Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Prediction of PM10 concentration in Seoul, Korea using Bayesian network

  • Minjoo Joa (Department of Statistics, Ewha Womans University) ;
  • Rosy Oh (Department of Mathematics, Korea Military Academy) ;
  • Man-Suk Oh (Department of Statistics, Ewha Womans University)
  • Received : 2023.04.10
  • Accepted : 2023.07.21
  • Published : 2023.09.30
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Abstract

Recent studies revealed that fine dust in ambient air may cause various health problems such as respiratory diseases and cancer. To prevent the toxic effects of fine dust, it is important to predict the concentration of fine dust in advance and to identify factors that are closely related to fine dust. In this study, we developed a Bayesian network model for predicting PM10 concentration in Seoul, Korea, and visualized the relationship between important factors. The network was trained by using air quality and meteorological data collected in Seoul between 2018 and 2021. The study results showed that current PM10 concentration, season, carbon monoxide (CO) were the top 3 effective factors in 24 hours ahead prediction of PM10 concentration in Seoul, and that there were interactive effects.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT, ME) (NRF-2022R1A2C1091256, 2020R1A2C1008699).

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