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.