otherwise unavailable, whilst maintaining a valuable oil product.

Abstract: This study aims to develop decision-making methods for equalizing urban electric vehicle (EV) charging services and apply them to the improvement of Wuhan’s charging infrastructure. Using grid units as the basic analytical units, the study constructs measurement models for two scenarios—daily commuting and weekend travel—including a spatial demand index based on classified population-distribution prediction, a spatial supply index derived from regional charging-facility statistics, and a supply–demand balance index. Grading systems are established for single-scenario demand, layout thresholds, and supply, together with an integrated classification combining both scenarios. According to the suitability of grid units for service improvement, three optimization strategies are proposed: adding charging stations, expanding existing stations, and converting parking lots. Evaluation methods using residential quarters and commercial/service POIs are designed to assess spatial equilibrium pre- and post-optimization. An empirical study of Wuhan’s main urban area shows that service satisfaction reaches 88.68% for residential quarters and 75.93% for commercial/service POIs under current conditions. The proposed scheme recommends 8 new stations, 31 station expansions, and 114 parking-lot conversions, increasing satisfaction to 99.24% and 92.35%, respectively. The model provides a feasible technical framework for urban EV charging-station planning.

Abstract: The southern coast of Java, Indonesia, lies along the active Sunda subduction margin, where coastal landforms record the interaction between sea-level change, wave erosion, sedimentation, and tectonic uplift. Marine terraces and raised coastal surfaces are important geomorphic indicators of vertical deformation, but their interpretation remains difficult where chronological control is limited and where coastal surfaces have been modified by erosion, deposition, karstification, or human activity. This study presents new Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) topographic profiles from four coastal sites: Pantai Ajah, Kalijali, Kulon Progo, and Wingko. The profiles were measured from the beachward side toward the landward side and were used to identify terrace treads, risers, slope breaks, residual topographic highs, and possible raised coastal platforms. These field data are integrated with published information on Holocene sea-level change, marine terraces, coastal uplift, and forearc deformation along the southern Java margin. The RTK profiles show variable terrace morphology between sites. Pantai Ajah preserves a prominent riser and a probable terrace tread at approximately 7–8.5 m elevation. Kalijali records a lower terrace-like surface at approximately 4–5 m, an upper surface at approximately 7–9 m, and a higher local topographic high near 12–13 m. Kulon Progo shows a subdued low-elevation raised coastal surface, while Wingko contains a distinct slope break at approximately 1450–1500 m from the beachward end and a broad landward surface at approximately 5–6.5 m elevation. The profiles suggest two tentative morphostratigraphic terrace groups: a lower group at approximately 4–6.5 m and an upper group at approximately 7–9 m. Higher local peaks, including the 12–13 m high at Kalijali and comparable elevated points at other sites, may represent remnants of older or more strongly uplifted coastal features. One possible interpretation is that some of these higher surfaces originated near the mid-Holocene sea-level highstand, when relative sea level in parts of Indonesia and Sundaland was higher than present, and were subsequently uplifted to different elevations according to local uplift rates. However, this hypothesis requires direct chronological and sedimentological confirmation. The raised terrace ridges and topographic highs may also act as partial natural barriers that reduce tsunami flow penetration inland, although they should not be treated as complete protection. Overall, RTK profiling provides a useful field method for recognizing coastal terrace morphology and identifying priority sites for future dating, tsunami-inundation modelling, and coastal-hazard planning.

Abstract: Floods triggered by intense precipitation represent one of the most significant natural hazards affecting Mediterranean regions, where complex terrain, rapid hydrological response and increasing urbanization can amplify flood impacts. This study presents a flood hazard assessment for two representative Eastern Mediterranean catchments: the Koiliaris River Basin in Crete-Greece and the Pediaios River Basin in wider Nicosia region in Cyprus. A composite Flood Hazard Index was developed by integrating three indicators representing key drivers of flood generation: the Topographic Wetness In-dex describing terrain-driven water accumulation, the Curve Number representing runoff potential, and the R20 precipitation frequency index. Spatial datasets including EU-DEM elevation data, CORINE land cover, European soil databases and precipita-tion information from the Copernicus CERRA reanalysis were used to derive the indi-cators. Each indicator was classified using the Natural Breaks method and combined through a weighted multi-criteria approach based on the Analytic Hierarchy Process. The resulting maps identify high-susceptibility areas mainly along river corridors and low-lying zones with high runoff and accumulation potential. Higher hazard levels occur in downstream areas of Koiliaris and urbanized zones of the Pediaios basin, par-ticularly around Nicosia. Historical flood events were also analyzed to validate the index and examine links between rainfall intensity and impact severity.

Abstract: The "spin-up" problem—where convection-permitting models require hours to develop realistic clouds from large-scale initial fields—critically limits short-term severe weather forecasting. Cloud analysis offers a potential solution by directly incorporating hydrome-teor information from remote sensing observations. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly aver-aged FY-2G satellite black-body temperature (TBB), and FY-2G total cloud water products, within a stepwise cloud-analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model's dynam-ic-thermal framework. Results demonstrate that the remote sensing-driven cloud analysis substantially enhances ensemble system performance across multiple dimensions: (i) spin-up time is significant-ly reduced, with precipitation forecasts exhibiting reasonable structure from the initial forecast hour; (ii) deterministic forecast accuracy improves systematically, with reduced RMSE for geopotential height, temperature, and wind fields across all levels; (iii) proba-bilistic forecasting skill is enhanced, evidenced by improved CRPS and AROC for surface elements and precipitation thresholds; (iv) ensemble reliability is optimized, with spread better matching forecast errors. Mechanistic analysis reveals that these improvements stem from physically coordinated hydrometeor-latent heat initial perturbations and sub-sequent cloud-radiation feedbacks that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for addressing spin-up challenges in convective-scale ensemble prediction.

Abstract: The dynamics of organic matter, nitrogen status, and biological activity in soils in southern Kazakhstan under various land-use systems were studied. A key feature of the research is the comprehensive comparison of humus status, nitrogen state, and biological activity of virgin and arable dark Kastanozem, Gleyic Calcisol, and Haplic Calcisol, as well as identification of their correlation with signs of functional depletion of organic component. The assessment was conducted using set of agrochemical and biological methods, including determination of humus content, available nitrogen forms, C/N ratio, microbial population, and enzymatic activity. It has been determined that the highest humus content is typical for dark chestnut soils under natural vegetation, while plowing of them is accompanied by decrease in humus content due to increased mineralization processes. Gleyic Calcisol - are characterized by more stable humus state, in some cases with increased organic matter content under arable conditions. Minimum humus values were found in Haplic Calcisol, due to arid conditions and limited supply of organic residues. It is shown that arable soils are characterized by a decreased C/N ratio and increased rates of organic matter transformation. Soil biological activity is linked to mineralization processes, as confirmed by microbial population dynamics and enzymatic activity. Additional assessment using digital tools reveals signs of functional depletion of organic component in agrocenoses. The obtained results indicate the need to consider biological indicators when assessing soil conditions and developing sustainable land management systems in arid climates.

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.