Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia
Air quality; Carbon monoxide; Errors; Forecasting; Linear regression; Nitrogen oxides; Sulfur dioxide; Wind; Accuracy; Forecasting modeling; Malaysia; Multiple linear regressions; Particulate Matter; Precautionary measures; Stepwise multiple linear regression; Trans-boundary; Particles (particulate...
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2023
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my.uniten.dspace-255562023-05-29T16:10:51Z Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia Abdullah S. Napi N.N.L.M. Ahmed A.N. Mansor W.N.W. Mansor A.A. Ismail M. Abdullah A.M. Ramly Z.T.A. 56509029800 57224902975 57214837520 56896999100 57211858557 57210403363 57193067284 57196459394 Air quality; Carbon monoxide; Errors; Forecasting; Linear regression; Nitrogen oxides; Sulfur dioxide; Wind; Accuracy; Forecasting modeling; Malaysia; Multiple linear regressions; Particulate Matter; Precautionary measures; Stepwise multiple linear regression; Trans-boundary; Particles (particulate matter); accuracy assessment; error analysis; forecasting method; haze; multiple regression; particulate matter; prediction; Malaysia Malaysia has been facing transboundary haze events every year in which the air contains particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to develop a PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. Therefore, this study aimed to develop and compare the best-fitted model for PM10 prediction from the first hour until the next three hours during transboundary haze events. The air pollution data acquired from the Malaysian Department of Environment spanned from the years 2005 until 2014 (excluding years 2007-2009), which included particulate matter (PM10), ozone (O3), nitrogen oxide (NO), nitrogen dioxide (NO), carbon monoxide (CO), sulfur dioxide (SO2), wind speed (WS), ambient temperature (T), and relative humidity (RH) on an hourly basis. Three different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed based on three different prediction hours, namely t+1, t+2, and t+3. The PM10, t+1 model was the best MLR model to predict PM10 during transboundary haze events compared to PM10,. t+2 and PM10, t+3 models, having the lowest percentage of total error (28%) and the highest accuracy of 46%. A better prediction and explanation of PM10 concentration will help the authorities in getting early information for preserving the air quality, especially during transboundary haze episodes. � 2020 by the authors. Final 2023-05-29T08:10:51Z 2023-05-29T08:10:51Z 2020 Article 10.3390/atmos11030289 2-s2.0-85082306221 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082306221&doi=10.3390%2fatmos11030289&partnerID=40&md5=ec07e9d096ed4bf68505f2273d2a695d https://irepository.uniten.edu.my/handle/123456789/25556 11 3 289 All Open Access, Gold, Green MDPI AG Scopus |
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Air quality; Carbon monoxide; Errors; Forecasting; Linear regression; Nitrogen oxides; Sulfur dioxide; Wind; Accuracy; Forecasting modeling; Malaysia; Multiple linear regressions; Particulate Matter; Precautionary measures; Stepwise multiple linear regression; Trans-boundary; Particles (particulate matter); accuracy assessment; error analysis; forecasting method; haze; multiple regression; particulate matter; prediction; Malaysia |
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56509029800 |
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56509029800 Abdullah S. Napi N.N.L.M. Ahmed A.N. Mansor W.N.W. Mansor A.A. Ismail M. Abdullah A.M. Ramly Z.T.A. |
| format |
Article |
| author |
Abdullah S. Napi N.N.L.M. Ahmed A.N. Mansor W.N.W. Mansor A.A. Ismail M. Abdullah A.M. Ramly Z.T.A. |
| spellingShingle |
Abdullah S. Napi N.N.L.M. Ahmed A.N. Mansor W.N.W. Mansor A.A. Ismail M. Abdullah A.M. Ramly Z.T.A. Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia |
| author_sort |
Abdullah S. |
| title |
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia |
| title_short |
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia |
| title_full |
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia |
| title_fullStr |
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia |
| title_full_unstemmed |
Development of multiple linear regression for particulate matter (PM10) forecasting during episodic transboundary haze event in Malaysia |
| title_sort |
development of multiple linear regression for particulate matter (pm10) forecasting during episodic transboundary haze event in malaysia |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806426712602312704 |
| score |
13.222552 |