Reactive Oxygen Species Production in Riparian Zones Governed by a Flow-Induced Chromatographic Separation Process
Bibliographic record
Abstract
Riparian zones are natural hotspots for reactive oxygen species (ROS) generation, yet the spatiotemporal dynamics of ROS within these zones remain poorly understood. In this study, we combine results from a flume experiment and reactive transport modeling to show that H 2 O 2 production is governed by a “chromatographic” separation process in which water flow modulates the interplay of O 2 and reductants. The riparian aquifer matrix acts as the stationary phase hosting various mobile and immobile reductants, while water flow serves as the mobile phase supplying oxidants, here dissolved oxygen (DO) and nitrate, during surface water inflow and flushing out of the mobile reductant species during flow reversal. The preferential consumption of DO by the reductants near the up-gradient boundary during surface water intrusion generates H 2 O 2, whereas less reactive oxidants like nitrate are transported further into the riparian aquifer, where they consume the reductants that have not reacted with DO. Although the inflow of nitrate reduces the overall ROS production capacity, it enables deeper DO penetration, hence expanding the ROS production area. The resulting coupling between hydrodynamic solute transport and biogeochemical redox reactions regulates the spatial separation and temporal evolution of H 2 O 2 across the simulated riparian aquifer. Overall, our study advances the mechanistic understanding of ROS dynamics in riparian zones with implications for redox-mediated contaminant attenuation and carbon cycling at the groundwater–river interface.
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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 | 0.000 |
| 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".