Arsenic Bioremediation by Biogenic Iron Oxides and Sulfides
Bibliographic record
Abstract
Microcosms containing sediment from an aquifer in Cambodia with naturally elevated levels of arsenic in the associated groundwater were used to evaluate the effectiveness of microbially mediated production of iron minerals for in situ As remediation. The microcosms were first incubated without amendments for 28 days, and the release of As and other geogenic chemicals from the sediments into the aqueous phase was monitored. Nitrate or a mixture of sulfate and lactate was then added to stimulate biological Fe(II) oxidation or sulfate reduction, respectively. Without treatment, soluble As concentrations reached 3.9 ± 0.9 μM at the end of the 143-day experiment. However, in the nitrate- and sulfate-plus-lactate-amended microcosms, soluble As levels decreased to 0.01 and 0.41 ± 0.13 μM, respectively, by the end of the experiment. Analyses using a range of biogeochemical and mineralogical tools indicated that sorption onto freshly formed hydrous ferric oxide (HFO) and iron sulfide mineral phases are the likely mechanisms for As removal in the respective treatments. Incorporation of the experimental results into a one-dimensional transport-reaction model suggests that, under conditions representative of the Cambodian aquifer, the in situ precipitation of HFO would be effective in bringing groundwater into compliance with the World Health Organization (WHO) provisional guideline value for As (10 ppb or 0.13 μM), although soluble Mn release accompanying microbial Fe(II) oxidation presents a potential health concern. In contrast, production of biogenic iron sulfide minerals would not remediate the groundwater As concentration below the recommended WHO limit.
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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) | 0.000 | 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".