Earth and Environmental Sciences

The study characterised the content and spatial variability of heavy metals and selected biogenic elements (Cd, Co, Cr, Cu, Ni, Pb, Zn, Al, Ca, Fe, K, Mg, Mn, Na, P, N, C) in one-year-old needles of Norway spruce and Scots pine from 262 peatlands in Poland in the light of previous studies and norms, and to determine the relationship between the content of elements in needles and the types of Histosol (fibric, hemic, sapric). The spatial variation in heavy metal content in needles is not related to southern regions of Poland with high human footprint index which suggests that episodic local sources of pollution and transboundary transport of air pollutants are likely to influence their bioaccumulation. It is also not related to the type of Histosol or the pH of water and soil, which indicates that heavy metals accumulate mainly passively from the atmosphere. Despite exceeding the optimal values for some elements, heavy metal levels do not pose a toxicological risk. Elements such as chromium in spruce and cadmium in pine can serve as markers of the impact of industrial emissions on peatland ecosystems. The concentrations of the most studied elements in Poland were lower than in other European countries.

In this research, a new Zr-MOF/CuCo2O4 nanocomposite was successfully synthesized and evaluated for the efficient removal of tetracycline (TC) from aqueous solutions. The composite displayed significant adsorption capacity, fast kinetics, and outstanding reusability. Analytical techniques including FT-IR, XRD, FE-SEM, BET, TGA, DSC, NMR and zeta potential confirmed the formation and stability of the nanocomposite. Optimization of the removal conditions was performed using Response Surface Methodology (RSM) with a Central Composite Design (CCD), achieving > 99% TC removal under optimum conditions (pH 7.80, 17.50 mgL⁻1 TC solution, 7.0 mg adsorbent, 46 min). Thermodynamic studies indicated a spontaneous and endothermic process, while Kinetic analysis indicated that the adsorption process followed a pseudo-second-order model. The nanocomposite maintained high stability and efficiency over seven reuse cycles. Real-sample analysis using seawater and pharmaceutical wastewater confirmed its strong potential for practical environmental applications. These findings highlight Zr-MOF/CuCo2O4 as a promising adsorbent for removing antibiotics from contaminated water, contributing to sustainable water purification and pollution control.

Seagrasses are essential blue carbon ecosystems that are increasingly threatened by the accumulation of emerging global pollutants, such as microplastics (MPs). However, in regions like Sri Lanka, MP contamination within seagrass beds remains critically understudied. This study investigated MP abundance in seagrass blades (Thalassia hemprichii), sediments, surface water, and sea cucumbers (Holothuria atra) and characteristics of isolated MPs from six selected sites along the Southern and Eastern coastlines of Sri Lanka (Indian Ocean). The average abundances of MPs in seagrass blades, Holothuria atra, surface water, vegetated sediments, and non-vegetated sediments were 1.59 ± 0.11 items/blade, 177.19 ± 53.28 items/kg, 9.60 ± 0.97 items/m3, 69.72 ± 8.38 items/kg, and 33.61 ± 2.15 items/kg, respectively. In this study, fibers were the most prevalent morphological type of MPs, while blue was the most dominant color. Fourier Transform Infrared Spectroscopy (FTIR) analysis revealed the predominant presence of low-density polyethylene (LDPE) in all types of samples, followed by polyethylene terephthalate (PET), high-density polyethylene (HDPE), polypropylene (PP), and polyamide or commonly known as nylon (PA) in variable amounts. A strong positive correlation between MPs on seagrass blades and those in sediments and surface water suggests that MPs from both water column and seabed contribute to their accumulation on seagrass. Furthermore, the ingestion of MPs by sea cucumbers indicates the potential for fragmentation and trophic transfer within the seagrass ecosystem, enabling MPs to enter and move through the marine food chain.

