An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis
Abstract 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 an...
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| Format: | Article |
| Language: | English |
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Springer
2023-10-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.230124 |
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| author | Mahsa Ashouri Frederick Kin Hing Phoa Chun-Houh Chen Galit Shmueli |
| author_facet | Mahsa Ashouri Frederick Kin Hing Phoa Chun-Houh Chen Galit Shmueli |
| author_sort | Mahsa Ashouri |
| collection | DOAJ |
| description | Abstract 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 m−3). 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). |
| format | Article |
| id | doaj-art-f671afe9ca194001b7f66e1ee8c354cd |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-f671afe9ca194001b7f66e1ee8c354cd2025-02-09T12:23:13ZengSpringerAerosol and Air Quality Research1680-85842071-14092023-10-01231211810.4209/aaqr.230124An Interactive Clustering-Based Visualization Tool for Air Quality Data AnalysisMahsa Ashouri0Frederick Kin Hing Phoa1Chun-Houh Chen2Galit Shmueli3Department of Biostatistics, University of MichiganInstitute of Statistical Science, Academia SinicaInstitute of Statistical Science, Academia SinicaInstitute of Service Science, National Tsing Hua UniversityAbstract 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 m−3). 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).https://doi.org/10.4209/aaqr.230124Time seriesClusteringWeb-based toolAir qualityEnvironmental protection agencies |
| spellingShingle | Mahsa Ashouri Frederick Kin Hing Phoa Chun-Houh Chen Galit Shmueli An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis Aerosol and Air Quality Research Time series Clustering Web-based tool Air quality Environmental protection agencies |
| title | An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis |
| title_full | An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis |
| title_fullStr | An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis |
| title_full_unstemmed | An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis |
| title_short | An Interactive Clustering-Based Visualization Tool for Air Quality Data Analysis |
| title_sort | interactive clustering based visualization tool for air quality data analysis |
| topic | Time series Clustering Web-based tool Air quality Environmental protection agencies |
| url | https://doi.org/10.4209/aaqr.230124 |
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