Pollution Characterization and Source Apportionment of Day and Night PM2.5 Samples in Urban and Suburban Communities of Tianjin (China)

Yan, Zhao; Li, Feng; Shang, Bodong; Li, Jianping; Lv, Guang; Wu, Yinghong

doi:10.1007/s00244-019-00614-z

Cited by 13 publications

(2 citation statements)

References 49 publications

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“…The sixth source was ship emission and typically characterized by a high level of V, Ni and Na + , accounting for 14.0% of PM 2.5 mass. V and Ni in the atmosphere are chiefly from fossil fuel combustion, especially heavy oil and crude oil, which is often attributed to ship emission [4,45,59]. Moreover, the ratio of V/Ni more than 0.7 is a sign of PM 2.5 affected by ship emission [46].…”

Section: Source Apportionment Resultsmentioning

confidence: 99%

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

Jiang

et al. 2020

Atmosphere

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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.

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Section: Source Apportionment Resultsmentioning

confidence: 99%

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

Jiang

et al. 2020

Atmosphere

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“…1. A study on the source apportionment of PM 2.5 in the air in Tianjin showed that the coal combustion and the vehicle exhaust were the predominant air pollution sources during the heating period (Zhao et al, 2019). The household car ownerships remained stable since 2013 when the car purchase and traffic restrictions policies were taking effect.…”

Section: Methodsmentioning

confidence: 99%

Health risks of PM2.5-bound polycyclic aromatic hydrocarbon (PAH) and heavy metals (PPAH&HM) during the replacement of central heating with urban natural gas in Tianjin, China

Zhang

Yan

et al. 2021

Environ Geochem Health

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To investigate the health effects of fine particulate matter (B 2.5 lm in aerodynamic diameter; PM 2.5 )-bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) before and after the implementation of the Urban Natural Gas Heating Project (UNGHP), the lifetime cancer risks, hazard quotients (HQs) of heavy metals and PAHs were calculated. Seven kinds of heavy metals (Al, As, Cd, Cr, Mn, Ni and Se) and 12 kinds of PAHs including acenaphthylene (ANY), acenaphthene (ANA), fluoranthene (FLT), pyrene (PYR), chrysene (CHR), benz[a]anthracene (BaA), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), dibenz[a,h]anthracene (DBA), benzo[ghi]perylene (BPE) and indeno[1,2,3-cd]pyrene (IPY) were analyzed and used for the health risk assessments. It was found that HQ of Mn fell from 1.09 in the coalburning period to 0.72 in the gas-burning period in the suburban area. And lifetime cancer risks of PAHs fell from 35.7 9 10 -6 in the coal-burning period to 17.22 9 10 -6 in the gas-burning period in the urban area. It could be concluded that, during the gasburning period, downward trends were observed for the lifetime cancer risks and HQs of most kinds of heavy metals and PAHs in all regions of Tianjin compared to those during the coal-burning period. The UNGHP was effective, and we should also take other measures to control the pollution.

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Study on Pollution Characterization and Source Apportionment of Daytime and Nighttime PM2.5 Samples in an Urban Residential Community in Different Weather Conditions

Yan

Wang

et al. 2020

Bull Environ Contam Toxicol

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Pollution Characterization and Source Apportionment of Day and Night PM2.5 Samples in Urban and Suburban Communities of Tianjin (China)

Cited by 13 publications

References 49 publications

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

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

Health risks of PM2.5-bound polycyclic aromatic hydrocarbon (PAH) and heavy metals (PPAH&HM) during the replacement of central heating with urban natural gas in Tianjin, China

Study on Pollution Characterization and Source Apportionment of Daytime and Nighttime PM2.5 Samples in an Urban Residential Community in Different Weather Conditions

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