Search Results (20,179)

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

Accurate estimation of peak water demand remains a challenge in institutional settings with floating populations, particularly in regions where design standards may require revision and validation to accommodate evolving consumption patterns. This pilot study assesses the potential of a probabilistic adaptation of Hunter’s method, calibrated through high-resolution flow and pressure monitoring, for peak flow estimation in five academic buildings in Loja, Ecuador. Over 62 days, usage parameters, duration (t), frequency (i), and peak period (h), were disaggregated from 1 min interval data to derive building-specific binomial probability distributions. The adapted model was compared against three benchmarks: the Neyman–Scott Rectangular Pulse Model (NSRPM), the Water Demand Calculator (WDC), and Ecuador’s Hydro-Sanitary Standard (NHE 2011). Results indicate the proposed approach estimates peak flows within −11.6% to +20.0% of observed values, outperforming WDC (systematic underestimation up to −81.5%) and NHE 2011 (average underestimation of −31.3%), though NSRPM achieved high accuracy for one site (−1.1%) with high inter-building variability (average −38.4%). While limited to a small sample in a single climatic context, this pilot demonstrates that temporal disaggregation of stochastic demand enables a context-sensitive recalibration of Hunter’s method, offering a methodologically sound basis for future validation across diverse institutional settings in the Global South. Full article

Wet-snow avalanches constitute a major geomorphic hazard in southeastern Tibet, where warm, humid climatic conditions and a steep, high-relief terrain generate failure mechanisms that are distinct from those in cold, dry snow environments. This study investigates the snowpack conditions underlying avalanche initiation in this region by integrating UAV-based multi-sensor surveys with field validation. Ground-penetrating radar (GPR), infrared thermography, and optical imaging were employed to characterize snow depth, stratigraphy, liquid water content (LWC), snow water equivalent (SWE), and surface temperature across an inaccessible avalanche channel. Calibration at representative wet-snow sites established an appropriate LWC inversion model and clarified the dielectric properties of avalanche-prone snow. Results revealed SWE up to 1092.98 mm and LWC exceeding 13.8%, well above the critical thresholds for wet-snow instability, alongside near-isothermal profiles and weak bonding at the snow–ground interface. Stratigraphic and UAV-based observations consistently showed poorly bonded, water-saturated snow layers with ice lenses. These findings provide new insights into the hydro-thermal controls of wet-snow avalanche release under monsoonal influence and demonstrate the value of UAV-based surveys for advancing the monitoring and early warning of snow-related hazards in high-relief mountain systems. Full article

Sediment plumes threatening benthic ecosystems are one of the environmental hazards associated with seafloor interventions such as bottom trawling, cabling, dredging, and marine mining operations. This study focuses on sediment plume release from hypothetical future deep-sea mining activities, emphasizing its interaction with turbulent ocean currents in regions characterized by complex seafloor topography. In such environments, turbulent lee waves may significantly enhance the scattering of released sediments, pointing to the clear need for appropriate impact assessment frameworks. Global-scale models are limited in their ability to resolve sufficiently high Reynolds numbers to accurately represent turbulence generated by seafloor topography. To overcome these limitations and effectively assess lee wave dynamics, models must incorporate the full physics of turbulence without simplifying the Navier–Stokes equations and must operate with significantly finer spatial discretization while maintaining a domain large enough to capture the full topographic signal. Considering a seamount in the Lofoten Basin of the Norwegian Sea as an example, we present a novel numerical analysis that explores the interplay between lee wave turbulence and sediment plume dispersion using a high-resolution Large Eddy Simulation (LES) framework. We show that the turbulence occurs within semi-horizontal channels that emerge beyond the topographic highs and extend into sheet-like tails close to the seafloor. In scenarios simulating sediment release from various sites on the seamount, our model predicts distinct behavior patterns for different particle sizes. Particles with larger settling velocities tend to deposit onto the seafloor within 50–200 m of release sites. Conversely, particles with lower settling velocities are more susceptible to turbulent transport, potentially traveling greater distances while experiencing faster dilution. Based on our scenarios, we estimate that the plume concentration may dilute below 1 ppm at about 2 km distance from the release site. Although our analysis shows that mixing with ambient seawater results in rapid dilution to low concentrations, it appears crucial to account for the effects of topographic lee wave turbulence in impact assessments related to man-made sediment plumes. Our high-resolution numerical simulations enable the identification of sediment particle size groups that are most likely affected by turbulence, providing valuable insights for developing targeted mitigation strategies. Full article

