Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

YE Lei; OU Xiangjun

doi:10.1007/s11769-019-1036-0

Volume 29 Issue 3

Jun. 2019

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YE Lei, OU Xiangjun. Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016[J]. Chinese Geographical Science, 2019, 20(3): 382-393. doi: 10.1007/s11769-019-1036-0

Citation:

YE Lei, OU Xiangjun. Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016[J]. Chinese Geographical Science, 2019, 20(3): 382-393. doi: 10.1007/s11769-019-1036-0

Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

doi: 10.1007/s11769-019-1036-0

YE Lei¹,
OU Xiangjun^2
,

1.
School of Geographic Science, Nantong University, Nantong 226000, China;
2.
School of Geography, Geomatics and Planning, Jiangsu Normal University, Nanjing 221116, China

Funds: Under the auspices of Key Projects of the National Social Science Fund (No. 16AJL015), Youth Project of Natural Science Foundation of Jiangsu Province (No. BK20170440), Open Foundation of Key Laboratory of Watershed Geographical Science (No.WSGS2017004), Project of Nantong Key Laboratory (No. CP12016005)

More Information

Corresponding author: OU Xiangjun. E-mail:oxjwmy@163.com
Received Date: 2018-01-05
Publish Date: 2019-06-01

Abstract

Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index (AQI) of the Yangtze River Delta region (YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn’. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.
- air quality index (AQI),
- spatial-temporal evolution,
- contributing factors,
- Yangtze River Delta (YRD)

