A new framework of regional collaborative governance for PM2.5

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


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.


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



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).


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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

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