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
- 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 tagAbstract
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.…
