Prediction of sharp change of particulate matter in Seoul via quantile mapping

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM10) using quantile mapping. To predict the current PM10 density in Seoul, we consider PM10 and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM10 distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM10. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute di fferences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM10 in Seoul.