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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Autors principals:	Vikas Kumar, Manoranjan Sahu, Pratim Biswas
Format:	Artigo
Idioma:	Inglês
Publicat:	Springer 2022-01-01
Col·lecció:	Aerosol and Air Quality Research
Matèries:	PM2.5 Source apportionment Receptor modeling Positive matrix factorization Machine learning Clustering algorithms
Accés en línia:	https://doi.org/10.4209/aaqr.210240
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