摘要
利用2018年261个乡镇环境空气自动监测站监测数据,结合GIS空间分析技术,对石家庄市PM10和PM2.5的时空污染特征进行了研究。结果表明,石家庄地区PM10和PM2.5污染的空间分布整体表现为西北部山区好于东南部的平原地区,主城区好于周边县(市、区)的特征。采暖期PM10和PM2.5的污染程度明显重于非采暖期。PM2.5稳定性差于PM10,PM10和PM2.5的稳定性与污染程度具有一定的负相关性,表现出污染越轻的区域稳定性越差。两者的日均值浓度变化在时间序列上呈极强正相关,且污染越重的区域时间相关性越强。与日均值相关性不同,污染程度越轻的区域PM10和PM2.5年均值的线性相关性越强。
In the article,the spatial and temporal distribution characteristics of PM10 and PM2.5 in Shijiazhuang were analyzed based on the monitoring date released by 261 county-level automatic air quality monitoring stations with the spatial analysis method GIS. The results showed that the annual concentration of PM10 and PM2.5 in the northwest mountainous areas were lower than the plain areas in the southeast,and the main urban areas were lower than surrounding counties (cities,districts). In respect of temporal variation,the concentration of PM10 and PM2.5 in heating period was significant higher than the rest of the year. The stability of PM2.5 was less than PM10,in other words,the daily relative variation of PM2.5 was higher than PM10.There was an inverse correlation between the stability and pollution level of PM10 and PM2.5.That was,the lower the concentration was,the poorer the stability would be. There was a positive correlation between the PM10 and PM2.5 in terms of the change of daily concentration,while the higher the concentration was,the stronger the time correlation would be. However,the linear fitting degree between annual averaged PM10 and PM2.5 concentrations was higher in less polluted area than that in heavier polluted areas.
作者
李秋芳
王杨
丁学英
李粟
谷金峰
刘翠棉
LI Qiufang;WANG Yang;DING Xueying;LI Su;GU Jinfeng;LIU Cuimian(Shijiazhuang Environmental Monitoring Center,Shijiazhuang 050022,China;College of Resources and Environmental Science,Hebei Normal University,Shijiazhuang 050024,China;Hebei Laboratory of Environmental Change and Ecological Constructon,Shijiazhuang 050024,China;Shijiazhuang Environmental Prediction Center,Shijiazhuang 050022,China)
出处
《中国环境监测》
CAS
CSCD
北大核心
2020年第2期173-183,共11页
Environmental Monitoring in China
基金
河北省自然科学基金资助项目(D2017205126)。