Forecasting daily total pollen concentrations on a global scale
László Makra, Luca Coviello, Andrea Gobbi, Giuseppe Jurman, Cesare Furlanello, Mauro Brunato, Lewis H. Ziska, Jeremy Hess, Athanasios Damialis, Maria Pilar Plaza Garcia, Gábor Tusnády, Lilit Czibolya, István Ihász, Áron József Deák, Edit Mikó, Zita Dorner, Susan K. Harry, Nicolas Bruffaerts, Ann Packeu, Annika Saarto, Linnea Toiviainen, Maria Louna‐Korteniemi, Sanna Pätsi, M. Thibaudon, G. Oliver, Athanasios Charalampopoulos, Despoina Vokou, Ewa Maria Przedpelska‐Wasowicz, Ellý Renée Guðjohnsen, Maira Bonini, Sevcan Çelenk, Cumali Özaslan, Jae‐Won Oh, Krista Sullivan, Linda Ford, Michelle M. Kelly, Estelle Levetin, Dorota Myszkowska, Elena Severova, Regula Gehrig, María Del Carmen Calderón‐Ezquerro, César Guerrero Guerra, Manuel A. Leiva G., German D. Ramón, Laura Barrionuevo, Jonny Peter, Dilys Berman, Connie Katelaris, Janet M. Davies, Pamela Burton, Paul J. Beggs, Sandra María Vergamini, Rosa María Valencia‐Barrera, Claudia Traidl‐Hoffmann
Abstract
BACKGROUND: There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. METHODS: The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. RESULTS: (DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28-100 cm depth, and past soil temperature in 0-7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. CONCLUSIONS: This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.