Defluoridation of fluoride-rich groundwater in Mayo Tsanaga River Basin-Cameroon using locally produced bone char
Bibliographic record
Abstract
With fluoride-rich groundwater causing a climatic-dependent fluorosis in Mayo-Tsanaga River Basin, the overall objective of this study was to reduce fluoride concentrations in drinking water to acceptable levels thereby improving the resilience of the population to this climate change induced pathology. The specific objectives were to: (1) assess water chemistry in the study area to re-affirm the undesirable fluoride levels; (2) assess the impact of seasons on the concentrations of fluoride; (3) construct and evaluate the performance of a household bone char-based adsorption defluoridation filter. A combination of hydrogeochemical and engineering analyses demonstrated that the groundwater is predominantly Ca+Mg-HCO3 type, which contains as much as 6.73 mg/l of undesirable concentrations of geogenic fluoride. These concentrations increased with elevated pH, electrical conductivity and in the dry season, and were reduced to less than 0.2 mg/l when the groundwater was subjected to filtration through 300 g of 0.2-0.8 mm faction of charred cow bones in a home-based defluoridation filter. The bone char in the filter can effectively reduce fluoride concentration to less than 0.7 mg/l, which is the local threshold limit, without negative impact on the organoleptic (taste, color and odor) characteristics of drinking water. Compared with the commercially activated carbon, the bone char has an additional capacity of adsorbing fluoride at a rate of 4 mg/liter in 30 minutes, which indicates that with a defined saturation time, the bone char filter can protect the population against climate change-induced fluoride enrichment in drinking water.Keywords: Groundwater. geogenic fluoride. climate dependent fluorosis. bone char defluoridation. water chemistry
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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".