Fine-Grained Individual Air Quality Index (IAQI) Prediction Based on Spatial-Temporal Causal Convolution Network: A Case Study of Shanghai

• Published in: Atmosphere Vol. 13; no. 6; p. 959
• Main Authors: Liu, Xiliang, Zhao, Junjie, Lin, Shaofu, Li, Jianqiang, Wang, Shaohua, Zhang, Yumin, Gao, Yuyao, Chai, Jinchuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Accuracy; Air; Air monitoring; Air pollution; Air quality; Air quality control; Bayesian analysis; Bayesian optimization; causal convolution network; Convolution; Deep learning; Feature extraction; Impact factors; individual air quality index prediction; Monitoring systems; multi-source factors; Neural networks; Nitrogen dioxide; Optimization; Outdoor air quality; Particulate matter; Pollutants; Predictions; Probability theory; Propagation; Quality control; Recurrent neural networks; Resampling; Shapley analysis; Time dependence; Time series; China
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13060959

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction.