VogCast: A Framework for Modeling Volcanic Air Pollution and Its Application to the 2022 Eruption of Mauna Loa Volcano, Hawai'i

Abstract

AbstractVolcanic activity and the associated gas emissions into the atmosphere often result in adverse air quality conditions and present a hazard to human health and the environment. Building on a decade‐long effort to provide operational surface sulfur dioxide and sulfate aerosol forecasts for the State of Hawai'i, we present an air quality modeling framework called VogCast. VogCast is designed to simplify ensemble air quality prediction on a regional scale by linking together multiple state‐of‐the‐art models of meteorology, emissions, and dispersion. The framework is open‐source and introduces a new dynamic plume‐rise algorithm for distributing pollutants vertically. Using radar and satellite data, we demonstrate that VogCast reasonably captured the mean injection height, the location, and the general envelope of the vog plume during Mauna Loa's 2022 eruption. The results suggest that during the 12‐day eruption period model performance varied between days with trade and non‐trade wind conditions. Our findings also highlight the importance of sulfur dioxide emission rate and vent parameter inputs for improving forecast accuracy. The broad goal of this work is to better our understanding of vog dispersion and improve air quality prediction for impacted communities.