Impact of Peat Extraction on Downstream Concentrations and Attenuation of Dissolved Organic Carbon and Nutrients
Bibliographic record
Abstract
Abstract Horticultural peat extraction can mobilize dissolved organic matter (DOM) and inorganic nutrients (nitrogen and phosphorous) to surface waters, harming aquatic ecosystems and water quality. However, it is uncertain how peat extraction affects solute concentration across hydrological and seasonal conditions and how biogeochemical processing in downstream drainage networks responds. Over two years, we used repeated, spatially extensive sampling in stream networks of two mixed land‐use catchments (<200 km2) on the subhumid interior plains of western Canada. We used random forest models to disentangle the effects of land cover, hydrology, and temperature on water chemistry. Peatlands were the dominant source of DOM to streams, but we detected no substantial effect of peat extraction on DOM concentration or composition. Stream discharge was the most important predictor of DOM composition, with generally humic‐like DOM becoming fresher during snowmelt and summer base flow. We detected no effect from peat extraction on soluble reactive phosphorous (SRP) or nitrate (NO3−). However, total ammonia nitrogen (TAN) was an order of magnitude higher in subcatchments with >40% extracted peatland cover (median: 1.5 mg TAN L−1) compared to catchments with similar intact peatland cover. Mass balance analysis suggested that DOM and inorganic nutrients synchronously attenuated during low flows. During high flows, DOM and inorganic nitrogen were conservatively transported, while SRP was attenuated, likely sorbing to suspended particles. Our study suggests that excess TAN mobilized by peat extraction is utilized in headwaters during low flow but propagates downstream during high flow, with implications for eutrophication that land managers should consider.
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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 | 0.001 |
| 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 | 0.000 |
| 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".