Reduction of Response Variable Influential Outliers Using M-Estimation in the Next Day Prediction of Ground-Level Ozone Concentration

Ground-level ozone concentration (O3) is a second significant air pollutant in Malaysia after particulate matter 10 micrometres or less in diameter (PM10) concentration. It is a secondary pollutant that created by photochemical reaction of primary pollutant such as volatile organic compound (VOCs) and nitrogen oxides (NOx) under the influence of solar radiation (UVB). O3 photochemical reactions used solar radiation with certain wavelength as the catalyst. In statistical analysis of prediction, the concentration level of O3 contains the influential outliers due to several factors such as offense in data recording and sampling, the error in data acquisition or data management and the damage of monitoring instrument in data recording that can lead to misleading result or information. The objective of this study is to predict the level of O3 concentration for next day (D+1) by using predictors of wind speed (WS), temperature (T), relative humidity (RH), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO) for selected urban area of Shah Alam by the method of minimizing influential outliers from response variable using M-estimation. The influential outliers from response variable is minimized using tuning constant approached at 95% level of efficiency. The improvement has been proved when Fair method has minimized 5.34% influential outliers from response variable and the average accuracy of the model is 0.513

Identification of Source Contributions to Air Pollution in Penang Using Factor Analysis

Penang is one of the rapidly developed states in Malaysia with large numbers of population industrial activities, motor vehicles density and development projects.  The concentrations of air pollution parameters in Penang were investigated and analyzed together with meteorological parameters in order to determine their characteristics and contributions to air pollution in Penang using factor analysis (FA).  The air pollution parameters include ground level ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and particulate matters of less than 10 microns in size (PM10) while the meteorological parameters include relative humidity, wind speed and temperature.  The data was obtained from the Department of Environment (DOE) for the Universiti Sains Malaysia (USM) monitoring station for the period of 10 years from 2004 to 2013.  In this study, concentrations of PM10 was found to be the highest among the air pollutants and the concentrations was at its highest between the months of June to September for almost all years of observation due to the southwest monsoon.  As for the source contributions of air pollutions, O3 and meteorological parameters were found to be the largest contributor to air pollutions in Penang, followed by the traffic emissions and industrial activities

Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia

Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu

Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia

Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu