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Open AccessArticle

Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support

1
Air Quality and Environment Research Group, Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, Kuala Nerus 21030, Malaysia
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Faculty of Science and Marine Environment, University Malaysia Terengganu, Kuala Nerus 21030, Malaysia
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Faculty of Engineering, University Tenaga Nasional, Bangi 43650, Malaysia
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Institute of Engineering Infrastructures, University Tenaga Nasional, Bangi 43650, Malaysia
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Faculty of Environmental Studies, University Putra Malaysia, Serdang 43400, Malaysia
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(11), 667; https://doi.org/10.3390/atmos10110667
Received: 18 August 2019 / Revised: 15 October 2019 / Accepted: 16 October 2019 / Published: 31 October 2019
(This article belongs to the Section Air Quality)
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. View Full-Text
Keywords: particulate matter; forecasting; air quality; Malaysia particulate matter; forecasting; air quality; Malaysia
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Abdullah, S.; Ismail, M.; Ahmed, A.N.; Abdullah, A.M. Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support. Atmosphere 2019, 10, 667.

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