Simulation of the distribution of main atmospheric pollutants and the influence of land use on them in central urban area of Nanchang City, China.

Abstract

With the rapid urbanization and industrialization in China, atmospheric pollution becomes increasingly urgent. It is of great importance to examine the distribution of atmospheric pollutants and the influence of land use for sake of reducing pollution. We simulated and analyzed the temporal and spatial distribution characteristics of the six main atmospheric pollutants in the central urban area of Nanchang City, i.e. PM2.5, PM10, SO2, NO2, CO and O3 based on land-use regression models (LUR). Four types of area, i.e. residential, commercial, educational and industrial area, were defined according to dominated land use type. Fifteen samples from each type were collected. The concentration of six air pollutants of fifteen sample areas for each type and each season were averaged to reduce the influence of meteorological factors. By means of double factor varian-ce analysis and multiple comparisons, we analyzed the effects of land use (expressed by sample area) on those atmospheric pollutants. The results showed that the concentrations of all the six atmospheric pollutants were well simulated by LUR model, with an average absolute error were 11.9%, 13.4%, 12.5%, 12.0%, 12.7% and 13.5% respectively. The concentration of six atmospheric pollutants showed obvious temporal and spatial distribution characteristics, with O3 presenting the highest in summer, then spring, autumn, and winter in order, and the remaining five pollutants peaked in winter, then spring, autumn and summer in order. The concentrations of PM2.5, PM10, SO2, NO2 and CO showed a decreasing trend from urban center to suburb, while the concentration of O3 was the opposite. The concentrations of varied seasons or land use sample areas were all significantly different, indicating that both meteorology and land use had significant effects on air pollution. The effects of land use on main atmospheric pollutants varied, with stronger effects on PM2.5, NO2 and O3 than on CO.