Search Results (107)

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

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

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

Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats. Full article

This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (particulate matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, Texas, USA, temporal variogram analysis was conducted on three distinct days with varying ${\mathrm{PM}}_{2.5}$ (particulate matter of size ≤ $2.5 \mathsf{\mu }\mathrm{m}$ in diameter) pollution levels. For the most polluted day, the optimal sampling interval for ${\mathrm{PM}}_{2.5}$ was determined to be 12.25 s. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained machine learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature, and wind speed could significantly impact the measurement time for ${\mathrm{PM}}_{2.5}$. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality. Full article

Lichens serve as effective bioindicators for air pollution studies, yet most biomonitoring research focuses primarily on the distance from pollution sources, often neglecting wind data that could elucidate the spread of airborne pollutants. In our previous study in Slavonski Brod, Croatia, we utilized data from a monitoring station, emphasizing the impact of meteorological conditions, particularly wind, on the dispersal of pollutants from a neighbouring oil refinery. To gain a deeper understanding of air pollution dynamics, here, we studied lichen vitality—measured through photochemical efficiency and photosynthetic pigments—alongside the metal (Ni, Zn, Cd, Pb) and non-metal (sulphur and nitrogen) content in native lichen species Flavoparmelia caperata across 17 plots within a 20 km radius of the refinery. Our analysis employed generalized linear models (GLMs) to incorporate various environmental predictors, including distance from the refinery, direction-specific wind speed and frequency, vegetation density, and the orientation of lichen samples with respect to north and the refinery. Findings show that pollution levels are significantly influenced, not only by distance but also by direction-specific wind patterns, underscoring the necessity of including these variables in future biomonitoring studies and highlighting a critical need for air quality management interventions. Full article

Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy. Full article

This paper explores the potential of Airborne Wind Energy Systems to revolutionize wind energy generation, demonstrating advancements over current methods. Through a series of controlled field experiments and the application of classical machine learning techniques, we achieved significant improvements in tether force estimation. Our XGBoost model, for example, demonstrated a notable reduction in error in predicting the tether force that can be extracted at a particular location, with a root mean square error of 52.3 Newtons and a mean absolute error of 32.1 Newtons, coupled with a $R^2$ error, which measures the proportion of variance explained by the model, achieved an impressive value of 0.93. These findings not only validate the effectiveness of our proposed methods but also illustrate their potential to optimize the deployment of Airborne Wind Energy Systems, thereby maximizing energy output and contributing to a sustainable, low-carbon energy future. By analyzing key input features such as wind speed and kite dynamics, our model predicts optimal locations for Airborne Wind Energy System installation, offering a promising alternative to traditional wind turbines. Full article

The phenomenal growth of remotely piloted aerial application systems (RPAASs) in recent years has raised questions about their impact on the off-target movement of plant protection products. The spray droplet spectrum is one of the important determining factors that govern droplet trajectories and off-target movement of pesticide particles. A field study was conducted to compare in-swath and downwind spray deposition on ground samplers from a 20 L RPAAS platform, equipped with three different nozzles, which provided fine, medium, and extra-coarse droplet spectra. A fluorescent dye was used as a tracer to determine spray deposition. Airborne spray droplets were measured at 10 and 20 m downwind. Downwind deposition measured on ground samplers showed that the extra-coarse nozzle received significantly fewer deposits than the medium or the fine nozzle. Similarly, the airborne deposition for the extra-coarse nozzle was significantly less compared to either the fine or the medium nozzle. Linear mixed effects modeling confirmed these results and showed that wind speed served as a covariate by refining the deposition differences among nozzles. Results indicated that spray drift from RPAAS platforms may be mitigated by using appropriate nozzles that produce larger droplet spectra. These results will provide aerial applicators with a better understanding of the best management practices to mitigate drift. Full article

Unmanned Aerial Vehicles (UAVs) continue to gain popularity in applications such as military reconnaissance, environmental monitoring in remote locations, and package delivery. High-Altitude Long-Endurance (HALE) UAVs can remain airborne for extended periods, enabling air pollution measurements to be conducted across a wide range of altitudes, from a few hundred meters above ground level to the lower stratosphere. However, the challenges posed by dynamic environmental conditions and strict energy limitations necessitate the use of adaptive path planning algorithms that account for UAV and environmental models. To address these challenges, we propose a two-tier Adaptive Path Planner (APP), which comprises a Global Path Planner (GPP) and a Local Path Planner (LPP). The GPP, operating offline, generates obstacle-free, energy-efficient paths that adhere to the UAV’s kinematic constraints, while the LPP dynamically recalculates alternative routes in real time when obstacles arise. The APP leverages a novel data-driven environmental model, integrating terrain, wind, airspace, and measurement maps. Extensive Model-in-the-Loop testing was conducted to evaluate various single-objective optimization algorithms for the GPP. Subsequently, the APP was successfully validated in simulation scenarios inspired by real-world pollution monitoring missions conducted in Poland and the Arctic. Additionally, the proposed approach was tested under real-world conditions, demonstrating significant application potential. A comparative analysis of the generated paths demonstrated that the APP effectively replaces human operators. Further testing confirmed the APP’s capability for adaptive re-planning during mission execution. Full article

