New Application of Fuzzy Markov Chain Modeling for Air Pollution Index Estimation

Air pollution is a problem faced by most countries across the globe. The modeling and evaluation of the probabilistic behavior of air pollution are crucial in providing useful information that can help in managing the environmental risk and planning for the adverse effects of air pollution. For modeling of air pollution, several statistical approaches have been considered; however, only a few approaches have been used for addressing the uncertainty in the analysis. This study proposes a new application of the Markov chain-based fuzzy states (MCFS) model using triangular fuzzy numbers for analysing the uncertainty in the occurrence of air pollution events and describing the transition behaviour of air pollution. In this study, the air pollution index (API) data collected from the city of Klang in Malaysia for a period between 2012 and 2014 is considered in the analysis. Based on the API data, a five-state Markov chain is considered for representing the five fuzzy states of air pollution. The fuzzy transition probabilities are estimated and used to determine the characteristics of air pollution such as the steady state probabilities and the mean first passage time for each state of air pollution. The findings show that, in general, the risk of occurrences for unhealthy events in Klang is small, nonetheless remains notably troubling. The results demonstrate that the MCFS can effectively model the air pollution index and it could be a better option in predicting air pollution. It may provide valuable information and more understanding about the dynamics of air pollution to the experts and policymakers. This will enable them to develop proper strategies to manage air quality.