Search Results (3,392)

Recycled powder (RP), a by-product with a particle size smaller than 150 μm, is generated during the processing of construction and demolition waste (CDW) for recycled aggregate production. RP mainly consists of recycled concrete powder and recycled brick powder. Previous studies have demonstrated that RP can serve as a supplementary cementitious material (SCM) in concrete production. Due to the heterogeneity of parent materials with different ages, service environments, and compositions, the physicochemical properties and reactivity of RP vary significantly, which largely accounts for the inconsistent results reported in the literature. This paper presents a critical review of the application of RP as an SCM in construction. The preparation technologies, chemical and physical properties, microstructural characteristics, and activation methods of RP are systematically examined. Owing to its irregular and rough surface morphology, RP tends to reduce workability and increase water demand when incorporated as an SCM. Nevertheless, when the replacement level and median particle size are limited (typically below 30% and 20 μm, respectively), RP can contribute through micro-filling, nucleation, and limited pozzolanic effects, thereby mitigating adverse impacts on mechanical and durability properties. The mechanisms and effectiveness of mechanical grinding, thermal activation, chemical activation, and CO₂ treatment are comparatively evaluated. Moreover, the incorporation of RP in cement-based materials offers significant economic and environmental benefits. Full article

Green roofs in Southern Europe are interest-growing nature-based solutions, capable of improving urban sustainability by positively impacting the water cycle, biodiversity, pollution, and, in some cases, energy consumption and carbon sequestration. Native plants adapted to Mediterranean climates exhibit drought-resistant traits, making them highly suitable for the challenging microclimate of green roofs. This microclimate features intense solar radiation, strong winds, and higher temperatures, in comparison to ground level, leading to increased atmospheric evaporative demand, driven by the interplay of radiation, wind, temperature, and humidity. Consequently, native plants from ecosystems resembling this microclimate are likely better suited for green roofs than local ground-level species. The current review synthesizes current knowledge on the use of native plants in Southern European green roofs, focusing on water management challenges given the region’s climate and scarce water resources. Out of roughly 12,500 native plant species in the Mediterranean basin, only about 124 have been examined in the past 20 years for green roof applications, with just 16% appearing in multiple scientific studies, highlighting a significant knowledge gap. The data indicate that ca. 85% of these species are perennials, valued for their low maintenance needs, a key consideration for green roof sustainability. Some of the studied species retain adequate aesthetic value when cultivated on green roofs with limited water availability. These species are mainly associated with four habitat types—rocky, coastal, dry, or well-drained environments—with a few linked to humid or adaptable conditions. This study aims to document the selection of drought-adapted native plant species best suited for green roof implementation in Southern Europe, contributing to enhancing sustainable urban design in the region, considering water management best practices and water use efficiency. Full article

Duckweed/algal bloom-type black–odorous waters (DAWs) exhibit composite optical properties of vegetation and pollution, posing intractable remote sensing identification challenges in complex environments. Current methods suffer from three critical limitations: a misclassification rate exceeding 25% due to spectral confusion with artificial green covers, an 18.7% false-negative rate for small patches (stemming from the imbalance between CNNs and Transformers), and insufficient feature dimensionality to characterize the dual properties of DAWs. To address these gaps, this study proposes a novel method that integrates the ASGICTVS feature set with a customized SwinTf-Unet model. The ASGICTVS feature set combines vegetation-sensitive metrics, optical water quality indicators, and visual features. The SwinTf-Unet model utilizes an optimized 4 × 4 window, an embedded feature fusion module, and an adaptive shifted window stride to balance global context capture and local detail reconstruction. Experiments on 21,104 GF-2 satellite samples demonstrate that the method achieves 87.50% precision, 88.41% recall, an 85.32% F1-score, and an 83.46% Intersection over Union (IoU), outperforming DeepLabV3+ by 14.56 percentage points in the IoU. With an inference time of 0.87 s per 512 × 512-pixel image and a stable performance across cross-regional datasets (IoU: 82.1–85.3%), it exhibits strong efficiency and generalization. This study resolves DAW spectral confusion, enables high-precision segmentation, and establishes a standardized feature threshold system, providing reliable technical support for large-scale automated DAW monitoring and regional water environment management. Full article

