Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis
Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Luo, Jinqi [verfasserIn] Huang, Xiaojuan [verfasserIn] Zhang, Junke [verfasserIn] Luo, Bin [verfasserIn] Zhang, Wei [verfasserIn] Song, Hongyi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 26(2019), 17 vom: 27. Apr., Seite 17685-17695 |
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| Übergeordnetes Werk: |
volume:26 ; year:2019 ; number:17 ; day:27 ; month:04 ; pages:17685-17695 |
| Links: |
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| DOI / URN: |
10.1007/s11356-019-05156-4 |
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| Katalog-ID: |
SPR018862136 |
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| 100 | 1 | |a Luo, Jinqi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis |
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| 520 | |a Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. | ||
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| 650 | 4 | |a Chengdu |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Huang, Xiaojuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Junke |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Hongyi |e verfasserin |4 aut | |
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10.1007/s11356-019-05156-4 doi (DE-627)SPR018862136 (SPR)s11356-019-05156-4-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Luo, Jinqi verfasserin aut Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. Single particles (dpeaa)DE-He213 Mixing state (dpeaa)DE-He213 Pollution evolution (dpeaa)DE-He213 SPAMS (dpeaa)DE-He213 Chengdu (dpeaa)DE-He213 Huang, Xiaojuan verfasserin aut Zhang, Junke verfasserin aut Luo, Bin verfasserin aut Zhang, Wei verfasserin aut Song, Hongyi verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 26(2019), 17 vom: 27. Apr., Seite 17685-17695 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:26 year:2019 number:17 day:27 month:04 pages:17685-17695 https://dx.doi.org/10.1007/s11356-019-05156-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 26 2019 17 27 04 17685-17695 |
| spelling |
10.1007/s11356-019-05156-4 doi (DE-627)SPR018862136 (SPR)s11356-019-05156-4-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Luo, Jinqi verfasserin aut Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. Single particles (dpeaa)DE-He213 Mixing state (dpeaa)DE-He213 Pollution evolution (dpeaa)DE-He213 SPAMS (dpeaa)DE-He213 Chengdu (dpeaa)DE-He213 Huang, Xiaojuan verfasserin aut Zhang, Junke verfasserin aut Luo, Bin verfasserin aut Zhang, Wei verfasserin aut Song, Hongyi verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 26(2019), 17 vom: 27. Apr., Seite 17685-17695 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:26 year:2019 number:17 day:27 month:04 pages:17685-17695 https://dx.doi.org/10.1007/s11356-019-05156-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 26 2019 17 27 04 17685-17695 |
| allfields_unstemmed |
10.1007/s11356-019-05156-4 doi (DE-627)SPR018862136 (SPR)s11356-019-05156-4-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Luo, Jinqi verfasserin aut Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. Single particles (dpeaa)DE-He213 Mixing state (dpeaa)DE-He213 Pollution evolution (dpeaa)DE-He213 SPAMS (dpeaa)DE-He213 Chengdu (dpeaa)DE-He213 Huang, Xiaojuan verfasserin aut Zhang, Junke verfasserin aut Luo, Bin verfasserin aut Zhang, Wei verfasserin aut Song, Hongyi verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 26(2019), 17 vom: 27. Apr., Seite 17685-17695 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:26 year:2019 number:17 day:27 month:04 pages:17685-17695 https://dx.doi.org/10.1007/s11356-019-05156-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 26 2019 17 27 04 17685-17695 |
| allfieldsGer |
10.1007/s11356-019-05156-4 doi (DE-627)SPR018862136 (SPR)s11356-019-05156-4-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Luo, Jinqi verfasserin aut Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. Single particles (dpeaa)DE-He213 Mixing state (dpeaa)DE-He213 Pollution evolution (dpeaa)DE-He213 SPAMS (dpeaa)DE-He213 Chengdu (dpeaa)DE-He213 Huang, Xiaojuan verfasserin aut Zhang, Junke verfasserin aut Luo, Bin verfasserin aut Zhang, Wei verfasserin aut Song, Hongyi verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 26(2019), 17 vom: 27. Apr., Seite 17685-17695 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:26 year:2019 number:17 day:27 month:04 pages:17685-17695 https://dx.doi.org/10.1007/s11356-019-05156-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 26 2019 17 27 04 17685-17695 |
| allfieldsSound |
