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Area of Science:

  • * Botany and Ecology: Focuses on Castanea sativa Miller, a significant component of European deciduous forests.
  • * Aerobiology and Climatology: Investigates pollen production, dispersal, and atmospheric concentration trends.
  • * Allergology: Addresses the impact of Castanea pollen on hypersensitive individuals and cross-reactivity risks.

Background:

  • * Castanea sativa is ecologically and economically important in European forests, particularly in North-Western Spain.
  • * Understanding Castanea flowering is crucial for assessing woodland conservation and climate change impacts.
  • * Castanea pollen is a significant allergen, posing risks of hypersensitivity and cross-reactivity.

Purpose of the Study:

  • * To develop and validate an Artificial Neural Network (ANN) model for predicting atmospheric Castanea pollen concentrations.
  • * To analyze a 20-year dataset of Castanea pollen concentrations in North-Western Spain.
  • * To assess the model's accuracy in forecasting pollen levels one, two, and three days in advance.

Main Methods:

  • * Utilized a 20-year dataset of Castanea pollen concentrations.
  • * Developed and implemented Artificial Neural Networks (ANNs) for predictive modeling.
  • * Evaluated model performance using linear correlation coefficients.

Main Results:

  • * Detected a significant increasing trend in total annual Castanea pollen concentrations over the 20-year study period.
  • * ANN models demonstrated a strong ability to predict pollen concentrations one, two, and three days ahead.
  • * The one-day ahead prediction model achieved high linear correlation coefficients (0.784 for individual ANN, 0.738 for multiple ANN).

Conclusions:

  • * Artificial Neural Networks provide a highly effective tool for forecasting atmospheric Castanea pollen concentrations.
  • * The developed ANN models outperform traditional methods like time series analysis and meteorological correlation for pollen prediction.
  • * Accurate pollen forecasting is vital for managing allergies and understanding ecological shifts related to Castanea distribution.