Fe₃₂Cr₂₁Co₂₁Al₁₆Ti₅B₅ (Coating 1) and Fe₄₃Cr₁₆Co₁₂Al₁₄Ti₅B₁₀ (Coating 2) were deposited on Q235 steel substrates using high-velocity oxy-fuel (HVOF) thermal spraying. Slurry erosion resistance was evaluated using a jet-type erosion tester, and surface degradation mechanisms were analysed through microstructural characterization. Material removal occurred through a combination of ploughing, cutting, platelet formation, abrasive grooving, and cracking. Parametric analysis revealed that impact velocity was the dominant factor influencing erosion, contributing approximately 65-68% to the total wear rate, followed by impingement angle (≈ 30-32%). The relative significance of control parameters followed the order: impact velocity > impingement angle > erodent feed rate > erodent size. Scanning electron microscopy indicated that erosion mechanisms varied with impingement angle, with micro-cutting, mixed cutting, and furrowing dominating at low angles, while platelet formation governed material removal at near-normal impact. Both coatings exhibited the formation of protective passive layers, as evidenced by higher corrosion potentials (E_corr) compared with the substrate. Taguchi analysis identified the optimal erosion-resistant conditions as an impact velocity of 10 m s⁻¹, impingement angle of 30°, erodent feed rate of 160 g min⁻¹, and erodent particle size of 105 μm. Overall, the results demonstrate that erosion behaviour is governed by impact-driven deformation mechanisms, with platelet detachment acting as the primary failure mode at higher impingement angles.

Despite the potential of Green-Adaptive Green Infrastructure (GAGI) to increase the provision of ecosystem services, and to mitigate urban climate risks while maintaining biodiversity, there is a critical research gap in the empirical identification of the context specific factors in developing nations. Therefore, this study examine the critical barriers, drivers and potential strategies to optimize GAGI by employing satellite imagery and survey data of 1232 respondents from Pakistan. The research rationale stems from the need to align urban expansion with ecosystem resilience. By employing remote sensing techniques on satellite imagery data, land use land cover analysis highlights the significant urban expansion in the study area. For the empirical analysis, a deep learning-based hybrid structural equation modeling-artificial neural network analysis was used to capture linear and complex non-linear relationships. Findings reveal that integrated urban transformation, socioeconomic equity, governance, and infrastructural barriers were critical barriers to implementing GAGI. Among the drivers, economic development and innovation, environmental sustainability, and infrastructure integration and efficiency are the main drivers of GAGI implementation. Moreover, education and R&D support, GAGI incentives, government standards and regulations, publicity programs, and awareness are the potential strategies for implementing GAGI in Pakistan. Specifically, sensitivity analysis of artificial neural network identifies governance and infrastructural barriers, economic development and innovation, and incentives towards GAGI as the most influencing factors. The study highlights the need for GAGI and provides a reference for decision-making on GAGI implementation and landscape transformation in response to extreme climate changes.

Expansive soils exhibit pronounced swelling-shrinkage behavior, low shear strength, and high moisture sensitivity, posing significant challenges to the stability of geotechnical structures such as embankments and tailings dam slopes. In this study, a sustainable stabilization strategy integrating enzyme-induced carbonate precipitation (EICP) with iron ore tailings is investigated to improve the hydro-mechanical performance of expansive soils. A comprehensive experimental program was conducted to evaluate changes in unconfined compressive strength (UCS), swelling pressure (Ps), hydraulic conductivity (Ks), cohesion (c), and internal friction angle (φ). Microstructural characterization using scanning electron microscopy and X-ray diffraction was performed to examine calcium carbonate precipitation and its cementation effects within the soil matrix. The results demonstrate that the combined EICP-iron ore tailings treatment significantly enhances soil performance, with UCS, c, and φ increasing by approximately 113%, 48%, and 98%, respectively, while Ps and Ks decrease by approximately 98% and 69%. Furthermore, seepage and slope stability analysis using GeoStudio (SEEP/W and SLOPE/W) indicate that the stabilized soil achieves a markedly higher factor of safety (FoS = 1.896) compared to untreated soils. The findings confirm that the synergistic integration of EICP and iron ore tailings provides an effective, environmentally sustainable, and engineering-feasible solution for stabilizing expansive soils and improving slope performance in tailings dam applications.

Skin infections caused by strong biofilm Pseudomonas aeruginosa (P. aeruginosa) are considered a serious public health issue because of the increased resistance toward the currently available antibiotics. Consequently, innovative therapeutic strategies have emerged to address these challenging infections. Among them, phage therapy stands out, in which highly potent lytic bacteriophages (phages) are specifically selected to target and eradicate the responsible pathogens. In this study, Pseudomonas phage E21 was recovered from sewage, and it genetically belongs to the Lavrentievirus genus, Casjensviridae family. The genetic characterization of the isolated phage reveals the presence of highly potent lytic enzymes, which play a critical role in effectively suppressing the growth of the targeted pathogens. The phage has high stability patterns over a wide range of temperatures and pH values (65 ℃ and 3-11). Carboxymethylcellulose was used to formulate a hydrogel for the evaluation of the bacteriophage's efficacy against biofilm-associated wound infection in a suitable animal model. The result of the preclinical study confirmed the efficacy of isolated phage in the therapy of biofilm-associated wound infection.

