Aerobiologia最新文献

Bioaerosols can be spread through coughing, sneezing, respiratory droplets and aerosol particles, and public awareness of the health risks of bioaerosols has increased. Based on bioaerosol culturable microbe concentration data collected from March–December in 2015, 2018 and 2019, the health risks of bioaerosols were assessed by air quality level, month, population, and particle size using an average daily dose rate model. The concentration of culturable microorganisms is related to the air quality index (AQI). Under AQI values ranging from 51–100, the concentration of culturable microorganisms was the highest, while the concentration of culturable microorganisms was the lowest for AQI values ranging from 101–150. The health risk in June and July 2015 was the highest, the change trends in 2018 and 2019 were similar, the health risk was the highest in October, and the health risk of bioaerosols along the inhalation route was 10³–10⁴ times that along the exposure route. The health risk of bioaerosols was generally higher in summer and autumn than in spring and winter over the three-year period. The health risk for different categories of individuals indicated the same trend over the 3-year period, with the health risk for adults exceeding that for children and the health risk for men exceeding that for women. The health risk of bioaerosols was high under particle sizes ranging from 1.10–4.70 μm. The study results could provide data support for the analysis of bioaerosol-related health risks and offer a reference for the prevention and control of urban microbial diseases.

Airborne pathogens pose a great threat to public health, and their appearance in bioaerosol also increases the contiguousness due to the long survival time and transmitting range. Real-time monitoring and rapid detection methods provide more effective prevention and control of airborne pathogens. The whole procedure could be divided into bioaerosol collection and detection processes. This review presents the basic principles and recent advances in commonly used methods for each of these two steps. We categorized four different kinds of collection methods based on their principles and discussed possible enrichment methods against a small amount of targets. Four different detection methods were compared regarding their ability to perform rapid testing. In the final section, we analyzed the latest trend in combining all these steps to set up a single device or platform for rapid, automated, and continuous on-site bioaerosol monitoring to overcome time and space constraints and increase the speed of the entire monitoring process. We conclude that an integrated all-in-one system using a microfluidic platform is the most promising solution for real-time monitoring of airborne pathogens, since they are capable of simplifying operational steps, efficient collection, and high-throughput detection, demonstrating the strong potential of field-deployable platforms.

Graphical abstract

Bioaerosols are particles of great importance for several fields of research, and spores produced by fungi can exist as bioaerosols when suspended in the air. Microbiological standards for environmental monitoring of outdoor air parameters can be achieved by analyzing the relationship between airborne microorganisms and the prevailing environmental conditions. The outdoor air of the Metropolitan Region of São Paulo and the rural area in a city of the state of São Paulo (Ibiúna/SP), both in Brazil, were evaluated for the presence of microorganisms using the MAS-100 ECO (Merck®, Fr.) and M Air T (Millipore®) air sample collectors. Dichloran Rose-Bengal Chloramphenicol and Tryptic Soy Agars were used for fungal and bacterial isolation, respectively. Bacterial colonies were counted, and the plates with fungal colonies were sent for phenotypic identification up to genus and species level, respectively. Data on pollutant concentrations were obtained from the Environmental Company of the State of São Paulo. The highest number of Colony-Forming Units/m³ (CFU/m³) of microorganisms was measured in the winter and summer seasons, respectively, but the greatest Spore-Forming Units (SFU) of fungi were found in the rural area, where pollutant concentrations were lower. Nitrogen dioxide (NO₂) had a slightly positive influence on the concentration of SFU of fungi in both areas studied. Sulfur dioxide (SO₂) pollutant concentrations had both positive and negative great relations showing influence on microbial counts in the air of the rural area. In the rural area, the low bacteria count was influenced negatively by the low concentration of carbon monoxide (CO). The microbial counts were related to each other, as well as to the concentrations of pollutants, shown by all the correlations seen, indicating microorganisms as biomarkers of pollution in outdoor areas. The influence of environmental factors on the population and outdoor air biome is also explicit.

Pollen, particularly from the Ambrosia genus, plays a pivotal role in triggering allergic rhinoconjunctivitis symptoms. This review delves into the global background of Ambrosia, focusing on its origins, invasive potential, and spread to South America. The ecological niche for Ambrosia species is explored, emphasizing its stability globally but exhibiting unique and dynamic features in South America. Information on Ambrosia pollen concentration in South America is summarized, revealing varying levels across countries. The establishment of new aerobiology stations, as highlighted in the latest findings, contributes valuable data for understanding allergen risk management in the region. The health perspective addresses the rise in allergic diseases due to climate change, emphasizing the need for continuous monitoring, especially in South America. Agricultural damage inflicted by Ambrosia is discussed, emphasizing its invasive potential, high seed production, and negative impact on crops, forage quality, and livestock. The review also positions Ambrosia as a marker of climate change, discussing the effects of global warming on pollen seasons, concentrations, and allergenic characteristics. The importance of expanding aerobiology stations in South America is underscored, requiring collaborative efforts from government, scientific societies, and academic institutions. The review concludes by advocating for increased monitoring to address potential challenges posed by Ambrosia, offering a basis for tailored interventions and future research in South American regions.

