Abstract

Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany; Kuopio, Finland, Leipzig, Germany; and Warsaw, Poland) during the ACTRIS-COVID-19 campaign in May 2020. The re-analysis lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 nm and 532 nm, and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (± 0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch dominant pollen periods; whereas for Hohenpeiβenberg and Leipzig 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 grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods: the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

Highlights

  • Pollen is recognized as one of the major agents of allergy-related diseases, such as asthma, rhinitis, and atopic eczema (Bousquet et al, 2008)

  • Out of all available data products, this study focused on particle backscatter coefficients (BSCs) at 355 nm and 532 nm, and particle linear depolarization ratios (PDRs) at 532 nm

  • 335 4 Summary and conclusions During the ACTRIS-COVID-19 campaign in May 2020, continuous lidar measurements were performed at EARLINET stations, with data publicly available after the centralized and automatic data processing with Single Calculus Chain (SCC)

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Summary

Introduction

Pollen is recognized as one of the major agents of allergy-related diseases, such as asthma, rhinitis, and atopic eczema (Bousquet et al, 2008). As one important type of biogenic particles, pollen has various climatic and environmental impacts (IPCC, 2013) They can affect the solar radiation reaching Earth causing cooling effect; whereas their interactions with long-wave radiation warm the atmosphere. 35 for workload reduction and online pollen monitoring These techniques are based on, e.g. image recognition such as Pollen Monitor BAA500 (Oteros et al, 2015), or fluorescence spectra such as Wideband Integrated Bioaerosol Sensor (WIBS) (Gabey et al, 2010; Savage et al, 2017) and Plair Rapid-E (Šauliene et al, 2019), or digital holography such as Swisens Poleno (Sauvageat et al, 2020), or light scattering such as pollen monitor KH-3000-01 (Miki and Kawashima, 2021). Study was conducted at four European lidar stations (Hohenpeißenberg, Germany; Kuopio, Finland, Leipzig, Germany; and Warsaw, Poland) for the pollen property retrieval They were selected based on the availability of lidar products and the possible pollen presence from measurements or models for dust-free periods during the campaign.

Stations and campaign
Lidars and data processing
Ancillary data
PDR vs BAE theory
Results
Characteristic values
Case examples
Kuopio – birch pollen An overview of the selected pollen period at
Background
375 Acknowledgements
Full Text
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