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

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.