A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe
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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
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.
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.
@article{2384418, author = {Makra, Laszlo and Matyasovszky, Istvan and Tusnady, Gabor and Ziska, Lewis H and Hess, Jeremy J and Nyul, Laszlo G and Chapman, Daniel S and Coviello, Luca and Gobbi, Andrea and Jurman, Giuseppe and Furlanello, Cesare and Brunato, Mauro and Damialis, Athanasios and Charalampopoulos, Athanasios and MuellerandScharer, Heinz and Schneider, Norbert and Szabo, Bence and Sumeghy, Zoltan and Paldy, Anna and Magyar, Donat and Bergmann, KarlandChristian and Deak, aron Jozsef and Miko, Edit and Thibaudon, Michel and Oliver, Gilles and Albertini, Roberto and Bonini, Maira and Sikoparija, Branko and Radisic, Predrag and Josipovic, Mirjana Mitrovic and Gehrig, Regula and Severova, Elena and Shalaboda, Valentina and Stjepanovic, Barbara and Ianovici, Nicoleta and Berger, Uwe and Seliger, Andreja Kofol and Rybníček, Ondřej and Myszkowska, Dorota and DabrowskaandZapart, Katarzyna and MajkowskaandWojciechowska, Barbara and WeryszkoandChmielewska, Elzbieta and Grewling, Lukasz and Rapiejko, Piotr and Malkiewicz, Malgorzata and Sauliene, Ingrida and Prykhodo, Olexander and Maleeva, Anna and Rodinkova, Victoria and Palamarchuk, Olena and Scevkova, Jana and Bullock, James M}, article_location = {AMSTERDAM}, article_number = {December 2023}, doi = {http://dx.doi.org/10.1016/j.scitotenv.2023.167095}, keywords = {Ambrosia; Aerobiology; Flowering phenology; Artificial intelligence; Climate change; Data reconstruction; Health risk; Invasive species}, language = {eng}, issn = {0048-9697}, journal = {Science of the Total Environment}, title = {A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe}, url = {https://www.sciencedirect.com/science/article/pii/S0048969723057224?via%3Dihub}, volume = {905}, year = {2023} }
TY - JOUR ID - 2384418 AU - 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 PY - 2023 TI - A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe JF - Science of the Total Environment VL - 905 IS - December 2023 SP - 1-18 EP - 1-18 PB - Elsevier SN - 00489697 KW - Ambrosia KW - Aerobiology KW - Flowering phenology KW - Artificial intelligence KW - Climate change KW - Data reconstruction KW - Health risk KW - Invasive species UR - https://www.sciencedirect.com/science/article/pii/S0048969723057224?via%3Dihub N2 - 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. ER -
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. \textit{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.