A land use regression model for estimating the NO2 concentration in Shanghai, China.

Limited by data accessibility, few exposure assessment studies of air pollutants have been conducted in China. There is an urgent need to develop models for assessing the intra-urban concentration of key air pollutants in Chinese cities. In this study, a land use regression (LUR) model was established to estimate NO2 during 2008-2011 in Shanghai. Four predictor variables were left in the final LUR model: the length of major road within the 2-km buffer around monitoring sites, the number of industrial sources (excluding power plants) within a 10-km buffer, the agricultural land area within a 5-km buffer, and the population counts. The model R(2) and the leave-one-out-cross-validation (LOOCV) R(2) of the NO2 LUR models were 0.82 and 0.75, respectively. The prediction surface of the NO2 concentration based on the LUR model was of high spatial resolution. The 1-year predicted concentration based on the ratio and the difference methods fitted well with the measured NO2 concentration. The LUR model of NO2 outperformed the kriging and inverse distance weighed (IDW) interpolation methods in Shanghai. Our findings suggest that the LUR model may provide a cost-effective method of air pollution exposure assessment in a developing country.