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Atmosphere
Special Issues
Atmospheric Bioaerosols: Detection, Characterization and Modelling
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Atmospheric Bioaerosols: Detection, Characterization and Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Aerosols".

Deadline for manuscript submissions: closed (2 February 2024) | Viewed by 5473

Special Issue Editors

Dr. David J. O'Connor

E-Mail Website
Guest Editor
School of Chemical Sciences, Dublin City University, D09 E432 Dublin, Ireland
Interests: bioaerosol monitoring; forecasting; characterization and source determination
Dr. Eoin McGillicuddy

E-Mail Website
Guest Editor
School of Chemical and Pharmaceutical Sciences, Technological University Dublin, D07 H6K8 Dublin, Ireland
Interests: outdoor air quality; real-time detection; bioaerosols
Dr. Meheal Fennelly

E-Mail Website
Guest Editor
School of Chemistry and Environmental Research Institute, University College Cork, T12 YN60 Cork, Ireland
Interests: indoor air quality; real-time detection; bioaerosols
Prof. Dr. John R. Sodeau

E-Mail Website
Guest Editor
School of Chemistry, University College Cork, T12 YN60 Cork, Ireland
Interests: atmospheric chemistry; aerobiology; photochemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The need to detect, characterize and model aerosols in our atmosphere has increased considerably more recently. This is primarily due to the detrimental effects that they can have on both human and plant health. An important component of these aerosols (both outdoors and indoors) is Primary Biological Aerosol Particles (PBAPs). These are comprised of materials such as viruses, bacteria, fungal spores, pollen, sub-pollen, and plant fragments. Therefore, we invite you to consider submitting your research for publication in this Special Issue of Atmosphere, entitled “Atmospheric Bioaerosols: Detection, Characterization and Modelling”. The aim is to communicate a selection of papers on the current state of field, laboratory and modeling/forecasting studies relevant to atmospheric bioaerosol loading and ambient interactions.

Current issues related to real-time pollen, fungal spore and bacteria monitoring and networking systems; the development of innovative bioaerosol sensors; the influence of climate change on PBAPs loadings;  bioaerosols within occupational settings both indoors and outdoors (e.g., hospitals or green waste sites); surface phenomena and reactions; the relevance of real-time measurements to ice nucleation, cloud condensation nuclei and other climate change issues; modelling and forecasting of bioaerosols.

Dr. David J. O'Connor
Dr. Eoin McGillicuddy
Dr. Meheal Fennelly
Prof. Dr. John R. Sodeau
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pollen
  • fungal spores
  • bacteria
  • real-time detection and analysis
  • climate change
  • health
  • sensor networks
  • surface reactivity
  • modeling
  • forecasting

Published Papers (4 papers)

