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. Lidar observations were analysed to characterizeatmospheric pollen at four EARLINET (European Aerosol Research LidarNetwork) stations (Hohenpeißenberg, Germany; Kuopio, Finland; Leipzig,Germany; and Warsaw, Poland) during the ACTRIS (Aerosol, Clouds and TraceGases Research Infrastructure) COVID-19 campaign in May 2020. Thereanalysis (fully quality-assured) lidar data products, after thecentralized and automatic data processing with the Single Calculus Chain(SCC), were used in this study, focusing on particle backscattercoefficients at 355 and 532 nm and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollendepolarization ratio was presented, based on the non-linear least squareregression fitting using lidar-derived backscatter-related Ångströmexponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and532 nm should be zero (±0.5) for pure pollen, the pollendepolarization ratios were estimated: for Kuopio and Warsaw stations, thepollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during thebirch-dominant pollen periods, whereas for Hohenpeißenberg andLeipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grasspollen. The method was also applied for the aerosol classification, usingtwo case examples from the campaign periods; the different pollen types (orpollen mixtures) were identified at Warsaw station, and dust and pollen wereclassified at Hohenpeißenberg station.

Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign