Simulation and Study of Poisonous Gas Diffusion Based on ArcEngine Techniques about the City after the Earthquake

Article Preview

Abstract:

This paper presents a dynamic simulation, using Gauss plume model and through component-based software development under ArcGIS Engine, of poisonous gas diffusion after an earthquake. Given certain meteorological condition, topography, and time interval, the simulation provides a satisfactory prediction about the locations and damage ranges of the diffusion process, and, therefore, helps the rescuers make emergency plans which could reduce disaster losses.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

1947-1950

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Ning He. Application of poisonous gas diffusion models to rescue of chemical accidents, Journal of Natural Disasters, Vol. 18(2009), p.197 (In Chinese).

Google Scholar

[2] Information on http: /www. arcgis. com.

Google Scholar

[3] Tiemin Liu, Husheng Li and Yuefeng Deng. Public incident emergency management information system for both peacetime and wartime, Journal of Safety Science and Technology, Vol. 1(2005), p.34 (In Chinese).

Google Scholar

[4] Chuansheng Jiang, Yunfeng Deng. Safety analysis on the evacuation of residents around Natural Gas Wells with High Hydrogen Sulfide Content. China Safety Science Journal, Vol. 17(2007), p.9 (In Chinese).

Google Scholar

[5] Zugang Chen, Yulong Wang, Yanhua Li and Jinxin Wang. Simulation and Realization of Gauss Puff Diffusion Model of Gas Based on ArcEngine Techniques, Journal of Geomatics, Vol. 36(2011). p.44 (In Chinese).

Google Scholar

[6] Benjei Tsuang, Chienlung Chen, Chunghsien Lin, Manting Cheng, Yingi Tsai, Chiapin Chio, Rongchang Pan and Peihsuan Kuo. Quantification on the source/receptor relationship of primary pollutants and secondary aerosols by a Gaussian plume trajectory model: Part II. Case study, Atmospheric Environment, Vol. 37(2003).

DOI: 10.1016/s1352-2310(03)00472-2

Google Scholar