WRF-Chem modeling of sulfur dioxide emissions from the 2008 Kasatochi Volcano

Abstract

We use the Weather Research Forecasting with Chemistry (WRF-Chem) model to simulate the evolution, dispersion and conversion of the sulfur dioxide (SO<sub>2</sub>) plume generated by the 2008 eruption of Kasatochi Volcano in Alaska, USA. About 1.7 Tg of SO<sub>2</sub> were dispersed into the atmosphere during three distinct explosive events. Stratospheric sulfur dioxide conversion chemistry is detailed and model output is compared to remote sensing retrievals from the Ozone Monitoring Instrument (OMI). WRF-Chem generated SO<sub>2</sub> column densities and plume locations similar to those from OMI retrievals as the plume traveled from the North Pacific through the continental United States and Canada. Analysis of SO<sub>2</sub> conversion established an eight day lifetime of SO<sub>2</sub> for the Kastaochi plume, which is a slightly shorter lifetime than derived by other modeling methods.

The Effect of Using a New Parameterization of Nucleation in the WRF-Chem Model on New Particle Formation in a Passive Volcanic Plume

“…WRF-Chem has proven to be an excellent candidate for the prediction of the formation, transport, dispersion, and sedimentation of different types of natural emissions [85]. A small number of recent studies focused on the effects of volcanic emissions within the atmosphere for eruptions or passive degassing at various volcanoes: Kasatochi volcano, 2008 [86], Eyjafjallajökull volcano, April-May 2010 [87], Etna volcano, 2015 [8], Grimsvötn volcano, 2011 [88], Eyjafjallajökull volcano, 2010 [89], and Mount Redoubt volcanic ash clouds using coupled PUFF and WRF-Chem dispersion models, 2009 [90]), as well as gasphase species, aerosol, and cloud properties [9,91]. In some of these studies, a module [85] was implemented to WRF-Chem to generate volcanic ash and sulfur dioxide emitted during the volcanic eruptions and passive degassing from a global emission database [92], based on field regional observations.…”

Bayesian network reasoning and machine learning with multiple data features: air pollution risk monitoring and early warning

“…The former methods mainly predict air quality by simulating the diffusion law of pollutants. For example, Rakowska et al (2014) established a prediction model by simulating the transmission of air pollutants in the street, but this method has certain parameter empirical assumption limitations (Egan et al 2014;Wang et al 2010).…”