Earth and Environmental Sciences

Abstract Wildfires in California’s Sierra Nevada during 2020–2021 killed giant sequoias ( Sequoiadendron giganteum ) at rates unseen for millennia, underscoring the vulnerability of highly fire-adapted trees to ongoing environmental change. Following a century of fire exclusion and fuel accumulation, the effectiveness of prescribed burns in reducing giant sequoia mortality from wildfire remained poorly quantified. Here we estimate mortality outcomes for 26,403 giant sequoias across 19 groves in Sequoia and Kings Canyon national parks following the Castle (2020) and KNP Complex (2021) wildfires using a Bayesian framework. We map tree mortality using a deep learning classifier integrating 3 m PlanetScope imagery, airborne lidar, and field observations. From an estimated 7,974 sequoia deaths (95% Bayesian credible interval (CI): 7,555–8,430), corresponding to 30.2% mortality (CI: 28.6–31.9%), we find previous prescribed burns (≤10 years prior) reduced mortality odds by 77% (CI: 69–83%), making treated trees nearly four times more likely to survive. Counterfactual simulations suggest that prescribed burns prevented at least 1,888 (CI: 1,487–2,302) deaths, and universal treatment would have saved an additional 3,888 (CI: 3,236–4,580) giant sequoias. These results show that prescribed burns substantially improve survival during extreme wildfires, offering guidance for conserving long-lived, fire-adapted forests under intensifying fire regimes.

Against the backdrop of the long-term entrenchment of high-carbon development pathways, identifying mechanisms for breaking carbon constraints is essential for advancing regional low-carbon transitions. Focusing on the Yangtze River Delta (YRD) urban agglomeration, this study develops a carbon unlocking level evaluation framework incorporating industrial, institutional, technological, and cultural dimensions. Carbon unlocking levels from 2012 to 2023 are measured, and intercity carbon unlocking level linkages are identified using a modified gravity model. Social network analysis is employed to reveal the spatial association network structure, while a temporal exponential random graph model (TERGM) is applied to examine the dynamic evolution of the network and its driving mechanisms. The findings are as follows: (1) The carbon unlocking level of the YRD exhibits a fluctuating upward trend, with pronounced heterogeneity across dimensions in evolutionary trajectories and spatial patterns. (2) Carbon-unlocked spatial correlation networks exhibit favourable connectivity, with increasing network density, yet overall redundancy remains low.(3) There is a pronounced functional division within the network, with the coexistence of primary overflow block, primary beneficiary block, and broker block; carbon unlocking spatial linkages occur primarily between modules, while intra-module cohesion remains relatively weak.(4) The evolution of the YRD network is driven not only by exogenous structural influences but also by endogenous self-organisation within the network itself. Reciprocity and transmissibility both exert positive influences on the formation of carbon unlocking networks, exhibiting notable stability. These findings contribute to deepening our understanding of collaborative mechanisms for regional low-carbon transition, while providing valuable reference for low-carbon governance and policy coordination at the urban cluster level.

The Tibetan Plateau is a major regulator of Asian hydroclimate, yet rapid changes in atmospheric demand are reshaping its water balance. Here, we examine whether the evaporation-paradox pattern has reversed by analysing meteorological records from 2000 to 2024 across the Nyainqêntanglha Mountains. Potential evapotranspiration (ET 0 ) increased across the region, but the mechanisms were strongly elevation dependent. In low-elevation valleys, ET 0 rebound was driven mainly by recovering wind speed and an expanding vapour pressure deficit, indicating increasing aerodynamic control. By contrast, high-altitude areas showed a cloud-brake effect, in which topographically enhanced cloudiness reduced radiation input and constrained ET 0 growth despite pronounced warming. Explainable machine-learning analyses showed that the dominant predictors identified by XGBoost and Random Forest were broadly consistent with Penman–Monteith sensitivity. Long Short-Term Memory models further reproduced ET 0 dynamics with strong predictive skill, supporting their use as virtual observation tools in data-sparse alpine environments. These findings indicate a transition from an energy-limited regime towards more dynamically controlled evaporative demand, with important implications for plateau water resources.