The accumulation of agricultural lignocellulosic waste, specifically rice straw, and the environmental impact of fossil fuel combustion necessitate the development of sustainable waste-to-energy technologies. Bioelectrochemical systems enable a simultaneous recovery of energy and the treatment of organic waste utilizing a mixed culture of bacteria. This study investigates for the first time the performance of rice straw-derived substrates with different levels of complexity, i.e. rice straw, rice straw hydrolysate, and xylan, to simultaneously produce bioelectricity and biohydrogen, with the objective of clarifying the influence of substrate characteristics on system efficiency. Microbial fuel cells (MFC) result indicated that xylan exhibited the highest performance, achieving a maximum cell potential of 852 ± 27 mV and a power density of 8.88 ± 0.27 W/m², representing a 2.4-fold increase over the control. This was supported by a high Chemical Oxygen Demand (COD) removal efficiency of 97.88 ± 1.32%. The higher values of power density are attributed to the employment of a mixed bacterial culture (sludge) that has already adapted to the degradation of lignocellulosic biomass, hence boosting its utilization and conversion to electrons, resulting in higher electricity generation. Electrochemical analysis indicated efficient electron transfer, reflected by increased current responses at higher scan rates and low charge transfer resistance values. Electrochemical characterization via cyclic voltammetry and electrochemical impedance spectroscopy (EIS) revealed that rice straw-powered cell exhibited the lowest charge transfer resistance (3.37 Ω), correlating with the highest recorded electrical current (9.23 ± 0.25 mA). Conversely, the microbial electrolysis cell (MEC) results showed that rice straw hydrolysate was the optimal substrate for biohydrogen production, yielding a maximum rate of 9.26 ± 0.08 mmol/day.L and a COD removal of 94.92 ± 0.88%, significantly outperforming xylan-based systems and the control cells. These findings indicate that while simpler carbohydrate structures like xylan favor electricity generation in MFCs, the bioavailable nutrients in alkaline-pretreated hydrolysates are more effective for electrohydrogenesis in microbial electrolysis cells. This research provides a critical comparative framework for selecting biomass fractions and system configurations to maximize the efficiency of agricultural waste-to-energy conversion strategies.

Deep geological sequestration is an emerging strategy for managing high-salinity coal mine water. However, the migration behavior of fluoride (F−) during sequestration poses significant risks to groundwater safety and system stability. Elucidating the mechanisms of F− migration and transformation is therefore critical for ensuring the long-term safety of sequestration projects. In this study, static batch experiments were conducted to investigate F− migration and retention in sequestration formations with different lithologies. Three representative rock types were collected from the Liujiagou Formation (fine sandstone–muddy sandstone and fine sandstone–mudstone) and the Shiqianfeng Formation (medium sandstone–mudstone) in the Ordos Basin. The experiments were carried out under simulated formation temperature (55 C) using synthetic high-salinity water with varying initial F− concentrations (20–150 mg/L) and pH conditions (2.0–10.0). Adsorption kinetics and isotherms were analyzed using pseudo-first-order, pseudo-second-order, and Freundlich models, while water–rock interaction mechanisms were evaluated through ionic ratio analysis, chloro-alkaline indices, and sodium adsorption ratio. Results showed that F− removal suggests dominance of chemical adsorption, following a rapid–slow–equilibrium pattern, and was well described by the pseudo-second-order kinetic model and Freundlich isotherm (R2 > 0.999). F− migration was likely governed by coupled processes of cation exchange, competitive adsorption, and mineral precipitation–dissolution. Fine sandstone–mudstone exhibited the strongest F− fixation capacity (462.7 mg/kg) due to synergistic adsorption and precipitation. Sensitivity analysis identified pH as the most influential factor. These findings provide a experimental basis for predicting F− pollution and support the safe implementation of deep geological sequestration in high-salinity coal mine water management.

