RESEARCH PAPER
Pollen grains as allergenic environmental factors – new approach to the forecasting of the pollen concentration during the season
| 1 | Department of Clinical and Environmental Allergology, Jagiellonian University Medical College, Cracow, Poland |
| 2 | Chair of Epidemiology and Preventive Medicine, Jagiellonian University Medical College, Cracow, Poland |
CORRESPONDING AUTHOR
Dorota Myszkowska
Department of Clinical and Environmental Allergology, Jagiellonian University Medical College, Cracow, Poland
Department of Clinical and Environmental Allergology, Jagiellonian University Medical College, Cracow, Poland
Ann Agric Environ Med. 2014;21(4):681–688
KEYWORDS
ABSTRACT
Introduction and objectives:
It is important to monitor the threat of allergenic pollen during the whole season, because of practical application in allergic rhinitis treatment, especially in the specific allergen immunotherapy. The aim of the study was to propose the forecast models predicting the pollen occurrence in the defined pollen concentration categories related to the patient exposure and symptom intensity.
Material and Methods:
The study was performed in Cracow (southern Poland), pollen data were collected using the volumetric method in 1991–2012. For all independent variables (meteorological elements) and the daily pollen concentrations the running mean for periods: 2-, 3-, 4-, 5-, 6- and 7 days before the predicted day were calculated. The multinomial logistic regression was used to find the relation between the probability of the pollen concentration occurrence in the selected categories and meteorological elements and pollen concentration in days preceding the predicted daily concentration. The models were constructed for each taxon using data in 1991–2011 (without 1992 and 1996 due to missing data in these years) and 1998–2011 pollen seasons.
Results:
The days classified among the lowest category (0–10 PG/m3) (pollen grains/m3 of air) dominated for all the studied taxa. The percentage of the obtained predictions of the pollen occurrence fluctuated between 35–78% which is a sufficient value of model predictions. Considering the studied taxon, the best model accuracy was obtained for models forecasting Betula pollen concentration (both data series), and Poaceae (both data series).
Conclusions:
The application of the recommended threshold values during the predictive models construction seems to be really useful to estimate the real threat of allergen exposure. It was indicated that the polynomial logistic regression models could be a practical tool for effective forecasting in biological monitoring of pollen exposure.
It is important to monitor the threat of allergenic pollen during the whole season, because of practical application in allergic rhinitis treatment, especially in the specific allergen immunotherapy. The aim of the study was to propose the forecast models predicting the pollen occurrence in the defined pollen concentration categories related to the patient exposure and symptom intensity.
Material and Methods:
The study was performed in Cracow (southern Poland), pollen data were collected using the volumetric method in 1991–2012. For all independent variables (meteorological elements) and the daily pollen concentrations the running mean for periods: 2-, 3-, 4-, 5-, 6- and 7 days before the predicted day were calculated. The multinomial logistic regression was used to find the relation between the probability of the pollen concentration occurrence in the selected categories and meteorological elements and pollen concentration in days preceding the predicted daily concentration. The models were constructed for each taxon using data in 1991–2011 (without 1992 and 1996 due to missing data in these years) and 1998–2011 pollen seasons.
Results:
The days classified among the lowest category (0–10 PG/m3) (pollen grains/m3 of air) dominated for all the studied taxa. The percentage of the obtained predictions of the pollen occurrence fluctuated between 35–78% which is a sufficient value of model predictions. Considering the studied taxon, the best model accuracy was obtained for models forecasting Betula pollen concentration (both data series), and Poaceae (both data series).
Conclusions:
The application of the recommended threshold values during the predictive models construction seems to be really useful to estimate the real threat of allergen exposure. It was indicated that the polynomial logistic regression models could be a practical tool for effective forecasting in biological monitoring of pollen exposure.
REFERENCES (34)
1.
Ring J, Krämer U, Schäfer T, Behrendt H. Why are allergies increasing? Current Opinion in Immunology 2001; 13: 701–708.
