Variations of disinfection by-product levels in small drinking water utilities according to climate change scenarios: a first assessment
Bibliographic record
Abstract
In Québec, Canada, shifts in climate patterns (i.e., rainfall increase) could have consequences on source water quality due to the intensification of surface/groundwater runoff contamination events, leading to a decline in drinking water treatment efficiency and ultimately disinfection by-products (DBPs) formation following treatment. To assess the impacts of climate change (CC) scenarios on DBP formation, a suite of models linking climate to DBPs was used. This study applies three emissions scenarios (B1, A1B and A2) for three 30-year horizons (2020, 2050 and 2080) in order to produce inputs to test several DBP models (total trihalomethanes (TTHMs), haloacetic acids and haloacetonitriles). An annual increase is estimated for all DBPs for each CC scenario and horizon. The highest seasonal increases were estimated for winter for all DBP groups or species. In the worst-case scenario (A2-2080), TTHMs could be affected more particularly during winter (+34.0%), followed by spring (+16.1%) and fall (+4.4%), whereas summer concentrations would remain stable (−0.3 to +0.4%). Potentially, small water utilities applying only a disinfection step could be more affected by rising TTHMs concentrations associated with CC than those having implemented a complete water treatment process (coagulation–flocculation, filtration and disinfection) with +13.6% and +8.2% increases respectively (A2-2080).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".