A 3D Visualization Framework for Real-time Distribution and Situation Forecast of Atmospheric Chemical Pollution

  • Haibo Wang
  • Jingeng Mai
  • Yi Song
  • Chaoshi Wang
  • Lin Zhang
  • Fei Tao
  • Qining Wang
Part of the Communications in Computer and Information Science book series (CCIS, volume 402)


The visualization system of pollutant distribution contributes to scientific decision-making in the emergency pollution affairs. In this paper, we propose a framework which supports 3D visualization for real-time distribution and situation forecast of atmospheric chemical pollution. The core of our framework is a distributed system infrastructure, which is designed for massive data storage and parallel computing. The stored data includes terrain elevation, vector maps, satellite maps, meteorological data and concentration data from gas sensors. High-performance computing generates gridded data for visualization. Web-based 3D visual applications with B/S structure support cross-platform terminal access.


visualization distributed system situation forecast web-based chemical pollution 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yu, J.J.Q., Li, V.O.K., Lam, A.Y.S.: Sensor Deployment for Air Pollution Monitoring Using Public Transportation System. In: 2012 IEEE World Congress on Computational Intelligence, WCCI 2012, pp. 1–7 (2012)Google Scholar
  2. 2.
    Xiao, J., Ying, W.: Monitoring System of Regional Environmental Pollution Index Based on Telecommunication Network. In: 2011 International Conference on Electrical and Control Engineering, ICECE 2011, Yichang, China, pp. 254–256 (2011)Google Scholar
  3. 3.
    Chen, W., Chen, S.: Application of GIS Technology in the Emergency Monitoring of Sudden Air Pollution Accident. In: 2010 2nd International Conference on Information Science and Engineering, ICISE 2010, Hangzhou, China, pp. 3550–3555 (2010)Google Scholar
  4. 4.
    Wang, C., Wan, T.R., Palmer, I.J.: Automatic Reconstruction of 3D Environment Using Real Terrain Data and Satellite Images. Intelligent Automation and Soft Computing 18(1), 49–63 (2012)CrossRefGoogle Scholar
  5. 5.
    Narashid, R.H., Mohd, W.M.N.W.: Air Quality Monitoring Using Remote Sensing and GIS Technologies. In: 2010 International Conference on Science and Social Research, CSSR 2010, Kuala Lumpur, Malaysia, pp. 1186–1191 (2010)Google Scholar
  6. 6.
    Sequeira, V., Wolfart, E., Bovisio, E., et al.: Hybrid 3D Reconstruction and Image-Based Rendering Techniques for Reality Modeling. In: Conference on Videometrics and Optical Methods for 3D Shape Measurement, San Jose, CA. SPIE, vol. 4309, pp. 126–136 (2001)Google Scholar
  7. 7.
    Hou, H.-D., Zhang, J.-F.: Research on Real-Time Visualization of Large-scale 3D Terrain. In: 2012 International Workshop on Information and Electronics Engineering, IWIEE 2012, Harbin, China, vol. 29, pp. 1702–1706 (2012)Google Scholar
  8. 8.
    Hu, Y., Li, D., He, X., Sun, T., Han, Y.: The Implementation of Wireless Sensor Network Visualization Platform based on Wetland Monitoring. In: 2009 Second International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2009, Tianjin, China, pp. 224–227 (2009)Google Scholar
  9. 9.
    Kamiński, L., Kulawiak, M., Ciżmowski, W., Chybicki, A.: Web-based GIS dedicated for marine environment surveillance and monitoring. In: IEEE Oceans 2009-Europe, Oceans, Bremen, Germany, vol. 1-2, pp. 1247–1253 (2009)Google Scholar
  10. 10.
    Ebrahimi, M., Jahangirian, A.: New Analytical Formulations for Calculation of Dispersion Parameters of Gaussian Model Using Parallel CFD. Environmental Fluid Mechanics 13(2), 125–144 (2013)CrossRefGoogle Scholar
