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Deep learning algorithms for prediction of PM10 dynamics in urban and rural areas of Korea

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Abstract

High concentrations of particulate matter (PM) are frequently associated with serious health problems, underlining the importance of accurate PM prediction. This study aimed to predict PM10 concentrations by analyzing air pollutant data in Korea (Seoul, Incheon, Daejeon, and Busan) using convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods. Real-time data from January 2014 to December 2020 were organized as hourly averages. The SO2, NO2, CO, O3, and PM10 data from 2014 to 2018 were used for training, and data from 2019 to 2020 were used as test data. The highest prediction accuracy was accomplished using all observations. The contribution ratio of each model component to the predictions was verified using SHapley Additive exPlanations (SHAP), and PM10 showed the greatest contribution. The other components, as secondary aerosol precursors, were divided by area. CO and O3 were found to be high in Seoul (Gwanak), which has been highly urbanized. On the other hand, CO and NO2 were found to be high in Incheon (Namdong), Daejeon (Yuseong), and Busan (Sasang), which are relatively suburban areas. The deep learning results indicated that the predicted PM10 concentration was most affected by past and present concentrations of PM10. It is considered that the atmospheric PM10 at the study sites mainly originated from direct emissions. We compared the proposed method with recent prediction methods using algorithms, machine learning, and deep learning. The R2, root mean square error, and mean absolute error evaluation indices supported the suitability of the proposed method for analyses at the study site.

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Acknowledgments

The authors wish to thank Chung-Mo Lee of the Korea Institute of Geoscience and Mineral Resources (KIGAM) for help with the mapping of the study area. This research was principally supported by the Basic Science Research Program through a National Research Foundation of Korea grant from the Ministry of Education (NRF-2018R1D1A1B07044596). This research was also supported by a grant from the Basic Research Project (21-3411) of KIGAM (Ministry of Science and ICT). Myungjoo Kang was supported by the NRF grant (2021R1A2C3010887). We thank the journal reviewers for providing thoughtful comments on the manuscript. The comments highly improved this paper.

CRediT authorship contribution statement

Han-Soo Choi: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Writing - original draft. Kyungmin Song: Methodology, Data curation, Formal analysis, Investigation, Resources. Myungjoo Kang: Writing - review & editing. Yongcheol Kim: Funding acquisition, Writing - review & editing. Kang-Kun Lee: Writing – review & editing. Hanna Choi: Supervision, Writing - review & editing.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Funding

This research was principally supported by the Basic Science Research Program through a National Research Foundation of Korea grant from the Ministry of Education (NRF-2018R1D1A1B07044596). This research was also supported by a grant from the Basic Research Project (21–3411) of KIGAM (Ministry of Science and ICT).

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Author notes

  1. Han-Soo Choi and Kyungmin Song are the first author and contributed equally to this work

Authors and Affiliations

  1. Research Institute of Mathematics, Seoul National University, Seoul, Republic of Korea

    Han-Soo Choi

  2. AI Research, LG CNS, Seoul, Republic of Korea

    Han-Soo Choi

  3. Optimization & Analytics, SK Innovation, Seoul, Republic of Korea

    Kyungmin Song

  4. Mathematical Science, Seoul National University, Seoul, Republic of Korea

    Myungjoo Kang

  5. Groundwater Research Center, Korea Institute of Geoscience and Mineral Resources, Daejeon, Republic of Korea

    Yongcheol Kim & Hanna Choi

  6. Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea

    Kang-Kun Lee

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  1. Han-Soo Choi
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  4. Yongcheol Kim
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Correspondence to Hanna Choi.

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Communicated by: H. Babaie

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Choi, HS., Song, K., Kang, M. et al. Deep learning algorithms for prediction of PM10 dynamics in urban and rural areas of Korea. Earth Sci Inform 15, 845–853 (2022). https://doi.org/10.1007/s12145-022-00771-1

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