[Temporal and spatial distribution of PM2.5 and PM10 pollution status and the correlation of particulate matters and meteorological factors during winter and spring in Beijing].

Fogs and hazes broke out many times in winter and spring of 2012-2013 in Beijing, inducing severe pollution of respirable particulate matters (PM10). As a fine particle component in PM10, PM2.5 would cause more severe air pollution if the proportion of PM2.5 to PM10 is high. Based on this, 30 monitoring stations recording the concentration of PM2.5 and PM1.0 all over Beijing were selected, and the contamination characteristics of particulate matters were analyzed, which further served to determine the characteristics of temporal and spatial pollution variations of PM2.5 and PM10. The distribution of PM2.5 and PM10 mass concentration in winter and spring in Beijing were derived by the Original Kriging interpolation method, and it was depicted from the figure that the concentration of particulate matters gradually increased from the northern mountain area to the southern part of Beijing; in the central urban area, the particulate concentration of the western region was generally higher than that of the eastern region, with certain differences between urban and rural area within some local areas. Monthly variation curve of PM2.5 and PM10 mass concentration showed single peak-valley pattern: the maximum was in January and the minimum was in April; daily variation indicated a good correlation between PM2.5 and PM10, both of which were significantly influenced by meteorological conditions; diurnal variation curve showed a double peak-valley type. Meteorological factors such as daily average temperature (degrees C), relative humidity (%), wind speed (wind scale), precipitation (mm) were chosen and their individual relationships with concentrations of PM10 and PM2.5 were investigated using Spearman rank correlation analyses. It was demonstrated that the concentrations of PM10 and PM2.5 were positively correlated with temperature and relative humidity, respectively, and strongly negatively correlated with wind speed; wind speed and relative humidity were two key factors affecting the distributions of PM2.5 and PM10 concentration.