Aerobiologia最新文献

An increasing global health concern is antibiotic resistance, involving antibiotic-resistant bacteria (ARB) and their antibiotic resistance genes (ARGs), particularly through inhalation of airborne bioaerosols. Airborne transmission helps spread resistant pathogens across regions and populations. Despite their substantial impact in human exposure, ARB and ARGs in air particles have received less attention than antibiotic resistance in soil and water. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines were implemented in conducting the review. The search focused on scientific articles focusing on ARB and ARGs disseminated through PM_2.5 and PM₁₀. The review was restricted to English-language works published between 2017 and 2024 that have been published in PubMed, Web of Science, and Scopus. Following screening of 352 publications across the globe, only 30 publications of note were identified. Some of the most commonly identified ARBs are the genera Pseudomonas spp., Staphylococcus spp., Burkholderia spp., Sphingomonas spp., Bacillus spp. and Bacteroides spp. Most prevalent ARGs that have been identified in the urban air include resistance to the multiple antibiotic classes, including tetracyclines (tetM, tetW, tetO), sulfonamides (sul1, sul2), macrolides (ermB, ermC), ß-lactams (blaTEM, blaCTX-M), quinolones (qnrS) and aminoglycosides (aadD). This report outlines current research on ARB and ARGs in urban air, where issues are increased by pollution and dense populations. It highlights key sources, including natural environments, industries, wastewater treatment and hospitals. Furthermore, it explores how meteorological factors such as temperature, humidity, wind speed, and air particles impact their dissemination.

Strawberry powdery mildew, caused by Podosphaera aphanis, is a major fungal disease affecting strawberry cultivation worldwide. Its rapid lifetime cycle and ability to spread under a wide range of favourable conditions make early detection and management particularly challenging. Understanding the impact of environmental factors on disease dispersal is crucial for improving forecasting and control strategies. This study investigated the spatiotemporal distribution of strawberry powdery mildew in a high tunnel with a specific focus on wind as a primary driver of inoculum release and dispersal. Disease spread was monitored from a single inoculum source, both under natural wind conditions and with minimized wind influence to assess pathogen dispersal efficiency under varying wind speeds. The infection rate was modelled using a Zero-Inflated Negative Binomial (ZINB) model according to airborne conidium concentration and distance from the inoculum. Results show that disease spread follows an exponential decay pattern with a dispersal rate of 1.65 m day⁻¹. Temperature and relative humidity significantly influence conidium release, with wind as the most critical factor driving pathogen dispersal. Wind contributes to the formation of heterogeneous infectious hotspots along the tunnel, shaping the spatial and temporal distribution of the disease. However, wind speed had no significant impact on quantitative disease progression, highlighting high pathogen dispersal efficiency even under low wind conditions.

This study aimed to identify culturable fungal bioaerosols in airborne particulate matter (PM₁₀) at a university campus located near Bogotá, Colombia, in the northern region of South America. Bioaerosols, which include living organisms or their byproducts, are significant for air quality, affecting human health, ecosystems, and climate. Fungal spores, a major component of bioaerosols, are known to cause respiratory and allergic diseases. Despite their importance, data on fungal bioaerosols in the Andean region of northern South America are limited. Samples were collected using a low-volume air sampler that captured PM₁₀ particles on filters, later analyzed for fungal colony-forming units (CFUs). The highest concentration observed was 900 CFU/m³. Molecular analysis identified predominant fungal genera, including Cladosporium sp., Penicillium sp., Xylariales sp., Aspergillus sp., and Trichoderma sp. Cladosporium species, such as C. asperulatum and C. cladosporioides, were notably abundant and have been associated with allergic reactions. Penicillium brevicompactum and Aspergillus fumigatus, both linked to respiratory irritations and lung infections, were also identified. Additionally, some fungal species detected are pathogenic to vegetation. These findings provide essential insights into airborne fungal species in South America, identifying potential allergenic and pathogenic organisms present on a university campus with a densely populated community regularly exposed to airborne particulate matter. This highlights the importance of continuous monitoring and the implementation of control measures to improve scientific understanding of bioaerosol dynamics and the associated health risks.

