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

Fan Yang (), Guangqiu Huang () and Yanan Li
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Fan Yang: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Guangqiu Huang: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Yanan Li: School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China

Sustainability, 2023, vol. 15, issue 12, 1-27

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 PM 2.5 , as an important index affecting air quality, has great significance. However, PM 2.5 concentration has a complex change process that makes its prediction challenging. By calculating both PM 2.5 concentration and that of other pollutants in the atmosphere and meteorological factors, it is evident that the variation in PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 prediction and can provide a theoretical basis for the formulation of environmental protection policies.

Keywords: air pollution; PM 2.5 prediction; correlation analysis; feature selection; bidirectional long short-term memory; encoder–decoder; inverse distance weighting; grid interpolation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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