Novel mixed a starfish-like shaped magnetic iron-nickel alloy with Polyvinylidene fluoride (PVDF) matrix membranes were developed for water desalination using vacuum membrane distillation (VMD). This study highlights the alloy's unique morphology that coated by the hydrophobic polymer, which enhances water vapor transport through innovative pore formation and its permanent magnetic properties, distinguishing it from existing research. The membranes were characterized using scanning electron microscopy (SEM), Energy Dispersive X‑ray (EDX) analysis and mapping, Fourier Transform Infrared Spectroscopy with Attenuated Total Reflection (FTIR-ATR), and Thermal Gravimetric Analysis (TGA), along with measurements of Liquid Entry Pressure (LEP), static water contact angle, tensile strength, thickness, roughness, porosity, pore size and pore size distribution. Performance tests in a VMD system showed that the iron-nickel alloy increased membrane productivity by 47% compared to pristine PVDF membranes. The 0.2 wt% alloy with 14% PVDF achieved the highest porosity (74.32%) and flux (29.1 kg/m²·h), balancing surface roughness and structural integrity. In contrast, higher polymer content (18 wt% PVDF) negatively impacted porosity and led to performance trade-offs. Thus, this study emphasizes the critical interplay between porosity, roughness, thickness, and the magnetic properties of the alloy in optimizing membrane performance for VMD applications.

The common leopard (Panthera pardus) is critically endangered in Pakistan, yet quantitative habitat assessments remain absent from the Himalayan foothills. This study provides the first detection-corrected occupancy assessment of leopards in Margalla Hills National Park (MHNP) using four years of systematic camera-trap data (2021-2024) across 20 sites and 2,400 sampling occasions per season. We modelled seasonal occupancy and detection probability during summer (May-August) and winter (November-February) using environmental and anthropogenic covariates. In summer, the null detection model was top-ranked (weight = 0.59), while prey relative abundance index (RAI) was the strongest occupancy predictor (β = 3.18 ± 1.31 SE, P = 0.015; ψ = 0.39 ± 0.19 SE). In winter, prey RAI strongly predicted detection probability (β = 0.437 ± 0.071 SE, P < 0.001), and occupancy was highest at sites distant from human settlements (β = 4.72 ± 2.19 SE, P = 0.031; ψ = 0.89 ± 0.13 SE), indicating active human-area avoidance during a season of heightened spatial overlap. These findings demonstrate the primacy of prey availability in driving leopard space use and identify settlement proximity as a key winter occupancy determinant. Conservation requires prey management, seasonal regulation of human activity in core leopard zones, and landscape-level planning across the MHNP-Murree Hills-Ayubia corridor.

Stored grains are highly vulnerable to insect pests, which can cause significant post-harvest losses worldwide. The invasive bark beetle Pagiocerus frontalis (Coleoptera: Curculionidae: Scolytinae) poses a major threat to maize and avocado seeds, yet the influence of temperature on its biology and damage potential is poorly understood. Here, we examined the effects of six constant temperatures (13, 17, 21, 25, 29, 33 °C) on survival, development, reproduction, and grain damage. Beetles thrived under moderately warm conditions (21, 25 and 29 °C), with the highest progeny, grain damage, and weight loss at 25 °C, while extreme temperatures (13 or 33 °C) suppressed activity and reproduction. These findings clarify the species' thermal preferences and suggest that controlling storage temperature, either by cooling or heat treatment, could reduce pest-induced losses, providing a sustainable, non-chemical strategy for protecting stored grains. This information can also be incorporated into climate-based predictive models to forecast pest survival, establishment, and potential damage.

Tetracycline is an emerging environmental threat, as its presence in water may contribute to antibiotic resistance in microorganisms. In this study, starch and activated carbon were extracted from potato peels and subsequently modified via free-radical polymerization using acrylic acid to develop a novel, green composite hydrogel for efficient tetracycline removal. Various adsorption parameters were optimized using the Taguchi method, including initial contaminant concentration, pH, adsorbent dosage, and contact time. The optimal conditions for tetracycline adsorption by the starch-acrylic acid/activated carbon biocomposite hydrogel identified were an initial concentration of 10 mg/L, a pH of 7, an adsorbent dosage of 2.5 g/L, and a contact time of 80 min. The incorporation of activated carbon significantly enhanced the adsorption capacity from 29.58 mg/g (for the starch-acrylic acid hydrogel) to 52.63 mg/g (for the starch-acrylic acid/activated carbon biocomposite hydrogel). Adsorption equilibrium was best described by the Temkin isotherm model, while kinetic studies confirmed that the process follows the pseudo-second-order model for starch-acrylic acid/activated carbon biocomposite hydrogel. Additionally, thermodynamic evaluations indicated that the process was both spontaneous and exothermic. Despite a moderate loss in efficiency over 8 reuse cycles from 99.99% to 58.06%, the developed starch-acrylic acid/activated carbon biocomposite hydrogel demonstrates a highly favorable adsorption capacity compared to previously reported green adsorbents, establishing it as a promising candidate for sustainable tetracycline removal.