Pollen presence in the atmosphere, as Primary Biological Aerosol (PBA) fraction, constitutes a significant proportion of aerosol. In recent years, research focusing on the role of pollen grains in the atmosphere has intensified toward understanding their impacts on environmental processes and human health. Investigation over 15 months, from January 2012 to March 2013, in Jawali, Kangra district of Himachal Pradesh, was undertaken to estimate the abundance and diversity of airborne pollen grains in ambient coarse particulate matter (CPM) sampled using a high-volume PM sampler. The average CPM load of 46.6 µg m⁻³ corresponded with an average pollen concentration of 219 pollen grains m⁻³ during the study. A large diversity of pollen grains in CPM was observed, with 54 different types of pollen grains belonging to 35 plant families, including two gymnosperm families. The major tree genera characterized were Mallotus, Pinus, Eucalyptus, Syzigium, Prosopis, Phyllanthus, Cassia, and Acacia, whereas the shrubs were Dodonaea, Ricinus, and Ephedra. Herbaceous pollen contributed a significant fraction of pollen grains belonging to Poaceae, Asteraceae, Brassicaceae, Cannabaceae, and Urticaceae. A significant number of pollen grains recorded in this investigation have been reported as allergenic in the literature. Most of the pollen grains in CPM belonged to the region's endemic vegetation, suggesting their local origin. The monthly concentration profile of pollen grains displayed significant variation over 15 months. Meteorological parameters, temperature, planetary boundary layer height, and wind speed statistically correlated with CPM. The insignificant correlations with pollen concentration indicated that the sources of CPM and pollen emissions differ. However, pollen showed a weak correlation with wind speed and a negative correlation with relative humidity.

Airborne bioparticles, including fungal spores, are of major concern for human and plant health, necessitating precise monitoring systems. While a European norm exists for manual volumetric monitoring, there's a growing interest in automated real-time methods. However, these methods rely heavily on machine learning, facing challenges due to diverse particle characteristics and limited training data availability, especially for fungal spores. This study aims to address this gap by outlining best practices for collecting reference material and creating tailored datasets for training algorithms. Using 17 fungal species from the Belgian fungi collection BCCM/IHEM, including five Alternaria species, key aspects such as in vitro cultivation, dry spore harvest, and aerosolization were addressed. Simple classification models were developed, achieving varying accuracies on different monitors. The Plair Rapid-E+ demonstrated accuracies ranging from 83.4% to 95.1% (macro average F1-score 0.61), with better recognition for Cladosporium spp. and Curvularia caricae-papayae. The SwisensPoleno Jupiter, initially achieving a macro average F1-score of 0.77 with holographic images of eight genera, improved to 0.83 when combined with fluorescence data. Accuracies ranged from 55 to 95%, with notable performance for Alternaria spp. and Curvularia caricae-papayae. Species differentiation was also shown to be possible for Cladosporium, but was more difficult for some Alternaria species, while the macro average F1-score remained good (0.72). Overall, this protocol paves the way for more efficient, standard, and accurate automatic identification of airborne fungal spores.

Airborne pollen monitoring depends on the precise and reproducible detection of pollen. In Europe, the volumetric Hirst standardized approach served as the baseline for the traditional method for pollen monitoring networks, requiring highly skilled technicians and which is a labor-intensive job. That is why there is a need for new automatic methodologies to solve those problems. This study evaluates and compares the technical characteristics of various automated pollen detection systems available on the market, providing a snapshot of the current state of technology. Particle size resolution, aspiration volume, storage capacity for high-definition particle pictures, and real-time data transfer were among the principal attributes of the systems examined. Our findings reveal that each system features unique advantages and limitations, with significant correlations between pollen concentrations detected by automatic systems and the manually operated Hirst sampler, especially with the Hund BAA-500 and Swisens Poleno devices. However, current systems require further enhancements in their classification algorithms and the development of comparable datasets for improved functionality. While this review provides an overview of the current scenario, the field is rapidly evolving, with continuous improvements and the potential for new players in the market.

Graphical abstract

Over the past few decades, the detection of airborne pathogens in various indoor and outdoor settings has emerged as a crucial area of research and development. Bioaerosols, stemming from natural or industrial sources and comprising airborne organisms or their fragments, pose potential public and industrial health risks. Hence, there is a growing emphasis on achieving early and dependable detection methods for these pathogens across different environments. This project investigates some possibilities for developing cost-effective “first alert” technology capable of detecting airborne bacteria, fungi and pollen in real time. The proposed approach shows significant promise as an initial alert system capable of alerting users to the possible presence of pathogens or allergens in the air, allowing for the timely implementation of personal protective measures. Although the device cannot differentiate between specific types of bacteria, fungi or pollen, it effectively collects and retains them in a liquid sample. This allows for their precise characterization to be conducted in the nearest laboratory. Subsequently, decisions regarding the retention or removal of protective equipment can be made based on the laboratory results, with further guidance sought from public health specialists as needed.

