Impact and management of iron corrosion by-products on drinking water quality in distribution systems
Bibliographic record
Abstract
Cast iron pipes were installed broadly in North American water utilities. Many of these cast iron pipes are corroded and are continuous sources of Fe(II) ions in drinking water distribution systems. Recent studies have reported that soluble or particulate iron decreases water quality in distribution systems. \n\nIn this study, an array of bench scale experiments were conducted to evaluate the impact of most common water parameters (e.g., pH, PO4, Cl2, and DOM) on the oxidation rate of Fe(II) ions, and on the formation of iron suspense in synthetic water samples. This was accomplished using a 24 full factorial design approach at a 95% confidence level. This study demonstrated that a lower content of iron suspension color, turbidity, and smaller particle size would appear to be obtained in presence of a phosphate based corrosion inhibitor at a pH value of 6.5 compared to a pH value of 8.5.\n\nTo investigate the impact of Fe(II) ions, phosphate, pH and reaction time, and their interaction on DBPs formation in water samples, this study was conducted following an experimental design approach. Considering all the significant (α = 0.05, p < 0.05) factors, mathematical models for HAAs and THMs prediction were developed using 80 experiments. The models’ adequacy was checked thorough the statistical and graphical diagnostics. Different sources of natural water samples collected from three main water treatment plants in Halifax, Canada, were used to validate the models. This study suggested that the models’ performance were found to be excellent under a wide range of studied variables. Consequently, the most predominant iron oxides (goethite and magnetite) were used to investigate their impact on chlorine decay and DBPs formation study. Goethite and magnetite were also used for the adsorption of DBPs precursor (DOM). The DOM adsorption data illustrated to fit well with the Langmuir adsorption isotherm, indicating monolayer coverage. Molecular weight (MW) distributions of DBPs precursor (DOM) revealed that the higher molecular weight fractions adsorbed preferentially onto goethite followed by magnetite surface. The change of MW distribution of DOM was found to be in reasonable agreement with the change of DBPs formation in iron-water systems.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".