Mangrove ecosystems play a vital role in supporting aquatic organisms by providing food sources, shelter, and spawning grounds. However, microplastic contamination has emerged as a significant environmental threat that may disrupt the ecological functions of these ecosystems. Unfortunately, studies investigating the vertical distribution of plastics and their relationship with sediment grain size in mangrove environments remain limited. Hence, this study aimed to investigate the abundance, vertical distribution, and characteristics of microplastics, as well as their relationship with sediment grain size in the mangrove sediments of Northern Aceh, Indonesia. A total of 40 sediment samples were collected and analyzed from eight mangrove areas across five sediment layers. The results showed that the abundance of microplastics in the sampling areas ranged from 210 to 440 items/kg dry weight. Higher concentrations of microplastics were observed in the upper layer (0–10 cm) (p < 0.05). Particles smaller than 500 μm, predominantly black and fragment-shaped, were most common. Polyethylene and polypropylene were the dominant polymers identified, followed by nylon and polystyrene. Microplastic abundance showed a significant correlation with sediment grain size, particularly with granules, very fine sand, and clay fractions. In contrast, a significant negative correlation was observed between microplastic abundance and the percentage of clay.
Desert lizards undergo hibernation (brumation) to survive extreme cold and food scarcity. This study investigated brumation-induced renal adaptations in four desert-dwelling species (Uromastyx aegyptia, Varanus griseus, Trapelus savignii, and Tarentola annularis) compared to their active states. Histological examination during brumation revealed kidney degeneration, including glomerulosclerosis, necrotic tubules, and cytoplasmic vacuolization. Morphometric analysis showed species‑specific renal changes during brumation; glomerular diameter significantly decreased in U. aegyptia as well as T. annularis but increased in V. griseus and T. savignii. Proximal tubule diameter increased in T. savignii but decreased in U. aegyptia, while distal tubule diameters increased significantly in T. savignii but decreased in U. aegyptia. Oxidative stress, indicated by malondialdehyde (MDA), increased significantly in all species during brumation. Superoxide dismutase (SOD) and catalase (CAT) activities increased with distinct patterns within each species. Furthermore, SOD expression increased by 0.77-fold, 1.79-fold, 0.12-fold, and 0.51-fold in U. aegyptia, V. griseus, T. savignii and T. annularis, respectively. CAT expression increased by 5.24-fold, 0.86-fold, and 0.35-fold in U. aegyptia, V. griseus, and T. annularis, but decreased in T. savignii by 0.90-fold. These findings indicate that desert lizards employ distinct, species-specific antioxidant strategies to mitigate renal oxidative stress induced by brumation, with obligate brumators (U. aegyptia and V. griseus) showing more consistent upregulation than facultative species (T. savignii and T. annularis), suggesting that the intensity of antioxidant defense correlates with the depth and duration of winter dormancy.
