Air quality secondary forecast model based on ARIMA time series model

With the further frequent of human living activities, natural meteorological changes will lead to some harmful substances into the atmosphere, the concentration of harmful substances is too high will harm people's health, air quality forecast can make people timely and effective response to bad air conditions In this paper, the concentrations of six common pollutants related to air quality are analyzed and predicted by combining existing air quality forecasting models and known data. The time series model is established and the secondary model is built on the basis of the original primary forecast, which improves the accuracy of air quality forecast.