References

[1]	Anhui Bureau of Statistics, 2015-2017. Anhui Statistical Year-book. Beijing:China Statistics Press.
[2]	Boichu M, Clarisse L, Péré J C et al., 2015. Temporal variations of flux and altitude of sulfur dioxide emissions during volcanic eruptions:implications for long-range dispersal of volcanic clouds. Atmospheric Chemistry and Physics, 15(4):8381-8400. doi: 10.5194/acpd-15-5031-2015
[3]	Buchholz S, Junk J, Krein A et al., 2010. Air pollution character-istics associated with mesoscale atmospheric patterns in northwest continental Europe. Atmospheric Environment, 44(39):5183-5190. doi: 10.1016/j.atmosenv.2010.08.053
[4]	Chen P, Wang T, Dong M et al., 2017. Characterization of major natural and anthropogenic source profiles for size-fractionated PM in Yangtze River Delta. Science of the Total Environment, 598:135-145. doi: 10.1016/j.scitotenv.2017.04.106
[5]	Correia A W, Pope Ⅲ C A, Dockery D W et al., 2013. Effect of air pollution control on life expectancy in the United States:an analysis of 545 U.S. counties for the period from 2000 to 2007. Epidemiology, 24(1):23-31. doi:10.1097/EDE. 0b013e3182770237
[6]	Dijkema M B A, van Strien R T, van der Zee S C et al., 2016. Spatial variation in nitrogen dioxide concentrations and cardi-opulmonary hospital admissions. Environmental Research, 151:721-727. doi: 10.1016/j.envres.2016.09.008
[7]	Dimakopoulou K, Gryparis A, Katsouyanni K, 2017. Using spa-tio-temporal land use regression models to address spatial var-iation in air pollution concentrations in time series studies. Air Quality, Atmosphere and Health, 10(9):1139-1149. doi: 10.1007/s11869-017-0500-1
[8]	Ding Lei, Liu Chao, Huang Yalin et al., 2016. Spatial and temporal characteristics of urban ambient air quality and its main influence factors in Hubei province. Economic Geography, 36(3):170-178. (in Chinese)
[9]	Dominick D, Latif M T, Juahir H et al., 2012. An assessment of influence of meteorological factors on PM₁₀ and NO₂ at selected stations in Malaysia. Sustainable Environment Research, 22(5):305-315.
[10]	Ebenstein A, Fan M, Greenstone M et al., 2013. Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy. Proceedings of the National Academy of Sciences of the United States of America, 110(32):12936-12941. doi: 10.1073/pnas.1300018110
[11]	Fu Xiaoxin, Wang Xinming, Hu Qihou et al., 2016. Changes in visibility with PM_2.5 composition and relative humidity at a background site in the Pearl River Delta region. Journal of Environmental Sciences, 40(2):10-19. (in Chinese)
[12]	George N J, Ekanem A M, Ibanga J I et al., 2017. Hydrodynamic Implications of Aquifer Quality Index (AQI) and Flow Zone Indicator (FZI) in groundwater abstraction:a case study of coastal hydro-lithofacies in South-eastern Nigeria. Journal of Coastal Conservation, 21(6):759-776. doi: 10.1007/s11852-017-0535-3
[13]	Han L J, Zhou W Q, Li W F et al., 2014. Impact of urbanization level on urban air quality:a case of fine particles (PM_2.5) in Chinese cities. Environmental Pollution, 194:163-170. doi: 10.1016/j.envpol.2014.07.022
[14]	Hu J L, Wang Y G, Ying Q et al., 2014. Spatial and temporal variability of PM_2.5 and PM₁₀ over the North China Plain and the Yangtze River Delta, China. Atmospheric Environment, 95:598-609. doi: 10.1016/j.atmosenv.2014.07.019
[15]	Jassim M S, Coskuner G, 2017. Assessment of spatial variations of particulate matter (PM₁₀, and PM_2.5) in Bahrain identified by air quality index (AQI). Arabian Journal of Geosciences, 10:19. doi: 10.1007/s12517-016-2808-9
[16]	Jiangsu Bureau of Statistics, 2015-2017. Jiangsu Statistical Yearbook. Beijing:China Statistics Press.
[17]	Kan H, Chen R, Tong S, 2012. Ambient air pollution, climate change, and population health in China. Environment Interna-tional, 42:10-19. doi: 10.1016/j.envint.2011.03.003
[18]	Karar K, Gupta A K, 2006. Seasonal variations and chemical characterization of ambient PM₁₀, at residential and industrial sites of an urban region of Kolkata (Calcutta), India. Atmos-pheric Research, 81(1):40-53. doi:10.1016/j.atmosres.2005. 11.003
[19]	Lin W L, Xu X B, Ma Z Q et al., 2012. Characteristics and recent trends of sulfur dioxide at urban, rural, and background sites in North China:effectiveness of control measures. Journal of Environmental Sciences, 24(1):34-49. doi: 10.1016/S1001-0742(11)60727-4
[20]	Lin Xueqin, Wang Dai, 2016. Spatio-temporal variations and socio-economic driving forces of air quality in Chinese cities. Acta Geographica Sinica, 71(8):1357-1371. (in Chinese)
[21]	Lu Shaowei, Liu Xiaona, Liu Bin et al., 2017. Spatial and temporal variations of PM₁₀ concentration in forest vegetation area of Beijing City in 2015. Acta Scientiae Circumstantiae, 37(2):469-476. (in Chinese)
[22]	Ma Limei, Zhang Xiao, 2014. The spatial effect of China's haze pollution and the impact from economic change and energy structure. China Industrial Economics, (4):19-31. (in Chinese)
[23]	Ministry of Housing and Urban-Rural Construction, 2015-2017. Urban Construction Statistical Yearbook of China. Beijing:China Planning Press.
[24]	Moran P A, 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society B, 37:243-251. doi: 10.2307/2332344
[25]	National Development and Reform Commission of China, 2016. The Development Plan of the Yangtze River Delta. Available at:http://www.ndrc.gov.cn/zcfb/zcfbghwb/201606/t20160603_806390.html
[26]	Ozcan H K, 2012. Long term variations of the atmospheric air pollutants in Istanbul city. International Journal of Environ-mental Research and Public Health, 9(3):781-790. doi: 10.3390/ijerph9030781
[27]	Patton A P, Perkins J, Zamore W et al., 2014. Spatial and temporal differences in traffic-related air pollution in three urban neighborhoods near an interstate highway. Atmospheric Envi-ronment, 99:309-321. doi: 10.1016/j.atmosenv.2014.09.072
[28]	Qiao Jiajun, 2004. Application of improved entropy method in Henan sustainable development evaluation. Resources Science, 26(1):113-119. (in Chinese)
[29]	Shanghai Bureau of Statistics, 2015-2017. Shanghai Statistical Yearbook. Beijing:China Statistics Press.
[30]	State Statistical Bureau, 2015-2017. Urban Statistical Yearbook of China. Beijing:China Statistics Press.
[31]	Tartakovsky D, Stern E, Broday D M, 2016. Dispersion of TSP and PM₁₀ emissions from quarries in complex terrain. Science of the Total Environment, 542:946-954. doi: 10.1016/j.scitotenv.2015.10.133
[32]	Tobler W R, 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46:234-240. doi: 10.2307/143141
[33]	Wang Zhenbo, Fang Chuanglin, Xu Guang et al., 2015. Spa-tial-temporal characteristics of the PM_2.5 in China in 2014. Acta Geographica Sinica, 70(11):1720-1734. (in Chinese)
[34]	Wong C M, Ma S, Hedley A J et al., 2001. Effect of air pollution on daily mortality in Hong Kong. Environmental Health Per-spectives, 109(4), 335-340. doi: 10.2307/3454891
[35]	Zhang X P, Gong Z Z, 2018. Spatiotemporal characteristics of urban air quality in China and geographic detection of their determinants. Journal of Geographical Sciences, 28(5):563-578. doi: 10.1007/s11442-018-1491-z
[36]	Zhang Y H, Hu M, Zhong L J et al., 2008. Regional integrated experiments on air quality over pearl river delta 2004 (PRIDE-PRD2004):overview. Atmospheric Environment, 42(25):6157-6173. doi: 10.1016/j.atmosenv.2008.03.025
[37]	Zhao J J, Chen S B, Wang H et al., 2012. Quantifying the impacts of socio-economic factors on air quality in Chinese cities from 2000 to 2009. Environmental Pollution, 167:148-154. doi: 10.1016/j.envpol.2012.04.007
[38]	Zhejiang Bureau of Statistics, 2015-2017. Zhejiang Statistical Yearbook. Beijing:China Statistics Press.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

1. School of Geographic Science, Nantong University, Nantong 226000, China;

2. School of Geography, Geomatics and Planning, Jiangsu Normal University, Nanjing 221116, China

Corresponding author: OU Xiangjun. E-mail:oxjwmy@163.com

Received Date: 2018-01-05

Publish Date: 2019-06-01

Key words:

Abstract: Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index (AQI) of the Yangtze River Delta region (YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn’. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.

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Reference (38)

[1]	Anhui Bureau of Statistics, 2015-2017. Anhui Statistical Year-book. Beijing:China Statistics Press.
[2]	Boichu M, Clarisse L, Péré J C et al., 2015. Temporal variations of flux and altitude of sulfur dioxide emissions during volcanic eruptions:implications for long-range dispersal of volcanic clouds. Atmospheric Chemistry and Physics, 15(4):8381-8400. doi:10.5194/acpd-15-5031-2015
[3]	Buchholz S, Junk J, Krein A et al., 2010. Air pollution character-istics associated with mesoscale atmospheric patterns in northwest continental Europe. Atmospheric Environment, 44(39):5183-5190. doi:10.1016/j.atmosenv.2010.08.053
[4]	Chen P, Wang T, Dong M et al., 2017. Characterization of major natural and anthropogenic source profiles for size-fractionated PM in Yangtze River Delta. Science of the Total Environment, 598:135-145. doi:10.1016/j.scitotenv.2017.04.106
[5]	Correia A W, Pope Ⅲ C A, Dockery D W et al., 2013. Effect of air pollution control on life expectancy in the United States:an analysis of 545 U.S. counties for the period from 2000 to 2007. Epidemiology, 24(1):23-31. doi:10.1097/EDE. 0b013e3182770237
[6]	Dijkema M B A, van Strien R T, van der Zee S C et al., 2016. Spatial variation in nitrogen dioxide concentrations and cardi-opulmonary hospital admissions. Environmental Research, 151:721-727. doi:10.1016/j.envres.2016.09.008
[7]	Dimakopoulou K, Gryparis A, Katsouyanni K, 2017. Using spa-tio-temporal land use regression models to address spatial var-iation in air pollution concentrations in time series studies. Air Quality, Atmosphere and Health, 10(9):1139-1149. doi:10.1007/s11869-017-0500-1
[8]	Ding Lei, Liu Chao, Huang Yalin et al., 2016. Spatial and temporal characteristics of urban ambient air quality and its main influence factors in Hubei province. Economic Geography, 36(3):170-178. (in Chinese)
[9]	Dominick D, Latif M T, Juahir H et al., 2012. An assessment of influence of meteorological factors on PM₁₀ and NO₂ at selected stations in Malaysia. Sustainable Environment Research, 22(5):305-315.
[10]	Ebenstein A, Fan M, Greenstone M et al., 2013. Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy. Proceedings of the National Academy of Sciences of the United States of America, 110(32):12936-12941. doi:10.1073/pnas.1300018110
[11]	Fu Xiaoxin, Wang Xinming, Hu Qihou et al., 2016. Changes in visibility with PM_2.5 composition and relative humidity at a background site in the Pearl River Delta region. Journal of Environmental Sciences, 40(2):10-19. (in Chinese)
[12]	George N J, Ekanem A M, Ibanga J I et al., 2017. Hydrodynamic Implications of Aquifer Quality Index (AQI) and Flow Zone Indicator (FZI) in groundwater abstraction:a case study of coastal hydro-lithofacies in South-eastern Nigeria. Journal of Coastal Conservation, 21(6):759-776. doi:10.1007/s11852-017-0535-3
[13]	Han L J, Zhou W Q, Li W F et al., 2014. Impact of urbanization level on urban air quality:a case of fine particles (PM_2.5) in Chinese cities. Environmental Pollution, 194:163-170. doi:10.1016/j.envpol.2014.07.022
[14]	Hu J L, Wang Y G, Ying Q et al., 2014. Spatial and temporal variability of PM_2.5 and PM₁₀ over the North China Plain and the Yangtze River Delta, China. Atmospheric Environment, 95:598-609. doi:10.1016/j.atmosenv.2014.07.019
[15]	Jassim M S, Coskuner G, 2017. Assessment of spatial variations of particulate matter (PM₁₀, and PM_2.5) in Bahrain identified by air quality index (AQI). Arabian Journal of Geosciences, 10:19. doi:10.1007/s12517-016-2808-9
[16]	Jiangsu Bureau of Statistics, 2015-2017. Jiangsu Statistical Yearbook. Beijing:China Statistics Press.
