Bioaccumulation of human pharmaceuticals in fish across habitats of a tidally influenced urban bayou
Bibliographic record
Abstract
Though pharmaceuticals and other contaminants of emerging concern are increasingly observed in inland water bodies, the occurrence and bioaccumulation of pharmaceuticals in estuaries and coastal ecosystems are poorly understood. In the present study, bioaccumulation of select pharmaceuticals and other contaminants of emerging concern was examined in fish from Buffalo Bayou, a tidally influenced urban ecosystem that receives effluent from a major (∼200 million gallons per day) municipal wastewater treatment plant in Houston, Texas, USA. Using isotope dilution liquid chromatography-tandem mass spectrometry, various target analytes were observed in effluent, surface water, and multiple fish species. The trophic position of each species was determined using stable isotope analysis. Fish tissue levels of diphenhydramine, which represented the only pharmaceutical detected in all fish species, did not significantly differ between freshwater and marine fish predominantly inhabiting benthic habitats; however, saltwater fish with pelagic habitat preferences significantly accumulated diphenhydramine to the highest levels observed in the present study. Consistent with previous observations from an effluent-dependent freshwater river, diphenhydramine did not display trophic magnification, which suggests site-specific, pH-influenced inhalational uptake to a greater extent than dietary exposure in this tidally influenced urban ecosystem. The findings highlight the importance of understanding differential bioaccumulation and risks of ionizable contaminants of emerging concern in habitats of urbanizing coastal systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".