Advertisement

Aerobiologia

, Volume 27, Issue 3, pp 247–259 | Cite as

Assessment of the daily ragweed pollen concentration with previous-day meteorological variables using regression and quantile regression analysis for Szeged, Hungary

  • László MakraEmail author
  • István Matyasovszky
Original Paper

Abstract

Time-varying parametric linear and time-varying nonparametric regression models as well as a time-varying nonparametric median regression model are developed to predict the daily pollen concentration for Szeged in Hungary using previous-day meteorological parameters and the daily pollen concentration. The models are applied to rainy days and non-rainy days, respectively. The most important predictor is the previous-day pollen concentration level, and the only other predictor retained by a stepwise regression procedure is the daily mean global solar flux for rainy days and the daily mean temperature for non-rainy days. Although the variance percentage explained by these two predictors is higher for non-rainy (55.2%) days than for rainy (51.9%) days, the prediction rate is slightly better for rainy than for non-rainy days. Nonparametric regression yields substantially better estimates, especially for rainy days indicating a nonlinear relationship between the predictors and the pollen concentration. The explained variance percentage is 71.4 and 64.6% for rainy and non-rainy days, respectively. Concerning the mean absolute error, the nonparametric median regression provides the best estimate. The quantile regression shows that probability distribution of daily ragweed concentration is much more skewed for non-rainy days, while the more concentrated probability distribution for rainy days exhibits relatively stable ragweed pollen concentrations. The possible lowest limits of concentrations are also calculated. Under highly favorable conditions for peak concentrations, the pollen level reaches at least 350 grains m−3 and 450 grains m−3 for rainy and non-rainy days, respectively. These values again underline the excessive ragweed pollen load over the area of Szeged.

Keywords

Time-varying linear regression Nonparametric regression Median regression Quantile regression Rainy days Non-rainy days 

Notes

Acknowledgments

The authors would like to thank Miklós Juhász for providing pollen data of Szeged, and Zoltán Sümeghy for the digital mapping in Fig. 1. The European Union and the European Social Fund have provided financial support to the project under the grant agreement no. TÁMOP 4.2.1./B-09/1/KMR-2010-0003.