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Research

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25 pages, 9084 KiB  
Article
Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration
by Maxamillian A. N. Moss, Dagen D. Hughes, Ian Crawford, Martin W. Gallagher, Michael J. Flynn and David O. Topping
Atmosphere 2023, 14(9), 1416; https://doi.org/10.3390/atmos14091416 - 08 Sep 2023
Viewed by 1606
Abstract
In a comparative study contrasting new and traditional clustering techniques, the capabilities of K-means, the hierarchal clustering algorithm (HCA), and GenieClust were examined. Both K-means and HCA demonstrated strong consistency in cluster profiles and sizes, emphasizing their effectiveness in differentiating particle types and [...] Read more.
In a comparative study contrasting new and traditional clustering techniques, the capabilities of K-means, the hierarchal clustering algorithm (HCA), and GenieClust were examined. Both K-means and HCA demonstrated strong consistency in cluster profiles and sizes, emphasizing their effectiveness in differentiating particle types and confirming that the fundamental patterns within the data were captured reliably. An added dimension to the study was the integration of an autoencoder (AE). When coupled with K-means, the AE enhanced outlier detection, particularly in identifying compositional loadings of each cluster. Conversely, whilst the AE’s application to all methods revealed a potential for noise reduction by removing infrequent, larger particles, in the case of HCA, this information distortion during the encoding process may have affected the clustering outcomes by reducing the number of observably distinct clusters. The findings from this study indicate that GenieClust, when applied both with and without an AE, was effective in delineating a notable number of distinct clusters. Furthermore, each cluster’s compositional loadings exhibited greater internal variability, distinguishing up to 3× more particle types per cluster compared to traditional means, and thus underscoring the algorithms’ ability to differentiate subtle data patterns. The work here postulates that the application of GenieClust both with and without an AE may provide important information through initial outlier detection and enriched speciation with an AE applied, evidenced by a greater number of distinct clusters within the main body of the data. Full article
(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling)
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20 pages, 12364 KiB  
Article
Towards a UK Airborne Bioaerosol Climatology: Real-Time Monitoring Strategies for High Time Resolution Bioaerosol Classification and Quantification
by Ian Crawford, Keith Bower, David Topping, Simone Di Piazza, Dario Massabò, Virginia Vernocchi and Martin Gallagher
Atmosphere 2023, 14(8), 1214; https://doi.org/10.3390/atmos14081214 - 28 Jul 2023
Cited by 2 | Viewed by 1345
Abstract
Biological particulate matter (BioPM) is a poorly constrained, ubiquitous, and diverse subset of atmospheric aerosols. They influence climate, air quality, and health via many mechanisms, spurring renewed interest in constraining their emissions to elucidate their impacts. In order to build the framework required [...] Read more.
Biological particulate matter (BioPM) is a poorly constrained, ubiquitous, and diverse subset of atmospheric aerosols. They influence climate, air quality, and health via many mechanisms, spurring renewed interest in constraining their emissions to elucidate their impacts. In order to build the framework required to assess the role of BioPM in these multidisciplinary areas, it is necessary to develop robust, high time-resolution detection methodologies so that BioPM emissions can be understood and characterized. In this study, we present ambient results from intensive monitoring at UK peri-urban and coastal ground sites using high time-resolution real-time bioaerosol spectrometers. We demonstrate the utility of a new dimensional reduction-driven BioPM classification scheme, where laboratory sample training data collected at the ChAMBRe facility were used to generate broad taxonomic class time series data of key species of interest. We show the general trends of these representative classes, spanning spring, early summer, and autumn periods between 2019 and 2021. Diurnal behaviors and meteorological relationships were investigated and contextualized; a key result arising from this study was the demonstration of rainfall-induced enhancement of nighttime Penicillium-like aerosol, where rainfall crucially only acts to enhance the quantity emitted without significantly influencing the early morning timing of peak spore liberation. Full article
(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling)
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15 pages, 4230 KiB  
Article
Bioaerosol Identification by Wide Particle Size Range Single Particle Mass Spectrometry
by Xuan Li, Lei Li, Zeming Zhuo, Guohua Zhang, Xubing Du, Xue Li, Zhengxu Huang, Zhen Zhou and Zhi Cheng
Atmosphere 2023, 14(6), 1017; https://doi.org/10.3390/atmos14061017 - 13 Jun 2023
Viewed by 1193
Abstract
The properties of bioaerosols are complex and diverse, and have a direct impact on the environment, climate, and human health. The effective identification of bioaerosols in the atmosphere is very significant with regard to accurately obtaining the atmospheric chemical characteristics of bioaerosols. To [...] Read more.
The properties of bioaerosols are complex and diverse, and have a direct impact on the environment, climate, and human health. The effective identification of bioaerosols in the atmosphere is very significant with regard to accurately obtaining the atmospheric chemical characteristics of bioaerosols. To improve the detection of large particle bioaerosol and non-bioaerosol interference in the process of bioaerosol recognition, this study detected a variety of bioaerosols and abiotic aerosols based on a single particle aerosol mass spectrometer (SPAMS). Furthermore, the bioaerosol particle identification and classification algorithm based on Zawadowicz the ratio of phosphate to organic nitrogen is optimized to distinguish bioaerosols from abiotic aerosols. The influence of ionized laser energy on classification methods is thoroughly explored here. The results show that 15 kinds of pure fungal aerosols were detected by SPAMS based on a wide size range sampling system, and that fungal aerosols with a particle size of up to 10 μm can be detected. Through the mass spectra peak ratio method of PO3−/PO2− and CNO−/CN−, when discriminating abiotic aerosols such as disruptive biomass combustion particles, automobile exhaust, and dust from pure bacterial aerosols, the discrimination degree is up to 97.7%. The optimized ratio detection method of phosphate to organic nitrogen has strong specificity, which can serve as the discriminant basis for identifying bioaerosols in SPAMS analytical processes. Full article
(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling)
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Review

Jump to: Research

25 pages, 1228 KiB  
Review
Navigating the Aerosolized Frontier: A Comprehensive Review of Bioaerosol Research Post-COVID-19
by Chengchen Zhang, Xiaorong Dai, Tedros Gebrezgiabhier, Yuan Wang, Mengrong Yang, Leiping Wang, Wei Wang, Zun Man, Yang Meng, Lei Tong, Mengmeng He, Bin Zhou, Jie Zheng and Hang Xiao
Atmosphere 2024, 15(4), 404; https://doi.org/10.3390/atmos15040404 - 25 Mar 2024
Viewed by 631
Abstract
In the wake of the COVID-19 pandemic, the scientific community has been galvanized to unravel the enigmatic role of bioaerosols in the transmission of infectious agents. This literature review, anchored in the extensive Web of Science Core Collection database covering the period from [...] Read more.
In the wake of the COVID-19 pandemic, the scientific community has been galvanized to unravel the enigmatic role of bioaerosols in the transmission of infectious agents. This literature review, anchored in the extensive Web of Science Core Collection database covering the period from 1990 to 2023, utilizes a bibliometric approach to chart the dynamic landscape of bioaerosol research. It meticulously documents the paradigm shifts and burgeoning areas of inquiry that have emerged in the aftermath of the pandemic. This review meticulously maps out the sources and detection strategies of pathogens in a variety of ecosystems. It clearly shows that impaction and filtration sampling methods, followed by colony counting and PCR-based detection techniques, were predominantly used in the scientific works within the previous three decades. It synthesizes the progress and limitations inherent in a range of models for predicting aerosol-mediated pathogen spread and provides a comparative analysis of eDNA technology and traditional analytical techniques for bioaerosols. The accuracy of these detection methods and forecasting models is paramount for the early recognition of transmission risks, which, in turn, paves the way for prompt and effective disease mitigation strategies. By providing a thorough analysis of the historical progression and current state of bioaerosol research, this review illuminates the path ahead, identifying the critical research needs that will drive the field’s advancement in the years to come. Full article
(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling)
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