#### Aerobiologia

Journal Information
ISSN / EISSN : 0393-5965 / 1573-3025
Current Publisher: Springer Science and Business Media LLC (10.1007)
Former Publisher:
Total articles ≅ 1,545
Current Coverage
SCOPUS
SCIE
LOCKSS
Archived in
SHERPA/ROMEO
Filter:

#### Latest articles in this journal

, Chamari B. A. Mampage, Dagen D. Hughes, Lillian M. Jones
Published: 5 May 2021
Aerobiologia pp 1-8; doi:10.1007/s10453-021-09702-x

Abstract:
Ragweed pollen is a prevalent allergen in late summer and autumn, worsening seasonal allergic rhinitis and asthma symptoms. In the atmosphere, pollen can osmotically rupture to produce sub-pollen particles (SPP). Because of their smaller size, SPP can penetrate deeper into the respiratory tract than intact pollen grains and may trigger severe cases of asthma. Here we characterize airborne SPP forming from rupturing giant ragweed (Ambrosia trifida) pollen for the first time, using scanning electron microscopy and single-particle fluorescence spectroscopy. SPP ranged in diameter from 20 nm to 6.5 μm. Most SPP are capable of penetrating into the lower respiratory tract, with 82% of SPP < 1.0 μm, and are potential cloud condensation nuclei, with 50% of SPP < 0.20 μm. To support predictions of the health and environmental effects of SPP, we have developed a quantitative method to estimate the number of SPP generated per pollen grain ( $${n}_{\mathrm{f}}$$ n f ) based upon the principle of mass conservation. We estimate that one giant ragweed pollen grain generates 1400 SPP across the observed size range. The new measurements and method presented herein support more accurate predictions of SPP occurrence, concentration, and air quality impacts that can help to reduce the health burden of allergic airway diseases. Graphic abstract Rupturing ragweed pollen releasing cellular components (right), viewed by an inverted light microscope.
Weihua Zhang, Guoxin Mo, Jie Yang, Xingshuo Hu, Hujie Huang, Jing Zhu, Pei Zhang, , Lixin Xie
Published: 4 May 2021
Aerobiologia; doi:10.1007/s10453-021-09708-5

Abstract:
To clarify the characteristics and distribution of hospital environmental microbiome associated with confirmed COVID-19 patients. Environmental samples with varying degrees of contamination which were associated with confirmed COVID-19 patients were collected, including 13 aerosol samples collected near eight patients in different wards, five swabs from one patient’s skin and his personal belongings, and two swabs from the surface of positive pressure respiratory protective hood and the face shield from a physician who had close contact with one patient. Metagenomic next-generation sequencing (mNGS) was used to analyze the composition of the microbiome. One of the aerosol samples (near patient 4) was detected positive for COVID-19, and others were all negative. The environmental samples collected in different wards possessed protean compositions and community structures, the dominant genera including Pseudomonas, Corynebacterium, Neisseria, Staphylococcus, Acinetobacter, and Cutibacterium. Top 10 of genera accounted for more than 76.72%. Genera abundance and proportion of human microbes and pathogens radiated outward from the patient, while the percentage of environmental microbes increased. The abundance of the pathogenic microorganism of medical supplies is significantly higher than other surface samples. The microbial compositions of the aerosol collected samples nearby the patients were mostly similar to those from the surfaces of the patient's skin and personal belongings, but the abundance varied greatly. The positive rate of COVID-19 RNA detected from aerosol around patients in general wards was quite low. The ward environment was predominantly inhabited by species closely related to admitted patients. The spread of hospital microorganisms via aerosol was influenced by the patients’ activity.
Published: 24 April 2021
Aerobiologia; doi:10.1007/s10453-021-09705-8