References

  1. Angosto, J. M., Moreno-Grau, S., Bayo, J., & Elvira-Rendueles, B. (2005). Multiple regression models for predicting total daily pollen concentration in Cartagena. Grana, 44, 108–114.CrossRefGoogle Scholar
  2. Asero, R. (2002). Birch and ragweed pollinosis north of Milan: A model to investigate the effects of exposure to “new” airborne allergens. Allergy, 57, 1063–1066.CrossRefGoogle Scholar
  3. Asero, R., Wopfner, N., Gruber, P., Gadermaier, G., & Ferreira, F. (2006). Artemisia and Ambrosia hypersensitivity: Co-sensitization or co-recognition? Clinical and Experimental Allergy, 36, 658–665.CrossRefGoogle Scholar
  4. Aznarte, J. L., Sánchez, J. M. B., Lugilde, D. N., Fernández, C. D. L., de la Guardia, C. D., & Sánchez, F. A. (2007). Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications, 32, 1218–1225.CrossRefGoogle Scholar
  5. Béres, I., Novák, R., Hoffmanné Pathy, Zs., & Kazinczi G. (2005). Distribution, morphology, biology, importance and weed control of common ragweed (Ambrosia artemisiifolia L.). [Az ürömlevelű parlagfű (Ambrosia artemisiifolia L.) elterjedése, morfológiája, biológiája, jelentősége és a védekezés lehetőségei.] Gyomnövények, Gyomirtás, 6, 1–48. (in Hungarian).Google Scholar
  6. Bousquet, J., Van Cauwenberge, P., Khaltaev, N., Ait-Khaled, N., Annesi-Maesano, I., Baena-Cagnani, C., et al. (2001). Allergic rhinitis and its impact on asthma. Journal of Allergy and Clinical Immunology, 108, S147–S334.CrossRefGoogle Scholar
  7. Cai, Z. (2007). Trending time-varying coefficient time series models with serially correlated errors. Journal of Econometrics, 136, 163–188.CrossRefGoogle Scholar
  8. Cecchi, L., Lorenzo, C., Morabito, M., Marco, M., Domeneghetti, M. P., Paola, D. M., et al. (2006). Long distance transport of ragweed pollen as a potential cause of allergy in central Italy. Annals of Allergy, Asthma & Immunology, 96, 86–91.CrossRefGoogle Scholar
  9. Cecchi, L., Malaspina, T., Albertini, R., Zanca, M., Ridolo, E., Usberti, I., et al. (2007). The contribution of long-distance transport to the presence of Ambrosia pollen in central northern Italy. Aerobiologia, 23, 145–151.CrossRefGoogle Scholar
  10. Chernozhukov, V. (2005). Extremal quantile regression. Annals of Statistics, 3, 806–839.CrossRefGoogle Scholar
  11. Comtois, P. (1998). Ragweed (Ambrosia sp.): The Phoenix of allergophytes. In: F. Th. M. Spieksma (Ed.), Ragweed in Europe. Satellite symposium proceedings of 6th international congress on Aerobiology, Perugia, (pp. 3–5). Horsholm DK: Alk—Abello′A/S.Google Scholar
  12. de Visiani, R. (1842), Flora Dalmatica. Vol. II.Google Scholar
  13. Draper, N., & Smith, H. (1981). Applied regression analysis (2nd ed.). New York: Wiley.Google Scholar
  14. Fan, J. (1992). Design-adaptive nonparametric regression. Journal of the American Statistical Association, 87, 998–1004.CrossRefGoogle Scholar
  15. Fan, J., & Yao, Q. (2005). Nonlinear time series: Nonparametric and parametric methods. New York: Springer.Google Scholar
  16. Fornaciari, M., Bricchi, E., Greco, F., Fascini, D., Giannoni, C., Frenguelli, G., et al. (1992). Daily variations of Urticaceae pollen count and influence of meteoclimatic parameters in East Perugia during 1989. Aerobiologia, 8, 407–413.CrossRefGoogle Scholar
  17. Fornaciari, M., Pieroni, L., Orlandi, F., & Romano, B. (2002). A new approach to consider the pollen variable in forecasting yield models. Economic Botany, 56, 66–72.CrossRefGoogle Scholar
  18. Fumanal, B., Chauvel, B., & Bretagnolle, F. (2007). Estimation of pollen and seed production of common ragweed in France. Annals of Agricultural and Environmental Medicine, 14, 233–236.Google Scholar
  19. Galán, C., Alcázar, P., Cariňanos, P., Garcia, H., & Domínguez-Vilches, E. (2000). Meteorological factors affecting daily urticaceae pollen counts in southwest Spain. International Journal of Biometeorology, 43, 191–195.CrossRefGoogle Scholar
