Published December 18, 2017
| Version v1
Journal article
Open
Measurements and modeling of surface–atmosphere exchange of microorganisms in Mediterranean grassland
Authors/Creators
- 1. Institute of Ecology, University of Innsbruck, Sternwartestrasse 15, Innsbruck, 6020, Austria
- 2. Institute of Biometeorology (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Via G. Caproni 8, 50145, Florence, Italy
- 3. Plant Pathology Research Unit, French National Institute for Agricultural Research (INRA), Allée des Chênes 67, Montfavet, 84143, France
- 4. FoxLab, Joint Research Unit Fondazione Edmund Mach – CNR IBIMET, Via E. Mach 1, San Michele all'Adige, 38010, Italy
- 5. IMèRA, Universitè Aix-Marseille 2, Place le Verrier, Marseille, 13004, France
Description
Microbial aerosols (mainly composed of bacterial and fungal cells) may
constitute up to 74 % of the total aerosol volume. These biological
aerosols are not only relevant to the dispersion of
pathogens, but they also have geochemical implications. Some bacteria and fungi
may, in fact, serve as cloud condensation or ice nuclei, potentially
affecting cloud formation and precipitation and are active at higher
temperatures compared to their inorganic counterparts. Simulations of the
impact of microbial aerosols on climate are still hindered by the lack of
information regarding their emissions from ground sources. This present work
tackles this knowledge gap by (i) applying a rigorous micrometeorological
approach to the estimation of microbial net fluxes above a Mediterranean
grassland and (ii) developing a deterministic model (the PLAnET model) to
estimate these emissions on the basis of a few meteorological parameters that
are easy to obtain. The grassland is characterized by an abundance of
positive net microbial fluxes and the model proves to be a promising tool
capable of capturing the day-to-day variability in microbial fluxes with a
relatively small bias and sufficient accuracy. PLAnET is still in its infancy
and will benefit from future campaigns extending the available training
dataset as well as the inclusion of ever more complex and critical phenomena
triggering the emission of microbial aerosol (such as rainfall). The model
itself is also adaptable as an emission module for dispersion and chemical
transport models, allowing further exploration of the impact of land-cover-driven microbial aerosols on the atmosphere and climate.
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