Search Results (257)

Ensuring the safe operation of Unmanned Aerial Vehicles (UAVs) is crucial for both mission-critical and safety-critical tasks. In scenarios where UAVs must track airborne targets, they need to follow the target’s path while maintaining a safe distance, even in the presence of unmodeled dynamics and environmental disturbances. This paper presents a novel collision avoidance strategy for dynamic quadrotor UAVs during target-tracking missions. We propose a safety controller that combines a learning-based Control Barrier Function (CBF) with standard sliding mode feedback. Our approach employs a neural network that learns the true CBF constraint, accounting for wind disturbances, while the sliding mode controller addresses unmodeled dynamics. This unified control law ensures safe leader-following behavior and precise trajectory tracking. By leveraging a learned CBF, the controller offers improved adaptability to complex and unpredictable environments, enhancing both the safety and robustness of the system. The effectiveness of our proposed method is demonstrated through the AirSim platform using the PX4 flight controller. Full article

Accurate prediction of the F-factor averaged over one kilometer (FBAR), a critical wind shear metric, is essential for aviation safety. A central F-factor is used to compute FBAR. i.e., compute the value of FBAR at a point using a spatial interval beginning 500 m prior to the point and ending 500 m beyond the point. Traditional FBAR estimation using the Vicroy method suffers from limited vertical wind speed (W${\phantom{,}}_h$) accuracy, particularly in complex, non-idealized atmospheric conditions. This foundational study proposes a hybrid CNN-BiLSTM-Attention deep learning architecture that integrates spatial feature extraction, sequential dependency modeling, and attention mechanisms to address this limitation. The model was trained and evaluated on data generated by the industry-standard Airborne Doppler Weather Radar Simulation (ADWRS) system, using the DFW microburst case (C1-11) as a benchmark hazardous scenario. Following safety assurance principles aligned with SAE AS6983, the proposed model achieved a W${\phantom{,}}_h$ estimation RMSE (root-mean-squared deviation) of $0.623$ $\mathrm{m} {\mathrm{s}}^{-1}$ (vs. Vicroy’s $14.312$ $\mathrm{m} {\mathrm{s}}^{-1}$) and a correlation of 0.974 on 14,524 test points. This subsequently improved FBAR prediction RMSE by 98.5% (0.0591 vs. 4.0535) and MAE (Mean Absolute Error) by 96.1% (0.0434 vs. 1.1101) compared to Vicroy-derived values. The model demonstrated a 65.3% probability of detection for hazardous downdrafts with a low 1.7% false alarm rate. These results, obtained in a controlled and certifiable simulation environment, highlight deep learning’s potential to enhance the reliability of airborne wind shear detection for civil aircraft, paving the way for next-generation intelligent weather avoidance systems. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

The mechanism for the upscale deposition of energy into the atmosphere from molecules and photons up to organized wind systems is examined. This analysis rests on the statistical multifractal analysis of airborne observations. The results show that the persistence of molecular velocity after collision in breaking the continuous translational symmetry of an equilibrated gas is causative. The symmetry breaking may be caused by excited photofragments with the associated persistence of molecular velocity after collision, interaction with condensed phase surfaces (solid or liquid), or, in a scaling environment, an adjacent scale having a different velocity and temperature. The relationship of these factors for the solution to the Navier–Stokes equation in an atmospheric context is considered. The scale invariant version of Gibbs free energy, carried by the most energetic molecules, enables the acceleration of organized flow (winds) from the smallest planetary scales by virtue of the nonlinearity of the mechanism, subject to dissipation by the more numerous average molecules maintaining an operational temperature via infrared radiation to the cold sink of space. The fastest moving molecules also affect the transfer of infrared radiation because their higher kinetic energy and the associated more-energetic collisions contribute more to the far wings of the spectral lines, where the collisional displacement from the central energy level gap is greatest and the lines are less self-absorbed. The relationship of events at these scales to macroscopic variables such as the thermal wind equation and its components will be considered in the Discussion section. An attempt is made to synthesize the mechanisms by which winds are generated and sustained, on all scales, by appealing to published works since 2003. This synthesis produces a view of the general circulation that includes thermodynamics and the defining role of turbulence in driving it. Full article

