Mahsa Ashouri This email address is being protected from spambots. You need JavaScript enabled to view it.1, Frederick Kin Hing Phoa2, Chun-Houh Chen2, Galit Shmueli3 

1 Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-1382, USA
2 Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan
3 Institute of Service Science, National Tsing Hua University, Hsinchu 30013, Taiwan

Received: May 28, 2023
Revised: October 13, 2023
Accepted: October 15, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Cite this article:

Ashouri, M., Phoa, F.K.H., Chen, C.H., Shmueli, G. (2023). An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis. Aerosol Air Qual. Res. 23, 230124.


  • Visualization tool helps analyze Taiwan PM2.5 patterns via regional clusters.
  • The web-based app is a user-friendly tool suitable for a wide range of users.
  • Results aid EPAs in solving environmental issues like traffic control.
  • The tool reveals an improvement in Taiwan's air quality since 2017.
  • Analysis isolates southern Taiwan, revealing an unusual air quality zone.


Examining PM2.5 (atmospheric particulate matter with a maximum diameter of 2.5 micrometers), seasonal patterns is an important research area for environmental scientists. An improved understanding of PM2.5 seasonal patterns can help environmental protection agencies (EPAs) make decisions and develop complex models for controlling the concentration of PM2.5 in different regions. This work proposes an R Shiny App web-based interactive tool, namely a “model-based time series clustering” (MTSC) tool, for clustering PM2.5 time series using spatial and population variables and their temporal features, like seasonality. Our tool allows stakeholders to visualize important characteristics of PM2.5 time series, including temporal patterns and missing values, and cluster series by attribute groupings. We apply the MTSC tool to cluster Taiwan’s PM2.5 time series based on air quality zones and types of monitoring stations. The tool clusters the series into four clusters that reveal several phenomena, including an improvement in Taiwan's air quality since 2017 in all regions, although at varying rates, an increasing pattern of PM2.5 concentration when moving from northern towards southern regions, winter/summer seasonal patterns that are more pronounced in certain types of areas (e.g., industrial), and unusual behavior in the southernmost region. The tool provides cluster-specific quantitative figures, like seasonal variations in PM2.5 concentration in different air quality zones of Taiwan, and identifies, for example, an annual peak in early January and February (maximum value around 120 µg m3). Our analysis identifies a region in southernmost Taiwan as different from other zones that are currently grouped together with it by Taiwan EPA (TEPA), and a northern region that behaves differently from its TEPA grouping. All these cluster-based insights help EPA experts implement short-term zone-specific air quality policies (e.g., fireworks and traffic regulations, school closures) as well as longer-term decision-making (e.g., transport control stations, fuel permits, old vehicle replacement, fuel type).

Keywords: Time series, Clustering, Web-based tool, Air quality, Environmental protection agencies

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