Abstract. 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.

Abstract. Pollen-induced allergy is among the most-prevalent non-contagious diseases, with about a quarter of European population 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, requires narrow specialization abilities and substantial time, so that the pollen data are always delayed, 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 the 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 (ANN) 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, Juniperus). Within these groups, pollen is practically non-distinguishable for the first-level recognition procedure. Construction of multi-steps algorithms with sequential discrimination of pollen inside each group seems to be one of possible ways forwards. In order to connect the classification experiment to existing technology, a short comparison with the Hirst measurements is presented and an issue of the false-positive pollen detections by Rapid-E is discussed.

The modification of the proportional–integral (PI) controller with the variable proportional constant for tracking of the grid-connected photovoltaic microinverter output current has been proposed. The obtained results show that in the case when the proportional constant of the PI controller varies in time according to the appropriate law, the microinverter output current sinus shape distortions decrease as compared to the case when the ordinary PI controller is used. The operation of the microinverter with the proposed controller was investigated for the cases when the electrical grid voltage sinus shape is not distorted and when it is distorted by the higher harmonics.

Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors.