Earth and Environmental Sciences

Abstract Activated carbon derived from waste pomegranate peels was investigated for the removal of amoxicillin (AMX) from aqueous solutions. The prepared adsorbent exhibited a high BET surface area (1307 m²/g) and a well-developed micro–mesoporous structure. Under optimal conditions (pH 2, 0.05 g, 25 °C, 50 mg/L), a maximum removal efficiency of 97% was achieved, while 87% removal was maintained at near-neutral pH (pH 6). Increasing the initial concentration reduced removal efficiency due to adsorption site saturation, whereas increasing temperature decreased adsorption, confirming the exothermic nature of the process. Kinetic studies showed that the pseudo-second-order model provided the best fit, indicating that surface-controlled interactions govern the adsorption rate. Equilibrium data were better described by the Freundlich model, suggesting heterogeneous adsorption behavior, although the Langmuir model also indicated a high monolayer adsorption capacity (qₘₐₓ = 100 mg g⁻¹). Thermodynamic parameters (ΔG° = −6.13 to − 5.16 kj/mol, ΔH° = −15.59 kj/mol, ΔS° = −32.29 J/mol K) confirmed that the adsorption process is spontaneous and exothermic. The relatively low ΔH° value indicates that adsorption is predominantly governed by physisorption mechanisms. Overall, the results demonstrate that this low-cost and sustainable adsorbent is a promising alternative for efficient antibiotic removal from water.

The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultural systems a critical focus in risk management. This study presents a geospatially integrated monitoring framework, the Optimized Multi-Scale Adaptive Graph Neural Network (OMSA-GNN), designed to mitigate risks associated with nutrient instability in hydroponic and aeroponic environments. The proposed system leverages a Raspberry Pi–based IoT network to monitor complex interactions among microclimatic variables, plant physiological health, and nutrient concentrations, treating them as localized geospatial data points. To enhance decision-making under environmental uncertainty, an Improved Sparrow Search Algorithm (ISSA) is employed to optimize the predictive performance of the GNN. The OMSA-GNN model incorporates visual plant indices as a proximal remote sensing approach to enable early detection of physiological stress that may lead to crop failure. Evaluated using a lettuce growth dataset, the framework demonstrates superior performance in forecasting growth trajectories and managing resource-related risks compared to conventional static models. The results highlight a scalable approach for improving the reliability of urban food systems, where traditional land-based agriculture is increasingly vulnerable to natural hazards.

Rice is a significant food that plays a vital part in delivering nutrition to the world’s population. Hence, approaches for assessing rice yield have received considerable study. The amount of rice seedlings (density) is a main agronomic module. It is related to harvest and also plays a significant part in the survival rate. Unmanned Aerial Vehicles (UAVs) are prepared with lightweight sensors, which creates a substantial effect in the field of crop phenotyping. The UAV was effectively used to measure germination rates and density in an accurate and effective method that would otherwise be laborious and expensive to obtain when compared to manual valuation. In image processing, mainly over the applications of deep learning (DL) models, there was a notable academic search for the value of UAV images for varied agricultural monitoring tasks. This work develops a Rice Seedlings for Assessing Germination Rates and Density using Aerial Images with Hierarchical Deep Network (RSAGRD-AIHDN) model. The goal of this paper is to assess germination rates and seedling density in rice fields using remote sensing (RS) or UAV-based imaging techniques for improved crop establishment monitoring. To accomplish that, the image pre-processing stage is initially applied with dual stages, such as image acquisition and pre-processing, to ensure high-quality and consistent inputs. Furthermore, the RSAGRD-AIHDN model employs the ConvNeXt method for the feature extraction process. For rice seed detection and classification, the RSAGRD-AIHDN model implements ensemble models, namely stacked autoencoder (SAE), bidirectional temporal convolution network (BiTCN), and Deep Q-Learning (DQL). The experimental assessment of the RSAGRD-AIHDN method is performed under the aerial dataset of rice seedlings. The experimentation of the RSAGRD-AIHDN method portrayed a superior accuracy value of 98.68% over existing approaches.

-N, Sal, Tur, Cond, Chl a, TDS and TN. SIMPER analysis revealed an average dissimilarity of 53.30% in FGs between 2018 and 2023, with Lo (29.42%) and M (17.78%) contributing most to this difference. In conclusion, the aggravation of eutrophication following the regime shift was the key driver of changes in the phytoplankton community of Caohai Lake. Nutrient enrichment created a turbid, eutrophic habitat that favored the proliferation of FGs adapted to such conditions, highlighting the critical role of nutrient regulation in restoring the lake's degraded ecosystem.

