Arsenic binding to organic and inorganic sulfur species during microbial sulfate reduction: a sediment flow-through reactor experiment
Bibliographic record
Abstract
Environmental context The use of water contaminated with arsenic for drinking and irrigation is linked to water and food borne diseases throughout the world. Although reducing conditions in soils and sediments are generally viewed as enhancing arsenic mobility in subsurface environments, we show they can actually promote As sequestration in the presence of reduced sulfur species and labile organic matter. We propose that sulfurisation of organic matter and subsequent binding of As to thiol groups may offer an innovative pathway for As remediation. Abstract Flow-through reactors (FTRs) were used to assess the mobility of arsenic under sulfate reducing conditions in natural, undisturbed lake sediments. The sediment slices in the FTRs were supplied continuously with inflow solutions containing sulfate and soluble AsIII or AsV and, after 3 weeks, also lactate. The experiment ran for a total of 8 weeks. The dissolved iron concentration, pH, redox potential (Eh), as well as aqueous As and sulfur speciation were monitored in the outflow solutions. In FTRs containing surface sediment enriched in labile organic matter (OM), microbial sulfate reduction led to an accumulation of organically bound S, as evidenced by X-ray absorption spectroscopy. For these FTRs, the inflowing dissolved As concentration of 20 µM was lowered by two orders of magnitude, producing outflow concentrations of 0.2 µM monothioarsenate and 0.1 µM arsenite. In FTRs containing sediment collected at greater depth, sulfide and zero-valent S precipitated as pyrite and elemental S, while steady-state outflow arsenite concentrations remained near 5 µM. The observations thus suggest that As sequestration is enhanced when sediment OM buffers the free sulfide and zero-valent S concentrations. An updated conceptual model for the fate of As in the anoxic As–C–S–Fe system is presented based on the results of this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".