Earth and Environmental Sciences

Abstract The geological origins of iron oxide-apatite (IOA) rocks, important resources for iron and rare-earth elements, are intensely debated. Using triple oxygen isotope data, we here show that magnetite from IOA deposits near Kiruna, northern Sweden, and related igneous rocks contain high concentrations of oxygen derived from evaporitic sulfate. To explain these observations, we propose that the Kiruna IOA assemblage formed in response to massive assimilation of evaporites by silicate magmas. sulfate from the evaporites would have oxidised ferrous iron in these magmas, facilitating the formation of immiscible ferric iron-rich melts and/or magnetite, which then separated from the magmas to form ore deposits. Ferric iron-bearing fluids with low Δ′ 17 O values, exsolved from the silicate magmas or the ore-forming melts, would have crystallised additional magnetite. An inventory study reveals that Proterozoic and Cambrian IOA deposits have lower Δ′ 17 O values than post-Cambrian IOA deposits. This shows that the Δ′ 17 O values of global IOA deposits reflect the changing isotope composition of atmospheric O 2 incorporated by evaporitic sulfate over time, and demonstrates that oxygen released from evaporitic sulfate is a common component in IOA deposits.

Seafloor hydrothermal deposits with extremely high concentrations of gold were recently discovered in the Higashi-Aogashima knoll caldera hydrothermal field, Izu-Ogasawara arc Japan. We report here the discovery of ultra-high concentrations of "invisible gold" (up to 1.9 wt%) in pyrite from the sulfide mounds and a black smoker chimney using a secondary ion mass spectrometry (SIMS). A notable feature of this study is that quantitative gold concentrations were obtained from all analyzed pyrites due to the high sensitivity (7 ppb) and wide measurement range of the SIMS analysis. The concentrations of gold and arsenic in pyrite are higher than those in the whole rock on any hydrothermal sites within the caldera, suggesting that gold and arsenic are concentrated in pyrite. The gold concentrations in pyrite vary greatly depending on the site of the deposit and morphology of pyrite. The extremely high gold concentrations are found from colloform pyrites with high lead and/or copper concentrations in addition to arsenic concentration in pyrite, which might have induced the high gold concentration in pyrite. No gold nanoparticles were observed in high spatial resolution SIMS depth profiles of pyrite, and it is believed that gold and other elements are contained within the pyrite crystal structure.

The paleosol horizons on the lower terrace of Wadi Shuwayhi are the first buried soils from the Holocene Humid Period in Central Oman have been studied; they occur in alluvial sediments (terrace fill) deposited approximately 11.5 to 7 ka, with radiocarbon ages of soil organic matter range from 10,600 to 5300 cal BP (8700-3500 cal BC). The youngest buried soil horizons in profile KS3 show disturbances in soil structure, consistent with Early Bronze cultivation at 4800-4400 cal BP (2800-2500 cal BC). Pedogenic features in the buried A and B horizons of all paleosols show subangular blocky and crumb macro- and microstructure, bioturbation features, and secondary calcification within root channels. In two soils, tabular and lenticular gypsum is particularly pronounced. Although the organic carbon content is low at all sites, soil formation suggests earlier vegetation establishment during the Holocene Humid Period as is also known from other arid areas. Alternating phases of alluvial aggradation and lateral erosion, i.e. stability and instability of the wadi terraces, are reflected in the preserved, albeit relic paleosol horizons. The study describes and classifies the buried soils, place them within a stratigraphic framework, and evaluates their significance as proxies of the HHP as well as early human-environment interactions in Southern Arabia.

In the version of this article initially published, due to a file conversion error, in Fig. 1a, black trace lines for collision boundaries in the Carpathians and Apennines were disconnected from their respective arrow points. The figure is now amended in the HTML and PDF versions of the figure.

