A review on short-term prediction of air pollutant concentrations

• Main Authors: Raffee, Ahmad Fauzi, Rahmat, Siti Nazahiyah, Abdul Hamid, Hazrul, Jaffar, Muhammad Ismail
• Format: Article
• Published: Science Publishing Corporation 2018
• Subjects: T Technology (General); TD Environmental technology. Sanitary engineering; TD172-193.5 Environmental pollution
• Online Access: http://eprints.uthm.edu.my/3982/; https://doi.org/10.14419/ijet.v7i3.23.17254

In the attempt to increase the production of the industrial sector to accommodate human needs; motor vehicles and power plants have led to the decline of air quality. The tremendous decline of air pollution levels can adversely affect human health, especially children, those elderly, as well as patients suffering from asthma and respiratory problems. As such, the air pollution modelling appears to be an important tool to help the local authorities in giving early warning, apart from functioning as a guide to develop policies in near future. Hence, in order to predict the concentration of air pollutants that involves multiple parameters, both artificial neural network (ANN) and principal component regression (PCR) have been widely used, in comparison to classical multivariate time series. Besides, this paper also presents comprehensive literature on univariate time series modelling. Overall, the classical multivariate time series modelling has to be further investigated so as to overcome the limitations of ANN and PCR, including univariate time series methods in short-term prediction of air pollutant concentrations.