Linking drugs of abuse in wastewater to contamination of surface and drinking water
Bibliographic record
Abstract
The concentrations of 17 drugs of abuse, including cocaine, several amphetamines, opioid drugs, and 2 metabolites--benzoylecgonine, a metabolite of cocaine, and 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrolidine, a metabolite of methadone--were investigated in an urban watershed that is heavily impacted by discharges of municipal wastewater. The artificial sweetener sucralose was also monitored as a persistent tracer of contamination from municipal wastewater. Monitoring was conducted in a municipal wastewater treatment plant (WWTP) and at sites upstream and downstream of the WWTP discharge, as well as in a drinking water treatment plant (DWTP) located 19 km downstream of the WWTP discharge that withdraws raw water from the river. Drug concentrations were monitored with polar organic chemical integrative samplers deployed for 2 wk in the river and in the WWTP and DWTP. Several of the investigated compounds exhibited a decrease in concentration with distance downstream from the wastewater discharge into the river, but there was little attenuation of sucralose, cocaine, benzoylecgonine, morphine, acetylmorphine, acetylcodeine, and oxycodone. Heroin and methadone were not detected at any sample locations. Amphetamine, methamphetamine, 3,4-methylenedioxy-methamphetamine, and 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrolidine were not detected in the samples collected at the drinking water intake. Many of the drugs of abuse were not removed effectively in the DWTP, including cocaine, benzoylecgonine, methylenedioxyamphetamine, ephedrine, and several prescription opioids, most probably because the DWTP was operating at or above its rated treatment capacity. These data indicate that there can be transport of drugs of abuse from wastewater sources into drinking water in urban watersheds.
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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 | 0.001 | 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".