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

Makra, László; Matyasovszky, István; Tusnády, Gábor; Ziska, Lewis H.; Hess, Jeremy J.; Nyúl, László G.; Chapman, Daniel S.; Coviello, Luca; Gobbi, Andrea; Jurman, Giuseppe; Furlanello, Cesare; Brunato, Mauro; Damialis, Athanasios; Charalampopoulos, Athanasios; Müller-Schärer, Heinz; Schneider, Norbert; Szabó, Bence; Sümeghy, Zoltán; Páldy, Anna; Magyar, Donát; Bergmann, Karl-Christian; Deák, Áron József; Mikó, Edit; Thibaudon, Michel; Oliver, Gilles; Albertini, Roberto; Bonini, Maira; Šikoparija, Branko; Radišić, Predrag; Josipović, Mirjana Mitrović; Gehrig, Regula; Severova, Elena; Shalaboda, Valentina; Stjepanović, Barbara; Ianovici, Nicoleta; Berger, Uwe; Seliger, Andreja Kofol; Rybníček, Ondřej; Myszkowska, Dorota; Dąbrowska-Zapart, Katarzyna; Majkowska-Wojciechowska, Barbara; Weryszko-Chmielewska, Elzbieta; Grewling, Łukasz; Rapiejko, Piotr; Malkiewicz, Malgorzata; Šaulienė, Ingrida; Prykhodo, Olexander; Maleeva, Anna; Rodinkova, Victoria; Palamarchuk, Olena; Ščevková, Jana; Bullock, James M. ORCID: 2023 A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe. Science of The Total Environment, 905, 167095. 18, pp.

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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 (, 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.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Biodiversity (Science Area 2017-)
ISSN: 0048-9697
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: ambrosia, aerobiology, flowering phenology, artificial intelligence, climate change, data reconstruction, health risk, invasive species
NORA Subject Terms: Ecology and Environment
Data and Information
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Date made live: 09 Nov 2023 15:43 +0 (UTC)

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