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Advances in Plume Dispersion Modeling Using Emissions Data from an Unmanned Aircraft System
Citation:
Gullett, B., F. Ngan, J. Aurell, M. Cohen, K. Stone, J. Adams, V. Scholl, AND Robert Owen. Advances in Plume Dispersion Modeling Using Emissions Data from an Unmanned Aircraft System. The International Oil Spill Conference 2024, New Orleans, LA, May 13 - 16, 2024.
Impact/Purpose:
This work addresses plume dispersion measurement from an oil spill burning on water. The presentation describes efforts to measure and model the plume for use in calibrating plume dispersion models. The significance of this work is that it is perhaps the first effort to obtain concentration data from a lofted plume for use in understanding plume dispersion coefficients. This presentation would be of interest to near source dispersion modelers and to emergency first responders.
Description:
An in situ oil burn plume was characterized to provide a unique aerial data set for development and calibration of near-source dispersion models. Seven in situ burns (ISBs) were conducted on a subset of a 1 ha artificial pond. The Alaska North Slope crude oil burn plume was sampled using multiple unmanned aircraft systems (UAS) for emissions and infrared/visible images. Meteorological data for near-field wind velocity, temperature, and relative humidity with altitude were measured using a balloon-lofted radiosonde. Emissions were sampled with a multirotor UAS carrying the “Kolibri” sampler which measured time- and spatially-resolved particulate matter of aerodynamic diameter 2.5 µm (PM2.5). Emissions data, together with meteorological data and parameters of the oil burn, are being used to evaluate and improve near-source dispersion models. A meteorological simulation using the Weather Research and Forecasting (WRF) Model was conducted, assimilating the wind and temperature measurements from a launched radiosonde to provide meteorological fields for the HYSPLIT model. This approach reduced the bias in the wind speed and direction prediction from WRF. By using the assimilated meteorological fields, the overall behavior of the modelled plume was consistent with the UAS measurement. Future analysis will investigate the full suite of experiments and include quantitative evaluation of the simulated mixing phenomena through comparison with UAS measurements. This work will advance dispersion modeling predictive capabilities and allow On-Scene Coordinators at emergency situations to predict plume paths more accurately and protect workers and downwind populations from potentially harmful exposures.
