A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe

Citation

Makra L, Matyasovszky I, Tusnády G, Ziska LH, Hess JJ, Nyúl LG, Chapman DS, Coviello L, Gobbi A, Jurman G, Furlanello C, Brunato M, Damialis A, Charalampopoulos A & Müller-Schärer H (2023) A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe. Science of The Total Environment, 905, Art. No.: 167095. https://doi.org/10.1016/j.scitotenv.2023.167095

Abstract
Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we have developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant's reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating seasonal pollen integrals than those of the GM approach. These are the first published modelled maps using altitude correction and flowering phenology to recover missing pollen information. We created a web page (http://euragweedpollen.gmf.u-szeged.hu/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions.

Keywords
Ambrosia; Aerobiology; Flowering phenology; Artificial intelligence; Climate change; Data reconstruction; Health risk; Invasive species

Notes
Additional authors: Norbert Schneider, Bence Szabó, Zoltán Sümeghy, Anna Páldy, Donát Magyar, Karl-Christian Bergmann, Áron József Deák, Edit Mikó, Michel Thibaudon, Gilles Oliver, Roberto Albertini, Maira Bonini, Branko Šikoparija, Predrag Radišić, Mirjana Mitrović Josipović, Regula Gehrig, Elena Severova, Valentina Shalaboda, Barbara Stjepanović, Nicoleta Ianovici, Uwe Berger, Andreja Kofol Seliger, Ondřej Rybníček, Dorota Myszkowska, Katarzyna Dąbrowska-Zapart, Barbara Majkowska-Wojciechowska, Elzbieta Weryszko-Chmielewska, Łukasz Grewling, Piotr Rapiejko, Malgorzata Malkiewicz, Ingrida Šaulienė, Olexander Prykhodo, Anna Maleeva, Victoria Rodinkova, Olena Palamarchuk, Jana Ščevková, James M. Bullock

Journal
Science of The Total Environment: Volume 905

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