Climate-Driven Increases in Source Water Natural Organic Matter: Implications for the Sustainability of Drinking Water Treatment
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This study presents an updated analysis spanning over two decades (1999–2023) of climate, water quality, and operational data from two drinking water facilities in Atlantic Canada that previously experienced gradual increases in the natural organic matter (NOM) concentration and brownification. The goal was to assess the impact of recent extreme weather events on acute NOM concentration increases and drinking water treatment processes. In 2023, a dry spring combined with a warm and wet summer caused NOM in the water supplies to increase by >67% (as measured by color). To mitigate increased NOM concentration, the alum dose nearly doubled in 2023 compared to that in 2022. Disinfection byproducts were elevated following the event but remained within the compliance levels. From 1999 to 2023, the two plants responded to gradual climate change impacts and brownification, with alum dose increases of between 4.1 and 8.3 times. Equivalent CO 2 emissions were estimated for alum usage, which increased by 3 to 7-fold in 2023 compared to when the plants were commissioned decades prior. The plants were not only adversely impacted by climate change but also contributed to the global CO 2 burden. Thus, a paradigm shift toward sustainable alternatives for NOM removal is required in the water sector, and climate change adaptation and mitigation principles are urgently needed.
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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) | 0.002 | 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".