Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model

Abstract

Air pollution is an important issue affecting sustainable development in China, and accurate air quality prediction has become an important means of air pollution control. At present, traditional methods, such as deterministic and statistical approaches, have large prediction errors and cannot provide effective information to prevent the negative effects of air pollution. Therefore, few existing methods could obtain accurate air pollutant time series predictions. To this end, a deep learning-based air pollutant prediction method, namely, the autocorrelation error-Informer (AE-Informer) model, is proposed in this study. The model implements the AE based on the Informer model. The AE-Informer model is used to predict the hourly concentrations of multiple air pollutants, including PM10, PM2.5, NO2, and O3.