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Application of k-means clustering and calendar view Visualisation for air pollution index analysis

Two years of diurnal concentration of particulate matter (PM10) and nitrogen dioxide with the addition of relative humidity measurement, collected from Putrajaya, Malaysia’s ground-based measurement station from January 2014 to December 2015, were analysed. Kmeans clustering was employed and optimal...

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Main Authors: Z Ali Omar, Siti Rahayu Mohd Hashim, Justin Sentian, Su Na Chin
Format: Proceedings
Language:English
English
Published: IOP Publishing Ltd 2022
Subjects:
TD878-894 Special types of environment Including soil pollution, air pollution, noise pollution
TP155-156 Chemical engineering
Online Access:https://eprints.ums.edu.my/id/eprint/41724/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41724/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41724/
https://www.researchgate.net/publication/365727238_Application_of_K-Means_Clustering_and_Calendar_View_Visualisation_for_Air_Pollution_Index_Analysis
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spelling my.ums.eprints.417242024-11-01T01:33:04Z https://eprints.ums.edu.my/id/eprint/41724/ Application of k-means clustering and calendar view Visualisation for air pollution index analysis Z Ali Omar Siti Rahayu Mohd Hashim Justin Sentian Su Na Chin TD878-894 Special types of environment Including soil pollution, air pollution, noise pollution TP155-156 Chemical engineering Two years of diurnal concentration of particulate matter (PM10) and nitrogen dioxide with the addition of relative humidity measurement, collected from Putrajaya, Malaysia’s ground-based measurement station from January 2014 to December 2015, were analysed. Kmeans clustering was employed and optimal clusters of four were identified for each year based on the most suggested number of clusters from internal cluster validation measures of the total within sum of square, silhouette index and gap statistics. Each cluster was then profiled where each mean pollutant sub-indices were calculated and the contributing pollutant to the air pollution index (API) was determined by looking at the maximum value from all subindices. This mechanism closely follows the Recommended Malaysian Air Quality Guidelines (RMG) for determining API. Particulate matter was found to be the dominant sub-index in all clusters and then paired with the mean relative humidity for visualisation. A calendar view was selected to show the temporal patterns and we observed a consistent cluster profile with the actual mean values of the selected parameters for most months. The calendar view also suggested that overall, the API (based on particulate matter) in 2014 was much better as compared to 2015. IOP Publishing Ltd 2022 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/41724/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41724/2/FULL%20TEXT.pdf Z Ali Omar and Siti Rahayu Mohd Hashim and Justin Sentian and Su Na Chin (2022) Application of k-means clustering and calendar view Visualisation for air pollution index analysis. https://www.researchgate.net/publication/365727238_Application_of_K-Means_Clustering_and_Calendar_View_Visualisation_for_Air_Pollution_Index_Analysis
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic TD878-894 Special types of environment Including soil pollution, air pollution, noise pollution
TP155-156 Chemical engineering
spellingShingle TD878-894 Special types of environment Including soil pollution, air pollution, noise pollution
TP155-156 Chemical engineering
Z Ali Omar
Siti Rahayu Mohd Hashim
Justin Sentian
Su Na Chin
Application of k-means clustering and calendar view Visualisation for air pollution index analysis
description Two years of diurnal concentration of particulate matter (PM10) and nitrogen dioxide with the addition of relative humidity measurement, collected from Putrajaya, Malaysia’s ground-based measurement station from January 2014 to December 2015, were analysed. Kmeans clustering was employed and optimal clusters of four were identified for each year based on the most suggested number of clusters from internal cluster validation measures of the total within sum of square, silhouette index and gap statistics. Each cluster was then profiled where each mean pollutant sub-indices were calculated and the contributing pollutant to the air pollution index (API) was determined by looking at the maximum value from all subindices. This mechanism closely follows the Recommended Malaysian Air Quality Guidelines (RMG) for determining API. Particulate matter was found to be the dominant sub-index in all clusters and then paired with the mean relative humidity for visualisation. A calendar view was selected to show the temporal patterns and we observed a consistent cluster profile with the actual mean values of the selected parameters for most months. The calendar view also suggested that overall, the API (based on particulate matter) in 2014 was much better as compared to 2015.
format Proceedings
author Z Ali Omar
Siti Rahayu Mohd Hashim
Justin Sentian
Su Na Chin
author_facet Z Ali Omar
Siti Rahayu Mohd Hashim
Justin Sentian
Su Na Chin
author_sort Z Ali Omar
title Application of k-means clustering and calendar view Visualisation for air pollution index analysis
title_short Application of k-means clustering and calendar view Visualisation for air pollution index analysis
title_full Application of k-means clustering and calendar view Visualisation for air pollution index analysis
title_fullStr Application of k-means clustering and calendar view Visualisation for air pollution index analysis
title_full_unstemmed Application of k-means clustering and calendar view Visualisation for air pollution index analysis
title_sort application of k-means clustering and calendar view visualisation for air pollution index analysis
publisher IOP Publishing Ltd
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/41724/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41724/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41724/
https://www.researchgate.net/publication/365727238_Application_of_K-Means_Clustering_and_Calendar_View_Visualisation_for_Air_Pollution_Index_Analysis
_version_ 1814935160490033152
score 13.211869

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