Abstract:
Ozone (03) is considered as one of the crucial air pollutant in atmosphere which
affects human health, vegetation and forests. According to DoE, the annual average
daily maximum one-hour ozone concentrations had slightly increased in 2014
compared to 2013. Thence, it is crucial to come out with a model that is suitable to
predict ground level ozone concentration in order to prevent adverse air pollution
effects to be aggravated. Therefore, the aim of this study is to predict ground level
ozone exceedences and return period at Cheras, Kuala Lumpur and Tanjung Malim,
Perak using distribution function. Data analysis in this study was done by MATLAB.
The selected distribution functions to fit 03 concentration data in 2012 are Gamma,
Inverse Gaussian/ Wald and Rayleigh. Next, parameter for each distribution was
estimated by Maximum Likelihood Estimator (MLE) method. The best fit distribution
was determined by the selected performance indicators based on the highest accuracy
measures which are close to 1 and the smallest error measures which are close to 0.
Results showed that the best distribution that fits the 03 observations was found to be
Gamma distribution for Cheras and Tanjung Malim. The probabilities of exceedences
were calculated and predicted the return period by the cumulative density function
(cdf) obtained from the best-fit distribution. For Cheras, it was predicted to exceed
O.lppm for 6.7 days with a return period of once per 55 days. Tanjung Malim was
predicted to exceed O.lppm for 3.6 days with a return period of once per 101 days. In
Malaysia, modelling using distribution function is still a new approach and it is
believe that it will be a good alternative for ground level ozone prediction.
