Global stocks and release pathways of pollutants in peatlands
Bibliographic record
Abstract
Peatlands have been widely recognised as important carbon stores, ecological habitats and natural hydrological buffers. However, comparatively less attention has been given to the role of peatlands as long-term stores of pollutants, particularly toxic metals and metalloids (TMMs). Furthermore, the potential for their release is poorly understood. An improved understanding of TMM distribution and release in peatlands is critical, because climate warming risks increasing their mobilisation, through enhanced decomposition and changes to hydrological processes, with potentially significant implications for natural ecosystems and human health. The PIPES project (Pollutants In Peatlands: from sink to Source) aims to identify global “hot spots” of peatland pollutants and establish likely release mechanisms of currently inert TMMs. We use a unique combination of observational and controlled-experimental approaches to address two research questions: (1) What is the content and distribution of pollutants in global peatlands? and (2) Under what conditions, and through which pathways, are these pollutants most likely to be released? In this presentation, we share early findings from both components of the PIPES project. Firstly, we present our ongoing analysis of the distribution of TMMs in global peatlands, with a primarily focus on spatial patterns identified across our comprehensive network of sites in the UK and Ireland. We quantify the total content of TMMs using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) in peat cores compiled by a network of > 90 international collaborators. Secondly, we present preliminary results from controlled environmental simulations of TMM release in peat monoliths from subarctic Sweden. We explore both pore-water and atmospheric release under scenarios of drought, climate warming and a shallow burn. Our findings provide crucial new insights into the potential fate of pollutants in global peatlands and their implications for human health and natural ecosystems.
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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 | 0.000 | 0.001 |
| 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".