An invasive aquatic macrophyte characterized by elevated lignocellulosic content and rapid propagation, water hyacinth (Eichhornia crassipes), serves as a feasible and sustainable biofuel resource. The extensive presence, affordability, and ecological detriment of this aquatic plant in freshwater ecosystems render its biofuel generation both economically and ecologically feasible, transforming waste biomass into a sustainable energy source. This study explored green pretreatment using petha wastewater alongside thermal co-pretreatment to augment biofuel generation from water hyacinth stems through anaerobic digestion. Green pretreatment used 15
Increasing agricultural intensification in the Argentine Pampas has heightened native amphibians’ exposure to herbicides. We evaluated the acute sublethal effects of Prometrex FW®, a commercial prometryn-based herbicide formulation containing 50
Heavy metal-contaminated soil (HMS) poses persistent environmental risks, while its heterogeneous composition and the generation of secondary solid wastes limit the effectiveness of conventional remediation methods. Cement-kiln co-processing offers a potential route for the safety disposal and resource utilization of HMS; however, suitable addition dosages and kiln operational windows that preserve clinker quality while stabilizing metals remain insufficiently defined. This study therefore investigated the effects of HMS addition dosage, calcination temperature, and residence time on clinker characteristics, heavy metal speciation, and leachability under kiln-relevant conditions. Microstructural and elemental analyses, XPS, sequential extraction, and leaching tests were combined with thermodynamic calculation to evaluate possible metal-phase evolution during calcination. The addition of 7.5
This study evaluates pristine fly ash–based geopolymers (FA-GP) and γ-Al₂O₃-modified geopolymers (FA-Al-GP) as low-cost sorbents for trivalent actinide and lanthanide sequestration, utilizing radiotracer 241Am(III) and Eu(III) as a chemical surrogate. Structural characterization confirmed the successful framework integration of γ-Al₂O₃. While this modification reduced the physical surface area from 41 m2/g (FA-GP) to 16 m2/g (FA-Al-GP), confirmed by Brunauer–Emmett–Teller-specific surface area analysis method, it produced a denser matrix with a higher proportion of octahedrally coordinated aluminum suggested by solid-state 27Al magic-angle spinning nuclear magnetic resonance. Consequently, FA-Al-GP exhibited superior macroscopic sorption, achieving >97
In response to the decreasing availability of high-grade kaolinitic clays, there is a growing shift toward using natural waste clays as supplementary cementitious materials. The performance of these waste clays is generally evaluated based on either strength or environmental impact, but the combination of both is rarely considered. This study undertakes a strength-normalized life cycle assessment to understand the interaction between these two parameters and utilizes the output to identify the most suitable activation process. The study used five Australian natural waste clays activated by calcination (600–900 °C) and high-shear mechanical grinding, replacing 30
Rapid urbanisation and industrialisation have substantially reduced cultivable land worldwide. To meet increasing food demand, dumping grounds are increasingly being reclaimed for agriculture. The distribution and temporal pollution trends of eight trace and toxic metals (Cr, Mn, Ni, Cu, Zn, Cd, Hg, and Pb) were assessed in reclaimed farmland soils of Dhapa (East Kolkata Wetlands Ramsar site, India). Tissue-specific metal transfer, bioaccumulation of Cd, Hg, and Pb in vegetables, dietary exposure, and probabilistic health risk assessment were integrated to evaluate farming safety on reclaimed landfill soils. A progressive increase in the PLI from 5.8 to 13.6 was observed over the past few decades, indicating a deterioration in soil quality in the study area. The soil to root metal transfer factor decreased as Cd > Pb > Hg, while their root to shoot transfer factor ranked as Hg = Cd > Pb, and bioaccumulation efficiency decreased as Cd ( 0.8) > Hg ( 0.03) > Pb ( 0.02). Metal accumulation in edible tissues resulted in elevated dietary exposure and health risks. The calculated combined HI was 1.0 for adults and 4.5 for children. The TCR was 1.8 × 10–3 for adults and 6.5 × 10–3 for children, substantially exceeding the acceptable risk range (10–6–10–4). This study proposed a bioaccumulation-based classification of vegetables, enabling selection of low-bioaccumulator crops for safer cultivation on contaminated soils, thereby minimizing dietary metal exposure. Future research should integrate species-specific metal uptake mechanisms into crop selection strategies to promote safer agriculture on reclaimed landfill soils.
The presence of microplastic (MP) particles in freshwater species is a growing global concern due to potential impacts on food security and human health. This study investigated MP contamination in two commercially important freshwater species, Macrobrachium rosenbergii (giant freshwater prawn, GP) and Litopenaeus vannamei (white leg shrimp, WS) cultured in inland and semi coastal aquaculture systems in Kollam, southwest India. A total of 307 MP items were identified from pooled gastrointestinal tract (GIT) samples, with average concentrations of 0.709 ± 2 MPs/g GIT in GP and 1.015 ± 2 MPs/g GIT in WS. MPs ranged in size from < 250 µm to 5 mm, with particles in the 500 µm−1 mm range being most prevalent. Blue colored MPs and fiber morphotypes dominated in both species, accounting for 47.31
Machine learning models are increasingly used in air pollution research, yet their application to long-term reconstruction of missing air-quality records remains limited. Pollutants such as PM2.5, PM10, O3, NO2, SO2, and CO pose major environmental challenges in megacities like Delhi, but accurate assessment requires long, complete, and spatially distributed monitoring records that are often unavailable. Delhi operated only four stations in 2014, expanding to 45 by 2024, producing fragmented datasets that hinder trend analysis and policy evaluation. This study develops an iterative, multi-model machine learning workflow to reconstruct missing daily pollution data for all six pollutants across 45 stations from 2014–2024. For each missing value, the algorithm identifies the four most correlated stations—assumed to share similar environmental conditions—and uses their observations as predictors in MATLAB’s Regression Learner to evaluate 35 ML models. The best-performing model is selected based on reconstruction accuracy, and the procedure iterates until all gaps are filled. Results show that models such as Fine Tree, Bagged Trees, Optimizable Ensemble, Fine Gaussian SVM, Rational Quadratic, and Exponential kernels consistently outperform multilinear regression. The reconstructed datasets enable computation of ensemble-mean pollutant records, yielding a more realistic and bias-corrected representation of Delhi’s air-quality evolution, which indicates modest improvement over the decade. Validation through artificial-gap experiments across pollutants, stations, and years demonstrates good performance under the tested conditions for PM2.5 and other regionally driven pollutants, with reduced accuracy for species dominated by local variability. These findings support the usefulness of the approach for long-term regional reconstruction while also highlighting its limitations for pollutants with strong local emission signatures.
The construction sector requires sustainable concrete that reduces natural aggregate consumption and valorises waste-derived resources. This study investigates recycled aggregate concrete (RAC) incorporating 100
Integrating heterogeneous water quality data from multiple monitoring agencies into a single reproducible analytical pipeline remains an unsolved challenge in environmental science. We present the Optimised Water Quality Monitoring Framework (OWQMF), an open-source four-block pipeline that harmonises multi-source monitoring data, computes dual water quality indices with objectively derived weights using a game-theory ensemble of three objective weighting methods, produces gradient-boosting ensemble forecasts with walk-forward cross-validation, and selects the best forecasting model through multi-criteria consensus with Adaptive Conformal Prediction coverage guarantees ( ≥ 90 n=13,230 ); Italy ( n=49,380 ); and Ireland ( n=13,380 ) under WFD 2000/60/EC: IQA-equivalent medians cluster in a narrow 81.9–86.1 band while compliance rates span 12.7
Seasonal aerosol variability over the Western Caribbean is strongly influenced by two recurrent atmospheric regimes: regional biomass burning (BB, March–May) associated with cold-front intrusions and dry-season circulation, and long-range transport of African dust (AD, June–August) during the boreal summer wet season. Despite their relevance for air quality and public health across the region, the combined influence of these regimes on near-surface aerosol composition and toxicological exposure remains insufficiently characterized. Here, we present an integrated assessment of meteorology, aerosol dynamics, fire activity, and combustion tracers based on continuous observations conducted in Mérida, Mexico, a representative near-coastal urban site within the Western Caribbean atmospheric domain. The analysis combines measurements of PM2.5, PM10, and particle-bound polycyclic aromatic hydrocarbons (pPAH) with satellite-based fire detections (FIRMS) and HYSPLIT back-trajectory simulations. Meteorological conditions exhibited a marked transition from dry, turbulent regimes dominated by southerly flow during BB to humid, easterly trade-wind conditions during AD. These shifts strongly modulated aerosol composition at both seasonal and episodic timescales: while PM2.5 and PM10 concentrations increased modestly during AD, the PM2.5/PM10 ratio decreased, reflecting an increased role of coarse mineral particles during dust intrusions. In contrast, BB was characterized by enhanced fine-mode aerosol contributions and episodic pPAH peaks linked to intense regional fire activity, consistent with satellite observations and trajectory analysis. Health-risk estimates based on benzo[a]pyrene-equivalent concentrations indicated low chronic carcinogenic risk under both seasonal regimes, which were statistically indistinguishable. The two regimes differed instead in the magnitude and diurnal timing of short-term excursions: peaks during BB reached substantially higher concentrations and occurred predominantly during afternoon hours, consistent with regional plume transport, whereas AD peaks clustered in the morning rush-hour window. Overall, this study demonstrates that the seasonal duality of BB and AD exerts a measurable influence on aerosol composition and exposure-relevant indicators across the Western Caribbean, with the distinct regional and local origins of BB and AD exposure peaks pointing toward correspondingly differentiated air-quality management strategies.
Land degradation represents a major environmental challenge in Iraq, particularly in arid and semi-arid regions experiencing intensive oil and gas development. Among the various anthropogenic drivers, drilling wastes generated during oil well construction remain an underinvestigated contributor to soil degradation and environmental deterioration. These wastes, including drilling muds and drill cuttings, contain complex mixtures of clays, chemical additives, hydrocarbons, salts, and trace metals and are commonly disposed of through on-site burial practices. This study develops an integrated analytical framework to evaluate the potential contribution of drilling waste disposal to land degradation in Iraq. The assessment synthesizes national and international literature, drilling activity records, and spatial information on oil field distribution to estimate drilling waste generation, affected land areas, and associated environmental risks under data-limited conditions. An uncertainty analysis is also included to acknowledge the limitations associated with the available datasets and estimation procedures. The results indicate that annual drilling waste generation in Iraq is estimated to range between 70,000 and 140,000 tons, with disposal sites primarily concentrated in the southern oil field–producing regions. Conventional burial practices may contribute to soil salinization, deterioration of soil physical properties, and long-term reductions in land productivity. Iraq’s predominantly arid climate, characterized by high evaporation rates, prolonged drought, and limited natural attenuation capacity, may further increase contaminant persistence and intensify land degradation processes. Over extended periods, cumulative impacts may expand beyond disposal sites through secondary contaminant migration pathways. The study identifies significant gaps in national environmental monitoring while demonstrating that improved drilling waste management practices, engineered containment systems, strengthened regulatory frameworks, and systematic environmental monitoring could substantially reduce long-term environmental risks. The proposed analytical framework provides a practical basis for supporting sustainable drilling waste management and land degradation mitigation strategies in Iraq and other oil field–producing regions facing similar environmental conditions.
Acid mine drainage (AMD) imposes severe global environmental risks and economic burdens, yet conventional remediation technologies often fail to recover valuable metals from this waste stream. Existing reviews primarily focus on pollutant removal rather than systematically integrating electrochemical metal recovery mechanisms, coupled process advancements, and scalability challenges specific to AMD matrices. This review addresses this critical gap by categorizing core electrochemical technologies (electrocoagulation, membrane-assisted systems, electrodeposition, capacitive deionization) based on their dual remediation-recovery mechanisms, summarizing recent progress in enhancing metal selectivity and recovery efficiency. It further analyzes emerging hybrid processes designed to mitigate matrix interference and low concentration limitations, discusses performance regulation strategies, and critically assesses technical bottlenecks (e.g., electrode fouling, high energy consumption) and industrial scaling barriers. Finally, it outlines future directions for adaptive, modular systems aligned with circular economy principles. This work provides a comprehensive framework to guide sustainable, value-added AMD management via electrochemical technologies.
