The number of children suffering from respiratory allergies and asthma has been increasing worldwide and, hence, it is crucial to understand the burden of inhalant biological particles present in school facilities, where children spend one third of their life. From the perspective of indoor air quality, while there are numerous studies on outdoor bioaerosol exposure, there are still uncertainties regarding the diversity and deposition of airborne pollen and fungi indoors. When it comes to schools, there is limited research as to the potential bioaerosol exposure. Here we studied the indoor environment of public schools aiming to reveal whether primary schools of different sizes and at localities of different levels of urbanization may exhibit a variability in the biodiversity and abundance of particles of biological origin, which could pose a risk to child health. To achieve this, 11 schools were selected, located in a variety of environments, from downtown, to city centre-periphery, and to the suburbs. Fungal and pollen samples were collected from various surfaces in school classrooms and corridors, using passive air sampling and swab sampling. We demonstrated that fungi and pollen are detected in school premises during and after the vegetation season. The highest diversity of bioaerosols was found on the top of cabinets and windowsills, with Penicillium, Cladosporium and Acremonium being the most abundant indoors. The levels of fungi were higher in schools with more students. The diversity and amount of pollen in the spring were significantly higher than in samples collected in autumn. Our findings complemented existing evidence that bioaerosol measurements in schools (including kindergartens or informal education facilities) are vital. Hence, we here suggest that, in addition to monitoring air quality and bacterial levels indoors, fungi and pollen measurements have to be integrated in the existing regular biomonitoring campaigns so as to prevent exposure, increase awareness and manage efficiently allergic symptomatology.

This study investigates the use of pollen elastically scattered light images for species identification. The aim was to identify the best recognition algorithms for pollen classification based on the scattering images. A series of laboratory experiments with a Rapid-E device of Plair S.A. was conducted collecting scattering images and fluorescence spectra from pollen of 15 plant genera. The collected scattering data were supplied to 32 different setups of 8 computer vision models based on deep neural networks. The models were trained to classify the pollen types, and their performance was compared for the test sub-samples withheld from the training. Evaluation showed that most of the tested computer vision models convincingly outperform the basic convolutional neural network used in our previous studies: the accuracy gain was approaching 10% for best setups. The models of the Weakly Supervised Object Detection approach turned out to be the most accurate, but also slow. However, even the best setups still did not provide sufficient recognition accuracy barely reaching 65%–70% in the repeated tests. They also showed many false positives when applied to real-life time series collected by Rapid-E. Similar to the previous studies, fusion of the new scattering models with the fluorescence-based identification demonstrated almost 15% higher skills than either of the approaches alone reaching 77–83% of the overall classification accuracy.

Airborne allergenic pollen affects a significant part of the population and the information on pollen load is a valuable tool for public health prevention. The messages should be provided in a form easily understandable for the population. The study provides new insight for the categorisation of pollen load by defining thresholds solely from aerobiological data. Using the long-term airborne pollen data of Corylus, Alnus, Betula, Poaceae, and Artemisia have been evaluated the regionality of pollen concentrations in Lithuania. SPIn and peak values of the main pollen season highlighted as regionality indicators. The largest differences between stations were found in the cases of Corylus and Artemisia.The principle enabling a group of pollen concentrations into levels has been analysed based on retrospective aerobiological data of five pollen types. Thresholds were determined by employing the lowest peak value of the pollen season and applying the 25% principle for selected pollen types. The results were verified by performing associations of defined thresholds with retrospective morbidity data of allergic rhinitis and allergic asthma in Lithuania. Determined pollen thresholds can be used in epidemiological studies requiring associations with pollen concentration. Thresholds could also complement air quality information by integrating pollen load data into public messages or contribute to the development of mHealth systems.

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.