The traditional land use regression(LUR) model does not consider the nonlinear complex relationship between impact factors and atmospheric pollutants. Taking PM10 as an example, Support Vector Machine Regression (SVR) has been used to improve the land use regression model modeling method to construct SVR-LUR model, and then the spatial distribution of PM10 around Nanpu Bridge in Shanghai, China is simulatedbased on the model. The results show that: 1) There is a high correlation between the PM10 concentration and the empty area in the 100 m buffer zone, the construction area, the empty area and the river area in the 150 m buffer zone, the green area and the river area in the 200 m buffer zone, as well as the humidity, traffic flow and background concentration. 2) the SVR-LUR model can better predict the spatial distribution of PM10 concentration in the study area. Compared with LUR model, SVR-LUR model has higher prediction accuracy. Compared with LUR model, the mean absolute error (MAE) and root mean squares error (RMSE) oftest set of SVR-LUR model reduces by are22.92%, 33.51% less than those of LUR model, MAE and RMSE respectively, and whilethe index of agreement (IA) value increases by 13.20%. Compared with the prediction results of single gradient spatial distribution obtained by ordinary Kriging interpolation model, SVR-LUR model can more effectively reveal the spatial differences in a small range. 3) The spatial distribution of PM10 concentration in the study area shows a general pattern of high concentration in the west and low in the east. The concentration is higher in the areas with dense buildings and road network, but relatively lower in the areas near the river and open space. The simulation results are consistent with the actual situation.