Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis

Particulate matter (PM) has caught scientific attention in scientific research due to its harmful effect on human health. While prediction is essential for future development in Keningau, temporal clustering in Keningau has yet to be studied. Thus, this research aims to determine whether monsoonal c...

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Main Authors: Muhammad Izzuddin Rumaling, F P Chee, J H W Chang, J Sentian
Format: Proceedings
Language:English
English
Published: IOP Publishing Ltd 2022
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/41763/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41763/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41763/
https://iopscience.iop.org/article/10.1088/1755-1315/1103/1/012003
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spelling my.ums.eprints.417632024-11-06T06:37:48Z https://eprints.ums.edu.my/id/eprint/41763/ Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis Muhammad Izzuddin Rumaling F P Chee J H W Chang J Sentian QH1-(199.5) General Including nature conservation, geographical distribution QH1-278.5 Natural history (General) Particulate matter (PM) has caught scientific attention in scientific research due to its harmful effect on human health. While prediction is essential for future development in Keningau, temporal clustering in Keningau has yet to be studied. Thus, this research aims to determine whether monsoonal clustering is required for meteorological and pollutant concentration data collected in Keningau. Missing data is first imputed using Nearest Neighbour Method (NNM). Then, wind direction and wind speed are converted into northern (Wy) and eastern (Wx) component of wind speed. Data is then temporal clustered based on monsoonal season (NEM, IM4, SWM, IM10). Both clustered and unclustered data are analysed using principal component (PC) analysis (PCA). The findings revealed that humidity in PC1 with average EV (explained variation) of 93.92 ± 0.52 contribute the most variation of PM10, followed by Wx in PC2 with average EV of 3.51 ± 0.48. Regression analysis shows that humidity and PM10 are negatively moderate to strongly correlated except for IM4 (intermonsoon April), which may be due to dry climate during the season. As for Wx, it has weak correlation with PM10. This may be due to location of Keningau at western part of Crocker range. However, the spread of PM10 due to eastern wind causes weak to zero correlation. Due to consideration of dry climate as revealed by the findings from IM4 cluster, there is need for data collected by Keningau to be clustered by monsoon. IOP Publishing Ltd 2022 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/41763/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41763/2/FULL%20TEXT.pdf Muhammad Izzuddin Rumaling and F P Chee and J H W Chang and J Sentian (2022) Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis. https://iopscience.iop.org/article/10.1088/1755-1315/1103/1/012003
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 QH1-(199.5) General Including nature conservation, geographical distribution
QH1-278.5 Natural history (General)
spellingShingle QH1-(199.5) General Including nature conservation, geographical distribution
QH1-278.5 Natural history (General)
Muhammad Izzuddin Rumaling
F P Chee
J H W Chang
J Sentian
Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
description Particulate matter (PM) has caught scientific attention in scientific research due to its harmful effect on human health. While prediction is essential for future development in Keningau, temporal clustering in Keningau has yet to be studied. Thus, this research aims to determine whether monsoonal clustering is required for meteorological and pollutant concentration data collected in Keningau. Missing data is first imputed using Nearest Neighbour Method (NNM). Then, wind direction and wind speed are converted into northern (Wy) and eastern (Wx) component of wind speed. Data is then temporal clustered based on monsoonal season (NEM, IM4, SWM, IM10). Both clustered and unclustered data are analysed using principal component (PC) analysis (PCA). The findings revealed that humidity in PC1 with average EV (explained variation) of 93.92 ± 0.52 contribute the most variation of PM10, followed by Wx in PC2 with average EV of 3.51 ± 0.48. Regression analysis shows that humidity and PM10 are negatively moderate to strongly correlated except for IM4 (intermonsoon April), which may be due to dry climate during the season. As for Wx, it has weak correlation with PM10. This may be due to location of Keningau at western part of Crocker range. However, the spread of PM10 due to eastern wind causes weak to zero correlation. Due to consideration of dry climate as revealed by the findings from IM4 cluster, there is need for data collected by Keningau to be clustered by monsoon.
format Proceedings
author Muhammad Izzuddin Rumaling
F P Chee
J H W Chang
J Sentian
author_facet Muhammad Izzuddin Rumaling
F P Chee
J H W Chang
J Sentian
author_sort Muhammad Izzuddin Rumaling
title Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
title_short Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
title_full Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
title_fullStr Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
title_full_unstemmed Effect of monsoonal clustering for pm10 concentration Prediction in Keningau, Sabah using principal component analysis
title_sort effect of monsoonal clustering for pm10 concentration prediction in keningau, sabah using principal component analysis
publisher IOP Publishing Ltd
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/41763/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41763/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41763/
https://iopscience.iop.org/article/10.1088/1755-1315/1103/1/012003
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score 13.214268