Li, Tingting1,2,3; Li, Jun1,2; Jiang, Hongxing1,2,3; Chen, Duohong4; Zong, Zheng5; Tian, Chongguo5
; Zhang, Gan1,2发表期刊ATMOSPHERE
2020-05-01
卷号11期号:5页码:18
关键词PM25C-14PMF modelBayesian mixing modelprimary sourcesecondary aerosolPearl River Delta (PRD)
DOI10.3390/atmos11050512
通讯作者Li, Jun(junli@gig.ac.cn); Chen, Duohong(chenduohong@139.com)
英文摘要To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM(2.5)in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (delta N-15-NO(3)(-)and delta O-18-NO3-), the Bayesian mixing model was performed to apportion the source of NO(3)(-)to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO(3)(-)between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM(2.5)were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from(14)C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than(14)C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning.
资助机构National Key R&D Program of China; Natural Science Foundation of China; Guangdong Basic and Applied Basic Research Foundation; Guangdong Foundation for Program of Science and Technology Research
收录类别SCI
语种英语
关键词[WOS]REGIONAL BACKGROUND SITE; ATMOSPHERIC HEAVY-METALS; FINE PARTICULATE MATTER; SOLUBLE INORGANIC-IONS; INDO-CHINA PENINSULA; RIVER DELTA REGION; CARBONACEOUS AEROSOLS; SOUTH CHINA; CHEMICAL CHARACTERISTICS; PARTICLE EMISSIONS
研究领域[WOS]Meteorology & Atmospheric Sciences
WOS记录号WOS:000541801900103
引用统计被引频次：3[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.cnhttp://ir.yic.ac.cn/handle/133337/28670
专题中科院海岸带环境过程与生态修复重点实验室_海岸带环境过程实验室
中科院海岸带环境过程与生态修复重点实验室
通讯作者Li, Jun; Chen, Duohong作者单位1.Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Peoples R China
2.Chinese Acad Sci, Guangzhou Inst Geochem, Guangdong Prov Key Lab Environm Protect & Resourc, Guangzhou 510640, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Guangdong Environm Monitoring Ctr, Guangzhou 510308, Peoples R China
5.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China
推荐引用方式
GB/T 7714Li, Tingting,Li, Jun,Jiang, Hongxing,et al. Source Apportionment of PM(2.5)in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon[J]. ATMOSPHERE,2020,11(5):18.
APALi, Tingting.,Li, Jun.,Jiang, Hongxing.,Chen, Duohong.,Zong, Zheng.,...&Zhang, Gan.(2020).Source Apportionment of PM(2.5)in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon.ATMOSPHERE,11(5),18.
MLALi, Tingting,et al."Source Apportionment of PM(2.5)in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon".ATMOSPHERE 11.5(2020):18.
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