Exceptionally low mercury concentrations and fluxes from the 2021 and 2022 eruptions of Fagradalsfjall volcano, Iceland
Bibliographic record
Abstract
Mercury (Hg) is naturally released by volcanoes and geothermal systems, but the global flux from these natural sources is highly uncertain due to a lack of direct measurements and uncertainties with upscaling Hg/SO2 mass ratios to estimate Hg fluxes. The 2021 and 2022 eruptions of Fagradalsfjall volcano, southwest Iceland, provided an opportunity to measure Hg concentrations and fluxes from a hotspot/rift system using modern analytical techniques. We measured gaseous Hg and SO2 concentrations in the volcanic plume by near-source drone-based sampling and simultaneous downwind ground-based sampling. Mean Hg/SO2 was an order of magnitude higher at the downwind locations relative to near-source data. This was attributed to the elevated local background Hg at ground level (4.0 ng m−3) likely due to emissions from outgassing lava fields. The background-corrected plume Hg/SO2 mass ratio (5.6 × 10−8) therefore appeared conservative from the near-source to several hundred meters distant, which has important implications for the upscaling of volcanic Hg fluxes based on SO2 measurements. Using this ratio and the total SO2 flux from both eruptions, we estimate the total mass of gaseous Hg released from the 2021 and 2022 Fagradalsfjall eruptions was 46 ± 33 kg, equivalent to a flux of 0.23 ± 0.17 kg d−1. This is the lowest Hg flux estimate in the literature for active open-conduit volcanoes, which range from 0.6 to 12 kg d−1 for other hotspot/rift volcanoes, and 0.5–110 kg d−1 for arc volcanoes. Our results suggest that Icelandic volcanic systems are fed from a particularly Hg-poor mantle. Furthermore, we demonstrate that the aerial near-source plume Hg measurement is feasible with a drone-based active sampling configuration that captures all gaseous and particulate Hg species, and recommend this as the preferred method for quantifying volcanic Hg emissions going forward.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".