The tropospheric zenith wet delay (ZWD) serves as a pivotal parameter for atmospheric water vapour inversion. By converting it into precipitable water vapour, high-temporal-resolution atmospheric humidity monitoring becomes feasible, providing crucial support for enhancing short-term rainfall forecast accuracy. However, ZWD exhibits significant non-stationarity due to complex influencing factors, and traditional models struggle to achieve precise predictions across all scenarios owing to limitations in local feature extraction. This article employs a ZWD prediction method based on the dynamic temporal decomposition module of TimesNet, re-constructing one-dimensional high-frequency ZWD time series into two-dimensional tensors to overcome the technical limitations of conventional models. Comprehensively considering topographical characteristics, climatic features, and seasonal factors, experiments were conducted using 30 s ZWD data from 20 IGS stations. This dataset comprised four consecutive days of PPP solutions for each season in 2023. Through comparative experiments with CNN-ATT and Informer models, the global prediction accuracy, seasonal adaptability, and topographical robustness of TimesNet were systematically evaluated. Results demonstrate that under the input-prediction window configuration where each can achieve the optimal accuracy, TimesNet achieves an average seasonal Root Mean Square Error (RMSE) of 5.73 mm across all seasonal station samples, outperforming Informer (7.89 mm) and CNN-ATT (10.02 mm) by 27.4% and 42.8%, respectively. It maintains robust performance under the most challenging conditions—including summer severe convection, high-altitude terrain, and climatically variable maritime zones—while achieving sub-5 mm precision in stable environments. This provides a reliable algorithmic foundation for short-term precipitation forecasting in Global Navigation Satellite System (GNSS) real-time meteorology. Full article

Laser pulses experience significant temporal broadening in underwater environments due to strong turbulence and scattering effects. As water turbidity increases, the likelihood of multiple scattering events rises, further intensifying pulse broadening and thereby degrading the ranging accuracy of underwater single-photon LiDAR systems. Accurate characterization of the return pulse shape is crucial for precise distance extraction, typically achieved via cross-correlation with the system’s Instrument Response Function (IRF). Conventional models often fail to accurately characterize the distinctive asymmetric shape of underwater LiDAR returns, which feature a rapid rise and a slow decay. To address this limitation, this paper proposes a Modified Biexponential Distribution (MBD) model, specifically designed to capture both the sharp leading edge and the gradual trailing decay of the pulses. This model enables a more accurate representation of the broadened pulse, effectively mitigating the ranging error induced by scattering. Experimental validation demonstrates that, at an attenuation length of 6.9, the Depth Absolute Error (DAE) is reduced from 3.82 cm to 3.15 cm (a 17.54% improvement), while the probability of achieving a DAE below 3.82 cm increases from 49.70% to 74.83%. These results confirm the effectiveness and robustness of the proposed model in enhancing the ranging accuracy of underwater photon-counting LiDAR systems. Furthermore, this study provides a model-driven analytical basis for improving underwater photon detection and bathymetric performance in turbid conditions. Full article

