The degradation behaviour of nine diverse contaminants in urban surface water and wastewater prior to water treatment
Bibliographic record
Abstract
An increasing diversity of emerging contaminants are entering urban surface water and wastewater, posing unknown risks for the environment. One of the main contemporary challenges in ensuring water quality is to design efficient strategies for minimizing such risks. As a first step in such strategies, it is important to establish the fate and degradation behavior of contaminants prior to any engineered secondary water treatment. Such information is relevant for assessing treatment solutions by simple storage, or to assess the impacts of contaminant spreading in the absence of water treatment, such as during times of flooding or in areas of poor infrastructure. Therefore in this study we examined the degradation behavior of a broad array of water contaminants in actual urban surface water and wastewater, in the presence and absence of naturally occurring bacteria and at two temperatures. The chemicals included caffeine, sulfamethoxazole, carbamazepine, atrazine, 17β-estradiol, ethinylestradiol, diclofenac, desethylatrazine and norethindrone. Little information on the degradation behavior of these pollutants in actual influent wastewater exist, nor in general in water for desethylatrazine (a transformation product of atrazine) and the synthetic hormone norethindrone. Investigations were done in aerobic conditions, in the absence of sunlight. The results suggest that all chemicals except estradiol are stable in urban surface water, and in waste water neither abiotic nor biological degradation in the absence of sunlight contribute significantly to the disappearance of desethylatrazine, atrazine, carbamazepine and diclofenac. Biological degradation in wastewater was effective at transforming norethindrone, 17β-estradiol, ethinylestradiol, caffeine and sulfamethoxazole, with measured degradation rate constants k and half-lives ranging respectively from 0.0082-0.52 d(-1) and 1.3-85 days. The obtained degradation data generally followed a pseudo-first-order-kinetic model. This information can be used to model degradation prior to water treatment.
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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) | 0.001 | 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".