Modification of craton margins influences topographic evolution, magmatism, and mineralization, though the scales, geometries, and driving mechanisms remain debated. Previous studies of the western North American craton margin focused primarily on the Colorado Plateau, invoking hydration by the subducting slab as the controlling factor. Here we construct a high-resolution 3-D shear-wave velocity model of this margin using full-wave ambient noise tomography. Our model reveals a distinct inward retreat of the high-velocity cratonic keel with depth along the entire margin, not limited to the edge of the Colorado Plateau, as previously thought. Two laterally confined (150-200 km wide) low-velocity zones, reflecting channelized lithospheric weakening, extend 500-600 km into the craton, reaching the Black Hills in two opposite directions. Their spatial correlation with strong mantle flows suggests that this flow can erode the craton itself. Kimberlite and basalt geochemistry data suggest that this entire margin undergoes progressive and laterally heterogeneous lithospheric erosion and may eventually delaminate, facilitating progressive inward and lateral lithospheric thinning. This progressive erosion process drives the migration of volcanism and regional topographic uplifts. Our findings provide insights into the detailed characteristics and mechanisms of lithospheric modification at craton margins, shaping the long-term evolution of cratonic lithosphere.

Sea ice primary production is a key component supplying carbon to higher trophic levels when few other resources are available. In bottom-ice habitats, this production is limited by light availability and nutrient supply from underlying seawater. Reports of low sea ice primary production from Greenland have reinforced the view that landfast ice is regionally unimportant. Here, we document a single early-spring observation of intense algal production in sea ice adjacent to marine terminating glaciers in West Greenland, an environment rarely examined in ice studies. The bloom included abundant pennate diatoms, including Nitzschia frigida, and reached a daily primary production of 146 ± 4.8 mg C m⁻² d⁻¹ and biomass accumulation of 42.4 ± 1.6 mg Chlorophyll a (Chl a) m⁻², exceeding previous Greenland observations. A biomass-specific production of 3.40 mg C mg Chl a⁻¹ d⁻¹ and maximum quantum yield (ΦPSII_max) of 0.44 indicated an active community. Strong silicic acid depletion in the presence of significant nitrate and phosphate concentrations suggested that silicic acid was the primary limiting nutrient within the ice. We propose that inflow-driven fjord circulation likely enhanced nutrient availability beneath the ice, while turbulence-driven fluxes across the ice-ocean interface represent a plausible mechanism for sustaining the observed high sympagic production during this sampling event. Sea ice in fjords with marine-terminating glaciers may therefore support high early-season production under favorable local conditions.

In this study, we reconstruct the Holocene environmental history of the Sinchang-dong wetland, a key archaeological site in South Korea, using a multi-proxy approach integrating diatom and phytolith assemblages with quantitative archaeological demographic data. A Bayesian age-depth model (ca. 9.0-1.0 ka) reveals contrasting ecosystem responses to the 8.2 ka and 4.2 ka climatic events and intensified human activity. Both events are marked by diatom-poor intervals and elevated chrysophyte/diatom ratios, indicating phases of hydro-ecological instability. Responses differed between the two intervals: the 8.2 ka event was followed by a more prolonged recovery, whereas the 4.2 ka event was followed by more rapid reorganization of the wetland system. A statistically significant regime shift at ca. 2.3 ka, identified using Sequential t-test Analysis of Regime Shifts (STARS), marks a transition from a climate-driven lacustrine system to an anthropogenically dominated agricultural landscape. This shift coincides with increased Oryza sativa phytoliths, adoption of iron farming tools, and regional demographic expansion. After ca. 2.3 ka, human activity surpassed climate as the dominant driver of wetland dynamics, illustrating the transition of small East Asian wetlands from climate-sensitive to human-dominated systems.

