Proposed Utilization of Classification Models for High Ozone Alert Communication in Seoul

A machine learning-based classification model was applied to identify the main influencing factors affecting O₃ advisory (triggered when hourly average O₃ concentrations exceed 0.12 ppm), using existing 25 urban air quality monitoring networks data from Seoul and meteorological data from Seoul automatic weather station (Jongno-gu). From May to September 2023, data were collected and analyzed. The dataset comprised 19 variables, including urban air quality metrics (such as O₃, PM_2.5, PM₁₀, NO_X) and meteorological parameters (such as wind speed, temperature, relative humidity, rain probability, and cloud cover), recorded on an hourly basis. Using this data, two classification models were developed: the first model (analysis model, ANM) employed decision tree and random forest algorithms to identify the main influencing factors affecting high O₃ concentration events. The second model (prediction model, PRM) was designed to predict the likelihood of O₃ advisory for the following day. Through the application of ANM, the main influencing factors affecting high O₃ concentration were identified, with PM_2.5, PM₁₀, and temperature emerging as significant variables affecting O₃ advisory. And both decision tree and random forest models have demonstrated strong classification performance. These results indicate that the models effectively classified the data into category 0 (no O₃ advisory) and category 1 (O₃ advisory). Additionally, a second classification model (PRM) was developed to predict the likelihood of O₃ advisory in Seoul for the following day. This model utilized seven independent variables: temperature, relative humidity, rain probability, cloud cover, and forecasted air quality levels (PM_2.5, PM₁₀, O₃). Overall, these findings suggest that PRM is a viable tool for predicting next-day O₃ advisory. In this study, the application of the proposed classification model methodology based on real-time air quality and meteorological data for a given region is expected to quantitatively explain the performance of PRM and be usefully utilized in reducing O₃ exposure for sensitive and vulnerable populations.