Search Results (181)

The Kubuqi Desert serves as a critical zone for both renewable energy development and ecological management in China. Large-scale photovoltaic (PV) deployment has fundamentally altered the regional underlying surface, impacting near-surface wind–sand dynamics. To elucidate these disturbance mechanisms, we selected three representative surfaces—a PV area, a resource base, and Qixing Lake—and conducted field observations from September to December 2023 using meteorological towers and wind erosion sensors. Results indicate that all surfaces significantly attenuated near-surface wind speeds by over 30% through modified flow field structures. A strong linear positive correlation existed between wind speed and friction velocity (R² ≈ 0.99). Notably, for the same friction velocity, the actual wind speed required to initiate sand movement was lowest in the PV zone (high k) and highest at Qixing Lake (low k), signifying enhanced surface stability due to PV infrastructure and moisture. Threshold analysis revealed distinct initiation speeds: >6.0 m·s⁻¹ in peripheral quicksand, >4.3 m·s⁻¹ in inter-panel zones, and >4.6 m·s⁻¹ beneath panels. The tilted PV panels accelerate airflow downward, generating cyclonic vortices that intensify sand particle impacts under and between panels. This study reveals the tri-dimensional mechanism of wind regulation–sand suppression–stability enhancement, providing theoretical support for mitigating wind–sand disasters while advancing green energy in desert regions. Full article

This systematic review summarizes experimental studies in atmospheric acoustics that quantify environmental influences on long-range sound propagation. A keyword-based search was conducted in Scopus and Google Scholar to identify relevant records. Studies were included if they were published in English between January 1977 and April 2026, investigated long-range sound propagation within the human audibility range using specific sound sources, and incorporated concurrent meteorological measurements. Two reviewers worked independently to assess eligibility of the studies included in this review. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, this systematic review surveys the methodological content of these studies with respect to sound sources, signal content and processing, microphone configuration, treatment of the ground and topography, and meteorological measurements to identify common practices. Some studies provide only limited information about the acoustic source properties, postprocessing of acoustic data, and/or configuration of meteorological measurements. Key experimental details for the 40 included studies are tabulated and summarized via histograms for reference. Most experimental acoustic studies have measured propagation within a range of 2 km on relatively flat land and have utilized tower-based meteorological measurements. The results of the studies surveyed here have implications for understanding long-range outdoor sound propagation, including development of accurate numerical models. Some contributing authors were funded by the Office of Naval Research: ONR Award N00014 24-1-2400, ONR Award N00014-24-1-2437. Full article

Conventional solar power tower (SPT) systems often suffer from significant heat transfer exergy destruction due to large temperature differences between the heat source and the working fluid during the heat exchange process. To overcome this limitation, a high–low dual-tower configuration based on segmented thermal utilization is proposed. In this arrangement, the high-temperature tower is mainly responsible for the evaporation, superheating, and reheating processes, whereas the low-temperature tower primarily handles feedwater preheating. Such a configuration improves the temperature matching characteristics during the heat exchange process. A comprehensive model integrating the heliostat field, receiver, thermal energy storage system, and power block was developed and validated against Solar Two experimental data, showing good agreement. Comparative analyses were conducted under identical solar resource and operating conditions. The results indicate that the proposed system achieves a comparable power output while reducing total heat transfer exergy destruction by approximately 24%, with a significant reduction of over 80% in the preheating section. Sensitivity analysis further reveals that optimizing the high tower outlet temperature can effectively reduce irreversibility and slightly enhance power output, although constrained by the pinch temperature difference. Dynamic simulations based on typical meteorological year data demonstrate that the system maintains stable operation and improves cycle efficiency. From an economic perspective, the proposed system reduces the levelized cost of electricity (LCOE) by about 6.6% and shortens the dynamic payback period, indicating enhanced long-term competitiveness. Overall, the high and low dual-tower system effectively improves thermodynamic and economic performance, providing a promising approach for high-efficiency concentrating solar power (CSP) development. Full article

