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Development of Pollen Concentration Prediction Models

Article information

J Korean Med Assoc. 2009;52(6):579-591
Publication date (electronic) : 2009 June 30
doi : https://doi.org/10.5124/jkma.2009.52.6.579
Jae-Won Oh, MD,
Department of Pediatrics, Hanyang University College of Medicine, Korea.
Korean Academy of Pediatric Allergy and Respiratory Diseases, Committee of Pollen Study, Korea. jaewonoh@hanyang.ac.kr

Abstract

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 µm, 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.

Keywords: Allergy; Pollen; Prediction model

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Copyright © 2009 Korean Medical Association

Figure 1

Figure 1

Monthly distribution of pollen counts: (A) all, (B) trees, (C) grasses, and (D) weeds.

Figure 2

Figure 2

Distribution of pollen counts of individual trees and weeds species (1998~2002).

Figure 3

Figure 3

Distribution of daily pollen counts according to temperature and precipitation in Seoul (1997~2002).

Figure 4

Figure 4

Surface weather chart at (A) 00UTC, (B) 03UTC, (C) 06UTC, (D) 09UTC, (E) 12UTC, (F) 15UTC, (G) 18UTC and (H) 21UTC 13 May 2004.

Figure 5

Figure 5

Distribution of allergenicity for (A) trees, (B) grasses, and (C) weeds based on daily observed pollen counts in Seoul (1997~2002).

Figure 6

Figure 6

Observed (blue) and predicted (pink) pine pollen counts in Seoul (A: April and B: May 2005).

Figure 7

Figure 7

Observed (blue) and predicted (pink) tree except pine pollen counts in Seoul (A: April and B: May 2005).

Figure 8

Figure 8

Observed (blue) and predicted (pink) weed pollen counts in Seoul (A: September and B: October 2004).

Table 1

Variation of pollen counts depending on meteorological factors

Table 1

Table 2

Risk index of allergenicity for pollen counts from American pollen network of American Academy of Asthma, Allergy and Clinical Immunology

Table 2

Table 3

Meteorological factors used in regression analyses for pine pollen counts

Table 3

Variables are MeanT: daily mean temperature, PRE: daily rainfall, WIND: average wind speed, HUM: daily relative humidity, MaxT: daily maximum temperature, MinT: daily minimum temperature, DR: daily temperature range, RT: continued rainfall hours, AS: accumulated sunshine hours, and AccumT: accumulated mean temperature *: significant at 95% confidence interval

Table 4

Meteorological factors used in regression analyses for tree pollen counts except pine

Table 4

Table 5

Meteorological factors used in regression analyses for weed pollen counts

Table 5

Table 6

Regression models for daily pollen counts of the trees (pine and except pine) in April and May, and weeds in September and October

Table 6

Variables are MeanT: daily mean temperature, PRE: daily rainfall, WIND: average wind speed, HUM: daily relative humidity, MaxT: daily maximum temperature, MinT: daily minimum temperature, DR: daily temperature range, RT: continued rainfall hours, AS: accumulated sunshine hours, and AccumT: accumulated mean temperature

Table 7

Clusters for daily pine pollen counts observed in May based on cluster analyses

Table 7

Table 8

Clusters for daily tree pollen counts except pine observed in May based on cluster analyses

Table 8

Table 9

Clusters for daily tree pollen counts except pine observed in May based on cluster analyses

Table 9

Table 10

Results from Discriminant analyses for pine pollen counts

Table 10

Table 11

Results from Discriminant analyses for tree pollen counts except pine

Table 11

Table 12

Results from Discriminant analyses for weed pollen counts

Table 12

Table 13

Daily allergenicity models for pine and the other trees in May and for weeds in September

Table 13

Table 14

Observed and predicted daily allergenicity by pine pollen counts for each cluster group in 2002~2004

Table 14

Table 15

Observed and predicted daily allergenicity by tree pollen counts except pine for each cluster group in 2002~2004

Table 15

Table 16

Observed and predicted daily allergenicity by weed pollen counts for each cluster group in 2002~2004

Table 16

Table 17

Validation results of the daily allergenicity models for pine pollen counts in 2005

Table 17

Table 18

Validation results of the daily allergenicity models for tree pollen counts except pine in 2005

Table 18

Table 19

Validation results of the daily allergenicity models for weed pollen counts in 2005

Table 19