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

Area of Science:

• Atmospheric chemistry and physics
• Environmental science
• Public health

Background:

• Atmospheric new particle formation (NPF) generates sub-10 nm particles globally, influencing air quality.
• Limited epidemiological studies link NPF to adverse health effects due to identification challenges.
• Manual identification of NPF events is time-consuming and difficult for large datasets.

Purpose of the Study:

• To develop an automated method for identifying regional new particle formation (NPF) events.
• To create a comprehensive, machine learning-based dataset of NPF events for global research.
• To facilitate future epidemiological studies on the health impacts of NPF-derived particles.

Main Methods:

• Development of a machine learning algorithm for automated NPF event identification.
• Application of the algorithm to particle number size distribution data from 65 global measurement sites.
• Generation of regional NPF event tags for the period 1996-2023.

Main Results:

• Successful creation of an automated regional NPF identifier.
• A global dataset of NPF events spanning over two decades (1996-2023) has been generated.
• The dataset covers 65 measurement sites worldwide, enabling large-scale analysis.

Conclusions:

• The automated NPF identifier simplifies and standardizes the detection of NPF events.
• The generated dataset provides a valuable resource for investigating the health effects of atmospheric particles.
• This work addresses a critical data gap, paving the way for more robust air quality and health research.