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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F23%3A00078418" target="_blank" >RIV/65269705:_____/23:00078418 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/23:00133787

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0048969723057224?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0048969723057224?pes=vor</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scitotenv.2023.167095" target="_blank" >10.1016/j.scitotenv.2023.167095</a>

Result language

angličtina

Original language name

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

Original language description

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&apos;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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30200 - Clinical medicine

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Science of the Total Environment

ISSN

0048-9697

e-ISSN

1879-1026

Volume of the periodical

905

Issue of the periodical within the volume

DEC 2023

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

18

Pages from-to

167095

UT code for WoS article

001160018800001

EID of the result in the Scopus database

2-s2.0-85173022583