Modeling the distribution of duration time for unhealthy air pollution events

The information about how long a severe unhealthy air pollution event will last is crucial for the purpose of planning a possible measure to mitigate its risk. Thus, analyzing the distribution of duration data on the past occurrences of air pollution events is important. This study analyzes the hourly data of air pollution index (API) in Klang City, Malaysia from 1997 to 2018. Air pollution duration data are determined from the period when API > 100, preceded and followed by periods when API < 100. In this study, four types of statistical distributions, namely, Exponential, Gamma, Lognormal, and Weibull are proposed as practical models. Goodness-of-fit measures are compared for each distribution to determine the best fitted one to describe the observed data. Results indicate that the Lognormal distribution provides the best fitted model among others. (paper)