Potassium modulating aquatic microbial processes: dynamics of nitrogen cycling and nitrous oxide
Bibliographic record
Abstract
Nitrous oxide (N 2 O) emissions from aquatic ecosystems, governed by microbial nitrogen cycling, significantly contribute to greenhouse gases (GHGs). Although the role of nitrogen and carbon in this process are well-documented, the influence of potassium has remained under-reviewed despite its ecological abundance and physiological importance. This review synthesizes the central yet complex role of potassium in regulating aquatic N 2 O fluxes. Potassium acts through two key mechanisms: serving as an essential cofactor for enzymes such as nitrate reductase and nitrous oxide reductase, and modulating the expression of denitrification genes (e.g., nirS, nirK, and nosZ). This dual regulatory role allows potassium to influence the efficiency of nitrification and denitrification pathways, often determining the critical N 2 O:N 2 product ratio. We emphasize the paradoxical nature of potassium: it can stimulate N 2 O production under high nitrate conditions, notably by enhancing l-arginine metabolism which facilitates the synthesis of nitric oxide via nitric oxide synthases, while also promoting the reduction of N 2 O by enzymatic processes to N 2 under optimal availability. The net effect depends on environmental co-factors like pH, temperature, and oxygen levels. Given increasing anthropogenic inputs from agricultural runoff, potassium must be integrated as a key predictive variable in nitrogen cycle models. This review offers a comprehensive mechanistic perspective of potassium’s role in microbial nitrogen cycling and its implications for GHG emissions, positioning it as a novel tool in the nexus of achieving climate and water quality goals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".