The agri-food sector is a major contributor to global greenhouse gas emissions while facing increasing demand for food production driven by population growth. Transitioning towards sustainable and low-carbon agricultural systems is therefore critical. Green hydrogen, produced from renewable energy sources, holds significant promise as a clean energy carrier and chemical feedstock to decarbonize multiple stages of the agri-food supply chain. This systematic review is based on a structured analysis of peer-reviewed literature retrieved from Web of Science, Scopus, and Google Scholar, covering over 120 academic publications published between 2010 and 2025. This review provides a comprehensive overview of hydrogen’s current and prospective applications across agriculture and the food industry, highlighting opportunities to reduce fossil fuel dependence and greenhouse gas emissions. In agriculture, hydrogen-powered machinery, hydrogen-rich water treatments for crop enhancement, and the use of green hydrogen for sustainable fertilizer production are explored. Innovative waste-to-hydrogen strategies contribute to circular resource utilization within farming systems. In the food industry, hydrogen supports fat hydrogenation and modified atmosphere packaging to extend product shelf life and serves as a sustainable energy source for processing operations. The analysis indicates that near-term opportunities for green hydrogen deployment are concentrated in fertilizer production, food processing, and controlled-environment agriculture, while broader adoption in agricultural machinery remains constrained by cost, storage, and infrastructure limitations. Challenges such as scalability, economic viability, and infrastructure development are also discussed. Future research should prioritize field-scale demonstrations, technology-specific life-cycle and techno-economic assessments, and policy frameworks adapted to decentralized and rural agri-food contexts. The integration of hydrogen technologies offers a promising pathway to achieve carbon-neutral, resilient, and efficient agri-food systems that align with global sustainability goals and climate commitments. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Livestock health knowledge graphs are essential for decision-making and reasoning in animal husbandry, yet existing knowledge is scattered across unstructured literature and encoded in narrowly scoped, species-specific models, resulting in semantic fragmentation and limited reusability. To address these issues, we proposed FARM (Four-dimensional Automated-Reasoning Multi-agent), a zero-shot multi-agent framework used for constructing multi-species livestock health knowledge graphs. FARM is grounded in a Four-Dimension Livestock Health Framework encompassing Rearing Environment, Physiological Status, Feed & Water Inputs, and Production Performance, and employs a unified ontology strategy that integrates cross-species general labels with species-specific constraints to achieve semantic alignment. The framework orchestrates five specialized agents—Coordination, Entity Extraction, Ontology Normalization, Relation Extraction, and Knowledge Fusion—to automate the construction process. Experiments on 2478 expertly annotated text samples demonstrate that FARM achieves an entity-level F1 score of 0.8070 (IoU ≥ 0.5), surpassing the strongest baseline by 0.1627. Moreover, it attains a corrected entity label accuracy of 90.44% and an F1 score of 0.9277 in relation existence identification, outperforming the baseline by 0.1114. Validation on 500 image samples further confirms its capability in multimodal evidence fusion. The resulting knowledge graph contains 29,064 entities and 26,662 triples, providing a reusable foundation for zero-shot extraction and unified cross-species semantic modeling. Full article

Understanding the behavior of light non-aqueous phase liquids (LNAPLs) in urban subsurface environments is essential to developing effective pollution control strategies, designing remediation systems, and managing waste and resources sustainably. Oil leakage from urban industrial facilities, underground pipelines, and fueling systems often leads to contamination that is challenging to characterize due to complex soil structures, limited access beneath densely built infrastructure, and dynamic groundwater conditions. In this study, we integrate a large-scale soil tank experiment with multiphase flow simulations to elucidate LNAPL distribution mechanisms under fluctuating groundwater conditions. A 2.4-m-by-2.4-m-by-0.6-m soil tank was used to visualize oil movement with high-resolution multispectral imaging, enabling a quantitative evaluation of saturation distribution over time. The results showed that a rapid rise in groundwater can trap 60–70% of the high-saturation LNAPL below the water table. In contrast, a subsequent slow rise leaves 10–20% residual saturation within pore spaces. These results suggest that vertical redistribution caused by groundwater oscillation significantly increases residual contamination, which cannot be evaluated using static groundwater assumptions. Comparisons with a commonly used NAPL simulator revealed that conventional models overestimate lateral spreading and underestimate trapped residual oil, thus highlighting the need for improved constitutive models and numerical schemes that can capture sharp saturation fronts. These results emphasize that an accurate assessment of LNAPL contamination in urban settings requires an explicit consideration of groundwater fluctuation and dynamic multiphase interactions. Insights from this study support rational monitoring network design, reduce uncertainty in remediation planning, and contribute to sustainable urban environmental management by improving risk evaluation and preventing the long-term spread of pollution. Full article

