Search Results (952)

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. Full article

Bacterial genera present in several areas of a pig farm were analyzed using high-throughput sequencing techniques. Samples were collected from the skin and feces of animals, as well as from surfaces, water, and air. The analyses revealed a strong correlation between air and skin samples, supporting the idea that bacterial growth on skin is potentially a mechanism of aerosolization and airborne transport. A water–air transmission route also appears to be present, although the direction of the transport mechanism cannot yet be determined. Other potential routes, such as contact with surfaces or feces, seem to be less efficient. Full article

Indoors, the infection risk of diseases transmitted through the airborne route is estimated from indoor carbon dioxide (CO₂) levels. However, the approaches to assess this risk do not account for the airborne concentration of pathogens, among other limitations. In this study, we analyzed the relationship between airborne SARS-CoV-2 levels and environmental parameters. Bioaerosols were sampled (n = 40) in hospital corridors of two wards differing in the COVID-19 severity of the admitted patients. SARS-CoV-2 levels were quantified using droplet digital PCR. SARS-CoV-2 was detected in 60% of the total air samples. The ward where the mildly ill patients were admitted had a higher occupancy, transit of people in the corridor, and CO₂ levels, but there were no significant differences in SARS-CoV-2 detection between wards. The mean CO₂ concentration in the positive samples was 569 ± 35.6 ppm. Considering all samples, the CO₂ levels in the corridor were positively correlated with patient door openings but inversely correlated with SARS-CoV-2 levels. In conclusion, airborne SARS-CoV-2 can be detected indoors with optimal ventilation, and its levels do not scale with CO₂ concentration in hospital corridors. Therefore, CO₂ assessment should not be interpreted as a surrogate of airborne viral presence in all indoor spaces. Full article

The characterization of airborne submicrometric composite structures’ debris is a challenge in the field of environmental monitoring and control. The work presented here aims to develop a new quantitative method to measure elemental mass concentrations via particle sampling and Transmission Electron Microscopy—Energy-Dispersive X-ray Spectroscopy (TEM-EDS). The principle is to collect airborne particles on a porous TEM grid, then add a certain mass of reference particles, and compare the relative mass percentages of elements from reference and sample particles via EDS. Diverse pairs of airborne particles (RbCl, CsCl, NaCl, SrCl₂, Ga(NO₃)₃, braking particles) were deposited on one TEM grid, and the experimental elemental mass ratios were measured by EDS and compared with the theoretical values. Results show that the quantitative and homogeneous collection of reference particles, such as RbCl, on the TEM grid could be suitable. For all the tested conditions, the absolute deviations between the theoretical elemental mass ratios and the experimental ratios remain lower than 8%. Thus, the mass concentration of Fe from the braking aerosol is calculated as 107 µg/m³. Compared to the cumbersome real-time instrument, this new method for mass characterization appears to be convenient, and requires a short time of aerosol sampling at the workplace. This approach ensures safety and practicability when assessing, e.g., the exposure risk of hazardous materials. Full article

This paper introduces an efficient 3D point cloud downsampling algorithm (DFPS) based on adaptive multi-level grid partitioning. By leveraging an adaptive hierarchical grid partitioning mechanism, the algorithm dynamically adjusts computational intensity in accordance with terrain complexity. This approach effectively balances the global feature retention of point cloud data with computational efficiency, making it highly adaptable to the growing trend of large-scale 3D point cloud datasets. DFPS is designed with a multithreaded parallel acceleration architecture, which significantly enhances processing speed. Experimental results demonstrate that, for a point cloud dataset containing millions of points, DFPS reduces processing time from approximately 161,665 s using the original FPS method to approximately 71.64 s at a 12.5% sampling rate, achieving an efficiency improvement of over 2200 times. As the sampling rate decreases, the performance advantage becomes more pronounced: at a 3.125% sampling rate, the efficiency improves by nearly 10,000 times. By employing visual observation and quantitative analysis (with the chamfer distance as the measurement index), it is evident that DFPS can effectively preserve global feature information. Notably, DFPS does not depend on GPU-based heterogeneous computing, enabling seamless deployment in resource-constrained environments such as airborne and mobile devices, which makes DFPS an effective and lightweighting tool for providing high-quality input data for subsequent algorithms, including point cloud registration and semantic segmentation. Full article

