Evaluating the efficiency of chlorine removal from potable tap water using off-gassing, boiling, and filtration treatment methods
Bibliographic record
Abstract
Background: One of the most frequent complaints to water distribution systems is the taste and odor of chlorine in consumer tap water. Chlorine is a common disinfectant used to inactivate and breakdown microbes and other contaminants. However, excess chlorine can result in an unpalatable chlorinous taste and odor. When water taste becomes too objectionable, consumers may search for alternative water sources, such as raw, untreated water that does not contain chemical additives. Raw, untreated water contains various contaminants, including disease-causing pathogens. To encourage consumers to drink treated tap water, and prevent disease, this study evaluated and compared the effectiveness of off-gassing, boiling and filtration as dechlorination methods for consumers to perform on their tap water. Method: Hach Method 8021 was performed to collect and analyse water samples following treatment with Off-gassing, Boiling and Filtration. Water samples were collected from BCIT SW1-1230. The Hach Pocket Colorimeter ™ II determined the free chlorine concentration of the water samples, and compared to a sample of untreated chlorinated tap water to see which method reduced chlorine concentrations the most. Results: Mean concentration of chlorine following off-gassing was determined to be 0.51 ppm, 0.24 ppm following boiling, and 0.55 ppm following filtration. It was determined that the boiling method was statistically significantly different from the mean values of chlorine concentration from the other two methods, as shown by the Kruskal-wallis test (P=0.000), and therefore was the most effective in dechlorinating tap water samples. This was further confirmed by the Scheffe’s Mutliple-Comparison Test and eyeball test. Conclusion: Based on the results, boiling water is the most effective method to dechlorinate potable tap water for consumer acceptability. The free chlorine levels found post-boiling were also found to be below the WHO’s threshold for tasting and smelling chlorine in drinking water (0.3 ppm), and above WHO’s minimum required 0.2 ppm chlorine residual. Therefore, drinking water following boiling will be safe for consumption, as well as free of chlorinous taste and smell. Public Health professionals can safely advise consumers of an effective method to encourage treated tap water consumption, and to discourage finding alternative water sources.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".