  20. Galán, C., Cariňanos, P., García-Mozo, H., Alcázar, P., & Domínguez-Vilches, E. (2001). Model for forecasting Olea europaea L. airborne pollen in South-West Andalusia. Spain. International Journal of Biometeorology, 45, 59–63.CrossRefGoogle Scholar
  21. Giner, M. M., García, J. S. C., & Sellés, J. G. (1999). Aerobiology of Artemisia airborne pollen in Murcia (SE Spain) and its relationship with weather variables: annual and intradiurnal variations for three different species. Wind vectors as a tool in determining pollen origin. International Journal of Biometeorology, 43, 51–63.CrossRefGoogle Scholar
  22. Helbig, N., Vogel, B., Vogel, H., & Fiedler, F. (2004). Numerical modelling of pollen dispersion on the regional scale. Aerobiologia, 20, 3–19.CrossRefGoogle Scholar
  23. Hirst, J. M. (1952). An automatic volumetric spore trap. Annals of Applied Biology, 39, 257–265.CrossRefGoogle Scholar
  24. Jäger, S. (2000). Ragweed (Ambrosia) sensitisation rates correlate with the amount of inhaled airborne pollen. A 14-year study in Vienna, Austria. Aerobiologia, 16, 149–153.CrossRefGoogle Scholar
  25. Jato, M. V., Rodríguez, F. J., & Seijo, M. C. (2000). Pinus pollen in the atmosphere of Vigo and its relationship to meteorological factors. International Journal of Biometeorology, 43, 147–153.CrossRefGoogle Scholar
  26. Koenker, R. (2005). Quantile regression. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  27. Koenker, R., & Bassett, G. B. (1978). Regression quantiles. Econometrica, 46, 33–50.CrossRefGoogle Scholar
  28. Köppen, W. (1931). Grundriss Der Klimakunde. Berlin: Walter De Gruyter & Co.Google Scholar
  29. Laaidi, M., Thibaudon, M., & Besancenot, J. P. (2003). Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France). International Journal of Biometeorology, 48, 65–73.CrossRefGoogle Scholar
  30. Makra, L., Juhász, M., Béczi, R., & Borsos, E. (2005). The history and impacts of airborne Ambrosia (Asteraceae) pollen in Hungary. Grana, 44, 57–64.CrossRefGoogle Scholar
  31. Makra, L., Juhász, M., Borsos, E., & Béczi, R. (2004). Meteorological variables connected with airborne ragweed pollen in Southern Hungary. International Journal of Biometeorology, 49, 37–47.CrossRefGoogle Scholar
  32. Makra, L., Tombácz, Sz., Bálint, B., Sümeghy, Z., Sánta, T., & Hirsch, T. (2008). Influences of meteorological parameters and biological and chemical air pollutants to the incidence of asthma and rhinitis. Climate Research, 37, 99–119.CrossRefGoogle Scholar
  33. Ocana-Peinado, F., Valderrama, M., & Aguilera, A. M. (2008). A dynamic regression model for air pollen concentration. Stochastic Environmental Research and Risk Assessment, 22, S59–S63. Supplement: Suppl. 1.CrossRefGoogle Scholar
  34. Oh, J. W. (2009). Development of pollen concentration prediction models. Journal of Korean Medical Association, 52, 579–591.CrossRefGoogle Scholar
  35. Peternel, R., Culig, J., Hrga, I., & Hercog, P. (2006). Airborne ragweed (Ambrosia artemisiifolia L.) pollen concentrations in Croatia, 2002-2004. Aerobiologia, 22, 161–168.CrossRefGoogle Scholar
  36. Ranzi, A., Lauriola, P., Marletto, V., & Zinoni, F. (2003). Forecasting airborne pollen concentrations: Development of local models. Aerobiologia, 19, 39–45.CrossRefGoogle Scholar
  37. Rodríguez-Rajo, F. J., Jato, V., & Aira, M. J. (2005). Relationship between meteorology and Castanea airborne pollen. Belgian Journal of Botany, 138, 129–140.Google Scholar
  38. Rodríguez-Rajo, F. J., Valencia-Barrera, R. M., Vega-Maray, A. M., Suarez, F. J., Fernandez-Gonzalez, D., & Jato, V. (2006). Prediction of airborne Alnus pollen concentration by using Arima models. Annals of Agricultural and Environmental Medicine, 13, 25–32.Google Scholar
  39. Ruiz, S. S., Bustillo, A. M. G., Morales, P. C., & Cuesta, P. (2008). Forecasting airborne Platanus pollen in the Madrid region. Grana, 47, 234–240.CrossRefGoogle Scholar