Sustainable agriculture aims to ensure food security while protecting natural resources and maintaining a responsible chain from soil to table. Input optimization in agricultural production can reduce energy use and carbon emissions without compromising yield. This study demonstrates that energy use and carbon emissions in corn production in Konya province, located in the Central Anatolia region of Türkiye, can be reduced through an input optimization approach and highlights potential policy measures for this purpose. DEA is a method that guides decision-makers on how to improve the efficiency of relatively inefficient units. The study results distinguish efficient from inefficient enterprises and suggest ways to reduce energy use and carbon emissions from inefficient enterprises. Input optimization can reduce total energy use by 12.01% (7646.2 MJ/ha) and carbon emissions by 10.25% (552.79 kg CO₂-eq/ha). Furthermore, the study reveals that if currently inefficient enterprises produce with the optimum input combination, energy use could be reduced by 18.1% and carbon emissions by 15.7%. These findings demonstrate that the environmental pressure of agriculture can be mitigated without compromising food production, and that the results indicate not only economic benefits but also sustainability in agriculture. This highlights that input optimization can serve as a basis for both economic and environmental policies.

Existing wastewater treatment methods often struggle to achieve slow, ineffective removal of complex organics, such as biochemical oxygen demand (BOD) and chemical oxygen demand (COD), particularly in challenging industrial applications, such as tannery wastewater treatment. Triethanolamine (TEA)-functionalized anthill clay was prepared as an effective adsorbent for the removal of BOD and COD from tannery wastewater. Raw anthill clay was purified by sedimentation, then subjected to acid activation and TEA functionalization. X-ray diffraction (XRD) analysis confirmed the retention of the aluminosilicate structure, while Fourier transform infrared (FTIR) spectra confirmed the incorporation of the amine functions through the formation of C-N and N-H bonds. Scanning electron microscope (SEM) images showed increased surface roughness, and Brunauer–Emmett–Teller (BET) surface area analysis indicated a high surface area (SBET = 138.34 m 2 /g) with retention of mesoporous characteristics. Thermogravimetric Analysis (TGA) revealed enhanced thermal stability of the modified clay. Batch studies demonstrated rapid uptake, reaching equilibrium within 60 min, with removal efficiencies of 95.82% for BOD and 96.98% for COD at the optimal dosage. There was an increase in removal capacity with rising temperature, indicating an endothermic nature. Sorption saturation capacities using the Langmuir model resulted in 125.07 mg/g (BOD) with 131.29 mg/g (COD), while the pseudo-second-order kinetics model (R 2 > 0.999) suggested the occurrence of chemisorption. Thermodynamic studies unveiled spontaneity with the enhancement of entropy. TEA-functionalized clay maintained > 50% removal capacity after six consecutive regeneration cycles, establishing its potency in the regeneration process.

Flood is one of the predominant natural hazards, inducing catastrophic impacts on lives, structures, and agriculture in low-elevated and riverine areas like Feni, Bangladesh, highly susceptible to flooding (flash and riverine floods), amplified by intense and prolonged rainfall in the monsoon. This study aims to assess flood-causing factors, risk profile by susceptibility maps, contrasting multi-criteria decision-making (MCDM) models, and sensitivity analysis for the Feni district. The novel aspect includes a comparison of eight MCDM models (subjective, objective, and ranking-based) for flood susceptibility mapping using traditional and DeLong-bootstrap receiver operating characteristic (ROC) curves, sensitivity, and confusion matrix analyses. This study explores twelve fundamental hydro-meteorological factors (Elevation, Slope, Aspect, Curvature, Drainage density, Topographic wetness index, Rainfall, Normalized difference vegetation index, Distance from river, Topographic position index, Land use land cover, and Stream power index), where each factor illustrated statistical significance according to a multicollinearity test applying MCDM methods: Analytical Hierarchy Process (AHP), Fuzzy AHP (FAHP), Analytic Network Process (ANP), Decision Making Trial and Evaluation Laboratory (DEMATEL), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIse Kriterijumska Optimizacijai Kompromisno Resenje (VIKOR), Entropy, and Evaluation Based on Distance from Average Solution (EDAS). The results indicate that 28-62.5% of the area is moderately susceptible to flooding, 0.22–16.84% is very highly susceptible, as assessed by the accuracy using the ROC curve and the area under the curve (AUC) value. The traditional ROC analysis yielded AUC values of 0.93, 0.93, 0.93, 0.93, 0.82, 0.70, 0.92, and 0.85 for AHP, FAHP, ANP, DEMATEL, TOPSIS, VIKOR, Entropy, and EDAS, respectively. Among these methods, DEMATEL, a subjective MCDM approach, exhibited the best predictive performance, achieving the highest AUC (0.935) when further validated using the DeLong bootstrap test, sensitivity, and confusion matrix analysis with supporting data from the Feni District. This study facilitates a novel methodology for flood susceptibility assessment in data-deficient Feni and analogous regions, guiding policymakers and governments for sustainable flood management and increasing the community’s resilience.

