Analysis of the persistent gas dispersion from Nyiragongo and Nyamuragira volcanoes using numerical modeling and satellite data

Volcanic activity can pose a serious threat to nearby populations, as continuous gas emissions remain dangerous even in the absence of eruptions. Nyiragongo and Nyamuragira volcanoes located in the East African Rift are among the largest global emitters of SO₂. Given the various environmental, climatic and health impacts of SO₂, studying its dispersion is important. In our study we use FALL3D model (Folch et al., 2020), an Eulerian atmospheric dispersal model that solves the advection-diffusion-sedimentation equation, combined with ensemble-based data assimilation technique to reduce uncertainties in eruption source parameters to simulate SO₂ dispersion during both eruptive and passive degassing phases.

Satellite observations from TROPOMI are processed using a trained AI algorithm based on machine learning that automatically detects and quantifies volcanic SO₂ emissions in near real-time filtering out non-volcanic sources (Corradino et al., 2024). The meteorological data used are from ERA5 reanalysis dataset.

Literature studies (e.g.Mingari et al., 2022) show that the inclusion of the satellite data in the model greatly improves the dispersion forecasts. Building on these results we aim to improve the dispersion forecasts of SO₂ from Nyiragongo volcano and develop probabilistic hazard maps of SO₂ exposure enabling an uncertainty informed assessment of potential impacts on populations and infrastructure surrounding the volcanoes. Our study will demonstrate the potential of combining observational data, numerical modeling, and ensemble-based data assimilation to improve volcanic hazard monitoring.