Jiang, Yanli, Xu, Ke, Gai, Wenmei, Salhi, Said (2022) Emergency response for tackling major accidental toxic gas releases: What should be done and when? Safety Science, 154 . Article Number 105819. ISSN 0925-7535. (doi:10.1016/j.ssci.2022.105819) (KAR id:95238)
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Official URL: https://doi.org/10.1016/j.ssci.2022.105819 |
Abstract
When there are toxic gas leaks, rapid emergency response planning is vital to protect public safety. In this study, an emergency response trade-off model to assist decision-makers in taking focused action for different personnel is developed. First, a modified Dijkstra algorithm and a minimum cost maximum flow algorithm are employed to determine the optimal evaluation routes, after which an as low as reasonably practical criterion is applied to evaluate the emergency response risk levels and identify the multiple emergency response windows of opportunity. Finally, a case study based on a real incident is given to illustrate the applicability of our method. It was found that an immediate evacuation of all members of the public in a target area would expose some of them to excessive risk. It was also discovered that there is a close and complex relationship between the emergency response risk and the shelter-in-place duration and the public emergency response. Another interesting finding is that the evacuation routes in the windows of opportunities differ significantly depending on the location, and the emergency response risks associated with using the same path to evacuate at different times. These interesting findings, which were based on the scientific assessment of emergency response risks, have a massive practical impact and could assist in more accurately formulating public protection strategies.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.ssci.2022.105819 |
Uncontrolled keywords: | Emergency response, Windows of opportunity, Risk assessment, Toxic gas release, Evacuation, Shelter-in-place |
Subjects: |
H Social Sciences T Technology > TA Engineering (General). Civil engineering (General) |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Said Salhi |
Date Deposited: | 31 May 2022 14:29 UTC |
Last Modified: | 01 Dec 2023 00:00 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/95238 (The current URI for this page, for reference purposes) |
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