Abstract This study investigates the long-term effects of partial recultivation on metal(loid) contamination and environmental risks at the historical (14-17th century) Cu–Pb smelting site at Polichno (Holy Cross Mountains, Poland). More than 50 years ago, the site underwent partial recultivation involving slag removal; however, residual slag fragments remain dispersed throughout the soil profile. The slag contains high mean concentrations of metal(loid)s, including 111,000 mg·kg − 1 Pb, 56,300 mg·kg − 1 Ba, 8370 mg·kg − 1 Zn, 465 mg·kg − 1 Cu, 110 mg·kg − 1 As, mainly hosted in sulfides, arsenides, sulfates, and a glassy matrix. Maximum soil concentrations reached 7010 mg∙kg − 1 Pb, 4180 mg∙kg − 1 Ba, 1500 mg∙kg − 1 Zn, 658 mg∙kg − 1 Cu, 45.4 mg∙kg − 1 As, with the highest levels in ash- and wood-containing horizons. Average pore water concentrations were 115 µg∙l − 1 Pb, 166 µg∙l − 1 Ba, 161 µg∙l − 1 Zn, 58.9 µg∙l − 1 Cu, and 1.9 µg∙l − 1 As. Batch leaching tests using deionized water classified the slag as non-hazardous waste rather than inert waste, as Pb release exceeded the regulatory threshold for inert waste. pH-static tests showed peak mobilization at pH 2 (Ba 39,000 mg·kg − 1 , Pb 4860 mg·kg − 1 , Zn 3870 mg·kg − 1 , As 21.7 mg·kg − 1 ), with minima at neutral pH. The formation of secondary phases during leaching indicates immobilization processes that limit metal(loid) mobility.

Abstract Anisakis spp are parasitic nematodes that can infect fish during larval stages, and, if consumed by humans, can cause anisakiasis and severe allergic reactions, including anaphylaxis. Here, we generated a 168 Mb genome assembly of A . s implex , the most abundant species in the Northeast Atlantic, which was used to develop SNP resources using a whole genome pooling resequencing strategy. 1,824,098 SNPs and 151,767 short structural variants were identified, showing the high polymorphism and complexity of A . simplex genome. A 481 SNP panel was developed for population screening using the Agriseq technology, through strict filtering and homogeneous distribution across the genome. This panel was used for genotyping 823 larvae and adults of A . simplex , 775 A . pegreffii and 402 putative hybrids collected from different hosts across the Northeast Atlantic, 275 SNPs being consistently genotyped in both species. Among them, 10 SNPs showing extreme genetic differentiation between A . simplex and A . pegreffii and no linkage disequilibrium in A . simplex , were used to design a cost-effective SNP tool for species and hybrid identification in population surveys. The genomic resources obtained will facilitate efficient surveillance programs, accurate discrimination of species and hybrids, effective anisakiasis management in fisheries, and improved seafood safety.

Microplastics (MPs) are increasingly recognised as chemically active interfaces that influence the environmental fate, transport, and persistence of coexisting contaminants. However, the molecular mechanisms governing antibiotic adsorption on environmentally transformed microplastics remain poorly understood. In this study, molecular dynamics (MD) simulations were employed to investigate the adsorption behaviour of the third-generation cephalosporin antibiotic cefixime on polyethylene (PE) microplastics, explicitly considering the effect of polymer aggregation and environmental aging. Pristine single-chain PE exhibited weak and reversible cefixime adsorption dominated by van der Waals interactions, resulting in high molecular mobility and frequent desorption events. In contrast, polymer aggregation substantially enhanced antibiotic retention by creating confined inter-chain domains that amplify cooperative dispersion interactions and reduced solvent competition at the polymer-water surface. Environmental aging further strengthened adsorption through the introduction of oxygen-containing functional groups capable of electrostatic interactions and hydrogen bonding with polar moieties of cefixime. These structural and chemical modifications collectively reduced molecular fluctuations, promoted the formation of stable binding sites, suppressed molecular diffusion, and significantly increased interaction energies. The findings demonstrate that environmental transformation of polyethylene microplastics enhance molecular adsorption of cefixime on polyethylene, with potential implications for contaminant transport, persistence, and ecological exposure in aquatic systems. The results provide mechanistic insight into the role of polymer ageing and aggregation in antibiotic adsorption and contribute to the understanding of antibiotic microplastic interactions.

