Assessing nutrient and pharmaceutical removal efficiency from wastewater using shallow wetland treatment mesocosms
Bibliographic record
Abstract
Wastewaters from rural sewage lagoons in Manitoba contain pharmaceuticals that are potentially harmful to non-target organisms and reduce overall water quality when released. An option for reducing exposure to wastewater contaminants and potential toxicity is surface flow treatment wetlands. However, little is known of the fate of pharmaceuticals in these types of systems. The fate and effects of six pharmaceuticals (carbamazepine, clofibric acid, fluoxetine, naproxen, sulfamethoxazole, sulfapyridine) were assessed in mesocosms simulating treatment wetlands in two separate 28-day experiments in the summer and fall of 2011, respectively: with and without significant aquatic plant communities, and with additional nutrients and harvesting of biomass. The removal of pharmaceuticals had half-lives that ranged from 0.23 to 9.4 days and 1.4 to 18 days during the summer and fall, respectively, and were predicted to occur primarily through photolysis and sorption. No overt toxicity from pharmaceuticals was observed for the common wetland macrophytes Myriophyllum sibiricum and Typha spp., but there was partitioning and bioaccumulation into macrophyte biomass. Treatment wetlands appeared to reduce pharmaceuticals and nutrients adequately, and may be a cost-effective means of treating rural wastewater.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".