Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/20.500.11765/16484
Atmospheric new particle formation identifer using longitudinal global particle number size distribution data
Título : Atmospheric new particle formation identifer using longitudinal global particle number size distribution data
Autor : Kecorius, SimonasMadueño, LeizelLovric, MarioRacic, NikolinaSchwarz, MaximilianCyrys, JosefCasquero Vera, Juan AndrésAlados Arboledas, LucasConil, SébastienSciare, JeanOndracek, JakubHallar, Anna GannetGómez Moreno, Francisco JavierEllul, RaymondKristensson, AdamSorribas, MarKalivitis, NikosMihalopoulos, NikolaosPeters, AnnetteGini, Maria I.Eleftheriadis, KonstantinosVratolis, StergiosJeongeun, KimBirmili, WolframBergmans, BenjaminNikolova, NinaDinoi, AdelaideContini, DanieleMarinoni, AngelaAlastuey, AndrésPetäjä, TuukkaRodríguez González, Sergio ORCID RESEARCHERID Autor AEMETPicard, DavidBrem, Benjamin T.Priestman, MaxGreen, DavidBeddows, David C. S.Harrison, Roy M.O'Dowd, ColinCeburnis, DariusHyvärinen, AnttiHenzing, BasCrumeyrolle, SuzannePutaud, Jean-PhilippeLaj, PaoloWeinhold, KayPlauškaitė, KristinaByčenkienė, Steigvilė
Palabras clave : Atmospheric new particle; Health effects; Machine learning; Algorithm
Fecha de publicación : 2024
Editor: Nature
Citación : Scientific Data. 2024, 11, 1239
Versión del editor: https://doi.org/10.1038/s41597-024-04079-1
Resumen : Atmospheric new particle formation (NPF) is a naturally occurring phenomenon, during which high concentrations of sub-10 nm particles are created through gas to particle conversion. The NPF is observed in multiple environments around the world. Although it has observable infuence onto annual total and ultrafne particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health efects. One plausible reason for this limitation may be related to the absence of NPF identifers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identifed manually from particle number size distribution contour plots. Identifcation of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifer, created using an automated, machine learning based algorithm. The regional NPF event tag was created for 65 measurement sites globally, covering the period from 1996 to 2023. The discussed data set can be used in future studies related to regional NPF.
URI : http://hdl.handle.net/20.500.11765/16484
ISSN : 2052-4463
Colecciones: Artículos científicos 2023-2026


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