摘要
PM2.5是造成空气污染的重要因素之一,为了对江苏省PM2.5空间变异性进行研究,以江苏省PM2.5监测站数据,利用地统计模型,分析江苏省13市大气中PM2.5的污染程度。结果表明:普通克里格法的指数模型较球状模型和高斯模型更为适合;晴、雨、雪都是中等的变异性,其空间变化是由结构性因素和随机性因素共同作用的结果;晴天的块金系数为16.7%,空间相关性较强,而雨雪天气的空间相关性较弱,证实了雨雪天气对PM2.5有着较大的影响。
PM2.5 is one of the key factors that cause air pollution. In order to analyze spatial variation of PM2.5 , geostatistics is used to analyze PM2.5 pollution based on data from Jiangsu PM2.5 monitoring station. The results showed that Exponential model is more suitable than Spherical model and Gaussian model; sunny, rainy, snowy are both with moderate variability, the spatial variation of them is caused by structural and stochastic factors simultaneously; the nugget coefficient in a sunny day is 16.7% and its spatial correlation is strong, but the nugget coefficients in rainy and snowy days are relatively weak, which confirm that rain and snow have a larger impact on PM2.5.
出处
《环境工程》
CAS
CSCD
北大核心
2014年第1期73-76,91,共5页
Environmental Engineering
基金
全国统计科学研究计划项目(2011LY062)
江苏省高校自然科学基金(12KJB11016)