Understanding the relationship between biodiversity and both the functioning and stability of ecosystems has been a central focus of ecologists for decades. A step-change in our understanding of the biodiversity-ecosystem functioning relationship was enabled by explicit measurement of the additional functioning provided by biodiversity through comparing expected and observed yields in multi-species communities. However, we lack an equivalent measure for stability. Here, we quantify the net biodiversity effect on stability using model simulations and a microcosm experiment that exposes different phytoplankton species and their combinations to temperature increases and fluctuations. As an emergent property of communities, stability frequently exceeds the expected stability of the combined component species, leading to a net biodiversity effect on stability analogous to the effect on functioning. In our simulations, these effects depend on the strength of competitive interactions as well as species composition and their thermal optima. Experimentally, the stabilising effect of biodiversity is, however, non-linear, greatest for two-species combinations, and varies with both community composition and disturbance regime. Quantifying the net biodiversity effect on ecological stability advances our mechanistic understanding of the biodiversity-stability relationship, and provides crucial information to support ecosystem management and conservation.

Robust estimates of biodiversity change are essential to inform management and conservation, and to address global biodiversity loss. However, common temporal data limitations may compromise trend accuracy. Here, we test how temporal resolution influences biodiversity trends using 1,353 river invertebrate time series collected annually for ≥ 10 years across 18 European countries. We simulate reduced sampling frequencies and durations and compare these trends to the complete time series. Reducing frequency from annual to every 2-6 years resulted in 87-73% of sites matching in trend directions, but only 78-39% matching in magnitude. Reducing duration from 10 to 9-2 years resulted in 88-52% direction matches and 86-8% magnitude matches. Similar results were observed for longer time series ( ≥ 20 years). Additionally, a comparison of two real-world monitoring datasets with different temporal resolutions shows that 53% of sites matched in direction, but only 12% in magnitude. Our findings indicate that biodiversity change magnitude is more sensitive to temporal resolution than direction. Consequently, accurate estimates of both direction and magnitude require high-resolution time series, whereas lower-resolution data may only reliably capture direction. These results highlight the value and limitations of temporal biodiversity data, and help plan future monitoring.

Ecological stability is essential for maintaining ecosystem functioning, but may be imperiled by biodiversity loss. Although the scaling of diversity-stability relationships from populations to communities and metacommunities has been studied within single trophic levels, it remains poorly understood when considering interactions between trophic levels. Here, we utilize data collected from a large-scale forest biodiversity experiment to investigate the scaling of temporal stability from populations, to communities, and meta-communities in a plant-herbivore system, allowing us to disentangle the relative role of top-down and bottom-up regulation. We observe that biodiversity has generally stabilizing effects within and between trophic levels. Specifically, species diversity of herbivores shows strong stabilizing top-down effects by enhancing species stability and asynchrony of plants that cascade to higher levels of organization. In contrast, bottom-up effects play a much smaller role. Our study therefore highlights the importance of top-down processes in safeguarding plant stability across levels of organization, while simultaneously providing a framework that allows the investigation of the multi-layered nature of stability mechanisms that needs to be considered for a successful and sustainable ecosystem management.

Abstract Integrating water purification membranes with electrolysis for in situ hydrogen (H 2 ) production from seawater offers a rapid pathway to net-zero, but is limited by salt crossover and insufficient water production in existing approaches. Here we overcome these limitations by integrating osmotic membrane distillation (OMD) with alkaline water electrolysis (AWE). Driven by dual thermal and osmotic gradients to enhance salt-free water vapour transport, the OMD-AWE delivers a H 2 production rate of 60 kg m –2 day –1 with excellent stability over 500 h of continuous operation. To eliminate the external heating energy penalty of OMD, we propose a thermally symbiotic architecture that converts the AWE’s waste heat to OMD’s thermal driving force while OMD simultaneously providing cooling to maintain AWE optimal temperatures, as validated by our thermal-water-hydrogen model. This thermal symbiosis not only makes OMD-AWE energy self-sufficient with energy efficiency of 51 kWh kg(H 2 ) –1 but also establishes a self-regulating mechanism that phase-locks thermal driving force to fluctuating electrical inputs, synchronising water supply with demand to overcome renewable intermittency. Our approach enables flexible component matching and thermal self-sufficiency at any scale, providing a framework for membrane-integrated electrolysis, demonstrating both technical excellence and economic viability towards a sustainable hydrogen economy.