In Europe, more than one quarter of the adult population and one third of the children suffer from pollinosis, but the geographical variability is large. In Belgium, at least ~ 10% of the people develop allergies due to birch pollen. These patients may benefit from a forecasting system that raises alerts when episodes with huge amount of airborne birch pollen grains are expected. Such a forecast system for birch pollen was established for the Belgian territory in 2023 based on the pollen emission and transport model System for Integrated modeLling of Atmospheric coMposition (SILAM). The question, however, is which uncertainty in modelling and forecasting airborne pollen levels can be expected? Here, we assess the uncertainty in modelling airborne birch pollen levels near the surface using SILAM in a Monte Carlo error approach summarized by the relative Coefficient of Variation (CV%) as descriptive statistic for the season of 2018 in Belgium. For the major inputs that drive the birch pollen model—the amount and location of birch trees (0.1° × 0.1° map), the start and end of the birch pollen season (1° × 1° map), and the ripening temperature of birch catkins—sets of 100 randomly sampled data layers were prepared for running SILAM 100 times. For each set of model input, 100 spatio-temporal maps of airborne birch pollen levels were produced and its spread was quantified by the CV%. We show that the spatial uncertainty of pollen emissions sources in SILAM is substantially high, but that the uncertainties of the parameters determining the start and end of the season are at least equally important. By accumulating the effects of all investigated model input uncertainties including the impact of the catkins-ripening temperature, CV% values of 50% and more are obtained when quantifying the variation of the modelled airborne birch pollen levels. These errors are in line with reported values from the current reference method for monitoring airborne birch pollen grains near the surface.

Biological particles known as bioaerosols are present in the atmosphere and have recently been implicated as influencing agriculture, cloud development, biogeography, and human health. The present study was conducted to characterize airborne bacterial heterogeneity at Jeju Island in Korea and at Saitama and Toyama in Japan, focusing on potential human pathogens. Air samples were collected during the winter, when the monsoon blows from the northwest. Samples were analyzed by high-throughput sequencing and denaturing gradient gel electrophoresis of PCR-amplified 16S rRNA genes to detect spatial differences in airborne bacteria and the possible spread of bacteria by transboundary transport. Compositions of the bacterial in samples collected on the same dates from the different sites were similar. Notably, bacteria from two genera that are potentially pathogenic for humans—Acinetobacter and Clostridium—were detected on the same day in both Korea and Japan. These results indicate the possibility of long-range transport of airborne bacteria and its potential impact on human health.

The viable bacterial assemblages in clouds at Réunion Island (Indian Ocean) were examined through culture-based approach. A total of 176 isolates were recovered from 15 independent cloud events collected during 3 field campaigns, and identified to the species level through full length 16S rRNA gene sequencing. As often in atmospheric samples, Alpha-, Beta- and Gamma-proteobacteria dominated, along with Actinobacteria, Firmicutes, and Bacteroidetes, depicting these as the backbone of the global aeromicrobiome. A comparative analysis with similar work from a cloud sampling site in Central France revealed site-specificities, and numerous common species. These latter included members of Pseudomonas, Sphingomonas, Bacillus, Staphylococcus, Rhodococcus and others, whose such widespread presence in clouds supports the existence of a pan-atmospheric microbiome. This also confirms that cultures remain powerful methods in the description of the viable microbial diversity by allowing deep taxonomic affiliation.

The advent of automatic pollen and fungal spore monitoring over the past few years has brought about a paradigm change. The provision of real-time information at high temporal resolution opens the door to a wide range of improvements in terms of the products and services made available to a widening range of end-users and stakeholders. As technology and methods mature, it is essential to properly quantify the impact automatic monitoring has on the different end-user domains to better understand the real long-term benefits to society. In this paper, we focus the main domains where such impacts are expected, using Europe as a basis to provide qualitative estimates and to describe research needs to better quantify impacts in future. This will, in part, also serve to justify further investment and help to expand monitoring networks.

In 2019, the world grappled with an unexpected and severe global health crisis—the Coronavirus disease (COVID-19) outbreak, which significantly impacted various aspects of human life. This case study, focusing on Bangladesh, aimed to uncover the complex spatial patterns and potential risk factors influencing the virus’s uneven spread across 64 districts. To analyze spatial patterns, two techniques, namely Moran I and Geary C, were employed to study spatial autocorrelation. Hotspots and coldspots were identified using local Moran I, while spatial hotspots were pinpointed using local Getis Ord G. Exploring spatial heterogeneity involved implementing two non-spatial models (Poisson–Gamma and Poisson-Lognormal) and three spatial models (Conditional Autoregressive model, Convolution model, and Leroux model) through Gibbs sampling. The Leroux model emerged as the optimal choice, meeting criteria based on the lowest values of deviance information criterion and Watanabe–Akaike information criterion. Regression analysis revealed that factors such as humidity, population density, and urbanization were associated with an increase in COVID-19 cases, while the aging index appeared to hinder the virus’s spread. The research outcomes provide a comprehensive framework adaptable to the evolving nature of COVID-19 in Bangladesh. It categorizes influential factors into distinct clusters, enabling government agencies, policymakers, and healthcare professionals to make informed decisions for controlling the pandemic and addressing future infectious diseases.