Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning

To further reduce atmospheric particulate matter concentrations,
there is a need for a more precise identification of their sources.
The SEM-EDS technology (scanning electron microscopy and energy-dispersive
X-ray spectroscopy) can provide high-resolution imaging and detailed
compositional analysis for particles with relatively stable physical
and chemical properties. This study introduces an advanced source
apportionment pipeline (RX model) that uniquely combines computer-controlled
scanning electron microscopy with computer vision and machine learning
to trace particle sources by integrating single-particle morphology,
size, and chemical information. In the evaluation using a virtual
data set with known source contributions, the RX model demonstrated
high accuracy, with average errors of 0.60% for particle number and
1.97% for mass contribution. Compared to the chemical mass balance
model, the RX model’s accuracy and stability improved by 75.6
and 73.4%, respectively, and proved effective in tracing Fe-containing
particles in the atmosphere of a steel city in China. This study indicates
that particle morphology can serve as an effective feature for determining
its source. The findings highlight the potential of electron microscopy
technology coupled with computer vision and machine learning techniques
to enhance our understanding of atmospheric pollution sources, offering
valuable insights for PM health risk assessment and evidence-based
policy-making.