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...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Vikas Kumar, Manoranjan Sahu, Pratim Biswas
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Springer 2022-01-01
Colecção:	Aerosol and Air Quality Research
Assuntos:	PM2.5 Source apportionment Receptor modeling Positive matrix factorization Machine learning Clustering algorithms
Acesso em linha:	https://doi.org/10.4209/aaqr.210240
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!