Dust Control Management Methods Achieve Air Quality Objectives, Oceano Dunes State Vehicular Recreation Area, Oceano CA

The Oceano Dunes in San Luis Obispo County, California, is a known source of fugitive dust emissions.  Under conditions of elevated wind speed exceedances of the US Federal standard (150 μg m⁻³) and the State of California standard (50 μg m⁻³) for 24-hour time-integrated concentrations of particulate matter ≤10 μm aerodynamic diameter (PM10) have been observed downwind of the dunes.  Part of the dune system lies within the Oceano Dunes State Vehicular Recreation Area (ODSVRA) and the off-highway recreational activity on the dune areas is known to augment dust emission potential.  A Stipulated Order of Abatement (SOA) requires the California Department of Parks and Recreation to reduce the PM10 attributable to the ODSVRA lowering the potential health risk for people living downwind.  As amended in 2022, the SOA requires that by the end of 2028, PM10 emissions from the ODSVRA be reduced to those modeled to approximate the conditions that existed in 1939.  This is prior to high levels of off-highway vehicle (OHV) activity.  The SOA requires that the annual modeling evaluation show that the total emissions (tonnes day^-1) of the current year be less than the total emissions from the 1939 scenario for the same meteorological conditions. As of July 1, 2024, Parks achieved compliance with the SOA.  Compliance with the SOA has also been reported for 2025.  To reduce regional PM10 levels has required direct intervention of this dynamic geomorphic system by implementing dust abatement management efforts, based largely on planting of vegetation and re-establishing a foredune area.

We use 14-years of hourly PM10 (µg m^-3) concentration and wind data to quantify the change in hourly PM10 concentration that has resulted from the evolution of dust management methods implemented from 2013 through 2025.  Here we demonstrate that the PM10 concentrations downwind of the ODSVRA are lower now, for similar wind conditions, than prior to the establishment of dust controls.  Additionally, a machine learning regression approach, based on the Random Forest (RF) algorithm, was used to evaluate how effective dust management was for reducing the potential number of exceedances of the State and Federal air quality standards compared to the number of exceedances prior to 2013, i.e., before the implementation of the dust management program.