Simulation of large particle transport near the surface under stable conditions: comparison with the Hanford tracer experiments |
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| Institution: | 1. School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China;2. Northwest Institute of Nuclear Technology, Xi''an 710000, China;3. Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, North China Electric Power University, Beijing 102206, China;4. Department of Civil and Environmental Engineering, University of Miyazaki, Japan;1. Department of Chemical Engineering, National Institute of Technology Warangal, India;2. Department of Mechanical Engineering, P. A. College of Engineering, (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru 574153, India;3. School of Engineering, The University of Newcastle, Callaghan, NSW 2308, Australia;4. School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083 |
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| Abstract: | A plume model is presented describing the downwind transport of large particles (1–100 μm) under stable conditions. The model includes both vertical variations in wind speed and turbulence intensity as well as an algorithm for particle deposition at the surface. Model predictions compare favorably with the Hanford single and dual tracer experiments of crosswind integrated concentration (for particles: relative bias=?0.02 and 0.16, normalized mean square error=0.61 and 0.14, for the single and dual tracer experiments, respectively), whereas the US EPA's fugitive dust model consistently overestimates the observed concentrations at downwind distances beyond several hundred meters (for particles: relative bias=0.31 and 2.26, mean square error=0.42 and 1.71, respectively). For either plume model, the measured ratio of particle to gas concentration is consistently overestimated when using the deposition velocity algorithm of Sehmel and Hodgson (1978. DOE Report PNL-SA-6721, Pacific Northwest Laboratories, Richland, WA). In contrast, these same ratios are predicted with relatively little bias when using the algorithm of Kim et al. (2000. Atmospheric Environment 34 (15), 2387–2397). |
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