Modeling air quality prediction using a deep learning approach: Method optimization and evaluation

• Published in: Sustainable cities and society 2021-02, Vol.65, p.102567, Article 102567
• Main Authors: Mao, Wenjing, Wang, Weilin, Jiao, Limin, Zhao, Suli, Liu, Anbao
• Format: Article
• Language: English
• Subjects: Air pollutant; Air quality prediction; Deep learning; Long-term prediction; Temporal sliding
• ISSN: 2210-6707; 2210-6715
• DOI: 10.1016/j.scs.2020.102567

•A temporal sliding model is proposed for PM2.5 concentration long-term predictions.•Integrating the optimal time lag of the spatiotemporal correlations can improve model performance.•The proposed model achieves higher prediction in the longer time series.•The proposed model has strong practicability in making atmospheric management decisions. Air pollution is one of the hot issues that attracted widespread attention from urban and society management. Air quality prediction is to issue an alarm when severe pollution occurs, or pollution concentration exceeds a specific limit, contributing to the measure-taking of relevant departments, guiding urban socio-economic activities to promote sustainable urban development. However, existing methods have failed to make full use of the temporal features from spatiotemporal correlations of air quality monitoring stations, and achieved poor performances in long-term predictions (up to or above 24h-predictions). In this study, we proposed a deep learning framework to predict air quality in the following 24 h: a neural network with a temporal sliding long short-term memory extended model (TS-LSTME). The model integrated the optimal time lag to realize sliding prediction through multi-layer bidirectional long short-term memory (LSTM), involving the hourly historical PM2.5 concentration, meteorological data, and temporal data. We applied the proposed model to predict the next 24 h average PM2.5 concentration in Jing-Jin-Ji region, with the most severe air pollution in China. The proposed model had better stability and performances with high correlation coefficient R2 (0.87), compared to the multiple linear regression (MLR), the support vector regression (SVR), the traditional LSTM, and the long short-term memory extended (LSTME) models. Moreover, the proposed model can achieve PM2.5 concentration predictions with high accuracy in long-term series (48 h and 72 h). We also tested the model to predict O3 concentration. The proposed model could be applied for other air pollutants. The proposed methods can significantly improve air quality prediction information services for the public and provide support for early warning and management of regional pollutants.