Vento Norte (VNOR; Portuguese for North Wind) is a downslope windstorm that develops over modest terrain in the central region of Rio Grande do Sul (RS), southern Brazil. The regional topography is characterized by an abrupt terrain transition with elevation differences of approximately 400–500 m. This atmospheric flow typically occurs during the cold season and is characterized by strong wind gusts, rapid warming, and drying of the planetary boundary layer (PBL). In this study, the performance of different PBL parameterization schemes in the Weather Research and Forecasting (WRF) model is assessed for simulating a VNOR event that occurred between 19 and 20 August 2021 in Santa Maria (SMA), RS. Five high-resolution numerical simulations were conducted using the Yonsei University (YSU), Asymmetric Convective Model version 2 (ACM2), Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN2.5), Quasi-Normal Scale Elimination (QNSE), and Three-Dimensional Turbulent Kinetic Energy (3DTKE) PBL schemes. Model results were evaluated against observations from a flux tower providing turbulence measurements, twice-daily radiosoundings, and hourly surface meteorological observations. Statistical metrics indicate that the MYNN2.5 scheme provided the most accurate representation of the nighttime stable boundary layer preceding the VNOR, as well as its onset and subsequent evolution. Although this study analyzes a single VNOR event and the results may be case-dependent, the overall performance of the MYNN2.5 scheme suggests that it is a promising option for the operational forecasting of VNOR events. These findings provide new insights into the ability of different PBL schemes to reproduce the mean boundary-layer structure and turbulence characteristics associated with downslope windstorms over modest terrain, contributing to the understanding of these events. Full article

The study of airborne pollen and spores in regions, communities, and campuses has gained importance in Nigeria in recent times. Aerospora sampling was carried out from November 2012 to February 2013 on the University of Ibadan campus Watch Tower. The Tower is the tallest building on campus, standing at 35 m. An Aero sampler was used to collect aeropalynomorphs monthly at the site. The recovered residues were acetolysed and studied microscopically. Meteorological data for this location were obtained from the Nigerian Meteorological Agency (NiMet) for the prevailing weather conditions. Statistical analysis using the Pearson Correlation Coefficient was used to evaluate the relationship between airborne pollen and spores and meteorological parameters. A variety of palynomorphs, characteristic of rainforest, secondary/open forest, savanna, and freshwater vegetation types, were recovered. The dominant ones belonged to the Arecaceae, Anacardiaceae, Amaranthaceae, Euphorbiaceae, Moraceae, and Poaceae families, as well as fungal spores. Pollen counts with meteorological data revealed variations in palynomorph types and concentrations that reflected the influence of the aerosampler location, weather parameters, and the degree of human activities on the floral composition. This work is the first aero-sampling on the University of Ibadan campus and a contribution to the aeropalynological data of campuses across Southwest Nigeria. Full article

The eastern coast of China, characterized by a pronounced climatic gradient and diverse ecosystems, is an ideal region for exploring the spatiotemporal dynamics of carbon fluxes and their drivers. Based on observations from eight flux tower sites, together with meteorological, remote sensing, and ecohydrological variables from 2001 to 2022, this study developed Back Propagation (BP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models to estimate regional gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP). Among them, RF performed best, achieving validation R² values of 0.92, 0.84, and 0.83 for GPP, ER, and NEP, respectively, and was therefore selected for regional upscaling. The regional mean GPP, ER, and NEP were 1578.38, 1286.05, and 334.56 g C m⁻² yr⁻¹, respectively, indicating that the region functioned as a net carbon sink during the study period. GPP, ER, and NEP exhibited a clear spatial gradient, with higher values in the south and lower values in the north. Total regional NEP increased from 344.12 Tg C in 2001 to 517.73 Tg C in 2022, reflecting a continuous strengthening of terrestrial carbon sink strength. Forests contributed most to the regional carbon sink, while the ecosystem-level NEP contribution of croplands increased over time; by contrast, the total carbon sink of wetlands declined because of area loss. These results suggest that ecological restoration, vegetation greening, and land cover optimization jointly enhanced the carbon sink along the eastern coast of China. These findings have important implications for ecological management and green low-carbon development along the eastern coast of China. Full article

