의학강좌-오재원_컬러

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1 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): 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 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

2 Oh JW 580

3 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

4 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 , 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

5 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

6 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 B D F H 584

7 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 WIND AS May AS MeanT Accum T Apr AccumT PRE AS MeanT May MeanT RT HUM Sep AccumT AS DR PRE Weeds Oct AccumT MinT RT 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 Precipitation E Wind Humidity MaxT MinT Daily range Raintime Sunshine AccumT Cluster Count 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 Precipitation Wind Humidity MaxT MinT Daily range Raintime Sunshine AccumT Cluster Count

8 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

9 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

10 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 Precipitation Wind Humidity MaxT MinT Daily range Raintime Sunshine AccumT Cluster Count Table 10. Results from Discriminant analyses for pine pollen counts Count (%) Cluster group Predicted group Total 1 17 (94.4) 1 (5.6) (4.2) 23 (95.8) (100) (100) 12 Table 11. Results from Discriminant analyses for tree pollen counts except pine Cluster group Predicted group Total 1 12 (92.3) 1 (7.7) 0 13 Count (%) (100) (3.8) 25 (96.2) 26 Table 12. Results from Discriminant analyses for weed pollen counts Cluster group Predicted group Total 1 32 (97.0) 0 1 (3.0) 33 Count (%) (100) (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 AS RT HUM WIND MeanT AccumT DR Pine RT PRE AccumT MeanT WIND AS RT AS WIND Tree except AccumT PRE MaxT pine HUM PRE AS RT MaxT AccumT AccumT MeanT MaxT Weed AccumT MeanT MeanT MaxT

11 Table 14. Observed and predicted daily allergenicity by pine pollen counts for each cluster group in 2002~2004 Cluster 1 Cluster 1 very low low high very high Cluster 3 Cluster 4 very low low high Table 15. Observed and predicted daily allergenicity by tree pollen counts except pine for each cluster group in 2002~2004 Cluster 1 very low low high Cluster 2 very low low high Cluster 3 very low low high Table 16. Observed and predicted daily allergenicity by weed pollen counts for each cluster group in 2002~2004 Cluster 1 very low low high Cluster 2 very low low high Cluster 3 very low low high

12 Oh JW Table 17. Validation results of the daily allergenicity models for pine pollen counts in 2005 very low low high very high Table 19. Validation results of the daily allergenicity models for weed pollen counts in 2005 very low low high Table 18. Validation results of the daily allergenicity models for tree pollen counts except pine in 2005 very low low high Lewis WH, Vinay P, Zenger VE. Airborne and allergenic pollen of North America. The Johns Hopkins University Press, Baltimore & London, Esch RE, Bush RK. Aerobiology of outdoor allergens. In Adkinson NF Jr, Yunginger JW, Busse WW, Bochner BS, Holgate ST, Simons FER, Middleton s allergy princiles and practice. 6th ed. St. Louis: Mosby, 2003: Taylor G, Walker J, Backley CH : A detailed description of the astonishing achievement of Backley in describing the causes of hay fever. Clin Allergy 1973; 3: Lewis W, Imber W. Allergy epidemiology in the St. Louis, 590

13 Missouri Area II, grasses. Ann Allergy 1975; 35: Anderson JH. Allergenic airborne pollen and spores in Anchorage, Alaska, Ann Allergy 1985; 54: Potter PC, Cadman A. Pollen allergy in South Africa. Clin Exp Allergy 1996; 26: Esch RE, Bush RK. Aerobiology of outdoor allergens. In Adkinson NF Jr, Yunginger JW, Busse WW, Bochner BS, Holgate ST, Simons FER, Middleton s allergy princiles and practice. 6th ed. St. Louis: Mosby, 2003: Solomon WR, Weber RW, Dolen WK. Common allergenic pollen and fungi. Bierman CW, Pearlman DS, Shapiro GG, Busse WW. Allergy, asthma and immunology from infancy to adulthood. 3rd ed, Philadelphia, WB Saunders, 1996: Oh JW. Characteristics and distribution of airborne pollen and mold. J Pediatr Allergy Respir Dis 1998; 8: Oh JW, Lee HL, Kim JS, Lee KI, Kang IJ, Kim SW, HB Lee. Aerobiological study of pollen and mold in the 10 states of Korea. Pediatr Allergy Respir Dis (Korea) 2000; 10: Oh JW, Pyun BY, Choung JT, Ahn KM, Kim CH, Song SW, Son JA, Lee SY, Lee SI. Epidemiological change of atopic dermatitis and food allergy in school-aged children in Korea between 1995 and J Korean Med Sci 2004; 19: Vázquez L M, Galán C, Domínguez-Vilches E. Influence of meteorological parameters on olea pollen concentrations in Cordoba (South-Western Spain), Int J Biometeorol, 2003; 48: Emberlin J, Savage M, Jones S. Annual variations in grass pollen seasons in London : trends and forecast models. Clinic Exp Allergy 1993; 23: Frenguelli G, Bricchi E. The use of phenoclimatic model for forecasting the pollination of some arboreal taxa, Aero-biologia 1998; 14: Galán C, Cari-anos P, García-Mozo H, Alcázar P, Domínguez- Vilches E. A model for forecasting Olea europaea L. airborne pollen in the South-West Andalucia, Spain, Int J Biometeorol 2001; 45: Garchia-Mozo H, Galán C, Gomez-Casero MT, Domínguez- Vilches E, 2000: A comparative study of different temperature accumulation methods for predicting the start of the Quercus pollen season in Córdoba (South West Spain), Grana 2000; 39: Smith M, Emberlin J. 2006: A 30-day-ahead forecast model for grass pollen in north London, United Kingdom. Int J Biometeorol 2006; 50: Beggs PJ. Impacts of climate change on aeroallergens: past and future. Clin Exp Allergy 2004; 34: Ziska LH, Gebhard DE, Frenz DA, Faulkner S, Singer BD, Straka JG. Cities as harbingers of climate change: common ragweed, urbanization, and public health. J Allergy Clin Immunol 2003; 111: Wayne P, Foster S, Conolly J, Bazzaz, Epstein P: Production of allergenic pollen by ragweed (Ambrosia artemisiifolia L.) is increased in CO2- enriched atmospheres. Ann Allergy Asthma Immunol 2002; 88: Peer Reviewers Commentary 591

03이경미(237~248)ok

03이경미(237~248)ok The recent (2001-2010) changes on temperature and precipitation related to normals (1971-2000) in Korea* Kyoungmi Lee** Hee-Jeong Baek*** ChunHo Cho**** Won-Tae Kwon*****. 61 (1971~2000) 10 (2001~2010).