This study investigates the composition, abundance, and seasonal variability of airborne pollen in Siirt, a transitional region between the Irano-Turanian and Mediterranean phytogeographical zones in southeastern Türkiye. The main objective was to assess pollen diversity and its relationship with meteorological parameters over a two-year period (2022–2023). Airborne pollen was collected using a Hirst-type volumetric pollen and spore trap; a total of 18,666 pollen grains/m³ belonging to 37 taxa were identified. Of these, 70.67% originated from woody taxa and 29.33% from herbaceous taxa. Peak concentrations occurred in April, with the lowest levels in December. The dominant taxa, all exceeding 1% of the total, were Pinaceae (31.00%); Cupressaceae/Taxaceae (27.79%); Poaceae (18.42%); Moraceae (4.23%); Amaranthaceae (2.42%); Urticaceae (2.13%); Quercus (1.55%); Fabaceae (1.29%); and Rumex (1.02%). Spearman’s correlation analysis revealed significant relationships between daily pollen concentrations and meteorological variables such as temperature, humidity, precipitation, and wind speed. These findings highlight that both climatic conditions and the surrounding vegetation, shaped by regional land cover, play a crucial role in determining pollen dynamics. In conclusion, this study provides the first aerobiological baseline for Siirt and contributes valuable data for allergy-risk forecasting and long-term ecological monitoring in southeastern Türkiye. Full article

Electrostatic spraying systems can improve the pesticide application efficiency by enhancing droplet deposition and coverage within crop canopies. This study evaluated the droplet size spectra and charge-to-mass ratio (CMR) of five electrostatically charged hollow-cone nozzles and one flat-fan nozzle paired with an electrode. Each nozzle was mounted on a moving boom in a wind tunnel and operated with the electrode and voltage that produced the highest CMR. Their effects on the spray coverage and deposition inside boxwood shrubs at wind speeds of 0 and 2.24 m s⁻¹ were assessed. The nozzles operated with the optimized electrode had average improvements in the canopy deposition and canopy coverage of 1.33 µg cm⁻² and 4.4% at a wind speed of 0 m s⁻¹ and 0.26 µg cm⁻² and 0.9% at a wind speed of 2.24 m s⁻¹. The airborne drift measurements at various heights above the wind tunnel floor showed an average 0.50 µg cm⁻² reduction in the drift at 0.1 m, variable results at 0.35 m, and minimal changes at heights of 0.7 m and above at a downwind distance of 2 m. These findings highlighted the potential of optimized electrostatic spraying systems to enhance pesticide deposition inside the crop canopy under various wind speeds while reducing the spray drift potential. Full article

The dynamic relationship between microplastics (MPs) in the air and on the Earth’s surface involves both natural and anthropogenic forces. MPs are transported from the ocean to the air by bubble scavenging and sea spray formation and are released from land sources by air movements and human activities. Up to 8.6 megatons of MPs per year have been estimated to be in air above the oceans. They are distributed by wind, water and fomites and returned to the Earth’s surface via rainfall and passive deposition, but can escape to the stratosphere, where they may exist for months. Anthropogenic sprays, such as paints, agrochemicals, personal care and cosmetic products, and domestic and industrial procedures (e.g., air conditioning, vacuuming and washing, waste disposal, manufacture of plastic-containing objects) add directly to the airborne MP load, which is higher in internal than external air. Atmospheric MPs are less researched than those on land and in water, but, in spite of the major problem of a lack of standard methods for determining MP levels, the clothing industry is commonly considered the main contributor to the external air pool, while furnishing fabrics, artificial ventilation devices and the presence and movement of human beings are the main source of indoor MPs. The majority of airborne plastic particles are fibers and fragments; air currents enable them to reach remote environments, potentially traveling thousands of kilometers through the air, before being deposited in various forms of precipitation (rain, snow or “dust”). The increasing preoccupation of the populace and greater attention being paid to industrial ecology may help to reduce the concentration and spread of MPs and nanoparticles (plastic particles of less than 100 nm) from domestic and industrial activities in the future. Full article

As the frequency of strong storms and cyclones increases, understanding wind risk in both existing and newly established plantation forests is becoming increasingly important. Recent advances in the quality and availability of remotely sensed data have significantly improved our capability to make large-scale wind risk predictions. This study models the loss of radiata pine (Pinus radiata D.Don) plantations following a severe cyclone within the Gisborne Region of New Zealand through leveraging repeat regional LiDAR acquisitions, optical imagery, and various surfaces describing key climatic, topographic, and storm-specific conditions. A random forest model was trained on 9713 plots classified as windthrow or no-windthrow. Model validation using 50 iterations of 80/20 train/test splits achieved robust accuracy (accuracy = 0.835; F1 score = 0.841; AUC = 0.913). In comparison to most European empirical models (AUC = 0.51–0.90), our framework demonstrated superior discrimination, underscoring its value for regions prone to cyclones. Among the 14 predictor variables, the most influential were mean windspeed during February, the wind exposition index, site drainage, and stand age. Model predictions closely aligned with the estimated 3705 hectares of cyclone-induced forest damage and indicated that 20.9% of unplanted areas in the region would be at risk of windthrow at age 30 if established in radiata pine. The resulting wind risk surface serves as a valuable decision-support tool for forest managers, helping to mitigate wind risk in existing forests and guide adaptive afforestation strategies. Although developed for radiata pine plantations in New Zealand, the approach and findings have broader relevance for forest management in cyclone-prone regions worldwide, particularly where plantation forestry is widely practised. Full article

