Long-Term Wet and Dry Deposition of Total and Methyl Mercury in the Remote Boreal Ecoregion of Canada
Bibliographic record
Abstract
Although a positive relationship between atmospheric loadings of inorganic mercury (Hg(II)) to watersheds and concentrations of methyl mercury (MeHg) in fish has now been established, net wet and dry deposition of Hg(II) and MeHg to watersheds remains challenging to quantify. In this study, concentrations and loadings of total mercury (THg; all forms of Hg in a sample) and MeHg in open area wet deposition, throughfall, and litterfall were quantified atthe remote Experimental Lakes Area in the boreal ecoregion, NW Ontario, Canada. Between 1992 and 2006, mean annual THg and MeHg loadings in the open were 36 +/- 17 and 0.5 +/- 0.2 mg ha(-1), respectively. Throughfall THg and MeHg loadings were generally 2-4 times and 0.8-2 times higher, respectively, than loadings in the open. Loadings of both THg and MeHg were highest under an old growth spruce/fir canopy and lowest under a deciduous maple canopy, whereas loadings under young jack pine and wetland spruce/pine/alder canopies were intermediate. Litterfall generally represented the largest input of THg (86-105 mg ha(-1)) and MeHg (0.7-0.8 mg ha(-1)) to the landscape on an annual basis. Using the "direct" method of estimating dry deposition (thoughfall + litterfall - open loadings), we calculated that annual dry deposition of THg and MeHg under forest canopies ranged from 105 to 201 mg ha(-1), whereas dry deposition of MeHg ranged from 0.7 to 1.2 mg ha(-1). Photoreduction and emission of wet-deposited Hg(ll) from canopy foliage were accounted for, resulting in 3-5% (5-6 mg ha(-1)) higher annual estimates of dry deposition than via the direct method alone. NetTHg and MeHg loadings to this remote landscape were lower than at any other previously studied forested site globally. This study shows that THg and MeHg loading can be extremely variable within a heterogeneous boreal landscape and that processes such as Hg photoreduction and emission from foliage should be considered when estimating dry deposition of Hg.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".