Time series model for Carbon Monoxide (Co) at several industrial sites in Peninsular Malaysia / Norshahida Shaadan … (et al.)

• Main Authors: Shaadan, Norshahida, Rusdi, Muhammad Soffi, Nik Mohd Azmi, Nik Noorul Syakirin, Talib, Shahira Fazira, Wan Azmi, Wan Athirah
• Format: Article
• Language: English
• Published: Penerbit UiTM 2019
• Subjects: Mathematical statistics. Probabilities
• Online Access: https://ir.uitm.edu.my/id/eprint/43818/1/43818.pdf; https://ir.uitm.edu.my/id/eprint/43818/; https://mjoc.uitm.edu.my
• Institution: Universiti Teknologi Mara

Abstract Malaysia is reported to experience explosive rise in the demand of transport vehicles in recent years due to rapid economic development and population growth. As a result, air pollution is expected to increase in conjunction with the increase in the number of the vehicles. In particular, Carbon Monoxide (CO) has been identified as the main component of the emission sources from vehicles other than Nitrogen Oxide (NOx), hydrocarbon lead and particulate matter of size less than 10 micron (PM10). This provides the reason why CO concentration is often used to reflect traffic density in an area. CO has both short-term and long-term effect on human’s health. Thus, knowledge on CO behaviour and the future levels at an area is important to help decision makers in managing air pollution due to vehicles emission in the country. This study was conducted to describe CO data and to determine a suitable time series model to enable the prediction of CO levels at two industrial sites; Perai and Pasir Gudang, Malaysia. The model obtained could help management to mitigate CO pollution at the sites. The analysis was conducted using daily maximum data which was obtained from the Department of Environment Malaysia from 2010 to 2014. The performance of the best model was determined using several performance measures such as MAE, RMSE and MAPE. The study has found that the most appropriate time series model for Perai is ARIMA (3,1,1) and for Pasir Gudang is SARIMA (2, 1, 8) (1, 1, 2)7.