Kecorius Simonas, Madueño Leizel, Lovric Mario, Racic Nikolina, Schwarz Maximilian, Cyrys Josef, Casquero-Vera Juan Andrés, Alados-Arboledas Lucas, Conil Sébastien, Sciare Jean, Ondracek Jakub, Hallar Anna Gannet, Gómez-Moreno Francisco J, Ellul Raymond, Kristensson Adam, Sorribas Mar, Kalivitis Nikolaos, Mihalopoulos Nikolaos, Peters Annette, Gini Maria, Eleftheriadis Konstantinos, Vratolis Stergios, Jeongeun Kim, Birmili Wolfram, Bergmans Benjamin, Nikolova Nina, Dinoi Adelaide, Contini Daniele, Marinoni Angela, Alastuey Andres, Petäjä Tuukka, Rodriguez Sergio, Picard David, Brem Benjamin, Priestman Max, Green David C, Beddows David C S, Harrison Roy M, O'Dowd Colin, Ceburnis Darius, Hyvärinen Antti, Henzing Bas, Crumeyrolle Suzanne, Putaud Jean-Philippe, Laj Paolo, Weinhold Kay, Plauškaitė Kristina, Byčenkienė Steigvilė
Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Environmental Science Center, University of Augsburg, Augsburg, Germany.
Sci Data. 2024 Nov 16;11(1):1239. doi: 10.1038/s41597-024-04079-1.
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.
大气新粒子形成（NPF）是一种自然发生的现象，在此过程中，通过气体到粒子的转化产生高浓度的亚10纳米粒子。NPF在世界各地的多种环境中都有观测到。尽管它对年度总粒子数浓度和超细粒子数浓度（分别为PNC和UFP）有可观测的影响，但只有有限的流行病学研究调查了这些粒子是否与不良健康影响有关。造成这种限制的一个合理原因可能与UFP和PNC数据集中缺乏NPF标识符有关。直到最近，区域NPF事件通常是从粒子数粒径分布等高线图中手动识别出来的。跨多年和多站点数据集识别NPF仍然是一项繁琐的任务。在这项工作中，我们引入了一个区域NPF标识符，它是使用基于机器学习的自动化算法创建的。为全球65个测量站点创建了区域NPF事件标签，涵盖1996年至2023年期间。所讨论的数据集可用于未来与区域NPF相关的研究。
