Toward an Assessment of the Global Inventory of Present-Day Mercury Releases to Freshwater Environments
Bibliographic record
Abstract
Aquatic ecosystems are an essential component of the biogeochemical cycle of mercury (Hg), as inorganic Hg can be converted to toxic methylmercury (MeHg) in these environments and reemissions of elemental Hg rival anthropogenic Hg releases on a global scale. Quantification of effluent Hg releases to aquatic systems globally has focused on discharges to the global oceans, rather than contributions to freshwater systems that affect local exposures and risks associated with MeHg. Here we produce a first-estimate of sector-specific, spatially resolved global aquatic Hg discharges to freshwater systems. We compare our release estimates to atmospheric sources that have been quantified elsewhere. By analyzing available quantitative and qualitative information, we estimate that present-day global Hg releases to freshwater environments (rivers and lakes) associated with anthropogenic activities have a lower bound of ~1000 Mg· a-1. Artisanal and small-scale gold mining (ASGM) represents the single largest source, followed by disposal of mercury-containing products and domestic waste water, metal production, and releases from industrial installations such as chlor-alkali plants and oil refineries. In addition to these direct anthropogenic inputs, diffuse inputs from land management activities and remobilization of Hg previously accumulated in terrestrial ecosystems are likely comparable in magnitude. Aquatic discharges of Hg are greatly understudied and further constraining associated data gaps is crucial for reducing the uncertainties in the global biogeochemical Hg budget.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".