Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support
Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM10) throughout the years. Studies have affirmed that PM10 influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM10 status for mitig...
| Main Authors: | , , , |
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| Format: | Article |
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MDPI AG
2019
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| _version_ | 1796980697114607616 |
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| author | Abdullah, Samsuri Ismail, Marzuki Ahmed, Ali Najah Abdullah, Ahmad Makmom |
| author_facet | Abdullah, Samsuri Ismail, Marzuki Ahmed, Ali Najah Abdullah, Ahmad Makmom |
| author_sort | Abdullah, Samsuri |
| collection | UPM |
| description | Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM10) throughout the years. Studies have affirmed that PM10 influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM10 status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000–2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM10 and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM10 compared to the linear model. The results are robust enough for precise next day forecasting of PM10 concentration on the East Coast of Peninsular Malaysia. |
| first_indexed | 2024-03-06T10:26:52Z |
| format | Article |
| id | upm.eprints-79937 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T10:26:52Z |
| publishDate | 2019 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | upm.eprints-799372023-03-30T03:27:33Z http://psasir.upm.edu.my/id/eprint/79937/ Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support Abdullah, Samsuri Ismail, Marzuki Ahmed, Ali Najah Abdullah, Ahmad Makmom Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM10) throughout the years. Studies have affirmed that PM10 influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM10 status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000–2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM10 and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM10 compared to the linear model. The results are robust enough for precise next day forecasting of PM10 concentration on the East Coast of Peninsular Malaysia. MDPI AG 2019 Article PeerReviewed Abdullah, Samsuri and Ismail, Marzuki and Ahmed, Ali Najah and Abdullah, Ahmad Makmom (2019) Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support. Atmosphere, 10 (11). art. no. 667. pp. 1-24. ISSN 2073-4433 https://www.mdpi.com/2073-4433/10/11/667 10.3390/atmos10110667 |
| spellingShingle | Abdullah, Samsuri Ismail, Marzuki Ahmed, Ali Najah Abdullah, Ahmad Makmom Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support |
| title | Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support |
| title_full | Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support |
| title_fullStr | Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support |
| title_full_unstemmed | Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support |
| title_short | Forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support |
| title_sort | forecasting particulate matter concentration using linear and non linear approaches for air quality decision support |
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