Nur Fariha Syaqina Zulkepli , Mohd Salmi Md Noorani, Fatimah Abdul Razak, Munira Ismail, Mohd Almie Alias


School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia



Received: October 8, 2018
Revised: May 1, 2019
Accepted: May 28, 2019
Download Citation: ||https://doi.org/10.4209/aaqr.2018.08.0315  

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Cite this article:
Zulkepli, N.F.S., Noorani, M.S.M., Razak, F.A., Ismail, M. and Alias, M.A. (2019). Topological Characterization of Haze Episodes Using Persistent Homology. Aerosol Air Qual. Res. 19: 1614-1624. https://doi.org/10.4209/aaqr.2018.08.0315


HIGHLIGHTS

  • Topological features of particulate matter are extracted using persistent homology.
  • Diverse pattern of topological features distinguish months with and without haze.
  • Summary statistics were calculated to summarize topological features.
  • Drastic changes of topological features were observed during haze episodes.

ABSTRACT


Haze is one of the major environmental issues that have continuously vexed countries worldwide, including Malaysia, for the last three decades. Therefore, this study aims to investigate the differences between the topological features of months with and those without haze episodes observed at air quality monitoring stations located in the areas of Jerantut, Klang, Petaling Jaya and Shah Alam. We employ persistent homology, which is a method of topological data analysis (TDA) that focuses on connected components and holes in the data, to characterize the local particulate matter (PM10). The summary statistics reveal drastic changes in the lifetimes of the topological data from every station during haze episodes, highlighting the possibility of developing an early detection system for haze based on our approach.


Keywords: Haze; Particulate matter; Persistent homology; Time delay embedding; Topological data analysis.

 


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