A1330
Title: Prediction of PM10 in Seoul, Korea using Bayesian networks
Authors: Man-Suk Oh - Ewha Womans University (Korea, South) [presenting]
Abstract: Recent studies revealed that fine dust in ambient air might 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. 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 results showed that current PM10 concentration, season, and carbon monoxide (CO) were the top 3 effective factors in predicting PM10 concentration in 24 hours in Seoul and that there were interactive effects.
