Abstract
Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision‐making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning technology, this paper proposes a spatiotemporal graph convolutional network based on the attention mechanism (STAM‐STGCN) to solve the PM2.5 concentration forecast problem. When constructing the adjacency matrix, we not only use the Euclidean distance between sites but also consider the impact of wind fields and the impact of pollution sources near the nodes. In the process of model construction, we first use the spatiotemporal attention mechanism to capture the dynamic spatiotemporal correlations in PM2.5 data. In the spatiotemporal convolution module, we use graph convolutional neural networks to capture spatial features and standard convolution to describe temporal features. Finally, the output module adjusts the output shape of the data to produce the final forecast result. In this paper, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as the performance evaluation metrics of the model, and the Dongmingnan Industrial Park atmospheric dataset is used to verify the effectiveness of the proposed algorithm. The experimental results show that our STAM‐STGCN model can more fully capture the spatial‐temporal characteristics of PM2.5 concentration data; compared with the most advanced model in the comparison model, the RMSE can be improved about 24.2%, the MAE is improved about 35.8%, and the MAPE is improved about 34.6%.
Highlights
PM2.5 refers to the particulate matter with a diameter less than or equal to 2.5 microns in the atmosphere, known as fine particulate matter or particulate matter that can enter the lung [1]
In order to solve this problem, we propose a spatiotemporal graph convolutional network based on the attention mechanism (STAMSTGCN), which is used to centrally forecast the PM2.5 concentration of each monitoring station in the industrial park; this model can capture the dynamic spatial-temporal characteristics of data more effectively
In the deep learning method, both the STGCN model and the model we proposed consider spatial-temporal correlations, and the results are better than traditional deep learning models, such as long short-term memory (LSTM) and GRU; this shows that in the industrial park scenario, considering the spatial-temporal correlations of monitoring stations is useful for forecasting PM2.5
Summary
PM2.5 refers to the particulate matter with a diameter less than or equal to 2.5 microns in the atmosphere, known as fine particulate matter or particulate matter that can enter the lung [1]. Scientists use PM2.5 concentration to indicate the content of such particles per cubic meter of air; the higher the value, the more serious the air pollution. The main sources are industrial fuels, dust, motor vehicle exhaust, photochemical smog, and other pollutants [2]. Fine particulate matter is only a small component of the earth’s atmosphere, it has an important impact on air quality and visibility. Compared with coarser atmospheric particulate matter, fine particulate matter has a small particle size and is rich in a large amount of toxic and harmful substances stay in the atmosphere for a long time, so they have a greater impact on human health and the quality of the atmospheric environment. There is a high correlation between the time of exposure to a high concentration of PM2.5 environment and mortality [3]. With the rapid development of our country’s economy, the process of industrialization and urbanization has accelerated, and air pollution problems caused by air pollutants mainly PM2.5 have become more and more prominent [4], which has caused
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