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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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Vikas Kumar, Manoranjan Sahu, Pratim Biswas
Format: Artigo
Sprache:Inglês
Veröffentlicht: Springer 2022-01-01
Schriftenreihe:Aerosol and Air Quality Research
Schlagworte:
PM2.5
Source apportionment
Receptor modeling
Positive matrix factorization
Machine learning
Clustering algorithms
Online-Zugang:https://doi.org/10.4209/aaqr.210240
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