A critical review of the occurrence, detection, and treatment of Δ<sup>9</sup>-tetrahydrocannabinol in aquatic environments
Bibliographic record
Abstract
The main psychoactive compound in marijuana, Δ9-tetrahydrocannabinol (THC), and its metabolites are emerging organic contaminants that have been detected in waste and surface waters around the globe. This paper aims to review the recent literature regarding the detection, occurrence, and treatment of THC and its main metabolites, 11-hydroxy-Δ9-tetrahydrocannabinol (THC-OH), and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH). Detection methods for THC, THC-OH, THC-COOH, and other cannabinoids have advanced in recent years with the development of sensitive analytical techniques (e.g., gas chromatography or liquid chromatography with mass spectrometry). Using online or offline solid phase extraction (SPE) has enabled detection at nanogram and picogram per litre concentrations. THC-COOH has been detected in untreated and treated wastewater up to 2500 and 750 ng·L–1, surface water over 500 ng·L–1, and tap water up to 1 ng·L–1. Adsorption is likely a significant pathway for the removal of THC that is unmetabolized in wastewater or surface water, due to its low aqueous solubility. THC and THC-COOH have been shown to cause oxidative stress to aquatic species. The few studies of potential treatments for THC-COOH have shown that chlorination and UV oxidation lead to potentially form transformation by-products, which would be toxic in aquatic environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".