Monitoring Adenosine Triphosphate Concentrations in a Chloraminated Drinking Water Distribution System for Risk and Asset Management
Bibliographic record
Abstract
Utilities rely on reliable and robust monitoring systems to inform decisions around asset operation and management in the drinking water distribution system (DWDS) to deliver high quality, biologically stable drinking water to consumers. However, traditional culture-based testing methods present challenges that make the timely detection of regrowth in the DWDS difficult. This study reports the results of an extensive adenosine triphosphate (ATP) monitoring campaign—a non-regulated parameter—in an urban, chloraminated drinking water system that analyzed over 5000 samples from two drinking water treatment plants (DWTPs), associated DWTP reservoirs, twelve outlying reservoirs and the DWDS between 2019–2022. ATP concentrations increased significantly between the two DWTP reservoirs and outlying reservoirs but decreased between the outlying reservoirs and DWDS samples. Relationships between ATP concentrations and other water quality variables varied depending on sampling location. Heterotrophic plate counts (HPC) were mainly non-detects (<1 CFU/mL) providing limited operational guidance compared to ATP. ATP concentrations exhibited temporal and spatial variation but did not exceed the proposed 10 pg/mL corrective action limit suggested by the manufacturer. ATP concentrations were also able to inform outlying reservoir management decisions. Monitoring ATP could serve as a useful indicator of biological stability in the DWDS for the utility of the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".