Examinando por Materia "Chemical characterization"

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    Aprendizaje automático para la identificación mineralógica de material particulado - Bogotá, Cali y Valle de Aburrá (Colombia)
    (Universidad EAFIT, 2023) Gutiérrez Silva, Juan Alberto; Duque Trujillo, José Fernando
    Identifying the mineral components present in particulate matter can be of great help to understand the dynamics of air pollution, especially to detect the presence of minerals that are dangerous for inhalation (such as asbestos). In this work is developed a methodology for the clustering of chemical data obtained through scanning electron microscopy with energy dispersive spectroscopy (SEM-EDX) in samples located in Bogota, Cali and Valle de Aburrá (Colombia). Rausch et al. (2022) and Avellaneda et al. (2020) develop and apply a methodology based on random forest algorithms to separate categories of particles, including minerals. In this work, a generalized algorithm based on DBSCAN is proposed as a complement. It allowed to analyze a set of 3716 samples previously classified as "mineral". The results reveal the presence of at least 15 different minerals. Despite a relatively low classification effectiveness (~20%), this work represents a significant advance in this area, as precedents are few or non-existent for this type of application. It is notable, also, that the presence of Serpentine (Antigorite variety) was detected in Medellín. The findings of this study reveal that most of the particles correspond to quartz, calcite, kaolinite and plagioclase. Despite the limitations, the algorithm demonstrates its effectiveness in mineral identification. However, improvements that could increase its accuracy are recognized. Overall, this study establishes a starting point for future chemical characterization analyses of particulate matter.