Search Results (5,107)

When developing strategies aimed at mitigating air pollution in densely populated urban areas, it is vital to accurately investigate the vertical distribution of airborne particulate matter (PM) and its primary influencing factors. For this study, field experiments were conducted to quantify the vertical distribution and dispersion processes of PM at five vertical heights related to trees—including at street level near vehicular emission sources (0.3 m), pedestrian breathing height (1.5 m), beneath the canopy (6 m), mid-canopy (9 m), and the top of the canopy (12 m)—within a street-facing building in Wuhan, China. Comparing the vertical dispersion patterns of PM with six particle sizes (PM₁, PM_2.5, PM₄, PM₇, PM₁₀, and total suspended particulates—TSPs), larger particles exhibited more pronounced variations with height, notably TSPs (correlation coefficient of −0.95) and PM₁₀ (−0.84). The findings consistently revealed a downward trend in PM concentrations across various particle sizes with increasing height, indicating a negative linear correlation between particle concentrations and altitude within the street canyon. For every 1% increase in vertical height, the PM_2.5 concentration decreased by approximately 5.44%, the PM₁₀ concentration decreased by 132.1%, and the TSP concentration decreased by 180.6%. These findings show potential for guiding building designers in developing effective strategies, such as optimal vent placement, in order to mitigate the intrusion of outdoor air pollution—particularly PM_2.5—into indoor environments. Furthermore, this research provides novel insights for residents living in street-facing buildings and individuals with respiratory diseases, aiding them in the selection of residential floors to minimize health risks associated with exposure to respirable PM. Full article

A global gravity field model (GGM) is essential to be validated with ground-based or airborne observational data for the accurate application of the GGM at a regional scale. Furthermore, accurately understanding the commission errors between the GGM and observational data are crucial for improving regional gravity fields. Taking the North China region as an example, to circumvent the omission errors, it is necessary to unify the spatial resolutions of the EIGEN-6C4 model and terrestrial gravity observational data to 110 km (determined by the distribution of gravity stations) by employing the spherical harmonic function for the EIGEN-6C4 model and the Slepian basis function for the gravity data, respectively. However, the application of spherical harmonic function expansions in the gravity model results in the Gibbs phenomenon, which may be a primary factor contributing to commission errors and impedes the accurate validation of the EIGEN-6C4 model with terrestrial gravity data. To effectively mitigate this issue, this study proposes a combination approach of window function filtering and regional eigenvalue constraint (based on the Slepian basis). Utilizing the EIGEN-6C4 gravity model to derive the gravity disturbance field at a resolution of 110 km (with spherical harmonic expansion up to the 180th degree and order), the combination approach effectively suppresses over 90% of high-degree (above the 120th degree) Gibbs phenomena. This approach also reduces signal leakage outside the region, thus enhancing the spatial accuracy of the regional gravity disturbance field. A subsequent comparison of the regional gravity disturbance field derived from the true model and terrestrial gravity data in North China indicates excellent consistency, with a root mean squared error (RMSE) of 0.80 mGal. This validation confirms that the combined approach of window function filtering and regional eigenvalue constraints effectively mitigates the Gibbs phenomenon and yields precise regional gravity fields. This approach is anticipated to significantly benefit scientific applications such as improving the accuracy of regional elevation benchmarks and accurately inverting the Earth’s internal structure. Full article

Serious farmland abandonment in hilly areas, and the resolution of commonly used satellite-borne remote sensing images are insufficient to meet the needs of identifying abandoned farmland in such regions. Furthermore, addressing the problem of identifying abandoned farmland in hilly areas with a certain level of accuracy is a crucial issue in the research of extracting information on abandoned farmland patches from remote sensing images. Taking a typical hilly village as an example, this study utilizes airborne multispectral remote sensing images, incorporating various feature factors such as spectral characteristics and texture features. Aiming at the issue of identifying abandoned farmland in hilly areas, a method for extracting abandoned farmland based on the OVR-FWP-RF algorithm is proposed. Furthermore, two machine learning algorithms, Random Forest (RF) and XGBoost, are also utilized for comparison. The results indicate that the overall accuracy (OA) of the OVR-FWP-RF, Random Forest, and XGboost classification algorithms have reached 92.66%, 90.55%, and 90.75%, respectively, with corresponding Kappa coefficients of 0.9064, 0.8796, and 0.8824. Therefore, by combining spectral features, texture features, and vegetation factors, the use of machine learning methods can improve the accuracy of identifying ground objects. Moreover, the OVR-FWP-RF algorithm outperforms the Random Forest and XGboost. Specifically, when using the OVR-FWP-RF algorithm to identify abandoned farmland, its producer accuracy (PA) is 3.22% and 0.71% higher than Random Forest and XGboost, respectively, while the user accuracy (UA) is also 5.27% and 6.68% higher, respectively. Therefore, OVR-FWP-RF can significantly improve the accuracy of abandoned farmland identification and other land use type recognition in hilly areas, providing a new method for abandoned farmland identification and other land type classification in hilly areas, as well as a useful reference for abandoned farmland identification research in other similar areas. Full article