Rapid urbanization has substantially transformed land use/land cover (LULC) patterns and intensified urban thermal environments, particularly in rapidly expanding cities of the Global South. Understanding the relationship between LULC dynamics and land surface temperature (LST) is essential for sustainable urban planning and climate adaptation. This study investigates the spatio-temporal changes in LULC and LST in Guwahati, Assam, India, over a 30-year period (1995–2024) using multi-temporal Landsat imagery processed on the Google Earth Engine (GEE) platform. LULC maps were generated using the Random Forest (RF) classifier, while LST was retrieved from Landsat thermal bands using the radiative transfer approach with Normalized Difference Vegetation Index (NDVI)-based emissivity correction. Long-term temperature trends were evaluated using the Mann–Kendall (MK) test and Sen’s slope estimator. In addition, Pearson’s correlation, linear regression, and one-way ANOVA were employed to quantitatively examine the relationship between vegetation, LULC, and LST using 5000 randomly sampled pixels from the 2024 dataset. The results revealed that the mean LST increased from 28.27 °C in 1995 to 32.07 °C in 2024, coinciding with rapid urban expansion and a substantial decline in vegetation and agricultural land. Built-up areas exhibited the highest mean LST (36.11 °C), whereas water bodies recorded the lowest (27.29 °C). A statistically significant warming trend was confirmed by the MK test (p < 0.05). Pearson’s correlation further demonstrated a significant inverse relationship between NDVI and LST (r = −0.492, p < 0.001), while linear regression indicated that vegetation explained approximately 24.2
This study presents an integrated remediation strategy and human health risk assessment for multimetal-contaminated industrial soil. The sequential treatment combined mechanical screening, high-intensity magnetic separation (HIMS), and chemical extraction using ethylenediaminetetraacetic acid (EDTA). Initial characterization revealed extreme contamination levels, with cadmium and thallium posing significant noncarcinogenic risks, particularly for children (hazard index = 169.4). Physical separation proved highly effective: Mechanical screening recovered 79
Over the past century, industrial activities have caused widespread soil contamination by potentially toxic elements (PTEs), making effective remediation increasingly urgent. Phytoremediation offers a sustainable strategy using selected plant species to remove, stabilize, or sequester pollutants. This study proposes an integrated agrivoltaic-phytoremediation framework for remediating PTE-contaminated soils. The framework integrates literature-based plant selection, photovoltaic system simulations, and techno-economic assessment, demonstrated through a representative case study in Augusta (Sicily, Italy). The effects of photovoltaic shading on C3 and C4 crops, together with PTE contamination, were estimated from literature data to quantify biomass reduction. A quantitative assessment was then performed by integrating literature data with PVsyst simulations of different photovoltaic configurations. The results indicate that the optimal configuration depends on the specific system objectives, such as maximizing energy production or biomass yield. Three agrivoltaic configurations, each combining a different photovoltaic technology with a selected plant species, were evaluated through a techno-economic assessment considering energy production, biomass yield under varying shading conditions, and net economic returns. The monofacial/Arundo donax L., bifacial/Chrysopogon zizanioides (L.) Roberty, and semi-transparent/Cannabis sativa L. configurations achieved annual energy productions of 568,660, 297,987, and 285,638 kWh, respectively, with corresponding biomass yields of 26.3, 60.0, and 14.0 t ha−1 year−1 and net economic returns of €2,906, €6,730, and €1,543 ha−1 year−1.
Accurate short-term ozone (O3) forecasting is critical for mitigating respiratory and ecological impacts, protecting public health, and guiding urban air quality management. This study presents the Residual-Aware Meta Ensemble (RAME), a hybrid deep learning framework that integrates bidirectional recurrent units with dilated convolutions to deliver high-resolution hourly surface ozone predictions, with a particular focus on peak concentration events and regulatory exceedances. The model first generates foundational forecasts through an ensemble of base learners trained on 24-h historical pollutant data and real-time meteorological parameters. These predictions are then refined via a residual-correction mechanism, where a meta-learner is trained to compensate for the discrepancy between the weighted ensemble forecast and actual concentrations. Using observations from eight monitoring stations across Tehran, RAME achieved city-wide mean 24-h averaged R2 of 0.87, RMSE of 8.12 ppb, and MAE of 5.30 ppb, consistently outperforming all individual baselines across heterogeneous urban sites. The model accurately identified 96
