Investigation of opportunistic pathogens in municipal drinking water under different supply and treatment regimes
Bibliographic record
Abstract
Changing regulations to lower disinfectant byproducts in drinking water is forcing utilities to switch disinfection from chlorine to monochloramine. It is generally unknown whether this will impact positively or negatively on the microbiological quality of drinking water. A utility in Florida, using water with relatively high organic carbon levels from deep wells in several wellfields, made the decision to change its disinfection regime from chlorine to chloramine in order to meet the new regulations. To assess the impacts of such a change on the microbiology of its water supplies, it undertook a number of studies before and after the change. In particular, the presence of the opportunistic pathogens Legionella and Mycobacterium, and also the composition of drinking-water biofilms, were examined. A preliminary synthesis and summary of these results are presented here. Legionella species were widely distributed in source waters and in the distribution system when chlorine was the disinfectant. In some samples they seemed to be among the dominant biofilm bacteria. Following the change to monochloramine, legionellae were not detected in the distribution system during several months of survey; however, they remained detectable at point of use, although with less species diversity. A variety of mycobacteria (21 types) were widely distributed in the distribution system when chlorine was the disinfectant, but these seemed to increase in dominance after chloramination was instituted. At point of use, only four species of mycobacteria were detected. Other changes occurring with chloramination included (a) an altered biofilm composition, (b) increased numbers of total coliforms and heterotrophs and (c) nitrification of water storage tanks. The results suggested that consideration should be given to the microbiological effects of changing disinfection regimes in drinking-water and distribution system biofilms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".