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

• Published in: Atmosphere Vol. 10; no. 11; p. 667
• Main Authors: Abdullah, Samsuri, Ismail, Marzuki, Ahmed, Ali Najah, Abdullah, Ahmad Makmom
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2019
• Subjects: Aerosols; Air pollution; Air quality; Airborne particulates; Algorithms; Artificial neural networks; Atmospheric models; Error reduction; Forecasting; Gaseous pollutants; Humidity; Industrial development; Industrial plant emissions; malaysia; Mathematical models; Mitigation; Multilayers; Neural networks; Nitrogen dioxide; Outdoor air quality; Particulate emissions; Particulate matter; Pollutants; Population; Precipitation; Radial basis function; Rain; Regression analysis; Rural areas; Suspended particulate matter; Urban areas; Terengganu Malaysia; Malaysia
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos10110667

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.