Detection of pollen and biomass burning particles using laser- induced aerosol fluorescence and in situ techniques during the PERICLES campaign 2023 in Switzerland

The Payerne lidaR and Insitu detection of fluorescent biomass burning and dust partiCLES and their cloud impact (PERICLES) campaign (May - June 2023) took place at the rural site of Payerne, Switzerland (46.82^o N, 6.94^o E, 491 m a.s.l.), at the premises of MeteoSwiss station. PERICLES aimed to understand the spatio-temporal variability of different types of bioaerosols (biomass burning, pollen, dust, etc.) in the Planetary Boundary Layer and aloft (typically up to 2-5 km asl.) and their role in cloud formation. As bioaerosols play a crucial role in cloud formation and on human health, there is strong need to characterize them, both at ground level and aloft. Recently, elastic and fluorescence lidars have become important tools for characterizing bioaerosols’ types. In this study, we used a synergy of in-situ and laser remote sensing instrumentation to discriminate between pollen, dust and biomass burning bioparticles and evaluate their role in cloud formation. Biomass burning particles originated from long-range wildfires in Canada and near-range ones in Germany. High concentrations of pollen were recorded by in situ instruments (Hirst-type volumetric trap, Swisens Poleno and WIBS 5 NEO) at ground level. The EPFL elastic-laser induced fluorescence (LIF) lidar was used to provide vertical profiles of the aerosol elastic (b_aer) and fluorescence backscatter (_bF) coefficients, along with the fluorescence capacity factor (G_F), during the study period. Typical values of b_F ranged from 1.5 to 8.5 x10^-4 Mm^-1 sr^-1, while G_F took values between 1-8 x 10^-4. A 32-channel spectrometer detected the bioaerosol fluorescence lidar signals aloft (from ground up to 1-1.5 km height). Application of machine learning algorithms we were able to determine the percentage distribution of various pollen types (e.g. Dactylis glomerata, Quercus robur, Fagus Sylvatica and Betula pendula), which correlate well with ground-level pollen data and number concentrations of ice-nucleating particles (INPs).