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Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016

LI Hua TONG Helong WU Xianhua LU Xiaoli MENG Shuhan

LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. 中国地理科学, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
引用本文: LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. 中国地理科学, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. Chinese Geographical Science, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
Citation: LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. Chinese Geographical Science, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5

Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016

doi: 10.1007/s11769-020-1157-5
基金项目: 

Under the auspices of the National Social Science Foundation of China (No.18ZDA052) and the National Natural Sci-ence Foundation of China (No. 41301154)

详细信息
    通讯作者:

    LI Hua.E-mail:lihua610@163.com

Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016

Funds: 

Under the auspices of the National Social Science Foundation of China (No.18ZDA052) and the National Natural Sci-ence Foundation of China (No. 41301154)

  • 摘要: The rapid development of China's economy and urbanization has given rise to noticeable environmental problems, among which the change of air quality has received extensive attention. The panel data of PM2.5 (particles with an aerodynamic diameter of 2.5 μm or less) in 343 prefecture-level cities in China from 1998 to 2016 were statistically analyzed to reveal the characteristics of the temporal evolution and spatial variation of China's air quality in the past two decades. The results show that: 1) the overall deterioration trend of air quality is obvious throughout the country. The variation trend of PM2.5 was divided into three phases: rapid-growth phase (1998–2007), lag phase (2006–2011) and mildly-incremental phase (2012–2016), with their average growth rates of 7.19%, −3.59% and 0.52%, respectively. 2) The spatial difference of PM2.5 values in China increased significantly with time. Since 2003, the high-value area in the east has expanded rapidly, and polarization became much more pronounced. The change rate of PM2.5 is high in the east and west and low in the middle. The change rates of most areas in the west exceed more than 80%, and in the east lie somewhere between 40% and 60%. In the midlands, the change rate is not large and some regions even show a negative growth. 3) The change rate of PM2.5 is also high in areas with higher values. However, in regions where the change rate of PM2.5 is high, the value of PM2.5 is not always high. The high change rate is mainly attributable to the low base value of PM2.5 and the cities concerned belong to sensitive areas. 4) According to the PM2.5 warning index, the number of strong, medium, weak and non-warning areas in China is 45, 85, 159 and 54, respectively.
  • [1] Badyda A J, Grellier J, Dąbrowiecki P, 2016. Ambient PM2.5 exposure and mortality due to lung cancer and cardiopulmonary diseases in polish cities. In:Pokorski M (ed). Respiratory Treatment and Prevention. Cham:Springer, 1-9. doi: 10.1007/5584_2016_55
    [2] Cao J J, Chow J C, Lee F S C et al., 2013. Evolution of PM2.5 measurements and standards in the U.S. and future perspectives for China. Aerosol and Air Quality Research, 13(4):1197-1211. doi: 10.4209/aaqr.2012.11.0302
    [3] Cheng B, Wang L J, 2019. Spatial and temporal variations of PM2.5 in North Carolina. Aerosol and Air Quality Research, 19(4):698-710. doi: 10.4209/aaqr.2018.03.0111
    [4] Dockery D W, 1993. Percentile curves for evaluation of repeated measures of lung function. Occupational Medicine (Philadelphia, Pa.), 8(2):323-338.
    [5] Fang C L, Wang Z B, Xu G, 2016. Spatial-temporal characteristics of PM2.5 in China:a city-level perspective analysis. Journal of Geographical Sciences, 26(11):1519-1532. doi: 10.1007/s11442-016-1341-9
    [6] Feng Zhe, Jiang Hongqiang, Lu Yaling, 2019. China's economic-environment comprehensive zoning based on big data method and SOFM clustering. Scientia Geographica Sinica, 39(2):242-251. (in Chinese)
    [7] Fu Hongchen, Sun Yanling, Chen Li et al., 2020. Temporal and spatial distribution characteristics of PM2.5 and PM10 in Xinjiang region in 2016 based on AOD data and GWR model. Acta Scientiae Circumstantiae, 40(1):27-35. (in Chinese)
    [8] Giannadaki D, Lelieveld J, Pozzer A, 2016. Implementing the US air quality standard for PM2.5 worldwide can prevent millions of premature deaths per year. Environmental Health, 15(1):88. doi: 10.1186/s12940-016-0170-8
    [9] Hayes R B, Lim C, Zhang Y L et al., 2020. PM2.5 air pollution and cause-specific cardiovascular disease mortality. International Journal of Epidemiology, 49(1):25-35. doi: 10.1093/ije/dyz114
    [10] Jiang Chao, Gong Jianzhou, Sun Jiaren et al., 2018. Spatial-temporal evolution of PM2.5 distribution in Pearl River Delta Region in 2013-2016. Ecology and Environmental Sciences, 27(9):1698-1705. (in Chinese)
    [11] Jin F, 2015. From urbanization to urban decay:the problems of modernization, urbanization and industrialization-the case of Detroit. In:Martinelli A (eds). Global Modernization Review. Singapore:World Scientific, 91-99. doi:10.1142/97898146 16072_0010
    [12] Kan H D, London S J, Chen G H et al., 2007. Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China. Environment International, 33(3):376-384. doi: 10.1016/j.envint.2006.12.001
    [13] Kelly J T, Jang C J, Timin B et al., 2019. A system for developing and projecting PM2.5 spatial fields to correspond to just meeting national ambient air quality standards. Atmospheric Environment:X, 2:100019. doi: 10.1016/j.aeaoa.2019.100019
    [14] Liu Qing, Liu Yu, Xu Jintao, 2016. Economic and environmental effects of improved auto fuel economy standard in China:a CGE analysis. Acta Scientiarum Naturalium Universitatis Pekinensis, 52(3):515-527. (in Chinese)
    [15] Lonati G, Crippa M, Gianelle V et al., 2011. Daily patterns of the multi-modal structure of the particle number size distribution in Milan, Italy. Atmospheric Environment, 45(14):2434-2442. doi: 10.1016/j.atmosenv.2011.02.003
    [16] Runze Y, Cui L L, Peng X M et al., 2019. Effect and threshold of PM2.5 on population mortality in a highly polluted area:a study on applicability of standards. Environmental Science and Pollution Research, 26(18):18876-18885. doi: 10.1007/s11356-019-04999-1
    [17] Sun Chunyuan, Li Lingjun, Zhao Wenji et al., 2016. Temporal and spatial characteristic and factors analysis of PM2.5 on the basis of wavelet transformation in Beijing. Ecology and Environmental Sciences, 25(8):1343-1350. (in Chinese)
    [18] Tian S L, Pan Y P, Liu Z R et al., 2014. Size-resolved aerosol chemical analysis of extreme haze pollution events during early 2013 in urban Beijing, China. Journal of Hazardous Materials, 279(8):452-460. doi: 10.1016/j.jhazmat.2014.07.023
    [19] Utell M J, Frampton M W, 2000. Acute health effects of ambient air pollution:the ultrafine particle hypothesis. Journal of Aerosol Medicine, 13(4):355-359. doi: 10.1089/jam.2000.13.355
    [20] Yadav J Y, Kharat V, Deshpande A, 2012. Comparative evaluation of selective methods in air quality classification:a case study. Pondicherry, India:National Conference on Fuzzy Soft Computing Mathematical Analysis.
    [21] Wang Chen, Shi Yue, Jing Yue, 2020. Spatial and temporal distribution characteristics of PM2.5 in Beijing-Tianjin-Hebei region based on remote sensing data. The Administration and Technique of Environmental Monitoring, 32(1):37-41. (in Chinese)
    [22] Wang L L, Liu Z R, Sun Y et al., 2015a. Long-range transport and regional sources of PM2.5 in Beijing based on long-term observations from 2005 to 2010. Atmospheric Research, 157:37-48. doi: 10.1016/j.atmosres.2014.12.003
    [23] Wang Y S, Yao L, Wang L L et al., 2014. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Science China Earth Sciences, 57(1):14-25. doi: 10.1007/s11430-013-4773-4
    [24] Wang Zhanshan, Li Yunting, Chen Tian et al., 2015c. Spatial-temporal characteristics of PM2.5 in Beijing in 2013. Acta Geographica Sinica, 70(1):110-120. (in Chinese)
    [25] Wang Zhenbo, Fang Chuanglin, Xu Guang et al., 2015b. Spatial-temporal characteristics of the PM2.5 in China in 2014. Acta Geographica Sinica, 70(11):1720-1734. (in Chinese)
    [26] Yadav J, Kharat V, Deshpande A, 2012. Comparative evaluation of selective methods in air quality classification:a case study. In:Proceedings of National Conference on Fuzzy Soft Computing Mathematical Analysis. Ramanujan School of Mathematical Sciences Pondicherry University Pondicherry. Available at https://xueshu.baidu.com/usercenter/paper/show?paperid=a3d1e05101a2f86cc28d6e03f194fd20&site=xueshuse
    [27] Yin Q, Wang J F, Hu M G et al., 2016. Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing. Journal of Environmental Sciences, 48(10):161-168. doi: 10.1016/j.jes.2016.03.024
    [28] Zhang Han, Wang Shigong, Xin Jinyuan et al., 2019. The temporal and spatial distribution characteristics of PM2.5 in the Sichuan Basin based on MODIS AOD revised by ground-based observations. Journal of Lanzhou University:Natural Sciences, 55(5):610-615, 623. (in Chinese)
    [29] Zhou L, Zhou C H, Yang F et al., 2019. Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015. Journal of Geographical Sciences, 29(2):253-270. doi: 10.1007/s11442-019-1595-0
    [30] Zhu Hongxia, Tao Xuemei, Wang Chao et al., 2020. Spatial and temporal distribution characteristics of Levoglucosan and its isomers in PM2.5 in Beijing and six surrounding cities. Environmental Science, 41(4):1544-1549. (in Chinese)
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Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016

doi: 10.1007/s11769-020-1157-5
    基金项目:

    Under the auspices of the National Social Science Foundation of China (No.18ZDA052) and the National Natural Sci-ence Foundation of China (No. 41301154)

    通讯作者: LI Hua.E-mail:lihua610@163.com

摘要: The rapid development of China's economy and urbanization has given rise to noticeable environmental problems, among which the change of air quality has received extensive attention. The panel data of PM2.5 (particles with an aerodynamic diameter of 2.5 μm or less) in 343 prefecture-level cities in China from 1998 to 2016 were statistically analyzed to reveal the characteristics of the temporal evolution and spatial variation of China's air quality in the past two decades. The results show that: 1) the overall deterioration trend of air quality is obvious throughout the country. The variation trend of PM2.5 was divided into three phases: rapid-growth phase (1998–2007), lag phase (2006–2011) and mildly-incremental phase (2012–2016), with their average growth rates of 7.19%, −3.59% and 0.52%, respectively. 2) The spatial difference of PM2.5 values in China increased significantly with time. Since 2003, the high-value area in the east has expanded rapidly, and polarization became much more pronounced. The change rate of PM2.5 is high in the east and west and low in the middle. The change rates of most areas in the west exceed more than 80%, and in the east lie somewhere between 40% and 60%. In the midlands, the change rate is not large and some regions even show a negative growth. 3) The change rate of PM2.5 is also high in areas with higher values. However, in regions where the change rate of PM2.5 is high, the value of PM2.5 is not always high. The high change rate is mainly attributable to the low base value of PM2.5 and the cities concerned belong to sensitive areas. 4) According to the PM2.5 warning index, the number of strong, medium, weak and non-warning areas in China is 45, 85, 159 and 54, respectively.

English Abstract

LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. 中国地理科学, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
引用本文: LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. 中国地理科学, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. Chinese Geographical Science, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
Citation: LI Hua, TONG Helong, WU Xianhua, LU Xiaoli, MENG Shuhan. Spatial and Temporal Evolution Characteristics of PM2.5 in China from 1998 to 2016[J]. Chinese Geographical Science, 2020, 30(6): 947-958. doi: 10.1007/s11769-020-1157-5
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