Forecasting of pollen dispersal of oak trees using the CMAQ system

Airborne pollens are important allergens for respiratory allergy and asthma (Sofiev et al., 2006). The concentration at the patients' level determines the severity of the symptom. NIMR and the Korea Meteorological Administration (KMA) provide daily pollen warnings based on statistical models that estimates the daily pollen concentration. There are several advanced systems for daily pollen forecast (Efstathiou et al., 2011). One of such systems forecasts the dynamics of pollen concentration based on the release, transport, and deposition of pollens. Technological development in numerical simulation models for airborne particles such as WRF-CMAQ enables us to predict the spatial distribution and concentration of the pollens (Oh et al., 2012). In this study, the operational 1.5 km resolution model (LDAPS: Local Data Assimilation and Prediction System) of the Korea Meteorological Administration (KMA) was adopted to POMO (POllen MOdel). It simulates the emission, transport, and deposition processes of the airborne pollens. The emission intensity of oak is estimated by the POMO for pollen concentration, which is a robust multiple regression model driven by air temperature, relative humidity, wind speed, rainfall, and day of year. Gridded emission is calculated at 1.5 km resolution by incorporating the spatial variability of the land cover of oak trees (vegetation cover at 1:5000 scale from the Forest Geospatial Information System (FGIS)). The released pollens from the emission process are removed by dry deposition of coarse particles during the transportation processes to produce the concentration of airborne pollens. Three Burkard traps were installed in and around Seoul in 2014 to observe daily and hourly pollen concentration. The hourly data were utilized in the development and optimization of pollen emission based on hourly meteorological conditions. It can be served as a component model in an integrated simulation system for air quality along with the ongoing improvement of the operational POMO.