Coastal regions face increasing threats from a range of natural and anthropogenic hazards, such as sea level rise, storm surges, coastal erosion and flooding. These hazards are intensified by climate change and rapid coastal development, placing both human populations and ecosystems at significant risk. Vulnerability to coastal hazards is influenced by geographic, socio-economic and infrastructural factors, with densely inhabited areas in low elevation being particularly susceptible. In this regard, the coastal zones are exposed to composite ocean hazards, including extreme water levels, sea level rise and shoreline change rate are used to generate the coastal multi-hazard zone (CMZ). Further, the Exposure Index (EI) at the village and town levels along the east coast was calculated to fill the gap in localised risk assessment. The study highlights the coastal villages/towns exposed to different degrees along the entire east coastline of India, spanning from Tamil Nadu to West Bengal. The most vulnerable districts from each state are Nagapattinam in Tamil Nadu, Krishna in Andhra Pradesh, Jagatsinghpur in Odisha and the North 24 Parganas in West Bengal have recorded more villages/towns under very high EI that are facing frequent inundation and need immediate attention. The top five districts in terms of percentage of population exposed are Karikal, North 24 Paraganas, Nagapattinam, Jagatsingpur and Thanjavur. The study provides different scenarios by utilizing the EI at inhabited villages/towns, through mere exposure. Though most of the deltaic and riverine environments have recorded large areas under exposure, the objective classification of villages/towns in high exposure zones adopted in this study differs in results. Hence, the EI derived in this work helps policymakers to draw site-specific interventions and disaster managers to focus on preparedness and adaptive measures to mitigate risks associated with coastal hazards in order to build resilient coastal communities.

Facies classification and channel detection remain challenging in geologically complex reservoirs. Faulting, thin-bed effects, and wavelet interference strongly influence seismic responses in such settings. This study presents an integrated multi-attribute seismic workflow utilizing Self-Organizing Maps (SOM) to enhance facies classification and delineate gas-bearing channels within the Permian Rotliegend reservoir of the Groningen Field, the Netherlands. A 3D post-stack seismic volume and four wells were utilized. The workflow integrates seismic interpretation, colored inversion, and unsupervised machine learning. Principal Component Analysis (PCA) was applied to evaluate attribute variability, followed by sensitivity testing to refine the final attribute set used in SOM classification. The SOM workflow successfully delineates NW-SE to NNW-SSE gas sand channels, consistent with the known fluvial-aeolian depositional framework of the Rotliegend Formation. Validation against well-log-derived lithofacies shows good agreement. The results highlight that multi-attribute SOM analysis provides clearer channel geometry and improved facies discrimination compared to individual seismic attributes.

Rare earth elements plus yttrium (REYs) are increasingly recognized as emerging contaminants, yet their ecological risks in agricultural soils remain are poorly understood. This study assessed REY contamination and ecological risk in agricultural soils adjacent to an abandoned coal gangue pile in a post-coal-mining area of Chongqing, Southwest China. Geochemical characteristics, together with stratigraphic and paleogeographic context and multivariate statistical analysis, suggest that coal gangue likely serve as a conduit for REYs sourced from volcanic materials associated with the Emeishan Large Igneous Province (ELIP) and their weathering products into nearby soils. Pollution indices indicate site-wide REY contamination. The pollution load index (PLI) values of 1.72–3.30 (mean 2.19) classified all soils as polluted. Element-specific geoaccumulation index (Igeo) values were predominantly within 0–1 in 96% of cases, indicating uncontaminated to moderately contaminated conditions for individual REYs, with a few subsoil cases reaching moderate contamination. Despite a generally consistent contamination level across the 0–30 cm profile, the 0–20 cm horizon exhibited slightly higher ΣREY concentrations and elevated PLI and RI values relative to the 20–30 cm layer. The potential ecological risk index (RI) is dominated by moderate risk conditions, with strength risk cases occurring mainly in the upper 0–20 cm layer. Elemental contributions to RI ranked, from highest to lowest, as Lu, Tb, Eu, Ho, and Tm, which together accounted for about 63% of the index. The HREYs (Lu, Tb, Ho, Tm) contributed about 51% of the RI, underscoring their dominant role in shaping ecological risk. Future studies should extend toward integrated, region-wide assessments of REY contamination, ecological risk, and potential human exposure in agricultural soils affected by coal gangue piles derived from Late Permian Longtan Formation coal mining, in order to support risk-based environmental management in post-coal-mining regions.

