PM10 and PM2.5 prediction in Seoul, South Korea using LDAPS meteorological data and tree-based machine learning

Particulate matter (PM) increases traffic accidents and mortality rates in the short term, and poses a long-term threat to public health, such as cardiovascular and respiratory diseases, deteriorating human health. Therefore, real-time monitoring and prediction are crucial for improving air quality, and efforts should be made to regulate and reduce the emissions of air pollutants at the national or regional level. In this study, tree-based machine learning algorithms (RF, XGB, LGB) were used with local data assimilation and prediction system (LDAPS) meteorological forecast data (four times a day, hourly forecast for 36 hours) to predict PM₁₀ and PM_2.5 in Seoul, South Korea. Seoul, being a densely populated megacity, experiences high local emissions of air pollutants and significant influx of high-concentration pollutants from neighboring countries and desert areas. Therefore, monitoring and prediction of PM concentrations are of utmost importance in Seoul. The prediction results were compared with the observed data of PM₁₀ and PM_2.5 from air-quality measurement station in Seoul. The results showed that LGB method among the tree-based ML algorithms was the most suitable for PM prediction, with hourly R²=0.83~0.86 and daily R²=0.996. These results showed that the prediction performance was significantly higher than the chemical transport model prediction results, with R² being more than 0.2 higher, and the prediction performance was superior especially in cases of high-concentration PM. Therefore, the high-accuracy PM prediction based on machine learning presented in this study can be useful for air quality monitoring and improvement.

 

Acknowledgments: This work was funded by the Korea Meteorological Administration Research and Development Program “Research on Weather Modification and Cloud Physics” under Grant (KMA2018-00224).