The data used in this study was generated by the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) and ACTRIS-2 H2020 research project (grant no 654109). Authors acknowledge the ACTRIS in situ EBAS Data Centre (EBAS@NILU https://ebas.nilu.no/), for providing datasets to the study. Some European sites and measurements were also supported by the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air pollutants in Europe (EMEP) under UNECE and the WMO program GAW. We would like to further extend our acknowledgments to the following data originators: Sellegri Karine, Erik Swietlicki, Peter Tunved, Lunder Chris, Markus Fiebig, Marcos Andrade, Fabian Lenartz, Zdenek Wagner, Miroslav Bitter, Alfred Wiedensohler, Cristina Reche, Sebastiao Martins dos Santos, and Zahra Hamzawi. Authors acknowledge Heinz Kaminski and Christof Asbach from Institute of Environment & Energy, Technology & Analytics, who in turn is grateful for the funding from North Rhine-Westphalia Office of Nature, Environment and Consumer Protection (LANUV). This work was supported by the funding from the Research Council of Lithuania (LMT LT), agreement No. S-MIP-22-57. Mario Lovric acknowledges funding by the EU-Commission Grant Nr. 101057497 – EDIAQI. Jakub Ondracek acknowledges the Ministry of Education, Youth and Sports of the Czech Republic under grant ACTRIS-CZ (LM2023030). David C. Green acknowledges funding by the National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Exposures and Health, a partnership between UK Health Security Agency (UKHSA) and Imperial College London. Roy Harrison and David Beddows acknowledge support from the UK Natural Environment Research Council through the National Centre for Atmospheric Science. ACTRIS observations in France are supported by the French Ministry for Research, French National Centre for Scientific Research (CNRS) and 22 French research performing organizations composing the ACTRIS-FR consortium.

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.

2024-05-02 - First online date 2024-07-29 - Posted date

Peer reviewed