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

• Published in: Aerosol and Air Quality Research Vol. 22; no. 3; pp. 1 - 13+ap1-20-005
• Main Authors: Kumar, Vikas, Sahu, Manoranjan, Biswas, Pratim
• Format: Journal Article
• Language: English
• Published: Taoyuan City 社團法人台灣氣膠研究學會 01.03.2022; Taiwan Association of Aerosol Research
• Subjects: Aerosols; Air pollution; Air temperature; Algorithms; Apportionment; Artificial intelligence; Burning; Carbon; Children; Cluster analysis; Clustering; Combustion; Datasets; Gasoline; Learning algorithms; Machine learning; Particulate matter; Pollution studies; Receptors; Variables; Vector quantization; PM_(2.5); Receptor modeling; Source apportionment; Clustering algorithms; Machine learning; Positive matrix factorization
• ISSN: 1680-8584; 2071-1409
• DOI: 10.4209/aaqr.210240

A source apportionment (SA) study was conducted on two PM_(2.5) 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 clustering (kMC) and spectral clustering (SC) as potential receptor models for source apportionment. The application of kMC produced unsatisfactory results, but the results obtained from SC demonstrated a significant correlation with the results obtained using positive matrix factorization (PMF). The clustering results obtained were associated with practical evidence available in the literature. SC identified six source factors on analyzing two carbon fractions data set and seven factors from eight temperature-resolved carbon fractions data set. The sources (source contribution in parentheses) identified are: combustion (45.9 ± 3.66%) and secondary sulfate (11.4 ± 1.09%), vegetative/wood burning (17.5 ± 1.46%), diesel (10.6 ± 0.92%) and gasoline (3.6 ± 0.33%) vehicles, soil/crustal (2.07 ± 0.2%), traffic (9.3 ± 0.81%), and metal processing (8.8 ± 0.72%). The source profiles obtained using SC also show similarity with the profiles derived using PMF. In summary, this study presented a basic framework for applying Machine Learning algorithms for SA analysis. Also, it presents SC as a potential receptor model technique for SA.