Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model
Particulate matter (PM10) is one of the key indicator of air quality index (API) during high particulate event (HPE). PM10 can cause adverse effect on human health and environment; hence, it is important to develop a reliable and accurate predictive model to be used as forecasting tool to alarm the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/74/e3sconf_icongeet2023_01002.pdf |
| _version_ | 1797658514044223488 |
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| author | A Rahim Nur Alis Addiena Mohamed Noor Norazian Mohd Jafri Izzati Amani Ul Saufie Ahmad Zia Madalina Boboc |
| author_facet | A Rahim Nur Alis Addiena Mohamed Noor Norazian Mohd Jafri Izzati Amani Ul Saufie Ahmad Zia Madalina Boboc |
| author_sort | A Rahim Nur Alis Addiena |
| collection | DOAJ |
| description | Particulate matter (PM10) is one of the key indicator of air quality index (API) during high particulate event (HPE). PM10 can cause adverse effect on human health and environment; hence, it is important to develop a reliable and accurate predictive model to be used as forecasting tool to alarm the citizen especially during HPE. This study aims to develop a modified Quantile Regression (QR) model to forecast the PM10 concentration during HPE in Malaysia. The performances of three predictive models namely Multiple Linear Regression (MLR), Quantile Regression (QR) and a modified QR models i.e. combination of QR with Relief-based were compared. The hourly dataset of PM10 concentration with other gaseous pollutants and weather parameters at Klang from the year with severe haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the accuracy of the predictive models. This study found that the Relief-QR model showed the best performance compared to MLR and QR models. The prediction of future PM10 concentration is very important because it can aid the local authorities to implement precautionary measures to limit the impact of air pollution. |
| first_indexed | 2024-03-11T18:01:09Z |
| format | Article |
| id | doaj.art-774179b5e46b41cc92195f13ef3232d4 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-11T18:01:09Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-774179b5e46b41cc92195f13ef3232d42023-10-17T08:53:04ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014370100210.1051/e3sconf/202343701002e3sconf_icongeet2023_01002Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified ModelA Rahim Nur Alis Addiena0Mohamed Noor Norazian1Mohd Jafri Izzati Amani2Ul Saufie Ahmad Zia3Madalina Boboc4Faculty of Civil Engineering & Technology, Universiti Malaysia PerlisFaculty of Civil Engineering & Technology, Universiti Malaysia PerlisFaculty of Civil Engineering & Technology, Universiti Malaysia PerlisSustainable Environment Research Group (SERG), Centre of Excellence Geopolymer and Green Technology (CEGeoGTech), Universiti Malaysia PerlisNational Institute for Research and Development in Environmental Protection Bucharest (INCDPM)Particulate matter (PM10) is one of the key indicator of air quality index (API) during high particulate event (HPE). PM10 can cause adverse effect on human health and environment; hence, it is important to develop a reliable and accurate predictive model to be used as forecasting tool to alarm the citizen especially during HPE. This study aims to develop a modified Quantile Regression (QR) model to forecast the PM10 concentration during HPE in Malaysia. The performances of three predictive models namely Multiple Linear Regression (MLR), Quantile Regression (QR) and a modified QR models i.e. combination of QR with Relief-based were compared. The hourly dataset of PM10 concentration with other gaseous pollutants and weather parameters at Klang from the year with severe haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the accuracy of the predictive models. This study found that the Relief-QR model showed the best performance compared to MLR and QR models. The prediction of future PM10 concentration is very important because it can aid the local authorities to implement precautionary measures to limit the impact of air pollution.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/74/e3sconf_icongeet2023_01002.pdf |
| spellingShingle | A Rahim Nur Alis Addiena Mohamed Noor Norazian Mohd Jafri Izzati Amani Ul Saufie Ahmad Zia Madalina Boboc Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model E3S Web of Conferences |
| title | Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model |
| title_full | Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model |
| title_fullStr | Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model |
| title_full_unstemmed | Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model |
| title_short | Prediction of PM10 Level During High Particulate Event in Malaysia Using Modified Model |
| title_sort | prediction of pm10 level during high particulate event in malaysia using modified model |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/74/e3sconf_icongeet2023_01002.pdf |
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