Real-Time Detection, Discrimination, and Forecasting of Bioaerosols
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Aerosols".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 1597
Special Issue Editors
Interests: aerosol and cloud particle detection systems; cloud microphysics; cloud-aerosol interactions; biometeorology; surface atmosphere exchange; micrometeorology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Biological aerosol, known as bioaerosol, is poorly constrained but has direct and indirect impacts on air quality, climate, and human health. Detecting and characterizing outdoor and indoor bioaerosol is critical as such knowledge is an essential prerequisite for policy interventions. Bioaerosols normally comprise viruses, bacteria, fungi, and pollen. Historically, offline techniques have been utilized to detect different types of bioaerosols and characterize their physical and chemical properties, as well as their environmental toxicity. Knowledge from integrated filter samples sometimes hinders our deep understanding of the release mechanisms of bioaerosols, which is the key for bioaerosol forecasting. With recent advances in fluorescence spectrometers, the detection, discrimination, and forecasting of different bioaerosols in real-time become possible.
In this Special Issue of Atmosphere, entitled “Real-Time Detection, Discrimination, and Forecasting of Bioaerosols”, we call for novel papers addressing the detection, discrimination, and forecasting of bioaerosols in real-time. The aim is to introduce recent advances in bioaerosol-related measurement science and data science. These studies include, but are not limited to, field measurement reports, exploring environmental drivers of release mechanisms of bioaerosols, health impacts and interventions of bioaerosols, and data analytics in bioaerosol science.
Prof. Dr. Martin Gallagher
Dr. Congbo Song
Guest Editors
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Keywords
- bioaerosol
- climate
- health
- clustering
- forecasting
- modeling
- intervention
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