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J Korean Med Assoc > Volume 52(6); 2009 > Article
Journal of the Korean Medical Association 2009;52(6):579-591.
DOI: https://doi.org/10.5124/jkma.2009.52.6.579   
Development of Pollen Concentration Prediction Models
Jae Won Oh
1Department of Pediatrics, Hanyang University College of Medicine, Korea.
2Korean Academy of Pediatric Allergy and Respiratory Diseases, Committee of Pollen Study, Korea. jaewonoh@hanyang.ac.kr
Air-borne pollen is known as one of the major causal agents to respiratory allergic reactions. The daily number of pollen grains was monitored using Burkard volumetric spore traps at eight locations including Seoul and Jeju during 1997-2005. Pollen grains were observed throughout the year especially from February to November. They showed similar distribution patterns of species among locations except Jeju, where Japanese cedar vegetation is uniquely found. The peak seasons for pollen grains from trees, grasses, and weeds were from March to May, May to September, and August to October. Tree pollens were mainly composed of pine, oak, alder, and birch. Weed pollens were mainly from Japanese hop, sagebrush, and ragweed. The diameter of pollen grains, which has a typical range of 20~60 micrometer, has close relationship with allergenicity. The allergenicity of trees and weed pollens is higher than that of grass pollens in general. Daily fluctuations in the amount of pollens have to do with a variety of meteorological factors such as temperature, rainfall, and the duration of sunshine. Temperature and rainfall are especially decisive in determining pollen concentrations. Ten weather elements that are thought to affect the concentration of pollens are used to develop equations for the pollen forecasts. Predictive equations for each pollen species and month are developed based on statistical analyses using observed data during the last 5 years in Seoul through a co-work with the Committee of Pollen Study in Korean Academy of Pediatric Allergy and Respiratory Diseases and National Institute of Meteorological Research.
Key Words: Allergy, Pollen, Prediction model


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