Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/16484
Atmospheric new particle formation identifer using longitudinal global particle number size distribution data
Title: Atmospheric new particle formation identifer using longitudinal global particle number size distribution data
Authors: 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
Keywords: Atmospheric new particle; Health effects; Machine learning; Algorithm
Issue Date: 2024
Publisher: Nature
Citation: Scientific Data. 2024, 11, 1239
Publisher version: https://doi.org/10.1038/s41597-024-04079-1
Abstract: 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
Appears in Collections:Artículos científicos 2023-2026


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