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Atmospheric new particle formation identifier using longitudinal global particle number size distribution data

Authors :
Simonas Kecorius
Leizel Madueño
Mario Lovric
Nikolina Racic
Maximilian Schwarz
Josef Cyrys
Juan Andrés Casquero-Vera
Lucas Alados-Arboledas
Sébastien Conil
Jean Sciare
Jakub Ondracek
Anna Gannet Hallar
Francisco J. Gómez-Moreno
Raymond Ellul
Adam Kristensson
Mar Sorribas
Nikolaos Kalivitis
Nikolaos Mihalopoulos
Annette Peters
Maria Gini
Konstantinos Eleftheriadis
Stergios Vratolis
Kim Jeongeun
Wolfram Birmili
Benjamin Bergmans
Nina Nikolova
Adelaide Dinoi
Daniele Contini
Angela Marinoni
Andres Alastuey
Tuukka Petäjä
Sergio Rodriguez
David Picard
Benjamin Brem
Max Priestman
David C. Green
David C. S. Beddows
Roy M. Harrison
Colin O’Dowd
Darius Ceburnis
Antti Hyvärinen
Bas Henzing
Suzanne Crumeyrolle
Jean-Philippe Putaud
Paolo Laj
Kay Weinhold
Kristina Plauškaitė
Steigvilė Byčenkienė
Source :
Scientific Data, Vol 11, Iss 1, Pp 1-10 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

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 influence onto annual total and ultrafine particle number concentrations (PNC and UFP, respectively), only limited epidemiological studies have investigated whether these particles are associated with adverse health effects. One plausible reason for this limitation may be related to the absence of NPF identifiers available in UFP and PNC data sets. Until recently, the regional NPF events were usually identified manually from particle number size distribution contour plots. Identification of NPF across multi-annual and multiple station data sets remained a tedious task. In this work, we introduce a regional NPF identifier, 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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.9b3c536832eb4a779fe51832dbc05a02
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-024-04079-1