A review of long-term change in surface water natural organic matter concentration in the northern hemisphere and the implications for drinking water treatment
Bibliographic record
Abstract
Reduced atmospheric acid deposition has given rise to recovery from acidification - defined as increasing pH, acid neutralization capacity (ANC), or alkalinity in surface waters. Strong evidence of recovery has been reported across North America and Europe, driving chemical responses. The primary chemical responses identified in this review were increasing concentration and changing character of natural organic matter (NOM) towards predominantly hydrophobic nature. The concentration of NOM also influenced trace metal cycling as many browning surface waters also reported increases in Fe and Al. Further, climate change and other factors (e.g., changing land use) act in concert with reductions in atmospheric deposition to contribute to widespread browning and will have a more pronounced effect as deposition stabilizes. The observed water quality trends have presented challenges for drinking water treatment (e.g., increased chemical dosing, poor filter operations, formation of disinfection by-products) and many facilities may be under designed as a result. This comprehensive review has identified key research areas to be addressed, including 1) a need for comprehensive monitoring programs (e.g., larger timescales; consistency in measurements) to assess climate change impacts on recovery responses and NOM dynamics, and 2) a better understanding of drinking water treatment vulnerabilities and the transition towards robust treatment technologies and solutions that can adapt to climate change and other drivers of changing water quality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".