Amino Acids as Potential Precursors to Odorous Compounds in Tap Water during Spring Runoff Events
Bibliographic record
Abstract
The onset of spring runoff in northern climates and tap water odor events are difficult to predict because common water quality parameters cannot fully explain the intermittent odor events that occurred over past decades. Studies have shown that small polar water-soluble compounds, such as amino acids (AAs), leach first from ice/snowmelt. AAs are known to produce odorous compounds, such as aldehydes and chloroaldimines, upon chlorination. Therefore, we proposed that AAs may serve as markers for small and soluble organics that contribute to the odor of chlorinated tap water. Here, we studied the occurrence of AAs in source water collected at two water treatment plants and the odor profiles of tap water at >300 homes during the 2021 and 2022 spring runoff events. AA concentrations were at baseline levels (<100 ng/L) during the 2021 runoff but much higher (up to 5500 ng/L) in 2022 and associated with an escalation in odor complaints. AA concentrations peaked at the onset of the 2022 spring runoff and corresponded with the strongest reported odor intensities in tap water. We obtained high resolution MS and MS/MS spectra of chloroaldimines and confirmed the formation of chloroaldimines under chlorination of the six AAs detected in source water. The results indicate that AAs signal the onset of spring runoff and represent small polar water-soluble compounds that may contribute to tap water odor problems.
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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 | 0.000 |
| 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 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".