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<p>Urban air pollution has brought great troubles to physical and mental health, economic development, environmental protection, and other aspects. Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts. In this paper, we propose an interval prediction method that considers the spatio-temporal characteristic information of PM<sub>2.5</sub> signals from multiple stations. K-nearest neighbor (KNN) algorithm interpolates the lost signals in the process of collection, transmission, and storage to ensure the continuity of data. Graph generative network (GGN) is used to process time-series meteorological data with complex structures. The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process, which is beneficial to improve the efficiency and robustness of the model. In addition, sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation (KDE) interval prediction. With the support of sparse strategy, sparse Bayesian regression kernel density estimation (SBR-KDE) is very efficient in processing high-dimensional large-scale data. The PM<sub>2.5</sub> data of spring, summer, autumn, and winter from 34 air quality monitoring sites in Beijing verified the accuracy, generalization, and superiority of the proposed model in interval prediction.</p>

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PM2.5 probabilistic forecasting system based on graph generative network with graph U-nets architecture

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Yan-fei Li,
  • Rui Yang,
  • Zhu Duan,
  • Hui Liu

摘要

Urban air pollution has brought great troubles to physical and mental health, economic development, environmental protection, and other aspects. Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts. In this paper, we propose an interval prediction method that considers the spatio-temporal characteristic information of PM2.5 signals from multiple stations. K-nearest neighbor (KNN) algorithm interpolates the lost signals in the process of collection, transmission, and storage to ensure the continuity of data. Graph generative network (GGN) is used to process time-series meteorological data with complex structures. The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process, which is beneficial to improve the efficiency and robustness of the model. In addition, sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation (KDE) interval prediction. With the support of sparse strategy, sparse Bayesian regression kernel density estimation (SBR-KDE) is very efficient in processing high-dimensional large-scale data. The PM2.5 data of spring, summer, autumn, and winter from 34 air quality monitoring sites in Beijing verified the accuracy, generalization, and superiority of the proposed model in interval prediction.