Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms

Particulate matter is a critical air pollutant in Malaysia as it is the utmost dominant pollutant, especially in industrial and urban areas. The development of a robust model for PM10 concentration forecasting provides invaluable information for local authorities to take precautionary measures and i...

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Main Authors: Abdullah S., Ismail M., Samat N.N.A., Ahmed A.N.
Other Authors: 56509029800
Format: Article
Published: Asian Research Publishing Network 2023
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spelling my.uniten.dspace-236692023-05-29T14:50:54Z Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms Abdullah S. Ismail M. Samat N.N.A. Ahmed A.N. 56509029800 57210403363 57204527316 57214837520 Particulate matter is a critical air pollutant in Malaysia as it is the utmost dominant pollutant, especially in industrial and urban areas. The development of a robust model for PM10 concentration forecasting provides invaluable information for local authorities to take precautionary measures and implement significant actions to improve air pollution status. This study aims to develop and assess the linear (Multiple Linear Regression, MLR) and nonlinear (Multilayer Perceptron, MLP) models forecasting capability in industrial area of Pasir Gudang, Johor. Daily observations of PM10 concentration, meteorological factors (wind speed, ambient temperature and relative humidity) and gaseous pollutants (SO2, NO2 and CO) from the year 2007-2014 were used in this study. Results showed that MLP model was able to explain 68.7% (R2 = 0.687) variance in the data compared to MLR model with 52.7% (R2 = 0.527). Overall, the MLP model able to increase the accuracy of forecasting by 29.9% and reducing the error by 69.3% with respect to MLR model. Thus, it is proven that nonlinear model has high ability in virtually representing the complexity and nonlinearity of PM10 in the atmosphere without any prior assumptions, unlike the linear model. � 2006-2018 Asian Research Publishing Network (ARPN). Final 2023-05-29T06:50:54Z 2023-05-29T06:50:54Z 2018 Article 2-s2.0-85055946389 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055946389&partnerID=40&md5=d04c764117d7efdc10a29f921288e3c3 https://irepository.uniten.edu.my/handle/123456789/23669 13 20 8227 8235 Asian Research Publishing Network Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Particulate matter is a critical air pollutant in Malaysia as it is the utmost dominant pollutant, especially in industrial and urban areas. The development of a robust model for PM10 concentration forecasting provides invaluable information for local authorities to take precautionary measures and implement significant actions to improve air pollution status. This study aims to develop and assess the linear (Multiple Linear Regression, MLR) and nonlinear (Multilayer Perceptron, MLP) models forecasting capability in industrial area of Pasir Gudang, Johor. Daily observations of PM10 concentration, meteorological factors (wind speed, ambient temperature and relative humidity) and gaseous pollutants (SO2, NO2 and CO) from the year 2007-2014 were used in this study. Results showed that MLP model was able to explain 68.7% (R2 = 0.687) variance in the data compared to MLR model with 52.7% (R2 = 0.527). Overall, the MLP model able to increase the accuracy of forecasting by 29.9% and reducing the error by 69.3% with respect to MLR model. Thus, it is proven that nonlinear model has high ability in virtually representing the complexity and nonlinearity of PM10 in the atmosphere without any prior assumptions, unlike the linear model. � 2006-2018 Asian Research Publishing Network (ARPN).
author2 56509029800
author_facet 56509029800
Abdullah S.
Ismail M.
Samat N.N.A.
Ahmed A.N.
format Article
author Abdullah S.
Ismail M.
Samat N.N.A.
Ahmed A.N.
spellingShingle Abdullah S.
Ismail M.
Samat N.N.A.
Ahmed A.N.
Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
author_sort Abdullah S.
title Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
title_short Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
title_full Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
title_fullStr Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
title_full_unstemmed Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
title_sort modelling particulate matter (pm10) concentration in industrialized area: a comparative study of linear and nonlinear algorithms
publisher Asian Research Publishing Network
publishDate 2023
_version_ 1806424223168593920
score 13.214268