首页|An improved GCN-TCN-AR model for PM2.5 predictions in the arid areas of Xinjiang,China
An improved GCN-TCN-AR model for PM2.5 predictions in the arid areas of Xinjiang,China
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An improved GCN-TCN-AR model for PM2.5 predictions in the arid areas of Xinjiang,China
As one of the main characteristics of atmospheric pollutants,PM2.5 severely affects human health and has received widespread attention in recent years.How to predict the variations of PM2.5 concentrations with high accuracy is an important topic.The PM2.5 monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM2.5 concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January-December in 2019 as the research period.On the basis of data from 21 PM2.5 monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN-TCN-AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM2.5 concentrations on the northern slope of the Tianshan Mountains.The GCN-TCN-AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN-TCN-AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28 pg/m3,respectively.The performance of the GCN-TCN-AR model was also compared with the currently neural network models,including the GCN-TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN-TCN-AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM2.5 concentrations.This study revealed the significant spatiotemporal variations of PM2.5 concentrations.First,the PM2.5 concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM2.5 concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM2.5 concentrations is highly important for the sustainable development of ecological environment in arid areas.
air pollutionPM2.5 concentrationsgraph convolution network(GCN)modeltemporal convolutional network(TCN)modelautoregression(AR)modelnorthern slope of the Tianshan Mountains
CHEN Wenqian、BAI Xuesong、ZHANG Na、CAO Xiaoyi
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School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education,College of Atmospheric Sciences,Lanzhou University,Lanzhou 730000,China
air pollution PM2.5 concentrations graph convolution network(GCN)model temporal convolutional network(TCN)model autoregression(AR)model northern slope of the Tianshan Mountains
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