Pre-monsoon (April-June) tropical cyclones (TC) over the North Indian Ocean (NIO) pose severe coastal hazards. Surprisingly, in recent years like in 2025 and 2026 pre monsoon season there was not a single named TC reported over NIO. In this study, we explored the drivers for such a suppressed activity. We considered 46 years (1980-2025) of India Meteorological Department (IMD) best-track data and found that such situation occurred in 12 specific years during the analysis period. We considered those 12 years as non-TC years and to examine the contrast, we identified 10 TC-active years, where at least one TC occurred in both Bay of Bengal (BoB) and Arabian sea (AS) basins. Genesis Potential Parameter (GPP) composites reveal a basin-wide suppression during non-TC years, with a statistically significant difference of 1.0 × 10⁻⁵ concentrated over the BoB and AS. Logarithmic decomposition of the GPP shows that low-level vorticity is the primary driver of enhanced GPP over both basins during TC years, while reduced vertical wind shear provides additional positive support over the BoB. In general, the conditions are less favourable for the TC formation over the AS and are favourable over the BoB. But, during TC years, warmer SST, higher mid-level moisture and strong low-level positive vorticity supported the TC activity over the AS. Enhanced atmospheric moisture transport (IVT) and stronger large-scale upper-tropospheric diabatic heating during TC years reflect a pre-existing convective environment driven by organized large-scale forcing rather than TC-induced latent heat release. We further identify the MJO as the dominant large-scale modulator of pre-monsoon cyclogenesis, with TC activity systematically phase-locked to long duration and high-amplitude MJO phases 2-4 during active years, while suppressed years are characterized by weak or inactive MJO convective envelopes (RMM amplitude < 1.0). Wavenumber-frequency spectral analysis exhibits enhanced power in equatorial Rossby, Kelvin, and MRG waves alongside active MJO during TC years. Analysis of ENSO and IOD states reveals no systematic difference between TC and non-TC years. These findings suggest role of intraseasonal variability in modulating the large-scale kinematic forcing, alongside local thermodynamic conditions behind the cyclogenesis activity over the NIO.

Abstract Oxygen and other light elements comprise up to 5 wt% of the Earth’s outer-core, and may significantly influence its physical properties and the operation of the geodynamo. Here we report in situ X-ray diffraction measurements of Fe, Fe + 4.5 FeO (atomic proportion), and Fe 2 O 3 melts at 177-440 GPa, achieved using laser-driven shock compression at an x-ray free-electron laser. The melts exhibit Fe-O coordination numbers between 4.0(0.4) and 4.5(0.4), indicating predominantly four-fold coordination environments. These coordination states are significantly smaller than those of Fe-bearing lower-mantle phases such as bridgmanite and ferropericlase. Shorter Fe-Fe interatomic distances in compressed iron oxide melts drive the denser packing relative to ambient melts, while the structural differences between Fe + 4.5 FeO and Fe 2 O 3 melts under shock indicate that the oxidation state modulates oxygen solubility in liquid Fe. At 177 GPa ( ~ 380 km below the core-mantle boundary) and 3800 K, Fe 2 O 3 melts exhibit higher Fe-O coordination, suggesting that local variations in oxygen content could contribute to the stratification in the uppermost outer-core inferred from seismological and geomagnetic observations.

Innate immunity is traditionally viewed as a broad defense system with limited specificity. However, increasing evidence suggests that innate immune cells can discriminate between distinct microbial partners. How such specificity arises in early-diverging animals remains unclear. Here, we identify in the sea anemone Nematostella vectensis a selective host innate immune mechanism mediated by nematosomes, motile multicellular bodies that differentially process bacterial cells. Nematosomes preferentially engulf non-native Vibrio isolates while showing reduced uptake of native host-associated strains. We identify the transcription factor cJUN as a key regulator of this process. CRISPR/Cas9-mediated knockout of cJUN reduces nematosome abundance, impairs lysosomal response, alters microbiome assembly, and increases susceptibility to bacterial infection. These results link immune gene function to microbial selectivity and demonstrate that even early-diverging animals exhibit sophisticated innate immunity mechanisms for microbiome regulation. Our findings support the idea that immune specificity can arise through repurposing deeply conserved pathways and may have deep evolutionary origin.

