Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas

The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentratio...
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Autor*in:	Jie Yang [verfasserIn] Xinran Fu [verfasserIn] Liping Qiao [verfasserIn] Lan Yao [verfasserIn] Fei Zhang [verfasserIn] Weiyue Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	air quality different functional areas weekday–weekend effect potential source contribution function Shanghai

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 15(2023), 3, p 2429
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:3, p 2429

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su15032429

Katalog-ID:	DOAJ08058229X

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520			\|a The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy.
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allfields	10.3390/su15032429 doi (DE-627)DOAJ08058229X (DE-599)DOAJaea51fb2d814444da56586424aa438ca DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Jie Yang verfasserin aut Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy. air quality different functional areas weekday–weekend effect potential source contribution function Shanghai Environmental effects of industries and plants Renewable energy sources Environmental sciences Xinran Fu verfasserin aut Liping Qiao verfasserin aut Lan Yao verfasserin aut Fei Zhang verfasserin aut Weiyue Li verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 3, p 2429 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:3, p 2429 https://doi.org/10.3390/su15032429 kostenfrei https://doaj.org/article/aea51fb2d814444da56586424aa438ca kostenfrei https://www.mdpi.com/2071-1050/15/3/2429 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 3, p 2429
spelling	10.3390/su15032429 doi (DE-627)DOAJ08058229X (DE-599)DOAJaea51fb2d814444da56586424aa438ca DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Jie Yang verfasserin aut Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy. air quality different functional areas weekday–weekend effect potential source contribution function Shanghai Environmental effects of industries and plants Renewable energy sources Environmental sciences Xinran Fu verfasserin aut Liping Qiao verfasserin aut Lan Yao verfasserin aut Fei Zhang verfasserin aut Weiyue Li verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 3, p 2429 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:3, p 2429 https://doi.org/10.3390/su15032429 kostenfrei https://doaj.org/article/aea51fb2d814444da56586424aa438ca kostenfrei https://www.mdpi.com/2071-1050/15/3/2429 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 3, p 2429
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allfieldsSound	10.3390/su15032429 doi (DE-627)DOAJ08058229X (DE-599)DOAJaea51fb2d814444da56586424aa438ca DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Jie Yang verfasserin aut Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy. air quality different functional areas weekday–weekend effect potential source contribution function Shanghai Environmental effects of industries and plants Renewable energy sources Environmental sciences Xinran Fu verfasserin aut Liping Qiao verfasserin aut Lan Yao verfasserin aut Fei Zhang verfasserin aut Weiyue Li verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 3, p 2429 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:3, p 2429 https://doi.org/10.3390/su15032429 kostenfrei https://doaj.org/article/aea51fb2d814444da56586424aa438ca kostenfrei https://www.mdpi.com/2071-1050/15/3/2429 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 3, p 2429
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callnumber-first	T - Technology
author	Jie Yang
spellingShingle	Jie Yang misc TD194-195 misc TJ807-830 misc GE1-350 misc air quality misc different functional areas misc weekday–weekend effect misc potential source contribution function misc Shanghai misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas
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topic_title	TD194-195 TJ807-830 GE1-350 Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas air quality different functional areas weekday–weekend effect potential source contribution function Shanghai
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc air quality misc different functional areas misc weekday–weekend effect misc potential source contribution function misc Shanghai misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc air quality misc different functional areas misc weekday–weekend effect misc potential source contribution function misc Shanghai misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas
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title_full	Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas
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title_sort	characteristics of atmospheric pollution in a chinese megacity: insights from three different functional areas
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title_auth	Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas
abstract	The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy.
abstractGer	The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy.
abstract_unstemmed	The most important atmospheric pollutants include PM<sub<2.5</sub<, PM<sub<10</sub<, SO<sub<2</sub<, NO<sub<2</sub<, CO and O<sub<3</sub<. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. This study emphasizes the prominent O<sub<3</sub< pollution in Shanghai, and argues that the prevention and control of O<sub<3</sub< pollution requires regional joint prevention and control strategy.
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title_short	Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas
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Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O<sub<3</sub<, exceeding PM<sub<2.5</sub<, has become the primary pollutant determining air quality in Shanghai. The frequency of O<sub<3</sub< as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM<sub<2.5</sub< (14–21%). NO<sub<2</sub< and SO<sub<2</sub<, precursors of PM<sub<2.5</sub<, presented a clear weekend effect, whereas PM<sub<2.5</sub< at weekends seems higher than that on weekdays. In the warm season, O<sub<3</sub< at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O<sub<3</sub< on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM<sub<2.5</sub< and O<sub<3</sub< were different, which brought challenges to the coordinated control of PM<sub<2.5</sub< and O<sub<3</sub< in Shanghai. 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Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas

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