A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe.
Makra L, Matyasovszky I, Tusnády G, Ziska LH, Hess JJ, Nyúl LG, Chapman DS, Coviello L, Gobbi A, Jurman G, Furlanello C, Brunato M, Damialis A, Charalampopoulos A, Müller-Schärer H, Schneider N, Szabó B, Sümeghy Z, Páldy A, Magyar D, Bergmann KC, Deák ÁJ, Mikó E, Thibaudon M, Oliver G, Albertini R, Bonini M, Šikoparija B, Radišić P, Josipović MM, Gehrig R, Severova E, Shalaboda V, Stjepanović B, Ianovici N, Berger U, Seliger AK, Rybníček O, Myszkowska D, Dąbrowska-Zapart K, Majkowska-Wojciechowska B, Weryszko-Chmielewska E, Grewling Ł, Rapiejko P, Malkiewicz M, Šaulienė I, Prykhodo O, Maleeva A, Rodinkova V, Palamarchuk O, Ščevková J, Bullock JM.
Makra L, et al.
Sci Total Environ. 2023 Dec 20;905:167095. doi: 10.1016/j.scitotenv.2023.167095. Epub 2023 Sep 23.
Sci Total Environ. 2023.
PMID: 37748607
Free article.
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) characteristi …
To achieve this, we have developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily rag …