Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling |
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| Authors: | RP Donnelly TJ Lyons T Flassak |
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| Institution: | 1. School of Environmental Science, Murdoch University, Murdoch, WA 6150, Australia;2. Ingenieurbuero Lohmeyer GmbH & Co. KG, D-76229 Karlsruhe, Germany;1. College of Mathematics and Computation, Hunan Science and Technology University, Xiangtan, Hunan 411201, China;2. Lab of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;1. Environmental Research Laboratory, Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, NCSR ‘DEMOKRITOS’, 15310, Ag. Paraskevi Attikis, Greece;2. Environmental Technology Laboratory, Department of Mechanical Engineering, University of Western Macedonia, Sialvera & Bakola Str., 50100, Kozani, Greece;3. Meteorological Institute, KlimaCampus, University of Hamburg, Bundesstrasse 55, D-20146, Hamburg, Germany;1. Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China;2. Department of Mathematics & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China;1. School of Energy and Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;2. State Key Laboratory of Technologies in Space Cryogenic Propellants, Beijing 100028, China;1. Niigata Institute of Technology, Kashiwazaki, Japan;2. Concordia University, Montreal, Canada |
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| Abstract: | WinMISKAM is evaluated from an emergency response perspective. Comparisons are made between ground level concentrations observed during selected Mock Urban Setting Test (MUST) field trials and predictions generated by the model. The model was driven by 5 min averaged on-site meteorological data, and used minimum grid spacing of 0.5 m in both the horizontal and vertical. The code was found to perform well, with 46% of all predictions (paired in time and space) and 83% of arc maxima predictions within a factor of two of observed concentrations. The model was found to perform better for neutral cases than stable cases with 27% of stable case predictions and 57% of neutral case predictions within a factor of two when compared in time and space. |
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