2.
D’Amato D, Cecchi L, Bonini S, Nunes C, Annesi-Maesano I, Behrendt H, Liccardi G, Popov T, van Cauwenberge P. Allergenic pollen and pollen allergy in Europe. Allergy 2007; 62: 976–990.
3.
Samoliński B, Sybilski AJ, Raciborski F, Tomaszewska A, SamelKowalik P, Walkiewicz A, Lusawa A, Borowicz J, Gutowska-Ślesik J, Trzpil L, et al. Prevalence of rhinitis in Polish population according to the ECAP (Epidemiology of Allergic Disorders in Poland) study. Otolaryngol Pol. 2009; 63: 324–330.
4.
Myszkowska D, Stępalska D, Obtułowicz K, Porębski G. The relationship between airborne pollen and fungal spore concentration and seasonal pollen allergy symptoms in Cracow in 1997–1999. Aerobiologia 2002; 18: 153–161.
5.
Frenz DA. Interpreting atmospheric pollen counts for use in clinical allergy: allergic symptomology. Ann Allergy Asthma Immunol. 2001; 86: 150–158.
6.
Canonica GW, Baena-Cagnani CE, Bousquet J, Bousquet PJ, Lockey RF, Malling HJ, Passalacqua G, Potter P, Valovirta E. Recommendations for standardization of clinical trials with Allergen Specific Immunotherapy for respiratory allergy. A statements of a World Allergy Organization (WAO) taskforce. Allergy 2007; 62: 317–324.
7.
Myszkowska D, Jenner B, Puc M, Stach A, Nowak M, Malkiewicz M, Chłopek K, Uruska A, Rapiejko P, Majkowska-Wojciechowska B, Weryszko-Chmielewska E, Piotrowska K, Kasprzyk I. Spatial variations in dynamics of Alnus and Corylus pollen seasons in Poland. Aerobiologia 2010; 26: 209–221.
8.
Laadi M. Predicting days of high allergenic risk during Betula pollination using weather types. Int J Biometeorol. 2001; 45: 124–132.
9.
Norris-Hill J. The modeling of daily Poaceae pollen concentrations. Grana 1995; 34: 182–188.
10.
Smith M, Emberlin J. Constructing a 7-dayahead forecast model for grass pollen at north London, United Kingdom. Clin Exp Allergy. 2005; 35: 1400–1406.
11.
Stach A, Smith M, Prieto Buena JC, Emberlin J. Long-term and shortterm forecast model for Poaceae (grass) pollen in Poznań, Poland, constructed using regression analysis. Environ Exp Bot. 2008; 62: 323–332.
12.
Piotrowska K, Kubik-Komar A. The effect of meteorological factors on air borne Betula pollen concentrations in Lublin (Poland). Aerobiologia 2012. doi: 10.1007/s10453–012–9249-z.
13.
Rodriguez-Rajo FJ, Valencia-Barrea RM, Vega-Maray AM, Suarez FJ, Fernandez-Gonzales D, Jato V. Prediction of airborne Alnus pollen concentration by using Arima models. Ann Agric Environ Med. 2006; 13: 25–32.
14.
Puc M. Artificial neural networks model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland). Int J Biometeorol. 2012; 56: 396–401.
15.
DellaValle CT, Triche EW, Bell ML. Spatial and temporal modeling of daily pollen concentrations, In J Biometeorol. 2012; 56: 183–194.
16.
Jantunen J, Saarinen K, Rantio-Lehtimäki A. Allergy symptoms in relation to alder and birch pollen concentrations in Finland. Aerobiologia. 2012; 28: 169–176.
17.
Viander M, Koivikko A. The seasonal symptoms of hyposensitized and untreated hay fever patients in relations to birch pollen counts: Correlations with nasal sensitivity, prick tests and RAST. Clinical Allergy. 1987; 8: 387–396.
18.