Improving the energy performance of existing multi-apartment residential buildings is critical for reducing energy consumption and greenhouse gas emissions in Central and Eastern Europe, where large stocks of post-war buildings with limited insulation are connected to district heating systems. This study evaluates façade insulation retrofit strategies for two representative typologies in Novi Beograd, Serbia—a high-rise tower and an elongated slab-type (‘lamella’) building—using calibrated dynamic energy models and cradle-to-use lifecycle assessment (LCA) over a 50-year service life. Models were calibrated against measured 2023–2024 heating consumption data (NMBE < 1%, CVRMSE < 15%) and normalized with Typical Meteorological Year weather for consistent scenario comparison. Retrofit scenarios applied expanded polystyrene (EPS) and cellulose insulation at 10, 12, and 15 cm thicknesses. Results show that external insulation reduces annual heating demand by approximately 19–20% compared to the uninsulated baseline (192 kWh/m²·a), with the majority of savings achieved at 10 cm and only marginal gains from additional thickness. Insulation thickness has a stronger influence on operational energy reduction than material choice, as differences between EPS and cellulose remain below 0.5%. LCA indicates 23.6–26.0% lower climate change impacts and 23.6–25.8% reduced cumulative energy demand in retrofit scenarios, with cellulose offering modest advantages due to lower embodied emissions and biogenic carbon storage. These findings support targeted envelope retrofits as an effective strategy for decarbonizing district-heated residential buildings in the region. Full article

To explicitly illustrate the relationship between heliostat field optimization and power generation, a coupled model was established in Simulink. By optimizing the geometric layout of the heliostat field, the solar heat collection efficiency can be significantly improved, thereby increasing the thermal input to the system. The optimized heliostat field design can convert solar energy into thermal energy more efficiently and transfer it to the steam generator through the molten salt loop, thereby driving power generation in the Rankine cycle. In this process, the Rankine cycle is responsible for converting the thermal energy supplied by the molten salt loop into mechanical work and ultimately into electrical power output. At the same time, real meteorological data from a commercial heliostat field were introduced, and annual power generation simulations demonstrated that the integrated modeling of the heliostat field, thermal storage, and power block based on actual meteorological boundary conditions and system parameters can effectively reflect the power generation performance of a commercial tower solar thermal power plant. Meanwhile, research on heliostat field optimization should further evolve from identifying general patterns toward parameter design and overall system performance improvement. For molten-salt tower solar thermal power plants, key design variables such as receiver tower height, receiver dimensions, heliostat dimensions, and heliostat field spacing parameters affect not only the annual average optical efficiency of the heliostat field and the thermal power output of the receiver, but also the annual power generation of the entire plant. By integrating SOLARPILOT 1.5.2 and SAM 2025.4.16, the design variables were systematically analyzed to investigate their effects on the annual average optical efficiency of the heliostat field, the number of heliostats, the receiver output power, and the annual power generation, and the reasonable value ranges of the heliostat field parameters were determined accordingly. The established Rankine cycle power block model was then coupled with the parameter optimization results to carry out a secondary optimization of the initial heliostat field. Through the above study, the aim is to realize a shift from single-objective geometric optimization of the heliostat field to comprehensive optimization oriented toward annual plant power generation performance and scenario adaptability, thereby providing a basis for scheme design and parameter selection of molten-salt tower solar thermal power plants. For external validation, the annual generation predicted for the Delingha 50 MW commercial plant was 142.15 GWh, corresponding to a relative deviation of 2.64% from the published design value of 146 GWh. This indicates that the coupled framework can reasonably capture the integrated response of the heliostat field, thermal storage system, and power block at the plant level. The model is therefore suitable for generation-oriented parameter screening and preliminary design of tower molten-salt CSP plants, while detailed component-level transient design still requires higher-fidelity engineering models. Full article

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R² = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R² = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices. Full article

