A Novel Approach : Automated SEM-EDS Analysis for the Individual Particle Classification of Air pollutants

Abstract

Airborne particulate matter consists of various molecules attached to particles, including organic compounds, minerals, and metals. Despite being extremely small and not visible to the naked eye, these particles pose serious health risks, contributing to cardiovascular and respiratory issues. Their characteristics vary depending on their sources, such as vehicle emissions, industrial areas, and agricultural activities. Accurately tracing the origin and emission contributions of airborne particulate matter is crucial for addressing potential health threats.

Scanning electron microscopy (SEM) combined with energy dispersive X-ray spectroscopy (EDS) is a valuable non-destructive technique for examining both the shape and chemical composition of individual particles. Unlike other analytical instruments that provide chemical information in elemental form, SEM-EDS analysis offers compound-level details, aiding in precise origin tracking. Also, Presented automated SEM-EDS analysis allows for the examination of thousands of particles, facilitating statistical analysis.

This study presents reliable statistical analysis based on chemical and morphological data of air pollutants collected using SEM/EDS. The goal is to track the sources of air pollutants by integrating their chemical and morphological characteristics.

Samples were collected at the Korea Institute of Science and Technology in Seoul, Republic of Korea, during winter between 2021 and 2023. High-concentration PM2.5 days were chosen for particle analysis using automated SEM-EDS, focusing on metal-based particle morphology and compound forms. The analysis utilized SEM equipment with EDS capabilities. Each sample underwent approximately 35 frames of analysis at a magnification of 10,000X, documenting 1,000 particles.

The analysis revealed that particulate matter samples consist of organic matter, mineral particles, Fe compound particles, and Non-Fe compound particles containing heavy metals. The proportions of these components varied across samples, influenced by atmospheric conditions, particle sources, and wind direction. Back trajectory analysis was conducted to provide a more precise source analysis.

The automated SEM-EDS analysis method offers efficient and comprehensive particle characterization, providing valuable insights into their origins. This information can be utilized for tracking pollution sources and predicting the health effects of air quality.