Predicting concentrations of atmospheric particle matters in Guangzhou by time series models

Abstract

Particulate matter is one of the major air pollutants closely related to human health. In order to predict atmospheric particulate matter concentrations effectively and accurately, this paper utilized ARIMA model, Holt-Winters model, STL-Holt model and STL-ARIMA model to carry out prediction experiments based on hourly PM2.5 and PM10 concentration historical data in Guangzhou city. The results showed that the four models were effective in predicting hourly PM2.5 and PM10 concentrations. The RMSE, MAE, MAPE, and metrics were used to evaluate the prediction accuracy of the models. It was found that the Holt-Winters model performed best among the four models. This study may provide guides for the environmental authorities in forecasting atmospheric particulate matter concentrations.