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km² in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less). Full article

Rural villages function as relatively self-sustained production and living units with well-developed infrastructure. In this setting, investigating the transmission pathways of airborne biological particles, including pathogenic microorganisms, is pivotal for ensuring the health of residents. This study investigated the sources and dispersion of biogenic particulate matter in rural ambient air and factors influencing their behavior. Potential bioaerosol sources including livestock farming areas, composting sites, garbage dumps, and sewage treatment facilities were investigated using a calibrated portable bioaerosol detector to collect and analyze the dispersion of bioaerosol particles. The dispersal characteristics of Enterobacteriaceae were explored using an Andersen six-stage sampler. Livestock farming areas were the primary source of bioparticles. The distribution of the bioparticles varied significantly with environmental conditions. Key factors influencing their distribution included the dispersal capabilities due to wind speed and the processes of aggregation and coagulation of particles. The dispersal pathway of Enterobacteriaceae indicated that the inhabitants of residences near the dispersion source might be exposed to health risks from pathogenic bacteria present in bioparticles indoors. Understanding such characteristics and transmission patterns of bioparticles in rural environments provides a scientific basis for risk assessment and management strategies, with important implications for improving air-quality monitoring, public health policies, and environmental management in rural areas. Full article

The eddy dissipation rate (EDR, or turbulence dissipation rate) is a crucial parameter in the study of the atmospheric boundary layer (ABL). However, the existing Doppler lidar-based estimates of EDR seldom offer long-term comparisons that span the entire ABL. Building upon prior research utilizing Doppler lidar wind-field data, we optimized the EDR retrieval algorithm using a genetic adaptive approach. The newly developed algorithm demonstrates enhanced accuracy in EDR estimation. The daily evolution of EDR reveals a distinct diurnal pattern in its variation. A detailed four consecutive days study of turbulence generated via low-level jets (LLJs) indicated that EDR driven by heat flux (~10⁻² m²/s³) is significantly stronger than that produced through wind shear (~10⁻³ m²/s³). Subsequently, we examined seasonal variations in EDR at different mixing layer heights (MLH, Zi): elevated EDR values in summer (~7 × 10⁻³ m²/s³ at 0.1Zi) contrasted with reduced levels in winter (~6 × 10⁻⁴ m²/s³ at 0.1Zi). In the early morning, EDR decreases with height for 1 magnitude, while in later stages, it remains relatively stable within 0.1 order of magnitude across 0.1Zi to 0.9Zi. Notably, the EDR during DJF exceeds that of MAM and SON in the afternoon. This suggests that ML turbulence is not solely dependent on surface fluxes (SHF + LHF) but may also be influenced by MLH. A lower MLH (smaller volume), even with reduced surface fluxes, could potentially result in a stronger EDR. Finally, we compared the evolution of the EDR and MLH in the boundary layer using Doppler lidar data from ARM sites and the PBL (Planetary Boundary Layer) Moving Active Profiling System (PBLMAPS) Airborne Doppler Lidar (ADL). The results show that the vertical wind data exhibit strong consistency (R = 0.96) when the ADL is positioned near ARM Southern Great Plains (SGP) sites C1 or E37. The ADL’s mobility and flexibility provide significant advantages for future field experiments, particularly in challenging environments such as mountainous or complex terrains. This study not only highlights the potential of utilizing Doppler lidar alone for EDR calculations but also extensively explores the development patterns of EDR within the ABL. Full article

An airborne wind energy system (AWES) harvests wind at a higher altitude above conventional wind turbines using tethered flying devices. For the design and development of an AWES, we need to know the representative wind speed profile, and its temporal variation is also quite important for the optimization of operation control. This study investigates wind speed profiles up to 3000 m, utilizing ERA5 data spanning from 2000 to 2022 and measured data from a laser wind radar. The long-term averaged wind profile is statistically analyzed, as well as wind profiles with different cumulative probabilities, which are generally consistent with the logarithmic law. Statistical results show that the frequency of negative shear is more than 85% in instantaneous wind profiles, with a greater likelihood at altitudes between 500 m and 1500 m. Fluctuations in wind speed and direction based on 10 min averaged wind speed data have also been provided, which are described by a normal distribution. The wind speed fluctuations primarily concentrate within 2 m/s, with a standard deviation of approximately 0.45 m/s. The wind direction fluctuations are severe at the ground layer and show a rapid decay trend with increasing altitude and averaged wind speed. These results can support the design and control optimization of the AWES. Full article

