[Seasonal Prediction Model for Airborne Pollen Content in Beijing Urban Area].

Airborne pollen is considered to be one of the air pollutants that can cause allergic reactions in humans, leading to the occurrence or aggravation of a series of allergic diseases. The latest study showed that the positive rate of pollen allergens in allergic rhinitis patients in urban areas of Beijing exceeded 80%. Accurate prediction of pollen content could provide more effective assistance to susceptible populations. Based on the measured data from multiple stations in the urban area of Beijing during the pollen season from 2021 to 2022, the spatiotemporal distribution characteristics of pollen content were analyzed. The results showed that the main meteorological factors affecting spring pollen content in the urban area of Beijing were daily average wind speed, 3-day average temperature, water vapor pressure, daily average, temperature, and accumulated temperature. The main meteorological factors affecting autumn pollen content were 3-day average temperature, water vapor pressure, minimum surface temperature, and daily average temperature. In addition, it was found that there was a consistent spatial correlation between the current air pollen content and meteorological elements in the urban area of Beijing, but this correlation had significant seasonal differences. Furthermore, the Granger causality test method was applied to select the main meteorological factors that affected airborne pollen content in the urban area of Beijing, and two prediction models for air pollen content in the Beijing urban area for different seasons were established based on the support vector machine method （SVM） and multiple linear regression theory. The test of the prediction results for 2023 showed that both the SVM model considering seasonal differences and the multiple linear regression model could predict the daily distribution trend of pollen content well. The overall correlation coefficients between the predicted pollen content and the measured values were 0.693 and 0.636 （