Wind energy stands as one of the most technologically mature renewable sources, playing a pivotal role in the mitigation of greenhouse gas emissions. However, wind farms and associated infrastructures increase collision risk for flying organisms. Implementing higher cut-in speeds is a proven mitigation strategy to significantly decrease wildlife mortality rates, particularly for bat species, by preventing turbine operation during low-wind periods of high activity. The suggested, non-standard, increased cut-in speed for wind turbines is generally 5.0 m/s. To test the effectiveness of cut-in speed increase, bat activity was monitored at three wind farms in northern Portugal (Gevancas, Azinheira, and Lagoa de Dom João e Feirão), to characterize spatial and temporal activity patterns and assess the potential associated risk. Ultrasonic acoustic detection was carried out at fixed stations, at heights of 55 m above ground level from March to October. Wind speed data were recorded concurrently using anemometers mounted on meteorological towers. Contradicting recommendations, the results show that significant bat activity might occur at wind speeds above the current curtailment values. Since turbine operation coincides with peak bat activity, it is imperative to implement site-specific mitigation strategies, such as optimized cut-in speeds, to minimize mortality risk. Full article

The accelerating development of offshore wind energy in the Great Lakes region necessitates cost-effective solutions for auxiliary infrastructure, such as meteorological masts. While monopile foundations are well-established for turbine generators, their high flexural rigidity and capital cost are often disproportionate for non-generating platforms. This study presents a parametric optimization of a guyed tower foundation situated in the nearshore limestone shelf of Lake Huron (Point Clark), specifically designed to balance strict signal serviceability with foundation economy. Using a non-linear static solver with Ernst equivalent cable moduli, a full factorial sweep of 48 design configurations was conducted under site-specific hydrodynamic loads (1300 kN Average/3500 kN Storm). The results demonstrate that while all configurations satisfied the 0.004 rad rotation limit mandated by TIA-222-H, significant non-linear trade-offs exist between structural stiffness and foundation demand. Specifically, a “cost of rigidity” was identified, where increasing cable pretension to 800 kN resulted in foundation overturning moments exceeding 9.6 × 10⁴ kN·m—a threefold increase compared to lower-pretension alternatives. To resolve this trade-off, a formal multi-objective scoring function was applied to rank designs based on rotation, moment, and displacement. The analysis identifies a “balanced” configuration comprising three guys with high-stiffness anchors (5 × 10⁷ N/m) and moderate pretension (300–500 kN) as the optimal design. This configuration leverages the competent bedrock to minimize cable tension requirements, offering a resilient and economically efficient solution for Great Lakes offshore monitoring. Full article

A vulnerability surface modeling method based on dual intensity metrics is proposed to assess the impact of typhoons and heavy rainfall disasters on the distribution pole-tower conductor system. A three-dimensional finite-element model is developed for a typical “three-pole four-conductor” distribution line, considering the uncertainties in both load-side and structural-side parameters. A spatially coherent turbulent wind field is generated using the Davenport spectrum and harmonic superposition method, while an equivalent rain load is derived based on raindrop spectrum integration. Nonlinear dynamic time-history analysis is then conducted under multiple combinations of basic wind speeds and rainfall intensities, extracting engineering demand parameters such as conductor axial tension and pole-base bending moments. Based on probabilistic demand analysis, the relationship between engineering demand parameters and dual intensity measures is regressed in the logarithmic domain to construct bivariate fragility surfaces for both the conductors and the poles. Critical failure curves are obtained by intersecting the fragility surfaces with the 10% exceedance probability level, enabling rapid classification of structural risk under the joint effects of wind and rain. The results show that the regression model provides a high fit, effectively revealing that wind speed is the dominant control factor, while rainfall intensity serves as a secondary amplifying factor. The resulting critical failure curves can be directly used as operation and maintenance warning thresholds and can be coupled with observed and forecast meteorological data for time-varying risk assessment. These findings provide methodological support and engineering guidance for risk assessment, operation and maintenance decision-making, and resilience enhancement of distribution networks under multi-hazard coupling. Full article

