Stephanie Bohlmann, researcher at the Finnish Meteorological Institute, studied in her doctoral dissertation, how lidar measurements could be used to detect pollen in the air. The research is another step forward in characterizing atmospheric pollen with active remote sensing, which could potentially lead to better modelling and forecasting of atmospheric pollen.
Atmospheric pollen is a well-known health threat causing allergy-related diseases. Birch, pine and spruce pollen are among the most abundant pollen types in Finland and especially birch pollen is known as one of the most allergenic pollen types in Europe.
“Pollen type and concentration are usually monitored close to ground, and little is known about the vertical distribution of pollen in the atmosphere. However, vertical information could improve our understanding of pollen distribution and transport”, explains Stephanie Bohlmann, researcher in the FMI’s Atmospheric Research Centre of Eastern Finland.
Lidar instruments are commonly used to investigate the vertical distribution of aerosols in the atmosphere. In this thesis, a multiwavelength Raman lidar instrument was operated at the rural forest site in Vehmasmäki (Kuopio). These measurements were combined with pollen observations on ground.
The results presented in the dissertation improve the understanding of optical properties of pollen and can improve the particle classification using lidar measurements. Furthermore, the evaluation of pollen transport models with lidar measurements can potentially improve the performance of pollen forecast models.
An algorithm can be used to estimate the contribution of pollen particles to observed lidar profiles
The dissertation investigated the applicability of lidar measurements to detect pollen in the atmosphere. The particle depolarization ratio, a lidar-derived optical parameter which describes the rotation of the backscattered light, was found to be the most valuable parameter for the detection of pollen. The particle depolarization ratio depends on the particle shape and can be used to detect non-spherical pollen particles.
One challenge is to distinguish pollen particles in lidar measurements as there is always a mixture of different aerosol types in the atmosphere. To separate pollen from background aerosols, an algorithm was developed. The algorithm can be used to estimate the shape information of pure pine and birch pollen in the air based on lidar measurements.
Using a Halo Streamline Doppler lidar, which is usually used for wind and turbulence studies, depolarization measurements were expanded by a third wavelength and a wavelength dependence of the particle depolarization ratio was found. This wavelength dependence could be characteristic for non-spherical atmospheric pollen and could be the key for distinguishing pollen from other depolarizing aerosol types.
Public examination can be followed online 15 September
The doctoral dissertation of MSc Stephanie Bohlmann, titled Characterization of atmospheric pollen with active remote sensing, is examined at the Faculty of Science and Forestry of the University of Eastern Finland 15 September 2021 at 12:00.
The opponent in the public examination will be Associate Professor Michaël Sicard from the Polytechnic University of Catalonia, and the custos will be Docent Mika Komppula, Head of the measurement group at the Atmospheric Research Centre of Eastern Finland (FMI).
The public examination will be held online, follow the examination here
More information:
Researcher Stephanie Bohlmann, Finnish Meteorological Institute, tel. +358 503570323, stephanie.bohlmann (a) fmi.fi
Dissertation is also available on Helda digital archive
Source: Finnish Meteorological Institute press release