To quantify the hospital burden of short-term wildfire-specific fine particulate matter (PM2.5), we linked 184.5 million patient-level hospitalizations across 449 Brazilian regions from 2000–2019 with daily wildfire-specific and non-wildfire PM2.5 estimates at 0.25° resolution. Using wind-driven variation in wildfire-specific PM2.5 within a space-time-stratified case-crossover framework, we estimated the effects of exposure on hospitalization costs and length of stay. Each 1 µg/m3 increase in wildfire-specific PM2.5 was associated with higher hospitalization costs for all-cause, respiratory, and cardiovascular diseases by 0.36%, 1.59%, and 0.25%, respectively, and longer stays by 0.63%, 1.72%, and 0.68%. Asthma and heart failure showed the largest cost increases, while asthma and pneumonia showed the largest increases in length of stay. Overall, wildfire-specific PM2.5 accounted for US$755.6 million in hospitalization costs and 30.8 million hospital days, with stronger relative effects among individuals aged 0–19 years and higher burdens in central-west Brazil. This study links 184.5 million hospitalizations in Brazil to wildfire-specific PM₂.₅, using wind-driven variation within a causal framework. It finds higher hospitalization costs and longer stays for all-cause, respiratory, and cardiovascular diseases, with stronger effects in children.

The co-optimization of enhanced oil recovery and carbon dioxide sequestration in shale reservoirs is fundamentally constrained by the competing physics of molecular diffusion and pressure-driven convection-a challenge that existing data-driven optimization frameworks fail to address due to their black-box nature and lack of physical fidelity. Here, this study introduce a physics-informed adaptive ensemble surrogate-assisted Bayesian optimization framework (AES-BO) that synergistically integrates Gaussian process regression, polynomial response surface, and radial basis function networks. By dynamically weighting these surrogates based on real-time cross-validation error, AES-BO embeds physical priors into the optimization loop, enabling it to navigate the complex, non-convex parameter space of CO₂-N₂ hybrid huff-n-puff while respecting the underlying diffusion-convection trade-off. Applied to a field-scale shale oil model, our framework achieved a global optimum net present value of 64.2 million-outperforming state-of-the-art differential evolution-artificial neural network, differential evolution-support vector regression and particle swarm optimization methods by 2.2-2.8%-while accelerating computation by up to 82.7%. Global sensitivity analysis revealed that the economic outcome is dominantly controlled by injection rate and soaking time, but is critically governed by a strong non-linear coupling between cyclic injection volume and the multi-well production regime. The optimized strategy enhances the recovery factor by 8.23% by using CO₂ for nano-scale oil mobilization and N₂ for macro-scale pressure maintenance, and identifies a clear economic limit of three huff-n-puff cycles. This work establishes a generalizable, physics-guided paradigm for the intelligent design of low-carbon subsurface energy systems, directly linking operational decisions to fundamental transport physics.

Understory plants have long been considered rare in mangrove forests, leaving their ecological roles poorly understood. This limits our understanding of how below-canopy vegetation contributes to sediment carbon cycling and benthic habitat dynamics in mangrove ecosystems. We examined the widespread mangrove fern Acrostichum aureum in a neotropical mangrove system to test whether fern-derived organic matter influences sediment properties and benthic macrofaunal assemblages. We established a litter-manipulation experiment (fern litter exclusion, control, procedural control, and mangrove-litter plots) in Bahía Málaga, Colombia. Sediments were sampled in October 2016 and March 2017 for pH, total dissolved solids, carbon:nitrogen ratio, organic matter content, and organic carbon, while macrofauna (crabs, molluscs, and polychaetes) were sampled after five months of manipulation. Macrofaunal species composition did not differ significantly among stations or treatments, whereas multivariate sediment characteristics differed significantly among treatments. Organic matter showed a treatment-dependent temporal response, increasing in plots receiving litter inputs and remaining lower in fern-litter exclusion plots by March. In contrast, organic carbon and other sediment variables showed no significant treatment effects over the experimental period. Overall, fern-litter exclusion was associated with reduced maintenance of sediment organic matter but did not restructure macrofaunal assemblages over five months, indicating that longer timescales or broader environmental gradients may be required for detectable faunal responses to changes in understory-derived litter. Together, these findings highlight the contribution of mangrove understory vegetation to sediment organic matter dynamics and the importance of longer-term perspectives when evaluating faunal responses to understory change.

