Groundwater nitrate contamination: Assessment and treatment using <i>Moringa oleifera</i> Lam. seed extract and activated carbon filtration
Bibliographic record
Abstract
The present research evaluates the groundwater nitrate concentration of the Maringá region (Paraná State, Brazil) aiming at nitrate removal by coagulation/flocculation using a natural coagulant obtained from the seeds of the Moringa oleifera Lam. plant (MO) combined with an activated carbon filter. Groundwater samples were collected in Maringá City metropolitan area and characterized by physicochemical analyses. The results showed groundwater NO3− concentrations ranging from 0–60 mg NO3− L−1. Thus, the nitrate variation in the water used in the coagulation/flocculation assays lay between 10–60 mg · L−1 of NO3−. In these assays both aqueous and saline MO raw extracts were added (concentrations between 0.25–11 g · L−1). Coagulation/flocculation treatment with aqueous MO extract presents nitrate removal of 73.3 %, and saline MO extract presents 85.4 %. The source that presented the highest nitrate concentration along with the ideal MO concentration in the coagulation/flocculation process was used for the coagulation/flocculation process followed by activated carbon filtration. The treatment with MO combined with the filter met Brazilian, American, and international potability standards for nitrate and turbidity. The present study reveals the presence of nitrate in the groundwater of the area and also enables new procedures for treating this kind of water in small rural properties and needy communities.
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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".