A New Combination Model for Air Pollutant Concentration Prediction: A Case Study of Xi’an, China

Abstract

As energy demand continues to increase, the environmental pollution problem is becoming more severe. Governments and researchers have made great efforts to avoid and reduce air pollution. The prediction of PM2.5, as an important index affecting air quality, has great significance. However, PM2.5 concentration has a complex change process that makes its prediction challenging. By calculating both PM2.5 concentration and that of other pollutants in the atmosphere and meteorological factors, it is evident that the variation in PM2.5 concentration is influenced by multiple factors, and that relevant features also influence each other. To reduce the calculated loss, with full consideration given to the influencing factors, we used the maximum correlation and minimum redundancy (MRMR) algorithm to calculate the correlation and redundancy between features. In addition, it is known from the Brock–Dechert–Scheinman (BDS) statistical results that the change in PM2.5 is nonlinear. Due to the outstanding performance of bidirectional long short-term memory (BiLSTM) neural networks in nonlinear prediction, we constructed an encoder–decoder model based on BiLSTM, named ED-BiLSTM, to predict the PM2.5 concentration at monitoring stations. For areas without monitoring sites, due to the lack of historical data, the application of neural networks is limited. To obtain the pollutant concentration distribution in the study area, we divided the study area into a 1 km × 1 km grid and combined the ED-BiLSTM model via the use of the inverse distance weighting (IDW) algorithm to obtain the PM2.5 concentration values in a region without monitoring stations. Finally, ArcGIS was used to visualize the results. The data for the case study were obtained from Xi’an. The results show that, compared with the standard long short-term memory (LSTM) model, the RMSE, MAE, and MAPE of our proposed model were reduced by 24.06%, 24.93%, and 22.9%, respectively. The proposed model has a low error for PM2.5 prediction and can provide a theoretical basis for the formulation of environmental protection policies.