Real-time pollen dynamics and automated detection: novel insights from Wrocław (Poland) 2024–2025

Abstract. Accurate monitoring and forecasting of airborne pollen are essential for public health and allergy management. This study evaluated a neural network model for real-time pollen monitoring using locally collected data in Wrocław, Poland (2024–2025) with the Swisens Poleno Jupiter detector. The retrained model, based on local data, outperformed the reference model trained on Swiss datasets and validated against a Hirst-type pollen trap. The coefficient of determination (R2) remained high (∼ 0.8) especially for Alnus, Betula and Quercus, while the root mean square error (RMSE) was lower, particularly at low and medium concentrations, showing improved sensitivity, real-time detection and better representation of seasonal and diurnal dynamics. Hourly analyses revealed distinct taxon-specific diurnal patterns in pollen release. Temperature and relative humidity were the main drivers of variability, while wind speed influenced all taxa except Pinus. Hourly pollen concentrations were positively correlated with planetary boundary layer height, especially for Betula and Alnus, highlighting the role of atmospheric mixing in pollen dispersion. Wind direction, particularly from southern and southeastern sectors, modulated local transport, reflecting land cover effects. Correlations with meteorological variables varied by month and flowering stage. Validation against Hirst-type data confirmed that the locally retrained model accurately captures taxon-specific pollen dynamics, demonstrating its effectiveness for real-time allergen monitoring and improving the reliability of allergy risk assessments.