1 results match your criteria: "France. Electronic address: claire.carvallo@sorbonne-universite.fr.[Affiliation]"
A supervised machine learning approach to classify traffic-derived PM sources based on their magnetic properties.
Environ Res
August 2023
Sorbonne Université, UMR 7590, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, F-75005, Paris, France. Electronic address:
Article Synopsis
- Environmental magnetism techniques can effectively map particulate pollutants on various accumulative surfaces, such as urban plant leaves and passive filters.
- The study uses a k-nearest neighbors algorithm (kNN) to classify pollution sources, primarily focusing on traffic-related pollutants like tire and brake pad residue.
- Results show that the model accurately identifies different traffic-related pollution sources and highlights the potential for magnetic mapping to enhance existing air quality measurement methods and improve pollutant dispersion models.