Biochar (BC) has been widely used for the lead (Pb(II)) immobilization in soil remediation. However, the mechanisms of dissolution-driven removal of Pb(II) by BC are still poorly understood. Herein, Pb(II) removal was investigated by differentiating BC- and dissolved organic carbon (DOC)-systems. As per the kinetics and isotherms, Pb(II) removal ranged from 31.11% to 80.92% in BC-system. This removal process was significantly influenced by dissolution-driven precipitation which contributed from 23.42% to 84.03% in DOC-system. During Pb(II) removal, an obvious reduction was observed in the released inorganic anions including carbonate (CO 3 2− ), sulfate (SO 4 2− ), and phosphate (PO 4 3− ). The CO 3 2− was a predominant contributor to facilitate Pb(II) precipitation. X-ray diffraction further confirmed the formation of cerussite (PbCO 3 ) and anglesite (PbSO 4 ) on BC surface after Pb(II) removal. Moreover, the decreased fluorescence responses of humic-like and fulvic-like substances were detected in DOC-system with the addition of Pb(II). At the same anion concentration, the enhanced Pb(II) amount was removed with increasing DOC concentration from 1 mg·L − 1 to 5 mg·L − 1 . These results indicated that released DOC exerted a significant contribution on Pb(II) precipitation due to its coagulation effect. This research provides mechanistic insights into the contributions of adsorption and dissolution-driven precipitation in overall Pb(II) removal through BC application.

Land use/cover change (LUCC) alters vegetation structure and carbon sequestration capacity, thereby reshaping regional carbon cycling. However, the mechanisms driving the spatial heterogeneity of carbon storage (CS) in rapidly urbanizing coastal regions remain insufficiently understood. In this study, the relationship between land use and CS was quantified using land-use data from 2000, 2010, and 2020 for the Chaoshan region. Future land-use patterns were simulated under three development scenarios using the PLUS model, CS dynamics were evaluated with the InVEST model, and the dominant drivers of CS spatial heterogeneity were identified using the Geographical Detector. The results indicate that between 2000 and 2020, artificial surfaces expanded significantly (889.23 km²), while cultivated land decreased substantially (782.98 km²), and wetlands nearly disappeared. Spatially, CS exhibited a pattern of low storage in the southeast and southwest and high storage in the central and north, a distribution primarily influenced by NDVI, DEM, and Slope. Notably, a strong interaction between DEM and NDVI (q = 0.62) highlights a critical terrain-vegetation synergy in shaping CS patterns. By 2030, CS is projected to decline under all three development scenarios, with the lowest reduction under the low-carbon development (LCD) scenario, emphasizing its significant advantage in mitigating carbon loss. This study proposes spatially targeted strategies integrating forest enhancement in high-carbon mountainous regions, compact urbanization with cropland protection in southeastern areas, and coastal-riparian wetland restoration, underpinned by carbon compensation mechanisms to balance development with ecological conservation.

