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机构地区:[1]山东建筑大学测绘地理信息学院,山东济南250101
出 处:《山东建筑大学学报》2017年第1期39-46,共8页Journal of Shandong Jianzhu University
基 金:教育部人文社会科学研究规划基金项目(12YJA790019);住房和城乡建设部研究开发项目(2012-K8-22)
摘 要:细颗粒物(PM2.5)是空气质量监测的重要对象,研究PM2.5的时空变化规律对于治理雾霾污染具有重要意义。文章基于山东省17个城市在2014~2015年共728 d的空气质量监测数据和相关气象数据,采用时间序列分析、相关分析、空间自相关分析和空间插值等方法研究了时空变化规律和气象因素对山东省PM2.5浓度的影响。结果表明:山东省PM2.5浓度呈U型变化,变化的时间序列自相性较强,一阶自相关系数达0.6896,PM2.5浓度空间分布规律明显;风向对山东省不同区域PM2.5浓度的聚集和扩散具有一定作用,与气象条件均呈现一定相关性,不同月份其相关性存在明显差异;PM2.5还与SO_2、CO、NO_2、PM10等污染物存在极强的正相关,与O_3存在负相关。PM 2.5 is an important target of air quality monitoring,and understanding the temporal and spatial variation of PM2.5 is of great significance for governing gray haze.Based on 728 days' air quality monitoring data from 2014 to 2015 and relevant meteorological data of 17 cities in Shandong province,using time series analysis,correlation analysis,spatial autocorrelation and spatial interpolation,we studied the spatial and temporal variation of the concentration of PM2.5 in Shandong Province and analyzed the influence of meteorological factors on PM2.5 concentration.The results show that the winter months present with a higher concentration,relatively lower in summer months,showing a U-shape changes.The time series autocorrelation of PM2.5 concentration changes was strong,and first order autocorrelation coefficient reaches 0.6896.The concentration distribution of PM2.5 shows obvious spatial agglomeration characteristics.The direction of wind has an obvious effect on the aggregation and diffusion of PM2.5 concentration;PM2.5 concentration and meteorological conditions show a certain correlation;in different months the correlation is significantly different.Meanwhile,the concentration of PM2.5 and SO_2,CO,NO_2,PM10 exists a highly positive correlation coefficient while a negative correlation with O_3.In the process of governing the haze,we should carry on the comprehensive management of various pollutants.
分 类 号:X513[环境科学与工程—环境工程]
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