Modeling the Air Pollution Index based on its structure and descriptive status

Abstract

Accurate air pollution modeling is essential for estimating the Air Pollution Index (API) effectively. The air quality assessment relies on the ability of the selected probability density function (PDF) to describe the observed air pollution data. This study characterizes the API data in Klang, Malaysia, for the period of January 2005 to December 2014. The study proposed three different approaches in modeling API characteristics, including conventional models, API structure models, and descriptive status models. The first approach is the conventional models, which are the most common distributions used for modeling the API and its pollutants. The fitted distributions of the observed and generated API data are used for comparisons to other proposed models. In addition, the selected distributions of pollutants were used as a basis in the construction of API structure models. The second approach is the API structure models, which involve a mixture of distributions for the critical pollutants. Finally, the third approach was based on the descriptive status of the API. The results show that the healthy status is able to be described using the conventional fitted models, while the generalized Pareto distribution (GPD) is found to be a good fitted model for the unhealthy status. In fact, based on the selection criteria, it was found that the API structure models are superior for modeling the API data. In addition, the API descriptive status models are useful for evaluating the unhealthy API return level. In summary, we conclude that the mixture distribution of the API components should be considered as a better method for simulating the API data.