A critical factor in the transition towards renewable energies is offshore wind, and therefore efficient site investigation to assure the safety and stability of the wind turbines' foundations is needed. Conventionally, borehole drilling and in-situ testing are used. Although these techniques can provide accurate geological data, their costs are prohibitive and their scope of survey is restricted. This drives the need for complementary geophysical survey methods. The Single-Channel Seismic (SCS) method is a cost-effective and rapid technique that can be employed for regional seabed survey. However, low signal-to-noise ratio (SNR) and poor stratigraphic continuity result when applying conventional processing workflow on seismic data obtained from complex settings, such as thick sand layers, shallow water environment with the presence of strong multiples, and sea surface swell conditions. To overcome these problems, an optimized SCS processing workflow which introduces three new techniques is proposed: (i) eigenvalue based swell correction with adaptive sliding window smoothing, (ii) shearlet transform based sparse representation of seismic data to remove random noise, and (iii) combined predictive deconvolution (for short period multiples) and SRME with Bayesian separation (for long period multiples). This algorithm has been applied to a field data survey at Fangchenggang, Guangxi (water depths: 0-25 m, total area is 99 km 2 ), and a substantial increase is observed: SNR enhancement from about 8-12 dB (from comparison with F-K spectrum analysis), the stratigraphic continuity improvement by factor of about 2.5 (estimated by reflection event tracing), and elimination of over 70% multiple energy within targeted range. The final result successfully delineates bedrock topography and structural settings required for wind farm installation site investigation. This new approach makes it possible to enhance shallow-marine seismic data quality even with difficult geological conditions and extract information indispensable for offshore wind farm development.

Understanding how applied voltage regulates metabolism in microbial electrochemical systems is challenging due to the lack of adequate electrophysiological tools for microorganisms. Here, we present an imaging platform based on electrochemiluminescence (ECL) that allows real-time, femtocoulomb-scale quantification of surface charge in single bacterial cells. The method exploits the electrostatic enrichment of cationic luminophores at bacterial membranes to amplify ECL signals, thereby linking surface charge dynamics to intracellular electron metabolism and interfacial electron flow. Using Shewanella oneidensis MR-1 as a model, we show voltage-activated metabolic enhancement mediated by outer-membrane cytochromes. By modulating direct and indirect electron-transfer pathways, we reveal that cytochrome-dependent direct electron transfer initiates activation, while mediator-enabled indirect electron transfer prevents charge saturation and sustains elevated metabolic turnover. Furthermore, dual-parameter screening enables identification of bacterial subpopulations with high metabolic activity and efficient electron transfer capability. Thus, our work provides a framework for microbial electrophysiology at the single-cell level and opens potential avenues for engineering high-performance electroactive strains for bio-electrochemical applications. Our understanding of how applied voltage regulates metabolism in microbial electrochemical systems is limited. Here, Xing et al. present an imaging platform based on electrochemiluminescence that allows real-time, femtocoulomb-scale quantification of surface charge in single bacterial cells.

The development of multifunctional membranes with tunable surface charge, high permeability, reactive pollutant removal, and bacterial resistance remains a critical challenge in wastewater treatment. Here, a poly(vinylimidazolium)-functionalized PVDF membrane incorporating g-C 3 N 4 -Fe 3 O 4 was fabricated through surface activation, composite deposition, free-radical graft polymerization, and subsequent hydroxide anion exchange to tailor surface physicochemical properties. The engineered membrane exhibited significantly enhanced hydrophilicity and high water permeability (109 L m − 2 h − 1 bar − 1 ), corresponding to a 25-fold increase over pristine PVDF. It showed strong removal of methylene blue (97%) but lower removal of methyl orange (30%) at 20 ppm, indicating charge-dependent reactive removal behavior. Methylene blue displayed a strong affinity for the modified surface, enabling effective interfacial adsorption and subsequent photocatalytic degradation under visible light, whereas methyl orange interacted much less effectively. Although most dye-removal experiments were conducted under static conditions to evaluate surface activity, pressure-driven filtration still achieved up to 83% methylene blue removal within the microfiltration pore-size range, confirming that pollutant removal was governed by reactive surface interactions rather than conventional size-exclusion mechanisms. Adsorption kinetics followed pseudo-second-order model and fit the Freundlich isotherm, suggesting heterogeneous interactions between dye molecules and the membrane surface. Interfacial adsorption governed pollutant localization in both dark and illuminated conditions, with photocatalysis providing an additional contribution under light. The membrane also exhibited excellent antibacterial activity, with near-complete flux recovery over eight filtration cycles. Overall, integrating polymeric ionic liquids with carbon nitride-based nanocomposites offers a promising route to low-pressure reactive membranes with enhanced pollutant removal and antibacterial properties for wastewater treatment.

