Construction and application of a pollen emissions model based on phenology and random forests

Abstract. In recent years, the intensification of global climate change and environmental pollution has led to a marked increase in pollen-induced allergic diseases. This study leverages 16 years of continuous pollen monitoring data, alongside meteorological factors and plant functional type data, to construct a pollen emissions model using phenology and random forests (RFs). This model is then employed to simulate the emission characteristics of three primary types of autumn pollen (Artemisia, chenopods and total pollen concentration), elucidating the emission patterns throughout the seasonal cycle in Beijing. Phenology and RFs precisely simulate the start and end day of year of pollen, as well as the annual pollen production. There are significant spatiotemporal differences among the three types of pollen. On average, pollen dispersal begins around 10 August, peaks around 30 August and concludes by 25 September, with a dispersal period lasting approximately 45 d. Furthermore, the relationship between pollen emissions and meteorological factors is investigated, revealing that temperature, relative humidity (RH) and sunshine hours (SSHs) significantly influence annual pollen emissions. Specifically, temperature and RH exhibit a strong positive correlation with annual pollen emissions, while SSHs show a negative correlation. Different pollen types display varied responses to meteorological factors. Finally, the constructed pollen emissions model is integrated into the ICTP Regional Climate Model (RegCM) and validated using pollen observation data, confirming its reliability in predicting pollen concentrations. This study not only enhances the understanding of pollen release mechanisms but also provides scientific evidence for the selection and planting of urban greening plants.