J 2023

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

MAKRA, Laszlo, Istvan MATYASOVSZKY, Gabor TUSNADY, Lewis H ZISKA, Jeremy J HESS et. al.

Basic information

Original name

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

Authors

MAKRA, Laszlo, Istvan MATYASOVSZKY, Gabor TUSNADY, Lewis H ZISKA, Jeremy J HESS, Laszlo G NYUL, Daniel S CHAPMAN, Luca COVIELLO, Andrea GOBBI, Giuseppe JURMAN, Cesare FURLANELLO, Mauro BRUNATO, Athanasios DAMIALIS, Athanasios CHARALAMPOPOULOS, Heinz MUELLER-SCHARER, Norbert SCHNEIDER, Bence SZABO, Zoltan SUMEGHY, Anna PALDY, Donat MAGYAR, Karl-Christian BERGMANN, aron Jozsef DEAK, Edit MIKO, Michel THIBAUDON, Gilles OLIVER, Roberto ALBERTINI, Maira BONINI, Branko SIKOPARIJA, Predrag RADISIC, Mirjana Mitrovic JOSIPOVIC, Regula GEHRIG, Elena SEVEROVA, Valentina SHALABODA, Barbara STJEPANOVIC, Nicoleta IANOVICI, Uwe BERGER, Andreja Kofol SELIGER, Ondřej RYBNÍČEK (203 Czech Republic, belonging to the institution), Dorota MYSZKOWSKA, Katarzyna DABROWSKA-ZAPART, Barbara MAJKOWSKA-WOJCIECHOWSKA, Elzbieta WERYSZKO-CHMIELEWSKA, Lukasz GREWLING, Piotr RAPIEJKO, Malgorzata MALKIEWICZ, Ingrida SAULIENE, Olexander PRYKHODO, Anna MALEEVA, Victoria RODINKOVA, Olena PALAMARCHUK, Jana SCEVKOVA and James M BULLOCK

Edition

Science of the Total Environment, AMSTERDAM, Elsevier, 2023, 0048-9697

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30225 Allergy

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 9.800 in 2022

RIV identification code

RIV/00216224:14110/23:00133787

Organization unit

Faculty of Medicine

UT WoS

001160018800001

Keywords in English

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

Tags

Tags

International impact, Reviewed
Změněno: 15/3/2024 14:12, Mgr. Tereza Miškechová

Abstract

V originále

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.