We present the application of particle-shape parameters for silt grains in the 4-8 µm, 8-15 µm, and 15-31 µm fractions obtained using automatic particle-shape image analysis (Morphologi G3SE). Four shape parameters (HS Circularity, Convexity, Solidity, and Aspect Ratio), supported by scanning electron microscopy (SEM) investigations and multivariate statistical analyses, were used to aid interpretation of processes and formulation of hypotheses regarding possible sedimentary environments responsible for grain shape. The analysis examined dust particles collected during four Saharan dust intrusions into Europe: Greece (2016, 2018) and Poland (2021, 2024). The results suggest that the Convexity parameter is the most useful for distinguishing individual dust events (2016, 2018, 2021, and 2024), identifying similarities between them (particularly between 2021 and 2024), and demonstrating internal variability within a single dust plume between different deposition sites during the 2021 event. In contrast, HS Circularity and Solidity exhibited lower discriminatory and interpretative potential. Grain-size fractions below 10 µm showed the strongest ability to differentiate between dust events and deposition sites. This analysis may provide a basis for linking particle shapes with environments that may have contributed to the formation of the final shape of dust particles and may reflect a long, multi-stage pathway (MSteP). More than 75% of the analyzed dust particles were classified as subrounded, rounded or well rounded, and exhibited more isometric shapes with shallow or absent microdepressions. The predominance/presence of subrounded and rounded grains may suggest processes characteristic of subaqueous (fluvial or beach) environments.

A new hierarchical model to elucidate the photo-mediated activation process of sulfite species in selective photodegradation of a fluoroquinolone antibiotic, ofloxacin, is introduced in this work, through the combination of RANSAC based high-order stochastically governed multi-outputs predictive modeling and an evolutionary parametric optimization, a set of coupled process parameters controlling the ofloxacin removal, biochemical oxygen demand (BOD) and chemical oxygen demand (COD) removals are determined. Systematic assessment of UV/sulfite system was performed to identify the effect of pH, UV intensity, sulfite concentration, starting drug concentration, contact time, and molar ratio of sulfite/ofloxacin, and strong predictive fidelity of COD and ofloxacin removal, and the complex BOD reduction behavior with moderate deviations under extreme conditions. RANSACRegressor was accurately captured non-linear and multivariate relations. Quantitative evaluation confirmed the stability of the model when using training and test data sets, that UV Intensity and initial ofloxacin concentration are the most important factors influencing the pollutant degradation and organic load reduction, whereas pH, contact time and sulfite/ofloxacin molar ratio are secondary but significant factors. Moreover, the evolutionary optimization protocol could identify the output-specific optimum settings, an increased UV intensity and sulfite concentration (when removal of ofloxacin and COD took place), compared with a decreased sulfite concentration and relatively alkaline pH (when removal of BOD took place). When combined, this hybrid photochemical-computational model gives improved predictive insight of the photon-induced sulfite activation mechanism and offer a robust predictive and operational framework of optimization of advanced oxidation in aqueous matrices to which antibiotics are added.

The rapid spread of the competing weed L. arvensis poses a major threat to wheat production; therefore, modern risk assessment methods are necessary for its management. This study developed and compared machine learning models (Random Forest [RF], Boosted Regression Trees [BRT], and Maximum Entropy [MaxEnt]) to evaluate habitat suitability for L. arvensis, a dominant weed in the wheat cropping systems of Pakistan's semi-arid regions. For this purpose, weed data from 402 wheat fields, along with 20 environmental factors, including topography, climate, soil characteristics, anthropogenic factors, and proximity metrics, were analysed. Soil texture (silt and clay), soil chemistry (EC, OM, TDS), and rainfall patterns were identified through a partial least squares (PLS) algorithm as major factors affecting the species distribution. The ROC-AUC results showed that MaxEnt (AUC = 0.93) and RF (AUC = 0.92) performed slightly better than BRT (AUC = 0.86). All models identified the eastern and southeastern regions as the main areas of highly suitable habitat. Although these models are reliable, their predictions may be affected by changes in environmental factors in cropland. These results demonstrate that machine learning methods are effective for mapping weed distribution and provide a scientific foundation for sustainable weed management in these regions.