  40. Saar, M., Gudzinskas, Z., Plompuu, T., Linno, E., Minkiene, Z., & Motiekaityte, V. (2000). Ragweed plants and airborne pollen in the Baltic states. Aerobiologia, 16, 101–106.CrossRefGoogle Scholar
  41. Sánchez Mesa, J. A., Galán, C., & Hervás, C. (2005). The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate. International Journal of Biometeorology, 49, 355–362.CrossRefGoogle Scholar
  42. Schueler, S., & Schlüntzen, K. (2006). Modeling of oak pollen dispersal on the landscape level with a mesoscale atmospheric model. Environmental Modeling & Assessment, 11, 179–194.CrossRefGoogle Scholar
  43. Šikoparija, B., Smith, M., Skjøth, C. A., Radišič, P., Milkovska, S., Šimič, S., et al. (2009). The Pannonian plain as a source of Ambrosia pollen in the Balkans. International Journal of Biometeorology, 53, 263–272.CrossRefGoogle Scholar
  44. Skjøth, C. A., Smith, M., Šikoparija, B., Stach, A., Myszkowska, D., Kasprzyk, I., et al. (2010). A method for producing airborne pollen source inventories: An example of Ambrosia (ragweed) on the Pannonian Plain. Agricultural and Forest Meteorology, 150, 1203–1210.CrossRefGoogle Scholar
  45. Smith, R. (1994). Nonregular regression. Biometrika, 81, 173–183.CrossRefGoogle Scholar
  46. Smith, M., & Emberlin, J. (2005). Constructing a 7-day ahead forecast model for grass pollen at north London, United Kingdom. Clinical and Experimental Allergy, 35, 1400–1406.CrossRefGoogle Scholar
  47. Smith, M., & Emberlin, J. (2006). A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom. International Journal of Biometeorology, 50, 233–242.CrossRefGoogle Scholar
  48. Sofiev, M., Siljamo, P., Ranta, H., & Rantio-Lehtimaki, A. (2006). Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study. International Journal of Biometeorology, 50, 392–402.CrossRefGoogle Scholar
  49. Stennett, P. J., & Beggs, P. J. (2004). Pollen in the atmosphere of Sydney, Australia, and relationships with meteorological parameters. Grana, 43, 209–216.CrossRefGoogle Scholar
  50. Turos, O. I., Kovtunenko, I. N., Markevych, Y. P., Drannik, G. N., & DuBuske, L. M. (2009). Aeroallergen monitoring in Ukraine reveals the presence of a significant ragweed pollen season. Journal of Allergy and Clinical Immunology, 123(2), S95–S95.CrossRefGoogle Scholar
  51. Vázquez, L. M., Galán, C., & Domínguez-Vilches, E. (2003). Influence of meteorological parameters on olea pollen concentrations in Cordoba (South-western Spain). International Journal of Biometeorology, 48, 83–90.CrossRefGoogle Scholar
  52. Verma, K. S., & Pathak, A. K. (2009). A comparative analysis of forecasting methods for aerobiological studies. Asian Journal of Experimental Sciences, 23, 193–198.Google Scholar
  53. Vogel, H., Pauling, A., & Vogel, B. (2008). Numerical simulation of birch pollen dispersion with an operational weather forecast system. International Journal of Biometeorology, 52(8), 805–814.CrossRefGoogle Scholar
  54. Wan, S. Q., Yuan, T., Bowdish, S., Wallace, L., Russell, S. D., & Luo, Y. Q. (2002). Response of an allergenic Ambrosia psilostachya (Asteraceae) to experimental warming and clipping: Implications for public health. American Journal of Botany, 89, 1843–1846.CrossRefGoogle Scholar
  55. Wand, M. P., & Jones, M. C. (1993). Comparison of smoothing parameterizations in bivariate kernel density estimation. Journal of the American Statistical Association, 88, 520–528.CrossRefGoogle Scholar
  56. Wopfner, N., Gadermaier, G., Egger, M., Asero, R., Ebner, C., Jahn-Schmid, B., et al. (2005). The spectrum of allergens in ragweed and mugwort pollen. International Archives of Allergy and Immunology, 138, 337–346.CrossRefGoogle Scholar
  57. Yu, K., & Jones, M. C. (1998). Local linear quantile regression. Journal of the American Statistical Association, 93, 228–237.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of Climatology and Landscape EcologyUniversity of SzegedSzegedHungary
  2. 2.Department of MeteorologyEötvös Loránd UniversityBudapestHungary

Personalised recommendations