Land Use Regression Model for Assessing Exposure and Impacts of Air Pollutants in School Children

Epidemiologic studies of air pollution need accurate exposure assessments at unmonitored locations. A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe, North America, and a few Asian region. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Metropolitan area in Korea. We used the annual-mean nitrogen dioxide (NO2) in 2010 in the study area. Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and elevation. Using geographical variables, we then constructed a model to predict the monitored levels of NO2. The mean concentration of NO2 was 30.71 ppb (standard deviation of 5.95) respectively. The final regression model for the NO2 concentration included five independent variables. The LUR models resulted in R-2 of 0.59. The mean concentration of NO2 of elementary schools was 34.04 ppb (standard deviation of 5.22) respectively. The present study showed that even if we used regulatory monitoring air quality data, we could estimate NO2 moderately well. These analyses confirm the validity of land use regression modeling to assign exposures in epidemiological studies, and these models may be useful tools for assessing health effects of long-term exposure to traffic related pollution.