Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece

Abstract

In the Mediterranean region, Olive (Olea europaea L.) is a primary source of airborne allergenic pollen, while Quercus contribute substantial quantities of pollen grains to the atmosphere, posing significant challenges in predicting their Main Pollen Seasons (MPS). This study addresses these challenges through the application of various predictive methodologies, including Thermal Time (TT) models, which integrate chilling and heat requirements, along with Partial Least Squares Regression (PLS), and Temperature-Photoperiod (TP) models. Pollen data from 2016 to 2022 in Thessaloniki, Greece, and meteorological data from ERA5 ECMWF reanalysis were analyzed. The results indicate that the mean duration of the Olea MPS was 58 days, starting in early April and peaking in mid-May, while the Quercus MPS lasted 55 days, starting in late March and peaking on average on April 25th. The predictive models were generally effective for forecasting the MPS, with a combination of TT and PLS methods providing the most robust predictions. PLS regression analysis highlighted the significant impact of spring and preceding autumn temperatures on the MPS. The chilling period for both pollen taxa typically begins in late November to early December, ending around early January, with an average requirement of about 1187 chilling hours. The heating requirements of the different predicting approaches for the start of MPS varied from 435 °C to 1113 °C for Olea and 544 °C to 1081 °C for Quercus. The results emphasize the importance of localized studies in aerobiology for accurate MPS predictions, which are crucial for public health planning and allergen management.

Keywords: Chilling; Heat requirements; Partial least squares regression; Pollen season; Temperature-photoperiod models.

Fig. 1

Distribution maps of fractional coverage of (a) Olea (CORINE Land Cover 2018–100 m) and (b) Quercus (Tree species maps for European forests 1 km – EFI, taxa in the study area.

Fig. 2

Mean maximum and minimum monthly temperatures for the period 2015–2022.

Fig. 3

Regression coefficients of important variables for Olea and Quercus taxa.

Fig. 4

Comparison of peak DOYs for Olea and Quercus taxa with predicted values from PLS and TP models.