In shallow-water high-speed mobile acoustic channels, severe non-uniform Doppler effects pose significant challenges to traditional equalization methods based on linear and time-invariant channel assumptions. Existing approaches typically rely on inverse compensation strategies, which are inadequate for handling path-dependent nonlinear Doppler distortions and fail to accurately reflect the underlying physical propagation process. To address these limitations, this paper proposes a forward reference-sample equalization (FRSE) method. Based on estimated channel parameters, forward channel modeling is performed for all possible transmitted symbols to generate a reference-sample matrix that is consistent with channel-induced distortions. At the receiver, a least-squares decision criterion is employed to match the received signal with the closest reference sample, thereby enabling reliable demodulation. Simulation results demonstrate that, at a high relative speed of 30 kn and a signal-to-noise ratio (SNR) of 8 dB, the proposed method achieves a bit error rate (BER) of $1.75\times {10}^{-4}$, significantly outperforming conventional equalization methods. Furthermore, sea trial experiments validate the robustness of the proposed approach in real shallow-water environments. By avoiding signal inversion, FRSE achieves improved detection reliability and strong robustness against non-uniform Doppler effects, highlighting its potential for practical underwater acoustic communication applications. Full article

Historical landfills in coastal environments are at increasing risk of erosion under changing climate conditions. Various studies have highlighted pollutant release associated with potentially toxic elements and flame retardants from such erosional processes, but there has been little focus on per- and poly-fluoroalkyl substance (PFAS) release as a result of physical erosion at such sites, despite landfills being highlighted as a key source of PFAS to the water environment. This study presents a rapid screening approach that could be adopted at scale by regulators to assess the presence and potential flux of PFAS released at three historical municipal waste landfill sites in the UK. The sites selected cover a range of epochs prior to rigorous environmental regulation from the second half of the twentieth century. At the older waste deposits (Withernsea: 1950s–1960s; Hessle: 1930s–1970s), all 52 PFAS analysed in solid materials were below the detection limits except for two samples where modest concentrations (0.92–1.98 ng/g) of perfluorooctane sulfonate (PFOS) and perfluoroethylcyclohexane sulfonate (PFecHS) were detected. At the more recently operational site (Crosby: 1970s–1980s), the legacy PFAS chemicals, PFOS and perfluorooctanoic acid (PFOA), were present in all samples in modest concentrations (6.01–8.22 ng/g for PFOS; 0.62–1.20ng/g for PFOA) below contaminated land thresholds. At this site, it was possible to model the flux of PFAS release based on LiDAR surveys of the eroding waste terrace over an 18-year period. This showed an annualised total solid phase PFAS (PFOS plus PFOA in this case) flux in the region of 2.5–16.9 g/yr, which is towards the lower end of the reported landfill leachate flux at inland sites. While such releases are relatively modest on an individual site basis, in transitional and coastal waters in heavily urbanised and (post-)industrial regions, the aggregated solid phase PFAS flux from the large number of eroding historical landfills (n = 114) could be significant. Full article

The continuous release of pharmaceutical compounds into aquatic environments poses significant challenges to environmental sustainability, as conventional wastewater treatment plants are often ineffective in removing recalcitrant and bioactive molecules. In this study, the adsorption performance of nanoclay was systematically evaluated for the removal of nystatin, a polyene antifungal of emerging environmental concern, from aqueous solutions. The effects of solution pH, adsorption kinetics, equilibrium isotherms, and adsorption mechanisms were investigated under environmentally relevant conditions. Nanoclay exhibited outstanding removal efficiency, exceeding 98% across a wide pH range (3–11), thereby demonstrating strong operational robustness and minimal sensitivity to pH variations. Structural and spectroscopic analyses (XRD and FTIR) confirmed that adsorption occurred predominantly on the external surface of the nanoclay, without significant disruption of its lamellar structure, and was governed mainly by hydrophobic interactions and hydrogen bonding. Kinetic data were best described by the pseudo-second-order model, with rapid equilibrium achieved within approximately 20 min, indicating high affinity between nystatin and the adsorbent surface. Equilibrium data were best fitted by the Sips isotherm model, reflecting surface heterogeneity and a favorable adsorption process, with a high maximum adsorption capacity of approximately 911 mg/g. A preliminary cost analysis revealed low raw material costs, while energy consumption, particularly during drying, was identified as the main economic limitation. Overall, the results highlight Nanoclay as an efficient, robust, and promising adsorbent for the sustainable removal of hydrophobic pharmaceutical contaminants from water and wastewater. Full article

