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Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China

PURPOSE: In the urban region of Shenzhen, the changes in the concentration of Black carbon (BC) have been evaluated throughout the dry season, and apportioned the BC sources, including in the form of fossil fuel (e.g., vehicle emissions) and biomass fuel (e.g., industrial emissions). METHODS: The ne...

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Autores principales: Chen, Wenqian, Cao, Xiaoyi, Ran, Haofan, Chen, Ting, Yang, Bohan, Zheng, Xuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911177/
https://www.ncbi.nlm.nih.gov/pubmed/35291691
http://dx.doi.org/10.1007/s40201-022-00793-3
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author Chen, Wenqian
Cao, Xiaoyi
Ran, Haofan
Chen, Ting
Yang, Bohan
Zheng, Xuan
author_facet Chen, Wenqian
Cao, Xiaoyi
Ran, Haofan
Chen, Ting
Yang, Bohan
Zheng, Xuan
author_sort Chen, Wenqian
collection PubMed
description PURPOSE: In the urban region of Shenzhen, the changes in the concentration of Black carbon (BC) have been evaluated throughout the dry season, and apportioned the BC sources, including in the form of fossil fuel (e.g., vehicle emissions) and biomass fuel (e.g., industrial emissions). METHODS: The new seven-channel aethalometer model (AE-33), PM(2.5), and meteorological data were collected in the dry season (October–May) from 2019 to 2020, to quantify BC emissions in urban Shenzhen. Explored the source allocation of BC based on Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) model. RESULTS: We revealed that the mean BC concentration was 2672 ± 1506 ng/m(3) in the dry season, with values of 4062 ± 1182 ng/m(3), 2519 ± 1568 ng/m(3), and 1900 ± 776 ng/m(3) in autumn, winter, and spring, respectively. Additionally, we found that fossil fuels have higher contributions to BC concentrations (86.3% to 86.8% in autumn and spring) in the dry season than biomass fuels (16% to 20% in autumn, spring and winter), which is different from Beijing, Nanjing and other large economic zones in China. The diurnal variation in BC and the contribution of fossil fuels indicate that there is a significantly greater increase in BC during peak traffic hours in urban Shenzhen than in other cities. Finally, meteorological parameters and PM(2.5) data provided supporting evidence that BC is sourced mainly from local vehicle emissions and industry-related combustion in the western and northeastern/southeastern parts of the study area. CONCLUSION: This study showed that the concentration of BC is lower than other regions, and the source allocation is mainly local fossil fuels (vehicle emission, etc.). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-022-00793-3.
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spelling pubmed-89111772022-03-11 Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China Chen, Wenqian Cao, Xiaoyi Ran, Haofan Chen, Ting Yang, Bohan Zheng, Xuan J Environ Health Sci Eng Research Article PURPOSE: In the urban region of Shenzhen, the changes in the concentration of Black carbon (BC) have been evaluated throughout the dry season, and apportioned the BC sources, including in the form of fossil fuel (e.g., vehicle emissions) and biomass fuel (e.g., industrial emissions). METHODS: The new seven-channel aethalometer model (AE-33), PM(2.5), and meteorological data were collected in the dry season (October–May) from 2019 to 2020, to quantify BC emissions in urban Shenzhen. Explored the source allocation of BC based on Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) model. RESULTS: We revealed that the mean BC concentration was 2672 ± 1506 ng/m(3) in the dry season, with values of 4062 ± 1182 ng/m(3), 2519 ± 1568 ng/m(3), and 1900 ± 776 ng/m(3) in autumn, winter, and spring, respectively. Additionally, we found that fossil fuels have higher contributions to BC concentrations (86.3% to 86.8% in autumn and spring) in the dry season than biomass fuels (16% to 20% in autumn, spring and winter), which is different from Beijing, Nanjing and other large economic zones in China. The diurnal variation in BC and the contribution of fossil fuels indicate that there is a significantly greater increase in BC during peak traffic hours in urban Shenzhen than in other cities. Finally, meteorological parameters and PM(2.5) data provided supporting evidence that BC is sourced mainly from local vehicle emissions and industry-related combustion in the western and northeastern/southeastern parts of the study area. CONCLUSION: This study showed that the concentration of BC is lower than other regions, and the source allocation is mainly local fossil fuels (vehicle emission, etc.). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-022-00793-3. Springer International Publishing 2022-03-10 /pmc/articles/PMC8911177/ /pubmed/35291691 http://dx.doi.org/10.1007/s40201-022-00793-3 Text en © Springer Nature Switzerland AG 2022
spellingShingle Research Article
Chen, Wenqian
Cao, Xiaoyi
Ran, Haofan
Chen, Ting
Yang, Bohan
Zheng, Xuan
Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China
title Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China
title_full Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China
title_fullStr Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China
title_full_unstemmed Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China
title_short Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China
title_sort concentration and source allocation of black carbon by ae-33 model in urban area of shenzhen, southern china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911177/
https://www.ncbi.nlm.nih.gov/pubmed/35291691
http://dx.doi.org/10.1007/s40201-022-00793-3
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