Advertisement

A new framework of regional collaborative governance for PM2.5

  • Chao Ye
  • Ruishan Chen
  • Mingxing Chen
  • Xinyue YeEmail author
Short Communication
  • 14 Downloads

Abstract

PM2.5 (Fine particulate matter in the atmosphere) pollution has become a major problem affecting human health and social development, especially in developing countries like China. The interaction between PM2.5 and the underlying factors across different regions or spatial scales are very complex, so it is helpful to study the spatial distribution and evolution of PM2.5, which is the foundation for carrying out regional collaborative governances. However, there is a paucity of this kind of research in China. This paper tries to put forward a new multi-scalar framework based on the spatiotemporal synthesis. After the spatial distribution and evolution of PM2.5 data is integrated with the methods of remote sensing, geographic information system and social ecological system analysis, we can more properly describe the evolving trajectory and characteristics of PM2.5 at different time–space scales. The paper defines and divides the space influenced by PM2.5 into four categories, which indicate the paths of spatial diffusion and finally puts forward the methodology of regional collaboration and environmental governance.

Keywords

PM2.5 Spatial distribution Regional collaborative governance Remote sensing Geographic information system 

Notes

Funding

This work is supported jointly by National Key Research and Development Program (No.2016YFC0503506), National Natural Science Foundation of China (No. 41571138, 41871143), and The Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23100301).

