Information on situation of air pollution is critically needed as input in four disciplines of research including risk management, risk evaluation, environmental epidemiology, as well as for status and trend analysis. Two normal practices were identified to evaluate daily air pollution situation; first, pollution magnitude has been treated as the common indicator, and second, the analysis was often conducted based on hourly average data. However, the information on the magnitude level alone to represent the pollution condition based on a rigid point data such as the average was seen as insufficient. Thus, to fill the gap, this study was conducted based on continuously measured data in the form of curves, which is also known as functional data, whereby pollution duration is emphasised. A statistical method based on curve ranking was used in the investigation. The application of the method at Klang, Petaling Jaya and Shah Alam air quality monitoring stations located in the Klang Valley, Malaysia, has shown that pollution duration decreases as the magnitude increases. Shah Alam has the longest pollution duration at low and medium magnitude levels. Meanwhile, all the three stations experienced quite a similar length of average pollution duration for the high magnitude level, that is, about 2.5 days. It was also shown that the occurrence of PM10 pollution at the area is significantly not random.
PM10 has been identified as being a common problem in Malaysia and many other countries all over the world. A Markov chain probability model is found to fit the average daily PM10 concentrations data of urban station (Shah Alam) and background area station (Jerantut) in Malaysia. This study aims to identify the occurrence of polluted and non-polluted days affected by PM10 concentrations based on data for 12 years’ period (2002-2013). The first order transition probability matrix of a Markov chain model and a two-state Markov chain, which are polluted days (1) and non-polluted days (0), were used for this purpose. The threshold value used in this study is referring to WHO 2006 guidelines (50µgm-3). Results of the analysis shows that there is a high probability that the next day event depends on what has happened on the previous day. The recurrence of the polluted day for Shah Alam is 4-5 days, while 2-3 days for Jerantut. By fitting the first order of Markov chain model, the results show that the higher order of Markov chain model is needed in order to get the best fitted distribution of polluted events at these two monitoring stations. Thus, the prediction of PM10 concentrations event can be made by considering the conditions of the previous day event;