Arsenic removal using oxidative media and nanofiltration
Bibliographic record
Abstract
Nanofiltration (NF) is a promising drinking water treatment technology for arsenic removal; however, most of the research on NF treatment of arsenic has used synthetic water. In this investigation, a pilot membrane system treated groundwater naturally contaminated with arsenic to test the performance of two NF membranes and one reverse osmosis (RO) membrane, both with and without oxidizing pretreatment using manganese dioxide (MnO 2 ). The arsenic concentration in the groundwater was ~ 40 μg/L, mostly present as arsenite, a neutral species. Without the oxidizing pretreatment, the two NF membranes provided almost no removal whereas the RO membrane provided ~ 25 to 50% arsenic removal, depending on operating conditions. Following pretreatment with MnO 2 , the treated arsenic concentration dropped to < 4 μg/L (90% removal) for all three membranes. The substantially improved performance for these negatively charged membranes was attributed to the oxidation of the neutrally charged arsenite to negatively charged arsenate. These results indicate that where arsenite is present, facilities with RO or NF processes can dramatically enhance their arsenic removal by adding a membrane‐compatible oxidation step such as MnO 2 filtration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".