Abstract. Pollen-induced allergies are among the most prevalent non-contagious diseases, with about a quarter of the European population being sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive and requires narrow specialized abilities and substantial time, so that the pollen data are always delayed and subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct a first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANNs) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence-based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better, exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species, ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus) and (Festuca, Artemisia and Juniperus). Within these groups, pollen is practically indistinguishable for the first-level recognition procedure. Construction of multistep algorithms with sequential discrimination of pollen inside each group seems to be one of the possible ways forward. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and the issue of false positive pollen detections by Rapid-E is discussed.

Populations worldwide have been increasingly affected by allergic rhinitis associated with pollen allergens. Alterations in the geographical distribution of pollinating flora resulting from climatic changes and increased production of pollens caused by higher atmospheric CO 2 , have changed the environment in which humans reside. This study, carried out in Lithuania, aimed to demonstrate how people with pollen allergy are affected by symptoms associated with changing pollen loads and to evaluate the resultant changes in human quality of life. This study assessed pollen loads in Lithuania during 2013-2014. Skin prick tests were carried out on 665 individuals aiming to evaluate sensitivity to pollen allergens. Selfreports on symptoms and their impact on quality of life were collected from each study subject. There were 249 to 429 differences in inter-annual peaks of Betula pollen concentrations, while differences of plant pollen concentrations of the other genus/families varied from 1.5 to 92 % on peak days. Positive reactions to pollen allergens were reported in 54 % of the study subjects; 90 % of these subjects had been continuously suffering from moderate-to-severe symptoms of allergic rhinitis. The most frequently reported symptoms were sneezing (99 %), runny nose (98 %) and stuffy nose (97 %). The dominant group of subjects reported symptoms during the entire period of vegetation (7 months). This study demonstrated that people with pollen allergies have attributed symptomassociated worsening of their quality of life to the typical periods of unavoidable exposure and have made only minimal changes in their lifestyle during the season of pollen load.

Abstract. Pollen-induced allergy is among the most-prevalent non-contagious diseases, with about a quarter of European population sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive, requires narrow specialization abilities and substantial time, so that the pollen data are always delayed, subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct the first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANN) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence- based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus), and (Festuca, Artemisia, Juniperus). Within these groups, pollen is practically non-distinguishable for the first-level recognition procedure. Construction of multi-steps algorithms with sequential discrimination of pollen inside each group seems to be one of possible ways forwards. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and an issue of the false-positive pollen detections by Rapid-E is discussed.

In this study, phenolic compounds and their antioxidant activity in the pollen of anemophilous Betula and Pinus were determined. Spectrophotometric, high-performance thin-layer and liquid chromatography methods were applied. Free phenolic compounds (free PC) and phenolic compounds bound to the cell wall (bound PC) were analysed in the pollen extracts. Regardless of the pollen species, their content was 20% higher than that in bound PC extracts. Pinus pollen extracts contained 2.5 times less phenolic compounds compared to Betula. Free PC extraction from the deeper layers of Pinus pollen was minimal; the same content of phenolic compounds was obtained in both types of extracts. The bioactivity of pollen (p < 0.05) is related to the content of phenolic compounds and flavonoids in Betula free PC and in bound PC, and only in free PC extracts of Pinus. Rutin, chlorogenic and trans-ferulic acids were characterised by antioxidant activity. Phenolic acids accounted for 70–94%, while rutin constituted 2–3% of the total amount in the extracts. One of the dominant phenolic acids was trans-ferulic acid in all the Betula and Pinus samples. The specific compounds were vanillic and chlorogenic acids of Betula pollen extracts, while Pinus extracts contained gallic acid. The data obtained for the phenolic profiles and antioxidant activity of Betula and Pinus pollen can be useful for modelling food chains in ecosystems.