Continuing Education Column Jae -Won Oh, MD Departments of Pediatrics, Hanyang University College of Medicine Korean Academy of Pediatric Allergy and Respiratory Diseases, Committee of Pollen Study E - mail : jaewonoh@hanyang.ac.kr J Korean Med Assoc 2009; 52(6): 579-591 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, 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 Abstract 579
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A B Figure 1. Monthly distribution of pollen counts: (A) all, (B) trees, (C) grasses, and (D) weeds. C D Figure 2. Distribution of pollen counts of individual trees and weeds species (1998~2002). 581
Oh JW Figure 3. Distribution of daily pollen counts according to temperature and precipitation in Seoul (1997~2002). Table 1. Variation of pollen counts depending on meteorological factorsdiagnostic criteria for Attention-Deficit / Hyperactivity Disorder Date Pollen count Temperature Precipitation Windspeed 5.10 112 14.9 10.5 2.6 5.11 716 17.3 0 2.0 5.12 164 16.9 13.5 1.5 5.13 2,412 15.9 1.5 2.9 5.14 548 19.7 0 1.8 5.15 80 15.9 0.5 1.8 Table 2. Risk index of allergenicity for pollen counts from American pollen network of American Academy of Asthma, Allergy and Clinical Immunology Species Pollen count (grains/m 3 ) Allergenicity Trees 0~14 very low 15~99 low 100~499 high > 500 very high Grasses 0~4 very low 5 ~ 9 low 10~199 high > 200 very high Weeds 0 ~9 very low 10~49 low 50~299 high > 300 very high 582
Table 3. Meteorological factors used in regression analyses for pine pollen counts Month Year Meteorological factor 2002 AccumT*, PRE Apr 2003 DR*, RT*, HUM*, AccumT, PRE 2005 RT, WIND, HUM, DR, PRE 2002 AccumT, AS, MeanT*, MaxT*, DR May 2003 MeanT*, WIND* 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 2004 AS, PRE, HUM, MeanT*, AccumT, RT*, MaxT, MinT 2005 AccumT*, HUM*, MeanT Table 4. Meteorological factors used in regression analyses for tree pollen counts except pine Month Year Meteorological factor 2002 PRE*, HUM*, AccumT 2003 DR, AS Apr 2004 MinT*, AccumT*, HUM, RT, WIND 2005 RT, AS, MinT*, AccumT, WIND, PRE, MaxT 2002 RT*, AccumT*, PRE, AS May 2003 DR, WIND*, MaxT 2004 AS*, PRE 2005 AccumT* Table 5. Meteorological factors used in regression analyses for weed pollen counts Month Year Meteorological factor 1997 AccumT*, HUM*, MaxT 2002 AccumT*, AS Sep 2003 MeaT, AccumT, PRE, WIND, MaxT, RT, HUM, AS 2004 MeanT, AS*, RT*, AccumT 1997 AccumT*, WIND*, AS 2002 AccumT*, RT, MinT, AS Oct 2003 RT*, AS, PRE 2004 DR, WIND 583
Oh JW A C E G 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. B D F H 584
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 Pollen Regression model R 2 P Pine Trees except pine Apr. 1.609848-0.328230 WIND + 0.001628 AS 0.474 0.02 May 1.577870-0.013258 AS + 0.117365 MeanT - 0.001257 Accum T 0.495 0.00 Apr. 0.494386 + 0.002296 AccumT - 0.009812 PRE - 0.012852 AS 0.693 0.00 + 0.047051 MeanT May 0.427347 + 0.042282 MeanT - 0.020994 RT + 0.005922 HUM 0.439 0.01 Sep. 3.105090-0.000521 AccumT - 0.011980 AS + 0.036886 DR 0.450 0.09 + 0.025268 PRE Weeds Oct. 5.419920-0.001308 AccumT + 0.023948 MinT - 0.044073 RT 0.743 0.00 + 0.008469 HUM 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 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Variables Mean Std. Mean Std. Mean Std. Mean Std. MeanT 17.7000 1.3996 20.5958 1.4710 14.6143 1.7865 17.5083 1.5347 Precipitation.5000 1.5435 2.083E- 02.1021 10.8571 7.0399 2.9167 5.0083 Wind 2.0389.3928 2.0458.5741 2.2571.8304 2.9583.9568 Humidity 58.2778 12.7994 52.4833 6.9805 86.7857 6.3017 74.4083 5.9560 MaxT 23.6889 1.9816 26.4792 1.6785 16.6000 2.4886 22.0583 2.4422 MinT 12.1056 1.4538 15.1625 1.3051 12.4000 1.6462 14.3083 1.1673 Daily range 11.5833 2.1236 11.3167 1.5319 4.2000 1.9807 7.7500 1.6545 Raintime 1.0006 2.5152.1104.5409 14.4671 4.6982 4.3833 4.1030 Sunshine 35.8944 9.5453 42.8458 15.3968 29.8143 8.7152 29.0750 12.7911 AccumT 838.1333 103.7194 1064.4083 131.9240 844.9286 132.7706 982.7250 154.7553 Cluster Count 18 24 7 12 Table 8. Clusters for daily tree pollen counts except pine observed in May based on cluster analyses Cluster 1 Cluster 2 Cluster 3 Variables Mean Std. Mean Std. Mean Std. MeanT 15.3615 1.7419 17.8826 1.2242 20.6654 1.4792 Precipitation 8.4231 7.0233.2174.7359.2308.8274 Wind 2.4692.8004 2.0913.3813 2.2462.9100 Humidity 80.8077 10.1861 60.6391 12.4727 54.8308 9.9201 MaxT 18.1385 2.7391 23.7348 1.6945 26.3654 1.6169 MinT 12.8385 1.5284 12.6304 1.5426 15.5654 1.7722 Daily range 5.3000 2.1339 11.1043 2.2237 10.8000 2.2514 Raintime 10.8885 5.7627.6157 1.6733.7623 2.2060 Sunshine 27.1769 7.8083 33.5435 10.0699 43.5846 15.1210 AccumT 866.0231 106.0416 872.9435 115.5075 1082.8885 142.3718 Cluster Count 13 23 26 585
