PREDICTION OF PM10 EXTREME CONCENTRATIONS IN URBAN MONITORING STATIONS IN SELANGOR, MALAYSIA USING THREE PARAMETERS EXTREME VALUE DISTRIBUTIONS (EVD)

Abstract

Objective: The purpose of the study was to determine the best distribution to predict the extreme concentrations of PM10 using the three parameters Weibull, Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD). Methodology: The study used maximum concentration of daily PM10 data recorded in the period of 2000-2012 in Klang and Shah Alam, Selangor. The parameters of all distributions were estimated using the method of Maximum Likelihood Estimator (MLE). The goodness of fit of the distribution was determined using performance indicators namely; the accuracy measures and error measures. The best distribution was selected based on the highest accuracy measures and the smallest error measures. Results: The findings showed that the three parameters GEV was the best fit for daily maximum concentration for PM10 in these two stations. The result also demonstrated that the predicted number of days in which the concentration of PM10 exceeded the Malaysia Ambient Air Quality Guideline (MAAQG) for daily concentrations of 150µg/m3 were more than 85% in compliance of the actual number of days. Hence, the GEV can be used for the prediction of the PM10 extreme concentrations.