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 thes...

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Main Authors: S. Papadogiannaki, K. Karatzas, S. Kontos, A. Poupkou, D. Melas
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-92259-3
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author S. Papadogiannaki
K. Karatzas
S. Kontos
A. Poupkou
D. Melas
author_facet S. Papadogiannaki
K. Karatzas
S. Kontos
A. Poupkou
D. Melas
author_sort S. Papadogiannaki
collection DOAJ
description 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.
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spelling doaj-art-9f1fb1a45b8742b1bf849c6ba12f3a0b2025-08-20T02:16:06ZengNature PortfolioScientific Reports2045-23222025-04-0115111510.1038/s41598-025-92259-3Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, GreeceS. Papadogiannaki0K. Karatzas1S. Kontos2A. Poupkou3D. Melas4Laboratory of Atmospheric Physics, School of Physics, Aristotle University of ThessalonikiEnvironmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of ThessalonikiLaboratory of Atmospheric Physics, School of Physics, Aristotle University of ThessalonikiResearch Centre for Atmospheric Physics and Climatology, Academy of AthensLaboratory of Atmospheric Physics, School of Physics, Aristotle University of ThessalonikiAbstract 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.https://doi.org/10.1038/s41598-025-92259-3Pollen seasonChillingHeat requirementsPartial least squares regressionTemperature-photoperiod models
spellingShingle S. Papadogiannaki
K. Karatzas
S. Kontos
A. Poupkou
D. Melas
Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece
Scientific Reports
Pollen season
Chilling
Heat requirements
Partial least squares regression
Temperature-photoperiod models
title Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece
title_full Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece
title_fullStr Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece
title_full_unstemmed Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece
title_short Comparative modeling approaches for predicting Olea and Quercus pollen seasons in Thessaloniki, Greece
title_sort comparative modeling approaches for predicting olea and quercus pollen seasons in thessaloniki greece
topic Pollen season
Chilling
Heat requirements
Partial least squares regression
Temperature-photoperiod models
url https://doi.org/10.1038/s41598-025-92259-3
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