A Case Analysis of Dust Weather and Prediction of PM10 Concentration Based on Machine Learning at the Tibetan Plateau

Abstract

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression model to predict PM10 concentration in this area. The results showed that: (1) The 24-h pressure change was positive when the front intruded on the surface; convergence of vector winds with a sudden drop in temperature and humidity led by a trough on 700 hPa; a “two troughs and one ridge” weather situation appeared on 500 hPa while the cold advection behind the trough was strong and a cyclone vorticity was formed in the east of Inner Mongolia. (2) The trajectory of air mass from the Hexi Corridor was the main air mass path influencing Xining City, in this case, since a significant lag in the peak of PM10 concentration appeared in Xining City when compared with Zhangye City. (3) The Multiple Linear Regression was not only timely and effective in predicting the PM10 concentration but had great abilities for anticipating the transition period of particle concentration and the appearance date of maximum values in such dust weather. (4) The MA and MP in the clean period were much lower than that in the dust period; the PM10 of Zhangye City as an eigenvalue played an important role in predicting the PM10 of Xining City even in clean periods. Different from dust periods, the prediction effect of Random Forest Optimized by Bayesian hyperparameter was superior to Multiple Linear Regression in clean periods.