The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980–2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.

Grass pollen is one of the major causes of allergy. Aerobiological monitoring is a necessary element of the complex of anti-allergic measures, but the similar pollen morphology of Poaceae species makes it challenging to discriminate species in airborne pollen mixes, which impairs the quality of aerobiological monitoring. One of the solutions to this problem is the metabarcoding approach employing DNA barcodes for taxonomical identification of species in a mix by high-throughput sequencing of the pollen DNA. A diverse set of 14 grass species of different genera were selected to create a local reference database of nuclear ITS1, ITS2, 5′-ETS, and plastome trnL-F DNA barcodes. Sequences for the database were Sanger sequenced from live field and herbarium specimens and collected from GenBank. New Poaceae-specific primers for 5′-ETS were designed and tested to obtain a 5′-ETS region less than 600 bp long, suitable for high-throughput sequencing. The DNA extraction method for single-species pollen samples and mixes was optimized to increase the yield for amplification and sequencing of pollen DNA. Barcode sequences were analyzed and compared by the barcoding gap and intra- and interspecific distances. Their capability to correctly identify grass pollen was tested on artificial pollen mixes of various complexity. Metabarcoding analysis of the artificial pollen mixes showed that nuclear DNA barcodes ITS1, ITS2, and 5′-ETS proved to be more efficient than the plastome barcode in both amplification from pollen DNA and identification of grass species. Although the metabarcoding results were qualitatively congruent with the actual composition of the pollen mixes in most cases, the quantitative results based on read-counts did not match the actual ratio of pollen grains in the mixes.