148 Plume Spread Under Drainage Conditions at Shallow Topography in SAVANT Field Campaign

Monday, 29 January 2024
Hall E (The Baltimore Convention Center)
Qidi Zhao, Chongqing Jiaotong Univ., Nanan, Chongqing, China; and J. Liu, J. Wang, S. R. Bhimireddy, D. A. Kristovich, A. L. Hiscox, J. Sun, and C. Nappo

In the nocturnal stable boundary layer, drainage flow occurs even in very shallow topographic airsheds. The dispersion of aerosols in drainage flow is different than that under other conditions in traditional dispersion models. Therefore traditional dispersion models may be inapplicable to the aerosol dispersion under drainage conditions. To fill this gap, field experiments and data analyses were conducted to deduce plume dispersion parameters and equations for drainage flows.

The field campaign experiment SAVANT (Stable Atmospheric Variability And Transport) measured smoke plume dispersions using lidars in the fall of 2018. The SAVANT site in Illinois has shallow slopes and multiple shallow gullies. The dispersion parameters (horizontal and vertical spreads, σy and σz) in drainage flows are calculated from lidar data. Using the traditional Passquill-Turner method for plume dispersion modeling, the atmospheric stability of drainage flows is an F category and dispersion parameters σy and σz range from 1 to 1.3 m at 165 m and 340 m away downwind, respectively, from the smoke-released location. However, the actual lidar measurements show a median value of σz 4.19 m and σy 43.75 m at 165 m, and 2.53 m and 43.45 m at 340 m. This shows that the general dispersion model does not apply to the calculation of aerosol dispersion in drainage flows. To deduce a new set of plume dispersion parameters for drainage flows, correlation and regression analyses were conducted. The independent variables are stability, air temperature, cloud cover (radiation), turbulence, wind speed, and wind direction. The dependent variables were σy and σz. Correlation analyses show the wind speed, wind direction, turbulence strength, stability, and air temperature had significant correlations with σy and σz . A regression equation is being deduced from these variables.

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