Ambient monitoring in chemical laboratories and industrial sites that use toxic, hazardous, or flammable materials is essential to protect the lives of workers, material resources, and infrastructure at these sites. In this research paper, we present an innovative approach for developing a low-cost and portable sensor node that detects and warns of hazardous chemical gas and vapor leaks. The system also enables leak location tracking using an indoor tracking and positioning system operating in ultra-wideband (UWB) technology. An array of sensors is used to detect gases, vapors, and airborne particles, while the leak location is identified through a UWB unit integrated with an Internet of Things (IoT) processor. This processor transmits real-time location data and sensor readings via wireless fidelity (Wi-Fi). The real-time indoor positioning system (IPS) can automatically select a tracking area based on the distances measured from the three nearest anchors of the movable sensor node. The environmental sensor data and distances between the node and the anchors are transmitted to the cloud in JSON format via the user datagram protocol (UDP), which allows the fastest possible data rate. A monitoring server was developed in Python to track the movement of the portable sensor node and display live measurements of the environment. The system was tested by selecting different paths between several adjacent areas with a chemical leakage of different volatile organic compounds (VOCs) in the test path. The experimental tests demonstrated good accuracy in both hazardous gas detection and location tracking. The system successfully issued a leak warning for all tested material samples with volumes up to 500 microliters and achieved a positional accuracy of approximately 50 cm under conditions without major obstacles obstructing the UWB signal between the active system units. Full article

In this paper, the authors describe an electromagnetic–hydrodynamic (EMHD) model of the airborne microbiological agent detection concept for the design of a sensor to identify the presence of airborne bacteria and viruses. Based on the model and a laboratory test, a methodology was proposed for the capture and subsequent detection of low-concentration bacterial and viral agents in airborne aerosols. A physical–biological approach was proposed to detect microorganisms based on their physical properties. The principle was validated in the laboratory on samples of defined concentrated water aerosols of Bacillus subtilis (BS) and feline infectious peritonitis virus (FIVP). Repeated tests with different concentrations were performed in the laboratory conditions. Full article

In this study, using in-house code I-COSTA, importance analyses are performed on the phenomenological parameters in the aerosol dynamics using International Standard Problem No. 44. The analyses consider twelve parameters used in multicomponent sectional equations and Mason equations. For the first step of the analysis, Latin hypercube sampling is performed for the aforementioned parameters, and the number of samplings is determined using a comparison of averages and standard deviations between those samplings and the ones gathered from continuous distributions of the parameters. Sensitivity analyses are then performed on the airborne concentrations of the aerosol particles using I-COSTA, and the results are used to obtain the correlation coefficients between the parameters and the airborne concentrations. From the analyses, the dynamic shape factor, which accounts for the drag force of the non-spherical aerosol particles, is found to be one of the most important parameters in the aerosol dynamics. The saturation ratio in the Mason equation is also found to be an important parameter for aerosol particles with high solubility since the mass of the aforementioned particles is sensitive to the hygroscopic growth rate. Full article

There is a profound interdependence between biodiversity and the UN Sustainable Development Goals (SDGs). Biodiversity underpins the functioning of global ecosystems and human welfare, and the achievement of numerous SDGs is directly or indirectly linked to protecting and sustainably managing biodiversity. In recent years, environmental DNA (eDNA) technology has exerted a great impact in the field of biodiversity monitoring. Airborne eDNA plays a significant role due to its non-invasive nature and the ability to monitor multiple taxonomic groups simultaneously. This review summarizes the technical principles, sampling methods, data analysis strategies of airborne eDNA and its application in biodiversity monitoring. In addition, it discusses the current technical challenges (e.g., pollution control, degradation mechanisms, and quantitative analysis) in the field, as well as future development directions, including optimizing sampling strategies, developing specific primers, integrating environmental RNA (eRNA), and establishing standardized monitoring systems. This review aims to provide a comprehensive assessment of airborne eDNA technology to promote airborne wide application of eDNA in global biodiversity monitoring. Full article