Reliable in-situ strength information is critical for construction-stage decisions, yet conventional UPV–UCS correlations are typically established on companion cylinders, which may overestimate the state of cast reinforced members. This study addresses this gap by developing an IoT-enabled embedded ultrasonic sensing system that tracks age-dependent UPV along a fixed internal path (200 mm) in cast reinforced concrete from casting to 28 days, and by quantifying the persistent discrepancy between embedded-path and cylinder-based surface UPV under identical mix designs. Across all mixes and ages, embedded-path UPV remained lower than surface UPV (ΔUPV = 118–415 m/s), suggesting that cylinder-based UPV may provide unconservative strength inference for cast members under the investigated configuration. By pooling embedded UPV with companion-cylinder UCS results, we establish a conservative empirical UCS–UPV model (UCS = 0.3846e0.0011 V p , R 2 = 0.82) with uncertainty metrics (RMSE and MAE). A cable-free LoRa–Wi-Fi architecture with gateway integrity checks and REST-based cloud upload demonstrates the feasibility of periodic near-real-time visualization for curing-stage monitoring, although full communication-performance benchmarking was beyond the scope of this study. Although direct member-strength verification of the cast specimens was not performed, the results support embedded-path UPV as a practical complementary indicator for construction-stage decision making.

Climate change is significantly impacting water resource management, with increasing droughts threatening water availability worldwide. This study assesses future water shortages at Boryeong dam in Chungnam Province, South Korea, under Coupled Model Intercomparison Project Phase 6(CMIP6) climate scenarios using the Soil and Water Assessment Tool (SWAT) model. The research integrates data from 18 Global Climate Models (GCMs) across multiple Shared Socioeconomic Pathways (SSPs) to analyze projected inflows, outflows, and reservoir storage capacity. The results indicate an overall increase in annual precipitation and inflow, with mean inflows rising from 5.26 m 3 / s (historical period: 1981–2010) to 6.64 m 3 / s under SSP5-8.5 by 2071–2100. While most projections suggest improved water supply resilience, certain models predict intensified shortages, with estimated shortage volumes ranging from 7.54 million m 3 to 1,914 million m 3 , highlighting the urgency of adaptive water management strategies. The study employs downscaling and bias correction techniques to enhance hydrological predictions, and simulations reveal significant uncertainties in future water levels. To mitigate potential shortages, policy recommendations include diversifying water sources, optimizing dam operations, and improving emergency water transfer infrastructure. The research underscores the importance of proactive planning in ensuring long-term water security in the face of climate variability.

Extreme heat causes by far the most fatalities of any type of hazardous weather (https://www.weather.gov/hazstat). As the globe continues to warm owing to the additional heat trapped by increasing concentrations of greenhouse gases in the atmosphere, areas prone to excessive heat have expanded, some verging on uninhabitable. We present a new metric - the accumulated dangerous heat index - to identify regions in Northern Hemisphere summers where the combination of temperature and humidity causes hazardous heat conditions. We assess changes in the magnitude and duration of dangerous heat, attribute those changes to shifts in temperature and/or humidity, and investigate possible links with amplified warming over high-latitude land. These results provide further motivation to reduce emissions of heat-trapping gases, and they will assist decisionmakers and planners in preparing for a future with an increased threat of fatal heat.

