Land-Use Regression Modeling to Estimate NO₂ and VOC Concentrations in Pohang City, South Korea

Land-use regression (LUR) has emerged as a promising technique for air pollution modeling to obtain the spatial distribution of air pollutants for epidemiological studies. LUR uses traffic, geographic, and monitoring data to develop regression models and then predict the concentration of air pollutants in the same area. To identify the spatial distribution of nitrogen dioxide (NO₂ ), benzene, toluene, and m-p-xylene, we developed LUR models in Pohang City, one of the largest industrialized areas in Korea. Passive samplings were conducted during two 2-week integrated sampling periods in September 2010 and March 2011, at 50 sampling locations. For LUR model development, predictor variables were calculated based on land use, road lengths, point sources, satellite remote sensing, and population density. The averaged mean concentrations of NO₂, benzene, toluene, and m-p-xylene were 28.4 µg/m³, 2.40 µg/m³, 15.36 µg/m³, and 0.21 µg/m³, respectively. In terms of model-based R² values, the model for NO₂ included four independent variables, showing R² = 0.65. While the benzene and m-p-xylene models showed the same R² values (0.43), toluene showed a lower R² value (0.35). We estimated long-term concentrations of NO₂ and VOCs at 167,057 addresses in Pohang. Our study could hold particular promise in an epidemiological setting having significant health effects associated with small area variations and encourage the extended study using LUR modeling in Asia.