Phosphate pollution caused by human activities has become a pressing environmental issue, leading to eutrophication and severe ecological risks. In this study, artificial humic acid (HA) and fulvic acid (FA) were synthesized from tung fruit and glucose, respectively, and further composited with ferrihydrite (Fh) to prepare HA/Fh and FA/Fh adsorbents for phosphate removal. The structural and morphological characteristics of the composites were confirmed by SEM, XRD, FTIR, and XPS analyses, which indicated successful complexation of HA or FA with Fh through ligand exchange and surface interactions. Batch adsorption experiments revealed that HA/Fh and FA/Fh exhibited significantly enhanced adsorption capacities compared to pristine Fh, with maximum Langmuir adsorption capacities of 33.67 mg g⁻¹ and 37.06 mg g⁻¹, respectively. The adsorption behavior was well described by the pseudo-second-order kinetic model and the Langmuir isotherm, suggesting a chemisorption-dominated process involving ligand exchange between surface –OH groups of Fh and phosphate ions, supplemented by electrostatic attraction. Coexisting ion studies demonstrated that Cl⁻ and SO₄²⁻ slightly promoted phosphate adsorption, while NO₃⁻ and CO₃²⁻ strongly inhibited it, highlighting the competition of multivalent anions with phosphate for Fe³⁺ active sites. Importantly, the phosphate-enriched adsorbents can be directly recycled as phosphorus fertilizers, providing a sustainable pathway for both environmental remediation and phosphorus resource recovery. Full article

George Town, the capital of Penang, Malaysia, was inscribed as a UNESCO World Heritage Site in 2008 and is renowned for its multicultural architectural surfaces. However, these historic façades face significant deterioration challenges, particularly biodeterioration caused by weed growth on wall surfaces under hot and humid equatorial conditions. Root penetration is a critical surface defect, accelerating mortar decay and threatening structural integrity. To address this issue, this study proposes YOLOv11-SWDS (Surface Weed Detection System), a lightweight and interpretable deep learning framework tailored for surface defect detection in the form of weed intrusion on heritage buildings. The backbone network was redesigned to enhance the extraction of fine-grained features from visually cluttered surfaces, while attention modules improved discrimination between weed patterns and complex textures such as shadows, stains, and decorative reliefs. For practical deployment, the model was optimized through quantization and knowledge distillation, significantly reducing computational cost while preserving detection accuracy. Experimental results show that YOLOv11-SWDS achieved an F1 score of 86.0% and a mAP@50 of 89.7%, surpassing baseline models while maintaining inference latency below 200 ms on edge devices. These findings demonstrate the potential of deep learning-based non-destructive detection for monitoring surface defects in heritage conservation, offering both a reliable tool for sustaining George Town’s cultural assets and a transferable solution for other UNESCO heritage sites. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article

Municipal solid waste incineration generates by-products like nitrogen oxides, sulfur dioxide, and hydrogen chloride, contributing to environmental issues such as acid rain, ozone depletion, and photochemical smog. While industrial sites use desulfurization and denitrification to reduce emissions, no studies have modeled the formation mechanisms and influencing factors of these pollutants from a pollution reduction perspective. This study first analyzes the municipal solid waste incineration process to identify the main factors affecting the concentration of pollutants related to desulfurization and denitrification. A coupled numerical simulation model for the whole life cycle desulfurization and denitrification process in real municipal solid waste incineration power plants is then constructed using a method that couples two software tools. Next, based on a double orthogonal experimental design, virtual simulation data are generated using the numerical simulation model. Finally, an improved interval type-II fuzzy broad learning algorithm is applied to construct a mechanism model for the whole process of desulfurization and denitrification-related pollutant concentration, using the obtained virtual simulated data. Using a Beijing incineration plant as a case study, the whole life cycle model is successfully established. The research provides data for optimizing pollutant reduction, examines influencing factors, and lays the groundwork for future intelligent control. Full article