Cleaning of public spaces for human activities is commonly employed and reduces the microbial load in these spaces. However, the mechanisms underlying changes in microbial community structure following cleaning remain poorly understood. As a pilot study, we investigated the bacterial community in public toilets before and after cleaning using 16S rRNA sequencing and computational simulations, as well as bacterial colony growth assays and metabolic functional prediction. The results revealed that cleaning initially imposes a strong disturbance on bacterial communities, followed by continuous stochastic loss and a concurrent influx of bacteria, resulting in changes in community structure. Culturable bacterial communities before and after cleaning showed equivalent growth properties, indicating that cleaning did not alter the original environment. Cleaning time was positively correlated with colony number but not with colony growth capacity, suggesting that bacteria surviving cleaning were fast-growing or highly abundant taxa. Metabolisms related to methane and to diverse environments showed increased contributions to survival after cleaning. Taken together, cleaning-mediated changes in bacterial community structure in public toilets were driven by a stochastic process that renewed bacterial diversity. This study provides new insights into the disturbance and recovery dynamics of the microbiome in public environments subjected to cleaning.

Reliable prediction of suspended sediment concentration is essential for reservoir operation, sustainable water management, and river ecosystem restoration. However, accurate forecasting remains challenging due to the strong nonlinearity and nonstationarity of sediment transport processes, as well as the complex interactions between hydrological variability and sediment supply dynamics.This study develops an integrated forecasting framework that combines signal decomposition, feature selection, deep learning, and optimization. In the proposed framework, variational mode decomposition is used to decompose suspended sediment concentration time series into multiple subcomponents, enabling the separation of temporal patterns at different scales. An improved sparrow search algorithm calibrates the decomposition parameters and improves component stability. Multigene genetic programming is then applied to select informative subcomponents and reduce redundancy. A hybrid convolution-transformer network is constructed to model both local fluctuation patterns and longer-range dependencies within the selected components. Finally, the improved sparrow search algorithm(ISSA) optimizes the weighting of individual model outputs to form an integrated adaptive predictor. The framework is evaluated using daily streamflow(Q) and suspended sediment concentration observations from 1977 to 1986 at the Tangnaihai Hydrological Station in the upper Yellow River Basin. The results show high predictive accuracy, achieving a Nash-Sutcliffe efficiency of 0.9535 on an independent test period with consistently low error metrics. Overall, the proposed framework provides a robust and generalizable approach for suspended sediment concentration forecasting under complex environmental conditions.

The quality of the soil is greatly influenced by soil management practices that either raise or decrease the criteria of soil quality. Therefore, it is crucial to monitor our soil to determine whether agricultural practices have a major positive or negative impact on it. The Havza district in Samsun, Turkey, which is a semi-humid climate region, experiences intensive agricultural land use, diverse soil-forming conditions, and increasing pressure from management practices, making it a vulnerable agroecosystem necessitating monitoring. Thus, this research was carried out to assess and predict the soil quality of this region. 217 soil samples were collected from the study area, and 33 soil quality parameters were selected and analyzed. The data were subjected to the Integrated Quality Index (IQI) and the Artificial Neural Network (ANN). This is to support sustainable land management, productivity, and long-term soil conservation under increasing human and climatic pressure. In addition, the Total Dataset (TDS) of soil parameters was subjected to Principal Component Analysis (PCA), and 13 soil quality parameters were chosen for the creation of a Minimum Dataset (MDS), and spatial distribution maps of the study area were created. The result showed that the Soil Quality Index (SQI) determined by IQI was similar to that predicted by ANN, with R2 values of 0.999, 0.970, and 0.987 for training, validation, and testing, respectively. The distribution maps show sporadic low-quality areas within the interior, with the lowest quality in the northern middle part of the study area. The overall soil quality was classified as medium quality. Also, the distribution maps provide valuable information for land management, ecosystem management, and the sustainability of agricultural farmlands.

