A very-high resolution (20m) measurement-based dust emissions and dispersion modeling approach for the Oceano Dunes, California

Abstract

This study shows the results from very high-resolution (20 m) dust emissions and transport simulations for the Oceano Dunes State Vehicular Recreation Area (ODSVRA), a coastal sand dune complex located in San Luis Obispo County, California. Field data from an enhanced observation period carried out in May–July 2013 helped estimate the emissions and flow conditions over the dune field. Emissions are based on a comprehensive emissions grid developed from in-situ measurements using the Portable In-Situ Wind ERosion Lab (PI-SWERL). PI-SWERL estimates the potential for a soil surface to produce PM10 dust emissions for a range of wind speeds. This approach provided a well-determined PM10 emissions field as a function of time and space. Wind and turbulence fields were estimated using the CALMET diagnostic meteorological model constrained with surface stations, upper air soundings, buoys, and the North American Reanalysis data. Hourly, three-dimensional wind flow and instability objective analysis fields were developed at 20 m resolution in order to consider the complex flow over realistic dune morphology, land use/land cover and terrain characteristics over and around the Oceano Dunes. The dust dispersion simulations were performed using a computationally efficient and vectorized Lagrangian Stochastic Particle Dispersion Model driven by the CALMET output and the PI-SWERL time-space variable emissions. The dispersion model is based on the Langevin formulation and includes the turbulent diffusion and stochastic particle motion (of millions of particles) in the inertial sub-range, and assuming particles as discrete units neglecting deposition. The model estimates diffusion of particles from an initial particle releases that scale according to the PI-SWERL time-variable emissions estimates. Results were then tested at two independent-downwind locations, with positive correlations for flow conditions (R2 = 0.89) and similar receptor PM10 concentrations (R2 = 0.85). Evaluations against those observations during mean flow conditions as well as for elevated dust events suggest that the model framework can capture the spatial and temporal characteristics of mean day-to-day and diurnal PM10 variability. In this study we describe the details of the model framework and its performance as well as its implementation to locate the dust sources that have the strongest impact in the receptor sites and to evaluate the impact of different dust reduction strategies used at the ODSVRA to mitigate PM10 at downwind receptors.