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This study compares phenological observations of Corylus (hazel) and Alnus (alder) flowering with airborne pollen counts of these taxa recorded using volumetric spore traps (2009–2011). The work was carried out in the Polish cities of Szczecin and Rzeszów that are located in different climatic regions. Correlations between pollen concentrations and meteorological data were investigated using Spearman’s rank correlation analysis. The timings of hazel and alder pollination and the occurrence of airborne pollen varied greatly and were significantly influenced by meteorological conditions (p < 0.05). The flowering synchronization of hazel and alder pollination in Szczecin and Rzeszów varied over the study period. Hazel and alder trees flowered notably earlier in stands located in places that were exposed to sunlight (insolated) and sheltered from the wind. On the other hand, a delay in the timing of pollination was observed in quite sunny but very windy sites. In Rzeszów, maximum hazel pollen concentrations did not coincide with the period of full pollination (defined as between 25 % hazel and alder and 75 % of flowers open). Conversely, in Szczecin, the highest hazel pollen concentrations were recorded during phenophases of the full pollination period. The period when the highest alder pollen concentrations were recorded varied between sites, with Rzeszów recording the highest concentrations at the beginning of pollination and Szczecin recording alder pollen throughout the full pollination period. Substantial amounts of hazel and alder pollen grains were recorded in the air of Rzeszów (but not Szczecin) before the onset of the respective pollen seasons.

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The objective of this study was to analyse the dynamics of the Alnus and Corylus pollen seasons in Poland with reference to spatial and seasonal differentiation. Aerobiological monitoring was performed in 10 cities, in 1994-2007. Five characteristics defining the pollen season were considered: 1. beginning and end dates of the season phases (5, 25, 50, 75, 95% of annual totals), 2. pollen season duration (90% method), 3. skewness and 4. kurtosis of airborne pollen curves, and 5. annual pollen totals. The beginning of the Corylus pollen season in Warsaw started on the 53rd day of a year. The Alnus pollen season started 9.5 days (SE = 1.4) later. The start of the season for both taxa was delayed by 3.3 (SE = 0.5) days for each 100 km towards the east. The Corylus pollen season lasted about 15 days longer than the Alnus season. Season duration for both taxa decreased towards the east by 3.5 days (SE = 0.7) and towards the north by 1.3 days (SE = 0.6) for each 100 km. Seasonal dynamics of both taxa are skewed to the right. In cities located west of Warsaw the dynamics are more skewed (except at Szczecin, Wroclaw). Asymmetry decreases towards the east by 0.16/100 km. Almost all kurtosis values of pollen-season dynamics were positive and higher for Alnus. Kurtosis values for both taxa increase together with delay of the pollen season beginning by 4% per day (p \ 0.0001). Mean pollen total increases: for Corylus mainly towards the north (by 64%/100 km), for Alnus mainly towards the west (by 15%/100 km). Geographical location (longitude and latitude) determines: the start and duration of the pollen season, skewness of the pollen curve, and annual totals.

The aim of the study was to determine the characteristics of temporal and space–time autocorrelation of pollen counts of Alnus, Betula, and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001–2005 and 2009–2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus, Betula, and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30–40 % of pollen count variation); (2) long-lasting factors (50–60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models.