PM2.5 and PM10 were the main air pollutants during winter in Lanzhou New District, China. In this paper, WRF model out-put combined with hourly monitoring data of pollutant concentration was used to analyze characteristics of the concentra-tion change and to study the relationship between meteorological elements and PM10/PM2.5 in Lanzhou New District in Jan-uary, 2018. Meanwhile, the concentration changes of PM2.5 and PM10 were predicted by wavelet analysis combined with BP neural network. The results show that:(1) Due to the cold front process in winter, PM2.5 was negatively correlated with the water vapor mixing rate. PM10 was positively correlated with air temperature and negatively correlated with air pres-sure. (2) There was an inversion layer in the atmosphere near the high value day of PM2.5 and PM10, the surface was con-trolled by low pressure, low wind speed, and the situation of low value day of PM2.5 was the opposite. On the day of high value of PM10, the air temperature below 600 hPa was higher, and the wind speed near the surface was also higher. (3) Wavelet analysis combined with BP (Back Propagation) neural network had a good prediction effect on PM2.5, which could basically reflect the hourly change of PM2.5 concentration. However, the simulation effect of PM10 was poor, and the input parameters of surrounding pollutants should be added to improve the prediction effect.