Numerous Aspergillus fumigatus (Af) airborne spores are inhaled daily by humans and animals due to their ubiquitous presence. The interaction between the spores and the respiratory epithelium, as well as its impact on the epithelial barrier function, remains largely unknown. The epithelial barrier protects the respiratory epithelium against viral infections. However, it can be compromised by environmental contaminants such as pollen, thereby increasing susceptibility to respiratory viral infections, including alphaherpesvirus equine herpesvirus type 1 (EHV-1). To determine whether Af spores disrupt the epithelial integrity and enhance susceptibility to viral infections, equine respiratory mucosal ex vivo explants were pretreated with Af spore diffusate, followed by EHV-1 inoculation. Spore proteases were characterized by zymography and identified using mass spectrometry-based proteomics. Proteases of the serine protease, metalloprotease, and aspartic protease groups were identified. Morphological analysis of hematoxylin-eosin (HE)-stained sections of the explants revealed that Af spores induced the desquamation of epithelial cells and a significant increase in intercellular space at high and low concentrations, respectively. The increase in intercellular space in the epithelium caused by Af spore proteases correlated with an increase in EHV-1 infection. Together, our findings demonstrate that Af spore proteases disrupt epithelial integrity, potentially leading to increased viral infection of the respiratory epithelium. Full article

This study aims to investigate the seasonal variation and source identification of fluorescent aerosol particles at the monitoring site of the University of Salento in Lecce, southeastern Italy. Utilizing a wideband integrated bioaerosol sensor (WIBS), this research work analyzes data from two specific monitoring days: one in winter (10 January 2024), marked by significant transport of anthropogenic particles from Eastern Europe, and another in early spring (6 March 2024), characterized by marine aerosol sources and occasional desert dust. This study focuses on the seven WIBS particle categories (A, B, C, AB, AC, BC, ABC), which exhibited distinct characteristics between the two days, indicating different aerosol compositions. Winter measurements revealed a predominance of fine-mode particles, particularly soot and bacteria. In contrast, spring measurements showed larger particles, including fungal spores, pollen fragments, and mineral dust. Fluorescence intensity data further emphasized an increase in biological and organic airborne material in early spring. These results highlight the dynamic nature of fluorescent aerosol sources in the Mediterranean region and the necessity of continuous monitoring for air quality assessments. By integrating WIBS measurements with air mass back-trajectories, this study effectively identifies fluorescent aerosol sources and their seasonal impacts, offering valuable insights into the environmental and health implications of aerosol variability in the investigated Mediterranean area. Full article

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map. Full article

Soil is an environment for huge diversity of fungi, which fulfill various tasks and support the maintaining of soil health. At the same time, varieties of soil fungal species, which produce numerous airborne spores and a range of mycotoxins, are known to be pathogenic for human health. The present review aims to summarize the current knowledge on soil fungi causing public health problems, including dermatoses, allergies, pulmonary diseases, wound infections, infections of the central nervous system, etc. Full article

Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given. To enhance UAV stability and resist atmospheric disturbances at sea, the configuration is optimized using an improved genetic algorithm. Then, three typical multi-UAV formations are analyzed. Finally, the trajectory is designed based on an analysis of its influence on EMI at the MAD. The simulation results show that the low-EMI design can keep MADs away from the EMI sources of UAVs and maintain flight stability. The thread-like formation is the best choice in terms of mutual interference and search width. The results also reveal the close relationship between the low-EMI design and flight trajectory. This research can provide a reference for the conceptual design and trajectory optimization of small, fixed-wing UAVs for magnetic anomaly detection. Full article

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy in trajectory prediction, even in scenarios with prolonged data interruptions. Initially, data outside the acceptable error range are discarded to mitigate the impact of interruptions on prediction accuracy. Subsequently, to address the irregular sampling caused by data elimination, NODEs are utilized to transform computational interpolation into an initial value problem (IPV), thus preserving informative features. Furthermore, this study enhances the Informer’s encoder through the utilization of time-series prior knowledge and introduces an ODE solver as the decoder to mitigate fluctuations in the original decoder’s output. This approach not only accelerates feature extraction for long sequence data, but also ensures smooth and robust output values. Experimental results demonstrate the superior performance of Node-former in trajectory prediction with interrupted data compared to traditional algorithms. Full article

