Along with the development of industrialization and urbanization, the pollution of the atmosphere increases and the air quality
decreases gradually, especially in the developed regions such as Beijing, Tianjin and Hebei in China. The frequent occurrence of
haze leads to the decrease of visibility day by day, which causes great inconvenience to people’s mind and body. Therefore, an indepth
study of regional weather quality impact factors can improve the air quality in the regions, which is of important significance
to haze warning. This study mainly explores the main causes of haze formation and forecasts the haze, and puts forward some
suggestions on haze control. This study proposes a space-time prediction model of air pollutants based on artificial neural network.
The selected research regions are cities in Beijing-Tianjin-Hebei region. The data are real-time daily data of monitoring stations
published by Beijing Environmental Protection Monitoring Center and China Meteorological Administration, and the air
environment changes of 13 cities in Beijing-Tianjin-Hebei region in 2017 are studied. The impact factors include PM2.5, PM10, SO2,
NO2, CO and O3. Through collecting and visualizing the data, evaluating with air quality index (AQI), this study designs, establishes,
trains, and simulates the backpropagation neural network, adjusts the corresponding parameters, obtains the optimal model and the
main research conclusion, as well as provides scientific reasonable suggestion.