Atmospheric bioaerosols modelling

Atmospheric bioaerosols include pollen from trees and grasses, fungi spores, various bacteria, viruses, semi-volatile biogenic organic aerosols, etc. They all have two common features: (i) their seasonal behavior is closely connected with the vegetation composition and state, (ii) their release and transport in the air is decided by the meteorological conditions. One can also add some of the smallest insects, such as aphids, into this list. Modelling of these aerosols starts from phenological model, which describes their maturation and presentation to some “ready-to-fly” storages. Under favorable meteorological conditions, the ready particles can be taken into the air by wind and turbulence, and then transported by the air flows. The most-significant impact of these aerosols refers to environmental and human health and well-being.


Sofiev, M., Siljamo, P., Ranta, H., Linkosalo, T., Jaeger,S., Rasmussen, A., Rantio-Lehtimaki, A., Severova, E., Kukkonen, J. (2012) A numerical model of birch pollen emission and dispersion in the atmosphere. Description of the emission module. Int.J.Biometeorology,, doi:10.1007/s00484-012-0532-z, PMID 22410824.

Ring, J; Akdis, C; Lauener, R; Schappi, G; Traidl-Hoffmann, C; Akdis, M; Ammann, W; Behrendt, H; Bieber, T; Biedermann, T; Bienenstock, J; Blaser, K; Braun-Fahrlander, C; Brockow, K; Buters, J; Crameri, R; Darsow, U; Denburg, J A; Eyerich, K; Frei, R; Galli, S J; Gutermuth, J; Holt, P; Koren, H; Leung, D; Muller, U; Muraro, A; Ollert, M; O'Mahony, L; Pawankar, R; Platts-Mills, T; Rhyner, C; Rosenwasser, L J; Schmid-Grendelmeier, P; Schmidt-Weber, C B; Schmutz, W; Simon, D; Simon, H U; Sofiev, M; van Hage, M; van Ree, R (2014) Global Allergy Forum and Second Davos Declaration 2013 Allergy: Barriers to cure - challenges and actions to be taken. Allergy, 69, 8, 978-982, doi:1-.1111/all.12406.

Clot, B., Gilge, S., Hajkova, L. Magyar, D., Scheifinger, H., Sofiev, M., Butler, F., Tummon, F. (2020) The EUMETNET AutoPollen programme: establishing a prototype automatic pollen monitoring network in Europe. Aerobiologia. doi:10.1007/s10453-020-09666-4.

Sofiev, M., Berger, U., Prank, M., Vira, J., Arteta, J., Belmonte, J., Bergmann, K.-C., Chéroux, F., Elbern, H., Friese, E., Galan, C., Gehrig, R., Khvorostyanov, D., Kranenburg, R., Kumar, U., Marécal, V., Meleux, F., Menut, L., Pessi, A.-M., Robertson, L., Ritenberga, O., Rodinkova, V., Saarto, A., Segers, A., Severova, E., Sauliene, I., Siljamo, P., Steensen, B. M., Teinemaa, E., Thibaudon, M., and Peuch, V.-H. (2015) MACC regional multi-model ensemble simulations of birch pollen dispersion in Europe, Atmos. Chem. Phys., 15, 8115-8130, doi:10.5194/acp-15-8115-2015,

Ritenberga, O., Sofiev, M., Kirillova, V., Kalnina, L., Genikhovich, E. (2016) Statistical modelling of non-stationary processes of atmospheric pollution from natural sources: example of birch pollen. Agriculture and Forest Meteorol. v.226–227, pp.96–107.

Sofiev, M., (2017) On impact of transport conditions on variability of the seasonal pollen index. Aerobiologia, 33, 1, pp. 167–179, doi:10.1007/s10453-016-9459-x.

Sofiev, M. (2019) On possibilities of assimilation of near-real-time pollen data by atmospheric composition models. Aerobiologia, doi:10.1007/s10453-019-09583-1,, 35(3), 523-531.