  11. 11.
    Leroy, C., Maro, D., Hebert, D., et al.: A study of the atmospheric dispersion of a high release of krypton-85 abovea complex coastal terrain, comparison with the predictions of Gaussian models (Briggs, Doury, ADMS4). Journal of Environmental Radioactivity, 937–944 (2010)Google Scholar
  12. 12.
    Roberti, D.R., Souto, R.P., de Campos Velho, H.F., et al.: Parallel Implementation of a Lagrangian Stochastic Model for Pollution Dispersion. In: Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing, Foz dolguacu, PR, Brazil, pp. 142–149 (2004)Google Scholar
  13. 13.
    Hao, Y., Yu, Q., Qu, J.: Application of ATSTEP in Decision Support System for Nuclear Emergency Management. Nuclear Power Engineering 23(4), 102–107 (2002)Google Scholar
  14. 14.
    Yao, R., Hao, H., Hu, E., Cao, J.: Comparison of Two Kinds of Atmospheric Dispersion Model Chains in RODOS. Radiation Protection 23(30), 146–155 (2003)Google Scholar
  15. 15.
    Bo, X., Ding, F., Xu, H., Li, S.-B.: Review of Atmospheric Diffusion Spersion Model CALPUFF Technology. Environmental Monitoring Management and Technology 21(3), 9–13 (2009)Google Scholar
  16. 16.
    Cai, X., Chen, J., Kang, L.: An Atmospheric Diffusion Model for Conditions of Nuclear Accident. Radiation Protection 23(5), 293–299 (2003)Google Scholar
  17. 17.
    Yang, B., Gu, X.-M., Zhang, F., Zhang, F.: Development of Simulated Evaluation and Decision Support System of Atmospheric Pollution Dispersion. Science of Surveying and Mapping 36(3), 147–149 (2011)Google Scholar
  18. 18.
    Cheng, Y.: Gaussian Model Used Gas Pipeline after the Gas Leak Spread. Petroleum Chemical Industry of Inner Mongolia (14), 49–51 (2010)Google Scholar
  19. 19.
    Li, H., Deng, J., Wang, X., Zhang, L.: Calculation of Diffusion of Radionuclides Cloud in Atmospheric Using the Gaussian Model. Radiation Protection 24(2), 92–99 (2004)Google Scholar
  20. 20.
    Dvorak, R., Zboril, F., Kapoun, M., Masek, I.: Modeling of Atmospheric Dispersion from Point Source. In: Second UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, pp. 46–51 (2008)Google Scholar
  21. 21.
    Briant, R., Seigneur, C., Gadrat, M., Bugajny, C.: Evaluation of roadway Gaussian plume models with large-scale measurement campaigns. Geoscientific Model Development 6(2), 445–446 (2013)CrossRefGoogle Scholar
  22. 22.
    Tong, X., Ben, J., Zhang, Y.: Design and Quick Display of Global Multi-Resolution Spatial Data Model. Science of Surveying and Mapping 31(1), 72–74 (2006)Google Scholar
  23. 23.
    Zhang, L., Tang, L.-W.: Study of Organizing Seamless Great Capacity Spatial Data Based on Quad-tree. Computer Technology and Development 21(1), 77–80 (2011)Google Scholar
  24. 24.
    Hoppe, H.: Smooth View-dependent Level-Of-Detail Control and its Application to Terrain Rendering. In: 9th Annual IEEE Conference on Visualization, VIS 1998, Research Triangle Park, NC, USA, pp. 35–42 (1998)Google Scholar
  25. 25.
    John, D.: A Novel Technique for Visualizing High-Resolution 3-D Terrain Maps. In: Conference on Stereoscopic Displays and Virtual Reality Systems XIV, San Jose, CA. SPIE, vol. 6490, pp. 49003–49013 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Haibo Wang
    • 1
  • Jingeng Mai
    • 1
  • Yi Song
    • 2
  • Chaoshi Wang
    • 1
  • Lin Zhang
    • 1
  • Fei Tao
    • 1
  • Qining Wang
    • 2
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.Intelligent Control Laboratory, College of EngineeringPeking UniversityBeijingChina

Personalised recommendations