Development of land use regression model and health risk assessment for NO2 in different functional areas: A case study of Xi'an, China

With the acceleration of urbanization, industrial prosperity and economic development, China has attracted great attention as one of the largest sources of nitrogen dioxide (NO2) pollution in Asia. Many studies have carried out on the source analysis, spatial and temporal distribution, physical and chemical properties of pollutants. However, there are a few studies on the high-resolution spatial distribution of atmospheric pollutants in small-scale cities, especially by using land use regression (LUR) model. LUR model is a common method for predicting the spatial variation of pollutants. It expresses spatially local changes in the results through potential variables in the equation, thus fully reflecting the spatial differentiation of small-scale pollutant concentrations. In our study, along with 40 types potential predictor, we used LUR model to simulate the concentration of NO2 in Xi'an and to generate a high-resolution spatial distribution map of the concentrations, to provide better support for health risk assessment. In addition to using traditional predictors, due to the different classifications of roads in our study area, the use of road length as a predictor did not reflect traffic volumes well. For this reason, we weighted the roads with grades and calculated their area to fit the model. The results show that the fitting effect of the model in this study is good (adjusted R-2 > 0.85), and the deviation (< 2.27%) and LOOCVRMSE (< 2.22 mu g/m(3)) are both small for all time periods. Compared with traditional interpolation models, the LUR model has better prediction accuracy and greater ability to express spatial differences. Using a grid of 500 m * 500 m, 3494 grids were defined in Xi'an to simulate the spatial distribution of NO2 concentrations. High NO2 values appeared around the factory and represented the highest health risk. Due to abundant surface vegetation coverage, the concentrations of NO2 and the consequent health risks in tourist spots, parks and universities were low.