Ticks use multiple sensory organs to facilitate host detection, including Haller’s organs (HOs) that allow ticks to sense infrared (IR) radiation from potential hosts. Additionally, ticks have primitive eyes to sense light sources. The possibility exists that these senses may detect stimuli that attract ticks to road edge habitat, where IR radiation tends to be elevated. We investigated the role of the HOs and eyes in the attraction of adult American dog ticks, Dermacentor variabilis, towards road edge habitat(s). Adult D. variabilis were collected from multiple study sites and separated into three groups: (1) Haller’s organs removed; (2) eyes painted with black nail polish; and (3) unmodified ticks (control). All tick groups were marked with a unique fluorescent paint color and released 7.5 m from the road edge at two study sites. Tick movements were tracked at night using ultraviolet lights, tick position(s) were recorded using flags, and measurements were recorded to track tick movement in relation to the release point and road edge. Surface temperatures were recorded at the road edge and in the field to detect a potential thermal stimulus. Mixed-effects models were applied to investigate the significance of tick proximity to the road edge between the groups and sites. Our results demonstrated that the control unmodified group was significantly closer to the road edge than the modified groups lacking Haller’s organ or eyes (p ≤ 0.0001, p = 0.0049), leading to the conclusion that unmodified ticks move towards road edges. Modifying ticks, either by removing the HO or eyes of adult D. variabilis decreased tick movement toward road edges. Full article

Despite the prevalence and toxicity of heavy metals in the environment, arsenic and cobalt are of particular concern due to their high mobility and bioaccumulation potential, particularly in contaminated groundwater. Herein, we studied the adsorption behavior of commercially available sorbents, including Fluorosorb-100 (FS-100), Fluorosorb-200 (FS-200), and Filtrasorb-400 (F-400), for the removal of arsenite (As(III)) and cobalt (Co(II)), aiming at the selection of filter media in terms of future groundwater remediation. Kinetic analysis revealed that As(III) adsorption followed a pseudo-second-order model, while Co(II) showed mixed first- and second-order behavior, reflecting sorbent-dependent mechanisms. Equilibrium isotherm modeling revealed strong correlations with both Langmuir and Freundlich models, confirming heterogeneous adsorption sites and multilayer interactions. FS-100 demonstrated the highest affinity for As(III) (qₘ = 0.46 mg/g) and F-400 exhibited the greatest adsorption capacity for Co(II) (qₘ = 1.00 mg/g), while FS-200 consistently showed relatively weaker adsorption for both metals. Desorption studies indicated predominantly irreversible binding, with minimal release of As(III) from F-400 and Co(II) from FS-200 and F-400, even at high concentrations. Overall, these findings highlight that commercially available sorbents can effectively capture arsenite and cobalt, offering cost-effective and scalable options for heavy-metal removal in groundwater remediation systems under realistic environmental conditions. Full article

Antibiotic resistance genes (ARGs) have emerged as globally concerning environmental contaminants, posing serious threats to ecosystem health and public safety. This systematic review summarizes global research trends on ARGs across three key aspects: (i) identification and distribution in river and lake ecosystems, (ii) sources and environmental behaviors, and (iii) ecological and human health risks. Concentration data of ARGs in various rivers and lakes across China were compiled to reveal their spatial distribution patterns. The analysis of ARGs sources and environmental behaviors provides essential insights for designing effective mitigation strategies. Furthermore, this review highlights the potential ecological and human health hazards of ARGs and discusses limitations and improvement directions of current risk assessment methodologies. The main findings indicate that ARGs are widely present in rivers and lakes across China; higher abundances occur in eastern and southern regions compared with central–western and northern areas, such as 4.93 × 10²–8.10 × 10³ copies/mL in Qinghai Lake and 6.7 × 10⁷–1.76 × 10⁸ copies/mL in Taihu Lake. The environmental behaviors of ARGs are highly complex, involving multiple mechanisms and influenced by climatic conditions, nutrient levels, and additional environmental factors. Based on these findings, future efforts should prioritize long-term site-specific monitoring, evaluate their prolonged impacts on aquatic ecosystems, and develop integrated risk assessment models to support evidence-based environmental management. Full article

