引用本文:段菁春,胡京南,谭吉华,陈红,等.特征雷达图的设计及其在大气污染成因分析中的应用[J].环境科学研究,2018,31(8):1329-1336.
DUAN Jingchun,HU Jingnan,TAN Jihua,CHEN Hong,et al.Design of Characteristic Radar Chart and Its Application in Air Pollution Analysis[J].Reserrch of Environmental Science,2018,31(8):1329-1336.]
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特征雷达图的设计及其在大气污染成因分析中的应用
段菁春1,2, 胡京南1,2, 谭吉华3, 陈红4
1. 中国环境科学研究院, 北京 100012;
2. 国家大气污染防治攻关联合中心, 北京 100012;
3. 中国科学院大学资源与环境学院, 北京 100049;
4. 广州宏泰环保科技有限公司, 广东 广州 510623
摘要:
为了更好地利用环境监测数据进行污染成因分析,提出数学算法,对多种污染物进行百分比成分谱化,并除以一定时期(或一定区域)的平均值,从而得到标准化特征谱,以消除污染物浓度变化的影响以及不同污染物间浓度值差异的影响;通过设计特征雷达图的方式,直观和快速地展现大气污染特征在时间序列和空间上发生的变化特征,为环境管理部门利用空气质量常规监测数据开展动态决策提供便利.该方法既可利用单点历史数据开展历史特征雷达图分析,也可基于区域多站点数据开展区域特征雷达图分析.该方法在时间序列上可以判断出偏沙尘污染型、偏燃煤污染型、偏二次颗粒物污染型、偏机动车污染型、偏烟花污染型等多个污染类型;在区域分布中可以判断出偏燃煤污染区、偏机动车污染区、偏钢铁污染区等多个区域类型.该方法除可应用于空气质量常规监测数据外,也可应用于其他组分数据如碳质组分、水溶性离子组分及元素组分等数据的分析.
关键词:  污染特征  时空变化  空气污染  来源解析  成因分析
DOI:10.13198/j.issn.1001-6929.2018.06.12
分类号:X513
基金项目:国家重点研发计划重点专项(No.2016YFC0208900);大气重污染成因与治理攻关项目(No.DQGG0303)
Design of Characteristic Radar Chart and Its Application in Air Pollution Analysis
DUAN Jingchun1,2, HU Jingnan1,2, TAN Jihua3, CHEN Hong4
1. Chinese Research Academy of Environmental Sciences, Beijing 100012, China;
2. National Joint Research Center for Tackling Key Problems in Air Pollution Control, Beijing 100012, China;
3. University of Chinese Academy of Sciences, College of Resources and Environment, Beijing 100049, China;
4. Guangzhou Hongtai Environmental Protection Technology Co., Ltd., Guangzhou 510623, China
Abstract:
In order to make better use of environmental monitoring data for pollution cause analysis, a mathematical algorithm is proposed, which creates the percentage spectrum of multiple pollutants; this is then divided by the average percentage spectrum over a given period (or region) to obtain the standard characteristic spectrum, so as to eliminate the influence of concentration and concentration differences between pollutants. By the design of the characteristic radar chart, the temporal and spatial characteristics of air pollution are displayed intuitively and quickly. It provides convenience for environmental management departments to make dynamic decisions by using air quality routine monitoring data. This method can make use of single point historical data to construct historical characteristic radar chart, and can also carry out regional characteristic radar charts based on regional multi-site data. At present, the characteristic radar chart has successfully identified the types of sand dust, coal combustion, secondary aerosol, vehicle emissions and firework emissions by use of historical characteristic radar chart; and the zone types of coal combustion, vehicle emissions, iron and steel pollution etc. through regional characteristic radar chart. The characteristic radar chart can also be applied in the analysis of other component data, such as carbon components, water-soluble ions and elements.
Key words:  pollution characteristics  temporal and spatial variation  air pollution  source apportionment  pollution analysis