How oceanic crust forms and intraplate volcanism arises remains central to resolving the mechanisms driving Earth's dynamic evolution. Anomalously thick oceanic crust is conventionally attributed to thermal mantle plumes, yet large igneous provinces such as the Azores Plateau, with its 8-30 km thick crust, dispersed volcanism, and distinctive water-rich geochemical signatures, challenge this paradigm. Here we use geodynamic numerical models to show that a migrating ridge over a locally hydrated layer (0.1-0.4 wt.% H₂O), generated by dehydration of the Mantle Transition Zone (MTZ), can trigger upwelling and melting sufficient to produce a 10-20 km-thick crust. This mechanism accounts for the plateau's anomalous crustal thickness, long-lived volcanism, and volatile-rich mantle source. We propose that recycled water, a subduction legacy stored in the MTZ, acts as a primary driver of intraplate volcanism, providing an alternative to the classical stationary mantle plume model. This mechanism may also help explain the widespread contamination of large-scale upper mantle domains by subduction-related fluid signatures, as in the DUPAL and South Atlantic domains.

This study focuses on the Umm Lajj region, situated along the eastern margin of the Red Sea, Northwest Saudi Arabia. The study integrates aeromagnetic and land gravity data to characterize subsurface geological structures influencing groundwater aquifers and their potential susceptibility to seawater intrusion. Data processing and interpretation were conducted employing a suite of filtering techniques to enhance data resolution and interpretability. A Butterworth filter was applied to distinguish between deep-seated and near-surface anomalies, while source edge detection and depth estimation methods were utilized to delineate structural features. The results reveal prominent NNW-, NW-, and NE-oriented fault systems, as well as associated grabens and horsts, which are structurally linked to Red Sea rifting processes. A significant structural basin was identified in the central coastal area and is interpreted as a potential groundwater aquifer. Gravity anomaly patterns also highlight low-density, fault-controlled zones that are likely conduits for seawater intrusion into the aquifer system. Conversely, NW-trending magmatic dikes and uplifted blocks along the western boundary may serve as partial barriers, restricting the lateral movement of seawater. The combined interpretation of magnetic and gravity data offers a detailed structural framework that enhances the understanding of regional hydrogeology and contributes to future groundwater resource management. These insights are particularly valuable for evaluating the risks of seawater intrusion and the vulnerability of groundwater systems in arid coastal settings.

To identify the contamination characteristics and health risks of heavy metals in karst groundwater systems in northern China, the Shentou Spring area was selected as the study site. A total of 53 karst water samples were collected, and ten heavy metal elements (As, Cr6+, Al, Cu, Pb, Zn, Ni, Mn, Co, and Fe) were analyzed. Multiple statistical analysis, the single-factor pollution index method, the Nemerow comprehensive pollution index method, and a health risk assessment model were comprehensively applied to systematically evaluate the distribution patterns, pollution levels, and associated human health risks of the aforementioned elements. The results showed that the concentrations of Al, Fe, and Mn exceeded the Class III threshold of the Groundwater Quality Standard (GB/T 14848-2017), with the contaminated samples mainly concentrated along the Maguan River. Multivariate statistical analysis revealed that Ni, Mn, and Co primarily originated from natural background and industrial activities; As and Cr6+ were controlled by agricultural pollution; Cu and Pb were dominated by natural factors; Fe was associated with industrial and human activities; Al originated from industrial and agricultural production; and Zn had complex sources. Pollution assessment indicated that Al, Fe, and Mn were at polluted levels, whereas the other elements were at non-polluted levels, suggesting generally good water quality. Health risk assessment showed that the risk values of all heavy metals were below the maximum acceptable risk level (5 × 10−5 a−1). The total health risk through drinking water exposure was higher for children (2.90 × 10−5) than for adults (2.52 × 10−5); through dermal contact, the total health risk was higher for adults (4.88 × 10−7) than for children (3.63 × 10−8). Drinking water was identified as the primary exposure pathway for heavy metal health risks in the study area. In summary, the groundwater quality in the study area generally meets the standard requirements at the regional scale, but certain local areas exhibit potential contamination risks that deserve attention in subsequent water resource management.