Electrochemical water splitting for hydrogen production is limited by the slow kinetics of the oxygen evolution reaction (OER). The tunable structure and anion-exchange capability of layered double hydroxides (LDHs) underpin their promise as OER catalysts. Consequently, the strategic incorporation of foreign anions is viewed as a powerful approach to engineer their active sites and boost catalytic activity. This review summarizes how doping with anions such as NO₃⁻, PO₄³⁻, Cl⁻, F⁻, and Sq²⁻ modifies the electronic structure of LDHs. These anions regulate the local coordination environment, induce oxygen vacancies, and alter metal oxidation states, thereby synergistically optimizing both the adsorption–evolution mechanism (AEM) and the lattice oxygen oxidation mechanism (LOM). For instance, NO₃⁻ promotes surface reconstruction, F⁻ activates lattice oxygen, PO₄³⁻ stabilizes the interface, Cl⁻ reshapes reaction pathways, and Sq²⁻ maintains interfacial alkalinity. Collectively, rational anion engineering lowers the overpotential, increases current density, and improves stability, establishing an effective design framework for advanced LDH-based OER electrocatalysts. Full article

Urban air pollution is a major public health concern, especially in densely populated cities. This problem also includes food safety issues in outdoor retail environments, where fresh products may be exposed to airborne pollutants. This study examines the presence of polycyclic aromatic hydrocarbons (PAHs) on fruits sold at indoor and outdoor locations across Budapest and several Hungarian cities. Results showed higher PAH concentrations on fruit sold outdoors, with benzo[a]pyrene (BAP) exceeding 2 µg/kg in 62% of outdoor samples and in 22% of indoor ones. Washing with water reduced contamination by 40–50% on average, with some samples showing over 65% reduction for BAP. Differences across fruit types were limited overall, though statistically significant for BAP in certain cases, highlighting compound-specific variability. Correlation analysis revealed weak but interpretable associations between PAH levels and ambient air quality indicators, with a moderate correlation for fine particulate matter ≤ 2.5 µm (PM_2.5) (r = 0.4355) and a weaker one for the calculated Air Quality Index (AQI) (r = 0.2148). These findings suggest that while urban microenvironments influence contamination, the general air quality indices may not predict surface PAH burden reliably. The study highlights the role of public wells in enabling citizen-level mitigation through rinsing and calls for integrated urban health strategies considering food exposure alongside infrastructural access. Full article

Herbicides are widely used agrochemicals and are increasingly recognised as contaminants of emerging concern in aquatic environments due to their extensive application, environmental persistence, and potential ecological and human health impacts. Their determination in water presents significant analytical challenges, as these compounds occur at trace to ultra-trace levels and encompass a wide range of chemical properties, including highly polar and ionic species as well as transformation products. This review provides a critical overview of recent advances in separation technologies for the analysis of herbicides in water, based on peer-reviewed studies published between 2020 and 2025 retrieved from the PubMed and Scopus databases. The discussion focuses on developments in sample preparation, extraction strategies, chromatographic separation, and detection techniques, with particular attention to analytical performance and sustainability. The reviewed studies demonstrate that solid-phase extraction remains central to achieving the lowest detection limits, while miniaturised and greener extraction approaches are increasingly adopted to reduce solvent consumption and simplify workflows. Advances in chromatographic separation and detection, especially liquid chromatography coupled to tandem mass spectrometry, have further enhanced sensitivity and selectivity for a broad range of herbicides. Overall, this review highlights current analytical capabilities and emerging trends, outlining future directions for reliable and sustainable monitoring of herbicides in aquatic environments. Full article