Аннотации:
Introduction: Allergies are the most common reason of the chronic diseases in developed countries and represent an important medical, social and economic issue, the relevance of which is growing both in these countries and in Ukraine. The most famous of these allergens group is the pollen of ambrosia and pollen of poaceae, which are ubiquitously
distributed in the subtropical and temperate climate. The aim: The objective of our study was to develop the mathematical models, which will be able to indicate the probability of the pollen circulation, and thus these models can simplify the forecast of symptoms risk and improve the prophylaxis of pollinosis. Materials and methods: The research was conducted on the basis of the research center of National Pirogov Memorial Medical University, Vinnytsia in the years 2012-2014. A volumetric sampler of the Hirst type was used for the air sampling. The observation was conducted from the first of April to the thirty-first of October. For the initial preparation of the tables and intermediate calculations, Excel software package was used. The software STATISTICA 10.0 was applied to calculate the average coefficients values and their statistical characteristics (beta-values, errors of the mean values, Student’s t-test, veracity and the factors percentage contribution into the function variation). Results: Statistically significant correlation between pollen concentrations of herbaceous plants and individual meteorological factors was found; classificational functions were designed by which it is possible to calculate the probability of presence or absence of Artemisia pollen in the atmosphere; the risks of increasing of the Artemisia pollen concentration are determined under exceeding of the critical temperature of 18С, relative humidity of 67% and atmospheric pressure of 980 Pa. Conclusions. The results of the research can be used to predict the emission of potentially hazardous concentrations of weed pollen grains in the atmosphere of the central region of Ukraine using the weather forecast.
