Abstract: Base on GIS, self-organizing map (SOM), back trajectory model and geostatistical method, this study investigates the spatiotemporal distribution of air pollution and its impacted factors in Nanchang City. For this purpose, the data of air quality index (AQI) and the main air pollutants in different time scales from 2014 to 2017 is selected, as well as the meteorological factors. Results show that the air quality of Nanchang City is not significantly improved in recent years. 51≤AQI≤100 is the main level of AQI with the ratios between 57% and 61%. But the primary pollutants are greatly changed, e.g. the number of polluted days of PM2.5 decrease, while the counterpart of NO2 and O3 increase. For seasonal variation, the air quality in Nanchang City is poor in winter and spring, with PM10 and PM2.5 as the primary pollutants. In comparison, the counterpart is good in summer, with O3 as the primary pollutant. The air quality is relatively bad on Monday and the weekends, and relatively good on Wednesdays and Thursday. It reveals that travel habit of people is not the only influence factor for the air quality in Nanchang City. The diurnal variation of air quality in Nanchang City shows a bimodal distribution peaked at 9:00-11:00 and 20:00-22:00, which is primarily related to the traffic exhaust during the rush hours. For spatial distribution, the air quality in Nanchang City is poor in the downtown area compared with that in the suburban area. Specifically, the concentrations of PM10, PM2.5, SO2, NO2, CO are higher in the downtown area. In contrary, O3 shows the higher concentration in the suburban area. Because there is more amount of NOx emission in the downtown area and O3 is easily consumed by rapid reaction with them. Back trajectories and cluster analysis suggest that the prevailing air masses were from the nearby area in Nanchang and PM10, PM2.5, NO2 and O3 pollution may mainly originate from these areas. Principal component analysis (PCA) demonstrates that the relative humidity and temperature is the main impact corresponding to the high concentration of PM2.5, PM10 and NO2 and the concentration of O3, respectively.