Journal of Visualization

, Volume 22, Issue 6, pp 1093–1105 | Cite as

Multivariate visualization for atmospheric pollution

  • Daying LuEmail author
  • Yao Ge
  • Laihua Wang
  • Dengming Zhu
  • Zhaoqi Wang
  • Xiaoru Yuan
Regular Paper


Multivariate visualization for atmospheric pollution is a challenging research topic. Appropriate algorithms and data structures based on modern graphics hardware are used to obtain high performance. 3D visualization of the atmospheric wind field and pollutant concentrations can easily result in visual perception problems such as occlusion and cluttering even artifacts. To solve the above issues, a K-means clustering technique is used in combination with a similarity metric between streamlines based on an iterative closest point method to cluster the initial streamlines. A small set of streamlines is then selected to represent the prominent structure of the wind field. The proper illumination model and the depth sorting method reduce the inter-occlusion between streamlines and isosurfaces to show much clearer wind field pattern and important features effectively. The atmospheric pollution data set is employed to evaluate the proposed algorithm framework.

Graphic abstract


Multivariate visualization Atmospheric pollution Occlusion Streamline Isosurface 



The authors would like to thank the reviewers for their valuable comments. Thanks go to Z.F. Wang from the Institute of Atmospheric Physics in Chinese Academy of Sciences for providing the air pollution data set in Pearl River Delta region. This work is supported and funded by the National Natural Science Foundation of China (Nos. 61379085, 61601261), funded by Science and Technology Program Foundation for Colleges and Universities in Shandong Province (No. J17KA062), funded by Industry-academy Cooperation and Cooperative Education Project of Ministry of Education (No. 201602028014) and supported by Laboratory Open Foundation of Qufu Normal University (No. sk201723).

Supplementary material

Supplementary material 1 (mp4 1220 KB)


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Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.College of SoftwareQufu Normal UniversityQufuChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Machine PerceptionPeking UniversityBeijingChina

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