The Salas y Gómez and Nazca Ridges (SGNRs) in the Southeast Pacific, recognized as an Ecologically or Biologically Significant Area (EBSA), host unique marine ecosystems with one of the highest rates of endemism on the planet. This study provides the first comprehensive trait-based assessment of seabird functional diversity in this globally significant region, focusing on their ecological contributions as top predators. Using at-sea abundance data from 11 oceanographic surveys (2014-2017) across 3,500 km of transects, we recorded 36 seabird species (8,179 individuals). We analysed functional diversity through ten foraging-related traits, including diet, foraging strata, and morphology. Multidimensional trait analyses revealed a seabird assemblage characterised by low functional richness (FRic = 0.0587), moderate-to-low evenness (FEve = 0.3649), and high divergence (FDiv = 0.6609), with non-random patterns confirmed by null models. Nesting (17 species) and non-nesting (19 species) groups showed distinct functional structures, with nesting seabirds exhibiting higher functional divergence and non-nesting seabirds greater functional evenness, though with 61% trait-space overlap. Low functional redundancy suggests that the loss of seabird species would likely translate into the loss of unique functional roles, potentially compromising ecosystem processes such as cross-system nutrient subsidies. With 73% of the SGNRs beyond national jurisdiction, seabirds face threats from unregulated fishing, plastic pollution, and seabed mining. These findings underscore the urgent need for conservation strategies under the High Seas Treaty (BBNJ treaty) to protect not only species richness but also functional roles, ensuring ecosystem resilience in this biodiversity hotspot of over 110 seamounts.

The canals in Dumki Upazila of Patuakhali district in Bangladesh are experiencing severe water contamination problems attributable to various human activities. This study aims to assess water quality and identify the major contaminants that affect the ecosystems of the canal. The physicochemical parameters, including dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), and electrical conductivity (EC), exceeds the permissible limits, indicating severe pollution and has negative impacts on the canal ecosystems. Moreover, dialogues with community residents and the responsible authorities identified untreated industrial effluents, domestic trash, and agricultural runoff as the major sources of contamination in the canal water. The expansion of the canal along with the uncontrolled disposal of garbage has exacerbated the environmental and social problems in the study area. The results demonstrate that the canal's water quality is insufficient for sustaining aquatic life and is unsafe for human consumption, thereby posing health hazards to the local community. Given these results, it is prudent to take immediate measures to reduce pollution levels of this important canal in Bangladesh.

Real-time water quality monitoring remains challenging due to the high latency of centralized laboratory analysis and the substantial computational payload of Machine Learning (ML) inference. While existing Internet of Things (IoT) frameworks deploy raw sensors, they frequently lack a standalone Edge calculation layer capable of operating completely independent of cloud infrastructure. This study proposes a mathematical-based Internet of Things Water Quality Index (IoT-WQI) architecture engineered specifically to act as an autonomous Fog/Edge Node for immediate first-line screening. The framework reduces continuous cloud dependency and network communication overhead by circumventing ML dependencies, replacing discrete look-up tables with mathematically continuous Gaussian and Polynomial Curve Fitting. This zero-training algorithmic optimization accelerates local computation by 83.55% over traditional linear interpolation and reduces network payload transmission bandwidth by 83.3%, while achieving an exceptional approximation fidelity ([Formula: see text]). Concurrently, a cross-sectoral Analytical Hierarchy Process (AHP) was structurally aligned with local river ecosystem profiles to extract robust contextual weights (CR = 0.02). Validated through a hardware-algorithmic stress test at the Troso River (Indonesia), the fault-tolerant topology yielded 6,779 validated records across a 6.49-hour deployment window via asynchronous queueing, achieving 98.1% of transmissions within 0-1 s end-to-end latency. Ultimately, this framework seamlessly integrates edge computational efficiency with cloud distribution frameworks, providing a highly scalable architecture for real-time anomaly triage in resource-constrained IoT environments.

The accurate prediction of particulate matter concentrations serves as the foundation for effective air quality management. Hisar City, which resides in north-west India, experiences high levels of PM 2.5 and PM 10 pollution because of vehicular and industrial emissions, construction and farming activities, and weather conditions. The research tests machine learning models to forecast PM 2.5 and PM 10 levels in Hisar City. Historical air quality data together with weather data for a period of 2020–2024 were gathered and an exploratory data analysis was performed to study the seasonal behaviour of particulate matter for the representative location. For modelling analysis, the periodic data from the year 2020 to 2023 was used for model training and the data from the year 2024 was used for testing. The research used Support Vector Regression (SVM), Random Forest (RF), Gradient Boosting, AdaBoost and M5 regression to create and test multiple machine learning models. For model explainability, SHAP analysis along with the sensitivity were performed to study the role and contribution of weather variables on the PM concentration estimation. The researchers used correlation coefficient (CC), root mean squared error (RMSE), and mean absolute error (MAE) as statistical indicators to assess the model performance. The study results showed that ensemble-based models (Gradient Boosting and RF) provide better results than the other regression models for predicting PM concentrations of the city. Correlation coefficient of 0.73 and 0.668 was achieved by Gradient Boosting model for PM 2.5 and PM 10 prediction along with the least RMSE values during testing. The RF model achieved CC values of 0.724 and 0.674 for PM 2.5 and PM 10 prediction, respectively, during testing. The proposed method shows that machine learning techniques can be used to predict air quality reliably, which helps policymakers develop effective emergency response plans.