References

  1. Bai Y, Yang F (2013) Lessons and experience of the U.S. PM2.5 control. Energy China 35(4):15–19Google Scholar
  2. Bao Z, Feng Y, Jiao L, Hong S, Liu W (2010) Characterization and source apportionment of PM2.5 and PM10 in Hangzhou. Environ Monit China 26(2):44–48Google Scholar
  3. Battelle Memorial Institute (BMI), and Center for International Earth Science Information Network (CIESIN)/Columbia University (2013) Global annual average PM2.5 grids from MODIS and MISR aerosol optical depth (AOD). NASA Socioeconomic Data and Applications Center (SEDAC), Palisades. http://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-avg-pm2-5-2001-2010. Accessed 3 Sept 2013
  4. Bors EK, Solomon S (2013) How a nested framework illuminates the challenges of comparative environmental analysis. Proc Natl Acad Sci 110(19):7531–7532Google Scholar
  5. Cao X, Kostka G, Xu X (2019) Environmental political business cycles: the case of PM2.5 air pollution in Chinese prefectures. Environ Sci Policy 93:92–100Google Scholar
  6. Cash DW, Moser SC (2000) Linking global and local scales: designing dynamic assessment and management processes. Glob Environ Change 10(2):109–120Google Scholar
  7. Cash DW, Adger WN, Berkes F, Garden P, Lebel L, Olsson P, Pritchard L, Young O (2006) Scale and cross-scale dynamics: governance and information in a multilevel world. Ecol Soc 11(2):8Google Scholar
  8. Chan CK, Yao X (2008) Air pollution in mega cities in China. Atmos Environ 42(1):1–42Google Scholar
  9. Chen Y, Ebenstein A, Greenstone M, Li H (2013) Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc Natl Acad Sci 110(32):12936–12941Google Scholar
  10. Chowdhury RR (2012) Revisiting the environmental Kuznets curve: an introduction to the special issue. Appl Geogr 32:1–2Google Scholar
  11. Dietz T, Ostrom E, Stern PC (2003) The struggle to govern the commons. Science 302(5652):1907–1912Google Scholar
  12. Fan X, Chen H, Xia X (2013) Progress in observation studies of atmospheric aerosol radiative properties in China. Chin J Atmos Sci 37(2):477–498Google Scholar
  13. Feng L, Liao W (2016) Legislation, plans, and policies for prevention and control of air pollution in China: achievements, challenges, and improvements. J Clean Prod 112:1549–1558Google Scholar
  14. Gu B, Ju X, Wu Y, Erisman J, Bleeker A, Reis S, Sutton M, Lam S, Chen D, Oenema O, Smith R, Ye X (2018) Cleaning up nitrogen pollution may reduce future carbon sinks. Glob Environ Change 48:56–66Google Scholar
  15. Gupta P, Christopher SA, Wang J, Gehrig R, Lee Y, Kumar N (2006) Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos Environ 40(30):5880–5892Google Scholar
  16. Han X, Wei W, Liu M, Hong W, Lu H, Zhang Y (2013) The influence of airflow on the concentrations of PM10, PM2.5 and PM1.0 in Urumqi, Xinjiang, China. J Desert Res 33(1):223–230Google Scholar
  17. He C, Huang Z, Ye X (2014) Spatial heterogeneity of economic development and industrial pollution in urban China. Stoch Environ Res Risk Assess 28(4):767–781Google Scholar
  18. Hoek G, Beelen R, de Hoogh K, Vienneau D, Gulliver J, Fischer P, Briggs D (2008) A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42(33):7561–7578Google Scholar
  19. Huang X, Song Y, Li M, Li J, Zhu T (2012) Harvest season, high polluted season in East China. Environ Res Lett 7(4):044033Google Scholar
  20. Khan M, Chang YC (2018) Environmental challenges and current practices in China—a thorough analysis. Sustainability 10(7):2547Google Scholar
  21. Kumar N, Chu A, Foster A (2008) Remote sensing of ambient particles in Delhi and its environs: estimation and validation. Int J Remote Sens 29(12):3383–3405Google Scholar
  22. Li W, Bai Z, Wei J, Liu A, Zhao Y, Jin T (2008) Pollution characteristics of PM2.5 and its main componts in Tianjin winter atmosphere. China Environ Sci 28(6):481–486Google Scholar
  23. Li L, Wang H, Cao L, Wang L, Liu H, Shi X (2013) Analysis of PM2.5 pollution situation and control measures in cities of China. Energy Energy Conserv 5:69–70Google Scholar
  24. Liu Y, Sarnat JA, Kilaru V, Jacob DJ, Koutrakis P (2005) Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. Environ Sci Technol 39(9):3269–3278Google Scholar
  25. Liu Y, Chen D, He K (2008) Review on multi-angle imaging spectral radiometer of atmospheric pollution. Sci China Press 38(3):384–396Google Scholar
  26. Liu Y, Cao G, Zhao N, Mulligan K, Ye X (2018) Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach. Environ Pollut.  https://doi.org/10.1016/j.envpol.2017.12.070 Google Scholar
  27. Luo Y, Chen J, Zheng X, Zhao T (2012) Climatology of aerosol optical depth over China from recent 10 years of MODIS remote sensing data. Ecol Environ Sci 21(5):876–883Google Scholar
  28. Marshall J (2013) PM2.5. Proc Natl Acad Sci USA 110(22):8756Google Scholar
  29. Martonchik JV, Kahn RA, Diner DJ (2009) Retrieval of aerosol properties over land using MISR observations. In: Satellite aerosol remote sensing over land. Springer, Berlin, Heidelberg, pp 267–293Google Scholar
  30. Ostrom E (1990) Governing the commons: the evolution of institutions for collective action. Cambridge University Press, CambridgeGoogle Scholar
  31. Ostrom E (2009) A general framework for analyzing sustainability of social-ecological systems. Science 325:419–422Google Scholar
  32. Ostrom E, Cox M (2010) Moving beyond panaceas: a multi-tiered diagnostic approach for social-ecological analysis. Environ Conserv 37(4):451–463Google Scholar
  33. People’s Network (2018) Beijing pushes 29 measures to deal with air pollution during the heating season: the responsibility for prevention and control is compacted to the township [EB/OL]. https://news.sina.com.cn/gov/2018-12-06/doc-ihprknvt4043721.shtml. 14 Nov 2018
  34. Ramanathan V, Crutzen PJ, Lelieveld J, Mitra A, Althausen D, Anderson J, Andreae M, Cantrell W, Cass G, Chung C (2001) Indian ocean experiment: an integrated analysis of the climate forcing and effects of the great Indo-Asian haze. J Geophys Res Atmos 106(D22):28371–28398Google Scholar
