Modeling the global atmospheric transport and deposition of mercury to the Great Lakes
Bibliographic record
Abstract
Abstract Mercury contamination in the Great Lakes continues to have important public health and wildlife ecotoxicology impacts, and atmospheric deposition is a significant ongoing loading pathway. The objective of this study was to estimate the amount and source-attribution for atmospheric mercury deposition to each lake, information needed to prioritize amelioration efforts. A new global, Eulerian version of the HYSPLIT-Hg model was used to simulate the 2005 global atmospheric transport and deposition of mercury to the Great Lakes. In addition to the base case, 10 alternative model configurations were used to examine sensitivity to uncertainties in atmospheric mercury chemistry and surface exchange. A novel atmospheric lifetime analysis was used to characterize fate and transport processes within the model. Model-estimated wet deposition and atmospheric concentrations of gaseous elemental mercury (Hg(0)) were generally within ∼10% of measurements in the Great Lakes region. The model overestimated non-Hg(0) concentrations by a factor of 2–3, similar to other modeling studies. Potential reasons for this disagreement include model inaccuracies, differences in atmospheric Hg fractions being compared, and the measurements being biased low. Lake Erie, downwind of significant local/regional emissions sources, was estimated by the model to be the most impacted by direct anthropogenic emissions (58% of the base case total deposition), while Lake Superior, with the fewest upwind local/regional sources, was the least impacted (27%). The U.S. was the largest national contributor, followed by China, contributing 25% and 6%, respectively, on average, for the Great Lakes. The contribution of U.S. direct anthropogenic emissions to total mercury deposition varied between 46% for the base case (with a range of 24–51% over all model configurations) for Lake Erie and 11% (range 6–13%) for Lake Superior. These results illustrate the importance of atmospheric chemistry, as well as emissions strength, speciation, and proximity, to the amount and source-attribution of mercury deposition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".