Prevalence and Detection of Pharmaceuticals in Hospital Wastewater: A Case of Referral and District Hospitals in Zanzibar
Bibliographic record
Abstract
Despite the great efforts taken by the developed and some developing countries in managing the release of pharmaceuticals from their primary sources to the environment; the pharmaceutical wastewater management in Zanzibar-Tanzania still has not received much attention as a bulk of pharmaceuticals and their metabolites are released to the environment without proper treatment. Therefore, this study has scrutinized the incidence of pharmaceuticals in wastewater released from the referral and district hospitals in Zanzibar, to ascertain the levels of pharmaceuticals present in hospital wastewater. Purposive sampling was implemented to collect a total of seventy-two (72) wastewater samples in a period of six months (March-August 2022). Samples were collected in the effluent of wastewater-streams (Mnazi Mmoja Hospital, MMH) and pit latrines (Kivunge District Hospital (KDH) and Makunduchi District Hospitals (MDH)). The pharmaceuticals in these samples were obtained via the solid phase extraction after which they were analyzed using the LC-tandem MS (Agilent 1,290 LC coupled to 6,460-triple quadrupole MS). The limit of detections (LODs) and limit of quantitations (LOQs) for the determination of pharmaceuticals were in the range of 0.021-0.037 μg L-1 and 0.033-0.059 μg L-1, respectively. The detected analytes belonged to antibiotics, anti-inflammatory, benzodiazepine, antipsychotic, antipyretic and anticonvulsant (anti-epileptic) drugs. Diclofenac (DIC), paracetamol (PCT), ciprofloxacin (CIP), sulfamethoxazole (SMZ) and azithromycin (AZM) were detected at higher concentrations (> 0.25 μg L-1) in wastewater samples collected from KDH and MDH. Lower concentrations of pharmaceuticals (< 0.10 µg L-1) were identified in MMH wastewater samples. These present findings provide estimable information on the incidence of pharmaceuticals in wastewater that can assist in strengthening the environmental strategies for the protection of marine and terrestrial life from pharmaceutical pollution in Zanzibar.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".