Skip to main content
Log in

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

  • Published:
Journal of Geographical Sciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Pope C A, 2000. Review: Epidemiological basis for particulate air pollution health standards. Aerosol Science & Technology, 32(1): 4–14.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stone B, 2008. Urban sprawl and air quality in large US cities. Journal of Environmental Management, 86(4): 688–698.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • Wu D, 2012. Hazy weather research in China in the last decade: A review. Acta Scientiae Circumstantiae, 32(2): 257–269.

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • Yi H, Hao J, Tang X L et al., 2007. Atmospheric environmental protection in China: Current status, developmental. Energy Policy, 35(2): 907–915.

    Article  Google Scholar 

  • Zhang Y, Cao F, 2015. Fine particulate matter (PM2.5) in China at a city level. Scientific Reports, 5: 1–11.

    Article  Google Scholar 

  • 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dongqi Sun.

Additional information

Foundation: The Strategic Priority Research Program of the Chinese Academy of Sciences, No.XDA19040401; China Postdoctoral Science Foundation, No.2016M600121; National Natural Science Foundation of China, No.41701173, No.41501137; The State Key Laboratory of Resources and Environmental Information System

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, L., Zhou, C., Yang, F. et al. Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015. J. Geogr. Sci. 29, 253–270 (2019). https://doi.org/10.1007/s11442-019-1595-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11442-019-1595-0

Keywords

Navigation