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Journal Abstract Search

117 related items for PubMed ID: 16768161

  • 1. [Prospective study of pollen dispersal prediction and identifying the usefulness of different parameters].
    Maeda M, Maguchi S, Nakamaru Y, Takagi D, Fukuda S.
    Nihon Jibiinkoka Gakkai Kaiho; 2006 May; 109(5):455-60. PubMed ID: 16768161
    [Abstract] [Full Text] [Related]

  • 2. [Prediction of the dispersion of birch pollen in Asahikawa].
    Kanaseki N.
    Nihon Jibiinkoka Gakkai Kaiho; 2004 Jul; 107(7):685-9. PubMed ID: 15346895
    [Abstract] [Full Text] [Related]

  • 3. [Relationship between birch pollen counts and meteorological factors for 8 years in Sapporo].
    Shirasaki H, Narita S, Watanabe K, Kanaizumi E, Satoh J, Konno N, Ogasawara H, Yamamoto T, Asakura K, Kataura A, Himi T.
    Nihon Jibiinkoka Gakkai Kaiho; 2003 Jun; 106(6):705-9. PubMed ID: 12872726
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  • 4. Forecasting the seasonal pollen index by using a hidden Markov model combining meteorological and biological factors.
    Tseng YT, Kawashima S, Kobayashi S, Takeuchi S, Nakamura K.
    Sci Total Environ; 2020 Jan 01; 698():134246. PubMed ID: 31505344
    [Abstract] [Full Text] [Related]

  • 5. Effect of meteorological factors on Betula, Fraxinus and Quercus pollen concentrations in the atmosphere of Lublin and Szczecin, Poland.
    Weryszko-Chmielewska E, Puc M, Piotrowska K.
    Ann Agric Environ Med; 2006 Jan 01; 13(2):243-9. PubMed ID: 17195996
    [Abstract] [Full Text] [Related]

  • 6. Impact of meteorological parameters and air pollutants on airborne concentration of Betula pollen and Bet v 1 allergen.
    Ščevková J, Dušička J, Zahradníková E, Sepšiová R, Kováč J, Vašková Z.
    Environ Sci Pollut Res Int; 2023 Sep 01; 30(42):95438-95448. PubMed ID: 37544949
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  • 9. [A relationship between birch pollen counts and meteorological factors in Sapporo].
    Shirasaki H, Yamamoto T, Saikawa E, Seki N, Asakura K, Kataura A, Himi T.
    Nihon Jibiinkoka Gakkai Kaiho; 2014 May 01; 117(5):653-7. PubMed ID: 24956742
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  • 11. Quantification of airborne birch (Betula sp.) pollen grains and allergens in Krakow.
    Madeja J, Wypasek E, Plytycz B, Sarapata K, Harmata K.
    Arch Immunol Ther Exp (Warsz); 2005 May 01; 53(2):169-74. PubMed ID: 15928586
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  • 12. Reevaluation of pollen quantitation by an automatic pollen counter.
    Muradil M, Okamoto Y, Yonekura S, Chazono H, Hisamitsu M, Horiguchi S, Hanazawa T, Takahashi Y, Yokota K, Okumura S.
    Allergy Asthma Proc; 2010 May 01; 31(5):422-7. PubMed ID: 20929610
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  • 13. Analysis of airborne betula pollen in Finland; a 31-year perspective.
    Yli-Panula E, Fekedulegn DB, Green BJ, Ranta H.
    Int J Environ Res Public Health; 2009 Jun 01; 6(6):1706-23. PubMed ID: 19578456
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  • 14. Influence of meteorological parameters and air pollution on hourly fluctuation of birch (Betula L.) and ash (Fraxinus L.) airborne pollen.
    Puc M.
    Ann Agric Environ Med; 2012 Jun 01; 19(4):660-5. PubMed ID: 23311785
    [Abstract] [Full Text] [Related]

  • 15. [Clinical and immunological analysis of Birch pollenosis].
    Takagi S.
    Hokkaido Igaku Zasshi; 1994 Nov 01; 69(6):1409-26. PubMed ID: 7705751
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  • 17. Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland).
    Puc M.
    Int J Biometeorol; 2012 Mar 01; 56(2):395-401. PubMed ID: 21573820
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  • 18. Spatiotemporal models for predicting high pollen concentration level of Corylus, Alnus, and Betula.
    Nowosad J.
    Int J Biometeorol; 2016 Jun 01; 60(6):843-55. PubMed ID: 26487352
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  • 20. A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe.
    Ritenberga O, Sofiev M, Siljamo P, Saarto A, Dahl A, Ekebom A, Sauliene I, Shalaboda V, Severova E, Hoebeke L, Ramfjord H.
    Sci Total Environ; 2018 Feb 15; 615():228-239. PubMed ID: 28972900
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