Pollen-induced allergies are among the most prevalent non-contagious diseases, with about a quarter of the European population being sensitive to various atmospheric bioaerosols. In most European countries, pollen information is based on a weekly-cycle Hirst-type pollen trap method. This method is labour-intensive and requires narrow specialized abilities and substantial time, so that the pollen data are always delayed and subject to sampling- and counting-related uncertainties. Emerging new approaches to automatic pollen monitoring can, in principle, allow for real-time availability of the data with no human involvement. The goal of the current paper is to evaluate the capabilities of the new Plair Rapid-E pollen monitor and to construct a first-level pollen recognition algorithm. The evaluation was performed for three devices located in Lithuania, Serbia and Switzerland, with independent calibration data and classification algorithms. The Rapid-E output data include multi-angle scattering images and the fluorescence spectra recorded at several times for each particle reaching the device. Both modalities of the Rapid-E output were treated with artificial neural networks (ANNs) and the results were combined to obtain the pollen type. For the first classification experiment, the monitor was challenged with a large variety of pollen types and the quality of many-to-many classification was evaluated. It was shown that in this case, both scattering- and fluorescence-based recognition algorithms fall short of acceptable quality. The combinations of these algorithms performed better, exceeding 80 % accuracy for 5 out of 11 species. Fluorescence spectra showed similarities among different species, ending up with three well-resolved groups: (Alnus, Corylus, Betula and Quercus), (Salix and Populus) and (Festuca, Artemisia and Juniperus). Within these groups, pollen is practically indistinguishable for the first-level recognition procedure. Construction of multistep algorithms with sequential discrimination of pollen inside each group seems to be one of the possible ways forward. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and the issue of false positive pollen detections by Rapid-E is discussed.

The paper considers the possibilities of modelling the release and dispersion of the pollen of common ragweed (Ambrosia artemisiifolia L.), a highly allergenic invasive weed, which is spreading through southern and central Europe. In order to provide timely warnings for the allergy sufferers, a model was developed for forecasting ragweed pollen concentrations in the air. The development was based on the system for integrated modelling of atmospheric composition (SILAM) and concentrated on spatio-temporal modelling of ragweed flowering season and pollen release, which constitutes the emission term.\ud \ud Evaluation of the new model against multi-annual ragweed pollen observations demonstrated that the model reproduces well the main ragweed pollen season in the areas with major plant presence, such as the Pannonian Plain, the Lyon area in France, the Milan region in Italy, Ukraine and southern Russia. The predicted start of the season is mostly within 3 days of the observed for the majority of stations in these areas. The temporal correlation between modelled and observed concentrations exceeds 0.6 for the bulk of the stations.\ud \ud Model application to the seasons of 2005–2011 indicated the regions with high ragweed pollen concentrations, in particular the areas where allergenic thresholds are exceeded. It is demonstrated that, due to long-range transport of pollen, high-concentration areas are substantially more extensive than the heavily infested territories