Avocado, a fruit consumed worldwide and essential for countries like Mexico and Chile, faces significant postharvest challenges, particularly during prolonged storage and transportation periods, where Botryosphaeriaceae and Glomerellaceae genera cause fruit rots that can generate substantial economic losses. This study investigated three Hass avocado orchards in the Valparaíso region of Chile to identify spore dispersion peaks, analyze the aerial dynamics of fungal inoculum, and evaluate the association with climatic conditions, as well as the incidence (I) and damage index (DI) of fruit rots. Spore traps were installed in symptomatic trees and monitored weekly over 13 months. Meteorological data were collected in parallel. Fruits from these orchards were sampled to evaluate postharvest rots, physiological maturity, and disease severity using molecular techniques, including DNA sequencing and phylogenetic analysis of isolated pathogens. The results revealed that spore peaks for both fungal families were closely associated with increased rainfall and high relative humidity, particularly from June to mid-September (winter season). The Santo Domingo orchard exhibited the highest disease pressure, with stem-end rot reaching an I of 44% and a DI of 17.25%, and anthracnose reaching an I of 23% and a DI of 12.25%. This study provides the first long-term, field-based evidence of airborne spore dynamics of Botryosphaeriaceae and Glomerellaceae in Chilean avocado orchards and their statistical relationship with environmental variables. These findings highlight the potential of incorporating climatic indicators—such as rainfall thresholds and humidity levels—into monitoring and early-warning systems to optimize fungicide application timing, reduce unnecessary chemical use, and improve postharvest disease management in avocado production. Full article

Soil carbon remote sensing has become a popular topic amongst scientists, policy makers, landholders, and others in recent years, as pragmatic perspectives on climate change, land productivity, and food security become increasingly important. Unfortunately, more than fifty years of existing research has not provided clarity or consensus on the best soil carbon remote sensing methods. A reliable, widely applicable, robust, and cost-effective means of soil carbon modelling remains elusive. As evidenced by aggregated data from 259 papers and 503 models published since 1969, much experimentation has been undertaken and soil carbon remote sensing shows promise, but the situation remains unresolved. First, this review and meta-analysis shows that soil carbon remote sensing model accuracy (via Pearson’s correlation coefficient R²) has decreased on average since 1969, and more rapidly since the year 2000. Second, the model R² does not correlate strongly with the spatial (airborne platforms compared with satellite platforms) or spectral (multispectral compared with hyperspectral) resolution of data. Third, no significant relationship between the model R² and the number of samples included in the training/test dataset is apparent. Fourth, the R² of non-parametric models (mean R² in 2022 = 0.58, n = 117) has declined more rapidly (decrease of 1.3% per year) since 1969 (mean R² in 1969 = 0.74, n = 1) than the R² of parametric models (decrease of 0.4% per year), suggesting that the algorithm applied during soil carbon modelling may be of importance. Finally, data compiled in this meta-analysis demonstrate a correlation between declining model R² and the increased use of satellite multispectral data and non-parametric algorithms, particularly machine learning, since the year 2000. There is no other evidence to suggest that prediction models prepared with multispectral data perform worse than other models, however. Hence, for the purpose of experimentation, it may be valuable to continue experimenting with the use of machine learning models for soil carbon prediction. However, when model performance is the priority, it is recommended that simple, parametric models (such as linear regression) are applied. Full article

The pre-training and fine-tuning paradigm has significantly advanced satellite remote sensing applications. However, its potential remains largely underexplored for airborne laser scanning (ALS), a key technology in domains such as forest management and urban planning. In this study, we address this gap by constructing a large-scale ALS point cloud dataset and evaluating its effectiveness in downstream applications. We first propose a simple, generalizable framework for dataset construction, designed to maximize land cover and terrain diversity while allowing flexible control over dataset size. We instantiate this framework using ALS, land cover, and terrain data collected across the contiguous United States, resulting in a dataset geographically covering 17,000 + ${\mathrm{km}}^2$ (184 billion points) with diverse land cover and terrain types included. As a baseline self-supervised learning model, we adopt BEV-MAE, a state-of-the-art masked autoencoder for 3D outdoor point clouds, and pre-train it on the constructed dataset. The resulting models are fine-tuned for several downstream tasks, including tree species classification, terrain scene recognition, and point cloud semantic segmentation. Our results show that pre-trained models consistently outperform their counterparts trained from scratch across all downstream tasks, demonstrating the strong transferability of the learned representations. Additionally, we find that scaling the dataset using the proposed framework leads to consistent performance improvements, whereas datasets constructed via random sampling fail to achieve comparable gains. Full article

