Single-Particle Metal Fingerprint Analysis and Machine Learning Pipeline for Source Apportionment of Metal-Containing Fine Particles in Air

Fine particulate matter (PM2.5) is a serious global
health concern requiring mitigation, but source apportionment is difficult
due to the limited variability in bulk aerosol composition between
sources. The unique metal fingerprints of individual particles in PM2.5 sources can now be measured and may
be used to identify sources. This study is the first to develop a
robust machine learning pipeline to apportion PM2.5 sources
based on the metal fingerprints of individual particles in air samples collected in Beijing, China. The metal fingerprints
of particles in five primary PM2.5 source emitters were
measured by single-particle inductively coupled plasma time-of-flight
mass spectrometry (spICP-TOF-MS). A novel machine learning pipeline
was used to identify unique fingerprints of individual particles from
the five sources. The model successfully predicted 63% of the test
data set (significantly higher than random guessing at 20%) and had
73% accuracy on a physically mixed sample. This strategy identified
metal-containing particles unique to specific PM2.5 sources
that confirms their presence and can potentially link PM2.5 toxicity to the metal content of specific particle types in anthropogenic
PM2.5 sources.