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 a 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, roč. 905, December 2023, s. 1-18. ISSN 0048-9697. Dostupné z: https://dx.doi.org/10.1016/j.scitotenv.2023.167095.
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Základní údaje
Originální název A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe
Autoři 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 Česká republika, domácí), 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 a James M BULLOCK.
Vydání Science of the Total Environment, AMSTERDAM, Elsevier, 2023, 0048-9697.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 30225 Allergy
Stát vydavatele Nizozemské království
Utajení není předmětem státního či obchodního tajemství
WWW URL
Impakt faktor Impact factor: 9.800 v roce 2022
Kód RIV RIV/00216224:14110/23:00133787
Organizační jednotka Lékařská fakulta
Doi http://dx.doi.org/10.1016/j.scitotenv.2023.167095
UT WoS 001160018800001
Klíčová slova anglicky Ambrosia; Aerobiology; Flowering phenology; Artificial intelligence; Climate change; Data reconstruction; Health risk; Invasive species
Štítky 14110317, rivok
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Tereza Miškechová, učo 341652. Změněno: 15. 3. 2024 14:12.
Anotace
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.
VytisknoutZobrazeno: 27. 4. 2024 13:57