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

Autoři	MAKRA Laszlo MATYASOVSZKY Istvan TUSNADY Gabor ZISKA Lewis H HESS Jeremy J NYUL Laszlo G CHAPMAN Daniel S COVIELLO Luca GOBBI Andrea JURMAN Giuseppe FURLANELLO Cesare BRUNATO Mauro DAMIALIS Athanasios CHARALAMPOPOULOS Athanasios MUELLER-SCHARER Heinz SCHNEIDER Norbert SZABO Bence SUMEGHY Zoltan PALDY Anna MAGYAR Donat BERGMANN Karl-Christian DEAK aron Jozsef MIKO Edit THIBAUDON Michel OLIVER Gilles ALBERTINI Roberto BONINI Maira SIKOPARIJA Branko RADISIC Predrag JOSIPOVIC Mirjana Mitrovic GEHRIG Regula SEVEROVA Elena SHALABODA Valentina STJEPANOVIC Barbara IANOVICI Nicoleta BERGER Uwe SELIGER Andreja Kofol RYBNÍČEK Ondřej MYSZKOWSKA Dorota DABROWSKA-ZAPART Katarzyna MAJKOWSKA-WOJCIECHOWSKA Barbara WERYSZKO-CHMIELEWSKA Elzbieta GREWLING Lukasz RAPIEJKO Piotr MALKIEWICZ Malgorzata SAULIENE Ingrida PRYKHODO Olexander MALEEVA Anna RODINKOVA Victoria PALAMARCHUK Olena SCEVKOVA Jana BULLOCK James M
Rok publikování	2023
Druh	Článek v odborném periodiku
Časopis / Zdroj	Science of the Total Environment
Fakulta / Pracoviště MU	Lékařská fakulta
Citace
www	https://www.sciencedirect.com/science/article/pii/S0048969723057224?via%3Dihub
Doi	http://dx.doi.org/10.1016/j.scitotenv.2023.167095
Klíčová slova	Ambrosia; Aerobiology; Flowering phenology; Artificial intelligence; Climate change; Data reconstruction; Health risk; Invasive species
Popis	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.