A Multi-Model Approach to Pollen Season Estimations: Case Study for Olea and Quercus in Thessaloniki, Greece

The accurate prediction of the Main Pollen Season (MPS) is crucial for public health and environmental management, particularly for allergenic and highly abundant taxa such as Olea and Quercus. This study presents a comparative evaluation of multiple predictive models for estimating MPS in Thessaloniki, Greece, from 2016 to 2022. The models examined include cumulative temperature-based approaches, Logistic Models (LM), the Distribution Method (DM), and Machine Learning Techniques (MLTs) such as Random Forest, Neural Networks, and Ensemble Learning. The results indicate that Double-Threshold temperature-based (DT) and LM models effectively capture the end of the pollen season, with differences from observed values ranging from 0 to 7 days. Meanwhile MLTs, particularly Random Forest, exhibit high accuracy in predicting its onset of the season, with deviations ranging from 0 to 10 days. Notably, the DT approach, which incorporates transition ranges, enhances the prediction reliability in complex urban environments. These findings contribute to the development of more robust aerobiological forecasting systems, supporting allergen exposure mitigation strategies and agricultural planning in Mediterranean climates. Future research should focus on multifold cross-validation techniques and advanced deep learning models, such as LSTMs (Long Short-Term Memory models), to further refine the prediction accuracy. These advancements would enable the development of more accurate and generalized forecasting models, contributing into a broader modeling system capable of predicting daily pollen concentrations, further supporting real-time pollen forecasting efforts.