A systematic review of the water treatment sludge toxicity to terrestrial and aquatic biota: state of the art and management challenges
Bibliographic record
Abstract
Safe drinking water' supply is an essential service and depends directly on the water treatment that produces water treatment sludge (WTS) as a product, whose final destination varies and remains a challenge. The ecotoxicity assessment of the WTS address the ecological implications of the WTS disposal but these information is still scarce in the literature. In this sense, we did a systematic review of the ecotoxicological studies on WTS using databases from six platforms. From the 785 papers recovered; 16 studies were eligible and showed the ecotoxicity assays' applicability to evaluate the WTS. We discussed WTS ecotoxicity considering sample characterization; terrestrial and aquatic toxicity assays; and WTS challenges. WTS proved to be a highly heterogeneous matrix composed mainly of coagulant precipitates, including Al and Fe. Studies lack consensus concerning the most representative/sensitive species for evaluating WTS' toxicity. Crustaceans were the most studied aquatic group, although algae species were more sensitive. Besides, soil ecotoxicity assessed only plant growth, and a single study used the earthworm. Even papers used bioassays to indicate the recycling WTS' feasibility, there is a lack of specific legislation regarding the WTS reuse. Furthermore, are necessary a regulation for WTS management that involves an ecological risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".