  35. Rivas-Perea P, Rosiles J, Cota-Ruiz J (2013) Statistical and neural pattern recognition methods for dust aerosol detection. Int J Remote Sens 34(21):7648–7670Google Scholar
  36. Rohde RA, Muller RA (2015) Air pollution in China: mapping of concentrations and sources. PLoS ONE 10(8):e0135749Google Scholar
  37. Shi W, Wong MS, Wang J, Zhao Y (2012) Analysis of airborne particulate matter (PM2.5) over Hong Kong using remote sensing and GIS. Sensors 12(6):6825–6836Google Scholar
  38. Silva RA, West JJ, Zhang Y, Anenberg SC, Lamarque JF, Shindell DT, Collins WJ, Dalsoren S, Faluvegi G, Folberth G (2013) Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change. Environ Res Lett 8(3):034005Google Scholar
  39. Sorek-Hamer M, Cohen A, Levy R, Ziv B, Broday D (2013) Classification of dust days by satellite remotely sensed aerosol products. Int J Remote Sens 34(8):2672–2688Google Scholar
  40. Superczynski SD, Christopher SA (2011) Exploring land use and land cover effects on air quality in Central Alabama using GIS and remote sensing. Remote Sens 3(12):2552–2567Google Scholar
  41. Van Donkelaar A, Martin RV, Park RJ (2006) Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. J Geophys Res Atmos.  https://doi.org/10.1029/2005JD006996 Google Scholar
  42. Van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, Villeneuve PJ (2010) Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect 118(6):847Google Scholar
  43. Van Donkelaar A, Martin RV, Levy RC, da Silva AM, Krzyzanowski M, Chubarova NE, Semutnikova E, Cohen AJ (2011) Satellite-based estimates of ground-level fine particulate matter during extreme events: a case study of the Moscow fires in 2010. Atmos Environ 45(34):6225–6232Google Scholar
  44. Wang D (2012) Look the release and supervision of public information from PM2.5 argument. Lawscience 9:75–81Google Scholar
  45. Wang W, Tang D, Liu H (2000) Research on current pollution status and pollution characteristics of PM2.5 in China. Res Environ Sci 13(1):1–5Google Scholar
  46. Wang JF, Hu MG, Xu CD, Christakos G, Zhao Y (2013a) Estimation of citywide air pollution in Beijing. PLoS ONE 8(1):e53400Google Scholar
  47. Wang Z, Liu Y, Hu M, Pan X, Shi J, Chen F, He K, Koutrakis P, Christiani DC (2013b) Acute health impacts of airborne particles estimated from satellite remote sensing. Environ Int 51:150–159Google Scholar
  48. Wang L, Zhang F, Pilot E, Yu J, Nie C, Holdaway J et al (2018) Taking action on air pollution control in the Beijing–Tianjin–Hebei (BTH) region: progress, challenges and opportunities. Int J Environ Res Public Health 15(2):306Google Scholar
  49. Wei Y, Ye X (2014) Urbanization, urban land expansion and environmental change in China. Stoch Environ Res Risk Assess 28(4):757–765Google Scholar
  50. Weng Q, Yang S (2006) Urban air pollution patterns, land use, and thermal landscape: an examination of the linkage using GIS. Environ Monit Assess 117(1–3):463–489Google Scholar
  51. Wu D (2012) Thinking and advice on the controlling strategy of PM2.5 the hot pollutant in the new ambient air quality standard of China. Environ Monit Forewarn 4(4):1–7Google Scholar
  52. Yang J (2012) Functions of soil and water conservation in improvement of air quality in Beijing. Chin Water Resour 2:21–22Google Scholar
  53. Ye X, Wei YD (2012) Regional development, disparities and polices in globalizing Asia. Reg Sci Policy Pract 4(3):179–182Google Scholar
  54. Young OR (2002) The institutional dimensions of environmental change: fit, interplay, and scale. The MIT Press, CambridgeGoogle Scholar
  55. Young OR (2011) Effectiveness of international environmental regimes: existing knowledge, cutting-edge themes, and research strategies. Proc Natl Acad Sci 108(50):19853–19860Google Scholar
  56. Young OR, King LA, Schroeder H (2008) Institutions and environmental change: principal findings, applications, and research frontiers. MIT Press, CambridgeGoogle Scholar
  57. Zhang X (2012) The analysis of power mechanism about government environmental information open: the event of “PM2.5 monitoring were included in the national standard” as an example. Urban Stud 19(9):61–67Google Scholar
  58. Zhang A, Qi Q, Jiang L, Zhou F, Wang J (2013) Population exposure to PM2.5 in the urban area of Beijing. PLoS ONE 8(5):e63486Google Scholar
  59. Zheng M, Salmon LG, Schauer JJ, Zeng L, Kiang C, Zhang Y, Cass GR (2005) Seasonal trends in PM2.5 source contributions in Beijing, China. Atmos Environ 39(22):3967–3976Google Scholar
  60. Zheng Z, Chen L, Zheng J, Zhong L, Liu Q (2011) Application of retrieved high-resolution AOD in regional PM monitoring in the Pearl River Delta and Hong Kong region. Acta Sci Circum 31(6):1155–1161Google Scholar
  61. Zheng X, Luo Y, Zhao T, Chen J, Kang W (2012) Geographical and climatological characterization of aerosol distribution in China. Sci Geogr Sin 32(3):265–272Google Scholar
  62. Zheng Y, Fan J, Liu J, Jiang J (2013) Characteristics of aerosol optical depth and aerosol particle size distribution based on ground-based remote sensing in the Taihu region. Acta Sci Circum 33(6):1672–1681Google Scholar
  63. Zhu X, Zhang Y, Zeng L, Wang W (2005) Source identification of ambient PM2.5 in Beijing. Res Environ Sci 18(5):1–5Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, Shanghai Key Laboratory for Urban Ecological Process and Eco-restoration, Institute of Eco-ChongmingEast China Normal UniversityShanghaiChina
  2. 2.Key Laboratory of Regional Sustainable Development ModelingInstitute of Geographic Sciences and Natural Resources Research, CASBeijingChina
  3. 3.University of Chinese Academy of Sciences, College of Resource and EnvironmentBeijingChina
  4. 4.Urban Informatics and Spatial Computing Lab, Department of InformaticsNew Jersey Institute of TechnologyNewarkUSA

Personalised recommendations