Gold plays a pivotal role in modern industry, technology, and national defense. While gold mining significantly drives economic development and employment, the disposal of mine tailings can facilitate the migration of potentially toxic elements (PTEs), leading to environmental contamination. This study focuses on the already exploited Wangjiawaizi gold mine in the Liaodong region, where 47 soil samples were collected from the surrounding areas. The study evaluates the pollution levels of potentially toxic elements (PTEs) in the soil and assesses the associated human health risks. Additionally, the PMF model was employed to analyze the sources of different elements. The geo-accumulation index results reveal the pollution intensity of heavy metals in the soil, ranked as As > Pb > Zn > Cd > Cu > Cr > Ni > Hg. The potential ecological risk index (RI) for PTEs in the study area is calculated at 443.53, indicating a high pollution level. Human health risk assessment highlighted oral ingestion as the dominant exposure pathway, and particular attention should be paid to the carcinogenic risk of arsenic (As) to children. Positive Matrix Factorization (PMF) resolved four pollution sources: agricultural activities (primary source of Hg), natural weathering (Cr, Ni, Cu), mining activities (Cd, Pb, Zn, As), and mining waste (As). Notably, As release was facilitated by two factors: mining activities and mine waste, with chemical oxidation and hydrological migration being the main pathways for As dispersion into the soil. Therefore, the specific human health risks posed by arsenic require urgent attention.

Rapti River (East) is a major Indo-Gangetic river in eastern Uttar Pradesh, India. Domestic sewage and industries along the river basin left the river polluted at numerous stretches. Water and sediment samples (n = 30) were collected across three seasons and analysed for heavy metal (HM) contamination, physicochemical properties, and associated risks. Pb exceeded permissible limits across multiple sites and seasons in water, while Ni exceeded the limit at S1 during monsoon; most HMs exceeded sediment quality guidelines (SQG), especially during monsoon. Zn and Cd were the highest and lowest in both compartments. Monsoonal flow altered sediment geochemistry, shifting Igeo status from unpolluted (Class 0) to highly polluted (Class 4) for Hg and Cd, with an extreme anomaly (Class > 6) for Hg at S5 during pre-monsoon. PLI and PERI showed low risk in water [max: 0.25; 31.17], and low to extremely high risk in sediment [PLI max: 4.10; PERI max: 1,030 during monsoon]. Non-carcinogenic risk (THI) was "low" in water and "high" in sediment; carcinogenic risk (TCR) exceeded safe thresholds in water during monsoon and was consistently high in sediment for children. PCA-based source apportionment identified lithogenic processes and upstream mining as primary sources of Cu, Zn, As, Cr, and Ni; industrial effluents contributed Pb and Cu at mid-to-downstream sites; and Hg reflected localized industrial inputs, with monsoonal flux as the primary basin-wide redistributor. Low DO and high BOD, TDS, and EC characterized urban and industrial stretches. Lead, arsenic, chromium, and nickel were the most concerning metals for long-term ecological and human health risks, underscoring the need for continuous monitoring and regulated management of HM contamination in the Rapti River Basin.

Wheat is a globally significant cereal crop and constitutes a primary source of essential nutrients for human consumption. Comprehensive understanding of the mineral composition of wheat grains, including the distribution of minerals among the whole grain, bran, and germ, as well as the nutrient concentrations in various bread types, is essential for accurately evaluating its nutritional contributions. This study aimed to quantify the concentrations of three essential minerals—potassium (K), calcium (Ca), and magnesium (Mg)—in wheat grains cultivated across Iran under both irrigated and rainfed agricultural systems and mineral partitioning between the germ and bran fractions. A total of 1876 wheat samples, representing both irrigated and rainfed cultivation systems, were collected from diverse agroecological zones throughout Iran and their K, Ca, and Mg concentrations were measured. The mean concentrations of K, Ca, and Mg in the wheat grains were 4059 mg kg −1 , 394 mg kg −1 , and 1046 mg kg −1 , respectively. Notably, all three minerals exhibited significantly higher concentrations in irrigated wheat compared to rainfed wheat. Spatial analysis indicated regional variability in mineral concentrations: potassium and calcium levels were lowest in northern provinces, which may be attributed to higher precipitation, intensified cultivation practices, and resultant soil nutrient depletion. Magnesium concentrations demonstrated a contrasting pattern, with reduced levels in northern and western provinces and increased concentrations in eastern and southern regions. A weak yet positive correlation was observed between mineral concentrations (K, Ca, Mg) and wheat grain yield. Furthermore, mineral concentrations were highest in the bran fraction, followed by whole grain and germ. Among different bread types analyzed, Baguette, Taftoon, and Sangak exhibited the highest concentrations of K, Ca, and Mg. These findings underscore considerable regional variability in the mineral content of wheat grains cultivated in Iran and suggest that irrigated agricultural practices generally enhance the accumulation of essential minerals in wheat.