Our understanding of how depolymerase sequence and structure determine substrate specificity is fragmentary due to the limited number of experimentally characterized enzymes. Here we show DepoCatalog - an experimentally validated collection of 129 recombinantly prepared Klebsiella phage depolymerases (90 enzymes produced in this study and 39 homologs from the literature), with specificity spanning 75 KL-types. Enzymes originated from podo-, sipho-, myo-, jumbo phages, and prophages. Using activity profiling, structural modeling, and domain dissection, we propose a five‑class framework that captures the architectural and functional diversity of these enzymes. DepoCatalog uncovers cross-reactivity and taxa‑specific enzymes. Structural comparisons indicate that specificity switching or extension is associated with modifications to the C‑terminal domain. We further hypothesize that podoviruses encoding up to two RBPs show greater receptor adaptability than jumbo phages with multiple specialized RBPs. Finally, we develop a publicly accessible, DepoCat dataset (https://depocat.uwr.edu.pl) for specificity, structural classification and comparison of newly identified depolymerases.

Biochar has improved soil fertility and crop productivity in various agricultural and industrial activities. This study assesses the application and characterization of biochar and compost derived from chicken excreta (CEB) and groundnut shells (GSB), evaluating their potential to enhance soil nutrient content and support sustainable agriculture. Collected samples were analyzed for proximate, heavy metals, and physicochemical parameters using FTIR and AAS. Results were analyzed using descriptive and two-way ANOVA statistical analysis for SPSS, and were compared with the WHO and USEPA set standards for soil quality. The obtained results for CEB, GSB and CP gave mean values (%) ranging from 1.13 ± 0.01-3.53 ± 0.01 for moisture content, volatile matter (2.10 ± 0.10-46.01 ± 0.01), ash content (6.18 ± 0.01-11.50 ± 0.05) and fixed carbon (46.77 ± 0.01-85.33 ± 0.00) for proximate analysis, physicochemical parameters gave 8.45 ± 0.01-9.35 ± 0.01 for pH, cation exchange capacity (5.33 ± 0.01-6.33 ± 0.01%) and organic carbon (5.97 ± 0.01-6.26 ± 0.01%), heavy metals gave 0.00±0.00-20.72±0.00 mg/kg for Zn, Cr, Pb and Cd for CP, CEB and GSB, and were significantly different at (p < 0.05). Results obtained were below the set standards by the WHO and USEPA for heavy metal concentration in soil for plant growth. The results demonstrated distinct properties of biochar derived from chicken excreta, groundnut shell, and composted biomass. Biochar exhibited lower moisture and volatile matter, and higher fixed carbon content, indicating its stability and suitability for long-term storage, suggesting its potential for carbon sequestration. In conclusion, essential mineral content (Nitrogen, Phosphorus, Potassium, Sodium, Calcium, and Magnesium) found in biochar and compost highlighted their potential to improve soil fertility, thereby providing a sustainable alternative to synthetic fertilizers in agricultural applications.

Microplastic (MP) pollution in freshwater ecosystems is an emerging environmental threat yet remains poorly documented in ecologically sensitive wetlands. Here, we present the first integrated assessment of MP occurrence, distribution, polymer composition, and associated risks in the surface waters of Loktak Lake (Manipur, India), a globally unique floating lake system and Ramsar site. Surface-water samples were collected during the pre-monsoon season (March 2024) from the 0-20 cm surface layer from 13 geospatially distributed sites. MP abundance exhibited pronounced spatial heterogeneity, ranging from 8 to 82 particles L⁻¹ (mean 47 ± 23.8 particles L⁻¹). MP hotspots are concentrated near densely inhabited islands, floating hutments, market-associated zones, and active fishing areas. Spatial concentration variation was statistically supported through a strong monotonic association between MP abundance and the anthropogenic intensity. Size profiling showed a dominance of small particles, with particles < 2000 μm accounting for 91.38% of total MPs, highlighting high bioavailability potential. Predominant MP morphotypes included fibers and fragments. Polymer identification confirmed the presence of common consumer and activity linked polymers, including polypropylene (PP), polyethylene terephthalate (PET), polyamide (PA), and polyvinyl chloride (PVC). EDX analysis revealed associated trace metals including Pb, Ru, and Pt, indicating potential polymer-metal interactions. An environmental risk evaluation integrating indices indicated elevated ecological concern in high-activity zones. The estimated human health risks (non-carcinogenic and carcinogenic) via ingestion remained within acceptable thresholds under current exposure assumptions. However, the presence of carcinogenic polymers such as PVC raises significant concern. This study provides a critical baseline to guide monitoring, wetland management, and targeted source-control interventions in Ramsar and high-value freshwater ecosystems.