This study presents a modern mobile laboratory to monitor outdoor air quality in Bucharest, Romania, with a focus on pollutants associated with transportation. Particulate matter (PM₂.₅, PM₁₀), carbon monoxide (CO), ozone (O₃), sulfur dioxide (SO₂), nitrogen oxides (NO, NO₂), and BTEX compounds (benzene, toluene, ethylbenzene, and xylenes) were among the significant pollutants that were examined in the lab. Meteorological variables such wind direction and speed, temperature, humidity, and solar radiation were also routinely observed in order to assess their influence on pollution levels. The study looked at two locations—a bustling city road in Bucharest and a remote community 40 kmawayin Snagov—under a range of weather conditions, including sunny, rainy, warm, and chilly days. The findings showed that the primary source of pollution in the urban area, which had significantly higher pollution levels than the rural site, was transportation. Particularly in the city, alarming concentrations of harmful particulate matter and carcinogens like benzene were found, underscoring the need for continuous air quality monitoring. The weather has a major impact on the dispersal of contaminants. Because of washout effects, rainy days decreased airborne pollutants, but sunny days showed higher pollution deposition. This study highlights the importance of outdoor air quality monitoring, particularly in urban environments, where traffic and weather have a significant impact on pollution levels. These findings provide crucial data that policymakers can utilize to implement targeted pollution control measures that protect human health. Full article

Soil loss due to wind erosion and dust deposition has become a growing concern, particularly in arid regions like Al-Baha, Saudi Arabia. The aim of this study was to quantitatively assess soil loss and dust deposition using three different dust collection methods across 20 sites during the summer of 2022. The methods include Big Spring Number Eight (BSNE), which measures airborne dust particles using passive samplers; Surface Dust Collector (SDC), designed to collect dust settling on the ground surface; and Marble Dust Collector (MDCO), which utilizes marble-coated surfaces to trap and measure dust deposition. These methods collectively provide a comprehensive evaluation of dust dynamics in the study area. The objective was to evaluate the effects of wind erosion and dust deposition on soil properties, offering insights into the mechanisms of soil loss in arid environments. The study revealed significant variations in soil characteristics, including low organic matter content (<1%), high calcite (up to 19.62%), and increased salinity levels, with notable quantities of Cl⁻ (211.58 meq kg⁻¹) and Na^⁺ (165.98 meq kg⁻¹). July showed the highest dust deposition (0.0133 ton ha⁻¹), particularly at site S11, while soil loss was lowest at site S5. This research offers novel insights into the nonlinear relationship between soil loss and time, contributing to sustainable soil management strategies. By aligning with Saudi Arabia’s Vision 2030 and the Sustainable Development Goals (SDGs), the findings underscore the need to mitigate soil loss to enhance environmental sustainability, prevent desertification, and promote long-term resilience in arid regions. Full article

Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km² or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR’s bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km² resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore (a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; (b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and (c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives. Full article

Bioaerosols, including airborne bacteria, are significant pollutants affecting both indoor and outdoor air quality, with implications for human health. Despite extensive research on indoor air quality, there is a notable lack of comprehensive data on ambient bacterial concentrations and their interactions with pollutants and meteorological factors. This review focuses on bacterial aerosols in the atmosphere, measured using the culture-based method, considered the “gold standard” for microorganism detection and identification. Studies reveal significant variability in bacterial concentrations across different environments and seasons, influenced by factors such as temperature, humidity, wind speed, solar radiation, and precipitation, underscoring the need for further research and monitoring to enhance health risk assessments and mitigation strategies. The presence of air pollutants such as particulate matter (PM) and ozone (O₃) further complicates these dynamics. The authors emphasize the need for more extensive research on outdoor bacterial aerosols and recommend that future studies prioritize detailed bioaerosol characterization to establish comprehensive exposure standards in ambient air, thereby improving public health protection and environmental management practices. Full article