Development of a statistical forecasting model for PM2.5 in Macau based on clustering of backward trajectories

Abstract

A daily PM2.5 forecasting model based on multiple linear regression (MLR) and backward trajectory clustering of HYSPLIT was designed for its application to small cities where PM2.5 level is easily affected by regional transport. The objective of this study is to investigate the regions that affect the fine particulate concentration of Macau and to develop an effective forecasting system to enhance the capture of PM2.5 episodes. By clustering the HYSPLIT 24-hr backward trajectories originated at Macau from 2015 to 2017, five potential transportation paths of PM2.5 were found. A cluster based statistical model was developed and trained with air quality and meteorological data of2015 and 2016. Then, the trained model was evaluated with data of 2017. Comparing to an ordinary model without backward trajectory clustering, the cluster based PM2.5 forecasting model yielded similar general forecast performance in 2017. However, the critical success index of the cluster based model was 11% higher than that of the ordinary model. This means the cluster based model has better model performance in PM2.5 concentration prediction and it is more important for the health of the public.