Energy exchange in the fire environment is highly influenced by the heterogeneous and dynamic coupling between the atmosphere, fire, and fuels. While it has long been known that radiation and convection are the dominant heat transfer mechanisms in wildfire spread, much is unknown about their relative contribution. This is largely due to significant challenges observing these processes in the extreme wildfire environment. We describe a low cost, easy to deploy, simple heat transfer sensor and associated model, which was developed to measure heat transfer in wildfire. The sensor consists of stainless steel thermocouple probes differing in emissivity and similar in geometry and size to fine fuel elements responsible for carrying fire. Field and laboratory tests indicate that stainless steel probes with 3.2 mm and 1.6 mm diameters alone are not able to resolve the highly-dynamic heat transfer ahead of the fire. However, coupling the probes with a fine-wire thermocouple significantly improves its sensitivity. Results presented here indicate that the sensor and model are capable of measuring physically realistic convective and radiative heat transfer in high-intensity crown fire and simple laboratory scenarios when compared to observations in literature. Some limitations are identified for future investigation and additional testing and validation are required.

Studying elusive carnivores in dense tropical habitats poses many logistical challenges. Commonly applied field techniques can entail substantial resources and human power, precluding short-term studies from generating a comprehensive understanding of species ecology. We leveraged Local Ecological Knowledge (LEK) in India's Western Ghats to better understand dholes (Cuon alpinus) across the forest and agroforest landscapes of Wayanad (Kerala) and Valparai (Tamil Nadu). Through interviews of 476 informants (forest department staff and local residents), we documented dhole sighting locations, pack sizes, and interactions with co-predators, domestic dogs, livestock, and humans. Our findings reveal a strong local awareness of dhole ecology, with vernacular names highlighting their cultural relevance. Pack sizes reported were consistent with those from previous studies. Dhole interactions with co-predators were less frequently reported compared to domestic dogs, suggesting potential risks of competition and disease transmission. Instances of livestock depredation were extremely rare, hinting at an adaptation to minimize negative interactions with people. Forest officials generally held positive perceptions toward dhole conservation, recognizing the species' ecological importance. Our study underscores how LEK can complement traditional research approaches and offer unique insights on elusive species in shared human-wildlife spaces. The framework used and the knowledge thus generated can aid in co-developing conservation strategies to safeguard megafauna populations beyond Protected Area boundaries.

Abstract Microplastic (MP) particles have emerged as ubiquitous and persistent contaminants in aquatic environments worldwide, posing increasing ecological concerns due to their widespread distribution and potential impacts on ecosystem health. To assess their occurrence in the Oman Gulf, water and sediment samples were collected during the summer of 2024 and analyzed for MP contamination. MP concentrations in sediments ranged from 2 to 397 MPs/Kg dry weight (mean: 94 MPs/Kg dry weight) and in water from 1 to 275 MPs/m 3 (mean: 63 MPs/m 3 ), with the highest levels in urban and recreational areas. The highest abundances were observed in urban areas.The samples were analyzed using a two-step density-based separation method, acid digestion, visual counting under a microscope, Micro-Raman spectroscopy, and SEM-EDX analysis. MPs fibers were more common in the water, probably due to the use of fishing nets and clothing. In contrast, fragmented MPs dominated the sediments due to the use of various buckets, toys, packaging and bottles. White was the most common color in all samples, probably due to the widespread use of white clothing, fishing nets and the hulls of fishing and recreational boats among the people of the region. PP was the most common polymer detected in most areas due to its widespread use in packaging, textiles and various industries. PHI values for sediment samples are classified as category III. PHI values for water samples are classified as risk class III and II. PLI for the sediments and water are in risk class II - IV. PERI values for water samples are classified as risk class I - IV. In this study, urban and recreational areas were found to have high risks. Given these findings, it is essential to implement measures such as environmental regulations for plastic production and consumption, large-scale plastic separation and recycling, and promoting public education and awareness about the harmful effects of plastics.

