Nur Fariha Syaqina ZulkepliMohd Salmi Md NooraniFatimah Abdul RazakMunira IsmailMohd Almie Alias2024-05-102024-05-102020Zulkepli, N. F. S., Noorani, M. S. M., Razak, F. A., Ismail, M., & Alias, M. A. (2020). A New Approach to Cluster Air Quality Monitoring Stations using Persistent Homology. Sains Malaysiana, 49(4), 963-970.https://repoemc.ukm.my/handle/123456789/569The issue of air pollution is a global problem that continues to be discussed today. Often, the use of quantitative approaches such as cluster analysis, correlation analysis and principal component analysis is used to analyze the similarity of air pollution between stations. However, studies related to qualitative approaches, especially topological approaches to analyzing the similarity of air pollution, have not been widely popularized in Malaysia. Therefore, this study is a pilot study conducted to investigate the similarity of air pollution between several stations in Malaysia using a technique in the topological data analysis known as persistent homology. The topological properties of air pollution are described by topological features such as connected components, holes, and void. The particulate matter (PM10) known as the main pollutant is used to describe the air pollution behavior at Klang, Petaling Jaya and Shah Alam air quality monitoring stations.enPersistent homologyPM10topological data analysistopological similarityWasserstein distanceJournal