Prediction models ranging from statistical probability to machine learning techniques have been employed to improve and manage urban air quality. However, the number of air quality monitoring stations (AQMS) for the collection of air quality information is limited. This study established a model that explains the relationship between six air pollutants–SO2, CO, O3, NO2, PM10, and PM2.5–measured by approximately 443 AQMS in South Korea and factors, such as the vegetation index, topography, and land cover elements. The model analyzed the impact of land cover changes on air pollutant concentrations and derived scenarios predicting changes in the air quality due to land use changes. Despite the relatively small sample size of approximately 360 AQMS, multiple regression analysis demonstrated higher explanatory power compared with Xtreme Gradient Boosting, a representative machine learning technique. The optimal spatial range for explaining air pollutant concentrations varied for each air pollutant. The highest R2 in the multiple regression analysis was 0.34 at a distance of 12,000 m for SO2; 0.27 at 11,000 m for CO; 0.50 at 6000 m for O3; 0.70 at 18,000 m for NO2; 0.49 at 18,000 m for PM10; and 0.48 at 11,000 m for PM2.5. Certain land cover characteristics were found to significantly affect air quality, whereas small-scale restoration had a minimal impact on air quality improvement, and large-scale development substantially increased pollutant concentrations. This study provides essential information for urban planning and policymaking aimed at improving urban air quality.
Keywords: Urban air pollution; Land cover; Multiple regression analysis; XGBoost