Oh JW A B C Figure 5. Distribution of allergenicity for (A) trees, (B) grasses, and (C) weeds based on daily observed pollen counts in Seoul (1997~2002). 586
Figure 6. Observed (blue) and predicted (pink) pine pollen counts in Seoul (A: April and B: May 2005). A B A B Figure 7. Observed (blue) and predicted (pink) tree except pine pollen counts in Seoul (A: April and B: May 2005). Figure 8. Observed (blue) and predicted (pink) weed pollen counts in Seoul (A: September and B: October 2004). A B 587
Oh JW Table 9. Clusters for daily tree pollen counts except pine observed in May based on cluster analyses Cluster 1 Cluster 2 Cluster 3 Variables Mean Std. Mean Std. Mean Std. MeanT 20.2788 1.1900 18.4000 1.5281 23.3710 1.3382 Precipitation 1.7758 4.5488 35.7000 18.8600 3.5484 9.5659 Wind 1.5424.4437 2.3600 1.8257 1.9548.8710 Humidity 65.4485 8.2360 86.0800 2.4386 70.7677 10.1954 MaxT 24.9758 1.8989 21.8200 1.2911 27.6000 1.9107 MinT 16.1515 1.3196 16.0200 1.0208 19.8355 1.6082 Daily range 8.8242 2.3137 5.8000.8888 7.7645 2.0086 Raintime.7461 1.4812 15.7420 2.9838 1.9335 2.7937 Sunshine 35.8152 9.9977 23.5000 14.0005 22.3355 13.5783 AccumT 3956.4152 108.2692 3630.5400 191.7676 3624.1032 126.4101 Cluster Count 33 5 31 Table 10. Results from Discriminant analyses for pine pollen counts Count (%) Cluster group Predicted group 1 2 3 4 Total 1 17 (94.4) 1 (5.6) 0 0 18 2 1 (4.2) 23 (95.8) 0 0 24 3 0 0 7 (100) 0 7 4 0 0 0 12 (100) 12 Table 11. Results from Discriminant analyses for tree pollen counts except pine Cluster group Predicted group 1 2 3 Total 1 12 (92.3) 1 (7.7) 0 13 Count (%) 2 0 23 (100) 0 23 3 0 1 (3.8) 25 (96.2) 26 Table 12. Results from Discriminant analyses for weed pollen counts Cluster group Predicted group 1 2 3 Total 1 32 (97.0) 0 1 (3.0) 33 Count (%) 2 0 5 (100) 0 5 3 0 0 31 (100) 31 Table 13. Daily allergenicity models for pine and the other trees in May and for weeds in September Pollen Cluster Regression model R 2 P 1 1.925318-0.008858 AS+0.069458 RT + 0.022064 HUM - 0.450859 WIND 0.61 0.01 2-0.894559 + 0.314224 MeanT - 0.002260 AccumT - 0.083394 DR 0.69 0.00 Pine 3 1.982743-0.103875 RT + 0.077967 PRE+0.002173 AccumT - 0.077289 MeanT - 0.078673 WIND 0.99 0.03 4 2.938907-0.033750 AS 0.38 0.03 1 3.734477-0.039974 RT - 0.032431 AS + 0.074371 WIND - 0.000864 Tree except AccumT + 0.022925 PRE - 0.037523 MaxT 0.78 0.13 pine 2 1.544558 + 0.005103 HUM - 0.209519 PRE - 0.010390 AS + 0.0056975 RT 0.45 0.04 3-0.031770 + 0.102436 MaxT - 0.000941 AccumT 0.50 0.00 1 4.668491-0.001345 AccumT + 0.215592 MeanT - 0.094537 MaxT 0.38 0.01 Weed 2-8.12693 + 0.00217 AccumT + 0.07439 MeanT 0.99 0.10 3 4.619065-0.213817 MeanT + 0.076789 MaxT 0.38 0.00 588
Table 14. Observed and predicted daily allergenicity by pine pollen counts for each cluster group in 2002~2004 Cluster 1 Cluster 1 very low 0 1 0 0 0 1 1 0 low 0 4 3 0 1 5 3 0 high 0 0 8 0 0 1 5 1 very high 0 0 2 0 0 0 1 5 Cluster 3 Cluster 4 very low 0 0 0 0 1 0 0 0 low 0 3 0 0 0 2 3 0 high 0 1 3 0 0 2 2 0 0 0 2 0 Table 15. Observed and predicted daily allergenicity by tree pollen counts except pine for each cluster group in 2002~2004 Cluster 1 very low 3 1 0 0 low 1 7 0 0 high 0 1 0 0 Cluster 2 very low 1 5 0 0 low 0 17 0 0 high 0 0 0 0 Cluster 3 very low 0 4 0 0 low 0 17 0 0 high 0 3 2 0 Table 16. Observed and predicted daily allergenicity by weed pollen counts for each cluster group in 2002~2004 Cluster 1 very low 1 11 0 0 low 0 14 1 0 high 0 5 1 0 Cluster 2 very low 2 0 0 0 low 0 3 0 0 high 0 0 0 0 Cluster 3 very low 0 2 3 0 low 0 4 5 0 high 0 4 13 0 589
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