Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China

doi:10.1038/s41598-017-10762-8

	Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China
	Wang, Xiaoping 1,2; Zong, Zheng 2; Tian, Chongguo2 ; Chen, Yingjun 3; Luo, Chunling 1; Li, Jun 1; Zhang, Gan 1; Luo, Yongming2 ; Tian, Chongguo(Chinese Acad Sci, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China)
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
	2017-09-06
卷号	7 页码:10648
关键词	CARBONACEOUS AEROSOLS ORGANIC-CARBON HAZE EVENTS SOUTH CHINA UNCERTAINTY GUANGZHOU TRACERS MODELS IMPACT VS.
研究领域	Multidisciplinary Sciences
DOI	10.1038/s41598-017-10762-8
产权排序	第2完成单位
作者部门	海岸带环境过程实验室
英文摘要	To explore the utility of combining positive matrix factorization (PMF) with radiocarbon (C-14) measurements for source apportionment, we applied PM2.5 data collected for 14 months at a national background station in North China to PMF models. The solutions were compared to C-14 results of four seasonally averaged samples and three outlier samples. Comparing the most readily interpretable PMF solutions and C-14 results revealed that PMF modeling was well able to capture the source patterns of PM2.5 with two and three irrelevant source classifications for the seasonal and outlier samples. The contribution of sources that could not be classified as either fossil or non-fossil sources in the PMF solution, and the errors between the modeled and measured concentrations weakened the effectiveness of the comparison. Based on these two factors, we developed an index for selecting the most suitable C-14 measurement samples for combining with the PMF model. Then we examined the potential for coupling PMF modeling and C-14 data with a constrained PMF run using the C-14 data as a priori information. The restricted run could provide a more reliable solution; however, the PMF model must provide a flexible dialog to input the priori restrictions for executing the constraint simulation.
文章类型	Article
资助机构	Key Laboratory of Coastal Environmental Processes and Ecological Remediation of the Chinese Academy of Sciences (CAS)(2016KFJJ01) ; Natural Scientific Foundation of China (NSFC)(41471413, 41473101) ; Guangzhou Science and Technology Plan Project(201504010002) ; Strategic Priority Research Program of the CAS(XDA11020402) ; NASA/HQ
收录类别	SCI
语种	英语
关键词[WOS]	CARBONACEOUS AEROSOLS ; ORGANIC-CARBON ; HAZE EVENTS ; SOUTH CHINA ; GUANGZHOU ; MODELS ; IMPACT ; VS.
研究领域[WOS]	Science & Technology - Other Topics
WOS记录号	WOS:000409439900066
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/23428
专题	中国科学院海岸带环境过程与生态修复重点实验室
通讯作者	Tian, Chongguo; Tian, Chongguo(Chinese Acad Sci, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China)
作者单位	1.Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Guangdong, Peoples R China 2.Chinese Acad Sci, Key Lab Coastal Zone Environm Proc & Ecol Remedia, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 3.Tongji Univ, Key Lab Cities Mitigat & Adaptat Climate Change S, Coll Environm Sci & Engn, Shanghai 200092, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xiaoping,Zong, Zheng,Tian, Chongguo,et al. Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China[J]. SCIENTIFIC REPORTS,2017,7:10648.
APA	Wang, Xiaoping.,Zong, Zheng.,Tian, Chongguo.,Chen, Yingjun.,Luo, Chunling.,...&Tian, Chongguo.(2017).Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China.SCIENTIFIC REPORTS,7,10648.
MLA	Wang, Xiaoping,et al."Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China".SCIENTIFIC REPORTS 7(2017):10648.