[17]	Kan H, Chen R, Tong S, 2012. Ambient air pollution, climate change, and population health in China. Environment Interna-tional, 42:10-19. doi:10.1016/j.envint.2011.03.003
[18]	Karar K, Gupta A K, 2006. Seasonal variations and chemical characterization of ambient PM₁₀, at residential and industrial sites of an urban region of Kolkata (Calcutta), India. Atmos-pheric Research, 81(1):40-53. doi:10.1016/j.atmosres.2005. 11.003
[19]	Lin W L, Xu X B, Ma Z Q et al., 2012. Characteristics and recent trends of sulfur dioxide at urban, rural, and background sites in North China:effectiveness of control measures. Journal of Environmental Sciences, 24(1):34-49. doi:10.1016/S1001-0742(11)60727-4
[20]	Lin Xueqin, Wang Dai, 2016. Spatio-temporal variations and socio-economic driving forces of air quality in Chinese cities. Acta Geographica Sinica, 71(8):1357-1371. (in Chinese)
[21]	Lu Shaowei, Liu Xiaona, Liu Bin et al., 2017. Spatial and temporal variations of PM₁₀ concentration in forest vegetation area of Beijing City in 2015. Acta Scientiae Circumstantiae, 37(2):469-476. (in Chinese)
[22]	Ma Limei, Zhang Xiao, 2014. The spatial effect of China's haze pollution and the impact from economic change and energy structure. China Industrial Economics, (4):19-31. (in Chinese)
[23]	Ministry of Housing and Urban-Rural Construction, 2015-2017. Urban Construction Statistical Yearbook of China. Beijing:China Planning Press.
[24]	Moran P A, 1948. The interpretation of statistical maps. Journal of the Royal Statistical Society B, 37:243-251. doi:10.2307/2332344
[25]	National Development and Reform Commission of China, 2016. The Development Plan of the Yangtze River Delta. Available at:http://www.ndrc.gov.cn/zcfb/zcfbghwb/201606/t20160603_806390.html
[26]	Ozcan H K, 2012. Long term variations of the atmospheric air pollutants in Istanbul city. International Journal of Environ-mental Research and Public Health, 9(3):781-790. doi:10.3390/ijerph9030781
[27]	Patton A P, Perkins J, Zamore W et al., 2014. Spatial and temporal differences in traffic-related air pollution in three urban neighborhoods near an interstate highway. Atmospheric Envi-ronment, 99:309-321. doi:10.1016/j.atmosenv.2014.09.072
[28]	Qiao Jiajun, 2004. Application of improved entropy method in Henan sustainable development evaluation. Resources Science, 26(1):113-119. (in Chinese)
[29]	Shanghai Bureau of Statistics, 2015-2017. Shanghai Statistical Yearbook. Beijing:China Statistics Press.
[30]	State Statistical Bureau, 2015-2017. Urban Statistical Yearbook of China. Beijing:China Statistics Press.
[31]	Tartakovsky D, Stern E, Broday D M, 2016. Dispersion of TSP and PM₁₀ emissions from quarries in complex terrain. Science of the Total Environment, 542:946-954. doi:10.1016/j.scitotenv.2015.10.133
[32]	Tobler W R, 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46:234-240. doi:10.2307/143141
[33]	Wang Zhenbo, Fang Chuanglin, Xu Guang et al., 2015. Spa-tial-temporal characteristics of the PM_2.5 in China in 2014. Acta Geographica Sinica, 70(11):1720-1734. (in Chinese)
[34]	Wong C M, Ma S, Hedley A J et al., 2001. Effect of air pollution on daily mortality in Hong Kong. Environmental Health Per-spectives, 109(4), 335-340. doi:10.2307/3454891
[35]	Zhang X P, Gong Z Z, 2018. Spatiotemporal characteristics of urban air quality in China and geographic detection of their determinants. Journal of Geographical Sciences, 28(5):563-578. doi:10.1007/s11442-018-1491-z
[36]	Zhang Y H, Hu M, Zhong L J et al., 2008. Regional integrated experiments on air quality over pearl river delta 2004 (PRIDE-PRD2004):overview. Atmospheric Environment, 42(25):6157-6173. doi:10.1016/j.atmosenv.2008.03.025
[37]	Zhao J J, Chen S B, Wang H et al., 2012. Quantifying the impacts of socio-economic factors on air quality in Chinese cities from 2000 to 2009. Environmental Pollution, 167:148-154. doi:10.1016/j.envpol.2012.04.007
[38]	Zhejiang Bureau of Statistics, 2015-2017. Zhejiang Statistical Yearbook. Beijing:China Statistics Press.

Spatial-temporal Analysis of Daily Air Quality Index in the Yangtze River Delta Region of China During 2014 and 2016

doi: 10.1007/s11769-019-1036-0

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References

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通讯作者: 陈斌, bchen63@163.com

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