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PM2.5 estimation and analysis of BiCNN model considering spatiotemporal characteristics: a case study of the middle reaches of the Yangtze River urban agglomeration

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Abstract

As one of China’s national urban agglomerations, the middle reaches of the Yangtze River urban agglomeration (MRYRA) has suffered from serious PM2.5 pollution for a considerable period. Using deep learning methods to fit the quantitative relationship between aerosol optical depth (AOD) and PM2.5 (AOD-PM2.5) has received extensive attention. In this study, based on the correlation between AOD and PM2.5 data, a new network model BiCNN is proposed by using the satellite-ground synergy method, aiming to obtain the spatial distribution of PM2.5 concentration in the MRYRA. Data from MRYRA 2019 is used for model training and testing. The results show that BiCNN exhibits the best estimation on the annual scale, with a fit coefficient R2 of 0.836 and the smallest error, RMSE of 6.746 and MAPE of 12.497 compared to the benchmark model. The model effectively improves the relationship between AOD-PM2.5. The spatial distribution of PM2.5 in the MRYRA from 2019 to 2022 by applying the BiCNN model inversion shows that the PM2.5 concentration is higher in 2020, but the overall air quality improves year by year. Seasonal evaluation results show that BiCNN has a significant advantage in PM2.5 estimation in autumn, with R2 reaching 0.883. The spatial distribution of PM2.5 shows a spatial pattern of higher in spring and winter, lower in summer and autumn, and transition in autumn. The BiCNN model integrates the advantages of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Neural Network (BiLSTM), makes full use of historical and future data, and can effectively capture the temporal and spatial characteristics between cofactors and PM2.5 data. It has achieved ideal results in the PM2.5 estimation of the MRYRA and provides an effective scientific method for air pollution control in urban agglomerations.

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Data availability

All datasets, raw or preprocessed, are available upon request from the corresponding author.

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Funding

This work is supported by Key Project of Natural Science Foundation of Jiangxi Province (20232ACB203025), Ministry of Education Project (202102245015), and Special Fund for Graduate Innovation (No. YC2022-S637).

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Authors and Affiliations

  1. School of Civil and Surveying & Map** Engineering, Jiangxi University of Science and Technology, No. 86 Hongqi Road, Ganzhou, 341000, Jiangxi, China

    Shuaiwen Wu, Hengkai Li & Yonglan He

  2. Information Technology Research Center, Bei**g Academy of Agriculture and Forestry Sciences, Bei**g, 100000, China

    Yanbing Zhou

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  1. Shuaiwen Wu
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  2. Hengkai Li
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  3. Yanbing Zhou
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  4. Yonglan He
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Contributions

Study conception and design: SW and YH; data collection and processing: SW; methodology: SW, YH and YBZ; analysis of results and discussion: SW, YH, and HL; and draft manuscript preparation: SW, YH, HL, and YZ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Hengkai Li or Yanbing Zhou.

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Wu, S., Li, H., Zhou, Y. et al. PM2.5 estimation and analysis of BiCNN model considering spatiotemporal characteristics: a case study of the middle reaches of the Yangtze River urban agglomeration. Theor Appl Climatol 155, 2787–2799 (2024). https://doi.org/10.1007/s00704-023-04775-9

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