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基于PMF模型的PM2.5中金属元素污染及来源的区域特征分析
引用本文:邓林俐,张凯山,殷子渊,李欣悦,武文琪,向锌鹏.基于PMF模型的PM2.5中金属元素污染及来源的区域特征分析[J].环境科学,2020,41(12):5276-5287.
作者姓名:邓林俐  张凯山  殷子渊  李欣悦  武文琪  向锌鹏
作者单位:四川大学建筑与环境学院, 成都 610065,四川大学建筑与环境学院, 成都 610065,四川大学建筑与环境学院, 成都 610065,四川大学建筑与环境学院, 成都 610065,四川大学建筑与环境学院, 成都 610065,四川大学建筑与环境学院, 成都 610065
基金项目:国家自然科学基金项目(41877395);四川泓远环保工程有限公司委托技术服务项目(18H0676)
摘    要:金属元素是大气PM2.5的重要组成成分,对人群危害性极强且兼具源特异性,分析不同经济模式地区大气细颗粒物中金属污染状况及来源差异,可以为科学规划城市产业布局和保护大气环境提供参考.通过霾/非霾期大气PM2.5采样,使用电感耦合等离子体发射光谱仪(ICP-OES)测定成都市及仁寿县样品中18种金属元素质量浓度,分析其污染水平,并基于正定矩阵因子分解模型(PMF)解析两地大气PM2.5中金属元素的来源.结果表明,成都市扬尘源、移动源和燃煤源特征元素占元素总和的比值大于仁寿县,而仁寿县生物质燃烧源、工业源以及燃油源特征元素占比则较高.两地Cr、Cd和As元素浓度均超标,表明PM2.5中重金属污染严重.随着霾污染加剧,两地PM2.5中金属元素总量上升,但增幅远低于PM2.5浓度增长.此外,不同元素在霾期和非霾期浓度比值存在差异,成都市变化范围为0.7(Al)~2.8(Ba),仁寿县介于0.8(Al)~3.1(Mn)之间,但总的来说两地大致呈现出燃煤和工业活动排放元素增幅较大,机动车污染源次之,扬尘源增幅较缓的状况.受地区产业布局、经济规模和发展模式的影响,大气PM2.5中金属元素污染水平及来源呈现出不同的区域特征.在重点发展第三产业的大型城市,如成都,更易遭受交通运输和城市建设等带来的大气污染,而在仁寿等第二产业占比不断增加的郊县,其污染主要受化石燃料燃烧和工业过程排放的影响.

关 键 词:PM2.5  金属元素  正定矩阵因子分解模型(PMF)  源解析  
收稿时间:2020/4/30 0:00:00
修稿时间:2020/6/21 0:00:00

Characterization of Metal Pollution of Regional Atmospheric PM2.5 and Its Sources Based on the PMF Model
DENG Lin-li,ZHANG Kai-shan,YIN Zi-yuan,LI Xin-yue,WU Wen-qi,XIANG Xin-peng.Characterization of Metal Pollution of Regional Atmospheric PM2.5 and Its Sources Based on the PMF Model[J].Chinese Journal of Environmental Science,2020,41(12):5276-5287.
Authors:DENG Lin-li  ZHANG Kai-shan  YIN Zi-yuan  LI Xin-yue  WU Wen-qi  XIANG Xin-peng
Institution:College of Architecture and Environment, Sichuan University, Chengdu 610065, China
Abstract:As important components of PM2.5, metal elements are extremely harmful to people and also have source specificity. Understanding the characteristics of PM2.5 metal pollution in the two different types of cities can help adjust the layout of regional industrial structure and improve the environment. PM2.5 samples during haze/non-haze periods were collected in Chengdu City and Renshou County. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine the mass concentrations of eighteen metal elements in collected PM2.5 samples. The positive matrix factorization (PMF) model was used for source apportionment analysis for metal elements in PM2.5. The analysis showed that the ratio of trace elements from fugitive dust, motor vehicle emissions, and coal burning to the total elements is greater in Chengdu City than that in Renshou County. The proportion of trace elements from biomass combustion, industrial, and fuel sources in Renshou County is higher than that in Chengdu City. In addition, concentrations of Cd, As, and Cr in both areas exceeded the standards, indicating the occurrences of heavy metal pollution. During the haze period, the total concentrations of compositional metal elements in PM2.5 increased, although the rate was much lower than that for PM2.5. The ratios of elements between haze and non-haze periods ranged from 0.7 (Al) to 2.8 (Ba) in Chengdu City, and from 0.8 (Al) to 3.1 (Mn) in Renshou County. Among all metal elements, the increase rate for trace elements from coal burning and industrial activities was relatively large but small for those from fugitive dust, with the growth in trace elements from motor vehicles being modest. The results of this study indicated that the characteristics of pollution and source of metal elements in PM2.5 varied by economic scale, development mode, and industrial layout. In large cities such as Chengdu City, where economic development is mainly focused on tertiary industry, air pollution is mainly caused by transportation and urban construction, while in suburban area such as Renshou County, where secondary or heavy industry are the focus for economic development, the pollution is mainly affected by energy consumption and industrial production.
Keywords:PM2  5  metal elements  positive matrix factorization (PMF)  source apportionment  haze
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