Unexpected anthropogenic emission decreases are required to explain recent atmospheric mercury concentration declines
Bibliographic record
Abstract
Anthropogenic activities emit ~2000 Mg yr-1 of the toxic pollutant mercury (Hg) into the atmosphere, leading to long-range transport and deposition to remote ecosystems. Global anthropogenic emissions inventories report increases in Northern Hemispheric (NH) Hg emissions during the last three decades, in contradiction with the observed decline in atmospheric Hg concentrations at NH measurement stations. Many factors can obscure the link between anthropogenic emissions and atmospheric Hg concentrations, including trends in the re-emissions of previously released anthropogenic (“legacy”) Hg, atmospheric sink variability, and spatial heterogeneity of monitoring data. Here we assess the observed trends in gaseous elemental mercury (Hg0) in the NH and apply biogeochemical box modeling and chemical transport modeling to understand the trend drivers. Using linear mixed effects modeling of observational data from 51 stations, we find negative Hg0 trends in most NH regions, with an overall trend for 2005–2020 of ‑0.011 ± 0.006 ng m-3 yr-1 (±2 SD). In contrast to existing emission inventories, our modelling analysis suggests that NH anthropogenic emissions must have declined by at least 140 Mg yr-1 between the years 2005 and 2020 to be consistent with observed trends. Faster declines in 95th percentile Hg0 values than median values in Europe, North America, and East Asian measurement stations corroborate that the likely cause is a decline in nearby anthropogenic emissions rather than background legacy re-emissions. Our results are relevant for evaluating the effectiveness of the Minamata Convention on Mercury, demonstrating that existing emissions inventories are incompatible with the observed Hg0 declines.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".