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, 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. A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe. Science of the Total Environment. AMSTERDAM: Elsevier, 2023, vol. 905, December 2023, p. 1-18. ISSN 0048-9697. Available from: https://dx.doi.org/10.1016/j.scitotenv.2023.167095.
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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
Original language English
Type of outcome Article in a journal
Field of Study 30225 Allergy
Country of publisher Netherlands
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 9.800 in 2022
RIV identification code RIV/00216224:14110/23:00133787
Organization unit Faculty of Medicine
Doi http://dx.doi.org/10.1016/j.scitotenv.2023.167095
UT WoS 001160018800001
Keywords in English Ambrosia; Aerobiology; Flowering phenology; Artificial intelligence; Climate change; Data reconstruction; Health risk; Invasive species
Tags 14110317, rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Tereza Miškechová, učo 341652. Changed: 15/3/2024 14:12.
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.
PrintDisplayed: 10/7/2024 21:08