Application of land use regression to regulatory air quality data in Japan

A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe and North America. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Shizuoka, Japan. We used the annual-mean nitrogen dioxide (NO₂) and suspended particulate matter (SPM) concentrations between April 2000 and March 2006 in the study area. SPM accounts for particulate matter with an aerodynamic diameter less than 8 μm (PM₈). Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and physical component. Using geographical variables, we then constructed a model to predict the monitored levels of NO₂ and SPM. The mean concentrations of NO₂ and SPM were 35.75 μg/m³ (standard deviation of 11.28) and 28.67 μg/m³ (standard deviation of 4.73), respectively. The final regression model for the NO₂ concentration included five independent variables. R² for the NO₂ model was 0.54. On the other hand, the regression model for the SPM concentration included only one independent variable. R² for the SPM model was quite low (R² = 0.11). The present study showed that even if we used regulatory monitoring air quality data, we could estimate NO₂ moderately well. This result could encourage the wide use of LUR models in Asian countries.