Finding the Best Statistical Distribution Model in PM10 Concentration Modeling by using Lognormal Distribution

Air pollution is one of the most important issues that are often discussed, it is important to carry out the study on air pollution modeling. Air pollution models play an important role and very useful because it can help local authorities to carry out suitable action to reduce the impact of air pollution. Finding the best model would allow prediction to be made accurately. Statistical distribution modeling plays an important role in predicting air pollutant concentration. Lognormal distribution is one of the distributions that widely used in environmental engineering. One of the important steps in statistical distribution modeling is parameter estimation. There are several methods can be used to estimate the parameter in fitting distribution for air pollutant concentration data. This research compared the performance of parameter estimator for two-parameter and three-parameter lognormal distribution by using PM10 concentration in Nilai, Negeri Sembilan, Malaysia. Two methods were used to estimate the parameters in this study which is method of moments and method of probability weighted moments. Five performance indicators are used to determine the best estimator and the best distribution to represent the PM10 concentration in Nilai, Negeri Sembilan from 2003 to 2009. Results show that three-parameter lognormal distribution performs better compared to two-parameter lognormal distribution.