珠三角地区PM_(2.5)浓度空间自相关分析

Spatial Autocorrelation Analysis of PM_(2.5) Concentration in the Pearl River Delta

PM_(2.5)空间变异规律是揭示污染机制的重要基础。研究获取珠三角地区共57个监测点2013年全年PM_(2.5)小时均值监测数据,汇总后得到监测点季度均值和年均值,采用空间自相关分析理论研究不同季节PM_(2.5)浓度空间自相关性的强弱与集聚模式。结果显示,珠三角地区PM_(2.5)污染季节差异显著,冬季PM_(2.5)浓度均值是夏季的3倍。空间自相关分析表明,90 km范围内,珠三角PM_(2.5)浓度均存在正空间自相关性且尺度效应明显,空间自相关性存在城市尺度和区域尺度2次递减;春、夏、秋、冬季PM_(2.5)浓度全局Moran's I指数分别为0.542、0.752、0.602和0.628,空间自相关性由高到低依次为夏、冬、秋和春季;珠三角PM_(2.5)浓度集聚模式明显,深圳等沿海地区表现为PM_(2.5)浓度低-低集聚(L-L),而离海岸稍远的广州等地区为高-高集聚(H-H)区域。

PM2.5 has been the primary air pollutant in many cities of China and an expanding public concern as well be-cause of its severe impacts on human health and visibility. The knowledge of spatial variability of PM2.5 is of great impor-tance to revelation of the mechanism of PM2.5 pollution. The monitoring data of hourly mean PM2.5 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 an-alyzing strength of the spatial autocorrelation and spatial clustering patterns of PM2.5 concentration relative to season. Re-sults show that in the Pearl River Delta, PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentra-tion appeared to be in a low-ow clustering pattern ( L-L) , while cities or areas quite far away from the coast, like Guang-zhou, in a high-high clustering pattern ( H-H) .