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Article

Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon

1
State Key Laboratory of Organic Geochemistry and Guangdong Province Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
2
University of Chinese Academy of Sciences, Beijing, 100049, China
3
Guangdong Environmental Monitoring Center, Guangzhou, 510308, China
4
Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China
*
Authors to whom correspondence should be addressed.
Atmosphere 2020, 11(5), 512; https://doi.org/10.3390/atmos11050512
Received: 9 April 2020 / Revised: 1 May 2020 / Accepted: 14 May 2020 / Published: 16 May 2020
(This article belongs to the Special Issue Atmospheric Carbonaceous Aerosols)
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 PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3 and δ18O-NO3), the Bayesian mixing model was performed to apportion the source of NO3 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 NO3 between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 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 14C 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 14C 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. View Full-Text
Keywords: PM2.5; 14C; PMF model; Bayesian mixing model; primary source; secondary aerosol; Pearl River Delta (PRD) PM2.5; 14C; PMF model; Bayesian mixing model; primary source; secondary aerosol; Pearl River Delta (PRD)
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MDPI and ACS Style

Li, T.; Li, J.; Jiang, H.; Chen, D.; Zong, Z.; Tian, C.; Zhang, G. Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon. Atmosphere 2020, 11, 512. https://doi.org/10.3390/atmos11050512

AMA Style

Li T, Li J, Jiang H, Chen D, Zong Z, Tian C, Zhang G. Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon. Atmosphere. 2020; 11(5):512. https://doi.org/10.3390/atmos11050512

Chicago/Turabian Style

Li, Tingting, Jun Li, Hongxing Jiang, Duohong Chen, Zheng Zong, Chongguo Tian, and Gan Zhang. 2020. "Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon" Atmosphere 11, no. 5: 512. https://doi.org/10.3390/atmos11050512

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