Published: 16 April 2021
Aerobiologia pp 1-17; doi:10.1007/s10453-021-09703-w

Abstract:
In 2016, the highest birch (Betula spp.) pollen concentrations were recorded in Kraków (Poland) since the beginning of pollen observations in 1991. The aim of this study was to ascertain the reason for this phenomenon, taking the local sources of pollen in Poland and long-range transport (LRT) episodes associated with the pollen influx from other European countries into account. Three periods of higher pollen concentrations in Kraków in 2016 were investigated with the use of pollen data, phenological data, meteorological data and the HYSPLIT numerical model to calculate trajectories up to 4 days back (96 h) at the selected Polish sites. From 5 to 8 April, the birch pollen concentrations increased in Kraków up to 4000 Pollen/m3, although no full flowering of birch trees in the city was observed. The synoptic situation with air masses advection from the South as well as backward trajectories and the general birch pollen occurrence in Europe confirm that pollen was transported mainly from Serbia, Hungary, Austria, the Czech Republic, Slovakia, into Poland. The second analyzed period (13–14 April) was related largely to the local flowering of birches, while the third one in May (6–7 May) mostly resulted from the birch pollen transport from Fennoscandia and the Baltic countries. Unusual high pollen concentrations at the beginning of the pollen season can augment the symptomatic burden of birch pollen allergy sufferers and should be considered during therapy. Such incidents also affect the estimation of pollen seasons timing and severity. Graphical Abstract
Amaia Fernández-Iriarte, , Jodelle Degois, Hamza Mbareche, Marc Veillette, Natalia Moreno, Fulvio Amato, Xavier Querol, Teresa Moreno
Published: 9 April 2021
Aerobiologia pp 1-17; doi:10.1007/s10453-021-09704-9

The publisher has not yet granted permission to display this abstract.
Asmaa Boullayali, Lakbira Elhassani, Asmae Janati, Lamiaa Achmakh,
Published: 26 March 2021
Aerobiologia pp 1-27; doi:10.1007/s10453-021-09700-z

The publisher has not yet granted permission to display this abstract.
, , Aníta Ósk Áskelsdóttir, Ellý Renée Guðjohnsen, Margrét Hallsdóttir
Published: 26 March 2021
Aerobiologia pp 1-18; doi:10.1007/s10453-021-09701-y

The publisher has not yet granted permission to display this abstract.
, Adina Paytan, Bailey W. Mitchell, Dale W. Griffin
Published: 19 March 2021
Aerobiologia pp 1-13; doi:10.1007/s10453-020-09686-0

The publisher has not yet granted permission to display this abstract.
Published: 19 March 2021
Aerobiologia pp 1-17; doi:10.1007/s10453-021-09696-6

The publisher has not yet granted permission to display this abstract.
Anna Muzalyova, Jens O. Brunner, Claudia Traidl-Hoffmann,
Published: 16 March 2021
Aerobiologia pp 1-22; doi:10.1007/s10453-021-09699-3

Abstract:
Airborne allergenic pollen impact the health of a great part of the global population. Under climate change conditions, the abundance of airborne pollen has been rising dramatically and so is the effect on sensitized individuals. The first line of allergy management is allergen avoidance, which, to date, is by rule achieved via forecasting of daily pollen concentrations. The aim of this study was to elaborate on 3-hourly predictive models, one of the very few to the best of our knowledge, attempting to forecast pollen concentration based on near-real-time automatic pollen measurements. The study was conducted in Augsburg, Germany, during four years (2016–2019) focusing on Betula and Poaceae pollen, the most abundant and allergenic in temperate climates. ARIMA and dynamic regression models were employed, as well as machine learning techniques, viz. artificial neural networks and neural network autoregression models. Air temperature, relative humidity, precipitation, air pressure, sunshine duration, diffuse radiation, and wind speed were additionally considered for the development of the models. It was found that air temperature and precipitation were the most significant variables for the prediction of airborne pollen concentrations. At such fine temporal resolution, our forecasting models performed well showing their ability to explain most of the variability of pollen concentrations for both taxa. However, predictive power of Betula forecasting model was higher achieving R 2 up to 0.62, whereas Poaceae up to 0.55. Neural autoregression was superior in forecasting Betula pollen concentrations, whereas, for Poaceae, seasonal ARIMA performed best. The good performance of seasonal ARIMA in describing variability of pollen concentrations of both examined taxa suggests an important role of plants’ phenology in observed pollen abundance. The present study provides novel insight on per-hour forecasts to be used in real-time mobile apps by pollen allergic patients. Despite the huge need for real-time, short-term predictions for everyday clinical practice, extreme weather events, like in the year 2019 in our case, still comprise an obstacle toward highly performing forecasts at such fine timescales, highlighting that there is still a way to go to this direction.