Arsenic removal by sand filtration for potable water in rural Newfoundland and Labrador
Bibliographic record
Abstract
The high concentration of arsenic in groundwater sources of Newfoundland has been reported by governmental departments and individual researchers. Arsenic uptake by human beings can cause the cancer of lungs, kidneys and skin. The National Health and Medical Research Council (2003), NHMRC, of Australia has set the maximum acceptable concentration (MAC) for arsenic in drinking water at 7 μg/L. The main objective of this research was to find an economical and sustainable water treatment method for rural Newfoundland. The locally available sand was used as a filter media to treat the groundwater without the aid of chemicals. Leaching column studies were conducted to determine the ability of sand to treat the water without exceeding the arsenic levels of 7 μg/L. 1400 mL of high strength Wabana water (As: 62.91 μg/L and Fe: 11825.84 μg/L) could easily be treated using a small sand filled column (6.7 cm x 6.7 cm (dia. x length)) as compared to the 4,000 mL and 10,500 mL for mixtures of the high strength and normal Wabana waters in the ratios of 1:1 and 1:3, respectively. Combining aeration and dilution, 9,000 mL and 18,000 mL of 1:1 and 1:3 mixtures, respectively, were treated without exceeding the arsenic limit of 7 μg/L. The Fe/As ratio was a major factor affecting arsenic adsorption and column tests were conducted with the high strength Wabana water mixed with Freshwater water in 1:10 (Mix-1), 2:10 (Mix-2) and 3:10 (Mix-3) proportions. The 2:10 mixture performed better than the Mix-1 or Mix-3 mixtures. The arsenic concentration after treating 39,000 ml was 8.735 μg/L for the Mix-2 water and 23.93 μg/L and 12.315 μg/L for the Mix-1 and Mix-3 waters, respectively. \n
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".