10.1007/s11356-019-05156-4 doi (DE-627)SPR018862136 (SPR)s11356-019-05156-4-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Luo, Jinqi verfasserin aut Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. Single particles (dpeaa)DE-He213 Mixing state (dpeaa)DE-He213 Pollution evolution (dpeaa)DE-He213 SPAMS (dpeaa)DE-He213 Chengdu (dpeaa)DE-He213 Huang, Xiaojuan verfasserin aut Zhang, Junke verfasserin aut Luo, Bin verfasserin aut Zhang, Wei verfasserin aut Song, Hongyi verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 26(2019), 17 vom: 27. Apr., Seite 17685-17695 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:26 year:2019 number:17 day:27 month:04 pages:17685-17695 https://dx.doi.org/10.1007/s11356-019-05156-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 26 2019 17 27 04 17685-17695 |
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Enthalten in Environmental science and pollution research 26(2019), 17 vom: 27. Apr., Seite 17685-17695 volume:26 year:2019 number:17 day:27 month:04 pages:17685-17695 |
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Luo, Jinqi @@aut@@ Huang, Xiaojuan @@aut@@ Zhang, Junke @@aut@@ Luo, Bin @@aut@@ Zhang, Wei @@aut@@ Song, Hongyi @@aut@@ |
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Luo, Jinqi |
| spellingShingle |
Luo, Jinqi ddc 333.7 bkl 43.00 bkl 43.50 bkl 58.50 misc Single particles misc Mixing state misc Pollution evolution misc SPAMS misc Chengdu Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis |
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333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis Single particles (dpeaa)DE-He213 Mixing state (dpeaa)DE-He213 Pollution evolution (dpeaa)DE-He213 SPAMS (dpeaa)DE-He213 Chengdu (dpeaa)DE-He213 |
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ddc 333.7 bkl 43.00 bkl 43.50 bkl 58.50 misc Single particles misc Mixing state misc Pollution evolution misc SPAMS misc Chengdu |
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ddc 333.7 bkl 43.00 bkl 43.50 bkl 58.50 misc Single particles misc Mixing state misc Pollution evolution misc SPAMS misc Chengdu |
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Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis |
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(DE-627)SPR018862136 (SPR)s11356-019-05156-4-e |
| title_full |
Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis |
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Luo, Jinqi |
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Environmental science and pollution research |
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Environmental science and pollution research |
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Luo, Jinqi Huang, Xiaojuan Zhang, Junke Luo, Bin Zhang, Wei Song, Hongyi |
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333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl |
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Elektronische Aufsätze |
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Luo, Jinqi |
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10.1007/s11356-019-05156-4 |
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333.7 690 |
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verfasserin |
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characterization of aerosol particles during the most polluted season (winter) in urban chengdu (china) by single-particle analysis |
| title_auth |
Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis |
| abstract |
Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. |
| abstractGer |
Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. |
| abstract_unstemmed |
Abstract Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. These results give a deeper understanding of $ PM_{2.5} $ pollution in Chengdu and the Sichuan Basin. |
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Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis |
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https://dx.doi.org/10.1007/s11356-019-05156-4 |
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| author2 |
Huang, Xiaojuan Zhang, Junke Luo, Bin Zhang, Wei Song, Hongyi |
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Huang, Xiaojuan Zhang, Junke Luo, Bin Zhang, Wei Song, Hongyi |
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320517926 |
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| doi_str |
10.1007/s11356-019-05156-4 |
| up_date |
2024-07-03T22:45:46.125Z |
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We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for $ PM_{2.5} $ and $ PM_{10} $ of 101 ± 60 and 162 ± 99 μg $ m^{−3} $, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate ($ KNO_{3} $) (12%), K-sulfate ($ KSO_{4} $) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from “excellent” (days with $ PM_{2.5} $ lower than 35 μg $ m^{−3} $) to “light pollution” ($ PM_{2.5} $ between 75 and 115 μg $ m^{−3} $) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to “heavy pollution” ($ PM_{2.5} $ higher than 150 μg $ m^{−3} $) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in $ PM_{2.5} $ concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of $ PM_{2.5} $ pollution in Chengdu. 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