Riverine fish communities are essential for the functioning of aquatic ecosystems and provide important ecosystem services for fisheries. Yet, anthropogenic environmental changes pose threats to fish communities and result in population collapses and reduced yields, underscoring the need to understand their stability from local to regional scales. In this study, we leverage long-term observational data of riverine fish communities in 108 hydrological basins across the globe to determine how anthropogenic activity, biodiversity, and habitat complexity jointly influence riverine fish community stability (i.e., temporal invariability of total fish abundance) at the site and basin scales. Our analyses show that anthropogenic activities represented by human footprint index decrease fish community stability across spatial scales; however, biodiversity and habitat complexity buffer these destabilizing effects by providing insurance effects at both site and basin scales. Specifically, biodiversity has consistently stabilizing effects across scales through enhancing the asynchrony within and/or among fish communities. At the basin scale, greater habitat area increases gamma stability by enhancing spatial community asynchrony. Our findings underscore the importance of conserving both fish biodiversity and habitat complexity to sustain the stability of riverine fish communities in the Anthropocene.

Abstract Bacteria resist toxic arsenite (As III ) in their environments by actively pumping the metalloid out of the cell via efflux pumps such as ArsB. However, the mechanism of extrusion remains poorly understood, which hinders the development of engineered bioremediation strategies. We report high-resolution cryo-EM structures of ArsB from the arsenic-tolerant bacterium Leptospirillum ferriphilum . ArsB adopts an inverted two-fold repeat architecture resembling that of other ion transporter (IT) superfamily proteins. Structures determined in the presence of arsenite and antimonite reveal that the metalloid substrates interact with polar residues at the core of the transmembrane domain primarily via hydrogen bonding. Mutagenesis and in vivo functional assays support these interactions. Our ArsB structures represent an ‘inward-facing’ conformation, where the metalloid-binding site is exposed to the cytoplasm, suitable for metalloid capture. Furthermore, we demonstrate that arsenite resistance conferred by ArsB varies with external pH, supporting that ArsB is a proton (H + )-coupled secondary transporter. Mutagenesis, in vivo functional assays, and pK a estimation imply that conserved aspartate residues near the metalloid-binding site likely mediate the H + -coupling mechanism. Our findings provide structural insights into metalloid recognition and H + /metalloid antiport in ArsB, laying a foundation for further elucidation of the molecular basis of toxic metalloid detoxification in bacteria.

Precise multivariate time series (MTS) forecasting, particularly in atmospheric applications like air quality monitoring, is still a challenging task because of high dimensionality, temporal correlations, and non-stationary interactions between features. The classical methods such as Auto Regressive Integrated Moving Average) ARIMA and isolated Long Short-Term Memory (LSTM) are likely to fail to capture nonlinear relationships and are highly sensitive to the scale of features, and Principal Component Analysis (PCA) based dimensionality reduction is likely to result in information loss. To mitigate these constraints, we introduce PIHS-Bi-LSTM-GRU, a deep learning hybrid model that combines PCA-ICA-based reduction of dimensions, multi-level hybrid scaling of features, and an improved Bidirectional LSTM- Gated Recurrent Unit (GRU) architecture with dual-layer normalization and dropout. Our approach begins by taking a weighted ensemble of Min-Max, Z-Score, and Robust scalers for stabilizing heterogeneous distributions of features. PCA is used to alleviate redundancy, followed by Independent Component Analysis (ICA) to yield statistically independent latent signals. The deep learning model subsequently learns temporal patterns from the transformed sequences. A new component-wise inverse transformation mechanism provides exact reconstruction in the original feature space. Comprehensive evaluation on actual air quality data shows that the proposed model considerably outperforms baseline methods in Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and [Formula: see text] on all features. The findings verify the effectiveness of the framework in picking up on intricate temporal-spatial relations and enhancing predictive reliability under multivariate prediction situations.

