Methylmercury Export From a Headwater Peatland Catchment Decreased With Cleaner Emissions Despite Opposing Effect of Climate Warming
Bibliographic record
Abstract
Abstract Peatlands are sources of bioaccumulating neurotoxin methylmercury (MeHg) that is linked to adverse health outcomes. Yet, the compounding impacts of climate change and reductions in atmospheric pollutants on mercury (Hg) export from peatlands are highly uncertain. We investigated the response in annual flow‐weighted concentrations (FWC) and yields of total‐Hg (THg) and MeHg to cleaner air and climate change using an unprecedented hydroclimatic (55‐year; streamflow, air temperature, precipitation, and peatland water tables), depositional chemistry (21‐year; Hg and major ions), and streamwater chemistry (∼17‐year; THg, MeHg, major ions, total organic carbon, and pH) data sets from a reference peatland catchment in Minnesota, USA. Over the hydroclimatic record, annual mean air temperature increased by ∼1.8°C, while baseflow and the efficiency that precipitation was converted to runoff (runoff ratio) decreased. Concurrently, precipitation‐based deposition of sulfate and Hg declined, where wet Hg deposition declined by ∼3–4 μg Hg m−2. Despite declines in wet Hg deposition over the study period, the catchment accumulated on average 0.04 ± 0.01 g Hg ha−1 yr−1 based on wet Hg deposition minus THg yield alone. Annual MeHg FWC was positively correlated with mean annual air temperatures (p = 0.03, r = 0.51), runoff ratio (p < 0.0001, r = 0.76), and wet Hg deposition concentration (p < 0.0001, r = 0.79). Decreasing wet Hg deposition and annual runoff ratios counterbalanced increased peatland MeHg production due to higher air temperatures, leading to an overall decline in streamwater MeHg FWC. Streamwater MeHg export may continue to decrease only as long as declines in runoff ratio and wet Hg deposition persistently outpace effects of increased air temperature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".