In this paper, a wind retrieval method based on genetic algorithm-particle swarm optimization (GA-PSO) for the coherent Doppler wind lidar (CDWL) is proposed. The algorithm incorporates an advanced optimization framework that considers wind field spatial continuity, simultaneously enhancing retrieval accuracy and computational efficiency. Comprehensive validations of the GA-PSO algorithm are conducted using a 1.5 μm all-fiber CDWL through ground-based and airborne experiments. In ground-based experiments, the GA-PSO algorithm extends the detection range by 20%~30% compared with traditional methods. The validation against meteorological tower data demonstrates excellent agreement, with mean deviations better than 0.27 m/s for horizontal wind speed and 3.07° for horizontal wind direction and corresponding RMSE values better than 0.36 m/s and 6.04°, respectively. During high-altitude airborne experiments at 5.5 km, the GA-PSO algorithm recovers up to 31% more horizontal wind speed and direction information compared with traditional algorithms, demonstrating exceptional performance in low signal-to-noise ratio (SNR) conditions. Both simulation analysis and field experiments demonstrate that the GA-PSO algorithm achieves processing speeds comparable to traditional real-time methods, establishing its suitability for real-time, three-dimensional wind retrieval applications. Full article

Background: There is a lack of automatic real-time monitoring of airborne pollens in China and no validation study has been performed. Methods: Two-year continuous automatic real-time pollen monitoring (n = 437) was completed in 2023 (3 April–31 December) and 2024 (1 April–30 November) in Shanghai, China, in parallel with the standard daily pollen sampling(n = 437) using a volumetric Hirst sampler (Hirst-type trap, according to the European standard). Daily ambient particulate matter and meteorological factors were collected simultaneously. Results: Across 2023 and 2024, the daily mean pollen concentration was 7 ± 9 (mean ± standard deviation (SD)) grains/m³ by automatic monitoring and 8 ± 10 grains/m³ by the standard Hirst-type method, respectively. The spring season had higher daily pollen levels by both methods (11 ± 14 grains/m³ and 12 ± 15 grains/m³) and the daily maximum reached 106 grains/m³ and 100 grains/m³, respectively. A strong correlation was observed between the two methods by either Pearson (coefficient 0.87, p < 0.001) or Spearman’s rank correlation (coefficient 0.70, p < 0.001). Compared to the standard method, both simple (R² = 0.76) and multiple linear regression models (R₂ = 0.76) showed a relatively high goodness of fit, which remained robust using a 5-fold cross-validation approach. The multiple regression mode adjusted for five additional covariates: daily mean temperature, relative humidity, wind speed, precipitation, and PM₁₀. In the subset of samples with daily pollen concentration ≥ 10 grains/m³ (n = 98) and in the spring season (n = 145), the simple linear models remained robust and performed even better (R² = 0.71 and 0.83). Conclusions: This is the first validation study on automatic real-time pollen monitoring by volumetric concentrations in China against the international standard manual method. A reliable and feasible simple linear regression model was determined to be adequate, and days with higher pollen levels (≥10 grains/m³) and in the spring season showed better fitness. More validation studies are needed in places with different ecological and climate characteristics to promote the volumetric real-time monitoring of pollens in China. Full article

Railway transportation is a critical component of global infrastructure which plays a significant role in ensuring the safe movement of goods and people. In desert environments, the effectiveness of railway transportation heavily relies on addressing key challenges such as shifting sand, migrating dunes, wind erosion, and sand deposition, which can disrupt operations and increase maintenance costs. To mitigate the significant threats posed by windblown sand to railway safety along the Lanzhou-Xinjiang High-Speed Railway, the technique of double rows of sand fences constructed from concrete columns and plates has been applied to the windward side of the railway. These structures are designed to reduce wind speed and capture moving sand, protecting the rail infrastructure. These fences reduce wind velocity on their leeward sides by 78% and 87% for the first and second rows, respectively. Additionally, due to the large openings in the fences, the sand-trapping efficiencies are 72% for the first row and 63% for the second. The effective shelter distance of the fence is ten times its height. However, advanced technologies like geographic information systems (GIS), geothermal energy solutions, and sustainable infrastructure practices are increasingly integrated into railway transportation to mitigate these risks and enhance safety and reliability. For the Etihad Railway, GIS techniques were utilized to identify areas vulnerable to sand accumulation and validate the substantial benefits of sand fences. Notably, a 40% reduction in wind speed and a significant 74% decrease in sand flux were observed post-installation, underscoring the effectiveness of these structures in disrupting sand mobility. Specifically, wind speed after fence installation was reduced by 40%. The threshold velocity for sand transport was approximately 0.206 m/s. The sand flux before fence installation was 19.95 kg/m²/s, reduced to 5.175 kg/m²/s after fence installation, marking a 74% reduction. The sand deposition behind the sand fence over a 500 m section was around 7387.5 kg/s. This demonstrates the significant role that sand fences play in reducing wind-driven sand transport, thus protecting the Etihad Railway from sand accumulation, and maintaining operational safety. Full article