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Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

Air pollution, such as PM 2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm), PM 10 (particulate matter with an aerodynamic equivalent diameter of less than 10 μm), NO x , and SO x , is a global concern because it may cause many chronic and fatal diseases, especially in developing countries. To better address air pollution problems, an important step is the timely and accurate prediction of air quality. Traditional methods are mainly based on meteorological data, regression model data, remote sensing data and different retrieval methods. Numerous studies on deep learning methods have suggested that these approaches may be able to perform accurate predictions for complex systems. In this paper, a long short-term memory (LSTM) approach for predicting air quality is proposed; moreover, meteorological data are used and Chinese social media is investigated as a proxy for public perceptions and responses for air quality prediction. We gathered daily air quality data, meteorological data and Weibo check-in data for Beijing, China from January 1, 2015 to December 31, 2016. The average sentiment of the related Weibo posts was selected as the public response proxy. The performance of our proposed model is evaluated based on real data. The root-mean-square error (RMSE) and the mean absolute error (MAE) indicated that our method presented better prediction results than traditional methods in terms of the PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 and CO concentrations. We focused on the prediction performance during the 2015 China Victory Day Parade period, during which social and political factors played an important role in air quality predictions. The results indicated that the proposed method, which incorporates public response data, was especially suitable for predicting the air quality in extreme short-term social events and provides a timely social measurement and feedback for environmental problems. • Social media were selected as a proxy for public perceptions for air quality. • LSTM model was proposed for air quality prediction. • Weibo data was suitable for air quality prediction during short-term social events. [ABSTRACT FROM AUTHOR]