Graphical abstract

Pollen grains cause negative effects on human health, especially for allergy sufferers. During the last years, allergenicity has increased due to air pollution. The present study analyzes the interactions between pollen concentrations of different pollen types, meteorological variables and air pollutants present in the atmosphere of a city. For this study, a ten-year pollen data series from the city of Badajoz (southwestern Spain) was used to analyze the correlations between pollutants (NO, NO₂, SO₂, CO, O₃) and particulate matter (PM₁, PM_2.5 and PM₁₀), benzene, toluene and xylene and the concentrations of pollen types (Poaceae, Olea, Fraxinus, Urticaceae, Amaranthaceae, Plantago, Rumex, Platanus, Cupressaceae, Myrtaceae, Alnus, Anthemideae, Castanea, Echium, Lactuceae, Pinus and Quercus). The analysis was performed using daily Spearman correlations, as well as with cumulative pollution values and with time lags between both variables. The correlation with atmospheric pollutants revealed that NO_x could be negatively associated pollen concentrations of all pollen types studied except for Alnus, Fraxinus and Urticaceae, for which positive r values were obtained. Benzene, toluene, and xylene also have negative r values except for the associations between Alnus and Urticaceae pollen and benzene and toluene. On the other hand, O₃ was positively correlated pollen concentrations of most pollen types except for Alnus, Cupressaceae, Fraxinus and Urticaceae. The strongest correlations (r values) between air pollutants and various pollen types, such as Olea and Poaceae, occur with a three-day lag. These results highlight the role that air pollution has on pollen concentrations, thus providing information on the behaviour of pollen in urban environments.

Certain fungal spores in the atmosphere are one of the main factors that cause asthma attacks and allergic rhinitis symptoms in susceptible individuals. Elevated concentrations of fungal spores cause a significant reduction in the quality of life of susceptible individuals, and an increase in workloads at hospitals. In addition, some fungal spores can contaminate agricultural crops, compromising plant health and food safety and inflicting economic losses. The analysis of atmospheric fungal spores through aerobiological studies and the determination of their relationship with meteorological factors can support reduced exposure to allergens and favor early precautions against agricultural pests. In this study, fungal spores in the atmosphere in Kastamonu, Türkiye were studied hourly throughout 2017 using the volumetric method. Through the study, fungal spores belonging to 41 different taxa were detected in Kastamonu’s atmosphere: Cladosporium, 58.76%; Leptosphaeria, 8%; and Pleospora, 5.01%; and Alternaria 4.98% were the dominant fungal spores in Kastamonu’s air. The annual spore integral was 3868 spores/m³ per day, and 4.48% of the taxa featured concentrations of less than 1%. In terms of total spore concentration, the highest readings were taken at 3:00–4:00 am in the summer, 6:00–7:00 am in the spring, 3:00–4:00 pm in the fall, and 12:00–1:00 am in the winter. Air temperature stands out as the most effective meteorological parameter, showing a positive correlation with all the dominant fungal spores. The direction and intensity of the correlation between meteorological parameters and fungal spores in the atmosphere vary significantly for each species. Longer-term studies are recommended to determine behavioral patterns and to better understand the abundance of fungal spores and their associated meteorological factors.

Examining the model of banana xanthomonas disease with treatment to stop the infections from spreading across the community due to pathogen attack is the main goal of this research. In order to examine the recovery rate of banana xanthomonas after taking control measures with treatment and continuous cutting of infected one, a mathematical model is established using the created hypothesis for healthy environment and the Fractal–Fractional operator is utilized to convert the model into a fractional-order model for continuous monitoring including reliable numerical solutions. In order to ascertain the stable status of the recently built fractional-order system, a qualitative and quantitative analysis is also conducted. Reliable results are ensured by examining the boundedness and uniqueness of the model, which are crucial attributes for comprehending the intricate dynamics. The global derivative is used to determine the rate of disease effects according to each sub-compartment and is validated for genuine positivity using linear growth and Lipschitz conditions. The global stability of the system is investigated using Lyapunov’s first derivative functions in order to evaluate the overall effect of the disease’s spread and control. The impacts of different parameters on the sickness are illustrated using numerical simulations, which are used to explore the effect of the fractional operator on the generalized form of the Mittag–Leffler kernel utilizing a two-step Lagrange polynomial approach for continuous monitoring. Simulations have been made to see the real behavior and control of xanthomonas disease caused by pathogen attack by taking control measures for healthy environment with hypersensitive response. Also, it can be observed the continuous monitoring with dimensional effects helps for identification of infected banana plants and can be removed by cutting process. This kind of research will be beneficial in determining how diseases spread and in creating control plans based on our validated findings for banana plants.