Accurate and reliable crop yield prediction before harvest is essential for informed decision-making, food security, resource optimization, climate change mitigation, and smart agricultural systems. Deep learning has significantly improved accuracy in predicting crop yields as compared to conventional methods. However, some challenges such as unstable gradients, single-model constraint, limited generalizability, and lack of interpretability still persist. Gradient instability is particularly acute in agricultural settings, where early-season weather anomalies influence final yield through long physiological lags, yet back-propagated signals in deep hybrid architectures often vanish or explode before reaching the responsible input layers. These gaps necessitate an innovative and composite problem solving approach. This paper presents a novel MHCNN-LSTM-MHA model which fuses multiple deep learning paradigms of Multihead Convolutional Neural Networks, Long Short-Term Memory, and Multihead Attention Mechanism to improve the prediction accuracy. This study employed a comprehensive dataset on U.S. soybean crop with features like weather conditions, soil properties, management practices, and historical yields. We tested the proposed model against single component models (CNN, LSTM, CNN-LSTM) and benchmarked models (CNN-RNN, Interaction Regression, CNN-DNN). The proposed MHCNN-LSTM-MHA model achieved an RMSE of 3.75 bushels per acre and [Formula: see text] of 0.905, showing a 9.86% improvement over the previous best benchmark on the same dataset. This performance is attributed to the advanced hybrid architecture that effectively model complex feature interactions and dependencies. The four-headed MHA improved generalizability and enhanced interpretability by dynamically prioritizing important features and time steps, while mitigating bias, in alignment with crop growth dynamics. SHAP-based interpretability revealed agronomically consistent feature importance, showing that crop yield in more sensitive to variations in weather components (precipitation, solar radiation, vapor pressure) exhibiting higher impact on yield outcome and moderately sensitive to soil properties (pH, Clay content). The contribution of this work is the enhancement of predictive accuracy alongside SHAP-based interpretability, which is essential for advancing explainable AI in crop yield prediction and facilitating its integration into smart agriculture systems.

Microbially induced calcium carbonate precipitation (MICP) faces persistent obstacles in transportation subgrade stabilization because rapid ureolysis can trigger premature clogging and pronounced spatial heterogeneity. This study develops a construction-oriented biocementation strategy that uses the urease inhibitor N-(n-butyl)-thiophosphoric triamide (NBPT) to regulate reaction kinetics and improve treatment uniformity under groundwater-relevant conditions. A multivariate optimization framework was established by jointly evaluating inhibitor dosage, cementation solution concentration, biological-to-chemical ratio, ambient temperature, and pH buffering. The optimal formulation was achieved at 0.1% NBPT, which delivered an unconfined compressive strength of 2.53 MPa compared with 2.72 MPa for the inhibitor-free control, indicating that strength was largely preserved while reaction kinetics were moderated. Calcium carbonate deposition proceeded steadily over 72 h and reached a carbonate content of 11.2 kg/m³ under the optimized protocol. This mineral accumulation reduced hydraulic conductivity from 1.7 × 10⁻³ m/s to 6.4 × 10⁻⁵ m/s, corresponding to a 96.3% decrease, while avoiding localized pore occlusion. Under simulated AASHTO T307 cyclic loading, the optimized treatment achieved a resilient modulus of 152 MPa and limited residual deformation to 0.32 mm per 1000 load cycles at an 80 kN axle load. The proposed NBPT-controlled MICP framework provides an operational window that balances strength gain, hydraulic functionality, and field implementability for subgrade applications.

Metal–organic frameworks (MOFs) have a high capability for the structure of membranes and photocatalysts to remove organic pollutants from water. In this study, a nanofiltration/photocatalysis coupled system was used to remove three antidepressant drugs—fluoxetine hydrochloride (FLX), sertraline hydrochloride (STL), and paroxetine hydrochloride (PRX)—from water. The thin film nanocomposite (TFN) nanofiltration membranes were loaded with varying amounts of NH2-MIL-101 (Fe) to filter the selected antidepressants under initial concentration of 10 mgL−1. The concentrated pharmaceuticals from the rejected stream of the nanofiltration system were then fed into the photocatalysis process. The NH2-MIL-101 (Fe)/TiO2 nanofibrous photocatalyst, in the presence of peroxymonosulfate (PMS) and solar light irradiation, was used to remove the antidepressants. After 48 h, the permeation fluxes and rejection percentages for FLX, PRX, and STL were 37.1 Lm−2.h−1 & 98.52%, 35.1 Lm−2.h−1 & 98.12%, and 37.8 Lm−2.h−1 & 98.74%, respectively. The final concentrations of FLX, PRX, and STL in the rejected stream after 48 h of membrane filtration experiment were 37.02, 35.06, and 37.80 mgL−1, respectively. The photocatalytic degradation efficiency of FLX, STL, and PRX using 0.5 gL−1 NH2-MIL-101 (Fe)/TiO2 nanofibrous photocatalyst, in the presence of 2 mM PMS and solar light irradiation, was 99.68%, 99.35%, and 99.21% during 30 min at a pH of 9. These results demonstrate that the coupling of membrane separation and photocatalysis processes, using MOFs-based TFN nanofiltration membranes/photocatalyst, is an effective strategy for treating pharmaceutical wastewater.

