Abstract:

PM_2.5 has been the primary air pollutant in many cities of China and an expanding public concern as well because of its severe impacts on human health and visibility. The knowledge of spatial variability of PM_2.5 is of great importance to revelation of the mechanism of PM_2.5  pollution. The monitoring data of hourly mean PM_2.5  concentration collected at the 57 monitoring posts in the Pearl River Delta region throughout the year of 2013 were pooled and analyzed and seasonal and annual means at each monitoring post were forked out. The theory of spatial autocorrelation analysis was adopted in analyzing strength of the spatial autocorrelation and spatial clustering patterns of PM_2.5  concentration relative to season. Results show that in the Pearl River Delta,PM_2.5  pollution varied sharply from season to season and its mean value in winter was 3 times as high as that in summer. Spatial autocorrelation analysis shows that (1) within the radius of 90 km, existed positive spatial autocorrelation of PM_2.5  concentration and its scale effect was apparent in the Delta, which indicates that spatial autocorrelation declined two times at the city scale and then the regional scale; and (2) the global Moran′s I of PM_2.5 concentration varied with season, being 0.542, 0.752, 0.602 and 0.628, in spring, summer, autumn and winter respectively, which demonstrates that spatial autocorrelation varied, following an order of  summer＞ winter ＞ autumn＞ spring. The spatial clustering pattern of PM2.5 concentration shows that in Shenzhen and the coastal areas PM_2.5  concentration appeared to be in a low-ow clustering pattern (L-L), while cities or areas quite far away from the coast, like Guangzhou, in a high-high clustering pattern (H-H).

Key words: air pollution, PM_2.5, spatial autocorrelation, spatial cluster, the Pearl River Delta