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Airborne pollen triggers allergic reactions in up to 40% of the global population. The incidence of pollen allergies is increasing in Thessaloniki, Greece and it is predicted that more than 50% of the European Union's inhabitants will suffer from allergic rhinitis by 2025. Thus, it is essential to investigate and predict high pollen concentrations to address this growing concern. This study utilized the Gradient Boosting Regression (GBR) technique, a machine learning approach, to estimate pollen concentrations of Oleaceae and Quercus taxa, using daily meteorological and land surface data obtained from the European Center for Medium-Range Weather Forecasts (ECMWF). The method accurately predicted pollen concentrations for both species, with an Index of Agreement (IoA) of 0.86 for Oleaceae and 0.78 for Quercus, despite the limited size of the dataset. [ABSTRACT FROM AUTHOR]

The SR-XRFA method has been first used to study the elemental composition of the Syringa L. (Lilac) in the urban ecosystem of Novosibirsk, under background conditions, and in soil samples. About 19 elements have been found in the leaves and shoots of the Syringa species – S. josikaea and S. vulgaris, represented by the three cultivars: cv. 'Pamyat' o Kirove', cv. 'Olimpiada Kolesnikova', and cv. 'Nadezhda', growing in urban and background conditions. In plants, the concentration of Mn, Fe, Cr, Br, Rb, and Nb increased and that of Zn and Mo decreased under technogenic impact as compared with the "background". In urban conditions, the strongest heavy metal pollution was recorded for S. vulgaris cv. 'Nadezhda'. The S. vulgaris cv. 'Pamjat' o Kirove' samples were most resistant to pollution and thus can be recommended both as reference and for use in green construction. [ABSTRACT FROM AUTHOR]