Source Apportionment of Particulate Matter by Application of Machine Learning Clustering Algorithms

Abstract A source apportionment (SA) study was conducted on two PM2.5 data sets, two carbon fractions and eight temperature-resolved carbon fractions collected during Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). This study aimed to evaluate two clustering algorithms: k-means cluste...

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Detalles Bibliográficos

Autores principales:	Vikas Kumar, Manoranjan Sahu, Pratim Biswas
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Springer 2022-01-01
Colección:	Aerosol and Air Quality Research
Materias:	PM2.5 Source apportionment Receptor modeling Positive matrix factorization Machine learning Clustering algorithms
Acceso en línea:	https://doi.org/10.4209/aaqr.210240
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