Near-fault ground motions significantly threaten bridges due to their distinct features, which are often inadequately considered in current seismic codes based mainly on far-field records. This study analyzes 941 near-fault records to evaluate the effects of site class, pulse-like motions, and vertical components on the peak acceleration ratio and normalized response spectra. A finite element model of a typical simply supported girder bridge is developed to examine how these factors affect pier internal forces. Results show that the peak acceleration ratio increases with softer sites and exhibits large scatter in near-fault regions, indicating that the conventional vertical-to-horizontal ratio of 0.65 may significantly underestimate vertical seismic actions. Pulse motions shift and broaden response spectra, raising seismic demands for medium- to long-period structures. Additionally, pulse effects combined with soft sites cause coupled amplification of internal forces. This work offers a theoretical basis for seismic design and assessment of similar bridges. Full article

Rapid urbanization and limited land resources have intensified parking shortages in China’s core and old urban districts, highlighting the tension between parking supply and public space. This study investigates the staged impacts of shared autonomous vehicles (SAVs) on private car ownership and parking demand within the context of urban renewal. Using a case study of Qintai Community in Wuhan, we combined resident surveys (135 valid samples), on-site parking facility assessments, and demand forecasting models to evaluate changes in parking requirements across different timeframes. Results indicate that SAVs can substantially reduce private car ownership and reshape parking demand structures, with short-term transitional pressures followed by long-term demand contractions. Furthermore, SAV adoption offers opportunities to reallocate parking land for multifunctional urban uses, alleviating land-use conflicts in high-density neighborhoods. The findings contribute to a dynamic framework for staged parking optimization, integrating technological innovation with community-level urban renewal strategies. This study underscores the importance of linking residents’ behavioral shifts with infrastructure adaptation, providing evidence-based guidance for sustainable urban transport and space management. Full article

Forest structure, including trees, deadwood and tree-related microhabitats, is a key determinant of forest biodiversity. Their relative contribution in shaping local (alpha) biodiversity and its variation (beta) between sites remains unclear. We assessed how forest characteristics shape alpha and beta diversity of beetle communities in mixed silver fir–beech forests within the Vallombrosa Nature Reserve (Tuscany, Italy). We sampled 47 circular plots recording single-tree attributes, deadwood volume and decay stage, and the occurrence of tree-related microhabitats. Beetle assemblages were surveyed using window flight traps, yielding over 11,000 individuals belonging to 187 species, 20% of those known from central-southern Italian forests, 58% of which were listed in the Italian Red List of Saproxylic Beetles and 10% of which were threatened. Statistical models (GLMs and GDMs) revealed that alpha diversity was driven by fine-scale features, including tree species composition, microhabitats (cavities, bark, epiphytes) and deadwood diversity. In contrast, beta diversity was shaped by stand structure and inter-stand heterogeneity. Our results highlight the need for conservation strategies that simultaneously maintain tree-level heterogeneity and secure variation across the landscape. Management should therefore combine retention of microhabitats and diverse deadwood substrates with promotion of structurally diverse, mixed stands to sustain beetle diversity at multiple spatial scales. Full article

The planetary bearing looseness fault can cause the planetary gear train to fail. Conventional modeling methods do not consider complex component-coupling relationships for fault feature analysis. As a result, a joint model is developed to examine the dominant relationship between planetary bearings and the planetary gear train. Firstly, the planetary bearing is modeled in the normal and fault states. Then, a refined joint planetary gear train dynamic model is constructed, which is composed of the planetary gears, the ring gear, the carrier, the sun gear, and the planetary bearings. Finally, the simulation results show that, when the planetary bearing is in the looseness fault state, its fault characteristic presents as the rotation frequency of the carrier and its harmonics. The on-site signal of a 2.0 MW wind turbine is used to verify the effectiveness of the model. The proposed model can provide the basis for the fault mechanism analysis and fault diagnosis of rolling bearing outer ring looseness. Full article