Climate Change and Cleaner Emissions Decrease Methylmercury Export from a Headwater Peatland Catchment
Bibliographic record
Abstract
Peatlands are sources of the bioaccumulating neurotoxin methylmercury (MeHg) and linked to adverse health outcomes, yet the impact of climate change and reductions in atmospheric pollutants on mercury (Hg) export from peatlands are highly uncertain. Here, we present 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-years; streamflow, air temperature, precipitation, regional and peatland water tables), depositional chemistry (21-years; Hg and major ions concentration and total mass), and streamwater chemistry (~17-years; THg, MeHg, major ions, total organic carbon, and pH) datasets from a reference peatland catchment in the north central USA. Over the hydroclimatic record, annual mean air temperature increased by ~1.8 ℃, decreasing baseflow and, subsequently, the efficiency that precipitation was converted to streamwater runoff (runoff ratio). Concurrently, precipitation-based deposition of sulfate and Hg declined, where wet Hg deposition rates declined to near pre-industrial levels. Annual MeHg FWC was positively correlated mean annual air temperatures (p=0.03, r=0.51), annual runoff ratio (p<0.0001, r=0.76), and wet Hg deposition concentration (p<0.0001, r=0.79). Over the study period, decreasing wet Hg deposition concentration and annual runoff ratios counterbalanced increased peatland MeHg production due to higher air temperatures, leading to an overall decline in streamwater MeHg FWC. Climate change and cleaner air were responsible for 0.51 and 0.32 of the variability in MeHg FWC, respectively. Streamwater MeHg export may continue to decrease only if declines in runoff ratio and wet Hg deposition concentration persistently outpace 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".