Groundwater exploration in arid regions of the Eastern Desert of Egypt requires an integrated understanding of the structural, geomorphic, and subsurface controls governing recharge and storage. This study presents a GIS-based groundwater potentiality model for a structurally complex rift-related zone along the southern Esh El Mellaha Block, between the Gulf of Suez and northern Red Sea. Sixteen topographical, meteorological, hydrological, and surface geological factors were systematically integrated with particular magnetic basement-depth modeling and subsurface fault architecture, which were weighted using the Analytical Hierarchy Process (AHP). Results demonstrate that regional tectonic configuration and structural geometry, rather than surface geomorphic factors alone, exert the primary control on groundwater distribution. High-potential zones are concentrated within major structural lows, including the Tarboul syncline, West Hurghada trough, and El Gouna fan system, where thick Quaternary deposits and enhanced infiltration prevail. The ENE-trending Bali Shear Zone acts as a key conduit for focused recharge by enhancing fracture permeability and linking the Gulf of Suez and Red Sea structural domains. Model validation yielded an AUC of 0.80, with balanced sensitivity and specificity (0.74), indicating reliable predictive performance. Single parameter sensitivity analysis confirms the robustness of the model and indicates that structural and geological factors exert the strongest control on groundwater potential distribution. The study emphasizes the role of structural architecture in groundwater assessment and supports future sustainable water-resource development in arid extensional tectonic settings.

The presented scientific study evaluates and quantifies the relationship of slope gradients to two fundamental types of rock masses in the Czech Republic. Investigating slope gradients within engineering geological zones of soil or rock and weathered rock masses (these areas were analyzed using a 1:50,000-scale map, whereas a previously published study used only a 1:500,000-scale map) is a fundamental scientific inquiry, as it compares the behaviour of these two basic mass types in engineering geology, geotechnics and geospatial analysis based on slope gradient values. Slope gradient is essentially the sole parameter observable on the surface across the entire country, allowing us to compare the behaviour of these two mass types. The study was divided into two parts, with the first study evaluating a group of engineering geological zones with Quaternary and pre-Quaternary geological structures (Study 1). The second study, which is of greater scientific significance (Study 2) assessed two groups of engineering geological zones with rocks and weathered rocks and their eluvium (rocks and weathered rocks), and zones with soil engineering geological characteristics. For all variations and zones, statistical characteristics were determined (average slope gradient, 25% quantile, 50% quantile, 75% quantile, and maximum slope gradient). It was found that the groups exhibit significant statistical differences in slope gradients in the Czech Republic. The most significant finding from Study 2 was that the group of engineering geological zones with rocks and weathered rocks and their eluvium (Group 2A) and the group of soil zones (Group 2B) had a difference between the minimums of average slope gradients of 3.0°, representing a 41% share of the total differences. In contrast, the difference between the maximums of average slope gradients in both groups of masses was even greater at 4.4°, representing a 59% share. When comparing the difference between the minimum and maximum of average slope gradients in the first (2A) and second group (2B), a difference of 5.6° (57% share of the total differences) was found in the first group, whereas in the second group, it was lower at 4.2° (43%). It is evident that soil masses and masses of rocks and weathered rocks must manifest differently due to primarily distinct physical-mechanical properties. This is logically reflected in slope gradients, as demonstrated and quantified in the Czech Republic through this study. Soil masses have lower slope gradients compared to masses of rocks and weathered rocks with their eluvium, which is entirely logical and corresponds to their material nature and the structure of the rock mass, reflecting in physical-mechanical properties observed on the surface in a single parameter, namely the assessed slope gradient.

Forest-based carbon assets are increasingly proposed as collateral for green credit, yet a central problem remains unresolved: a higher collateral valuation does not necessarily translate into greater lendable value. Here we develop a contract-consistent framework for pricing forest-based carbon collateral under uncertainty and apply it to the Ning'er afforestation case in Yunnan, China, covering 51,365 mu and 719,680 t [Formula: see text] e of expected carbon assets. The framework combines weekly carbon-price forecasting from thin and irregular China Certified Emission Reduction (CCER) trading data with threshold-based option valuation and a lender-oriented pledge-rate equation anchored to the observed three-year loan structure. Using weekly pre-loan transactions from 31 August 2018 to 30 September 2022, we show that incorporating uncertainty and timing flexibility raises the option-adjusted collateral value to CNY 71.87 million. However, once effective deliverability, downside protection and prudential threshold calibration are imposed, the model-implied lower-bound pledgeable amount is only CNY 5.92 million, below the realised loan of CNY 12 million. Sensitivity analysis further shows that financing capacity is jointly shaped by carbon price, effective deliverability, volatility identification and threshold calibration. Our results show that valuing forest-based carbon assets for lending is not a single-stage asset-pricing problem, but a constrained translation problem between economic collateral value and lender-recognised lendable value.