Prediction of PM10 concentration in Seoul, Korea using Bayesian network

Minjoo Jo, Rosy Oh, Man Suk Oh

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)517-530
Number of pages14
JournalCommunications for Statistical Applications and Methods
Issue number5
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.


  • air pollution
  • Bayesian network
  • machine learning
  • PM10
  • prediction


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