Time series analysis of PM10 concentration in Parit Raja residential area
Parit Raja is one of the sub-urban area that rapidly grow due to its location containing industrial and education hub. Pollution from factories and the increasing number of vehicles are the main contributors of PM10. Since PM10 can give the adverse effect to human health such as asthma, cardiovascul...
| Main Authors: | , , , |
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| Format: | Article |
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Science Publishing Corporation
2018
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| Online Access: | http://eprints.uthm.edu.my/3826/ |
| _version_ | 1848888123984969728 |
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| author | Raffee, Ahmad Fauzi Hamid, Hazrul Abdul Radin Mohamed, Radin Maya Saphira Jaffar, Muhammad Ismail |
| author_facet | Raffee, Ahmad Fauzi Hamid, Hazrul Abdul Radin Mohamed, Radin Maya Saphira Jaffar, Muhammad Ismail |
| author_sort | Raffee, Ahmad Fauzi |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Parit Raja is one of the sub-urban area that rapidly grow due to its location containing industrial and education hub. Pollution from factories and the increasing number of vehicles are the main contributors of PM10. Since PM10 can give the adverse effect to human health such as asthma, cardiovascular disease and lung problem, appropriate action mainly involve short-term prediction maybe required as a precaution. This research was conducted to predict the PM10 concentration using the best time series model in Parit Raja, Batu Pahat, Johor. Primary data was obtained using E-Sampler at three monitoring stations; Sekolah Menengah Kebangsaan (SMK) Tun Ismail, Kolej Kediaman Melewar and Sekolah Rendah Kebangsaan Pintas Raya. ARIMA time series model was used to predict the PM10 concentration and the most suitable model is identify using by Akaike Information Criterion (AIC). Prediction of PM10 concentration for for the next 48 hours at all monitoring locations was verified using three error measures which are mean absolute error (MAE), normalized absolute error (NAE) and root mean square error (RMSE). After comparing the time series model, the short term prediction model for station 1 is AR(1), station 2 is ARMA(1,1) and station 3 is ARMA(2,1) based on the smallest AIC value and the best time series model that used for prediction at Parit Raja residential area is AR(1). Since the best model was identified for Parit Raja residential area, PM10 concentration can be predicted using AR(1) model to identify the value of PM10 concentration in the next day. |
| first_indexed | 2025-11-15T20:05:17Z |
| format | Article |
| id | uthm-3826 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T20:05:17Z |
| publishDate | 2018 |
| publisher | Science Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-38262021-11-22T04:02:29Z http://eprints.uthm.edu.my/3826/ Time series analysis of PM10 concentration in Parit Raja residential area Raffee, Ahmad Fauzi Hamid, Hazrul Abdul Radin Mohamed, Radin Maya Saphira Jaffar, Muhammad Ismail T Technology (General) TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution Parit Raja is one of the sub-urban area that rapidly grow due to its location containing industrial and education hub. Pollution from factories and the increasing number of vehicles are the main contributors of PM10. Since PM10 can give the adverse effect to human health such as asthma, cardiovascular disease and lung problem, appropriate action mainly involve short-term prediction maybe required as a precaution. This research was conducted to predict the PM10 concentration using the best time series model in Parit Raja, Batu Pahat, Johor. Primary data was obtained using E-Sampler at three monitoring stations; Sekolah Menengah Kebangsaan (SMK) Tun Ismail, Kolej Kediaman Melewar and Sekolah Rendah Kebangsaan Pintas Raya. ARIMA time series model was used to predict the PM10 concentration and the most suitable model is identify using by Akaike Information Criterion (AIC). Prediction of PM10 concentration for for the next 48 hours at all monitoring locations was verified using three error measures which are mean absolute error (MAE), normalized absolute error (NAE) and root mean square error (RMSE). After comparing the time series model, the short term prediction model for station 1 is AR(1), station 2 is ARMA(1,1) and station 3 is ARMA(2,1) based on the smallest AIC value and the best time series model that used for prediction at Parit Raja residential area is AR(1). Since the best model was identified for Parit Raja residential area, PM10 concentration can be predicted using AR(1) model to identify the value of PM10 concentration in the next day. Science Publishing Corporation 2018 Article PeerReviewed Raffee, Ahmad Fauzi and Hamid, Hazrul Abdul and Radin Mohamed, Radin Maya Saphira and Jaffar, Muhammad Ismail (2018) Time series analysis of PM10 concentration in Parit Raja residential area. International Journal of Engineering and Technology, 7 (3.23). pp. 15-21. ISSN 2227-524X https://doi.org/10.14419/ijet.v7i3.23.17252 |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution Raffee, Ahmad Fauzi Hamid, Hazrul Abdul Radin Mohamed, Radin Maya Saphira Jaffar, Muhammad Ismail Time series analysis of PM10 concentration in Parit Raja residential area |
| title | Time series analysis of PM10 concentration in Parit Raja residential area |
| title_full | Time series analysis of PM10 concentration in Parit Raja residential area |
| title_fullStr | Time series analysis of PM10 concentration in Parit Raja residential area |
| title_full_unstemmed | Time series analysis of PM10 concentration in Parit Raja residential area |
| title_short | Time series analysis of PM10 concentration in Parit Raja residential area |
| title_sort | time series analysis of pm10 concentration in parit raja residential area |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution |
| url | http://eprints.uthm.edu.my/3826/ http://eprints.uthm.edu.my/3826/ |