Siamese-based trackers have been widely utilized in UAV visual tracking due to their outstanding performance. However, UAV visual tracking encounters numerous challenges, such as similar targets, scale variations, and background clutter. Existing Siamese trackers face two significant issues: firstly, they rely on single-branch features, limiting their ability to achieve long-term and accurate aerial tracking. Secondly, current tracking algorithms treat multi-level similarity responses equally, making it difficult to ensure tracking accuracy in complex airborne environments. To tackle these challenges, we propose a novel UAV tracking Siamese network named the contextual enhancement–interaction and multi-scale weighted fusion network, which is designed to improve aerial tracking performance. Firstly, we designed a contextual enhancement–interaction module to improve feature representation. This module effectively facilitates the interaction between the template and search branches and strengthens the features of each branch in parallel. Specifically, a cross-attention mechanism within the module integrates the branch information effectively. The parallel Transformer-based enhancement structure improves the feature saliency significantly. Additionally, we designed an efficient multi-scale weighted fusion module that adaptively weights the correlation response maps across different feature scales. This module fully utilizes the global similarity response between the template and the search area, enhancing feature distinctiveness and improving tracking results. We conducted experiments using several state-of-the-art trackers on aerial tracking benchmarks, including DTB70, UAV123, UAV20L, and UAV123@10fps, to validate the efficacy of the proposed network. The experimental results demonstrate that our tracker performs effectively in complex aerial tracking scenarios and competes well with state-of-the-art trackers. Full article

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated. Full article

The consequences of new airspace entrants, such as novel aircraft concepts with innovative propulsion systems, on air traffic management operations need to be carefully identified. This paper aims to assess the impact of future aircraft with different performance envelopes on the European air traffic network from a flow-based perspective. The underlying approach assumes that all certification-related questions concerning airworthiness have been resolved and do not take into account any economic factors related to airline operations. For example, for an innovative propulsion system, a short range all-electric aircraft is considered in this study. Aircraft trajectory calculations are based on the dataset of base of aircraft data (BADA), which are developed and maintained by EUROCONTROL. The new design concept is integrated into BADA as well, resulting in a new set of coefficients for the all-electric aircraft. In addition to the adjusted parameters which affect airborne performances, ground-related aspects are also taken into account. This includes assumptions on operational procedures, charging capacities and adaptions in infrastructure. Investigations are carried out at the trajectory level as well as at the airport and the entire network. Full article

The fractionation of airborne particles based on multiple characteristics is becoming increasingly significant in various industrial and research sectors, including mining and recycling. Recent developments aim to characterize and fractionate particles based on multiple properties simultaneously. This study investigates the fractionation of a technical aerosol composed of a mixture of micron-sized copper and silicon particles by size and material composition using a classifying aerodynamic lens (CAL) setup. Particle size distribution and material composition are analyzed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) for samples collected from the feed stream (upstream of CAL) and product stream (downstream of CAL) at varying operational pressures. The experimental findings generally agree with the predictions of an analytical fractionation model but also point to the importance of particle shape as a third fractionation property. Moreover, the results suggest that material-based fractionation is efficient at low operational pressures, even when the aerodynamic properties of the particle species are similar. This finding could have significant implications for industries where precise particle fractionation is crucial. Full article

Although mask-wearing is now widespread, the knowledge of how to quantify or improve their performance remains surprisingly limited and is largely based on empirical evidence. The objective of this study was to visualize the expiratory airflows from facemasks and evaluate aerosol transmission between two persons. Different visualization methods were explored, including the Schlieren optical system, laser/LED-particle imaging system, thermal camera, and vapor–SarGel system. The leakage flows and escaped aerosols were quantified using a hotwire anemometer and a particle counter, respectively. The results show that mask-wearing reduces the exhaled flow velocity from 2~4 m/s (with no facemask) to around 0.1 m/s, thus decreasing droplet transmission speeds. Cloth, surgical, and KN95 masks showed varying leakage flows at the nose top, sides, and chin. The leakage rate also differed between inhalation and exhalation. The neck gaiter has low filtration efficiency and high leakage fractions, providing low protection efficiency. There was considerable deposition in the mouth–nose area, as well as the neck, chin, and jaw, which heightened the risk of self-inoculation through spontaneous face-touching. A face shield plus surgical mask greatly reduced droplets on the head, neck, and face, indicating that double face coverings can be highly effective when a single mask is insufficient. The vapor–SarGel system provided a practical approach to study interpersonal transmission under varying close contact scenarios or with different face coverings. Full article

The rapid spread and accumulation of microplastics (MPs) in environmental ecosystems result from extensive plastic usage. MPs have been found in both indoor and outdoor air. Outdoor MP levels vary widely across global cities, with reported ranges from 36 to 118 MPs m⁻² day⁻¹. However, differing measurement units complicate comparisons. Indoor MPs are particularly concerning due to the significant amount of time people spend indoors. For instance, MP concentrations in workplaces like reception areas and nail salons were found to be 309 ± 214 and 46 ± 55 MPs m⁻³, respectively. Technological limitations hinder the identification of MPs, with methods like µ-ATR-FTIR, µ-FTIR, and µ-Raman identifying MPs of different sizes. MPs smaller than 0.3 µm pose a health risk as they can be internalized in lung cells, while MPs larger than 10 µm are too large to enter alveolar macrophages. This review highlights the current understanding of airborne MPs, focusing on their sources, transport, and deposition mechanisms. It aims to provide a foundation for further studies to deeply assess the presence, abundance, and occurrence of MPs in aerosols, a subject that remains underexplored. Full article