Palynological assessment of the atmosphere in the city of Cherepovets

The article presents the results of the study of aeropalinological spectra of the city of Cherepovets (59 ° 07′59 ″ N, 37 ° 53′59 ″ E), carried out during the growing season 2014-2015. The method of gravimetric sampling was used (Durham’s pollen trap). Data was obtained on 22 taxa and the dynamics of dusting of various palynomorphs; the dominant taxa of palinospectrum were identified, as well as non-pollen palynomorphs in the air. It is shown that Betula pollen grains prevail in the atmosphere of the city. The pollen of woody plants occupies 80% of the total spectrum, and the pollen of herbaceous plants – 20%. Seasonal highs are recorded twice: in May and in late June – early July. This information must be taken into account when accompanying patients with hay fever.

Variability in the Betula pollen concentrations in the atmosphere of six urban areas in Slovakia in 2018

Betula pollen is one of the most important aeroallergens during the spring months in the central European countries. In 2018, pollen monitoring was conducted in six urban areas (Bratislava, Banská Bystrica, Košice, Nitra, Trnava, and Žilina) in Slovakia. Investigations were carried out using a volumetric Hirst-type pollen trap (Burkard). Betula pollen season timing was determined by the 90% method when the start and end of the season were defined as the date when 5% and 95%, respectively of the total pollen sum was reached. The pollen season start date was recorded earliest in Bratislava (April 8th) and latest in Banská Bystrica (April 12th). The highest both seasonal total pollen concentration (7,390 P/m3) and birch pollen allergen risk were found in Banská Bystrica. The shortest pollen season was recorded in Žilina (13 days) and the longest in Košice (25 days). Peak daily pollen concentrations ranged between 1,567 P/m3 in Žilina and 202 P/m3 in Košice.

Forecasting of Corylus, Alnus, and Betula pollen concentration in the air in Poland

Understanding of the behavior of atmospheric pollen concentration, as well as developing predictive models, can greatly help allergic sufferers. The aims of this study were (i) to determine mean multi-year characteristics of temporal and space–time autocorrelation of the pollen counts of Corylus, Alnus, and Betula in Poland, (ii) to create and evaluate Corylus, Alnus, and Betula pollen concentration levels predictions based on previous pollen count values from given sites, and (ii) to develop spatiotemporal predictive models of Corylus, Alnus, and Betula pollen concentration levels, using preprocessed gridded meteorological data. The monitoring of the concentrations of Corylus, Alnus, and Betula pollen in the air was conducted in 11 cities in Poland. Additionally, AGRI4CAST Interpolated Meteorological Data were used as predictor variables. The autocorrelation and cross-correlation functions were used to investigate temporal and spatial patterns. Random forest method was used to predict the high pollen concentration level of Corylus, Alnus, and Betula. The study provided an understanding of the temporal and spatiotemporal autocorrelation of Corylus, Alnus, and Betula pollen counts. The final models also proved to be capable of pollen levels predicting in continuous areas rather than in a single location.