New approaches in detection of pathogens and aeroallergens

Cofinanciado por:
Acronym | Adopt
Project title | New approaches in detection of pathogens and aeroallergens
Project Code | OC-2018-2-23475
Main objective | Reforçar a Investigação, o desenvolvimento tecnológico e a inovação

Region of intervention | Europa

Beneficiary entity | Universidade de Évora(líder)

Approval date | 04-06-2019
Start date | 21-11-2019
Date of the conclusion | 20-11-2023

Total eligible cost | 6000 €
European Union financial support | - 6000 €
National/regional public financial support |
Apoio financeiro atribuído à Universidade de Évora | 6000 €

Summary

Bioaerosols are among the most complex components in the atmosphere. Bioaerosols are relevant as important pathogens in crops and on trees, as aeroallergens in relation to human health and as catalysts for physical processes in relation to climate such as cloud formation processes. For decades the backbone in the European monitoring network of bioaerosols in relation to crop and human health has been simple impactors that trap the bioaerosols on a sticky surface followed by optical identification using microscopes. This approach is both time consuming, expensive and limiting with respect to the progress of science. The last five to ten years a range of new techniques have become available that enable a number of scientific breakthroughs in the general understanding of bioaerosols and how they interact with the environment.  This COST action will establish an interdisciplinary network of experts currently involved in the detection of bioaerosols using both existing methods as well as upcoming technologies such as real or near real-time technologies from atmospheric chemistry and physics or eDNA methods used in molecular biology. A main objective is to critically address the barriers that limits the penetration of new methods in detection of bioaerosols. The cost action will stimulate both research and technological development, e.g. by developing approaches for integration of multiple methods for detecting bioaerosols and how to handle data using numerical approaches in a big data environment by using fungal spores and pollen as examples.


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