Effect of easily biodegradable organic compounds on bacterial growth in a bench-scale drinking water distribution system
Bibliographic record
Abstract
Many engineered (e.g., disinfectant residual concentration) and environmental (e.g., temperature) factors influence bacterial regrowth in drinking water distribution systems. This paper examines the effect of nutrients, specifically biodegradable organic matter (BOM) composition, BOM concentration, and hydraulic retention time on bacterial growth in an annular reactor (AR). Drinking water that had an alkalinity of 300 mg/L as CaCO3and a free chlorine residual of approximately 0.2 mg/L was used as process water in the ARs. Prior to entering the ARs, the water was filtered through granular activated carbon (GAC) to remove background chlorine and background organic matter. A cocktail of easily biodegradable organic compounds consisting of carboxylic acids, aldehydes, and free amino acids were spiked into the ARs as the primary carbon source. It was found that the influent BOM concentration (p value = 0.013) and the presence of free amino acids in the BOM cocktail (p value = 0.009) significantly increased the number of viable culturable cells in the biofilm, as measured by heterotrophic plate counts (HPCs). The interaction between the BOM concentration and the presence of amino acids also significantly increased the number of biofilm HPCs (p value = 0.021). Alternatively, the BOM concentration and the amino acid fraction did not affect the number of bulk (i.e., suspended) bacteria. The number of biofilm HPCs in the reactor was approximately 10 times greater than the number of bulk HPCs at high influent BOM concentrations and low retention times (i.e., high BOM loading rates). At low loading rates, the ratio of number of biofilm to bulk cells was less than 2. Consequently, it was deduced that the BOM was utilized predominately by the biofilm cells. This indicates that removal of easily biodegradable organic compounds is an important factor for controlling biofilm growth in distribution systems.Key words: drinking water, distribution systems, biofilm, annular reactor, regrowth.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".