Escalating global climate change has intensified the frequency and scale of wildfires in mountainous regions hosting transmission line infrastructure. These conflagrations act as extreme meteorological events, capable of generating localized heatwaves that compromise the air insulation of power lines and trigger protective relay operations, thereby posing systemic threats to regional grid stability. To enhance wildfire early-warning efficacy for grid security, this study formulates wildfire early warning for power transmission corridors as a regression-based risk prediction problem and proposes a hierarchical “global screening–local refinement” risk assessment framework. The primary contribution of this study lies in the integration of a machine-learning-based global wildfire risk screening model with tower-level spatial refinement using geographically weighted regression (GWR), enabling coordinated global–local wildfire risk characterization along power transmission corridors The framework employs a predictive model built on a Gradient Boosting Decision Tree algorithm, integrating geospatial and statistical analyses. A global risk model, utilizing historical data from the Himawari-8 satellite alongside meteorological, topographic, and anthropogenic variables, produces a composite risk index. This index is spatially interpolated via Kriging to generate stratified wildfire risk maps for broad-area assessment. For precise corridor-level analysis, these Globally Projected Risk Indices, along with localized terrain features, inter-tower clearance distances, and proximity to historical ignition points, are incorporated into a Geographically Weighted Regression model. This yields a spatially calibrated wildfire risk index along critical routes. The results show that the GBDT-based model achieved the best predictive performance among the evaluated regression models, with an R² of 0.626 and a mean squared error of 0.178. This approach offers a scientifically robust and operationally viable reference for wildfire prevention strategies in power line maintenance. Full article

With the rapid advancement of clean energy, wind farm planning and construction are expanding worldwide, increasing the demand for accurate resource assessments. This study investigates the influence of vertical wind characteristics on wind farm siting and energy production, using measured meteorological data from the Hangjinqi wind farm. Results show that both mean wind speed increase substantially with altitude, indicating that upper layers provide richer and more stable wind resources. The estimated annual energy production of the site reaches 23,500 MWh, with capacity factors ranging from 35% to 42%, well above the national average. Seasonal and diurnal variations are evident: wind speeds strengthen during winter and spring, particularly at night, while turbulence intensity peaks in the daytime and decreases with height. Carbon dioxide (CO₂) reduction also increases with hub height, with the most pronounced seasonal reductions in spring (3367.6–5041.1 tCO₂, +49.7%) and winter (3215.7–5380.0 tCO₂, +67.4%), equivalent to several thousand tons of standard coal per turbine annually. Optimal performance is observed at 100–140 m, demonstrating efficient utilization of mid- to high-altitude resources. Nevertheless, discrepancies in turbine performance at different hub heights suggest untapped potential at higher elevations. These findings highlight the importance of incorporating vertical wind characteristics into wind farm siting decisions, and support the deployment of turbines with tower heights ≥140 m alongside intelligent scheduling and forecasting strategies to maximize energy yield and economic benefits. Full article

Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-precision wind speed information. In this study, a hierarchical data fusion strategy is proposed to enhance both the quality and efficiency of multi-sensor fusion on wind measurement towers. At the local fusion stage, multi-sensor wind speed data are denoised and fused using an unscented Kalman filter enhanced with fuzzy logic and a robustness factor (FLR-UKF). At the global decision fusion stage, decision-level fusion is achieved through an extreme learning machine (ELM) neural network optimized by a Q-learning-improved Aquila optimizer (QLIAO-ELM). By incorporating a spiral surrounding attack mechanism and a Q-learning-based adaptive strategy, QLIAO-ELM significantly enhances global search capability and convergence speed, enabling the ELM network to obtain superior parameters within limited computational time. Consequently, the accuracy and efficiency of decision fusion are improved. Experimental results show that, during the local fusion phase, the RMSE of FLR-UKF is reduced by 26.46% to 28.6% compared to the traditional UKF; during the global fusion phase, the RMSE of QLIAO-ELM is reduced by 27.1% and 14.0% compared to ELM and ISSA-ELM, respectively. Full article