Flow cytometry and machine learning enable identification of allergenic urban tree pollen

Abstract. Exposure to allergenic pollen is a major public health concern, as it is a key trigger for respiratory allergies, including seasonal allergic rhinitis, which affects approximately 20 % of the global population. Monitoring airborne pollen is essential for prevention and clinical management, yet traditional identification methods, such as light microscopy, are time-consuming and often limited to genus- or family-level resolution. Here, we present a high-throughput approach combining flow cytometry with machine learning to identify pollen from urban environments. We collected a reference database of pollen from 97 species across 34 genera, representing the dominant allergenic trees and other common airborne taxa in Montreal, Canada. Using flow cytometry, we measured particle size, granularity, and fluorescence intensity across multiple excitation and emission channels, and applied a Random Forest classifier to distinguish pollen taxa. At the species level, the model achieved a mean F1-score of 0.76, while genus-level classification reached 0.90, with misclassifications largely occurring among closely related species. Granularity and fluorescence parameters from the violet and blue lasers were the most distinctive features. Our results demonstrate that flow cytometry combined with machine learning provides an efficient, scalable alternative to microscopy, with potential for large-scale urban pollen monitoring.