Journal of Geographical Sciences

, Volume 29, Issue 2, pp 253–270 | Cite as

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

  • Liang Zhou
  • Chenghu Zhou
  • Fan Yang
  • Lei Che
  • Bo Wang
  • Dongqi SunEmail author


High concentrations of PM2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM2.5 concentrations for regional air quality control and management. In this study, PM2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3. (2) PM2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM2.5. (3) The center of gravity of PM2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM2.5 concentrations have moved eastward, while low-value PM2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM2.5 concentration in China.


air pollution PM2.5 haze spatio-temporal evolution environmental influence China 


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  1. Austin E, Coull B A, Zanobetti A et al., 2013. A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition. Environment International, 59(3): 244–254.CrossRefGoogle Scholar
  2. Beckerman B S, Jerrett M, Serre M et al., 2013. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States. Environmental Science & Technology, 47(13): 7233–7241.CrossRefGoogle Scholar
  3. Bell M L, Dominici F, Ebisu K et al., 2007. Spatial and temporal variation in PM2.5 chemical composition in the United States for health effects studies. Environmental Health Perspectives, 115(7): 989–995.CrossRefGoogle Scholar
  4. Cao G L, Zhang X Y, Gong S L et al., 2011. Emission inventories of primary particles and pollutant gases for China. Atmospheric Environment, 45(37): 6802–6811. (in Chinese)CrossRefGoogle Scholar
  5. Charron A, Harrison R M, 2005. Fine (PM2.5) and coarse (PM2.5–10) particulate matter on a heavily trafficked London highway: Sources and processes. Environmental Science & Technology, 39(20): 7768–7776.CrossRefGoogle Scholar
  6. Cheng S, Yang L X, Zhou X et al., 2011. Evaluating PM2.5 ionic components and source apportionment in Jinan, China from 2004 to 2008 using trajectory statistical methods. Journal of Environmental Monitoring, 13(6): 1662–1671.CrossRefGoogle Scholar
  7. Chow J C, Chen L W, Watson J G et al., 2006. PM2.5 chemical composition and spatiotemporal variability during the California regional PM10/PM2.5 air quality study (CRPAQS). Journal of Geophysical Research Atmospheres, 111(D10): 1–17.CrossRefGoogle Scholar
  8. Chu H J, Huang B, Lin C Y, 2015. Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship. Atmospheric Environment, 102(2): 176–182.CrossRefGoogle Scholar
  9. Delfino R J, Sioutas C, Malik S, 2005. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environmental Health Perspectives, 113(8): 934–946.CrossRefGoogle Scholar
  10. Dockery D W, Pope CA, Xu X et al., 1994. An association between air pollution and mortality in six US cities. New England Journal of Medicine, 329(24): 1753–1759.CrossRefGoogle Scholar
  11. Franklin M, Koutrakis P, Schwartz P, 2008. The role of particle composition on the association between PM2.5 and mortality. Epidemiology, 19(5): 680–689.CrossRefGoogle Scholar
  12. Gao M, Cao J, Seto E. A, 2015. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environmental Pollution, 199(4): 56–65.CrossRefGoogle Scholar
  13. Gelencsér A, May B, Simpson D et al., 2007. Source apportionment of PM2.5 organic aerosol over Europe: Primary/ secondary, natural/anthropogenic, and fossil/biogenic origin. Journal of Geophysical Research Atmospheres, 112(D23): 1–12.CrossRefGoogle Scholar
  14. Gramsch E, Cereceda-Balic F, Oyola P et al., 2006. Examination of pollution trends in Santiago de Chile with cluster analysis of PM10 and ozone data. Atmospheric Environment, 40(28): 5464–5475.CrossRefGoogle Scholar
  15. Guo J P, Zhang X Y, Wu Y R et al., 2011. Spatio-temporal variation trends of satellite-based aerosol optical depth in China during 1980–2008. Atmospheric Environment, 45(37): 6802–6811.CrossRefGoogle Scholar
  16. Henderson S B, Beckerman B, Jerrett M et al., 2007. Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environmental Science & Technology, 41(7): 2422–2428.CrossRefGoogle Scholar
  17. Hoek G, Brunekreef B, Goldbohm S et al., 2002. Association between mortality and indicators of traffic-related air pollution in the Netherlands: A cohort study. The Lancet, 360(9341): 1203–1209.CrossRefGoogle Scholar
  18. Huang, Y, Yan Q, Zhang C, 2018. Spatial-temporal distribution characteristics of PM2.5 in China in 2016, Journal of Geovisualization and Spatial Analysis, 2(2): 1–12.CrossRefGoogle Scholar
  19. Hueglin C, Gehrig R, Baltensperger U et al., 2005. Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland. Atmospheric Environment, 39(4): 637–651.CrossRefGoogle Scholar
  20. Jiang Y A, Chen Y, Zhao Y Z et al., 2013. Analysis on changes of basic climatic elements and extreme events in Xinjiang, China during 1961–2010. Advances in Climate Change Research, 4(1): 20–29.CrossRefGoogle Scholar
  21. Kioumourtzoglou M A, Schwartz J, Weisskopf M et al., 2016. Long-term PM2.5 exposure and neurological hospital admissions in the Northeastern United States. Environmental Health Perspectives, 124(1): 23–29.CrossRefGoogle Scholar
  22. Kloog I, Nordio F, Coull B et al., 2012. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. Environmental Science & Technology, 46(21): 11913–11921.CrossRefGoogle Scholar
