Allergenic weed pollen forecast under the mathematical modeling method implementation in ukraine.

Area of Science:

• Environmental Science
• Aerobiology
• Biometeorology

Background:

• Allergies, particularly from Ambrosia and Poaceae pollen, are prevalent chronic diseases in developed nations and Ukraine.
• Pollen allergies (pollinosis) pose significant medical, social, and economic burdens, necessitating improved forecasting and prophylaxis.

Purpose of the Study:

• To develop mathematical models for predicting pollen circulation probability.
• To simplify the forecasting of allergy symptom risk.
• To enhance the prophylaxis of pollinosis.

Main Methods:

• Air sampling using a Hirst-type volumetric sampler from April to October (2012-2014).
• Statistical analysis using STATISTICA 10.0 to determine correlations and model parameters.
• Development of classification functions based on meteorological data.

Main Results:

• A statistically significant correlation was found between herbaceous plant pollen concentrations and meteorological factors.
• Classification functions were created to predict the presence or absence of Artemisia (Ambrosia) pollen.
• Increased risk of Artemisia pollen concentration is associated with temperatures exceeding 18°C, relative humidity above 67%, and atmospheric pressure above 980 Pa.

Conclusions:

• The developed models can predict potentially hazardous concentrations of weed pollen.
• These predictions can be utilized for weather-forecast-based management of pollinosis in central Ukraine.