Quantifying haze effect using air pollution index data

Abstract

Malaysia has been misfortunate with intermittent haze episodes since 1997 which affect the air quality tremendously. In Malaysia, an instrument named air pollution index (API) is utilized in determining the quality of air, which is influenced by the presence of haze. API values are calculated by considering the concentration of harmful particles in haze. Therefore, any haze episode heavily affects the API values and can be considered as a determining factor. Since Malaysia is prone to haze, it is crucial to identify and quantify the haze effect on the API values. Therefore, a regression model with autoregressive integrated moving average errors (ARIMAX) is employed. It is found that ARIMAX (4,0,1) with non-zero mean is the best model in describing the API data with presence of haze as external regressor based on the smallest adequacy and error measures for training and test datasets. In conclusion, the effect of haze is significant in describing the API values and thus, proper health managements is required during haze episodes.