Arctic atmospheric mercury: Sources and changes
Bibliographic record
Abstract
Global anthropogenic and legacy mercury (Hg) emissions are the main sources of Arctic Hg contamination, primarily transported there via the atmosphere. This review summarizes the state of knowledge of the global anthropogenic sources of Hg emissions, and examines recent changes and source attribution of Hg transport and deposition to the Arctic using models. Estimated global anthropogenic Hg emissions to the atmosphere for 2015 were ~2220 Mg, ~20% higher than 2010. Global anthropogenic, legacy and geogenic Hg emissions were, respectively, responsible for 32%, 64% (wildfires: 6-10%) and 4% of the annual Arctic Hg deposition. Relative contributions to Arctic deposition of anthropogenic origin was dominated by sources in East Asia (32%), Commonwealth of Independent States (12%), and Africa (12%). Model results exhibit significant spatiotemporal variations in Arctic anthropogenic Hg deposition fluxes, driven by regional differences in Hg air transport routes, surface and precipitation uptake rates, and inter-seasonal differences in atmospheric circulation and deposition pathways. Model simulations reveal that changes in meteorology are having a profound impact on contemporary atmospheric Hg in the Arctic. Reversal of North Atlantic Oscillation phase from strongly negative in 2010 to positive in 2015, associated with lower temperature and more sea ice in the Canadian Arctic, Greenland and surrounding ocean, resulted in enhanced production of bromine species and Hg(0) oxidation and lower evasion of Hg(0) from ocean waters in 2015. This led to increased Hg(II) (and its deposition) and reduced Hg(0) air concentrations in these regions in line with High Arctic observations. However, combined changes in meteorology and anthropogenic emissions led to overall elevated modeled Arctic air Hg(0) levels in 2015 compared to 2010 contrary to observed declines at most monitoring sites, likely due to uncertainties in anthropogenic emission speciation, wildfire emissions and model representations of air-surface Hg fluxes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".