Modeling the behavior of sulfur in magmatic systems from source to surface: Application to Whakaari/White Island, Aotearoa New Zealand, and Etna, Italy
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Abstract
Our understanding of the role of volcanoes in the global sulfur cycle and how volcanic gas emissions can be used to monitor volcanoes is limited by the complex interactions between hydrothermal systems and volcanic sulfur emissions. Hydrothermal systems influence the amount and speciation of volcanogenic sulfur, which is ultimately released to the surface/atm via a range of physicochemical processes. To understand the effect of the hydrothermal system, on surface emissions we model the magmatic-hydrothermal systems at Whakaari/White Island, Aotearoa New Zealand, and Etna, Italy. We quantify the magmatic sulfur inputs using mass balance and MELTS modeling (thermodynamic model of crystallization); model the effects of degassing using Sulfur_X (an empirical model of melt-gas equilibria); and model the influence of the hydrothermal system using CHIM-XPT and EQ3/6 (thermodynamic and kinetic models of gas+water±rock reactions), which we compare to measured plume and fumarole compositions. We find that the sulfur inputs can broadly equal sulfur outputs over long timescales. However, the hydrothermal system can modulate the total mass of sulfur released and its H2S/SO2 ratio on shorter timescales, especially as the system evolves from water- to gas-dominated through the development of dry, gas-dominated pathways.
Copyright and License
© 2023 Elsevier.
Acknowledgement
This project originated at the Cooperative Institute for Dynamic Earth Research (CIDER) 2019 Summer Program: Volcanoes funded by NSF Grant EAR-1664595 to Bruce Buffett, Barbara Romanowicz, Roland Burgmann, Michael Manga, and Richard Allen. ECH was supported by a Geology Option Post-Doctoral Fellowship from Caltech, CA USA, and the New Zealand Ministry of Business, Innovation and Employment (MBIE) through the Hazards and Risk Management and New Zealand Geothermal Futures programmes (Strategic Science Investment Fund, contract C05X1702). JB was supported by NSF EAR Award #2052963. IMF was supported at UC Berkeley by an NSF Graduate Research Fellowship.
Data Availability
All data collated from the literature and used for this study are included in supplementary, as are any results from the modeling described in the paper.
Conflict of Interest
Authors declare there are no conflicts of interest.
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Funding
- National Science Foundation
- EAR-1664595
- California Institute of Technology
- National Science Foundation
- EAR-2052963
- National Science Foundation
- Graduate Research Fellowship