AIR QUALITY MODELING FOR PREDICTING THE EXCEEDANCES OF PARTICULATE MATTER (PM10) CONCENTRATION

Central fitting distribution such as log-normal distribution can give a good result for fitting the mean concentration of air pollutants data. However, many researches find that it cannot precisely fit the high concentration region. Therefore, this research compares the central fitting distribution (CFD) represented by the lognormal distribution with the extreme value distribution (EVD) represented by Gumbel distribution to fit the hourly particulate matter (PM10) concentration data in year 2007 in Zagazig city. Method of moments (MOM) is used to estimate the distributions parameters. Goodness of fit criteria is used in order to select distribution that gives the best fit to the data. Furthermore, the exceedences of a critical PM10 concentration over the Egyptian Air Quality Limit value (EAQLV) are estimated using the best distribution. The results show that the extreme value distribution gives better fit to the actual PM10 concentration than the central fitting distribution. The exceedences over the EAQLV were also successfully predicted by using this method. In 2007, the exceedences are 305 days.