Classical risk factors for cardiovascular disease (CVD) are well established. Although the association between air pollution (AP) and CVD is also well recognized, its integration into cardiovascular mortality risk prediction models remains limited, particularly in the context of machine learning approaches. We conducted a study within the prospective EP-PARTICLES cohort to quantify the improvement in CVD mortality risk prediction achieved by integrating environmental factors into prediction models using the ‘ePM-years Index’ which reflects the proportion of cumulative long-term PM2.5 exposure exceeding a reference threshold relative to total exposure and advanced machine learning (ML) techniques. The study included 6935 patients (mean age 64.5 ± 10.1 years; 46% women) with a median observation period of 3140 days (IQR 2377–4169 days). We analysed 21 classical risk factors with CV death as the primary endpoint. The ‘ePM-years Index’ was calculated for each patient. The χ2 test and Monte Carlo strategy selected the most discriminating predictors. ML techniques were applied to improve the accuracy of mortality risk predictions and to identify key risk factors. Among the patients, 2646 (38.2%) had prevalent CVD, and 927 (13.4%) died from CVD. Feature selection identified 14/21 (66.7%) most discriminative predictors. The best ML model without the ePM-years Index achieved the Matthews correlation coefficient (MCC) of 0.281 ± 0.021, correctly classifying 72.59% of patients. Adding the ‘ePM-years Index’ improved the ML model’s performance to an MCC of 0.657 ± 0.027, correctly classifying 92.75% of patients. The Area Under the Curve for the ML model exploiting the ‘ePM-years Index’ ranged from 0.88 to 0.91. Long-term exposure to PM2.5 was associated with higher CVD mortality risk in high risk groups. ML incorporating PM2.5 demonstrated greater predictive abilities and clinical utility. Including environmental factors into CVD mortality risk scales improves risk stratification, and ML tools can help build simpler, more clinically useful models. Trial registration The study was registered at ClinicalTrials.gov (NCT05198492).

Pan evaporation (Epan) is one of the crucial parameters in hydrological studies, sustainable agricultural development, and water resources management. Predicting Epan remains a challenging problem among researchers worldwide because of its dependency to the diverse climate elements. Hence, it is necessary to precisely predict Epan time series through establishing reliable predictive models. A Chebyshev Polynomial-Based Kolmogorov-Arnold Network (CKAN) is proposed in this study for Epan prediction of two stations located in Australia (Perth and Sydney). Besides the CKAN, three deep learning methods comprising Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformer (TFR), and two machine learning models, namely Classification and Regression Tress (CART) and eXtreme Gradient Boosting (XGBoost) were also developed. The findings demonstrated that the proposed CKAN model performed better than other methods used for predicting Epan at both the stations. Two interpretable techniques, including Shapely Additive eXplanations (SHAP) and local interpretable model-agnostic explanations (LIME) were used to reveal the most important inputs. The outcomes for the superior CKAN model under full-input scenario indicated that solar radiation at Perth and minimum temperature at Sydney were found to show most contributions for the global SHAP method, whereas in the selected samples of LIME, mean temperature at Perth and relative humidity at Sydney were generally emerged as the influential input parameters. Finally, a K-fold cross validation technique was utilized for the superior CKAN model, denoting the effectiveness and generalizability of proposed CKAN for prediction of Epan.

