A new approach for source apportionment of Black Carbon from Raman Spectroscopy

Black Carbon is a major contributor to air pollution and climate change. Current techniques using an Aethalometer can differentiate biomass burning and fossil fuel combustion aerosols, but their accuracy is limited by assumptions and BC properties. This study explores using Raman spectroscopy, statistical analysis and machine learning to improve BC source apportionment, considering more BC emission sources: diesel, gasoline, and biomass burning. Preliminary results indicate over 95% accuracy in classifying BC sources, providing a promising innovative method for effective air quality management.