Towards a Predictive Model for Initial Chlorine Dose in Humanitarian Emergencies
Bibliographic record
Abstract
Free chlorination is a widely employed disinfection method in humanitarian water provision due to its many advantages. However, its effective application is hindered by the challenge in determining adequate initial doses to achieve free chlorine residuals that satisfy both health and aesthetic requirements. Current guidelines show varying recommended dosing strategies, and many do not adequately consider chlorine decay mechanisms that occur during water storage. Even though turbidity is commonly used as a criterion for deciding chlorine dose, it may not be an adequate proxy for the water quality in many cases. This paper addresses the fundamental relationships between chlorine decay kinetics and selected key water parameters (i.e., natural organic matter, water temperature, chlorine demand) by conducting chlorine decay tests in controlled conditions and in jerrycans (i.e., simulating humanitarian water treatment conditions). Chlorine decay constant from the Feben and Taras’s empirical model and first order model formed linear and exponential relationships with two water parameters (UVA254 and 30-min chlorine demand). With these relationships, the two chlorine decay models can be calibrated quickly and frequently in the field, allowing effective determination of initial chlorine dose. These two models calibrated based on the suggested water parameters from the study could predict chlorine decay in water having a main chlorine demand-inducing constituents as natural organic matter. However, they underpredicted chlorine decay in surface water with additional chlorine reactants. Further research on additional chlorine decay mechanisms is needed to expand the applicability of the models.
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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) | 0.001 | 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 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".