Airborne bacteria in coastal zones and the marine influence on them remain poorly understood. Here, we investigated airborne bacterial communities at a coastal site in Ostend, Belgium, during late spring and summer using Nanopore full-length 16S rRNA gene sequencing. By comparing glass and quartz fiber filters across multiple sampling durations, we found that quartz filters with 4-12 h sampling provided sufficient DNA while avoiding longer sampling durations that may introduce bias. These samples were characterized by air mass trajectories, environmental conditions, and comparison with bacteria detected in local seawater. Air mass origin and temperature were associated with bacterial community composition. Samples associated with oceanic air masses, particularly under conditions favorable for sea spray aerosol generation, tended to show greater overlap with bacteria detected in local seawater. Proteobacteria, Firmicutes, Bacteroidota, and Actinobacteriota predominated across samples. Marine-influenced aerosols tended to show higher relative abundance of Gammaproteobacteria and higher predicted representation of pathways related to protein export and diverse metabolic processes, whereas less marine-influenced samples showed greater representation of chemotaxis- and xenobiotic-associated metabolism pathways. Potential habitat affiliations suggested stronger ocean-related signatures in marine-influenced aerosols and broader terrestrial affiliations in less marine-influenced samples. These findings highlight marine-terrestrial interactions in shaping coastal airborne bacterial communities.

Prolonged radiation exposure continues in Fukushima Prefecture, Japan, due to the presence of long-lived artificial radionuclides (mostly 134Cs and 137Cs) discharged from the Fukushima Daiichi Nuclear Power Plant in 2011. Extensive decontamination has reduced the ambient gamma dose rates, allowing the government to lift evacuation orders. However, the external effective doses additionally received from the discharged radionuclides should be evaluated by considering the heterogeneous spatial distribution of ambient gamma dose rates from primordial radionuclides (238U- and 232Th-series elements and 40K) in the affected areas. The present study aimed to comparatively evaluate the doses of artificial and primordial radionuclides in Kawauchi Village and Tomioka and Okuma Towns by discriminatively measuring the dose rates associated with these radionuclides using a car-borne survey technique, thus elucidating the prolonged radiological impact over a decade after the nuclear accident. The results showed that the annual external effective doses of 134Cs and 137Cs ranged from 0 to 1.7 mSv (median range: 0.04–0.55 mSv), whereas those of primordial radionuclides ranged from 0.11 to 0.35 mSv. At more than 90% of the 938 measurement points, the annual doses of 134Cs and 137Cs were well below 1 mSv, which represent the lowest value of the reference level band (1–20 mSv) used for existing exposure situations conceptualised by the International Commission on Radiological Protection. The additional external dose remained below 1 mSv and was of the same order of magnitude as the external dose from the primordial radionuclides because the evacuation order was lifted. Government bodies can incorporate such data to promote public understanding of radiation in the affected areas to reduce concern surrounding radiation exposure on returnees, migrants, and evacuees.

This study details the synthesis and application of a novel one-part geopolymeric hybrid composite (OP-GPHC) derived from glauconite, talc, and olive seed waste-based activated carbon for the efficient sequestration of Congo red (CR) dye from contaminated water. The hybrid binder was synthesized by impregnating activated carbon-based biogenic waste into a thermally treated glauconite/talc matrix, followed by alkali-activation with NaOH. Comprehensive characterization via XRD, FTIR, BET, TG/DTG, FESEM/EDX, and elemental mapping confirmed the material's exceptional adsorptive properties. A Box-Behnken design (BBD) optimization established the optimal operational conditions: pH 2.0, adsorbent dosage 0.07 g/ 25 ml, contact time 77.5 min, initial CR concentration 150 mg/L, and temperature 328 K, achieving a removal efficiency of 99.2%. Equilibrium data were best described by the Langmuir isotherm, yielding a maximum adsorption capacity of 367 mg/g at 328 K, while kinetic data followed the pseudo-first-order (PFO) model. Advanced statistical physics modeling revealed a multimolecular adsorption mechanism with a vertical orientation of CR molecules at the active sites, independent of temperature, and binding energies in the range of 19.25-21.46 kJ/mol, consistent with physisorption dominated by hydrogen bonding, π-π interactions, and electrostatic forces. Thermodynamic parameters confirmed the endothermic and spontaneous nature of the process. The OP-GPHC adsorbent exhibited excellent reusability (87.8% after five cycles) and a low production cost of $0.032/g. Based on batch-derived data, treatment of 100 L of CR-contaminated water (50 mg/L) is projected to cost approximately $1.68, highlighting its strong potential for industrial-scale tertiary treatment applications.