De Weger L, Bergmann K-Ch, Rantio-Lehtimäki A, Dahl A, Buters J, Déchamp Ch, Belmonte J, Galán C, Waisel Y. Impact of pollen. In: Sofiev M, Bergmann K-Ch. Allergenic pollen. Springer, Dordrecht, 2013.p.161–215.
19.
Frei T, Gassner E. Trends in prevalence of allergic rhinitis and correlation with pollen counts in Switzerland. Int J Biometeorol. 2008; 43: 191–195.
20.
Burr ML, Emberlin J, Treu R, Cheng S, Pearce NE. Pollen counts in relations to the prevalence of allergic rhinoconjunctivitis, asthma and atopic eczema in the International Study of asthmna and allergies in Childhood (ISAAC). Clin Exp Allergy. 2003; 33: 1675–1680.
21.
Samoliński B, Rapiejko P, Lipiec A, Kurzawa R. Metody ograniczenia narażenia na alergen. In: Kruszewski J, Kowalski ML. Standardy w alergologii. Część I. Medycyna praktyczna, Kraków 2010.p.143–149 (in Polish).
22.
Woś A. Klimat Polski. Państwowe Wydawnictwo Naukowe PWN, Warszawa, 1999 (in Polish).
23.
Stach A, Kasprzyk I. Metodyka badań zawartości pyłku roślin i zarodników grzybów w powietrzu z zastosowaniem aparatu Hirsta. Bogucki Wydawnictwo Naukowe, Poznań, 2005 (in Polish).
24.
Myszkowska D. Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series. Aerobiologia 2013; 29: 31–44.
26.
Jones AM, Harrison RM. The effect of meteorological factors on atmospheric bioaerosol concentrations – a review. Science of the Total Environment. 2004; 326: 151–180.
27.
Matyasovszky I, Makra L, Guba Z, Pátkai Z, Páldy A, Sümeghy Z. Estimating the daily Poaceae pollen concentration in Hungary by linear regression conditioning on weather types. Grana 2011; 50: 208–216.
28.
Norris-Hill J. The influence of ambient temperature on the abundance of Poaceae pollen. Aerobiologia 1997; 13: 91–97.
29.
Schäppi GF, Taylor PE, Kenrick J, Staff IA, Suphioglu C. Predicting the grass pollen count from meteorological data with regard to estimating the severity of hayfever symptoms in Melbourne (Australia). Aerobiologia 1998; 14: 29–37.
30.
Green BJ, Dettmann M, Yli-Panula E, Rutherford S, Simpson R. Atmospheric Poaceae pollen frequencies and associations with meteorological parameters in Brisbane, Australia: a 5-year record, 1994–1999. Int J Biometeorol. 2004; 48: 172–178.
31.
Aboulaich N, Achmakh L, Bouziane H, Mar Trigo M, Recio M, Kadiri M, Cabezudo B, Riadi H, Kazzaz M. Effect of meteorological parameters on Poaceae pollen in the atmosphere of Tetouan (NW Morocco). Int J Biometeorol. 2013; 57: 197–205.
32.
Mendez J, Comtois P, Iglesias I. Betula pollen: One of the most important aeroallergens in Ourense, Spain. Aerobiological studies from1993 to 2000. Aerobiologia. 2005; 21: 115–123.
33.
Rantio-Lehtimaki A, Koivikko A, Kupias R, Makinen Y, Pohjola A. Significance of sapling height of airborne particles for aerobiological information. Allergy 1991; 46: 68–76.
34.
Thibaudon M. Allergy risk associated with pollens in France. Eur Ann Allergy and Clinical Immunology. 2003; 35: 170–172.
RELATED ARTICLE
Improvement of visual identification of the journal - task financed under the agreement No. 618/P-DUN/2019 by the Minister of Science and Higher Education allocated to the activities of disseminating science.
Generation of the DOI (Digital Object Identifier) - task financed under the agreement No. 618/P-DUN/2019 by the Minister of Science and Higher
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.