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成都市金牛区冬季PM2.5化学特征和源解析

Chemical characteristics and source apportionment of PM2.5 at Jinniu District of Chengdu city in winter
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摘要 通过对2017年12月-2018年1月成都市金牛区大气细颗粒物PM2.5质量浓度及其组成进行分析,获得含碳组分有机碳-元素碳(OC/EC)、水溶性离子和微量元素等的变化特征,并利用正定矩阵因子分解(PMF)模型进行PM2.5来源解析。结果表明:2个采样点的含碳组分物质主要来自二次有机碳(OC/EC质量比值均在5.0左右);水溶性离子中首要组分是NO3-,表明金牛区受机动车污染影响大;微量元素S、Cl、Cr、Cu、Zn、Ni和Pb均出现较高的富集因子(EF)值,说明已经明显受到人为活动的影响;PM2.5中二次无机气溶胶(SNA)组分占比接近50%,说明二次气溶胶是PM2.5中的重要组分;源解析表明首要污染源是二次气溶胶(38.4%,质量分数,下同),其次是交通源(21.2%)、燃煤(15.1%)、生物质燃烧(13.6%)和土壤尘(11.7%)。 In this paper,by analyzing the PM2.5 mass concentration and species of atmospheric particulate matter in Jinniu district of Chengdu city from December 2017 to January 2018,we obtained the characteristics of species composition changes such as OC-EC,water-soluble ions and trace elements. PM2.5 source analysis was performed by the PMF model. The results show that the carbonaceous components of the two sampling points are mainly from secondary organic carbon (the value of OC/EC is about 5.0). The main component of water-soluble ions is NO3-,indicating that the ambient air is greatly affected by motor vehicle pollution in Jinniu district. Trace elements of S,Cl,Cr,Cu,Zn,Ni and Pb have higher EF values and obviously affected by human activities. The SNA component in PM2.5 is close to 50%,indicating that the secondary aerosol is an important component in PM2.5. Offline PMF source analysis shows that the primary source of pollution is secondary aerosol (38.4%),followed by traffic sources (21.2%),coal burning (15.1%),biomass burning (13.6%) and soil dust (11.7%).
作者 王平 赵由之 丁文慈 李曾曾 刘随心 WANG Ping;ZHAO Youzhi;DING Wenci;LI Zengzeng;LIU Suixin(School of Marine Science and Technology,Hainan Tropical Ocean University,Sanya 570022,China;Key Laboratory of Aerosol Chemistry and Physics,Institute of Earth Environment,Chinese Academy of Sciences,XiJ an 710061,China)
出处 《中国粉体技术》 CAS CSCD 2020年第5期14-21,共8页 China Powder Science and Technology
基金 国家自然科学基金项目,编号:41867046 中国科学院地球环境研究所黄土与第四纪地质国家重点实验室开放基金项目,编号:SKLLQG1830 中国科学院气溶胶化学与物理重点实验室开放基金项目,编号:KLACP2001 2019年海南省基础与应用基础研究计划(自然科学领域)高层次人才基金项目,编号:2019RC243 2018年三亚市院地科技合作项目,编号:2018YD14。
关键词 细颗粒物 正定矩阵因子分解模型 滤膜采样 源解析 fine particulate matter positive definite matrix factorization model filter sampling source apportionment
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