Droughts are among the most damaging natural hazards, exerting severe impacts on ecosystems, economies, and societies. While typically catastrophic, their effects can vary across regions, including polar areas where outcomes may differ unexpectedly. Accurate forecasting is therefore essential for climate adaptation and resource planning. This study analyzes monthly precipitation data from 1970 to 2025 across four Norwegian cities (Bergen, Kristiansand, Oslo, and Tromsø). Standardized Precipitation Index (SPI12) values were calculated and used as inputs to predictive models based on Convolutional Neural Networks (CNN). To enhance performance, the CNN framework was combined with Random Forest (RF) and also optimization techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), as well as signal decomposition methods such as Variational Mode Decomposition (VMD) and the Tunable Q-Factor Wavelet Transform (TQWT). Four input configurations were tested, and results show that CNN-TQWT hybrid models consistently outperformed other approaches across all study sites, highlighting their potential for reliable drought prediction in diverse climatic settings. Specifically, the most effective models were identified as: Bergen: CNNTQWTM03 (R = 0.9732, NSE = 0.9231) Kristiansand: CNNTQWTM03 (R = 0.9879, NSE = 0.9568) Oslo: CNNTQWTM02 (R = 0.9727, NSE = 0.9172) Tromsø: CNNTQWTM01 (R = 0.9777, NSE = 0.9395). The findings highlight the superior accuracy of the TQWT decomposition method across all stations, underscoring its potential to support decision-makers in developing effective agricultural and water management policies. Furthermore, the findings of this study will contribute to the formulation of drought preparedness and development plans. By providing a technical foundation for proactive planning, these results will assist in mitigating the impacts of drought through more resilient management strategies.

The ecosystem of plateau mountain cities is highly vulnerable to damage and possesses limited ecological resilience. Influenced by human activities, they face prominent issues such as ecosystem degradation. Scenario-based analysis of land use and ecosystem services (ESs) is therefore essential for ensuring ecological sustainability in such regions, taking Kunming, a typical plateau mountain city, as the study area. We employed an integrated modeling approach that combined Markov-FLUS and InVEST to examine water yield, soil conservation, carbon storage, and habitat quality under different land use scenarios from 2030 to 2050. Trade-offs/synergies among ESs were examined through Spearman correlation analysis. Results demonstrate that: (1) Under the natural development scenario (NDS), construction land will expand substantially. Under the cropland protection scenario (CPS), construction land will expand slightly, and cropland will increase. Under the ecological protection scenario (EPS), forest land and grassland will increase, while construction land will remain almost unchanged. (2) Under NDS and CPS, water yield will increase, mainly in the metropolitan center of Kunming and its adjacent areas. However, soil conservation, carbon storage, and habitat quality will decrease in these areas. Under EPS, water yield will decrease, while soil conservation, carbon storage, and habitat quality will increase. (3) There will be a trade-off enhancement of ESs in Kunming under NDS, while synergies will be enhanced under CPS and EPS. This study provides a scientific foundation for the adjustment of land use structure, the optimization of ecosystems, and sustainable urban development in cities with similar plateau mountain characteristics.

Mosquito host contact determines arboviral transmission efficiency. Aedes aegypti and Culex quinquefasciatus are important vectors of dengue, Zika, chikungunya, West Nile virus, and other arboviruses, yet their feeding patterns remain poorly characterized in many tropical regions. We used bloodmeal metabarcoding to detect DNA from multiple vertebrate species within individual blood-fed mosquitoes collected from rural Guatemala and south Texas, USA. Mosquitoes were collected using aspiration in Guatemala and BG-Sentinel traps in south Texas. We calculated forage ratios (FR) to assess host utilization relative to availability. In Guatemala, Ae. aegypti exhibited strong anthropophilic behavior (human DNA: 90.2% of bloodmeals and FR = 3.62 (95% CI: 2.70-4.54), indicating significant over-utilization. In south Texas, Ae. aegypti strongly over-utilized dogs (88.2% of bloodmeals; FR = 4.65, 95% CI: 2.43-6.87) while under-utilizing humans (FR = 0.53, 95% CI: 0.25-0.81). In Guatemala, Cx. quinquefasciatus displayed high anthropophilic behavior (85.3% of bloodmeals; FR = 2.60, 95% CI: 2.24-2.97). Mixed bloodmeals were common in both species at both sites (19.5-85.3%), with up to four host species detected in single mosquitoes. These results demonstrate that mosquito host selection is variable and context-dependent and underscore the need for location-specific surveillance to inform vector control strategies.