Urbanization has been suspected to increase the allergic rate of people, and its impact on airborne fungi and potential allergens has drawn attention. In this study, aerosol samples were collected concurrently at proximate urban and rural sites of Shanghai during the four seasons to analyze the changes in abundance and community composition of airborne fungi. In summer, there were significantly higher concentrations of fungi in the urban atmosphere compared to at the rural site. Ascomycota and Basidiomycota were the top two fungal phyla, and Cladosporium was the most abundant genus year round. Alternaria was the second highest genus in spring and winter (only the rural site), whereas Nigrospora ranked second during summer and autumn due to it largely being sourced from marine organisms and predominantly marine-influenced air masses in these seasons. Airborne fungal richness was relatively higher at the rural site than in urban during winter. Allergenic fungal species were found to be more abundant in winter than in other seasons; particularly, the relative abundance of Cladosporium sp. was significantly higher (p < 0.001), and Fusarium culmorum and Cladosporium herbarum also increased more in urban than in rural areas, which may be one of the key factors contributing to the rising allergic rate in the urban population. Full article

Polyaryletherketone (PAEK) materials, including polyetheretherketone (PEEK) and polyetherketoneketone (PEKK), possess excellent mechanical properties and biocompatibility; however, their inherently low surface energy limits effective bonding with resin cements. This study investigated the effects of hydrofluoric acid (HF) etching and handheld nonthermal plasma (HNP) treatment on enhancing the adhesive performance of PAEK surfaces. Disk-shaped PEEK (BP) and PEKK (PK) specimens were divided into four groups: APA (airborne-particle abrasion), PLA (nonthermal plasma treatment), LHF (5.0% HF), and HHF (9.5% HF). Surface characterization was performed using a thermal field emission scanning electron microscope (FE-SEM). Surface wettability was evaluated using contact angle goniometry. Cytotoxicity was evaluated using HGF-1 cells exposed to conditioned media and analyzed via PrestoBlue assays. Shear bond strength (SBS) was measured after three aging conditions—NT (no aging), TC (thermocycling), and HA (highly accelerated aging)—using a light-curing resin cement. Failure modes were categorized, and statistical analysis was performed using one-way and two-way ANOVA with Tukey’s HSD test (α = 0.05). Different surface treatments did not affect surface characterization. PLA treatment significantly improved surface wettability, resulting in the lowest contact angles among all groups, followed by HF etching (HHF > LHF), while APA showed the poorest hydrophilicity. Across all treatments, PK exhibited better wettability than BP. Cytotoxicity results confirmed that all surface treatments were nontoxic to HGF-1 cells, indicating favorable biocompatibility. SBS testing demonstrated that PLA-treated specimens achieved the highest and most stable bond strength across all aging conditions. Although HF-treated groups exhibited lower bond strength overall, BP samples treated with HF showed relatively less reduction following aging. Failure mode analysis revealed a shift from mixture and cohesive failures in the NT aging condition to predominantly adhesive failures after TC and HA aging conditions. Notably, the PLA-treated groups retained mixture failure patterns even after aging, suggesting improved interfacial durability. Among the tested methods, PLA treatment was the most effective strategy, enhancing surface wettability, bond strength, and aging resistance without compromising biocompatibility. In summary, the PLA demonstrated the greatest clinical potential for improving the adhesive performance of PAEK when used with light-curing resin cements. Full article

Traditional reliability methods using fixed removal plans often overlook withdrawal randomness, leading to biased estimates for asymmetric data. This study advances classical and Bayesian frameworks for the inverse Chen distribution, which is suited for modeling asymmetric data under adaptive progressively Type-II censoring with binomial removals. Here, removals post-failure follow a dynamic binomial process, enhancing a more realistic approach for reliability studies. Maximum likelihood estimates are computed numerically, with confidence intervals derived asymptotically. Bayesian approaches employ gamma priors, symmetric squared error loss, and posterior sampling for estimates and credible intervals. A simulation study validates the methods, while two asymmetric real-world applications demonstrate practicality: (1) analyzing diamond sizes from South-West Africa, capturing skewed geological distributions, and (2) modeling failure times of airborne communication transceivers, vital for aviation safety. The flexibility of the inverse Chen in handling asymmetric data addresses the limitations of symmetric assumptions, offering precise reliability tools for complex scenarios. This integration of adaptive censoring and asymmetric distributions advances reliability analysis, providing robust solutions where traditional approaches falter. Full article