Literature DB >> 1628252

Prediction of annual variations in atmospheric concentrations of grass pollen. A method based on meteorological factors and grain crop estimates.

J Subiza1, J M Masiello, J L Subiza, M Jerez, M Hinojosa, E Subiza.   

Abstract

We performed an aerobiologic observation of the grasses present in Madrid for 14 years (1978-1991), using volumetric air samplers. The counts obtained show that the major grass pollen release period (average daily grass pollen counts greater than 50 grains/m3 of air) occurs in the months of May and June, although lower counts can occur some days from the end of January onward. There are wide year-to-year variations in total atmospheric grass pollen counts, expressed as the total sum of the mean daily concentrations from April 1st to July 30th (ranging from 2568 to 6624). A strong, statistically significant correlation, based on Spearman's rank test and/or simple and multiple linear regressions, was found between the total grass seasonal count and preseasonal rainfall from October to March (R2 = 0.64; P = 0.0429). The meteorological variable which gave the correlation with greatest statistical significance (R2 = 0.97; P = 0.0016) was the average monthly preseasonal humidity from October to March. A good correlation was also found between March estimates of wheat, rye and barley crops and the total grass count (R2 = 0.73; P = 0.006). A model was designed from the above mentioned humidity variable through a multilinear regression analysis, and it was possible to predict, at the beginning of April, total seasonal counts for 1989 (predicted = 5468; actual = 4410; average error = 24%), 1990 (5033; 6090; -17%) and 1991 (3930; 2568; 53%). These data may help clinicians to predict and prepare themselves for the intensity of the grass pollen season and to explain yearly variations in the severity of symptoms.

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Year:  1992        PMID: 1628252     DOI: 10.1111/j.1365-2222.1992.tb00163.x

Source DB:  PubMed          Journal:  Clin Exp Allergy        ISSN: 0954-7894            Impact factor:   5.018


  5 in total

1.  Atmospheric Poaceae pollen frequencies and associations with meteorological parameters in Brisbane, Australia: a 5-year record, 1994-1999.

Authors:  Brett James Green; Mary Dettmann; Eija Yli-Panula; Shannon Rutherford; Rod Simpson
Journal:  Int J Biometeorol       Date:  2004-03-02       Impact factor: 3.787

2.  Diurnal temperature range and emergency room admissions for chronic obstructive pulmonary disease in Taiwan.

Authors:  Wen-Miin Liang; Wen-Pin Liu; Hsien-Wen Kuo
Journal:  Int J Biometeorol       Date:  2008-11-07       Impact factor: 3.787

Review 3.  Effect of meteorological parameters on Poaceae pollen in the atmosphere of Tetouan (NW Morocco).

Authors:  Nadia Aboulaich; Lamiaa Achmakh; Hassan Bouziane; M Mar Trigo; Marta Recio; Mohamed Kadiri; Baltasar Cabezudo; Hassane Riadi; Mohamed Kazzaz
Journal:  Int J Biometeorol       Date:  2012-06-29       Impact factor: 3.787

4.  A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function.

Authors:  Kyu Rang Kim; Mijin Kim; Ho-Seong Choe; Mae Ja Han; Hye-Rim Lee; Jae-Won Oh; Baek-Jo Kim
Journal:  Int J Biometeorol       Date:  2016-07-07       Impact factor: 3.787

5.  Wind-mediated horseweed (Conyza canadensis) gene flow: pollen emission, dispersion, and deposition.

Authors:  Haiyan Huang; Rongjian Ye; Meilan Qi; Xiangzhen Li; David R Miller; Charles Neal Stewart; David W DuBois; Junming Wang
Journal:  Ecol Evol       Date:  2015-06-17       Impact factor: 2.912

  5 in total

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