India's escalating water scarcity, combined with declining groundwater quality, calls for decentralised and low-cost wastewater treatment solutions that can ensure sustainable household water reuse and groundwater recharge. This study developed and evaluated a laboratory-scale greywater filtration and soil aquifer treatment (SAT) system using sand and granular activated carbon (GAC) as primary filter media. Greywater was collected from kitchen and bathroom-laundry sources in Vellore, Tamil Nadu, in a 3:7 ratio and treated through a two-stage system comprising a slow sand-GAC filtration column followed by a soil infiltration pond. The filtration column achieved significant removal of suspended solids and organics, while the SAT unit provided advanced polishing through microbial degradation and ion exchange. Analytical results revealed sharp reductions in turbidity (72.4%), total solids (15.7%), COD (99.5%), nitrate (91.1%), TKN (63.6%), and chlorides (94.8%). The final effluent parameters complied with CPCB discharge and approached BIS drinking-water standards, confirming its suitability for non-potable reuse and groundwater recharge. Compared to previous natural-media filtration systems the present system which is combination of filtration and SAT achieved superior efficiency with shorter retention time and minimal operational demand. This compact, low-energy, and easily replicable unit demonstrates a promising pathway for household-scale wastewater management in water-stressed regions.

Air pollution, especially particulate matter (PM), poses significant environmental and health risks to humans. This study examines the toxicological effects of atmospheric PM collected over Rourkela, an industrial city in eastern India, using Drosophila melanogaster as an in-vivo model. The PM samples, evaluated using a Field Emission Scanning Electron Microscope (FE-SEM) and Energy Dispersive X-ray (EDX) Spectroscopy, revealed a complex composition of heavy metals and rare-earth elements, suggesting potential contributions from industrial and other anthropogenic sources. Food consumption exposed flies in the larval stage to varying PM exposure volumes. Histochemical staining, locomotor tests, and adult phenotypic screening were used to evaluate the effects. The results indicated volume-based PM genotoxicity, cytotoxicity, membrane damage, altered lipid metabolism, reduced climbing behaviour, and morphological abnormalities, particularly in the compound eyes. These results offer experimental evidence of the biological impacts of atmospheric PM exposure in a Drosophila model, shedding light on potential mechanisms relevant to human health.

Anthropogenic pressures, particularly high fishing effort and ocean warming are reshaping marine ecosystems and influencing the population dynamics of key fisheries species. While these stressors have been widely studied in isolation, their interactive effects across taxonomically distinct groups remain poorly understood. Here, we examine how fishing pressure and increasing temperature jointly affect the size spectra of nine commercial species (three bony fish, three crustaceans, and three cephalopods) in the central Mediterranean Sea. Using size spectra analyses applied to fishery-independent survey data from 2000 to 2023, we evaluate population-level responses to these stressors. Our findings reveal taxon-specific patterns: under high fishing effort and high temperatures, fish populations exhibit a higher proportion of smaller individuals, consistent with synergistic fishing-induced truncation and temperature-driven metabolic constraints. In contrast, crustaceans and cephalopods show different responses, reflecting their greater physiological plasticity and shorter life cycles, which may buffer against environmental changes. These results suggest that the interactive effects of fishing and climate change could disproportionately reduce fish biomass, while taxa characterized by greater physiological and life-history plasticity may display comparatively higher resilience to environmental and anthropogenic disturbances. Our results emphasize the need for adaptive management strategies that incorporate both environmental change and fishing pressure simulations to maintain sustainable yields and ecosystem resilience in the face of ongoing climate-driven shifts.