  23. Laden F, Neas L M, Dockery D W et al., 2000. Association of fine particulate matter from different sources with daily mortality in six US cities. Environmental Health Perspectives, 108(10): 941–947.CrossRefGoogle Scholar
  24. Laden F, Schwartz J, Speizer F E et al., 2006. Reduction in fine particulate air pollution and mortality. American Journal of Respiratory and Critical Care Medicine, 173(6): 667–672.CrossRefGoogle Scholar
  25. Lindner A, Pitombo C S, 2018. A conjoint approach of spatial statistics and a traditional method for travel mode choice issues. Journal of Geovisualization and Spatial Analysis, 2(1): 1–13.CrossRefGoogle Scholar
  26. Lin G, Fu J, Jiang D et al., 2013. Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China. International Journal of Environmental Research and Public Health, 11(1): 173–186.CrossRefGoogle Scholar
  27. Liu Y, Paciorek C J, Koutrakis P et al., 2009. Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environmental Health Perspectives, 117(6): 886–892.CrossRefGoogle Scholar
  28. Liu Y, Sarnat JA, Kilaru V et al., 2005. Estimating ground-level PM2.5 in the eastern using satellite remote sensing. Environmental Science & Technology, 39(9): 3269–3278.CrossRefGoogle Scholar
  29. Liu Y S, Yang R, 2012. The spatial characteristics and formation mechanism of the county urbanization in China. Acta Geographica Sinica, 67(8): 1011–1020. (in Chinese)Google Scholar
  30. Lu B, Kong S F, Han Bin, 2011. Inventory of atmospheric pollutants discharged from biomass burning in China continent in 2007. China Environmental Science, 31(2): 186–194. (in Chinese)Google Scholar
  31. Merbitz H, Buttstädt M, Michael S et al., 2012. GIS-based identification of spatial variables enhancing heat and poor air quality in urban areas. Applied Geography, 2012, 33(4): 94–106.CrossRefGoogle Scholar
  32. Pope C A, 2000. Review: Epidemiological basis for particulate air pollution health standards. Aerosol Science & Technology, 32(1): 4–14.CrossRefGoogle Scholar
  33. Pope C A, Burnett R T, Thun M J et al., 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Jama, 287(9): 1132–1141.CrossRefGoogle Scholar
  34. Pope C A, Dockery D W, Schwartz J, 1995. Review of epidemiological evidence of health effects of particulate air pollution. Inhalation Toxicology, 7(1): 1–18.CrossRefGoogle Scholar
  35. Samet J M, Dominici F, Curriero F C et al., 2000. Fine particulate air pollution and mortality in 20 U.S cities, 1987–1994. New England Journal of Medicine, 343:(24): 1742–1749.CrossRefGoogle Scholar
  36. Stone B, 2008. Urban sprawl and air quality in large US cities. Journal of Environmental Management, 86(4): 688–698.CrossRefGoogle Scholar
  37. Wang H, Dwyer-Lindgren L, Lofgren K T et al., 2012. Age specific and sex-specific mortality in 187 countries, 1970–2010: A systematic analysis for the global burden of disease study 2010. The Lancet, 380(9859): 2071–2094.CrossRefGoogle Scholar
  38. Wang J, Christopher S A, 2003. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophysical Research Letters, 30(21): 1–4.Google Scholar
  39. Wang J F, Li X H, George Christakos et al., 2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1): 107–127.CrossRefGoogle Scholar
  40. Wang Z B, Fang C L, Xu G et al., 2015. Spatial-temporal characteristics of the PM2.5 in China in 2014. Acta Geographica Sinica, 70(11): 1720–1734. (in Chinese)Google Scholar
  41. Wu D, 2012. Hazy weather research in China in the last decade: A review. Acta Scientiae Circumstantiae, 32(2): 257–269.Google Scholar
  42. Xu W, He F, Li H et al., 2014. Spatial and temporal variations of PM2.5 in the Pearl River Delta. Research of Environmental Sciences, 27(9): 951–957.Google Scholar
  43. Xue W, Wu W, Fu F et al., 2015. Satellite retrieval of a heavy pollution process in January 2013 in China. Environmental Science, 36, (3): 794–800. (in Chinese)Google Scholar
  44. Xue W B, Fu F, Wang J N et al., 2014. Numerical study on the characteristics of regional transport of PM2.5 in China. China Environmental Science, 34(6): 1361–1368. (in Chinese)Google Scholar
  45. Yi H, Hao J, Tang X L et al., 2007. Atmospheric environmental protection in China: Current status, developmental. Energy Policy, 35(2): 907–915.CrossRefGoogle Scholar
  46. Zhang Y, Cao F, 2015. Fine particulate matter (PM2.5) in China at a city level. Scientific Reports, 5: 1–11.CrossRefGoogle Scholar
  47. Zhang Y, Zhang W, Wang J et al., 2015. Establishment and application of pollutant inventory-chemical mass balance (I-CMB) model for source apportionment of PM2.5. Transactions of Atmospheric Sciences, 38(2): 279–284. (in Chinese)Google Scholar

Copyright information

© Science in China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Liang Zhou
    • 1
    • 2
  • Chenghu Zhou
    • 2
  • Fan Yang
    • 3
  • Lei Che
    • 4
  • Bo Wang
    • 5
  • Dongqi Sun
    • 2
    Email author
  1. 1.Faculty of GeomaticsLanzhou Jiaotong UniversityLanzhouChina
  2. 2.Institute of Geographic Sciences and Natural Resources Research, CASBeijingChina
  3. 3.School of Geographic and Oceanographic SciencesNanjing UniversityNanjingChina
  4. 4.College of Geography and Environment SciencesNorthwest Normal UniversityLanzhouChina
  5. 5.Department of GeographyThe University of Hong KongHong KongChina

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