Development of a Mobile Dust Source Parameterization using an Inverse Lagrangian Stochastic Modeling Technique

Abstract

In arid regions, mechanical disturbances along the desert floor can result in large fluctuations of dust particles into the atmosphere. Rotorcraft operation near the surface may have the greatest potential for dust entrainment per vehicle. Due to this, there is a need for efficient tools to estimate the risk of air quality and visibility impacts in the neighborhood of rotorcraft operating near the desert surface. In this study, a set of parameterized models were developed to form a multi-component modeling system to simulate the entrainment and dispersion of dust from a rotorcraft wake. A simplified scheme utilizing momentum theory was applied to predict the shear stress at the ground under the rotorcraft. Stochastic dust emission algorithms were used to predict the PM10 emission rate from the wake. The distribution of dust emission from the wake was assigned at the walls of a box-volume that encapsulates the wake. The distribution was determined using the results of an inverse Lagrangian stochastic particle dispersion modeling study, using a dataset from a full-scale experiment. All of the elements were put together into a model that simulates the dispersion of PM10 dust from a rotorcraft wake. Downwind concentrations of PM10 estimated using the multi-component modeling system compared well to a set of experimental measurements.